Incorporate day-ahead robustness and real-time incentives for electricity market design. (15th February 2023)

• Record Type: Journal Article
• Authors: Guo, Yi; Han, Xuejiao; Zhou, Xinyang; Hug, Gabriela
• Is Part Of: Applied energy. Volume 332(2023)
• Publication Date: 2023-02-15
• Subjects: Distribution networks -- Electricity market mechanism -- Online optimization -- Power systems -- Stochastic optimization; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.120484
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: In this paper, we propose a two-stage electricity market framework to explore the participation of distributed energy resources (DERs) in a day-ahead (DA) market and a real-time (RT) market. The objective is to determine the optimal bidding strategies of the aggregated DERs in the DA market and generate online incentive signals for DER-owners to optimize the social-welfare taking into account network operational constraints. Distributionally robust optimization is used to explicitly incorporate data-based statistical information of renewable forecasts into the supply/demand decisions in the DA market. We evaluate the conservativeness of bidding strategies distinguished by different risk aversion settings. In the RT market, a bi-level time-varying optimization problem is proposed to design the online incentive signals to tradeoff the RT imbalance penalty for distribution system operators (DSOs) and the costs of individual DER-owners. This enables tracking their optimal dispatch to provide fast balancing services, in the presence of time-varying network states while satisfying the voltage regulation requirement. Simulation results on both DA wholesale market and RT balancing market demonstrate the necessity of this two-stage design, and its robustness to uncertainties, the performance of convergence, the tracking ability and the feasibility of the resulting network operations. Highlights: A two-stage electricity market framework is proposed to explore the participation of distributed energy resources (DERs) in day-ahead and real-time markets. Distributionally robust optimization is applied to model the stochasticity of renewable resources in a day-ahead market. An online incentive-based optimal algorithm is proposed to tradeoff the real-time imbalance penalty for distribution systems operators and the cost of individual DER-owners. The case studies demonstrate the necessity of this two-stage market design, its robustness to uncertainties and the feasibility of the distribution network operational constraints.