Two‐stage risk‐constrained stochastic optimal bidding strategy of virtual power plant considering distributed generation outage

Abstract

Abstract This paper presents an optimal bidding strategy for a technical and commercial virtual power plant (VPP) in medium‐term time horizon. A VPP includes various distributed energy resources (DERs) that can participate in the Pool and Futures markets. Although medium/long‐term scheduling provides the opportunity to participate in the futures market, it also raises the possibility of unit failure. In this regard, the impact of distributed generation (DG) units’ failure, as an important challenge in VPP, is incorporated in the proposed model. The model is formulated as a risk‐constrained two‐stage stochastic problem. The VPP signs futures market contracts in the first stage, and in the second stage, it participates in the day‐ahead (DA) market and manages its DERs. Long short‐term memory neural network and scenario generation and reduction methods are used to capture the uncertainty parameters of electrical load, DA market prices, wind speed, and solar radiation in the proposed model. The performance of proposed model is investigated in different cases. The obtained results show that the VPP can compensate the losses caused by the DG units’ failure through taking advantage of the arbitrage opportunity.

Keywords

• distributed power generation
• power markets
• power system economics
• power system management
• recurrent neural nets