rebecca johnson ph d puc smart grid policy specialist e mail rebecca johnson@dora state co us n.
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Rebecca Johnson, Ph.D. PUC Smart Grid Policy Specialist E-mail: rebecca.johnson@dora.state.co.us. 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 rebecca johnson@dora state co us
Rebecca Johnson, Ph.D.

PUC Smart Grid Policy Specialist

E-mail: rebecca.johnson@dora.state.co.us

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

presentation overview
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
sources of savings epri
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
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
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
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
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
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
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
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
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
Experimental Variables:Wind Integration
  • Smart grid supports wind integration by aligning demand with renewable generation.
results wind integration
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).
sources of energy efficiency
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)
experimental variables plug in hybrid electric vehicles
Experimental Variables:Plug-in Hybrid Electric Vehicles

Sources: Ventyx Consulting, General Motors

results phevs
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.
policy implications energy efficiency
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
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
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
Questions?

E-mail: rebecca.johnson@dora.state.co.us

acknowledgements
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