Rebecca johnson ph d puc smart grid policy specialist e mail rebecca johnson@dora state co us
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Rebecca Johnson, Ph.D. PUC Smart Grid Policy Specialist E-mail: [email protected] Smart Grid: Carbon and Economic Implications for Colorado April 29, 2010. Presentation Overview. Results from national studies on the energy and CO2 impacts of smart grid

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Rebecca Johnson, Ph.D.

PUC Smart Grid Policy Specialist

E-mail: [email protected]

Smart Grid: Carbon and Economic Implications for ColoradoApril 29, 2010


Presentation Overview

  • Results from national studies on the energy and CO2 impacts of smart grid

  • Colorado smart grid case study

    • Evaluation of Colorado-specific changes in CO2 and levelized cost under a variety of smart grid scenarios

  • Key policy implications


Results from National Studies on the Energy and CO2 Impacts of Smart Grid


Sources of Savings - EPRI

Source: Electric Power Research Institute. “The Green Grid: Energy Savings and Carbon Emissions Reductions Enabled by a Smart Grid”. 2008


Sources of Savings - PNNL

Source: Pacific Northwest National Laboratory. “The Smart Grid: An Estimation of the Energy and CO2 Benefits”. 2010


Sources of Savings – Brattle Group

Source: The Brattle Group. “How Green is the Smart Grid?”. 2009


Why it is Important to Understand Smart Grid Implications at the State Level

  • National-to-state and state-to-state electricity fuel mixes vary dramatically.

  • Changes in CO2 due to changes in the electricity infrastructure are fuel mix dependent and are therefore state specific.

  • Electricity policy is largely developed at the state level.

Source: EIA 2006 Electricity Profiles


Colorado Smart Grid Case Study

  • Quantified Colorado-specific changes in CO2 and levelized cost under a variety of smart grid scenarios.

  • Modeled all generating units in the state plus Laramie River Station in Wyoming (coal unit owned by Tri-State)

  • Evaluated smart grid enabled:

    • demand response

    • large scale wind integration

    • energy efficiency

    • plug-in hybrid electric vehicle (PHEV) integration


Research Design:Experimental Variables

  • Degrees of Grid Intelligence

  • Demand Response (Demand Flattening)

  • Wind Generation

  • Energy Efficiency (Demand Destruction)

  • Plug-in Hybrid Electric Vehicles (PHEVs)


Experimental Variables:Degrees of Grid Intelligence

  • Conventional Grid

    • Business-as-usual operation.

  • Intermediate Grid (non-dynamic load shaping)

    • Time-of-use pricing, enhanced consumer information, and programmable appliances shift demand from peak to off-peak.

    • Demand curve is flattened in a predictable way, but system does not have the ability to dynamically shape demand to match supply.

  • Advanced Grid (dynamic load shaping)

    • Dynamic demand shaping via real-time pricing, enhanced consumer information, price-responsive programmable appliances, and direct load control.

    • System dynamically matches supply and demand using all generating options, storage, and demand response.

    • Managed PHEV load follows renewable generation.


  • Experimental Variables:Demand Response

    Intermediate Grid

    (non-dynamic load shaping)

    • Time-of-use pricing, enhanced consumer information, and programmable appliances shift demand from peak to off-peak.

    • Demand curve is flattened in a predictable way, but system does not have the ability to dynamically shape demand to match supply.

    • Advanced Grid

    • (dynamic load shaping)

      • Dynamic demand shaping via real-time pricing, enhanced consumer information, price-responsive programmable appliances, and direct load control.

      • System dynamically matches supply and demand using all generating options, storage, and demand response.

      • Managed PHEV load follows renewable generation.


    Results: Demand Response

    • Without wind, perfect ability to flatten load increases CO2 by 1% and decreases levelized costs by 0.2%.

      • More relevant to municipalities and rural electric associations than to PSCo.

    • With 20% wind, demand response reduces wind integration costs by up to $18 million per year. Smart grid contributes <1% of total CO2 reductions.

    • With 50% wind, demand response reduces wind integration costs by up to $226 million per year. Smart grid contributes up to 9% of total CO2 reductions.


    Experimental Variables:Wind Integration

    • Smart grid supports wind integration by aligning demand with renewable generation.


    Results: Wind Integration

    • Smart grid reduces wind integration costs by reducing curtailment.

    • Curtailment expense is calculated as levelized cost plus foregone production tax credit ($86.50 per MWh).


    Experimental Variables:Energy Efficiency

    Source: Ventyx Consulting


    Sources of Energy Efficiency

    Modeled 5% and 15% energy efficiency improvements

    • Consumer demand reductions – highly uncertain

      • Feedback

        • 4% to 12% (Neenan & Robinson, 2009; PNNL, 2010)

      • Time-based pricing

        • 4% (King & Delurey, 2005)

    • Reductions in Transmission and Distribution Losses – relatively certain

      • 2.4% (Xcel Energy, 2008)


    Results: Energy Efficiency Impacts on CO2 and Levelized Costs


    Experimental Variables:Plug-in Hybrid Electric Vehicles

    Sources: Ventyx Consulting, General Motors


    Results: PHEVs

    • A ‘typical’ PHEV in Colorado would emit 48% less CO2 than an internal combustion vehicle.

    • Very high penetrations of PHEVs would rarely overwhelm system generating capabilities.

    • However, highly problematic from the distribution level perspective (7/1 CIM).

    • Managed charging is critical.


    CO2 Summary


    Levelized Cost Summary


    CO2 and Levelized Cost Reductions Are Not Aligned


    Comparison of Results


    Policy Implications:Energy Efficiency

    • Problem:

      • The traditional utility business model is a disincentive to efficiency.

    • Potential State-Level Policy Solutions:

      • Alternate Business Models

        • Shared Savings

        • Bonus Return on Equity

        • Virtual Power Plant

      • Performance-Based Renewable Energy and Energy Efficiency Targets


    Policy Implications:Wind Integration

    • Smart grid’s wind integration benefits require consumer adoption.

    • If consumers don’t adjust their behavior in response to smart grid, the technology will become an expensive mechanism to marginally improve electric utility operational efficiency.

    • Consumer-centric mechanisms to promote adoption.

      • Outreach and education

      • Time-based pricing

      • Incentives and rebates

      • Privacy and data security assurance

      • Data ownership clarity


    Upcoming Commissioner Informational Meetings

    • June 7th, 9:00 am to 11:00 am

      • Topic: Electricity use feedback and customer behavior

      • Speakers:

        • Dr. Ahmad Faruqui, The Brattle Group

        • Dr. Karen Ehrhardt-Martinez, formally with NRRI, now consulting with her own firm, Human Dimensions Research Associates

        • Nancy Brockway, former NH PUC Commissioner and current consultant on consumer and low income issues

    • July 1st, 9:00 am to 11:00 am

      • Topic: Smart grid’s role in emerging markets

      • Speakers:

        • Peter Fox-Penner, the Brattle Group

          • Emerging markets overview

        • Paul Denholm, the National Renewable Energy Laboratory

          • Plug-in hybrid electric vehicles impact on the electric grid

    • August , date and time tbd

      • Technical aspects of smart grid

        • Communications platforms

        • IT infrastructure

        • Interoperability standards


    Questions?

    E-mail: [email protected]


    Acknowledgements

    • Research supported by CU’s Renewable and Sustainable Energy Institute (RASEI)

    • Data provided by Ventyx Consulting

    • Research guidance from the National Renewable Energy Laboratory (NREL), Ventyx Consulting, and Xcel Energy


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