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26.11.2012
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Prof. Dr. Reinhard Madlener
Full Professor of Energy Economics and ManagementRWTH School of Business & Economics
Head, Institute for Future Energy Consumer Needs and Behavior (FCN)RWTH Director JARA-Energy Research Professor DIW Berlin
SAEE Conference 2012 „Demand Side Management: Potentiale und Erfahrung“
November 21, 2012
(Duales) Demand Side Management: Alter Wein in neuen Schläuchen?
Future Energy Consumer Needs and Behavior
Presentation outline
1. Introduction: Brief history, elements of DSM (strategies, typology of measures, impact areas, dynamic pricing)
2. Challenges, opportunities, drivers, barriers of DSM
3. Investment trade-offs (expenditures vs. expected savings)
4. Levers of effective DSM
5. Demand Response (DR)
6. Potentials of DSM
7. Conclusion
1. Brief history of DSM
DSM started in the late 1970s in the U.S. (west coast) Gradually spread to the east cost, north central and other regions of
the U.S., as well as to British Columbia, Ontario and other Canadian provinces
Later, it also spread to Australia, Europe, Latin America and Asia, although efforts were more limited than in North America
1970s: Information (educate – by energy audits and printed materials) and loan
(subsidized interest rates) programs Early ESCO activities, load management programs (reduce peak demand) Insights gained: education alone has limited impact, majority of customers
not interested in loans consideration of rebates
1980s: Integrated Resource Planning (IRP) – conserved power / energy at lower
cost than new power plants, market transformation programs (building codes, efficiency mandates; take over utility’s responsibility) Source: Nadel/Geller (1996)
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1. Typology of DSM programs in 1992
Information Programs (educational brochures, industrial energy audits)
Load Management Programs
Rebate Programs (lighting, end-use, air conditioning and motor rebate programs)
Loan Programs
Performance Contracting Programs (Energy Service Companies / ESCOs receive payments for each kWh they save)
Comprehensive Direct Installation Programs (one-stop shopping for customers – e.g. audits, measure installation, financing assistance, operations, maintenance)
Bidding Programs
Source: Nadel (1992)
1. DSM techniques today
Night-time heating with load switching
Direct load control: remotely controllable switch that can turn power to a load or appliance on or off
Load limiters: limit the power that can be taken by individual consumers
Commercial/industrial programs: i.e. load-interruptible programs
Frequency regulation: dealing with fluctuation in frequency
Time-of-use pricing: reflect the production and investment cost structure where rates are higher (lower) during peak (off-peak) periods
Demand bidding: customer reduces the consumption of electricity at a certain predetermined price
Smart metering: tracking amount of electricity using to manage costs and consumption
Source: Strbac (2008)
1. Impact areas of DSM
Source: McKinsey (2010)
1. Influencing load with DSM
Source: Qureshi et al. (2011)
Load shifting, load reduction
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1. Dynamic pricing in a new environment
Well-established pricing schemes:
Time-of-use pricing (TOU)
Critical peak pricing (CPP)
Real-time pricing (RTP)
Peak-time rebates (PTR)
Idea: Price signal to final consumer as a supply scarcity indicator
Consumer acceptance of strong price discrimination?
Changing price elasticities of demand?
Shaving peak load and peak pricing?
2. Drivers for introducing DSM
Historically: The prospect of increasing the efficiency of system operation
and the existing investment in generation and transport of electricity
Today and in the near future: Climate change, peak oil, NIMBY-ism, other societal challenges Liberalized (competitive) energy markets (no captive customers,
new business opportunites) Development information & communication technologies (ICT) Rising demand for energy (esp. electricity), curse for growth Ageing assets in the energy infrastructure (need for replacements
and new investments; e.g. grids, power plants) Increasingly complex & interrelated systems (agent-based control) Consumers turn into active “prosumers” (distributed generation)
2. Challenges for DSM 1/2
Lack of ICT infrastructure DSM requires much more intensive deployment of various
sensors and advanced measurement and control devices– Facilitate the control of generators, loads, and various network devices– Integration of two systems: electrical delivery and information system
Although the key elements of the technology exist, targeted trialsare required to gain experience with DSM and network operation
Electricity demand patterns start shifting in unpredictable ways
Lack of understanding of the benefits of DSM solutions There has been insufficient clarity regarding the business case
for DSM (difficulties in the quantification of costs and benefits, lots of risks, lacking and/or split incentives, lack of policy support)
Source: Strbac (2008), own additions
2. Challenges for DSM 2/2
DSM-based solutions are often not competitive when com-pared with traditional approaches Technical, economic and environmental performance of the existing
and future DSM schemes need to be comprehensively assessed
DSM-based solutions increase the operational complexity of the system when compared with traditional solutions Complexity is increasing by operating the power systems with a
corrective control approach
Inappropriate market structure, lack of incentives Need to develop appropriate financial incentives Benefits may not be accessible to those who invest in the technology,
as the developed market arrangements support conventional solutions
New market mechanisms are required Source: Strbac (2008)
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2. Opportunities for DSM: High variation in capacity utilization and demand
Generation: Average utilization capacity is below 55%
Bulk Transmission and Distribution: Circuits in the interconnected transmission network are generally
loaded below 50%
Consumption: Demand in summer nights is about 30% of the winter peak
Idea: Use of the “Smart Grid/s” to enhance the potentials of DSM
Source: Strbac (2008)
2. Benefits of DSM to the distribution network
1. Differing new network investment
2. Increasing the amount of distributed generation that can be connected to the existing distribution network infrastructure
3. Relieving voltage-constrained power transfer problems
4. Relieving congestion in distribution substations
5. Simplifying outage management and enhancing quality and security of supply to critical load customers
6. Providing a corresponding carbon reduction
Source: Strbac (2008)
3. Investment trade-offs: complex assessment
-2 000
-1 500
-1 000
- 500
0
500
1 000
billi
on d
olla
rs (2
000)
Difference
Additional demand-sideinvestment
Efficiencymeasures
Avoided supply-sideinvestment
Generation
Transmis-sion
Distribu-tion
Additional investments on the demand side are more than offset by lower investments on the supply side.
IEA’s World Energy Outlook: Difference in Electricity Investment in the Alternative vs. Reference Scenario, 2003-2030
Source: Nilsson (2009) / WEO 2004
3. DSM expenditures vs. predicted energy savings
Source: Auffhammer et al. (2008)
Between 1989–1999, U.S. electric utilities spent $14.7 bn on DSM programs
DSM expenditures lowered electricity sales by 0.6–1.2% (at 6-12 US-ct/kWh)
Source: Loughran/Kulick (2004)
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4. Levers of effective DSM
By 2020, the U.S. could cut its end-use energy consumption by over one fifth of total projected demand (McKinsey, 2010) Real-time access to information provided through smart grid
networks can cut energy consumption by up to 18%
Six Levers of DSM (McKinsey, 2010): Rates (CPP, TOU, RTP etc.) Incentives (rebates etc.) Access to information (billing etc.) Utility controls (smart appliances) Education and marketing (targeted) Customer insight and verification
Econ. incentive
Information
Source: McKinsey (2010)
5. Demand Response (DR): Old wine, but new technologies and market players
DR as part of the “Smart Grid/s” concept (intelligent load-shifting)
Key actor: Curtailment Service Provider (“DR Aggregator”)
Prominent players (mostly start-ups): EnerNoc, Comverge, Akuacom, Site Controls*, Sequentric, etc. (U.S.) Entelios (Germany)
Recent DR study for Germany: (Von Roon/Grabmaier, 2010)
By 2020, DR = 28 GW for replacing peak-load capacities (avoided investment cost: €20 bn), reduction of 59 TWh p.a.
Industry: Economic potential of 2.8 GW (1’350 GWh) Private households: heat pumps, nighttime heat storage
* acquired by Siemens
5. Evolution of DR: Diffusion and understanding of net benefits in uncertain environment is key
Source: Entelios
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5. Demand Response (DR): Old wine, but a very competitive environment
Source: Entelios Source: McKinsey (2010) / FERC
6. Schematic load-shifting potential until 2020 (stylized)
Source: Klobasa (2007), cited in Von Roon/Gobmaier (2010)
6. Technical potential of switchable load, by switch-off times (Germany)
Source: FfE, Von Roon/Gobmaier (2010)
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7. Conclusion
1. Supply side: increasing relevance of DSM (intermittent renewables, ageing energy system infrastructure, lacking social acceptance of new large-scale projects)
2. Demand side: increasing levels of controllable load (continued electrifi-cation and increasing no. of intelligent devices – “Smart Grid-ready”)
3. Technical and managerial innovation: New opportunities for DSM through ICT and new service providers (e.g. aggregators), dynamic load management
4. Demand Response (DR): old wine in new bottles (DR as part of the Smart Grid, e.g. “Virtual negative reserve power plants”), split incentives problem
5. Demand-side (equipment, needs & behavior, heterogeneity, susceptibility, privacy): likely more complex and diverse in the future and not easier to manage, energy savings depend on optimized man–technology interactions
6. Policy support and predictable regulatory framework needed for transition period; but cost-benefit analysis remains challenging (e.g. for determining social welfare gain – justifiable policy intervention?)
Thank you for your kind attention!
Any questions or comments?
Prof. Reinhard MadlenerTel. +49-241-80 49 820
email: [email protected]
www.eonerc.rwth-aachen.de/FCN
References
Auffhammer M., Blumstein C., Fowlie M. (2008). Demand-Side Management and Energy Efficiency Revisited, The Energy Journal, 29(3): 91-104 .
Davito B., Tai H., Uhlander R. (2010). The Smart Grid and the Promise of Demand Side Management, McKinsey (http://www.smartgridnews.com/artman/uploads/1/mckinsey_demand_side_mgtm.pdf)
Forschungsstelle für Energiewirtschaft (FfE) (2010). Demand Response in der Industrie:Status und Potenziale für Deutschland, Endbericht, Dezember.
Loughran D.S., Kulick J. (2004). Demand-Side Management and Energy Efficiency in the United States, The Energy Journal, 25(1): 19-43.
Nadel S. (1992). Utility Demand-Side Management Experience and Potential – A Critical Review, Annu. Rev. Energy Environ, 17: 507-535.
Nadel S., Geller H. (1996). Utility DSM: What Have We Learned? Where are We Going? Energy Policy, 24(4): 289-302.
Qureshi W.A., Nair N.K.C., Farid M.M. (2011). Impact of Energy Storage in Buildings on Electricity Demand Side Management, Energy Conversion & Mgt., 52(5): 2110–2120 .
Strbac G. (2008). Demand-Side Management: Benefits and Challenges, Energy Policy, 36: 4419-4426.
50Hz Transmission, Amprion, TenneT TSO, TransnetBW (2012). NetzentwicklungsplanStrom 2012, 2. überarbeiteter Entwurf, August.