Optimal Energy Management of Wind-Battery Hybrid Power System With Two-Scale Dynamic Programming

This study is concerned with the optimal energy management for a wind-battery hybrid power system (WBHPS) with local load and grid connection, by including the current and future information on generation, demand, and real-time utility price. When applying typical dynamic optimization schemes to such a problem with a single time scale, the following dilemma usually presents: it is more beneficial to plan the (battery) storage setpoint trajectory for the longer horizon, while prediction of renewable generation, utility price, and load demand is more accurate for the shorter term. To relieve such conflict, a two-scale dynamic programming (DP) scheme is applied based on multiscale predictions of wind power generation, utility price, and load. A macro-scale dynamic programming (MASDP) is first performed for the whole operational period, based on long-term ahead prediction of electricity price and wind energy. The resultant battery state-of-charge (SOC) is thus obtained as the macro-scale reference trajectory. As the operation proceeds, the micro-scale dynamic programming (MISDP) is applied to the short-term interval based on short-term three-hour ahead predictions. The MASDP battery SOC trajectory is used as the terminal condition for the MISDP. Simulation results show that the proposed method can significantly decrease the energy cost compared with the single scale DP method.