A data-driven hybrid robust optimization approach for microgrid operators in the energy reserve market considering different wind power producers’ strategies

Abstract

In recent years, an increasing number of studies have indicated that wind power producers (WPP) have the potential to provide reserve capacity, enabling WPP to profit in the reserve market. However, the inherent uncertainty of wind power may affect the stability of this service. Therefore, WPP need to develop capacity strategies that account for the uncertainty of wind power while also serving the microgrids (MG). Moreover, inappropriate allocation of reserve capacity within the MG may lead to increased total operational costs. To address this issue, this paper proposes a new energy management framework aimed at optimizing the joint scheduling of MG in the day-ahead energy reserve market. Specifically, the information gap decision theory (IGDT) method is employed to model the capacity strategies of WPP while considering wind power uncertainty, and data-driven distributionally robust optimization (DDRO) techniques are utilized to determine the optimal reserved reserve capacity allocation for the MG. Experimental results demonstrate that different strategies significantly impact the trading of MG in the energy reserve market, and an analysis of the risk-return profiles of WPP under various strategies is provided. Additionally, the DDRO reduces the conservativeness of the results while ensuring a certain level of robustness.