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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

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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: Rmadlener@eonerc.rwth-aachen.de

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.