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Identification of marginal generation units based on publicly available information

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  • Hu, Tingli
  • Wang, Caisheng
  • Miller, Carol

Abstract

Identification of marginal generation units is essential to the development of effective demand response (DR) programs and quantification of locational marginal emissions (LMEs). The real-time marginal units, however, are not revealed by the ISOs/RTOs. This paper develops a framework to identify marginal units from the perspective of a market participant. Through the analysis of the relationship between marginal units and locational marginal prices (LMPs), it is impossible to determine the marginal generators based solely on knowledge of the LMPs. In the proposed framework, a simple data driven approach of exclusion is developed using LMPs, masked bid prices with a 4-month delay, and annual data on power generation and fuel consumption; all are publicly available. Using the link between load profiles and marginal units, the identification results based on historical data can be used for marginal unit predictions assuming the dispatch merit order in the prediction case is the same as in the historical case. A simulation study shows that the proposed framework is effective in detecting marginal units when system load levels are relatively high. Two applications, one to trace load changes back to marginal units and the other to calculate locational marginal emissions, are provided to show the practical value of marginal unit identification in power systems.

Suggested Citation

  • Hu, Tingli & Wang, Caisheng & Miller, Carol, 2021. "Identification of marginal generation units based on publicly available information," Applied Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920315014
    DOI: 10.1016/j.apenergy.2020.116073
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    References listed on IDEAS

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    1. Wang, Y. & Wang, C. & Miller, C.J. & McElmurry, S.P. & Miller, S.S. & Rogers, M.M., 2014. "Locational marginal emissions: Analysis of pollutant emission reduction through spatial management of load distribution," Applied Energy, Elsevier, vol. 119(C), pages 141-150.
    2. Noel, Lance & Brodie, Joseph F. & Kempton, Willett & Archer, Cristina L. & Budischak, Cory, 2017. "Cost minimization of generation, storage, and new loads, comparing costs with and without externalities," Applied Energy, Elsevier, vol. 189(C), pages 110-121.
    3. Rogers, Michelle M. & Wang, Yang & Wang, Caisheng & McElmurry, Shawn P. & Miller, Carol J., 2013. "Evaluation of a rapid LMP-based approach for calculating marginal unit emissions," Applied Energy, Elsevier, vol. 111(C), pages 812-820.
    4. Fuentes-Cortés, Luis Fabián & Ma, Yan & Ponce-Ortega, Jose María & Ruiz-Mercado, Gerardo & Zavala, Victor M., 2018. "Valuation of water and emissions in energy systems," Applied Energy, Elsevier, vol. 210(C), pages 518-528.
    5. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
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    Cited by:

    1. Andrianesis, Panagiotis & Biskas, Pandelis & Liberopoulos, George, 2021. "Evaluating the cost of emissions in a pool-based electricity market," Applied Energy, Elsevier, vol. 298(C).
    2. Huo, Yuchong & Bouffard, François & Joós, Géza, 2022. "Integrating learning and explicit model predictive control for unit commitment in microgrids," Applied Energy, Elsevier, vol. 306(PA).

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