Novel Frequency Regulation Scenarios Generation Method Serving for Battery Energy Storage System Participating in PJM Market

Yichao Zhang, Amjad Anvari-Moghaddam, Saeed Peyghami and Frede Blaabjerg ()
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Yichao Zhang: Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark
Amjad Anvari-Moghaddam: Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark
Saeed Peyghami: Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark
Frede Blaabjerg: Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark

Energies, 2024, vol. 17, issue 14, 1-16

Abstract: As one of the largest frequency regulation markets, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market allows extensive access of Battery Energy Storage Systems (BESSs). The designed signal regulation D (RegD) is friendly for use with BESSs with a fast ramp rate but limited energy. Designing operating strategies and optimizing the sizing of BESSs in this market are significantly influenced by the regulation signal. To represent the inherent randomness of the RegD signal and reduce the computational burden, typical frequency regulation scenarios with lower resolution are often generated. However, due to the rapid changes and energy neutrality of the RegD signal, generating accurate and representative scenarios presents challenges for the methods based on shape similarity. This paper proposes a novel probability-based method for generating typical regulation scenarios. The method relies on the joint probability distribution of two features with a 15-min resolution, extracted from the RegD signal with a 2 s resolution. The two features can effectively portray the characteristic of RegD signal and its influence on BESS operation. Multiple regulation scenarios are generated based on the joint probability distributions of these features at first, with the final typical scenarios chosen based on their probability distribution similarity to the actual distribution. Utilizing regulation data from the PJM market in 2020, this paper validates and analyzes the performance of the generated typical scenarios in comparison to existing methods, specifically K-means clustering and the forward scenarios reduction method.

Keywords: frequency regulation; typical frequency regulation scenario; BESS sizing optimization and operating strategy; probability distribution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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