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23rd International Conference on Electricity Distribution Lyon, 15-18 June 2015
Paper 0425
CIRED 2015 1/5
OPPORTUNITIES PRESENTED BY SMART GRIDS TO IMPROVE NETWORK
PLANNING, OPTIMISING ELECTRICAL VEHICLE, DER AND LOAD INTEGRATION
Ricardo PRATA Pedro MOUSINHO Diogo MOREIRA
EDP - Distribuição – Portugal EDP - Distribuição – Portugal EDP - Distribuição – Portugal
[email protected] [email protected] [email protected]
ABSTRACT
Presently at EDP Distribuição, LV networks are planned
using probabilistic methods. With InovGrid, the smart
grid project developed in Portugal, real data from LV
networks is becoming available for planning purposes,
enabling new planning methods. Part I of this paper
analyses the results obtained with both approaches,
probabilistic and chronological, while planning a real LV
network.
Furthermore, Part II of the paper analyses the impact on
the distribution MV networks of three different strategies
of electric vehicle (EV) charging – Direct Charging,
Minimum Cost and Renewable-Following. Finally, we
performed a critical analysis of the results obtained, in
order to find the strengths and weaknesses of each
charging solution, as well as the impact that these have
on the medium voltage distribution network.
INTRODUCTION
The probabilistic method used in EDP Distribuição for LV network planning estimates the contribution of each LV customer to the peak demand, considering its contracted power and a simultaneity factor that allows to estimate power flows with 95 % probability of not exceedance. With smart meter deployment, the grid planner has more detailed information regarding load diagrams of consumption, production and power demand in secondary substations – allowing for the development of a chronological method. In the first section of this paper, simulations of a real network are performed with data yielded through smart meters, in order to analyse actual conditions during peak demand and off-peak demand. The connection of new micro-generation units (G) and loads, including electrical vehicles (EV) with different charging strategies, are also considered in the simulations. The results obtained by the chronological method are compared with those obtained by the probabilistic method, concerning the investment needs identified through each methodology, in order to compare them. Regarding the EV, mobility represents a significant
portion of world’s economy and is essential to the way
we live and organize ourselves. Transport accounts
approximately 30% of energy consumption in Portugal
and Europe. [1]
Power Systems face new challenges associated with the
potential massive integration of EV in the future. The
increased integration of EV will translate into a new
paradigm in terms of energy consumption, with
electricity becoming the most used energy source for
mobility instead of fossil fuels.
Thus arises the need to analyze the impact that a larger
EV fleet will have on distribution networks.
PARAMETERS
In all the simulations was considered that an EV has a
constant charging rate of 3kW [2], requiring six hours to
fully load. Charging is done at home and it is assumed
that it can be done within a 12 hours window, after the
vehicle arrival, and it can be done intermittently. Thus it
is necessary to find the best charging strategy and analyse
the impact in the distribution grid.
During the 12 hours window available for charging the
EV, the vehicle is connected to the same busbar to which
it originally connected. It is assumed that all EVs start the
charging period with the same battery state of charge
(SOC) and that after 6 hours of charge the SOC reaches
100%.
TRADITIONAL VS. FUTURE PLANNING
The LV network selected in order to perform the
simulations is mostly residential and it is fed by a
secondary substation equipped with a distribution
transformer of 630kVA. In order to compare both
methods, the day analysed with the chronological method
corresponded with the day where the 2014 peak load of
the transformer was achieved (Figure 1 and Figure 2).
Figure 1 - Daily consumption measured in the secondary
substation
0.0
0.5
1.0
1.5
2.0
2.5
3.0
01-01-2014
08-01-2014
15-01-2014
22-01-2014
29-01-2014
05-02-2014
12-02-2014
19-02-2014
26-02-2014
05-03-2014
12-03-2014
19-03-2014
26-03-2014
Dai
ly M
Wh
23rd International Conference on Electricity Distribution Lyon, 15-18 June 2015
Paper 0425
CIRED 2015 2/5
Figure 2 - Load diagram of the secondary substation on Feb.
9th 2014
In the following sub-sections, the comparison between
both methods is represented graphically in terms of the
performance of the network related to voltage and current
values obtained in nodes and branches, respectively,
according to the following presented on Figure 3,
Figure 3 – Colour coding used to present voltage and currents
where, Umax is equal to 110% p.u. of nominal voltage, as
stated in EN50160, and Umin is equal to 92% p.u. of
nominal voltage, which corresponds to a voltage drop of
8% p.u. up to the point of common coupling, thus
allowing for a further voltage drop of 2% p.u. up to the
customer’s equipment (10% p.u. of total voltage drop).
Imax is the nominal current of the conductor.
A node or branch coloured in red or blue represents an
unacceptable scenario that must be overcome by means
of new investments in the network or through new
configurations of the network, if possible.
LV network as is
Figure 4 shows the actual state of the network, simulated
according to probabilistic and chronological methods.
Probabilistic (Peak)
Chronological (14h15m)
Chronological (20h45m)
Figure 4 - Actual state of the LV network analysed
Although the three scenarios have nodes coloured in
yellow, the lowest node’s voltage is lower in the
probabilistic approach (92.1% of nominal voltage) than in
the chronological approach (93.5% of nominal voltage).
Connection of new consumers
Figure 5 shows the behaviour of the network after
connecting 2 new consumers of 6.9kVA each (1 in phase
A and 1 in phase C), in 2 of the most unfavourable nodes:
Probabilistic (Peak)
Chronological (14h15m)
Chronological (20h45m)
Figure 5 - Performance of the network after connecting 2 new
consumers
The conclusions yielded through the two approaches are
different: while the probabilistic method shows that the
connection of the 2 consumers requires grid
reinforcements, the chronological method states that the
connection is possible without violating voltage or
current limits.
On this example, the consequence of connecting 2 new
consumers regarding voltage profiles is less severe under
the more rigorous chronological method, which considers
actual load profiles and network unbalance.
Connection of new producers (peak load)
After connecting 2 new consumers as referred in the
previous section, Figure 6 illustrates the behaviour of the
network after connecting 2 new micro generator units of
3.7kVA each (solar), 1 in phase B and 1 in phase C, again
in 2 unfavourable nodes:
0
40
80
120
160
200
0:15
1:15
2:15
3:15
4:15
5:15
6:15
7:15
8:15
9:15
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kW
Voltage (nodes): Current (branches):
U > Umax
Umax*98% < U < Umax I < Imax*75%
Umin*102% < U < Umax*98% Imax*75% < I < Imax*90%
Umin < U < Umin*102% Imax*90% < I < Imax
U < Umin I > Imax
23rd International Conference on Electricity Distribution Lyon, 15-18 June 2015
Paper 0425
CIRED 2015 3/5
Probabilistic (Peak)
Chronological (14h15m)
Chronological (20h45m)
Figure 6 - Performance of the network after connecting 2 new
producers, during peak demand
As in the previous example, probabilistic method would
support the decision for network reinforcements that
might not be necessary. According with the chronological
method, there is an improvement regarding the voltage
profile on the point of common coupling (PCC) of the
generation units, thus sparing the need for further
investments.
Again, in this example the conclusions provided by the
chronological method are more realistic, as it considers
real load and production diagrams and real unbalance
conditions. Furthermore, on residential networks the
chronological method is more effective in analysing
voltage profile variation during the analysis period, given
that solar production and load consumption tend to occur
on different occasions (production will be higher at noon
and peak consumption occurs late afternoon).
Connection of new producers (off-peak load)
The connection of production units must be assessed also
during off-peak demand, due to the potential increase of
voltage values. To illustrate this effect, 9 producers of
3.7kVA each are connected along the furthest branch
from the secondary substation (5 in phase A, 2 in phase B
and 2 in phase C). The off-peak demand is simulated in
the probabilistic approach, considering zero consumption
in the grid, while in the chronological approach considers
the day of 2014 with the smallest consumption (Figure 7).
Figure 8 shows the results of the simulations performed.
Figure 7 - Load diagram of the secondary substation on Jun. the
27th 2014
Probabilistic (Off-Peak)
Chronological (14h15m)
Chronological (20h45m)
Figure 8- Performance of the network after connecting 9 new
producers, during off-peak demand
On this example, the probabilistic method returns a
maximum voltage of 111% p.u. during off-peak demand,
leading to the conclusion that the connection would
require grid reinforcements. The use of the chronological
method in the same example returns a maximum voltage
of 109% p.u., which is within the acceptable voltage
values.
The main reason for the differences observed is related
with the off-peak demand conditions considered on the
probabilistic approach, which are more conservative as it
considers zero or near zero consumption on the grid,
while in the chronological approach the off-peak demand
conditions considers the real lowest consumption
observed. The advantage of using real data under off-
peak conditions is even more important in secondary
substations feeding industrial or services loads, where the
period of maximum solar production matches the highest
consumptions and not the off-peak consumptions (Figure
9), allowing for the possibility of connecting more
distributed generation units and minimizing investment
requirements.
Services-type secondary substation
Solar production
Figure 9 - Load diagram of a Services-type secondary
substation
Connection of electrical vehicles
The analysis associated with the connection of EVs is
similar to the analysis of the connection of new
customers, regarding the purpose of comparing
probabilistic and chronological methods, as addressed in
the previous sub-section. The purpose of this section is to
0
20
40
60
80
100
0:15
1:15
2:15
3:15
4:15
5:15
6:15
7:15
8:15
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kW
23rd International Conference on Electricity Distribution Lyon, 15-18 June 2015
Paper 0425
CIRED 2015 4/5
analyse two different EV charging strategies, and assess
their impact in the grid using the chronological method:
Direct charging - the EV starts to charge when it
arrives at the charging point (normally between
18h and 20h);
Minimum cost - the EV’s charging occurs when
the cost of energy is lower (after midnight).
Figure 10 presents the behaviour of the grid while
feeding the charge of 33 EVs (~1/5 of the number of
consumers) equally divided by the 3 phases. Direct charging (20h45m)
Minimum cost (00h15m)
Figure 10 - Performance of the network after connecting 33
EVs
Real load diagrams usage allows the grid planner to
compare different charging strategies, according to the
load profile of each secondary substation.
Having more information to support the chronological
methodology, the capability of the network to
accommodate the charging of EVs is better assessed, with
less conservative error margins, encouraging this way the
dissemination of EVs.
STRATEGIES FOR ELECTRICAL VEHICLE
CHARGING
The analysis of the EV integration impact on MV
distribution networks will follow the parameters defined
initially, and it is necessary to define the different
charging strategies used for the case study.
Charging strategies
Three different loading strategies are analysed, including
Direct Charging, Minimum Cost and Renewable-
following.
Direct Charging
The Direct Charging model considers that the EV starts
to charge as soon as it arrives at the loading site, and it
finishes after 6 hours. This type of charging does not
impose any restrictions to the owners of EV. This
strategy requires no control or fleet management of EV
charging, since the charging process is to be done
continuously and immediately upon arrival of the EV at
the loading site.
Minimum Cost
The Minimum Cost strategy consists on the minimization
of the energy cost associated with charging the EV,
shifting the load to off-peak hours, when the energy price
is lower. In order to define when the EV charges, it is
necessary to solve an optimization problem. Given that
minimizing the cost of a singular EV is independent of
the others, this can be done individually. Total cost is
obtained by adding the hourly energy cost associated with
the energy consumed. The Minimum Cost strategy
requires the existence of an aggregator element that,
based on market prices, defines when the EV should be
loaded (fleet manager).
The equations that describe the optimization function are:
𝑀𝑖𝑛 𝑍 = ∑ ∑ 𝐶𝑘 × 𝐸𝑉𝑖,𝑘
24
𝑖=14
24
𝑘=1
Subject to:
∑ 𝐸𝑉𝑖,𝑘 = 6 ∀𝑖
24
𝑘=1
𝐸𝑉𝑖,𝑘 ≤ 𝐷𝑖,𝑘 ∀𝑖, 𝑘
𝑖 − 𝑎𝑟𝑟𝑖𝑣𝑎𝑙 𝑡𝑖𝑚𝑒 𝑜𝑓 𝑡ℎ𝑒 𝐸𝑉
𝑘 − 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 ℎ𝑜𝑢𝑟
𝐸𝑉𝑖,𝑘 − 𝐵𝑖𝑛𝑎𝑟𝑦 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒, 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑖𝑛𝑔 𝑡ℎ𝑒 𝑠𝑡𝑎𝑡𝑒 𝑜𝑓 𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔
𝐶𝑘 − 𝐸𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑠𝑡
𝐷𝑖,𝑘 − 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡𝑜 𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔
Renewable following
The Renewable following model aims charge the
batteries when there is higher renewable production on
the network, in order to benefit from the advantages of
distributed generation for loading the EV, trying to match
demand with offer. Contrary to the other strategies, it
isn’t the owner of EV who defines when it is loaded, but
an entity responsible for EV fleet management. In
Renewable Following strategy, EVs are not independent
of each other to charge, so the user will only have control
in defining the overall time period during which she/he
will require the vehicle to be fully charged.
The equations that describe the optimization function are:
𝑀𝑖𝑛 𝑋 = ∑ 𝑍𝑘
24
𝑘=1
− ∑ ∑ (𝛼 × 𝐸𝑉𝑖,𝑘 × 𝑁𝑘 − 𝑅𝑘)
24
𝑖=14
24
𝑘=1
Subject to:
∑ 𝐸𝑉𝑖,𝑘 = 6 ∀𝑖
24
𝑘=1
𝐸𝑉𝑖,𝑘 ≤ 𝐷𝑖,𝑘 ∀𝑖, 𝑘
𝑍𝑘 ≤ 0 ∀𝑘
𝑍𝑘 ≥ ∑ (𝛼 × 𝐸𝑉𝑖,𝑘 × 𝑁𝑘 − 𝑅𝑘) ∀𝑘
24
𝑖=14
𝑖 − 𝑎𝑟𝑟𝑖𝑣𝑎𝑙 𝑡𝑖𝑚𝑒 𝑜𝑓 𝑡ℎ𝑒 𝐸𝑉
23rd International Conference on Electricity Distribution Lyon, 15-18 June 2015
Paper 0425
CIRED 2015 5/5
𝑘 − 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 ℎ𝑜𝑢𝑟
𝐸𝑉𝑖,𝑘 − 𝐵𝑖𝑛𝑎𝑟𝑦 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒, 𝑖𝑛𝑑𝑖𝑐𝑎𝑡𝑖𝑛𝑔 𝑡ℎ𝑒 𝑠𝑡𝑎𝑡𝑒 𝑜𝑓 𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔
𝑍𝑘 − 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝐸𝑉 𝑙𝑜𝑎𝑑 𝑎𝑛𝑑 𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛
𝑁𝑘 − 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐸𝑉 𝑙𝑜𝑎𝑑𝑖𝑛𝑔
𝐷𝑖,𝑘 − 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡𝑜 𝑐ℎ𝑎𝑟𝑔𝑖𝑛𝑔
𝑅𝑘 − 𝑅𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒
𝛼 − 𝐿𝑜𝑎𝑑𝑖𝑛𝑔 𝑟𝑎𝑡𝑒 (3𝑘𝑊)
An example of the charging load profile for 1,200 EVs
obtained with a give renewable production diagram is
presented on Figure 11
Figure 11 - Load profile for Renewable Following
Charging availability
The profile used is based on the data of the number of EV
arrivals per day, obtained from a study on mobility held
in the North [3] region of Portugal. The original data
regarding the expected number of EVs on 2030 can be
considered optimistic, (863,000 EV in Portugal), given
the trend meanwhile verified. According to [4], it is only
expected a large EV integration of EV after 2030, in
Portugal. In order to adapt these studies to the values
used on this case study, that data was normalized to
values between 0 and 100%, corresponding to 100% of
the total number of EV present in the network under
study.
It is assumed that EV arrivals at the charging points occur
between 14:00h and 00:00h.
Case study description
On this case study was analysed a radial MV distribution
network, with 69 buses, having distributed renewable
production of wind and photovoltaic type associated with.
Wind generation is represented by three wind farms,
while the PV is present on the buses with energy
consumption. The amount EV is a parameter in the
network, as well as the profiles of renewable production,
in order to cover more possibilities.
Results
The results to consider are the daily losses, minimum
voltage values throughout the day, the total number of
limits violated and the maximum load line flow. In
addition to these, are still analysed the energy costs
associated with charging the EV as well as the amount of
renewable energy used. The hybrid strategy is a mix one,
on which half of consumers use the Direct Charging
strategy and the other half the Minimum Cost strategy.
The results of the simulation for all strategies are shown
in Table I:
Results/Strategies Direct Cost Renewable Hybrid
Losses (MWh) 2.4 2.031 1.946 2.084
Min. Voltage (p.u.) 0.9 0.957 0.959 0.958
Limits violated
(N.º) 15 0 0 0
Capacity usage (%) 52.3 42.3 39.6 45.7
Energy Cost (€) 1380 1221 1229 1301
Renewable
Energy Used (%) 34.4 32.7 34.8 34.7
Table 1 – Results for 1200 EV
CONCLUSIONS
Chronological information from InovGrid allows to
assess more precisely the actual conditions verified
during peak demand and low load demand. In all the
examples presented, the benefit of having more
information regarding real load diagrams led to different
conclusions concerning the possibility of connecting new
loads and new generation units without requiring further
investments on grid reinforcements. From the results
obtained, it can be concluded that a methodology that
considers actual load and generation profiles can boost
the increase of distributed generation and electrical
vehicles connected to the grid.
Analysing the results of each strategy, it is concluded that
the production Renewable Following strategy is the one
with less negative impacts on the networks, including
losses, minimum voltage, line flow, and total limits
violated per day. Thus, a greater integration of EV in
networks is favoured by the strategy, without causing
constraints in the functioning of networks and leading to
lower losses. This solution is not only the best in energy
costs, losing to the Minimum Cost strategy, which has the
lowest cost for all cases, but leads to worst results in the
other parameters studied.
REFERENCES
[1] J. Amador, “Produção e consumo de energia em Portugal:
Factos estilizados,” Boletim Económico, 2010.
[2] J. F. N. Soares, “Impact of the deployment of electric
vehicles in grid operation and expansion,” dissertação para
obtenção do Grau de Doutorado em Engenharia Electrotécnica e
de Computadores pela FEUP, 2011. [3] I. N. de Estatística, “Inquérito à mobilidade da população
residente,” Inquérito à Mobilidade da População Residente
2000, Direcção Geral de Transportes Terrestres.
[4] I. PORTO, “Carro elétrico: Massificação só em 2030,”
http://www2.inescporto.pt/noticias-eventos/notas-de-
imprensa/carroeletrico-massificacao-so-em-2030/, acedido em
Abril de 2013.
