Power Optimization and Real-time Management of Grid-connected Microgrid with Nonlinear Battery Degradation Model in Energy Market

This paper presents a method for optimal power management of grid-connected microgrids. Dynamic programming (DP) algorithm is utilized to forecast the global optimal solution to microgrid power flow dispatch, considering day-ahead forecasts of PV power and loads. The impact of various retail electricity pricings, which include fixed-price, time-of-use (TOU) pricing and real-time pricing (RTP) based on locational marginal pricing (LMP), are discussed. Battery storage with its nonlinear charging/discharging cost model is also exploited for system power optimization. The objective is to achieve the minimum microgrid system operation cost, in other words, the maximum economic benefits for end-users. Rule-based (RB) algorithm is also studied as a baseline for comparison to the DP solution. Typical cases under different weather scenarios have been studied, simulated and compared.