A Linear Chance Constrained Model for a Virtual Power Plant in Day-ahead, Real-time and Spinning Reserve Markets

Abstract

Integration of distributed energy resources into a virtual power plant (VPP) can realize the scale merit. This study proposes a novel approach to derive an optimal offering strategy of a VPP participating in the day-ahead (DA), the spinning reserve (SR), and the real-time (RT) markets. The considered VPP comprises of stochastic generating units, dispatchable generating units, storage units, and flexible loads. Uncertainties involved in DA and RT market energy prices, SR market capacity prices, stochastic power production, and called balancing power are hedged against using a chance constrained programming approach. Moreover, we develop a tractable solution method based on the big-M approach to reformulate the chance constrained model into a mixed-integer linear programming formulation. Finally, numerical results for a realistic case study demonstrate the effectiveness of the proposed approach.