Optimization Scheduling Model Based on Source-Load-Energy Storage Coordination in Power Systems

To improve the wind power and photovoltaic power accommodation rate and reduce the power system operation costs, this paper considers thermal power units, price-based demand response (DR) and battery energy storage system (BESS) as scheduling resources and establishes an optimization scheduling model based on source, load and energy storage coordination. A two stages optimization method is proposed in order to minimize the system operational costs including thermal power units operation cost, wind power and photovoltaic power curtailment cost and price-based DR scheduling costs. The first stage optimization uses binary particle swarm optimization algorithm (BPSO) to minimize the sum of wind power and photovoltaic power curtailment cost and thermal power units start-up cost. Based on the optimization results at the first stage, the second stage optimization uses double layers' continuous particle swarm optimization (CPSO) algorithm to minimize the sum of price-based DR scheduling cost and fuel consumption cost. The simulation results verify the feasibility and effectiveness of this optimization scheduling model.