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Does a detailed model of the electricity grid matter? Estimating the impacts of the Regional Greenhouse Gas Initiative

Author

Listed:
  • Shawhan, Daniel L.
  • Taber, John T.
  • Shi, Di
  • Zimmerman, Ray D.
  • Yan, Jubo
  • Marquet, Charles M.
  • Qi, Yingying
  • Mao, Biao
  • Schuler, Richard E.
  • Schulze, William D.
  • Tylavsky, Daniel

Abstract

The consequences of environmental and energy policies in the U.S. can be severely constrained by physical limits of the electric power grid. Flows do not follow the shortest path but are distributed over all lines in accordance with the laws of physics, so grid operators must select which generation units to operate at each moment, not only to minimize production costs, but also to prevent the system from collapsing because of line overloads. Because of the complexity of power grid operation, computing limitations have until very recently made it impossible to solve a policy analysis or planning model that combines realistic modeling of flows with a detailed transmission system model and the prediction of generator investment and retirement. We construct and solve a model of the eastern US and Canada that combines these characteristics. Then, because a smaller model would be usable for some additional purposes, we explore the effects of transmission model simplification on the accuracy of simulation results. To evaluate the amount of detail necessary, we simulate the short- and long-term effects of imposing a price on the carbon dioxide emissions from the power plants in nine northeastern US states, as the Regional Greenhouse Gas Initiative does. We consider three grid models that simplify the actual 62,000-node system to varying degrees. Our 5000-node model matches the 62,000-node model very closely. We use it as the basis for evaluating the more simplified models: a 300-node model and a model with just one node, i.e. no transmission constraints. With each of the three models, we predict the carbon dioxide emission impacts, electricity price impacts, and generator entry and exit impacts of the emission price, over the next 20 years. We find that most of the impact predictions produced by the 300- and one-node models differ from those of the 5000-node model by more than 20%, and some by much more. Fortunately, the 5000-node model, and others with its combination of transmission detail, realistic flows, entry prediction, and retirement prediction can be used for many useful purposes.

Suggested Citation

  • Shawhan, Daniel L. & Taber, John T. & Shi, Di & Zimmerman, Ray D. & Yan, Jubo & Marquet, Charles M. & Qi, Yingying & Mao, Biao & Schuler, Richard E. & Schulze, William D. & Tylavsky, Daniel, 2014. "Does a detailed model of the electricity grid matter? Estimating the impacts of the Regional Greenhouse Gas Initiative," Resource and Energy Economics, Elsevier, vol. 36(1), pages 191-207.
  • Handle: RePEc:eee:resene:v:36:y:2014:i:1:p:191-207
    DOI: 10.1016/j.reseneeco.2013.11.015
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    References listed on IDEAS

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    1. Shmuel S. Oren, 1997. "Economic Inefficiency of Passive Transmission Rights in Congested Electricity Systems with Competitive Generation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 63-83.
    2. Hogan, William W, 1992. "Contract Networks for Electric Power Transmission," Journal of Regulatory Economics, Springer, vol. 4(3), pages 211-242, September.
    3. Roger E. Bohn & Michael C. Caramanis & Fred C. Schweppe, 1984. "Optimal Pricing in Electrical Networks over Space and Time," RAND Journal of Economics, The RAND Corporation, vol. 15(3), pages 360-376, Autumn.
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    Citations

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    Cited by:

    1. Xu, Qingyu & Hobbs, Benjamin F., 2021. "Economic efficiency of alternative border carbon adjustment schemes: A case study of California Carbon Pricing and the Western North American power market," Energy Policy, Elsevier, vol. 156(C).
    2. Picciano, Paul & Aguilar, Francisco X. & Burtraw, Dallas & Mirzaee, Ashkan, 2022. "Environmental and socio-economic implications of woody biomass co-firing at coal-fired power plants," Resource and Energy Economics, Elsevier, vol. 68(C).
    3. Frew, Bethany A. & Jacobson, Mark Z., 2016. "Temporal and spatial tradeoffs in power system modeling with assumptions about storage: An application of the POWER model," Energy, Elsevier, vol. 117(P1), pages 198-213.
    4. Shawhan, Daniel L. & Picciano, Paul D., 2019. "Costs and benefits of saving unprofitable generators: A simulation case study for US coal and nuclear power plants," Energy Policy, Elsevier, vol. 124(C), pages 383-400.
    5. Rebecca J. Davis & J. Scott Holladay & Charles Sims, 2022. "Coal-Fired Power Plant Retirements in the United States," Environmental and Energy Policy and the Economy, University of Chicago Press, vol. 3(1), pages 4-36.
    6. Zhou, Yishu & Huang, Ling, 2021. "How regional policies reduce carbon emissions in electricity markets: Fuel switching or emission leakage," Energy Economics, Elsevier, vol. 97(C).
    7. Fell, Harrison & Maniloff, Peter, 2018. "Leakage in regional environmental policy: The case of the regional greenhouse gas initiative," Journal of Environmental Economics and Management, Elsevier, vol. 87(C), pages 1-23.
    8. Yan, Jingchi, 2021. "The impact of climate policy on fossil fuel consumption: Evidence from the Regional Greenhouse Gas Initiative (RGGI)," Energy Economics, Elsevier, vol. 100(C).

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    More about this item

    Keywords

    Electricity market; Simulation modeling; Cost–benefit analysis; Long term; Carbon dioxide;
    All these keywords.

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects

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