Identification of marginal generation units based on publicly available information. (1st January 2021)

• Record Type: Journal Article
• Authors: Hu, Tingli; Wang, Caisheng; Miller, Carol
• Is Part Of: Applied energy. Volume 281(2021)
• Publication Date: 2021-01-01
• Subjects: Data driven approach -- LME -- LMP -- Marginal units identification -- Optimization analysis; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.116073
• Languages: English
• ISSNs: 0306-2619

Abstract

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. Highlights: Identification of marginal units is critical to effective demand response. System-wide LMPs alone do not suggest any information regarding marginal units. A data-driven approach uses public data without compromising market confidentiality. Locational Marginal Emissions due to electricity generation can be quantified.