Robust bidding strategy for multi-energy virtual power plant in peak-regulation ancillary service market considering uncertainties

Li, Yong, Deng, Youyue, Wang, Yahui, Jiang, Lin ORCID: 0000-0001-6531-2791 and Shahidehpour, Mohammad (2023) Robust bidding strategy for multi-energy virtual power plant in peak-regulation ancillary service market considering uncertainties. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 151. p. 109101.

Text
MEVPP-Manuscript-IJEPE Author accepted version 2rd revision.pdf - Author Accepted Manuscript
Download (1MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.ijepes.2023.109101

Abstract

Multi-energy virtual power plant (MEVPP) can aggregate flexible resources such as energy storage and flexible loads that decentralized in the region to meet the access conditions in the peak-regulation ancillary service market. However, the uncertainties in energy sources and loads bring adverse impact on the operation of MEVPP. Therefore, this paper proposes a day-ahead robust bidding strategy for MEVPP to participate in the peak-regulation market. Firstly, this paper analyzes the impact of uncertainties for MEVPP on the peak-regulation market. On this basis, the operation mechanism for MEVPP in the peak-regulation market is proposed by considering the integrated demand response (IDR). Additionally, the day-ahead two-stage robust bidding model is established to minimize the operation cost of MEVPP. Finally, the case studies show that the day-ahead robust bidding strategy can effectively reduce the peak-regulation deviation penalty compared with traditional deterministic optimization. Specifically, with the proposed robust bidding strategy, the total revenue in the actual operation stage is increased by 5.16% and 8.45% on sunny day and raised by 8.28% and 15.35% on cloudy day when the predicted deviations are respectively 20% and 30%, comparing with traditional deterministic optimization.

Item Type:	Article
Uncontrolled Keywords:	Multi -energy virtual power plant, Peak -regulation market, Robust bidding strategy, Uncertainty
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	19 May 2023 07:25
Last Modified:	11 Apr 2024 01:30
DOI:	10.1016/j.ijepes.2023.109101
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3170480