Fluctuation Reduction of Wind Power and Sizing of Battery Energy Storage Systems in Microgrids

The intermittency and uncertainty of the renewable energy deteriorate the stability of microgrids. In this article, we focus on a grid-connected microgrid with the wind power and a battery energy storage system (BESS). The electricity load of the microgrid is satisfied by the power from the wind turbine, the BESS, and the grid, together. The purpose is to reduce the fluctuation of grid power by charging or discharging the BESS dynamically. A Markov chain is used to depict the randomness of the wind power and the fluctuation is measured by the variance of grid power. Since the variance measurement is quadratic and nonadditive, the dynamic optimization problem of the BESS does not fit the standard model of Markov decision processes (MDPs) and the dynamic programming (DP) is not applicable. In this article, we introduce the sensitivity-based optimization theory to derive a difference formula to overcome this obstacle. Based on the difference formula, an iterative optimization algorithm is developed and its efficiency is similar to the policy iteration in MDPs. The optimal scheduling policy of the BESS can be derived based on this approach. Besides, we propose a divide-and-conquer method that is much faster than traditional methods of computing the steady-state probability distribution of the large Markov chain. The performance of our approach is verified through numerical experiments using real data. Simulation results demonstrate the efficiency of our approach. In addition, the type selection and capacity sizing of the battery storage are also investigated, which can provide industry guidance for the design of BESS. Note to Practitioners—This article is motivated by the problem of reducing the long-term fluctuation of renewable energy and increasing the stability of microgrids. The frequent and tremendous power exchange between the microgrid and the main grid has an impact on the stability and economy of the main grid since the other plants, such as fuel-burning generators connected to the main grid, must quickly response. Also, the associated cost of conservative spinning reserve and transmission line capacity would be large. Therefore, it is of great practical interest to optimally schedule the battery energy storage system (BESS) to make the total power generated by the BESS and the wind turbine match the demand such that the long-term fluctuation of the grid power is reduced and the stability and economy of the grid are improved. In this article, we use a Markov chain to depict the randomness of wind power and formulate the long-term fluctuation reduction problem as an Markov decision process (MDP) problem. We apply the variance of grid power to the measurement of the fluctuation and develop an iterative algorithm to obtain the optimal policy. Another challenge is the high cost of the BESS. To make a tradeoff between the economic cost and performance, we investigate the problem of type selection and capacity sizing of the BESS. It is shown that the NaS batteries have the highest performance–cost ratio among different batteries considering the capital budget, lifespan, battery availability, and performance by case study. The optimal capacity of batteries is also investigated. These results can provide insights for implementing the BESS in renewable energy systems.