A three-stage bi-level model for joint energy and reserve scheduling of VPP considering local intraday demand response exchange market

Abstract

In this paper, a three-stage bi-level stochastic programming approach is proposed for joint energy and reserve scheduling of a virtual power plant (VPP). In this framework, the VPP can provide demand response (DR) services and reserve capacity from external DR providers (DRPs) by participating in a local intraday demand response exchange (IDRX) market and trading with internal load aggregators (LAs). The VPP tries to reach a proper balance between allocating spinning reserve and DR services to reduce the penalty cost resulting from the difference between the day-ahead (DA) scheduled power and the real-time dispatched. To this end, a bi-level problem is formulated, in which at the upper level the objective of the VPP is to maximize its profit, and in the lower level, the LAs maximize their social welfare. The interaction between the VPP and the LAs is modelled as a Stackelberg game; then, by reformulating the lower-level problem using Karush–Kuhn–Tucker optimality conditions, a mathematical programming with equilibrium constraints (MPEC) is achieved. The intended problem is converted into a convex mixed-integer quadratic problem (MIQP) by applying the strong duality theorem. Simulation results demonstrate that providing DR services from the internal LAs and the local IDRX market noticeably affects the VPP’s decisions improving the profit by more than 7% and reducing the imposed imbalance penalty in the balancing market by nearly 50%. © 2022 Elsevier Ltd