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GHENT UNIVERSITY
FACULTY OF ECONOMICS AND BUSINESS
ADMINISTRATION
ACADEMIC YEAR 2013 – 2014
The potential of demand-side
management in Belgium
Master thesis submitted to obtain the degree of
Master of Science in Business Engineering: Operations Management
Louise Van Isterdael
under direction of
Prof. dr. Johan Albrecht and Ruben Laleman
GHENT UNIVERSITY
FACULTY OF ECONOMICS AND BUSINESS
ADMINISTRATION
ACADEMIC YEAR 2013 – 2014
The potential of demand-side
management in Belgium
Master thesis submitted to obtain the degree of
Master of Science in Business Engineering: Operations Management
Louise Van Isterdael
under direction of
Prof. dr. Johan Albrecht and Ruben Laleman
PERMISSION
The undersigned declares that the content of this thesis may be consulted and/or reproduced,
subject to acknowledgement of source.
Louise Van Isterdael
I
Foreword
This thesis is written as completion to my master degree in business engineering at the University of
Ghent. It was a great challenge to explore the interesting world of electricity.
I would like to thank my supervisor Professor Johan Albrecht for giving me the opportunity to study
this topic.
I would also like to thank Ruben Laleman for his valuable insights and support that gave me guidance
to complete the research and write this thesis.
All the effort put into this master thesis would not be enough without the support of my family and
friends. In particular, I would like to thank my father, mother and sister to encourage me during the
last months as well as the five instructive years at university.
II
Content Foreword .................................................................................................................................................. I
Abbreviations ......................................................................................................................................... IV
List of figures and tables ........................................................................................................................ VI
Dutch summary .................................................................................................................................... VIII
Introduction ............................................................................................................................................. 1
1. Chapter 1 Trends in energy markets ............................................................................................... 3
1.1 General context ....................................................................................................................... 3
1.2 Challenges of intermittency .................................................................................................... 5
1.2.1 Intermittency ................................................................................................................... 5
1.2.2 Flexibility .......................................................................................................................... 7
1.3 Who pays the bill? ................................................................................................................... 8
1.3.1 Retail price composition .................................................................................................. 8
1.3.2 Merit order effect .......................................................................................................... 10
2. Chapter 2 Demand-side concepts and resources .......................................................................... 13
2.1 Demand-side management: definitions ................................................................................ 13
2.1.1 Energy efficiency ........................................................................................................... 13
2.1.2 Demand response.......................................................................................................... 13
2.2 Demand response: load types ............................................................................................... 13
2.3 Demand response: participation in wholesale markets ........................................................ 14
2.3.1 Energy markets .............................................................................................................. 14
2.3.2 Ancillary services market ............................................................................................... 16
2.3.3 Capacity market ............................................................................................................. 18
2.4 Classification of demand-side resources ............................................................................... 19
2.4.1 General overview .......................................................................................................... 19
2.4.2 Dispatchable demand response programs .................................................................... 21
2.4.3 Non-dispatchable demand response programs ............................................................ 25
3. Chapter 3 Demand response case studies .................................................................................... 27
3.1 Demand response cases in the U.S. ...................................................................................... 28
3.1.1 Regulations enabling demand response ....................................................................... 28
3.1.2 Evolution of demand response in the U.S. .................................................................... 28
3.1.3 Contributors .................................................................................................................. 30
3.1.4 Case studies in the U.S. ................................................................................................. 32
3.1.5 Conclusions from U.S. experiences ............................................................................... 40
III
3.2 Case studies in Belgium ......................................................................................................... 41
3.2.1 Total demand response ancillary services ..................................................................... 41
3.2.2 Elia’s R1 Load product ................................................................................................... 42
3.2.3 Elia’s R3 Interruptible Contract Holder product ............................................................ 43
3.2.4 Elia’s R3 Dynamic Profile product ................................................................................. 43
3.2.5 Strategic reserve ............................................................................................................ 44
3.2.6 Bid ladder platform ....................................................................................................... 44
3.2.7 Smart offers day-ahead market .................................................................................... 45
4. Chapter 4 Potential of industrial and residential demand response in Belgium .......................... 46
4.1 Potential of industrial demand response in Belgium ............................................................ 46
4.2 Potential of residential demand response in Belgium .......................................................... 48
4.2.1 Policy of the residential building sector ........................................................................ 48
4.2.2 Electrical household appliances .................................................................................... 49
4.2.3 Field measurements ...................................................................................................... 50
4.2.4 Flexibility of electric vehicles ......................................................................................... 56
4.2.5 Total flexible capacity available today .......................................................................... 60
4.2.6 Effect on the total load curve ........................................................................................ 63
4.2.7 Effect on load in emergency situations ......................................................................... 66
4.2.8 Residential demand response as balancing resource ................................................... 67
4.2.9 Effect of battery storage on the total load curve .......................................................... 69
Conclusion ............................................................................................................................................. 72
References ................................................................................................................................................ I
Appendix
Appendix 1 Overview of the U.S. Ancillary services.........................................................Appendix 1.1
Appendix 2 Hourly consumption field measurements.................................................... Appendix 2.1
Appendix 3 Synthetic load profiles S21 and S22 households.......................................... Appendix 3.1
IV
Abbreviations
AMI Advanced metering infrastructure
ARP Access Responsible Party
BRP Balancing Responsible Party
CCGTs Combined-cycle gas turbines
CPP Critical peak pricing
CRM Capacity remuneration mechanism
CSS Clean spark spread
DP Dynamic Pricing
DR Demand response
DSM Demand-side management
DSO Distribution system operator
FERC Federal Energy Regulatory Commission
GHG Greenhouse gas
ICAP Installed capacity
ICH Interruptible Contract Holder
IEA International Energy Agency
ISO Independent system operator
NYISO New York Independent System Operator
OCGTs Open-cycle gas turbines
OTC Over-the-counter
PJM PJM Interconnection
PV Photovoltaic
RES Renewable energy sources
RPM Reliability Pricing Model
RTO Regional Transmission Organization
RTP Real-time pricing
SLP Synthetic load profile
SRMCP Synchronized Reserve Market clearing price
TOU Time-of-use
TSO Transmission system operator
UCAP Unforced capacity
VAT Value Added Tax
V2H Vehicle-to-home
V
kW kilowatt
kWh kilowatt hour
MW megawatt
MWh megawatt hour
VI
List of figures and tables
Figure 1 Electricity generation resources (based on EURELECTRIC Powerstats 2013) ........................... 4
Figure 2 Share RES in gross final energy consumption and electricity consumption in Belgium ........... 5
Figure 3 Variability of intermittent renewables (Pöyry, 2011) ............................................................... 6
Figure 4 Flexibility of the electricity system (IEA, 2012) ......................................................................... 7
Figure 5 Composition of the household electricity invoice in 2014 in Belgium (FEBEG, 2014) .............. 9
Figure 6 Merit order in Belgium (Sia Partners, 2013) ........................................................................... 10
Figure 7 Positive clean spark spread in Belgium (Sia Partners, 2013) ................................................... 11
Figure 8 Energy Efficiency (Charles River Associates, 2005c)................................................................ 13
Figure 9 Curtailable load (Massin, 2014) ............................................................................................... 14
Figure 10 Load Shifting (Massin, 2014) ................................................................................................. 14
Figure 11 Balancing electricity markets (European Commission, 2013b) ............................................. 16
Figure 12 Comparison of European and US Ancillary Services (Hurley et al., 2013) ............................. 17
Figure 13 Capacity market policies Europe (EURELECTRIC, 2013) ........................................................ 19
Figure 14 Demand-side management categories (Hurley et al., 2013) ................................................ 20
Figure 15 Time-of-Use (FERC, 2006) ...................................................................................................... 25
Figure 16 Critical peak pricing (FERC, 2006) .......................................................................................... 25
Figure 17 Real-time Pricing (FERC, 2006) .............................................................................................. 26
Figure 18 Two-part real-time pricing (FERC, 2006) ............................................................................... 26
Figure 19 Number of demand response projects worldwide (Navigation Research, 2013) ................. 27
Figure 20 Total reported potential peak load reduction for 2006 through 2012 FERC Surveys ........... 29
Figure 21 Actual peak load reduction residential, commercial & industrial DR in the U.S. (SEDC, 2013)
............................................................................................................................................................... 29
Figure 22 Reported potential peak load reduction by customer class (FERC, 2012) ............................ 30
Figure 23 Reported potential peak reduction by program type and by customer class in 2012 FERC
Survey .................................................................................................................................................... 31
Figure 24 Full Emergency Load Response program .............................................................................. 32
Figure 25 Demand response in PJM forward capacity auction with weighted average clearing price
(Hurley et al., 2013) ............................................................................................................................... 34
Figure 26 Response from demand resources in NYISO on the 2nd of August 2006 (Hurley et al., 2013)
............................................................................................................................................................... 36
Figure 27 Synchronized Reserve Market program ................................................................................ 37
Figure 28 Change in residential peak-period energy use CPP program (Charles River Associates,
2005a) .................................................................................................................................................... 38
Figure 29 Average percent reduction in peak-period energy use on critical days (Charles River
Associates, 2005a) ................................................................................................................................. 39
Figure 30 Flexible capacity of industrial consumers (Febeliec, Elia, & Energy Ville, 2013) ................... 47
Figure 31 Residential consumer load mix (European University Institute, 2013) ................................. 49
Figure 32 Overview flexible capacity dishwasher, washing machine and tumble dryer per cycle ....... 54
Figure 33 Flexible capacities available per day per household of the aggregated pool ....................... 55
Figure 34 Timeline flexible hours dishwasher, washing machine and tumble dryer ............................ 56
Figure 35 Flexible capacity available per day per electric vehicle......................................................... 57
Figure 36 Timeline flexible hours of all shiftable load available ........................................................... 58
Figure 37 Flexible capacity available per day for the total household pool ......................................... 61
VII
Figure 38 Flexible capacity available per day for the total vehicle pool ............................................... 62
Figure 39 Total load curve 7 and 8 May 2014 (Elia) .............................................................................. 63
Figure 40 Effect all flexible products on the load of 7-8 May 2014 ...................................................... 65
Figure 41 Total load curve 2-3 February 2012 ...................................................................................... 67
Figure 42 Effect battery storage on the total load curve of 2 February 2012 ...................................... 70
Table 1 Full Emergency Load Response programs (PJM, 2013) ............................................................ 33
Table 2 Impact of DR on the Reliability Pricing Model (The Independent Market Monitor for PJM,
2013)...................................................................................................................................................... 34
Table 3 Average SRMCP and average SRMCP when all cleared synchronized reserves are DR resources
(Monitoring Analytics LLC, 2013) .......................................................................................................... 37
Table 4 Evolution of the contracted volumes of ancillary services (MW) (Elia, 2013c) ........................ 41
Table 5 Contracted volumes and average prices DR products Elia (Elia, 2014a) .................................. 42
Table 6 Field measurement: specifications household ......................................................................... 50
Table 7 Results dishwasher ................................................................................................................... 52
Table 8 Results washing machine .......................................................................................................... 52
Table 9 Results tumble dryer ................................................................................................................ 53
Table 10 Results dishwasher, washing machine and tumble dryer for each cycle ............................... 53
Table 11 Adjusted results dishwasher, washing machine and tumble dryer per cycle ........................ 54
Table 12 Flexible capacity available per day per household of the aggregated pool ........................... 55
Table 13 Flexible hours dishwasher, washing machine and tumble dryer ........................................... 56
Table 14 Specifications electric vehicle ................................................................................................. 57
Table 15 Flexible hours electric vehicle ................................................................................................. 58
Table 16 Average daily electricity production (Ed) from PV solar panels of 4 kW (European
Commission, n.d.) .................................................................................................................................. 59
Table 17 Residential grid connections (Synergrid) ................................................................................ 60
Table 18 Flexible capacity total household pool per day ...................................................................... 61
Table 19 Flexible capacity and energy per day per period total household pool ................................. 61
Table 20 Flexible capacity total vehicle pool per day ............................................................................ 62
Table 21 Overview aggregated household pool .................................................................................... 64
VIII
Dutch summary
Europa focust met haar 20-20-20 doelstellingen op energie-efficiëntie en hernieuwbare energie, die
beiden moeten zorgen voor een daling van de broeikasgassen. Deze beleidsmaatregelen hebben een
positieve impact op het milieu, maar veroorzaken ook heel wat problemen. Een groot deel van de
hernieuwbare energie bestaat uit variabele en onvoorspelbare wind- en zonneproductie. Dit zorgt
ervoor dat er extra flexibiliteit nodig is om het elektriciteitssysteem in evenwicht te houden. Deze
flexibiliteit kan geleverd worden door back-up centrales, voldoende interconnectiecapaciteit met
buurlanden, grootschalige opslag van elektriciteit en vraagbeheer.
De elektriciteitsvraag kan beheerd worden door energie-efficiëntie te stimuleren en flexibele
consumenten aan te moedigen om hun vraag naar elektriciteit aan te passen aan de toestand van
het net, beter gekend als ‘demand response’ (DR). Consumenten kunnen hun consumptie
verschuiven van piek- naar daluren om zo de elektriciteitsrekening te doen dalen. Dit wordt in België
bijvoorbeeld al jarenlang gestimuleerd door de verschillende dag- en nachttarieven.
Daarnaast kunnen consumenten hun flexibiliteit aanbieden op de elektriciteitsmarkt. Grote
industriële verbruikers hebben sinds enkele jaren contracten met de Belgische
transmissienetbeheerder Elia om hun consumptie te verschuiven en zo het net te balanceren. Meer
en meer sluit Elia ook contracten met aggregatoren die de flexibele consumptie van kleinere
consumenten gezamenlijk aanbieden als reserve. Er bestaan verschillende DR programma’s en
contracten die gesloten kunnen worden tussen consumenten, aggregatoren, leveranciers,
evenwichtsverantwoordelijken en netbeheerders onderling.
Ongeveer 95% van alle DR programma’s in de wereld wordt aangeboden in Noord-Amerika, in het
bijzonder in de U.S. In 2012 werd geschat dat deze DR programma’s samen in staat waren om 9.2%
van het totale piekverbruik in de U.S. te reduceren. Het grootste deel hiervan is te wijten aan de
flexibiliteit van industriële consumenten en aggregatoren. Slechts 12% van deze reductie werd tot
stand gebracht door residentiële klanten.
In België hebben Elia, Febeliec en Energy Ville een studie uitgebracht over de potentiële flexibele
consumptie van 29 grote industriële verbruikers die samen zorgen voor 13.6% van de totale
elektriciteitsconsumptie in 2012. Uit de studie is gebleken dat ze een totaal van 631 MW aan
flexibele capaciteit bezitten waarvan 134 MW nog niet is ingezet in een DR programma. Ook blijkt
dat andere verbruikers zeer enthousiast zijn om in de toekomst hun flexibiliteit aan te bieden. Echter
blijkt dat kwantitatieve extrapolaties naar het totale potentieel in België niet mogelijk zijn door het
IX
uiteenlopende consumptieprofiel van de verschillende industriële sectoren. Verder onderzoek naar
het totale potentieel van de industrie in België is echter gepland.
In deze thesis is onderzocht hoeveel potentiële flexibiliteit de residentiële consumenten kunnen
aanbieden. Om een realistische inschatting te maken zijn veldmetingen van één huishouden gebruikt
om met behulp van realistische assumpties en berekeningen het totale potentieel bij de huishoudens
in België in te schatten.
Uit deze berekeningen is gebleken dat vaatwassers, wasmachines en droogkasten vandaag in België
elke dag 4,724 MW flexibele capaciteit kunnen aanbieden. Daarvan kan 1,574 MW voor 8 uur
verschoven worden tussen 8 uur en 16 uur en 3,149 MW voor 9 uur tussen 20 uur en 5 uur ’s nachts.
Ook werd de potentiële flexibele capaciteit van elektrische auto’s becijferd. Vandaag bezitten 1,437
huishoudens in België een elektrische auto. Er werd geschat dat het totale elektrische wagenpark
elke dag 3,880 MW flexibele capaciteit kan leveren tussen 21 uur en 4 uur ’s nachts.
Vervolgens werd geschat in welke mate de totale piekbelasting zou kunnen dalen indien alle
huishoudens samen in één aggregator pool zouden zitten. Als voorbeeld werd de lastcurve van 7 mei
2014 gebruikt. Ongeveer 578 MW of 6 % van de hoogste belasting die dag werd gereduceerd. s’
Avonds kon er 1,265 MW of 13.3% van de hoogste belasting in de avond gereduceerd worden.
Echter zou deze potentiële reductie onmogelijk zijn indien de piekbelasting buiten de flexibele
periode valt waarin er last kan verschoven worden. In deze zin zou het interessant zijn om een DR
noodprogramma te ontwikkelen waaraan gezinnen kunnen participeren. In noodgevallen zouden
deze gezinnen het verbruik van hun wasmachines, droogkasten, vaatwassers en elektrische auto’s
kunnen uitstellen naar de volgende dag, en dit slechts enkele keren per jaar. Hierdoor wordt de
flexibele periode uitgebreid en wordt residentiële DR interessanter.
Het is belangrijk om na te gaan of een aggregator van gezinnen voldoende omzet kan halen uit zijn
diensten. Indien de aggregator flexibele capaciteit aanbiedt aan evenwichtsverantwoordelijken is er
geschat dat hij maximaal € 23.5 per gezin per jaar kan verdienen. Indien hij capaciteit aanbiedt aan
Elia als ondersteunende dienst is dit slechts maximaal € 1.59 per gezin per jaar. Dit is echter wat de
aggregator ontvangt, niet de winst die hij maakt. De kosten zijn sterk afhankelijk van de
noodzakelijke investeringen in slimme meters en of gezinnen al dan niet een vergoeding krijgen voor
hun flexibiliteit.
X
Als laatste is onderzocht wat de mogelijke invloed zou kunnen zijn op de totale lastcurve indien elk
gezin in België een batterij zou installeren in combinatie met zonnepanelen. Ik verwacht dat dit in de
toekomst zeer veel voordelen kan bieden. De prijs voor een batterij per kWh opslagcapaciteit is
immers sterk aan het dalen, evenals de investeringskost van zonnepanelen. Indien elk gezin in België
een batterij met een opslagcapaciteit van 15 kWh zou hebben, zou de variabiliteit van de decentrale
zonneproductie én de variabiliteit van de dagelijkse consumptie sterk verminderd worden.
Daarenboven kunnen ze de bevoorradingszekerheid positief beïnvloeden doordat de opgeslagen
energie gebruikt kan worden tijdens piekuren en noodsituaties. Het maximale positieve effect is
geïllustreerd aan de hand van de lastcurve van 2 februari 2012, wanneer een koudegolf in België voor
een noodsituatie zorgde.
Uit de resultaten en schattingen verwacht ik dat residentiële consumenten in staat zijn om een
aanzienlijk deel van hun verbruik te verschuiven en daardoor de piekbelasting van het net sterk te
kunnen verminderen. Echter is dit niet in elk geval zo, waardoor de betrouwbaarheid en waarde van
residentiële flexibiliteit daalt. Ook is de winst die aggregatoren kunnen maken laag, zeker wanneer ze
moeten instaan voor grootschalige investeringen in slimme meters en de gezinnen een monetaire
bijdrage moeten geven.
Indien aggregatoren hun flexibele pool diversifiëren door bijvoorbeeld ook contracten te sluiten met
industriële consumenten, kunnen ze een meer waardevolle dienst aanbieden aan
evenwichtsverantwoordelijken en Elia.
Daarnaast raad ik aan via een aangepast woningenbeleid batterijen in combinatie met zonnepanelen
te stimuleren. Batterijen kunnen het evenwichtsprobleem sterk verminderen en de
bevoorradingszekerheid verbeteren. In de toekomst zou er verder onderzoek moeten gedaan
worden naar het potentieel van dit beleid.
1
Introduction
The world faces today one of the greatest environmental challenges due to climate change. Europe
has chosen to focus its efforts on renewables and energy efficiency. A significant share of renewable
electricity resources are solar and wind energy, which both have a relative unpredictable and
variable output. To this end, there’s an increasing need for flexible supply- and demand-side
resources, such as back-up capacity, interconnection, storage technologies and demand-side
management. This trend and the problems it creates are discussed in the first chapter.
Demand-side management is seen in literature as the umbrella term for energy efficiency and
demand response. The focus of the thesis is on demand response in Belgium, which can be provided
by residential, commercial and industrial electricity consumers. These customers can shift
consumption from peak hours to cheaper off-peak hours in order to lower their electricity bill. This
also decreases the consumption level of both the electricity supplier and the balancing responsible
party, which results in a lower necessary peak load in Belgium. Customers can also offer their flexible
load on the electricity market, i.e. energy bids in wholesale markets, ancillary services and capacity
tenders for balancing reserves. This is often done through concluding contracts with a third party,
such as a load aggregator. The different services flexible customers can offer and the various
program types in which they can participate are discussed in the second chapter.
Roughly 95% of all demand response programs are deployed in North America, in particular in the
U.S. The potential peak load reduction through demand response programs in the U.S. was estimated
to be nearly 72,000 MW in 2012, which is about 9.2% of total U.S. peak demand. To this end some
important case studies of demand response programs in the U.S. are discussed in chapter three. Also
Belgian case studies are described to see what already exists in our country.
In chapter 4 is investigated how much potential demand response of industrial and in particular
residential consumers can be offered on the electricity market in Belgium. In the first section of this
chapter the results and conclusions of a study conducted by Elia, Febeliec and Energy Ville about the
potential of large industrial consumers in Belgium is shortly discussed.
In the second section of chapter 4 the amount of flexible capacity that residential consumers can
offer is estimated. This is calculated based on field measurements, theoretical calculations and
realistic assumptions. Furthermore the different services this flexibility can provide to balance the
system and increase the adequacy of the electricity grid are discussed. Also the potential revenue of
an aggregator who manages a pool of households is estimated.
2
In the last section of chapter 4 the advantages of batteries installed in combination with PV solar
panels at home are described. Also their maximum positive effect on the total load curve is
estimated to show the potential of batteries to reduce the total peak load in Belgium. This is
illustrated for the total load on 2 February 2012, which was an extreme cold day.
3
1. Chapter 1 Trends in energy markets
1.1 General context
Climate change is generally regarded as one of the greatest environmental challenges the world is
facing today. The achievement of a low-carbon economy is one of the main objectives to prevent
global warming. A lot of studies and expert reports show that energy efficiency plays an important
role in transforming the global energy system.
The IEA1 publishes since 2006 every two years its Energy Technology Perspectives, which is its most
ambitious publication on new global developments in energy technology. They are of the opinion
that the power generation sector is the backbone of a clean energy future. Reductions in the demand
for energy services, savings due to energy efficiency improvements and savings due to fuel and
technology switching are of paramount importance. The latter is possible by for example switching
from gasoline to electric cars and use heat pumps for space and water heating. (IEA, 2012)
The IPCC2 affirms the importance of energy efficiency in one of its expert reports and proposes an
ambitious but achievable goal for the G8, i.e. the eight leading industrialized countries:
“They should double their historical rate of energy efficiency improvement in sectors such as
buildings, transportation, industry, and energy supply by accelerating deployment of existing
technologies and altering economic incentives.”(IPCC, 2007, pg.i)
As one of the policy options to improve energy efficiency, they advise the following:
“Structure utility rates to provide higher rates of return on investments in end-use energy
efficiency than on investments in energy supply, and ensure that at least 30% of demand for
new capacity is met by demand-side management.” (IPCC, 2007, pg.21)
In 2008, the European Union adopted its first package of climate and energy goals, which have to be
attained by 2020. Three targets are set up front, known as the “20-20-20” targets: a 20% reduction in
EU greenhouse gas (GHG) emissions from 1990 levels, raising the share of EU energy consumption
produced from renewable energy sources (RES) to 20% and a 20% improvement in the EU’s energy
efficiency. (European Commission, 2013c)
1 The International Energy Agency is an autonomous intergovernmental organization and energy policy advisor
to its member states (only member states of the OECD can become an IEA member) 2 The Intergovernmental Panel on Climate Change is an organisation of the United Nations and an international
accepted authority on climate change.
4
In January 2014 the new EU framework on climate and energy for the period up to 2030 was
proposed by the European Commission. The pillars of the framework are the following: a reduction in
GHG emissions by 40% below the 1990 level, an EU-wide binding target for renewable energy of at
least 27%, renewed ambitions for energy efficiency policies, a new governance system and a set of
new indicators to ensure a competitive and secure energy system. (European Commission, 2014a)
Many Directives3 are being implemented by the European Union to stimulate the transition in
different industry sectors. Collective efforts of all sectors are needed, but especially the power
generation sector is critical.
In the power generation sector, RES are becoming a dominant input. In Figure 1, the different
generation shares are given for the EU in 2012 and forecasted for 2020. In 2012 the total RES share
accounted for 22.38%, which is less than the half of the share of fossil fuel based generation. By 2020
renewables are predicted to be the second largest component of the EU energy mix accounting for
34% of total generation, which is only 1%-point less than the share of fossil fuel based generation.
Figure 1 Electricity generation resources (based on EURELECTRIC Powerstats 2013)
In Figure 2 the share of renewable energy in gross final energy consumption and the electricity
generated from RES as percentage of gross electricity consumption is given for Belgium for the
period 2004 through 2012, plus the target set for 2020. This graph is based on data from Eurostat in
2014.
3 Inter alia the RES Directive (2009/28/EC), the Energy Efficiency Directive (2012/27/EU), and the Emissions
Trading Directive (2003/87/EC).
22,38%
27,01% 3,27%
48,38%
Electricity Generation EU-27 - 2012
RES
Nuclear
Pumped Hydroand Other
Fossil Fuel Fired
34%
24% 7%
35%
Electricity Generation EU-27 - 2020
RES
Nuclear
Pumped Hydroand Other
Fossil Fuel Fired
5
Figure 2 Share RES in gross final energy consumption and electricity consumption in Belgium
The target for electricity generated from RES is 20.9% of gross electricity consumption in Belgium.
This is an increase of 88% compared to the RES share in electricity consumption of 11.1% in 2012.
This creates extra challenges for the electricity system in Belgium, as explained in the next section.
1.2 Challenges of intermittency
As shown in the previous section, RES are a significant share of the total electricity generation in
Europe and Belgium. A significant part of these RES are intermittent energy sources, i.e. wind and
solar energy, and to a lesser extent wave and tidal energy.
It’s very important to take into account intermittency impacts when wind and solar deployment
reach their target levels, since they require significantly more back-up and storage capacity than
conventional power stations.
1.2.1 Intermittency
In the context of electricity generation systems, intermittency refers to the non-continuous output of
power plants. An intermittent electric generator or intermittent resource can be interpreted as “an
electric generating plant with output controlled by the natural variability of the energy resource
rather than dispatched based on system requirements.” (U.S. Energy Information Administration,
2013)
Two main components can be used to define the intermittent character of RES: relative
unpredictability and non-controllable variability. (Hirst, 2001)
6,8
13 11,1
20,9
0
5
10
15
20
25
2004 2005 2006 2007 2008 2009 2010 2011 2012 TARGET2020
Share of renewable energy in gross final energy consumption (%)
Electricity generated from renewable sources as % of gross electricityconsumption
6
The first element is the relative unpredictability of intermittent energy sources. The output of
dispatchable conventional power stations such as coal- or gas-fired power plants is highly predictable
because these plants can be controlled by operators. This is less the case for solar and wind energy
because they depend on weather conditions, which can’t be perfectly forecasted by the operators.
Forecasting methods for wind speed and solar insolation have become more and more accurate, but
still a certain amount of uncertainty remains. (Hirst, 2001)
The second element that makes up intermittency is non-controllable variability. Variability can be
observed in the short term (seconds, minutes, hours, days) as well as in the long term (seasons,
years). The volatility, i.e. minute-to-minute variations in output, of most generators can also be
controlled, because their output can be controlled as well. However, wind and solar output is
determined by time-varying wind speed and solar radiation, which can’t be perfectly predicted.
(Hirst, 2001)
In Figure 3, the impact of large amounts of solar and wind energy generation and the resulting
variability is shown for Northern Europe for January, April, July and October. There’s a stark contrast
between the output of the intermittent renewables in 2010 and what may be expected in 2035.
Research has proven that weather patterns, especially wind speeds, across North Europe are very
similar. The conclusion of the study is that the overall output of the intermittent generation will be
highly variable, and it will not average out in Northern Europe because of geography and weather, as
can be seen from the diagram. (Pöyry, 2011)
Figure 3 Variability of intermittent renewables (Pöyry, 2011)
7
1.2.2 Flexibility
Today, not all energy generated from intermittent RES can be fed continuously into the electricity
grid. Power consumption is also subject to a characteristic pattern that varies over the day, week or
year. At any given time, the electricity grid has to be in balance – supply has to be exactly the same
as demand, in order to prevent collapsing of the grid. (Deutsche Bank, 2012)
To this end the availability of flexible supply- and demand-side resources in the electricity system is
very important. The need for this flexibility is triggered by contingencies and fluctuations in the net
load, such as demand variability and uncertainty, and intermittent renewables. Within the last few
years, large amounts of data have been collected regarding demand fluctuations. This allows making
increasingly accurate demand predictions. However, flexibility needs created by intermittent
renewables are less easy to forecast, as explained in the previous section.
The need for flexibility can be met by four flexible resources, as well as their potential synergies:
power generation plants, energy storage facilities, interconnection with adjacent markets, and
demand-side management and response, which is the focus of the thesis. In Figure 4, an overview is
given. All different flexible resources are in a different stage of maturity.
Figure 4 Flexibility of the electricity system (IEA, 2012)
Open-cycle gas turbines (OCGTs) play a significant role in providing flexibility, but also combined-
cycle gas turbines (CCGTs) and other power generation technologies such as nuclear, hydro and coal
plants. Interconnection with adjacent markets is technically mature. Belgium has several projects to
increase interconnection capacities: BRABO, STEVIN, NEMO, ALEGrO and more.4
4 For more information on these projects: (Elia, 2013c).
8
Demand response is still in an early phase in Belgium, but interest has grown in recent years. A lot of
studies and research has been done to investigate the potential of demand response in our country.5
Both industry and households can contribute to grid stability by making parts of their consumption
flexible and by responding to possible shortages in peak periods. Today some very large consumers
already offer their flexibility to Elia, the transmission system operator (TSO) in Belgium. Nevertheless
a lot of flexible demand available in our country is still not used. Case studies of demand response
experiences are discussed in chapter 3 and estimations of the potential flexible demand response
available in Belgium are given in chapter 4.
1.3 Who pays the bill?
The ambitious climate change and energy sustainability targets set by the EU have required several
legislative acts, such as the Renewable Energy Directive and the Fuel Quality Directive6. These acts do
not always result in the expected outcomes, which makes State aid necessary. Member States,
Belgium included, grant support by using market mechanisms such as green certificates that
guarantee demand for the RES production at a higher price than the market price for production by
conventional plants. (European Commission, 2013a) These support mechanisms influence the retail
electricity prices. In Belgium the subsidies are passed on the distribution network costs, as explained
in the next section.
1.3.1 Retail price composition
When evaluating the evolution of electricity retail prices, it’s important to analyse its constituents
instead of the total retail price. The electricity invoice includes the following three cost components:
(1) energy cost, i.e. cost for consumed electricity, (2) network cost, i.e. transmission and distribution
cost, and (3) taxes, i.e. VAT7 and other taxes.
To enable the integration of renewables, significant infrastructure investments are necessary which
have an influence on the network cost. (European Commission, 2011)
In Figure 5 an example of the composition of a Belgian household invoice is given for 2014, with
special attention paid to the distribution and transmission network costs. (FEBEG, 2014) The
distribution and transmission network costs account for 55% of total household invoice, while the
energy cost is only 29% of the invoice.
5 (FORBEG, 2014), (Febeliec et al., 2013)
6 The Fuel Quality Directive (Directive 98/70/EC) amends a number of elements of the petrol and diesel
specifications as well as introducing a requirement on fuel suppliers to reduce the GHG intensity of energy supplied for road transport (Low Carbon Fuel Standard). 7 Value Added Tax
9
Figure 5 Composition of the household electricity invoice in 2014 in Belgium (FEBEG, 2014)
It is expected that the future network costs will rise even more in Belgium. Due to the collapsed
market for certificates, Eandis and Infrax, the two main Flemish overarching distribution system
operators8 (DSO), own today a huge amount of unmarketable certificates. These accumulated debts
haven’t been charged to consumers for years. The SERV9 has calculated that these debts will be
added up to 1.8 billion euro by the end of 2015, thereby increasing the network costs and thus
electricity bills in the future. (SERV, 2014)
I think that it should be fairer to purify the electricity bill from costs which have little to do with
private consumption and instead include them in the general government budgets. As a result the
costs will be carried conform the purchasing power of the consumers and thereby limit the increase
in energy poverty.
Renewables for electricity generation do not only incur subsidies, but also increase the need for
substantial additional back-up capacity to provide sufficient generation capacity in times of low wind
and solar supply. (Méray, 2012) However, many EU Members such as Belgium have given wind and
solar production priority access to the electricity networks. This has a negative effect on the
profitability of conventional power plants because of the merit order effect, which is explained
below.
8 “a natural or legal person responsible for operating, ensuring the maintenance of and, if necessary,
developing the distribution system in a given area and, where applicable, its interconnections with other systems, and for ensuring the long term ability of the distribution system to meet reasonable demands for the distribution of electricity” (EURELECTRIC, 2004) 9 The “Sociaal Economische Raad van Vlaanderen” or the Economic and Social Council of Flanders
10
1.3.2 Merit order effect
Adding renewables to the power mix has a significant influence on wholesale electricity prices,
known as the merit order effect. The ranking of electricity generation assets by their marginal cost of
production provides the supply curve, also known as the merit order. (European Commission, 2014b)
The available generation assets are country dependent.
The demand curve is relatively steep, which indicates the inelasticity of demand for electricity. This
means that demand remains almost unchanged when prices change, because electricity falls into the
category of goods that are a necessity, and substitutes are few and difficult to obtain. (Pöyry, 2010)
The merit order is a good approach to assess the impact of intermittent RES on the wholesale
market. In Figure 6 the merit order for the electricity generation mix in Belgium is shown.
Figure 6 Merit order in Belgium (Sia Partners, 2013)
The differences in marginal costs (€/MWh) are mainly due to fuel costs and technology
characteristics. Wind and solar power have zero fuel costs, so enter at the bottom of the merit order.
In periods of high wind and solar generation, the supply curve shifts to the right and lowers the
wholesale price. In this way investments in conventional peaking power plants are unprofitable
11
because their operating hours decrease significantly. As the share of RES in the Belgian generation
mix increases each year, the operating hours of OCGTs decrease significantly.
The profitability of gas fired power plants can be assessed through the clean spark spread formula.
This is the difference between the price a generator can get from selling 1 MWh of electricity and the
cost of the fuel and required number of carbon allowances needed to generate that MWh of
electricity. All other costs such as operation, maintenance, capital and other financial costs must be
covered from the clean spark spread. (U.S. Energy Information Administration, 2013)
In Figure 7 the number of days with a positive clean spark spread (CSS) for gas-fired power plants in
Belgium are shown, plus their average daily energy production for 2008 through 2012. In 2009, there
were 327 days with a positive clean spark spread, while in 2012 this was only 77 days, a decrease of
more than 76% in three years.
Figure 7 Positive clean spark spread in Belgium (Sia Partners, 2013)
More and more electric utilities in Belgium are forced to shut down their gas-fired plants, such as
OCGTs and classic STEG plants. E.ON announced in 2013 the closure of its 385 MW gas-fired plant in
Vilvoorde. EDF Luminus announced also in 2013 the closure of its 460 MW gas plant in Seraing. In
2014 this year Electrabel notified Elia that it plans to close the Drogenbos plant in October 2015. The
plant has a capacity of 460 MW and would reduce Electrabel’s gas-fired capacity by 14%.
These gas plant closures together with the planned nuclear phase out in Belgium may have a serious
impact on the preservation of the adequacy10 of the electricity system. As a result, Belgium takes
measures to support gas plants to recover their fixed costs by remunerating them based on the
installed capacity (€/MW) instead of the output (€/MWh). The Belgian government has called for 800
10
The ability of the electric system to supply the aggregate electrical demand and energy requirements of the customers at all times, taking into account scheduled and reasonably expected unscheduled outages of system elements (ENTSO-E, 2004)
12
MW of new gas-fired capacity under a tender that guarantees price support for 6 years per MW
installed. (Power in Europe, 2014) The objective is to create investment incentives for new plants.
This, however, creates a vicious cycle of subsidies and contradicts the goal of phasing out subsidies
for fossil fuels. And what to do with the existing gas plants? This focus on new power units not
interacting with the market should be replaced by a clear and honest capacity remuneration
mechanism (CRM) for at least all gas fired power plants.
Other EU Member States are facing the same problems. The European Commission advises to
consider in the first place alternative ways to achieve generation adequacy, such as increasing the
interconnection capacity and invest in demand-side management. (European Commission, 2013a)
Demand-side management (DSM) and in particular demand response (DR) is the focus of this thesis
because there’s a growing awareness of its importance for the electricity grid.
DSM originates from the U.S. electric power industry circa 1970. In 2012 the potential peak load
reduction through DR programs in the U.S. is estimated to be nearly 72,000 MW, which is about 9.2%
of the total U.S. peak demand. (FERC, 2012) To this end it’s interesting to discuss DR experiences in
the U.S. This is done in chapter three, where also Belgian case studies are given to show what already
exists in our country.
First in the next chapter DSM concepts and resources are explained, as well as the various DR
program types already existing in the U.S.
13
Figure 8 Energy Efficiency (Charles River Associates, 2005c)
2. Chapter 2 Demand-side concepts and resources
In this chapter DSM concepts, DR participation in wholesale markets and the classification of
demand-side programs are explained.
2.1 Demand-side management: definitions
There are various definitions and interpretations in literature for DSM. EnerNOC11 defines DSM as
“the planning, implementation, and monitoring of strategies designed to reduce or shift electric
consumption or improve energy efficiency at an end-user facility”. (EnerNOC, 2014) DSM is often
seen in literature as the umbrella term for energy efficiency and demand response, both explained
below. (AEIC Load Research Committee, 2009) The focus of this thesis is on demand response.
2.1.1 Energy efficiency
Energy efficiency is “the reduction of electrical consumption via upgrades
and/or retrofits while retaining the same output or performance.”
(EnerNOC, 2014) The effect on energy use is shown in Figure 8.
2.1.2 Demand response
Demand response can be seen as a load management tool that provides a cost-effective alternative
to traditional supply-side solutions to address demand during peak hours.
“Demand response can be defined more specifically as changes in electric usage by end-use
customers from their normal consumption patterns in response to changes in the price of electricity
over time, or to incentive payments designed to induce lower electricity use at times of high
wholesale market prices or when system reliability is jeopardized.” (FERC, 2012, pg.21)
2.2 Demand response: load types
The changes in electric usage through customer response can be provided by all types of customers:
residential, commercial and industrial customers. DR can be achieved through four different load
types: curtailable load, self-generation, shiftable load and storable load.
Customers can curtail electricity consumption during peak hours when prices are high or contingency
conditions occur and this without changing their consumption pattern during off-peak hours (see
Figure 9). This DR action may involve a temporary loss of comfort, because the reduced load can’t be
11
EnerNOC is a leading provider of energy intelligence software and technology and has partnered with hundreds of utilities and grid operators worldwide to meet their demand-side management objectives.
14
Figure 9 Curtailable load (Massin, 2014)
Figure 10 Load Shifting (Massin, 2014)
shifted to off-peak periods. For example thermostat settings of air
conditioners or heaters can be temporarily changed, or lighting
can be switched off. (Albadi & El-Saadany, 2008)
The second DR load type is on-site generation. Customers can
generate their own power by using back-up generators. This has
the same effect as curtailable load DR as shown in Figure 9.
The third DR load type is shifting peak demand operations to off-
peak periods when high electricity prices or contingency conditions
occur. The effect is shown in Figure 10. For example dishwashers,
pool pumps, washing machines and tumble dryers can be used to
shift load to off-peak hours. There will be no loss of comfort and
total electricity demand will remain the same. (Albadi & El-Saadany, 2008) When industrial
customers decide to reschedule operations to off-peak hours, rescheduling costs should be limited or
reduced to zero.
DR resources able to store electricity can align consumption with generation output. This can be
done by for example charging batteries (e.g. to store solar energy), freezing ice for cold storage,
heating water, pumping water, or compressing air during off-peak periods. (Hurley, Peterson, &
Whited, 2013) This has the same effect as in Figure 10.
In the next section the participation of DR resources in wholesale markets is explained. They can
fulfill an important role in all available wholesale markets: the energy, ancillary services and capacity
market.
2.3 Demand response: participation in wholesale markets
It’s important to have a clear view of the various wholesale electricity markets to have an insight in
how DR resources can participate in it. There are three main wholesale markets: energy markets,
ancillary services markets, and capacity markets. Assets participating in the last two markets also
participate in the energy market when activated.
2.3.1 Energy markets
A DR customer can participate in energy markets as a single end-customer or can have a contract
with a third party, e.g. an aggregator. A third party gives the DR customer predetermined payments
for the DR service offered. The third party then uses its portfolio of aggregated DR customers to
15
participate in energy markets as a single DR resource. The smaller the end-customer, the more he is
obligated to participate in energy markets through an aggregator.
Electricity can be transferred via two types of exchange markets: over-the-counter (OTC) trading and
exchange platforms.
OTC trading is done directly between two parties, without any supervision of an exchange. The trade
can be obtained through bilateral contacts, via brokers or via electronic platforms, but it always ends
in a bilateral contract between two parties. Prices and amounts of the bilateral contracts are not
made public. Beside standard products also customized products are traded. (Pöyry, 2010)
In case of exchange platforms bids and offers are visible, but bidder and seller remain anonymous. A
clearing house is an intermediate between buyer and seller to guarantee the anonymity and
facilitates the contract between both. Only standard products are traded and prices are higher due
to the higher process costs.
There are two spot markets in Belgium, facilitated by Belpex: the day-ahead market and intraday
market. In order to efficiently and economically balance system changes, intraday markets are
established because system conditions, available generation and anticipated load can differ from the
time the day-ahead market is run till real-time markets. Intraday markets become more and more
important with the increasing share of RES.
DR resources are not always allowed to participate in energy markets in the same way as supply-side
production assets. A distinction can be made between fully-integrated market-based DR and market-
reactive DR programs. (Hurley et al., 2013)
Fully-integrated market-based DR implies that DR resources can set the market clearing price and are
thus equally treated as supply-side production assets. This isn’t the case for Belgium however.
Market-reactive DR programs offer similar services by reacting to a market signal (high prices,
contingency conditions), but are not able to set the market clearing price. When DR resources bid
less than the day-ahead energy market clearing price, they are scheduled for dispatch in real-time.
The price in the day-ahead market isn’t influenced by DR participation. This is only the case in
Belgium when DR resources participate in the tertiary reserve Interruptible Contract Holder program
of Elia (see infra p.44). The DR resource receives the clearing price set by the merit order, but is not
able to set the price. More information about Belgian programs is given in chapter 3.
16
2.3.2 Ancillary services market
Ancillary services are required to continuously uphold the balance between electricity load and
generation, and recover energy unbalances after unexpected outages in supply and demand. In
general, market forces push towards an optimal system balance point, as shown in Figure 11. ARP’s12
are responsible for balancing their balancing perimeter. However, due to imperfections these market
forces are not enough to perfectly balance the electricity grid. TSOs are obliged to correct the
resulting imbalance, using ancillary services. In Belgium this is the responsibility of Elia. (European
Commission, 2013b)
Figure 11 Balancing electricity markets (European Commission, 2013b)
In the next paragraphs the different ancillary services are defined. A distinction is made between
European and U.S. ancillary service nomenclature. This is important to fully understand the DR case
studies in the U.S. and Belgium, both described in the third chapter.
In Figure 12 a comparison is made between the European and U.S. terminology used. In Appendix 1
the various ancillary services in the U.S. are clearly summarized. (Hurley et al., 2013)
A distinction can be made between services used for normal conditions and for contingency
conditions. The former are frequency regulation, regulating reserve and load following services. The
latter are spinning, non-spinning and supplemental reserves.
12
Access Responsible Parties
17
Figure 12 Comparison of European and US Ancillary Services (Hurley et al., 2013)
Frequency regulation provides reliability services on automatic generation control in response to
changes in system frequency. These services are performed by reserves able to respond at full
capacity within seconds. In the European Union this service type is called primary reserve (Hurley et
al., 2013) ENTSO-E13 manages the frequency control of the interconnected European grid, and
establishes rules on how the national TSOs, such as Elia in Belgium, have to manage the frequency
regulation of 50Hz.
Regulating reserve, or regulation, is a reserve on automatic generation control able to start after
maximum 30 seconds, with full availability within minutes, in response to constantly changing load
conditions and altering frequency. In the European Union these services are generally called fast
secondary reserves. (Hurley et al., 2013) These reserves are continuously automatically activated, and
this within maximum 30 seconds for at least 15 consecutive minutes. (Elia, 2013a)
Load following is the process of regulating supply to follow the changes in net demand, by ramping
up or down production or demand (DR). In the U.S. these services are supplementary to regulating
reserves, but have a slower response time. They are a bridge between regulating reserves and hourly
energy markets. Regulating reserve and load following are necessary to correct imbalances under
normal conditions, i.e. current or expected imbalances, for each trading period. (Hurley et al., 2013)
Spinning reserve is online but unloaded generation or responsive load ready to be activated when the
TSO requests it in case contingency conditions occur. Full output can be reached within 10 minutes.
13
European Network of Transmission System Operators for Electricity
18
Non-spinning reserve is not connected to the system, but full output can be reached within 10
minutes as well. This reserve consists of e.g. back-up generators. (Hurley et al., 2013)
Tertiary reserve is used in the European Union to manage congestions, follow load, control frequency
changes, and restore secondary control reserve when secondary reserve is not sufficient. Manual
dispatching is required, thus in contrast to primary and secondary reserve not automated. (Hurley et
al., 2013) Response time is within 15 minutes and the duration, max 8 hours, depends on whether
congestion and imbalance problems are solved or not. (Elia, 2013a)
Supplemental, operating, or replacement reserves in the U.S. and elsewhere have the same
characteristics of tertiary reserves, but the response time is between 15 minutes and 1 hour at most
to come online. (Hurley et al., 2013)
In the U.S. DR participants able to curtail within a maximum of 30 minutes may participate in
ancillary services markets. They can help maintaining the balance between electricity load and
generation, and recover energy unbalances after unexpected outages in supply and demand (Hurley
et al., 2013)
3 April 2014 the Belgian federal ministry for energy approved the creation of an 800 MW strategic
reserve of power capacity by Elia by November 2014, and this for three years. This strategic reserve
can be dispatched when the other reserves, i.e. primary, secondary and tertiary reserve, aren’t
sufficient. How this reserve will be created is to be confirmed. Elia will consider bids from suppliers
offering generation capacity as well as from large manufacturers offering to curtail load in return for
a premium. Tenders for industrial DR will be closed for a period of one year from November 2014.
(EDEM, 2014)
2.3.3 Capacity market
The objective of capacity markets is to guarantee adequacy of the system, i.e. long-term security of
supply. Capacity markets cover a range of payment mechanisms developed to remunerate
participants based on their availability and installed amount of capacity to produce power or curtail
demand, i.e. CRMs. To this end generators and DR resources get a more predictable and stable
income flow to recover their fixed costs. These mechanisms can reduce the missing-money problem
(see supra Merit order effect p.10). (EURELECTRIC, 2011)
In Figure 13 an overview is given of the various capacity market policies in European countries. As
mentioned earlier, Belgium is taking measures to support gas plants to recover their fixed costs by
remunerating them based on the installed capacity (€/MW) instead of the output (€/MWh). The
19
Belgian government has called for 800 MW of new gas-fired capacity under a tender that guarantees
price support for 6 years per MW installed. However, no CRM is proposed for all generation assets or
DR resources, which strongly limits the competition between different assets.
The creation of an 800 MW strategic reserve by Elia, as said in the previous section (see supra p.18),
is also a sort of CRM, but bids from suppliers offering generation capacity are separated from
industrial DR resources bids. This again limits competition between the different strategic reserve
suppliers.
Figure 13 Capacity market policies Europe (EURELECTRIC, 2013)
2.4 Classification of demand-side resources
2.4.1 General overview
Figure 14 gives an overview of the different general demand-side programs available. As previously
explained, DR and energy efficiency are the two enablers for DSM. Energy efficiency is not the
subject of the thesis and is therefore not discussed. DR resources can generally be classified as
dispatchable or non-dispatchable resources.
The purpose of dispatchable resources is to directly bid the flexibility of the consumer on the market:
e.g. energy bids in wholesale markets, ancillary services and capacity tenders for balancing reserves.
20
To value the flexibility of the consumer, specific rules and contracts are in place to organise the
energy transfer from the consumer to the buyer of the flexibility.
The purpose of non-dispatchable resources is to lower the consumption level of the electricity
supplier and of the balancing portfolio of balancing responsible parties (BRPs). This sort of flexibility
is created by the development of time-of-use tariffs, such as the day and night tariffs in Belgium,
which stimulate consumers to shift consumption in the day to the cheaper night hours.
Figure 14 Demand-side management categories (Hurley et al., 2013)
Dispatchable resources
Dispatchable resources are supervised by a third party or aggregator. A contract between the DR
resource and the aggregator is concluded in which predefined payments and other specifications are
defined. An aggregator can be a curtailment service provider14 or an electric utility15. In the
remainder of the thesis the term aggregator is used in order to preserve clarity. (FORBEG, 2014)
14
“Businesses that sponsor demand response programs that recruit and contract with end users, and sell the aggregated demand response to utilities, RTOs and ISOs. A Curtailment Service Provider is sometimes called an aggregator and is not necessarily a load-serving entity.” (FERC, 2012, p.64) 15
“A corporation, person, agency, authority, or other legal entity or instrumentality producing, transmitting, or distributing electricity for use primarily by the public.” (FERC, 2012, p.65)
21
An aggregator requests its portfolio of dispatchable resources to reduce load to participate in energy
markets. This load reduction can be done by reducing non-shiftable load, shift load to later hours,
use stored energy or use on-site back-up generators (see ‘Demand response load types’ p.13). The
aggregator decides which of its different dispatchable resources he should request depending on
how much is needed.
When the customer responds to the request, one speaks of a program event. Dispatchable programs
include direct load control of customer electrical equipment, curtailable and interruptible load
contracts and a range of other programs. (FERC, 2010) These are described in the section
Dispatchable demand response programs below.
Non-dispatchable resources
Non-dispatchable resources are customers who decide whether and when they reduce consumption,
based on variable retail electricity rates. They participate in retail price-based DR programs charging
higher prices during peak hours and lower prices during off-peak hours. The timing and volume of
the load reduction depends on the retail price and the customer decides himself when he responds.
This makes these resources non-dispatchable. (FERC, 2010)
Price-based DR programs, such as time-of-use, critical peak pricing and real-time pricing tariffs are
described in section ‘Non-dispatchable demand response programs’, p.25. These programs let
customers participate in short term energy markets and may become more widespread for
residential and small commercial consumers as smart metering systems are installed. (Aalami, Parsa
Moghaddam, & Yousefi, 2010)
In case smart meters have a large installed customer base, smart metering can make it possible to
aggregate consumers on a large scale so they can provide dispatchable services to aggregators.
(Hurley et al., 2013)
2.4.2 Dispatchable demand response programs
In this section, the various dispatchable DR programs existing in the U.S. (see Figure 14) are
discussed. The programs are described in its most general form, since several variants exist. Besides,
they can also overlap. This is for example the case when a DR aggregator has a portfolio of end-
customers participating in customized interruptible load programs offered by the aggregator, while
the aggregator himself provides its DR portfolio for example to a TSO, i.e. Elia in Belgium, through an
ancillary services market program.
22
This section describes all possible dispatchable programs existing in the U.S. In Europe only some of
them have already been implemented.
Direct Load Control Programs
Participants in direct load control programs are mainly residential customers and small commercial
customers. For limited periods, the aggregator remotely shuts down electrical equipment, e.g. water
heaters, air conditioners, heat pumps, swimming pool pumps, typically during the times of system
peak demand. The participating customers agree to the maximum number of events and their
durations, defined by hours a day and season of the year. (U.S. Department of Energy, 2006) Direct
load control programs are voluntary programs and do not penalize if customers do not curtail
consumption when requested by the aggregator. (Aalami et al., 2010)
New technologies have enabled more sophisticated remote switches, allowing more targeted
reductions. (FERC, 2006)
Curtailable Load Programs
In case of curtailable load programs, the targeted participants are large commercial and industrial
consumers that are able to curtail their load to a minimum pre-specified level per event, e.g. 100 kW,
or curtail a specific block of electric load. Consumers with continuous processes (e.g. silicon chip
production) or twenty four seven operations are not suitable, as well as schools and hospitals obliged
to continue providing service. (FERC, 2006)
Again, the number of events and their durations are agreed upon in advance. When the aggregator
calls for curtailment, the participating customer is asked to reduce load to predefined values,
typically within thirty minutes to two hours. These electricity reduction practices include air
conditioning, lighting, crushing and compressing operations, ventilation, process heating and cooling,
and ventilation.
Each call for curtailment is managed by the customer himself, so control of the aggregator is limited.
The customer decides whether he participates in the event or not, but the contracted number and
duration of events have to be met to avoid penalty costs or reduced payments. (Peak Load
Management Alliance, 2002) An interval meter or advanced metering16 infrastructure (AMI) is used
to calculate load reduction for the duration of each event. (Aalami et al., 2010)
16
“Advanced meters are meters that measure and record usage data at hourly intervals or more frequently, and provide usage data to both consumers and energy companies at least once daily. Data are used for billing and other purposes. Advanced meters include basic hourly interval meters, meters with one-way
23
Interruptible Load Programs
Interruptible load programs require significant amounts of load to be available for reduction, e.g.
minimum 1 MW. Participants are industrial customers with operations able to curtail large parts or
even all load and commercial customers, particularly if on-site back-up generators can foresee a
major part of the load. Customers with continuous operations, service or processes are not suitable.
Each call for interruption, ten minutes to a few hours prior to the event, is managed by the customer
or the aggregator.
Unlike the curtailable load programs, participation in the interruptible load program is mandatory
and may be required at all times during the year or within pre-agreed periods. Financial penalties are
applied when contractual commitments aren’t satisfied. AMI is essential to measure the participant’s
performance. (Peak Load Management Alliance, 2002)
Ancillary Services Market Programs
Ancillary services DR programs permit participating consumers to offer their load reductions or load
increases to TSOs as operating reserves or as regulation. In Belgium large industrial consumers and
aggregators of smaller consumers are already enabled to provide ancillary services to Elia. These
Belgian case studies are given in chapter three.
It is required as a participant to adjust load quickly when a reliability event occurs, with a response
rate depending on the type of reserve being provided (see supra p.17). These DR programs involve a
relatively high minimum required amount of load reduction compared to other programs, and
require AMI, more specifically real-time meters, which limits the number of suitable participants. The
targeted DR resources are for example large industrial processes, remote automatic control of
electrical equipment (e.g. air conditioners) and large water pumping load. (FERC, 2006) A Belgian
aggregator REstore has its own AMI that enables them to shut down customers’ industrial processes
in a fully automated way within 30 seconds. This is discussed more in detail in chapter three.
Capacity Market Programs
Capacity market programs give large consumers guaranteed payments in exchange for mandatory
load curtailment on short notice when notified. Program participants face penalties if they do not
reduce load when dispatched in the energy market. (Aalami et al., 2010) The guaranteed payments
consist of reservation payments defined by capacity market prices (€/MW). In some programs, also
communication, and real-time meters with built-in two-way communication capable of recording and transmitting instantaneous data.” (FERC, 2012, p.7)
24
energy payments are given for the amount actually reduced when dispatched. This is the case for
one DR program in Belgium, described in chapter three.
Capacity programs are included among the planned reserves of TSOs and therefore part of the
capacity resources designed to meet system reserve requirements. (U.S. Department of Energy,
2006)
Emergency DR Programs
Emergency DR programs offer incentive payments to participating consumers for measured load
reductions when called upon by the aggregator or TSO in case reserve short falls arise. These
payments are often linked to real-time wholesale market prices or to the outage cost of the
participating customer. The customer doesn’t receive up-front capacity payments since emergency
DR programs are not considered as system resources for planning purposes, but as supplemental
resources to assure reliability when unexpected reserve shortfalls occur. Emergency DR programs are
dispatched only when available capacity and operating reserves are fully used, hence reducing the
probability of forced outages. (U.S. Department of Energy, 2006)
Emergency DR programs are voluntary programs and do not penalize if customers don’t reduce
consumption when requested by the aggregator or TSO. Because no commitment on the consumer’s
part is required, emergency programs have attracted a lot of customers in the U.S. who can curtail
consumption on a pay-for-performance basis. (U.S. Department of Energy, 2006)
However, the voluntary nature of the emergency DR programs has consequences for their usage in
planning and grid operation, since precisely forecasting the extent of the load curtailment when the
program is activated is impossible. (FERC, 2006) In Belgium no such programs exist.
Demand Bidding or Buyback Programs
Demand bidding or buyback programs are dispatchable economic programs as can be seen on Figure
14. The primary focus is the economic benefit gained by reacting to high electricity prices. These
programs encourage large consumers to bid into wholesale electricity markets and offer load
reductions at a certain price they’re willing to respond, or offer a certain reduction volume they’re
willing to reduce at a given price. (Aalami et al., 2010) When the customer’s load reduction offer is
accepted he is obliged to reduce load as contracted, or has to deal with penalties. (U.S. Department
of Energy, 2006)
These programs allow the participating customer to remain at fixed rates, but use demand bidding to
obtain higher payments for their demand reductions when wholesale prices are high. (FERC, 2006)
25
Figure 16 Critical peak pricing (FERC, 2006)
2.4.3 Non-dispatchable demand response programs
Time-of-use pricing
Time-of-use (TOU) pricing is the basic type of
price-responsive DR programs.
The TOU rates can vary by season and by time
of day, e.g. peak hours versus off-peak hours,
and are generally predefined for several
months or years. (U.S. Department of Energy,
2006) Rates during off-peak periods are lower
than those during peak periods. The size of the
spread between peak and off-peak prices is important to incentivise customers. The peak period can
last between 9 a.m. and 8 p.m. Off-peak hours are usually in the evening, night, and weekends. A
TOU example is given in Figure 15. Regulation has to decide how many different periods are relevant,
e.g. peak and off-peak, two daily, weekends as off-peak and seasonal differences. (FERC, 2006)
In the U.S., TOU rates are widely used by residential customers, large commercial and industrial
customers. Meters are necessary to register cumulative usage during the different periods, unless
only seasonal rates are defined. (U.S. Department of Energy, 2006) Also in Belgium a lot of
consumers have separate meters for day and night consumption.
Critical peak pricing
Critical peak pricing (CPP) is a form of TOU
pricing. The difference is that during critical
peak periods, i.e. when system contingencies
or high peak demand occur, a very high price
is charged. An example is shown in Figure 16.
These critical peak periods account for a
small number of days or hours per year, have
much higher prices than normal peak prices
and occur when there are system
contingencies. (FERC, 2006)
Figure 15 Time-of-Use (FERC, 2006)
26
Figure 17 Real-time Pricing (FERC, 2006)
Figure 18 Two-part real-time pricing (FERC, 2006)
Real-time pricing
Real-time pricing (RTP) rates fluctuate hourly,
directly reflecting the wholesale electricity price.
An example is shown in Figure 17. This
connection between retail and wholesale rates
enables price-responsiveness in the retail
market, often referred to as dynamic real-time
pricing. Typically prices are displayed to
participating consumers on an hour-ahead or day-
ahead basis. (U.S. Department of Energy, 2006)
Across the U.S., several variants exist: day-of or hour-ahead pricing versus day-ahead pricing,
mandatory versus voluntary, and one-part versus two-part pricing. (FERC, 2006)
Day-ahead RTP
Customers are given one-day notification of the electricity prices for each of the 24 hours next day,
giving them the possibility to plan responses (e.g. shifting load to off-peak periods, using on-site
generation) in advance.
Two-part RTP
In this variant, each customer has its
historical baseline layered with hourly
fluctuating prices for marginal use
deviating from the baseline. This protects
the customer from real-time pricing
volatility and enables bill savings by
shifting their marginal use during price
hikes to lower price periods. Figure 18
shows how two-part RTP works.
Large customers can use this pricing tariff as an alternative to the standard tariff, when they do not
want to expose their entire energy cost to real-time prices, but are willing to take some price risk.
27
3. Chapter 3 Demand response case studies
In recent years, DR programs have begun to emerge in a few European Union countries, e.g. the UK,
Italy and Spain, but there are still only few benefits achieved from the implementation. The uptake of
DR programs at EU level does not evolve as fast as in the U.S. where DR programs have been
developed on a large scale. (European Commission, 2014c)
In Figure 19 the share of North America, Europe, Asia Pacific and the rest of the world in the total
existing DR programs worldwide is shown. According to a study by Navigant Research17 (Navigation
Research, 2013) almost 95% of all programs are located in North America. Generally, capacity market
programs have been successful business models since participants receive reliable monthly capacity
payments in response for their reduction or dispatchable back-up on-site generation. Worldwide
capacity programs represent 54% of all programs. DR programs participating in ancillary services
markets account for only 1.4%.
Figure 19 Number of demand response projects worldwide (Navigation Research, 2013)
It is clear that Europe and Belgium can learn a lot from North American and in particular U.S.
experiences. To this end it’s important to review some important case studies of DR programs
implementations in the U.S. A description of the existing DR programs in Belgium gives a better view
on our country’s current status.
17
Navigation Research. (2013). Demand Response Tracker 4Q13 Executive Summary. Navigant Research is a market research and consulting team that provides in-depth analysis of global clean technology markets, with offices located in the U.S., Europe and Asia.
28
3.1 Demand response cases in the U.S.
3.1.1 Regulations enabling demand response
Before going into detail about the U.S. DR cases, it’s important to have an insight into the two recent
regulations in the U.S. that enabled the increase in DR participation: FERC Order No. 745 and Order
No. 755.
An important enabler for DR customers participating in U.S. energy markets is FERC Order No. 745, in
effect since March 2011. “FERC Order No. 745 established several guidelines for demand response
participation in energy markets:
1. Demand response can provide benefits similar to generation resources
2. Payments to demand response resources should be at the full market price of energy
3. A net-benefits threshold needs to be established
4. Demand response should be able to set the energy market clearing price, if it can provide the
next available megawatt in economic order” (Hurley et al., 2013, pg.43)
Before the order was issued in 2011, some regions in the U.S. already paid DR resources the market
clearing price for participating in the energy markets. However, full compliance with the order will
gradually expand these rules to other areas. Today, most regions are still developing plans for full
compliance. (Hurley et al., 2013)
The participation of DR resources in ancillary services markets has been very slow spread. FERC
Order No. 755 on compensation for regulation services, issued on October 2011, is thereby an
important step for DR resources to offer ancillary services. The order is issued to avoid that
traditional generation resources are preferred by system operators over other resources, such as
electric storage technologies, thermal storage, batteries, and DR resources. FERC ordered regional
system operators to offer payments partly based on the response rate and availability of the
resources to provide regulation services. (Hurley et al., 2013)
3.1.2 Evolution of demand response in the U.S.
Every two years the FERC has surveyed electricity entities to gather information about the potential
load reductions and participation rate of DR programs. In Figure 20 the evolution of the total
reported potential peak load reduction through DR programs in the U.S. is given for all FERC Survey
years, i.e. 2006 through 2012. These values do not represent the actual potential total load reduction
in the U.S. since not all existing entities responded to the survey.
29
From 2010 to 2012 the reported potential peak reduction from DR increased from 53,062 MW to
66,351 MW, which is a 25% increase.
Figure 20 Total reported potential peak load reduction for 2006 through 2012 FERC Surveys
In the U.S. the potential peak load reduction through DR programs is estimated to be nearly 72,000
MW in 2012, which is about 9.2% of the total U.S. peak demand. (FERC, 2012)
In Figure 21 the actual peak reduction of residential, commercial and industrial DR programs is
shown for all independent system operators (ISOs) and regional transmission organizations18 (RTOs)
in the U.S. in 2010. The mean actual DR peak load reduction is around 4.5% of total peak load.
Figure 21 Actual peak load reduction residential, commercial & industrial DR in the U.S. (SEDC, 2013)
18
ISOs and RTOs operate the high-voltage system, manage electricity generation and control long-term regional planning for a defined region.
30
3.1.3 Contributors
The yearly steady growth of the reported potential peak load reduction in Figure 20 has many
contributors, varying across customer class, ownership type, and program type. In Figure 22 the
reductions by customer class are shown for the four survey years.
Figure 22 Reported potential peak load reduction by customer class (FERC, 2012)
Commercial customers include various facility types, i.e. office buildings, hospitals, federal facilities,
local government facilities, state facilities, universities, retail establishments, non-profit organization
facilities, master-metered apartment buildings, homes on military bases, and institutional living
quarters. (FERC, 2012)
The industrial sector encompasses energy-intensive industrial activities, such as manufacturing,
agriculture, forestry and fisheries, processing, mining, and construction. (FERC, 2012)
The reported potential peak reductions provided by commercial and industrial customers, as shown
in Figure 22, steadily increased due to better reporting in the 2012 survey and of course due to new
and expanded DR programs. In 2012 the reported potential reduction was 28,088 MW, which is
6,683 MW more than in 2010. A large contributor to this increase is the TOU pricing program of
Oklahoma Gas and Electric, the largest electric utility in the state of Oklahoma. They welcomed 900
MW extra DR capability from commercial and industrial customers between 2010 and 2012. (FERC,
2012)
The residential sector are private households having a variety of electric-powered devices, such as
water and space heating, air conditioning, tumble dryers and washing machines.
31
Residential customers’ reported potential reduction grew from 7,189 MW in 2010 to 8,134 MW in
2012, which is a 13% increase in two years. Increases in direct load control and time-based tariff
programs are the largest contributors. For example Baltimore Gas and Electric, Maryland’s largest gas
and electric utility, offers a residential direct load control program which increased significantly from
272 MW to 763 MW potential peak load reduction. The program was activated during an emergency
event in July 2011. The impact was measured at roughly 600 MW. (FERC, 2012)
Wholesale customers include aggregators, ISOs, RTOs, wholesale electric marketers who are
engaged in buying and selling electricity without certainly owning generators or transmission
facilities, joint action agencies which are wholesale power suppliers owned or created by municipal
utilities, and generation and transmission corporations. (FERC, 2012)
Wholesale customers had a DR reduction capability of 22,884 MW in 2010 which increased to 28,807
MW in 2012, a growth of almost 26%. This is primarily due to the increased enrollment in DR
programs offered by PJM Interconnection (PJM) and MISO, two ISOs. (FERC, 2012)
In Figure 23 the reported potential peak reduction in 2012 is given by DR program type and customer
class. Four of the DR programs make up 80% of the total reported potential peak reduction of 66,351
MW in 2012: load as capacity resource represents 29%, followed by interruptible load programs with
24%, direct load control programs with 15% and time-of-use programs with 12%.
Figure 23 Reported potential peak reduction by program type and by customer class in 2012 FERC Survey
32
It’s interesting to take a look at the experiences of these programs widely used in the U.S. and see
how they can provide insights for the development of Belgium’s DR programs.
3.1.4 Case studies in the U.S.
First, the influence of DR programs implemented by two important ISOs is discussed. The ISOs are
PJM and the New York Independent System Operator (NYISO). Two programs of PJM and one
program of NYISO are described. The case studies show how dispatchable DR resources have an
impact on capacity and reserve market clearing prices and how they can contribute to avoid power
outage in contingency situations.
Also two case studies of non-dispatchable DR programs are shortly described. The first pricing
program is the State-wide Pricing Pilot in California, which was a pilot time-of-use and critical peak-
pricing program in which residential and small commercial & industrial customers were involved. The
second non-dispatchable case study is Georgia’s real-time pricing program for large commercial and
industrial customers.
PJM’s Full Emergency Load Response program
PJM’s capacity market model is called the Reliability Pricing Model (RPM), which is implemented in
2007. It’s a three-year forward capacity market to provide the PJM region with long-term price
signals enabling the necessary investments to maintain existing generation assets and stimulate new
capacity resources, including DR capability. The same market rules for generation assets apply for DR
resources. (PJM, 2014b)
DR resources are allowed to submit offers in the forward RPM auctions by participating in the Full
Emergency Load Response program, which is a capacity market program instead of an emergency DR
program as the name suggests. In Figure 24 the different roles and their relations are visualized.
Figure 24 Full Emergency Load Response program
33
End-customers participate in the programs through aggregators who act as their agents or on their
own when the customer is large enough. Aggregated end-customers participate in interruptible load
programs of an aggregator. This aggregator notifies its customers when the load has to be reduced.
Each aggregator manages his own portfolio of DR customers to meet commitments and avoid
financial penalties when cleared in the RPM through participation in the Full Emergency Load
Response program. (PJM, 2014a)
When an offer is cleared, the DR provider is obliged to reduce load in the promised delivery year
when requested to and receives the capacity clearing price in return as availability payment, and an
activation payment when actually dispatched in the day-ahead or real-time market in the promised
delivery year. (PJM, 2014a)
Since 2011 the Full Emergency Load Response program has three different variations: the Limited DR,
the Extended Summer DR and Annual DR program. Specifications are given in Table 1.
Period Hours within day Duration event # Events/year
Limited Weekdays
June - September 12 p.m. – 8 p.m. ≥ 6 hours ≤ 10 times
Extended Summer
All days May - October
10 a.m. – 10 p.m. ≥ 10 hours No limit
Annual All days
June – May next year 10 a.m. – 10 p.m. ≥ 10 hours No limit
Table 1 Full Emergency Load Response programs (PJM, 2013)
In Figure 25 the amount of DR offered and cleared in RPM auctions is shown, together with the
weighted average clearing price in $/MW-day. Before the delivery year June 2012/May 2013 (auction
held in 2009) there was also a capacity program that didn’t participate in the RPM. The huge increase
from delivery year 2011/2012 to 2012/2013 is due to the requirement for all DR resources to
participate in the RPM.
From the delivery year 2012/2013 there’s a steady growth in the amount of DR offered and cleared,
with 14,832 MW DR offers cleared in delivery year 2015/2016 (auction held in 2012). Weighted
average clearing prices range from $100 to $160 per MW-day for delivery years 2012/2013 to
2015/2016.
34
Figure 25 Demand response in PJM forward capacity auction with weighted average clearing price (Hurley et al., 2013)
The impact of DR on the RPM auctions is shown in Table 2. The actual auction results are compared
to the results when no DR offers are made, assuming everything else remains the same. This means
that there are no limited and extended summer products anymore, but only the annual product type
without annual DR is offered. Results are shown for three different areas of the RPM.
In case no DR bids are offered, the RTO clearing price would increase to $401.42 per MW-day, which
is an increase of 195% for the annual clearing prices, and the total cleared unforced capacity (UCAP)19
would decrease to 156,381.4 MW. For the MAAC20 region and the ATSI21 region the annual clearing
price would increase with 140% and 51%, respectively.
Table 2 Impact of DR on the Reliability Pricing Model (The Independent Market Monitor for PJM, 2013)
19
Unforced capacity, or UCAP, is the actual available installed capacity (ICAP) adjusted for performance, and the unit used for selling and buying ICAP. (Westcott, 2003) 20
Mid-Atlantic Area Council, a regional reliability council responsible for ensuring the adequacy, reliability, and security of the bulk electric supply systems of the MAAC Region through coordinated operations and planning of generation and Transmission Facilities. (The Independent Market Monitor for PJM, 2013) 21
American Transmissions System, Inc. is a multi-state transmission-only utility in the U.S. (The Independent Market Monitor for PJM, 2013)
35
The effect of DR offers on the clearing price in an auction mechanism varies over the
different RPM areas, but it is obvious that DR offers in the RPM have a huge influence on the
clearing price. When introduced, there’s a lower clearing price in the market, which means
that other resources such as generation assets offering higher prices will be pushed out of
the market (merit order effect). However, DR resources can offer relatively cheap capacity,
which is positive for end-customers’ electricity bill.
NYISO’s Special Case Resource program
In NYISO’s capacity market buyers and sellers are matched through the clearing price methodology. A
DR resource can only participate in the capacity market when it is registered as a Special Case
Resource, and market rules for DR and generation assets are the same.
Participants of the Special Case program are mainly industrial and commercial companies and only
reduce load when the NYISO requests for it two hours in advance. Small customers can also
participate through aggregators. The relations are the same as in Figure 24. Participants of the
Special Case program are obliged to curtail load when asked to and therefore receive capacity
payments. (New York ISO, 2014a)
The Special Case Resource program is the most significant and largest program operated by the
NYISO as regards the number of MWs and number of individual DR resources participating. (New
York ISO, 2014b) In 2011 the total Special Case Resource capacity reached 2,053 MW. (New York ISO,
2014c)
During summer heat waves in 2006, the peak load in New York climbed to more than 35,000 MW on
2 August in the afternoon. DR prevented power outages by reducing load with more than 1,000 MW
called upon by the NYISO, thereby limiting the peak load to less than 34,000 MW, as shown in Figure
26. The blue line was the expected load without DR and the green line the actual system load.
(Hurley et al., 2013)
36
Figure 26 Response from demand resources in NYISO on the 2nd
of August 2006 (Hurley et al., 2013)
Another heat wave in 2011 made the peak load in New York surge to more than 35,000 MW. DR
resources responded with more than 1,400 MW, which is 68.2% of the installed Special Case
Resource capacity and 4% of the peak load occurred. (Hurley et al., 2013)
NYISO’s Special Case Resource program shows how DR resources can contribute to a
sufficient provision of capacity in cases of extreme weather conditions. They are able to
prevent power outages and may reduce the need for extra, unattractive generation asset
investments, which will only produce energy for a couple of hours a year.
PJM’s Synchronized Reserve Market program
In case generation outage or disruption in the transmission system occurs, PJM is required to use
primary reserves that provide load until the system balance is recovered. PJM experienced 127
spinning events from January 2009 through December 2012. The events lasted on average 11.4
minutes. (Monitoring Analytics LLC, 2013)
PJM operates a Synchronized Reserve Market and Non-Synchronized Reserve Market to fulfil the
primary reserve requirement. The Synchronized Reserve Market consists of two tiers: Tier 1 and Tier
2. DR resources, whether aggregated or not, are allowed to participate on a voluntary basis in Tier 2
in case they can provide a minimum reduction of 0.1 MW and are able to react within 10 minutes.
When DR resources are cleared in the market, they’re obliged to curtail load when events occur.
(PJM, 2013) In Figure 27 the different roles and their relations are visualized.
37
Figure 27 Synchronized Reserve Market program
In 2012 the primary reserve requirement was 2,063 MW, of which at least 1,375 MW had to be
synchronized reserves. DR resources are relatively low cost and their participation lowers the
Synchronized Reserve Market prices. Before 2012, DR resources were limited to 25% of total
synchronized reserve requirement. Since 2012, the limit has increased to 33%. In 2012 the DR
resources share of the total Synchronized Reserve Market increased from 17.7% in 2011 to 29.8% in
2012. Its share of the cleared Tier 2 synchronized reserves was 23% in 2011 and 36% in 2012.
(Monitoring Analytics LLC, 2013)
In Table 3 the average Synchronized Reserve Market clearing price (SRMCP) is given for all months in
2012, as well as the average SRMCP in the hours when all cleared Tier 2 reserves were DR resources,
which was the case in 5% of total hours in 2012. The average SRMCP in 2012 for all cleared hours was
$8.02, while the SRMCP was $0.96 on average in the hours when all Tier 2 reserves were DR
resources, which is $7.06 less on average.
Table 3 Average SRMCP and average SRMCP when all cleared synchronized reserves are DR resources (Monitoring Analytics LLC, 2013)
38
It is obvious that DR resources bidding in ancillary services markets strongly influence the
average market clearing prices. This pushes other non-DR resources out of the merit order
and makes investments in those resources unattractive. However, for utilities obliged to
balance a certain region, providing sufficient synchronized reserves becomes cheaper when
DR resources are more and more allowed to bid in ancillary services markets.
California’s State-wide Pricing Pilot
In 2003 and 2004, the three investor-owned utilities of California operated the U.S.’ first pilot test
with dynamic pricing, called the State-wide Pricing Pilot, in which around 2,500 residential and small
commercial & industrial customers were involved for a period of two years. Time-of-use (TOU) and
critical peak pricing (CPP) programs were tested.
TOU rates were two-part: peak period (2 p.m. to 7 p.m.) and off-peak period. The CPP program used
TOU rates during 350 days a year, and 15 critical peak days. (Charles River Associates, 2005b)
In Figure 28 the change in residential peak period energy use is shown for different zones (%). Energy
reduction reached state-wide to more than 13% on critical weekdays, with the highest change in the
zones with the hottest climate. On normal weekdays there was on average a 4.7% reduction.
Figure 28 Change in residential peak-period energy use CPP program (Charles River Associates, 2005a)
In Figure 29 the impact of different peak prices on the energy used on critical days of the CPP
program is shown. The State-wide Pricing Pilot used an average critical peak price of $0.59/kWh
causing a 13% reduction impact (see also Figure 28). The pilot demand models can be used to predict
the impact of prices that differ from those used in the experiment and for different populations. The
conclusion is that in all zones higher critical peak prices would cause higher energy reductions per
hour. (Charles River Associates, 2005a)
39
Figure 29 Average percent reduction in peak-period energy use on critical days (Charles River Associates, 2005a)
The CPP program was a success. More than 70% of the participants chose to continue on the
experimental rates even when the pilot program ended and new metering infrastructure was
charged at a monthly price of $3 to $5.
The TOU program caused a state-wide peak period demand reduction of around 6% in 2003.
However, in the second year the TOU customers failed to sustain their DR behaviour.
The pilot case revealed that a CPP program on weekdays should be combined with a
traditional TOU program in weekends.
The yearly consumption didn’t change which means that customers increased energy use in
off-peak periods by almost exactly the same amount that they decreased during peak
periods. So the programs didn’t have an effect on annual energy use.
The pilot case also showed that customers are more likely to participate when they are fully
informed and when the state makes a strong commitment to pricing programs. The
development of AMI will further boost participation, as well as making DR a default option.
Georgia’s real-time pricing program
The investor-owned utility Georgia Power manages a RTP program with large commercial and
industrial consumers as target group. FERC calculated in 2010 that roughly 1,200 medium to large
commercial and industrial customers with a minimum size of 250 kW had registered for the program,
amounting to more than 4,000 MW of the 15,379 MW summer peak demand, i.e. 26%.
The customers can participate in the day-ahead or hour-ahead program. Most of them choose for
the day-ahead program and around 100 of the largest industrial participants choose to register for
40
the hour-ahead program. In general RTP customers require a fairly price of minimum $0.20-
0.30/kWh to be prepared to respond. (FERC, 2010)
On days with moderate prices, maximum load reductions were around 4% for day-ahead and 10% for
hour-ahead participants. On the high-price days the load reductions increased to 7% and even 30%
for day-ahead and hour-ahead participants, respectively, thereby reducing their electricity bills.
(FERC, 2010)
Georgia Power’s RTP program has indicated that well-designed programs are cost-effective
and attract a lot of medium and large commercial & industrial customers. It has also showed
that including price protection and risk management products has a positive effect on the
participation rate of DR programs.
3.1.5 Conclusions from U.S. experiences
DR has proven to be a multi-billion business in direct revenues per year to local businesses in the
U.S., and a lot of investments in generation, transmission and distribution have been avoided.
However, some important rules are crucial to encourage successful DR participation in wholesale
markets.
DR resources should be treated in the same way as supply-side resources. Regulation has to include
different provisions for demand resources, such as baseline measurement, telemetry and
communication requirements, minimum load requirements and performance penalties.
These regulation provisions should be provided at the level of the aggregator instead of the
consumers. An aggregator has to be treated as a single resource. In this way industrial, commercial
and residential consumers don’t have to stand in for the registration, prequalification, measurement
and communication requirements.
Consumer friendly products have to be in place to encourage participation. It’s important to develop
a wide variety of customer incentive schemes for various customer segments. In this way more and
more customers will be encouraged to participate in the customized DR programs.
Appropriate payment structures have to be in place. Market structures have to pay for flexibility by
giving capacity payments for being available and/or activation payments when actually dispatched in
energy markets.
Variable prices in the retail market have to be visible to consumers participating in non-dispatchable
DR programs: time-of-use, critical peak pricing and real-time pricing. When the prices are not
41
available to the end-customer, which is the case for Belgium, aggregators may mitigate this limitation
by pooling customers and sell the aggregated load in the energy wholesale markets. Thereby
consumers can benefit from energy price variations.
3.2 Case studies in Belgium22
Since a few years the Belgian TSO Elia has DR contracts with large industrial customers connected to
the transmission grid to provide balancing reserves. Recently also aggregators are allowed to provide
tertiary reserves and since 2014 the Belgian aggregator REstore is allowed to participate in the
primary reserve of Elia. This is the first time an aggregator has concluded a primary reserve contract
with Elia. This is possible through REstore’s fully automated platform that enables it to react within
15 seconds. Another scoop this year is the participation in Elia’s tertiary reserves contracts of
aggregators with a consumer pool connected to the distribution grid. The different existing DR
products in Belgium are explained below.
3.2.1 Total demand response ancillary services
In Table 4 the evolution of Elia’s contracted ancillary services volumes is given for 2011 to 2014. The
last two years Elia supported DR participation through the development of different new demand-
side based ancillary services programs. The total volume of DR ancillary services reached 338 MW
this year. Elia’s three available DR products in 2014 are R1 Load, R3 Interruptible Contract Holder
(ICH) and R3 Dynamic Pricing (DP).
MW 2011 2012 2013 2014
R1 Production 106 95 60.5 55
R1 Load 0 0 30.5 27
R1 Total 106 95 91 82
R3 Production 400 400 400 350
R3 ICH 261 261 261 261
R3 DP 0 0 0 50
R3 Total 661 661 661 661
Total DR ancillary services 261 261 291.5 338
Table 4 Evolution of the contracted volumes of ancillary services (MW) (Elia, 2013c)
DR ancillary services can be remunerated through reservation and/or activation payments. The
former are capacity payments (€/MW) for the 100% availability of the capacity. The latter are energy
22
More detailed information on existing DR products in Belgium can be found in the following studies: (FORBEG, 2014); (Elia, 2013b); (Elia, 2013c); (Elia, 2014b)
42
payments (€/MWh) for the actual activation of the service. Today only the R3 ICH product receives
both payments, while R1 Load and R3 DP only receive reservation payments.
Reservation payments
Payments for provision are made through monthly advances. The average power reserve activated
and thus provided to Elia is compared to the contractually agreed reference reserve. When greater
reserves are activated, Elia will pay surpluses. When they are less than the contracted amount,
advances are recovered and in some cases penalties are imposed. (Elia, 2014b)
The reservation payment is a fixed price defined in the yearly tenders, which are held for all three
demand-side products. Reservations are made per hour. In Table 5 the average price for the three DR
products are given. The tendering and contracting period is 2013 and the delivery period 2014.
Contracted volume
(MW) Average price
(€/MW/h)
R1 Load 27 *5…6+
R3 ICH 261 1.41
R3 DP 50 3.38
Table 5 Contracted volumes and average prices DR products Elia (Elia, 2014a)
Activation payments
R3 ICH is the only DR product that receives activation payments on top of the reservation payments.
The activation payment is linked to the bid prices for the R3 Production reserves. The incremental R3
Production bid selected by Elia based on the merit order principle in the day-ahead market sets the
activation payment. The average Belpex day-ahead market price was €50/MWh in 2013. However,
the minimum activation payment for R3 ICH is set at €75/MWh. (Elia, 2014b)
3.2.2 Elia’s R1 Load product
R1 refers to primary reserves and is used for the 50 Hz frequency regulation. The R1 Load product
introduced in 2013 is a DR product that may be activated only in case large frequency dips occur (i.e.
from < 49.900 Hz). Only industrial customers connected to the transmission grid of Elia, whether
aggregated or not, can participate in the yearly tendering process. The R1 Load reserves have a max
activation time of 30 seconds for the entire contracted volume. The low amount of yearly activations,
around 10, limits the impact on industrial processes. Elia is allowed to contract 50% of the total R1
volume with R1 Load reserves. The supplier is only remunerated for its hourly reservations through
capacity payments, but no activation fees are paid. (Elia, 2013b)
43
In 2013 the Belgian aggregator REstore has made a contract with Elia for providing a part of the total
primary reserve in 2014. Details about the contract are not made public, but it will be at least 10% of
total primary reserve. REstore has its own patented automated DR platform called Flexpond that
enables them to fully automate their DR pool and react within 15 seconds. REstore has interruptible
load contracts with industry sites in the cement, pulp & paper and chemical industry, which are
pooled together and offered to Elia, National Grid UK23, and EDF Belgium24. (Power in Europe, 2013)
3.2.3 Elia’s R3 Interruptible Contract Holder product
R3 refers to the tertiary reserves which is manually activated by remote control by Elia and can be
provided by spinning reserve, fast start up units and DR resources. R3 Interruptible Contract Holder
(ICH) is a highly flexible product to deal with imbalances and congestion problems and covers about
40% of total R3’s contracted yearly volume. Industrial customers connected to the transmission grid
can participate in the yearly capacity tendering process for R3 ICH. (Elia, 2014b)
Customers are obliged to reduce load below the contracted shedding limit within 3 minutes, without
being notified in advance. Generally there are only 2 to 4 activations per year with at least 24h
between each one. The duration per event is between 15 minutes and 2 to 8 hours, depending on
the contract specifications. The total sum of the duration of interruptions over the contracted period
is maximum 16 hours or 24 hours, also depending on the contract type. The supplier is remunerated
for its hourly reservations through capacity and activation payments. (Elia, 2013b)
Since a couple of years, Elia contracts 261 MW R3 ICH reserve with large industrial consumers. In
2013 a part of Elia’s R3 ICH reserve is contracted with Energy Pool, a French aggregator offering 20
MW DR capacity. This is the first time Elia closed a contract with an aggregator. Energy Pool has a
total virtual power pool of 1,200 MW and has contracts in 4 countries outside France. Their main
Belgian consumers are operational in the steel industry, electrometallurgy, pulp and paper industry,
cement industry and agri-food industry. (Energy Pool, 2014)
3.2.4 Elia’s R3 Dynamic Profile product
The R3 Dynamic Profile (DP) product is a new product introduced in 2014. It was created in 2012 as a
pilot study in collaboration with the Belgian aggregator REstore to exploit the flexibility of customers
connected to the distribution grid. Besides, also industrial customers connected to the transmission
grid can participate in the yearly capacity tendering process. (Elia, 2013b)
23
TSO of the UK 24
Electricity producer, also acting as a Balancing Responsible Party
44
The R3 DP competes with tertiary production reserves (R3 Production) in the capacity allocation
process. The R3 DP volume contracted by Elia is thereby deducted from the 400 MW capacities that
Elia will contract with production reserves (see Table 4). (FORBEG, 2014)
The reserve supplier is only remunerated for its hourly reservations through capacity payments, no
activation fees are paid. (Elia, 2013b)
Elia contracted 50 MW R3 DP with three parties: REstore, EDF Belgium and Actility Benelux. Actility
Benelux is another DR aggregator and EDF Belgium uses the back-up generators located at the sites
of industrial customers. Maximum 41 activations per year are allowed with a maximum duration of 2
hours per activation and a response time of max 15 minutes. Offered volumes were substantially
higher than the target, which shows that there is significant potential for flexibility located in the
distribution grid.
When the nuclear power plant Doel 1 was shut down unexpectedly on 13 February 2014, Elia
activated its R3 DP product. The use of this flexible power connected to the distribution grid was a
scoop for Belgium. (Power in Europe, 2013)
3.2.5 Strategic reserve
In 2013 the Minister Council has approved “plan Wathelet” to ensure security of electricity supply in
Belgium. One of its ambitious goals is a supplementary capacity of 400 MW DR resources by 2015,
provided by large industrial consumers and aggregators. (FORBEG, 2014) This means more than
double of the existing DR capacity contracted by Elia. However, details about how these resources
will be selected and remunerated are not yet published.
3.2.6 Bid ladder platform
Elia currently started the design phase of a future balancing platform, called the bid ladder platform.
This new platform will allow market players to offer on top of centralized production units all
available balancing flexibility coming from load and/or decentralized production units connected to
the transmission or distribution grid. There will be three types of balancing energy products: fast
standard, slow standard and emergency products. Response time is maximum 15 minutes and
activation durations are max 45 minutes. (FORBEG, 2014) This will offer new opportunities for DR
resources.
45
3.2.7 Smart offers day-ahead market
In the near future Belpex will implement “smart offers” in the day-ahead market. Non-reserved
flexibility on the supply-side can be offered, activated and remunerated to keep the control area in
balance. In a later phase non-reserved flexibility of demand-side and storage resources will also be
able to offer in the day-ahead market through these “smart offers”. (FORBEG, 2014)
These new developments will increase DR participation motivated by arbitrage gains and can solve
peak power and intermittent RES issues. It will be a game changer for many industries and
aggregators in Belgium.
46
4. Chapter 4 Potential of industrial and residential demand
response in Belgium
Following on the plans for strategic reserve, a bidding ladder platform and smart offers in the day-
ahead market, it’s important to have an insight into the flexibility potential of both large and small
electricity consumers in Belgium. Both industry and households can contribute to grid stability by
making (parts of) their consumption flexible and by responding to possible shortages in peak periods.
As explained in the previous chapter some very large consumers already offer their flexibility to Elia.
Also smaller industrial consumers are more and more allowed to contribute to grid stability through
contracts with aggregators. However, there’s not yet taken advantage of the flexibility of households.
To this end it’s interesting to investigate how much flexibility a household can offer.
In the first section of this chapter the results and conclusions of a study conducted by Elia, Febeliec
and Energy Ville about the potential of large industrial consumers in Belgium is shortly discussed.
In the second section the potential of residential consumers is investigated. It is estimated how much
flexible capacity households can offer, what service this flexibility can provide to balance the system
and increase the adequacy of the electricity grid, and how much money an aggregator can get for his
service.
4.1 Potential of industrial demand response in Belgium25
In 2013 Elia, Febeliec and Energy Ville have conducted a study to which 29 industrial consumers
connected to the Elia grid have contributed. They did a survey to investigate the Belgian industrial DR
potential. The consumers who responded to the survey represent 13.6% of the Belgian electricity
consumption of 2012. They appear to have a total flexible capacity of 631 MW of which 134 MW is
not yet operated on a smart basis to reduce energy costs.
In Figure 30 the results are shown. A distinction is made between the different response times. The
green bars represent the cumulative flexible capacity. Those able to respond within 15 minutes can
participate in the balancing market, and a large part of it already provides ancillary services for Elia.
Flexible capacity with a response time of 1 to 2 hours can participate in the intraday market and
those with longer response times only in the day-ahead market.
25
Detailed information about the study and results of the survey are available in the following study: (Febeliec et al., 2013).
47
About 228 MW of total potential flexible capacity is not suitable for balancing services (response
time > 15 minutes), but can benefit from participating in the energy markets, i.e. the intraday and
day-ahead market, through specific agreements with Balancing Responsible Parties (BRPs), and can
benefit from participating in the strategic reserve (plan Wathelet). A significant part of this capacity is
not yet used.
Figure 30 Flexible capacity of industrial consumers (Febeliec, Elia, & Energy Ville, 2013)
There is certainly unaddressed potential for large consumers that can participate in the energy
markets through contracts with BRPs. Elia only handles residual imbalances that could not be
balanced by BRPs, so the volume traded is relatively low compared to the balancing portfolio
volumes of BRPs. A huge opportunity for DR lies within agreements with BRPs and this will become
increasingly attractive when roles and responsibilities for the different parties, i.e. the aggregator,
BRP, TSO, DSO and supplier, are clearly designed.
Quantitative extrapolations for the Belgian industry can’t be made due to the biased industry
population in the survey. However, the study shows that large industrial electricity consumers in
Belgium clearly have a substantial potential of capacity able to participate in DR. The study also
revealed that less strict contractual obligations and longer announcement times, i.e. minimum 1
hour, will help to address new flexible sources in the industry. Nevertheless, the majority of
industrial customers are enthusiastic about the DR programs. (Febeliec et al., 2013)
In the next section the potential of residential DR is calculated. This is the focus of the chapter, since
there are currently no experiences with DR programs for households and no studies are made yet to
estimate their potential flexibility today.
48
4.2 Potential of residential demand response in Belgium26
Two important evolutions in the residential sector strongly influence the electricity grid: the
increasing amount of intermittent PV solar panels installed at home and an increasing electricity
demand due to the shift from fossil-fuelled systems towards electrical systems. This policy of the
residential building sector is described in the first section.
Thereafter the potential flexibility present at residential houses is calculated. Only electrical
equipment that does not intervene in the comfort of the households is taken into account.
Field measurements were made at one house for one week to calculate the flexible capacity and
energy available at one household that has a dishwasher, a washing machine and a tumble dryer.
After that also the daily flexibility of an electric vehicle is calculated. The total flexibility available
today in Belgium is then defined.
Next, it is described which balancing services households can offer and how much an aggregator
managing these households can get for his service.
To investigate the influence of residential flexibility on the total load curve of Belgium, it is calculated
how much necessary capacity can be replaced during one specific day in May this year.
However, a new evolving technology can’t be ignored: batteries. The advantages of batteries
installed at households are discussed at the end of the chapter.
4.2.1 Policy of the residential building sector
The European targets set to stimulate RES and increase energy efficiency are translated into stricter
measures for the residential building sector. The measures are focused on isolation, energy efficiency
and renewables. From 1 January 2014 a minimum amount of energy consumption for new or
renovated houses should come from renewables. The Flemish government has listed possible actions
to meet this measure, such as installing a solar-boiler, a heat pump and photovoltaic (PV) solar
panels. (Vlaams Energieagentschap, 2014)
Less energy will be consumed due to the energy efficiency targets and the upcoming of passive
houses. However the electricity consumption may rise because of the shift from fossil-fuelled heating
systems towards electrical equipment with a higher efficiency such as heat pumps and the transition
to electric vehicles.
26
The results and calculations presented in the thesis are the rounded version of the numbers in Excel.
49
These evolutions strongly burden the electricity grid. To this end it’s interesting to investigate how
much flexibility the residential customers can offer through DR to mitigate the effects of these
evolutions. In the next sections both the terms ‘residential consumer’ and ‘household’ refer to an
average household in Belgium.
4.2.2 Electrical household appliances
The categorization of residential consumers according to their load mix helps to define how they can
offer flexibility to the grid. Consumer load, i.e. the power consumed from the electricity grid, is
categorized in four load types: storable load, shiftable load, curtailable load and self-generation. The
different types are already discussed in section ‘Demand response: load types’ p.13. In Figure 31 the
load types and some examples for residential customers are given.
Figure 31 Residential consumer load mix (European University Institute, 2013)
A fifth load type is given in the figure: base load. This includes load that cannot be shifted in time or
interrupted during end-use service. As a result, this load cannot be used for DR and is not further
discussed.
The four different load types of residential consumers can be used to provide flexibility to the grid.
However, it’s important to take into account the comfort of the households. When they are asked to
curtail or shift the power consumption of TV’s and lighting, their comfort may be reduced.
This is less the case when tumble dryers, washing machines and dishwashers are shifted to off-peak
hours. Therefore only these three shiftable electric devices are taken into account for the
calculations, as well as electric vehicles. The latter is categorized as storable load in Figure 31.
However, this is only the case when a household uses the electricity stored in the battery of the
electric vehicle to reverse-charge their home, known as vehicle-to-home (V2H) power. For the
calculations the electric vehicles are assumed to only charge load at home and no V2H power
50
reverse-charging is possible. In this case the electric vehicles’ consumption is categorized as shiftable
load instead of storable load.
Field measurements are done at one house for one week to calculate the available flexibility of a
washing machine, a tumble dryer and a dishwasher. The method and results of the field
measurements are described in the next section. For the electric vehicles only theoretical
calculations are made in the section thereafter, ‘Flexibility of electric vehicles’ p.56.
4.2.3 Field measurements
To have a realistic idea of the available flexibility of a washing machine, a tumble dryer and a
dishwasher of one household, I measured the hourly electricity consumption at home for a period of
one week, 5 to 11 May 2014. My house has the following important specifications:
Size household 3
Source of central heating gas
Annual electricity consumption Day Night
4,211 kWh 1,945 kWh 2,266 kWh
Table 6 Field measurement: specifications household
These specifications are close to the specs of an average household. Although the annual
consumption is higher than the average, i.e. 3,500 kWh, this has no influence on the calculations
since the consumption of washing machines, tumble dryers and dishwashers highly depends on the
number of people living in the house, which is for this household close to the average in Belgium.
(VREG, 2014)
Method
The tumble dryer has a capacity of 3 kW and both the dishwasher and washing machine have a
capacity of 2.5 kW. However, within one cycle the power usage varies depending on the phase of the
cycle. For example, a washing machine uses more energy per time unit in the beginning when
heating the water than during the washing itself. This causes a variable capacity rate during the cycle.
To this end it’s important to calculate the average capacity used during the cycle to know the
capacity that is available for shifting.
Each time one of the three electric devices was switched on, the start and end times were noted. The
electricity consumption for each hour block for each day of the week is shown in Appendix 2. The
51
yellow bars in the graphs indicate that during these hour blocks at least one of the three devices was
switched on.
To calculate the flexibility of the three devices in terms of the amount of shiftable capacity and
energy, the steps below are followed for each device and for each cycle during the week:
1. The total energy consumed during the hour block(s) the device is switched on is
counted27 (X)
2. The total energy consumed during the same hour block(s) on the other days of the week
is counted
3. The average energy consumed during the same hour block(s) on the other days of the
week is calculated28 (Y)
4. The energy consumption of the device = X – Y (E)
5. The duration of the cycle = end time – start time (D)
6. The capacity level during the cycle = E/D
When these six steps are calculated for all cycles occurred during the week, the average energy
consumption, average duration and average capacity of the device can be calculated.
Results
The results of these six steps for one cycle are given below for the dishwasher in order to clearly
explain the method. The dishwasher was switched on three times during the week: on Wednesday,
Friday and Sunday.
The dishwasher is used on Wednesday from 2 p.m. till 3.50 p.m. The results for the cycle on
Wednesday are the following:
1. The total energy consumed in the hour blocks 1.30 p.m. - 2.30 p.m., 2.30 p.m. - 3.30 p.m.
and 3.30 p.m. – 4.30 p.m. is 2.3 kWh (see Appendix 2 graph Wednesday)
2. The total energy consumed during the same hour blocks on the other days of the week is
shown below (in kWh):
27
Although the device is not switched on during the whole hour block(s), the energy consumption during the whole hour block(s) is counted. 28
In case that one of the other devices is switched on during these hours on another day, that day is not taken into account in order to avoid an overestimation of the calculated base load, and thereby an underestimation of the device’s energy consumption level.
52
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
0.3 0.4 0.6 0.5 0.7 -
During (one of) the three hour blocks on Sunday, the tumble dryer was switched on, so this day isn’t
taken into account to avoid overestimation of the base load during these two hour blocks.
3. The average energy consumed during the same hour blocks on the other days of the
week is
= 0.5 kWh
4. The energy consumed by the dishwasher on Wednesday is 2.3 kWh – 0.5 kWh = 1.8 kWh
5. The duration of the cycle is 1.83 hours
6. The capacity =
= 0.98 kW
The same six steps can be applied for the other cycles of the dishwasher on Friday and Sunday. The
averages of the results of all cycles of the dishwasher during the week are then calculated. Results for
the dishwasher are given in Table 7.
Energy consumption (kWh) Duration (hours) Capacity (kW)
Cycle Wednesday 1.80 1.83 0.98
Cycle Friday 1.32 1.83 0.72
Cycle Sunday 1.60 1.67 0.96
Average 1.58 1.78 0.89
Table 7 Results dishwasher
The same calculation method is applied for the washing machine and the tumble dryer. Results are
shown in Table 8 and Table 9.
Energy consumption (kWh) Duration (hours) Capacity (kW)
Cycle Wednesday 2.38 1.67 1.43
Cycle Friday 1.37 1.67 0.82
Cycle Saturday 1.05 1.00 1.05
Cycle Sunday - - -
Average 1.60 1.44 1.10
Table 8 Results washing machine
53
The washing machine was switched on four times during the week. The last cycle on Sunday started
at 10 a.m. until 11.40 a.m. The total energy consumed in the hour blocks 9.30 a.m. – 10.30 a.m.,
10.30 a.m. – 11.30 a.m., and 11.30 a.m. – 12.30 p.m. reached a high level of 5.1 kWh. The average
consumption during the same hour blocks on the other days of the week was 0.65 kWh, which leads
to a consumption of 4.45 kWh by the washing machine on Sunday. However, this is an unrealistic
result, because with its duration of 1.67 hours on Sunday the capacity would reach a level of 2.67
kW. This is impossible, because the highest capacity level of the washing machine is 2.5 kW. This
means that other activities were consuming electricity in these hour blocks that did not happen on
other days during these hour blocks, such as cooking and watching TV. To this end the measurements
of Sunday are not taken into account to calculate the average numbers for the washing machine.
Energy consumption (kWh) Duration (hours) Capacity (kW)
Cycle Thursday 2.30 1.50 1.53
Cycle Saturday 3.06 1.50 2.04
Cycle Sunday 1.56 1.00 1.56
Average 2.31 1.33 1.71
Table 9 Results tumble dryer
The tumble dryer was switched on three times during the whole week. This device has the highest
average capacity used during its cycles and consumes on average the most of all three devices. In
Table 10 an overview is given of the results of the field measurements for all three devices.
Energy consumption (kWh) Duration (hours) Capacity (kW)
Dishwasher 1.58 1.78 0.89
Washing machine 1.60 1.44 1.10
Tumble dryer 2.31 1.33 1.71
Table 10 Results dishwasher, washing machine and tumble dryer for each cycle
The dishwasher has a flexible capacity of 0.89 kW that is needed for 1.78 hours each cycle, the
washing machine has 1.10 kW flexible capacity that is needed for 1.44 hours, and the tumble dryer
has the highest flexible capacity, 1.71 kW, needed each cycle for 1.33 hours. In Figure 32 the results
per cycle are visualized.
54
To use these flexible capacities on a large scale, i.e. for an aggregated pool of households, it is easier
to work with the same number of shiftable hour blocks for all three devices. The capacities can be
levelled out over 2 hour blocks instead of 1.78, 1.44 and 1.33 hours. This makes it easier to calculate
the total available capacities for all devices together for each household and to shift the capacities to
other hour blocks. In Table 11 the adjusted capacities per cycle for all three devices are given.
Energy consumption (kWh) Duration (hours) Capacity (kW)
Dishwasher 1.58 2 0.79
Washing machine 1.60 2 0.80
Tumble dryer 2.31 2 1.15
Table 11 Adjusted results dishwasher, washing machine and tumble dryer per cycle
It should be noted that a large pool of aggregated households makes it possible to adjust the flexible
capacity available per cycle to the flexible capacity available per day. In reality, not all households in
the pool use their devices on the same days. It is assumed that each day the same number of devices
is switched on, which is not an unrealistic assumption when the pool consists of e.g. 1,000
households.
To calculate the daily available flexible capacity per household, the calculations below are made:
1. Average number of cycles per day =
2. Flexible capacity per day (kW) = capacity * average number of cycles per day
Results are given in Table 12.
Figure 32 Overview flexible capacity dishwasher, washing machine and tumble dryer per cycle
55
Capacity (kW)
Number of cycles per week
Average number of cycles per day
Flexible capacity available per day
(kW)
Dishwasher 0.79 3 0.43 0.34
Washing machine 0.80 4 0.57 0.46
Tumble dryer 1.15 3 0.43 0.49
Table 12 Flexible capacity available per day per household of the aggregated pool
The resulted flexible capacities available per household and per day are visualized in Figure 33 for all
three devices.
Flexible hours
In reality the households will indicate the deadline of the cycle when they start the device. The
aggregator that manages the pool of households then chooses which of the devices has to start,
depending on the conditions of the electricity grid.
It’s important to define the period in which the available flexible capacity can be shifted. I assume
two periods that apply for all three devices: one during the day and one at night. Part of the
households wants to start their devices in the morning when they leave to work and want them to be
ready when coming home in the evening. The other part wants to take advantage of the lower night
tariffs by starting their devices in the evening and want them to be ready in the morning.
In Table 13 the two periods are specified. Period 1 starts at 8.00 a.m. and the deadline is set at 6.00
p.m. The duration of the cycle, i.e. 2 hours, has to be deducted from the deadline to determine the
flexible period, i.e. when the device is able to start and when it must start at last. The number of
Figure 33 Flexible capacities available per day per household of the aggregated pool
56
flexible hours for period 1 is 8. Period 2 starts at 8.00 p.m. and the deadline is at 7.00 a.m. The
number of flexible hours for period 2 is 9.
Start time Deadline Flexible period Number of flexible
hours
Period 1 8.00 a.m. 6.00 p.m. 8.00 a.m. – 4.00 p.m. 8
Period 2 8.00 p.m. 7.00 a.m. 8.00 p.m. – 5.00 a.m. 9
Table 13 Flexible hours dishwasher, washing machine and tumble dryer
In Figure 34 the flexible hours are visualized on a timeline for two consecutive days (x and x+1).
Figure 34 Timeline flexible hours dishwasher, washing machine and tumble dryer
4.2.4 Flexibility of electric vehicles
Not only electric devices such as a dishwasher, a washing machine and a tumble dryer are interesting
flexible products, also electric vehicles can provide flexibility to the electricity grid. As mentioned
previously, the electric vehicles are assumed to only charge load at home and no V2H power reverse-
charging is possible. Another important assumption is that the electric vehicle is only charged at
home and on each day. The calculation method and results are described below.
Method and results
The specifications of the electric vehicle used in the calculations are based on the Nissan Leaf, a
middle-class electric vehicle of which more than 100,000 cars are sold worldwide. The Nissan Leaf
57
has a lithium-ion battery with an energy storage volume of 24 kWh. The calculations are also
applicable for other electric vehicles in Belgium with the same electricity consumption level.
The 2013 version of the Nissan Leaf has a range of 220 km (Nissan, 2014). However, the New
European Driving Cycle estimated the range at 200 km, while the U.S. Environmental Protection
Agency’s official range for this model is only 121 km. For the calculations below, the consumption
level of the Nissan Leaf calculated in a study of the KUL and VITO is used. (Van Roy et al., 2011)
Although the study dates from 2011, they took into account representative driving cycles, travel
behaviour and car characteristics. Table 14 gives an overview of the numbers and results.
Consumption level vehicle (kWh/km)
Average driving distance per
vehicle per day (km)
Electricity consumption per vehicle per day
(kWh)
Charge efficiency
Electricity charged from
the grid per day (kWh)
Load capacity
(kW)
0.174 41.95 7.30 90% 8.11 2.70
Table 14 Specifications electric vehicle
The electricity consumption per vehicle per day is 7.30 kWh. This is calculated by multiplying the
consumption level of the electric vehicle with the average driving distance per day of 41.95 km29. The
electricity actually charged from the grid is 8.11 kWh per day, which is higher than 7.30 kWh due to
the 90% charge efficiency. (CREG, 2010)
The load capacity is not limited by the battery itself, but by the power of the distribution network. A
study by CREG has defined a load capacity of 3 kW. (CREG, 2010) The charge duration each day is 2.7
hours. However, for the sake of simplicity a daily
charge duration of three hour blocks is taken
instead of 2.7 hours, which means that the load
capacity for all three hours is 2.70 kW30. This is not
an unrealistic assumption, because the battery will
probably not be charged continuously at full
power.
The available capacity of one electric vehicle each
day for three hour blocks is visualized in Figure
35.
29
(FOD Mobiliteit en Vervoer, 2013) 30
Load capacity = 8.11 kWh / 3 hours = 2.70 kW
Figure 35 Flexible capacity available per day per electric vehicle
58
Flexible hours
To define the period in which the available flexible capacity can be shifted, I assume that households
want their car to be available in the morning at 7.00 a.m. when they leave for work and that the car
may be charged from 9 p.m. in the evening. The number of flexible hours is 7 (see Table 15).
Start time Deadline Flexible period Number of flexible
hours
9.00 p.m. 7.00 a.m. 9.00 p.m. – 4.00 a.m. 7
Table 15 Flexible hours electric vehicle
In Figure 36 the flexible hours of the electric vehicle are visualized on a timeline for two consecutive
days (x and x+1). The flexible hours of the dishwasher, washing machine and tumble dryer are also
shown to give a clear overview of all the flexible products considered.
Figure 36 Timeline flexible hours of all shiftable load available
Flexibility of batteries
The evolution of batteries for electricity storage is an important factor for electric vehicles, certainly
when the electric vehicles are used for V2H reverse-charging. On top of that, batteries can be used
separate from an electric vehicle and be installed at each house. In this case, they can store
electricity from PV solar panels at home and/or charge the battery with electricity from the grid
59
during off-peak hours. The stored energy can then be used during peak hours and emergency
situations.
PV solar panels are increasingly stimulated due to the policy of the residential building sector (see
supra p.48). When households are obliged to install PV solar panels in combination with a battery at
home, this will create a lot of advantages. The intermittency of decentralized PV solar production as
well as the intermittency caused by the consumption of the household itself can be mitigated, so
balancing will become less difficult. On top of that it can decrease peak consumption, which
improves the adequacy of the system.
The cost of batteries decreases significantly. Tesla Motors, an American producer of electric vehicles,
claims that the cost can decrease from $200/kWh today to $125/kWh by 2020. From 2020 the cost
will decrease yearly by 3%. (Slim Beleggen, 2014)
The average energy consumption of a household per day is 9.5 kWh31. PV solar panels with a total
nominal power of 4 kW have an average daily electricity production of 9.95 kWh, with a maximum
daily average of 15 kWh in June (see Table 16) These results are for solar panels installed in Ghent.
Table 16 Average daily electricity production (Ed) from PV solar panels of 4 kW (European Commission, n.d.)
To store all solar production of one day, a battery of 15 kWh should be installed. A battery of 15 kWh
has an investment cost today of $200/kWh * 15 kWh = $3,000 or € 2,190. This is 22.8% of the
investment cost of PV solar panels with a nominal power of 4 kW, which is € 9,60032.
31
Average annual consumption one households = 3,500 kWh. 32
The investment cost of PV solar panels is around €2,400/kW today and €2,000/kW by 2020. (European Photovoltaic Industry Association, 2014)
60
By 2020, the investment cost of the battery will be $125/kWh * 15 kWh = $1,875 or €1,369. This is
17% of the total cost of PV solar panels with a capacity of 4 kW, which is € 8,000.
During days with an electricity production lower than the electricity consumed, electricity from the
grid may be used to fully charge the battery during off-peak.
The battery investment could be financed by the government through a premium for each household
that installs a battery. As already said, it offers a lot of advantages. The effect of fully loaded batteries
on the total load in Belgium on an emergency day in February 2012 is shown at the end of this
chapter in the section ‘Effect of battery storage on the total load curve’ p.69.
4.2.5 Total flexible capacity available today
The total flexible capacity available today in Belgium is calculated in this section. It’s important to
stress that dishwashers, washing machines and tumble dryers cannot be switched off during a cycle.
Only the electric vehicles of the households can be switched off while charging, because this has no
influence on the comfort of the households as long as the vehicles are fully charged by the deadline.
However, they can all be shifted to later hours and are thus able to reduce consumption when
needed.
Electric devices
About 70% of all Belgian households have a dishwasher, 90% a washing machine and again 70% have
a tumble dryer. (Bond Beter Leefmilieu, 2014) The number of residential distribution grid
connections per DSO is given in Table 17. The Belgian DSOs together have 4,754,848 residential grid
connections.
S2133 S22
Flanders EANDIS 1,682,827 420,620
INFRAX 508,342 122,319
Brussels SIBELGA 492,338 27,795
Wallonia34
ORES 950,213 221,564
TECTEO 232,203 82,544
WAVRE 11,763 2,320
Total per SLP 3,877,686 877,162
TOTAL 4,754,848
Table 17 Residential grid connections (Synergrid)
33
A distinction is made between two different household types. S21 are households with a night/day consumption ratio less than 1.3 and S22 has a night/day ratio of 1.3 or more. 34
I didn’t receive information about the two other DSOs that operate in Wallonia: AIEG and AIESH, but their amount of residential connections is negligible.
61
In case all households having one or more of the three devices at home are aggregated in one large
pool, the total flexible capacity per day needed for 2 hours long would be 4,723,602 kW, which is a
total flexible energy volume of 9,447,204 kWh per day.
% of all households owning the
device
Number of households owning the
device
Capacity available for 2h per day per
household (kW)
Capacity available for 2h per day for
total pool (kW)
Dishwasher 70% 3,328,394 0.34 1,126,899
Washing machine 90% 4,279,363 0.46 1,956,280
Tumble dryer 70% 3,328,394 0.49 1,640,423
Total 4,723,602
Table 18 Flexible capacity total household pool per day
This is visualized in Figure 37.
However, not all capacity in the total household pool can be shifted during the same flexible period. I
assume that one third of all households starts their device(s) in the morning and wants it to be ready
at 6.00 p.m., i.e. period 1. The other two thirds of the households want their device(s) to start in the
evening and be ready in the morning, i.e. period 2 (see supra Table 13 p.56). I assume a larger part of
the households in period 2 each day, because two-metered households want to take advantage of
the lower tariff at night. Results for the two periods are shown in Table 19.
Total Period 1 Period 2
Capacity (kW) 4,723,602 1,574,534 3,149,068
Energy (kWh) 9,447,204 3,149,068 6,298,136
Table 19 Flexible capacity and energy per day per period total household pool
Figure 37 Flexible capacity available per day for the total household pool
62
Electric vehicles
Today around 1,437 households in Belgium have an electric car. (Febiac, 2014) In case all these
vehicles are aggregated in one pool, the total capacity available for 3 hours each day is 3,880 kW (see
Table 20). The aggregator that manages this pool can choose when and how much of the 3,880 kW
will be needed and has to be supplied by the electricity grid for 3 hours. The total amount of flexible
energy that can be shifted between 9 p.m. and 7 a.m. is thereby 11,640 kWh.
Capacity available for 3h per day per vehicle
(kW)
Capacity available for 3h per day for total vehicle fleet
(kW)
Flexible energy volume per day for total vehicle fleet
(kWh)
2.70 3,880 11,640
Table 20 Flexible capacity total vehicle pool per day
In Figure 38 this result is visualized.
Smart meters
An important factor is the development and deployment of smart meters to manage the aggregated
pool of electric household devices and electric vehicles. The question that rises today is when and
how many smart meters will be installed in the near future and how much this will cost.
Due to the evolution of wireless internet access electric appliances at home could be easily managed.
A good example is the Smart Energy Box offered by Electrabel. The box includes 4 smart energy plugs
that have to be plugged in into the socket of the device you want to manage. These smart plugs
enable households to remotely switch on and switch off electric devices and measure their
Figure 38 Flexible capacity available per day for the total vehicle pool
63
consumption through the use of an application (app) on a computer, smartphone or tablet. The
Smart Energy Box has a cost price of € 139 and an extra € 3 per month. (Electrabel, 2014)
When such smart plugs are installed at each house, an aggregator could easily manage dishwashers,
washing machines, tumble dryers and electric vehicles. In case a household wants to start a device,
he indicates on the app when it has to be ready. From that moment the aggregator controls the
smart plugs and gives the smart plugs a signal through internet to start the device when it suits the
conditions of the grid. This way the aggregator has a view on the available capacity at any time.
Now it’s of course important to know what can be done with this aggregated pool of households. In
the next section the effect on the total load curve in Belgium is investigated.
4.2.6 Effect on the total load curve
It’s interesting to investigate the influence of the flexible residential load shifted from peak hours to
off-peak hours on a normal day, i.e. without extreme peak consumption. When load is shifted from
peak hours to off-peak hours the total capacity needed that day may decrease. To see the influence
of the flexible pool of household devices35 on the total load curve of two consecutive days, the load
of 7 and 8 May 2014 are used as an example.
The total load curve for 7 and (part of) 8 May 2014 is shown in Figure 39. The curve reflects the total
load needed each quarter for all consumers in Belgium, i.e. large industrial consumers, small
industrial & commercial consumers, and residential consumers.
Figure 39 Total load curve 7 and 8 May 2014 (Elia)
35
Dishwashers, washing machines, tumble dryers and electric vehicles.
7,0
7,5
8,0
8,5
9,0
9,5
10,0
12
:15
a.m
.
1:3
0 a
.m.
2:4
5 a
.m.
4:0
0 a
.m.
5:1
5 a
.m.
6:3
0 a
.m.
7:4
5 a
.m.
9:0
0 a
.m.
10
:15
a.m
.
11
:30
a.m
.
12
:45
p.m
.
2:0
0 p
.m.
3:1
5 p
.m.
4:3
0 p
.m.
5:4
5 p
.m.
7:0
0 p
.m.
8:1
5 p
.m.
9:3
0 p
.m.
10
:45
p.m
.
12
:00
a.m
.
1:1
5 a
.m.
2:3
0 a
.m.
3:4
5 a
.m.
5:0
0 a
.m.
6:1
5 a
.m.
7:3
0 a
.m.
GW
Total load 7-8 May 2014
64
The total load is higher than 9 GW between 7.30 a.m. and 2.15 p.m. and 6.45 p.m. to 11.30 p.m. on
the 7th of May. The highest total load is at 11.15 a.m. reaching 9,647,705 kW. During the peak period
in the evening, the highest total load is at 10.30 p.m. reaching 9,515,704 kW.
For the sake of simplicity the dishwashers, washing machines and tumble dryers are from now on
referred to as “Flex Product A” and the electric vehicles as “Flex Product B”. An overview of the
flexible capacity and flexible energy available in the aggregated pool is given in Table 21. A distinction
is made between the two flexible periods for Flex Product A, i.e. period 1 and period 2, as explained
in Table 13 p.56.
Capacity (kW)
Number of hours per cycle (h)
Energy (kWh) Shiftable period for the energy volume
Flex Product A Period 1
1,574,534 2 3,149,068 8.00 a.m. – 6.00 p.m.
Flex Product A Period 2
3,149,068 2 6,298,136 8.00 p.m. – 7.00 a.m.
Flex Product B 3,880 3 11,640 9.00 p.m. – 7.00 a.m.
Table 21 Overview aggregated household pool
Shift of devices in Period 1
An energy volume of 3,149,068 kWh or 3.15 GWh of Flex Product A can be shifted between 8.00 a.m.
and 6.00 p.m. The optimal situation shown in Figure 40 occurs when roughly 1,790,000 kWh or 1.79
GWh would normally be consumed between 8.00 a.m. and 1.45 p.m., and can be shifted to the
period between 1.45 p.m. and 6.00 p.m. This is only possible when 1.79 GWh or 57% of the total 3.15
GWh flexible energy available in the pool would normally be consumed between 8.00 a.m. and 1.45
p.m. This is not an unrealistic assumption, because a large part of the households start their devices
in the morning before leaving to work.
The highest peak load on 7 May was 9,647,705 kW at 11.15 a.m. Due to the flexible energy shifted to
off-peak, the highest peak load could be 9,070,000 kW. This is a reduction of 577,705 kW or roughly
578 MW, which is 6% of the highest peak load. This is more than the capacity of the nuclear power
plants of Doel 1 and Doel 2, which is for both plants 433 MW, and more than the largest gas plant in
Belgium, which is 460 MW (CREG, 2014).
65
Figure 40 Effect all flexible products on the load of 7-8 May 2014
Shift of devices in Period 2
An energy volume of 6,298,136 kWh or 6.3 GWh of Flex Product A can be shifted between 8.00 p.m.
and 7.00 a.m. This period overlaps with Flex Product B, which has a flexible energy volume of 11,640
kWh that can be shifted between 9.00 p.m. and 7.00 a.m.
The optimal situation shown in Figure 40 occurs when roughly 3,900,000 kWh or 3.9 GWh would
normally be consumed between 8.00 p.m. and 12.30 a.m. the next day. This volume can be shifted to
the next day between 12.30 a.m. and 6.15 a.m. This is only possible when 3.9 GWh or 61.8% of the
total 6.3 GWh flexible energy available in the pool would normally be consumed between 8.00 p.m.
and 12.30 a.m. the next day. This is again not an unrealistic assumption, because the largest part of
the households starts their devices when coming home after work. Households with a day and night
meter will probably start their devices when the night tariff starts, which is in the most cases at 9.00
p.m. or 10 p.m.36
The highest total load in the evening was at 10.30 p.m., reaching 9,515,704 kW. Due to the flexible
energy shift to off-peak, the highest peak load in the evening could be reduced to 8,250,000 kW. This
is a reduction of 1,265,704 kW or 1,265 MW, which is 13.3% of the highest peak load in the evening.
This is more than the capacity of the largest nuclear plant in Belgium, Tihange 3, which is 1,046 MW.
(CREG, 2014)
36
Day and night hours vary between DSOs and intercommunals.
7,0
7,5
8,0
8,5
9,0
9,5
10,0
12
:15
a.m
.1
:15
a.m
.2
:15
a.m
.3
:15
a.m
.4
:15
a.m
.5
:15
a.m
.6
:15
a.m
.7
:15
a.m
.8
:15
a.m
.9
:15
a.m
.1
0:1
5 a
.m.
11
:15
a.m
.1
2:1
5 p
.m.
1:1
5 p
.m.
2:1
5 p
.m.
3:1
5 p
.m.
4:1
5 p
.m.
5:1
5 p
.m.
6:1
5 p
.m.
7:1
5 p
.m.
8:1
5 p
.m.
9:1
5 p
.m.
10
:15
p.m
.1
1:1
5 p
.m.
12
:15
a.m
.1
:15
a.m
.2
:15
a.m
.3
:15
a.m
.4
:15
a.m
.5
:15
a.m
.6
:15
a.m
.7
:15
a.m
.
GW
Effect all Flex Products on the load of 7-8 May 2014
66
It should be stressed that these numbers are the result for 7 May 2014 and the night/morning of 8
May 2014, and are discussed as an example. It is also assumed that all potential devices and vehicles
of the Belgian households today are available in the pool of a large aggregator through direct load
control programs (see supra p.22). This means that an aggregator is able to control all dishwashers,
washing machines, tumble dryers and electric vehicles in Belgium. Although not all flexible capacity
and energy is used each time the aggregator has to shift load, it creates a margin to offer a reliable
service.
However, it’s important that an aggregator diversifies its DR pool, for example by concluding also
interruptible load contracts with industrial customers. They may offer supplementary flexible load, as
well as complementary flexible load, i.e. when they are able to shift load during other flexible hours
than residential customers. In this case, aggregators can afford it to have a smaller number of
households in their pool without lowering the reliability of its services. This will certainly be the
situation in the near future when only a small proportion of the households may have smart
metering infrastructure.
Speculations and estimations about smart metering roll-out differ to a large extent. Accenture
estimates that by 2020 three million smart meters will be installed. (Accenture, 2013) A different
estimation is a roll-out of 20% of all households and firms by 2020. (Jurres, 2014) A lot of studies are
being conducted on how to enable smart grids and deploy smart meters.37 However, no clear policy
or ‘smart regulation’ is yet developed in Belgium, which makes it difficult to make reliable
estimations in the meantime.
4.2.7 Effect on load in emergency situations
It must be emphasized that the availability of flexible residential load highly depends on the period in
which this load can be shifted. In case the highest peaks occur outside the flexible periods, an
aggregator with only residential flexible load in its pool isn’t able to reduce the total capacity need in
peak hours.
Another important fact is that an aggregator shifts an amount of energy from peak hours to later
hours under the assumption that the same energy volume can be shifted to off-peak hours within the
flexible period. To clarify this, an example is given in Figure 41.
During the first two weeks of February 2012 a cold wave occurred and electricity demand reached
high peaks. The total load curve of Thursday 2 and Friday 3 February 2012 is shown in Figure 41.
37
e.g. (CEER, 2014), (Smart Grids Flanders, 2014), (FORBEG, 2014)
67
Figure 41 Total load curve 2-3 February 2012
The blue curve is the load for Thursday and the red curve represents Friday. The aggregator could
shift load from Thursday morning to the afternoon and from Thursday evening to Friday night. If the
aggregator would again reduce load on Friday morning, he wouldn’t be able to start the shifted
devices in the afternoon before the deadline without increasing the total peak load on Friday,
because there’s no real off-peak period in the afternoon as there is on normal days.
To this end it’s interesting to offer emergency DR products that are called upon only in extreme
circumstances. Households could participate in an emergency program by delaying the consumption
of their dishwashers, washing machines, tumble dryers and electric vehicles longer than the usual
deadline, e.g. until the next night or next day. This offers a lot more flexibility and higher value to the
system.
As already mentioned in chapter 3, Baltimore Gas and Electric, i.e. Maryland’s largest gas and electric
utility, offers a residential direct load control program that was able to reduce around 600 MW of the
peak load of Maryland during an emergency event in July 2011. (FERC, 2012)
4.2.8 Residential demand response as balancing resource
In this section it is discussed how the flexible household pool can be offered as balancing resource
and what an aggregator can receive in return for the service.
Balancing responsible party
The flexibility of the aggregated pool of households can be offered to balancing responsible parties
(BRPs) to balance their portfolio. A bilateral contract between the aggregator and the BRP can be
9,0
9,5
10,0
10,5
11,0
11,5
12,0
12,5
13,0
13,5
12
:15
a.m
.
1:3
0 a
.m.
2:4
5 a
.m.
4:0
0 a
.m.
5:1
5 a
.m.
6:3
0 a
.m.
7:4
5 a
.m.
9:0
0 a
.m.
10
:15
a.m
.
11
:30
a.m
.
12
:45
p.m
.
2:0
0 p
.m.
3:1
5 p
.m.
4:3
0 p
.m.
5:4
5 p
.m.
7:0
0 p
.m.
8:1
5 p
.m.
9:3
0 p
.m.
10
:45
p.m
.
12
:00
a.m
.
GW
Total load curve 2 and 3 February 2012
Thursday 02-02
Friday 03-02
68
concluded through the over-the-counter trading platform (see supra p.15). When for example a
BRP’s portfolio is unbalanced, the BRP can contract with an aggregator to shift a certain amount of
devices in its pool to later hours. The imbalance price in 2013 was on average €50/MWh. In case the
BRP takes more energy from the grid than he injects, he has to pay this penalty for each MWh
shortage to Elia. This is thus the maximum price per MWh an aggregator can get for his service;
otherwise it’s cheaper for the BRP to pay the penalty to Elia. (CREG, 2014)
To have insight in what can be earned with a pool of residential flexibility, I use the example of an
aggregator able to offer 1 MW each flexible hour to BRPs. The flexible periods of the shiftable
devices are between 8.00 a.m. and 4.00 p.m. and between 8.00 p.m. and 5.00 a.m. (see supra Table
13 p.56), which is together 17 hours a day. Over one year, the aggregator can thus offer 17 hours *
365 = 6,205 hours. The annual energy the aggregator can offer is thereby 1 MW * 6,205 hours =
6,205 MWh. The maximum price the aggregator can get from a BRP for this volume is €50/MWh *
6,205 MWh = € 310,250 per year.
I assume for this example that each household in the pool has all three devices, i.e. a washing
machine, a dishwasher and a tumble dryer, but no electric vehicle38. These devices offer together
1.29 kW per day per household (see supra Figure 33 p.55). The service of 1 MW each hour can only
be offered in case the pool consists of (1,000 kW each hour * 17 hours per day) / 1.29 kW per day =
13,178 households who’s load is shifted every day. As a result 13,178 households is the upper
minimum necessary to offer the service of 1 MW each flexible hour. This means that the aggregator
receives maximum € 310,250/13,178 households = € 23.5 per year per household. The profit per
household for the aggregator is below this price, because no variable and fixed costs of the
aggregator are yet deducted.
Tertiary reserve Elia
The flexibility of the aggregated pool of households can also be offered to Elia to provide ancillary
service. It can be offered as a tertiary reserve through the R3 Dynamic Profile (DP) product. The R3
DP product is described in the previous chapter p.43. This product is new since 2014 and enables
consumers connected to the distribution grid to exploit their flexibility. Only industrial consumers
connected to the distribution grid were contracted until today, and no residential consumers.
In case households are aggregated to offer sufficient flexibility, the aggregator could participate in
the R3 DP product tender. When the aggregator is notified by Elia, he has to check the state of his
38
The electric vehicle fleet consists of only 1,437 cars.
69
pool to see which of the devices or vehicles can be shifted to later hours (reduction of consumption
level).
The average price for delivery year 2014 is € 3.38 per MW per hour reserved during a year (see supra
Table 5 p.42). This means that in case you can offer each hour 1 MW, you get € 29,609 for the whole
year (there are 8,760 hours in a year).
To see how much an aggregator can get in return per household for this service to Elia, the same
example as above is used. The aggregator can provide 1 MW each flexible hour. He is able to offer
6,205 hours per year as calculated in the previous section. The aggregator receives thereby 6,205
hours per year * € 3.38 per MW per hour = € 20,973 per year for 1 MW.
As calculated above, there are at least 13,178 households needed to provide this service, which
means that the aggregator receives €20,973 divided by 13,178 households or € 1.59 per household
per year. It should also be stressed that the profit of the aggregator is even below this amount, since
the costs are not deducted.
This is a much lower price than in case he offers his service to BRPs. Thereby it’s a lot more
interesting to offer residential flexibility to BRPs than to Elia as R3 DP product.
4.2.9 Effect of battery storage on the total load curve
As previously said, the service of flexible residential load highly depends on the period in which this
load can be shifted to maintain the comfort of households. In the case of 7 May 2014, the aggregated
pool could replace a large gas plant in the noon and a large nuclear plant of more than 1,000 MW in
the evening. However, this is not always the case, as described in the section ‘Effect on load in
emergency situations’ p.66. Residential DR could offer emergency service by delaying consumption
to a larger extent than normally, but this is only possible a few times per year.
A new evolving technology, however, can play a significant role in the balancing and adequacy of the
system. In the section ‘Flexibility of batteries’ p.58 the advantages of batteries installed at home are
described. The intermittency of decentralized PV solar production as well as the intermittency
caused by the consumption of the household itself can be mitigated. It can also decrease peak
consumption and thereby improving the adequacy of the system.
In Figure 42 the optimal effect of batteries installed at households on the total load curve is shown
for the extreme cold day 2 February 2012. The blue curve is the total load on this day. The red curve
70
is what the total load curve would have been if all households had a full battery that day and would
not have needed electricity from the grid.
Figure 42 Effect battery storage on the total load curve of 2 February 2012
To calculate the red curve, the S21 and S22 synthetic load profiles (SLP)39 of 2 February 2012 for
households are used to determine the total quarterly residential load that day. The total yearly
residential energy consumption in 2012 was 20,779,382,353 kWh or 20.78 TWh. This residential
consumption is allocated over the two household SLP types, i.e. S21 and S22, based on their share in
the total number of grid connections (see supra Table 17 p.60). 82% of the grid connections are S21
households and 18% are S22 households. Results are shown in Appendix 3.
The total energy consumed by the households on 2 February 2012 is 67,445,934 kWh. This is on
average 14 kWh per household (67,445,934 kWh/4,754,848 households40). When all households
would have installed a battery of 15 kWh and the battery would be fully charged at the beginning of
the day, the grid would have to supply 67,445,934 kWh less in total, or the maximum total peak load
would reduce from 13,001,244 kW at 6.15 p.m. to 9,592,290 kW at 7.45 a.m. This is a decrease of
3,408,954 kW or 3,409 MW. This is 26% of the max total peak load normally that day.
39
A consumption profile depending on the consumer type: household (S21 and S22) or non-household. S21 are households with a night/day consumption ratio less than 1.3 and S22 are households with a night/day ratio of 1.3 or more. 40
See supra Table 17 p.60.
7,0
8,0
9,0
10,0
11,0
12,0
13,0
14,0
12
:15
a.m
.
1:1
5 a
.m.
2:1
5 a
.m.
3:1
5 a
.m.
4:1
5 a
.m.
5:1
5 a
.m.
6:1
5 a
.m.
7:1
5 a
.m.
8:1
5 a
.m.
9:1
5 a
.m.
10
:15
a.m
.
11
:15
a.m
.
12
:15
p.m
.
1:1
5 p
.m.
2:1
5 p
.m.
3:1
5 p
.m.
4:1
5 p
.m.
5:1
5 p
.m.
6:1
5 p
.m.
7:1
5 p
.m.
8:1
5 p
.m.
9:1
5 p
.m.
10
:15
p.m
.
11
:15
p.m
.
GW
Total load 2 February 2012
Total load Total load without residential load
71
It should be stressed that this is only the case when all households possess a battery of 15 kWh and
that they are all fully charged at the beginning of the day through PV solar production and/or
electricity from the grid stored during previous days.
This is of course a very optimal situation, but it is meant to show how much potential the batteries
have to reduce total peak load.
The completion of the advantages of batteries depends on the size of the battery, the energy
consumption of the household, the energy available in the battery, the total PV solar production
profile and the ability to charge the battery from the grid during previous off-peak hours.
However, I think that batteries at home in combination with PV solar panels are the future. As said
before, batteries can mitigate the intermittency of PV solar panels and thereby improve the
utilization of renewables, and can improve adequacy of the system by decreasing necessary peak
load. On top of that batteries have the advantage that they do not depend on flexible hours, do not
have to take into account the comfort of households and the management of batteries is a lot easier
than managing different devices per household. Compared to the flex products taken into account in
the previous calculations, batteries can offer symmetric services (up- and down scaling of the load).
72
Conclusion
This thesis has investigated the potential flexibility industrial and in particular residential customers
can offer on the electricity market in Belgium by participating in demand response (DR) programs.
The literature study of DR programs in the U.S. electric power industry has revealed that commercial
and industrial consumers can offer a large amount of flexible load. The total reported potential peak
load reduction from DR in the U.S. was 66,351 MW in 2012. Roughly 43.2% of this potential
reduction could be provided by commercial and industrial consumers, 44.3% through wholesale
programs with aggregators and other third parties, and only 12.5% through residential consumers
participating in direct load control programs and time-based tariff programs.
Although the potential DR contribution of residential consumers to peak load reductions only
presents a small portion of the total potential in the U.S., it’s important to investigate the residential
flexibility potential in Belgium. It is calculated how much flexible capacity households can provide,
what services this flexibility can offer to balance the system and increase the adequacy of the
electricity grid, and how much money an aggregator can get for his service.
Since a few years Elia has concluded interruptible load contracts with large industrial customers
connected to the transmission grid to provide ancillary services. Recently also aggregators of smaller
customers, connected to the transmission grid as well as the distribution grid, are allowed to provide
reserves for Elia. The total contracted volume of DR ancillary services reached 338 MW this year.
Only a short description of a study by Elia, Febeliec and Energy Ville as regard the potential flexibility
of industrial consumers is given. The conclusion of this study is that quantitative extrapolations for
the whole Belgian industry are difficult to make, because the potential highly depends on the
industry sector investigated. To this end further research on the total industrial potential is planned.
To estimate the potential flexibility of residential consumers, field measurements at one house for
one week are used. Realistic assumptions and extrapolations to the total residential sector in
Belgium revealed that the total number of dishwashers, washing machines and tumble dryers
available today have a daily flexible capacity of 4,724 MW. 1,574 MW can be shifted for 8 hours,
from 8.00 a.m. to 4.00 p.m., and 3,149 MW for 9 hours, from 8.00 p.m. to 5.00 a.m. the next day.
On top of that the flexibility of electric vehicles is estimated. Today, around 1,437 households in
Belgium have an electric car. This total vehicle fleet can possibly offer 3,880 kW flexible capacity per
day, shiftable between 9.00 p.m. and 4 a.m. the next day.
73
To investigate the possible influence of this flexible pool of households on the total load curve in
Belgium, two consecutive days 7 and 8 May 2014 are used as an example. The highest peak load on 7
May occurred at noon and could be reduced by shifting peak load to off-peak hours in the afternoon.
Roughly 578 MW could be reduced, which is 6% of the highest peak load that day. This is more than
the 433 MW capacity of the nuclear power plants Doel 1 and Doel 2 and more than the 460 MW
largest gas plant in Belgium. The peak load in the evening could be reduced by 1,265 MW, which is
13.3% of the highest peak load in the evening. This is even more than the 1,046 MW capacity of
Tihange 3, which is the largest nuclear plant in Belgium.
However, in case the highest peaks occur outside the flexible periods of the households, an
aggregator with only residential flexible load in its pool won’t be able to reduce the total capacity
need in peak hours. To this end it’s interesting to offer emergency DR products that are only called
upon in extreme circumstances. Households could participate in an emergency program by delaying
the consumption of their dishwashers, washing machines, tumble dryers and electric vehicles longer
than the usual deadline, e.g. until the next night or next day, and only a few times per year.
An estimation of the possible revenue for the aggregator is made based on the assumption that the
flexibility of the aggregated pool of households is offered to balancing responsible parties (BRPs). The
maximum revenue the aggregator can get from a BRP for offering the flexibility of one household is
estimated at € 23.5 for a whole year. In case the flexibility of the aggregated pool of households is
offered to Elia as ancillary service, the aggregator receives only € 1.59 per household per year.
However, it should be stressed that the profit of the aggregator is even below this amount, since the
costs of the aggregator have to be deducted. Consequently offering residential flexibility to Elia or
BRPs to balance the grid may be interesting for aggregators only in case all households already have
smart meters and households do not receive any incentives for their flexibility.
Residential flexible products may be able to reduce a large amount of peak load during some days,
but this highly depends on the period in which the flexible products can be shifted. To this end it’s
important that an aggregator diversifies its DR pool, for example by concluding also interruptible load
contracts with industrial customers. They may offer supplementary flexible load and may be able to
shift load during other flexible hours than residential customers. In this case, aggregators can afford
it to have a smaller number of households in their pool without lowering the reliability of its services.
This will certainly be the situation in the near future since only a small proportion of the households
may have smart metering infrastructure.
74
In the future, the new evolving battery technology will be a significant game changer for the
electricity grid as the price per kWh storage capacity is decreasing. If all households in Belgium would
install a battery of 15 kWh in combination with solar panels, the intermittency of the solar
production as well as the intermittency caused by the consumption of the household itself can be
mitigated. On top of that the adequacy of the system will strongly improve, because the electricity
stored in all residential batteries can be used in peak hours and emergency situations. This is
illustrated in the thesis for the total load on 2 February 2012, which was an extreme cold day.
The batteries of electric vehicles will create major advantages through the vehicle-to-house and
vehicle-to-grid principle. They can be used for balancing as well, but this burdens the grid to higher
extent than the separate batteries do, because the batteries at home are directly connected to the
solar panels. On top of that vehicles are not always available to the grid. When small batteries are
installed at home the intermittency problem is tackled at the source and the grid will be less loaded.
To conclude, I think that the flexibility of industrial consumers has a lot of potential to provide
reliable services to the electricity grid. Calculations revealed that aggregated residential consumers
may be able to offer a large amount of flexible capacity to balance the grid and preserve adequacy.
However, it doesn’t appear to be a very profitable business for an aggregator. The profit per
household is low, certainly when taking into account smart metering investments and incentives for
the households.
A policy focused on small batteries in combination with decentralised solar panels at home will
certainly have a major positive impact on the potential of residential DR programs, both for balancing
and adequacy services. Further research should be done to investigate the real potential of this
policy.
I
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Appendix 1.1
Appendix
Appendix 1 Overview of the U.S. Ancillary services
Appendix 2.1
Appendix 2 Hourly consumption field measurements
00,10,20,30,40,50,60,70,80,9
11
2:3
0:0
0 A
M
1:3
0:0
0 A
M
2:3
0:0
0 A
M
3:3
0:0
0 A
M
4:3
0:0
0 A
M
5:3
0:0
0 A
M
6:3
0:0
0 A
M
7:3
0:0
0 A
M
8:3
0:0
0 A
M
9:3
0:0
0 A
M
10
:30
:00
AM
11
:30
:00
AM
12
:30
:00
PM
1:3
0:0
0 P
M
2:3
0:0
0 P
M
3:3
0:0
0 P
M
4:3
0:0
0 P
M
5:3
0:0
0 P
M
6:3
0:0
0 P
M
7:3
0:0
0 P
M
8:3
0:0
0 P
M
9:3
0:0
0 P
M
10
:30
:00
PM
11
:30
:00
PM
kWh
Hourly consumption Monday 5 May 2014
00,10,20,30,40,50,60,70,80,9
1
12
:30
:00
AM
1:3
0:0
0 A
M
2:3
0:0
0 A
M
3:3
0:0
0 A
M
4:3
0:0
0 A
M
5:3
0:0
0 A
M
6:3
0:0
0 A
M
7:3
0:0
0 A
M
8:3
0:0
0 A
M
9:3
0:0
0 A
M
10
:30
:00
AM
11
:30
:00
AM
12
:30
:00
PM
1:3
0:0
0 P
M
2:3
0:0
0 P
M
3:3
0:0
0 P
M
4:3
0:0
0 P
M
5:3
0:0
0 P
M
6:3
0:0
0 P
M
7:3
0:0
0 P
M
8:3
0:0
0 P
M
9:3
0:0
0 P
M
10
:30
:00
PM
11
:30
:00
PM
kWh
Hourly consumption Tuesday 6 May 2014
0
0,5
1
1,5
2
2,5
12
:30
:00
AM
1:3
0:0
0 A
M
2:3
0:0
0 A
M
3:3
0:0
0 A
M
4:3
0:0
0 A
M
5:3
0:0
0 A
M
6:3
0:0
0 A
M
7:3
0:0
0 A
M
8:3
0:0
0 A
M
9:3
0:0
0 A
M
10
:30
:00
AM
11
:30
:00
AM
12
:30
:00
PM
1:3
0:0
0 P
M
2:3
0:0
0 P
M
3:3
0:0
0 P
M
4:3
0:0
0 P
M
5:3
0:0
0 P
M
6:3
0:0
0 P
M
7:3
0:0
0 P
M
8:3
0:0
0 P
M
9:3
0:0
0 P
M
10
:30
:00
PM
11
:30
:00
PM
kWh
Hourly consumption Wednesday 7 May 2014
Appendix 2.2
0
0,5
1
1,5
2
2,5
3
12
:30
:00
AM
1:3
0:0
0 A
M
2:3
0:0
0 A
M
3:3
0:0
0 A
M
4:3
0:0
0 A
M
5:3
0:0
0 A
M
6:3
0:0
0 A
M
7:3
0:0
0 A
M
8:3
0:0
0 A
M
9:3
0:0
0 A
M
10
:30
:00
AM
11
:30
:00
AM
12
:30
:00
PM
1:3
0:0
0 P
M
2:3
0:0
0 P
M
3:3
0:0
0 P
M
4:3
0:0
0 P
M
5:3
0:0
0 P
M
6:3
0:0
0 P
M
7:3
0:0
0 P
M
8:3
0:0
0 P
M
9:3
0:0
0 P
M
10
:30
:00
PM
11
:30
:00
PM
kWh
Hourly consumption Thursday 8 May 2014
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
12
:30
:00
AM
1:3
0:0
0 A
M
2:3
0:0
0 A
M
3:3
0:0
0 A
M
4:3
0:0
0 A
M
5:3
0:0
0 A
M
6:3
0:0
0 A
M
7:3
0:0
0 A
M
8:3
0:0
0 A
M
9:3
0:0
0 A
M
10
:30
:00
AM
11
:30
:00
AM
12
:30
:00
PM
1:3
0:0
0 P
M
2:3
0:0
0 P
M
3:3
0:0
0 P
M
4:3
0:0
0 P
M
5:3
0:0
0 P
M
6:3
0:0
0 P
M
7:3
0:0
0 P
M
8:3
0:0
0 P
M
9:3
0:0
0 P
M
10
:30
:00
PM
11
:30
:00
PM
kWh
Hourly consumption Friday 9 May 2014
0
0,5
1
1,5
2
2,5
12
:30
:00
AM
1:3
0:0
0 A
M
2:3
0:0
0 A
M
3:3
0:0
0 A
M
4:3
0:0
0 A
M
5:3
0:0
0 A
M
6:3
0:0
0 A
M
7:3
0:0
0 A
M
8:3
0:0
0 A
M
9:3
0:0
0 A
M
10
:30
:00
AM
11
:30
:00
AM
12
:30
:00
PM
1:3
0:0
0 P
M
2:3
0:0
0 P
M
3:3
0:0
0 P
M
4:3
0:0
0 P
M
5:3
0:0
0 P
M
6:3
0:0
0 P
M
7:3
0:0
0 P
M
8:3
0:0
0 P
M
9:3
0:0
0 P
M
10
:30
:00
PM
11
:30
:00
PM
kWh
Hourly consumption Saturday 10 May 2014
Appendix 2.3
0
0,5
1
1,5
2
2,5
12
:30
:00
AM
1:3
0:0
0 A
M
2:3
0:0
0 A
M
3:3
0:0
0 A
M
4:3
0:0
0 A
M
5:3
0:0
0 A
M
6:3
0:0
0 A
M
7:3
0:0
0 A
M
8:3
0:0
0 A
M
9:3
0:0
0 A
M
10
:30
:00
AM
11
:30
:00
AM
12
:30
:00
PM
1:3
0:0
0 P
M
2:3
0:0
0 P
M
3:3
0:0
0 P
M
4:3
0:0
0 P
M
5:3
0:0
0 P
M
6:3
0:0
0 P
M
7:3
0:0
0 P
M
8:3
0:0
0 P
M
9:3
0:0
0 P
M
10
:30
:00
PM
11
:30
:00
PM
kWh
Hourly consumption Sunday 11 May 2014
Appendix 3.1
Appendix 3 Synthetic load profiles S21 and S22 households
The SLPs are provided online by Synergrid.
0
200 000
400 000
600 000
800 000
1 000 0001
2:1
5 a
.m.
1:1
5 a
.m.
2:1
5 a
.m.
3:1
5 a
.m.
4:1
5 a
.m.
5:1
5 a
.m.
6:1
5 a
.m.
7:1
5 a
.m.
8:1
5 a
.m.
9:1
5 a
.m.
10
:15
a.m
.
11
:15
a.m
.
12
:15
p.m
.
1:1
5 p
.m.
2:1
5 p
.m.
3:1
5 p
.m.
4:1
5 p
.m.
5:1
5 p
.m.
6:1
5 p
.m.
7:1
5 p
.m.
8:1
5 p
.m.
9:1
5 p
.m.
10
:15
p.m
.
11
:15
p.m
.
kWh
SLP S21 2 February 2012
0
50 000
100 000
150 000
200 000
250 000
300 000
350 000
12
:15
a.m
.
1:1
5 a
.m.
2:1
5 a
.m.
3:1
5 a
.m.
4:1
5 a
.m.
5:1
5 a
.m.
6:1
5 a
.m.
7:1
5 a
.m.
8:1
5 a
.m.
9:1
5 a
.m.
10
:15
a.m
.
11
:15
a.m
.
12
:15
p.m
.
1:1
5 p
.m.
2:1
5 p
.m.
3:1
5 p
.m.
4:1
5 p
.m.
5:1
5 p
.m.
6:1
5 p
.m.
7:1
5 p
.m.
8:1
5 p
.m.
9:1
5 p
.m.
10
:15
p.m
.
11
:15
p.m
.
kWh
SLP S22 2 February 2012
0
200 000
400 000
600 000
800 000
1 000 000
1 200 000
12
:15
a.m
.
1:1
5 a
.m.
2:1
5 a
.m.
3:1
5 a
.m.
4:1
5 a
.m.
5:1
5 a
.m.
6:1
5 a
.m.
7:1
5 a
.m.
8:1
5 a
.m.
9:1
5 a
.m.
10
:15
a.m
.
11
:15
a.m
.
12
:15
p.m
.
1:1
5 p
.m.
2:1
5 p
.m.
3:1
5 p
.m.
4:1
5 p
.m.
5:1
5 p
.m.
6:1
5 p
.m.
7:1
5 p
.m.
8:1
5 p
.m.
9:1
5 p
.m.
10
:15
p.m
.
11
:15
p.m
.
kWh
SLP Total energy 2 February 2012
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