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In this paper, an optimal control strategy is presented for grid-connected microgrids with renewable generation and battery energy storage systems (BESSs). In order to optimize the energy cost, the proposed approach utilizes predicted data on renewable power, electricity price, and load demand within a future period, and determines the appropriate actions of BESSs to control the actual power dispatched to the utility grid. We formulate the optimization problem as a Markov decision process and solve it with a dynamic programming algorithm under the receding horizon approach. The main contribution in this paper is a novel cost model of batteries derived from their life cycle model, which correlates the charge/discharge actions of batteries with the cost of battery life loss. Most cost models of batteries are constructed based on identifying charge–discharge cycles of batteries on different operating conditions, and the cycle counting methods used are analytical, so cannot be expressed mathematically and used in an optimization problem. As a result, the cost model proposed in this paper is a recursive and additive function over control steps that will be compatible with dynamic programming and can be included in the objective function. We test the proposed approach with actual data from a wind farm and an energy market operator.

Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting