Strategic Bidding of a Virtual Power Plant in Day-Ahead Joint Energy and Reserve Markets

With the increasing penetration of distributed energy resources, Virtual Power Plants (VPPs) are evolving from price-takers to price-makers in modern electricity markets. This study investigates the strategic bidding of a VPP in joint energy and reserve markets by developing a Stackelberg-based bi-level optimization model that maximizes VPP profit while simulating ISO market clearing and locational marginal pricing (LMP). To ensure computational efficiency, the bi-level mixed-integer linear programming (BMILP) problem is transformed into a single-level Mathematical Program with Equilibrium Constraints (MPEC) using KKT conditions. Furthermore, the strong duality theorem and the big-M method are employed to linearize the bilinear terms in the objective function and the complementary slackness conditions, thereby converting the model into a mixed-integer linear programming (MILP) problem for solution. Case studies on a modified IEEE 30-node system demonstrate that the VPP successfully transitions to a price-maker role, significantly enhancing its profitability by strategically acting as a marginal unit to influence market clearing prices. These findings reveal the strategic mechanisms of VPPs and highlight the economic value of internal resource synergy in complex market environments.