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Economic impact assessment and operational decision making in emission and transmission constrained electricity markets

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  • Nanduri, Vishnu
  • Kazemzadeh, Narges

Abstract

Carbon constrained electricity markets are a reality in 10 northeastern states and California in the US, as well as the European Union. Close to a Billion US Dollars have been spent by entities (mainly generators) in the Regional Greenhouse Gas Initiative in procuring CO2 allowances to meet binding emissions restrictions. In the near future, there are expected to be significant impacts due to the cap-and-trade program, especially when the cap stringency increases. In this research we develop a bilevel, complete-information, matrix game-theoretic model to assess the economic impact and make operational decisions in carbon-constrained restructured electricity markets. Our model is solved using a reinforcement learning approach, which takes into account the learning and adaptive nature of market participants. Our model also accounts for all the power systems constraints via a DC-OPF problem. We demonstrate the working of the model and compute various economic impact indicators such as supply shares, cost pass-through, social welfare, profits, allowance prices, and electricity prices. Results from a 9-bus power network are presented.

Suggested Citation

  • Nanduri, Vishnu & Kazemzadeh, Narges, 2012. "Economic impact assessment and operational decision making in emission and transmission constrained electricity markets," Applied Energy, Elsevier, vol. 96(C), pages 212-221.
    • Handle: RePEc:eee:appene:v:96:y:2012:i:c:p:212-221
    DOI: 10.1016/j.apenergy.2011.12.012
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    Cited by:

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    2. Liam Wagner & Lynette Molyneaux & John Foster, 2012. "Magnifying uncertainty: the impact of a shift to gas under a carbon price," Energy Economics and Management Group Working Papers 11-2012, School of Economics, University of Queensland, Australia.
    3. Razeghi, Ghazal & Brouwer, Jack & Samuelsen, Scott, 2016. "A spatially and temporally resolved model of the electricity grid – Economic vs environmental dispatch," Applied Energy, Elsevier, vol. 178(C), pages 540-556.
    4. Chen, Yizhong & He, Li & Li, Jing & Cheng, Xi & Lu, Hongwei, 2016. "An inexact bi-level simulation–optimization model for conjunctive regional renewable energy planning and air pollution control for electric power generation systems," Applied Energy, Elsevier, vol. 183(C), pages 969-983.
    5. Wagner, Liam & Molyneaux, Lynette & Foster, John, 2014. "The magnitude of the impact of a shift from coal to gas under a Carbon Price," Energy Policy, Elsevier, vol. 66(C), pages 280-291.
    6. Feijoo, Felipe & Das, Tapas K., 2014. "Design of Pareto optimal CO2 cap-and-trade policies for deregulated electricity networks," Applied Energy, Elsevier, vol. 119(C), pages 371-383.
    7. Nanduri, Vishnu & Saavedra-Antolínez, Ivan, 2013. "A competitive Markov decision process model for the energy–water–climate change nexus," Applied Energy, Elsevier, vol. 111(C), pages 186-198.
    8. Motalleb, Mahdi & Ghorbani, Reza, 2017. "Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices," Applied Energy, Elsevier, vol. 202(C), pages 581-596.
    9. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).

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