A Computationally Efficient Optimization Approach for Battery Systems in Islanded Microgrid

In islanded microgrids, it is a challenge to optimize battery energy storage systems (BESSs) with other power supply units (e.g., renewable energy and traditional power generator) and achieve the minimum daily operational cost. In this paper, we propose a computationally efficient near optimal control approach to tackle this problem. Specifically, a new islanded microgrid model, including the power supply and demand as well as battery lifetime characteristics, has been formulated based on Markov decision process. Then, we propose an approximate dynamic programming approach to solve this energy optimization problem, and achieve near minimum operational cost efficiently. We use linear programming (LP) and dynamic programming (DP) approaches to validate the percentage of optimality of our proposed approach for deterministic and stochastic case studies, respectively. Our proposed approach can obtain 100% optimality in deterministic cases comparing with results from LP, and can obtain competitive percentages of optimality with around 50% less computational time in stochastic cases comparing with results from DP. Moreover, we quantify that discharging the BESS at a certain state of charge range can increase the battery lifetime and the yearly net savings of the system. In addition, we validate our proposed approach for different sets of large data samples and achieve 18.69 times faster response than that of the traditional DP approach for 0.5 million of data samples.