Grid-price Dependent Optimal Energy Storage Management Strategy for Grid-connected Industrial Microgrids

This paper presents an optimal energy management strategy for the operation of multiple energy storage units (batteries) in grid-connected industrial microgrids with high-penetration renewables in a variable grid-price scenario. The approach is based on a regrouping particle swarm optimization (RegPSO) formulated over a day-ahead scheduling horizon with one hour time interval, considering forecasted renewable energy generations and electric load demands. Besides satisfying its local energy demands, the microgrid considered in this paper (a real industrial microgrid, "Goldwind Smart Microgrid System" in Beijing, China), participates in energy trading with the main grid; it can either sell power to the main grid or buy from the main grid. Performance objectives include minimization of operation and maintenance costs and energy purchasing expenses from the main grid, and maximization of financial profit from energy selling revenues to the main grid. Simulation results demonstrate the effectiveness of various aspects of the proposed strategy in different scenarios. To validate the performance of the proposed strategy, obtained results are compared to a genetic algorithm (GA) based reference energy management approach and reveal that the RegPSO based strategy was able to find a global optimal solution in considerably less computation time than the GA based reference approach.