Energy storage system operation: Case studies in deterministic and stochastic environments

As more renewable energy sources (solar, wind, hydro, etc.) are being incorporated into the smart grid, the problem of balancing generation and load demand gets more attention in the field of power system optimal economic operation. As a solution, grid-level energy storage systems (ESSs) have been used to increase the efficiency of the power supply by smoothing load fluctuations. In this paper, monotone adaptive dynamic programming (MADP) is investigated for the optimal operation of ESSs. MADP is capable of solving optimal policies for time-dependent and finite-horizon energy storage problems. Six types of real-world ESSs, with different storage capacities, charging/discharging rates, and efficiencies are compared under energy storage optimal benchmark problems. Simulation results discuss the impact of ESS parameters on the total revenue. The robust performance of the algorithm is also validated for both deterministic and stochastic energy storage benchmark problems.