Optimal Price-maker Trading Strategy of Wind Power Producer Using Virtual Bidding

This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy wind power producer using virtual bidding, which is kind of financial tool available in most electricity markets United States. In proposed model, bidding used to improve producer's market day-ahead (DA) by at multiple buses, are not limited locations units. The joint and generated solving bi-level nonlinear model. upper-level problem maximizes total expected profit while conditional value risk (CVaR) management. lower-level represents clearing process DA market. By Ka-rush-Kuhn-Tucker (KKT) conditions, duality theory, big- $M$ method, firstly transferred into an equivalent single-level mathematical program with equilibrium constraints (MPEC) then mixed-integer linear programming (MILP) can be solved existing commercial solvers. To reduce computational cost large systems, method reducing number buses considered simplify MPEC its decision variables related bidding. Case studies performed show effectiveness impacts transmission limits, unit location, aversion parameters, volatility, capacities on also studied through case studies.