Identification of marginal generation units based on publicly available information

Tingli Hu, Caisheng Wang and Carol Miller

Applied Energy, 2021, vol. 281, issue C, No S0306261920315014

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.

Keywords: Data driven approach; LME; LMP; Marginal units identification; Optimization analysis (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:281:y:2021:i:c:s0306261920315014

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DOI: 10.1016/j.apenergy.2020.116073

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