Intelligent Determination of a Battery Energy Storage System Size and Location Based on RBF Neural Networks for Microgrids

Abstract

As an installation of Battery Energy Storage Systems (BESS) at a non-optimum size and location in a microgrid can cause stability issues, increase in cost, system losses and larger BESS size. Thus, the optimum size and location of BESS is a main challenge for combining BESS into a microgrid. For this reason, this paper proposes a novel method to evaluate an optimal size and location of the BESS using the Radial Basis Function Neural Networks (RBFNN). The proposed method consists of a two-stage based on an optimum size process and optimum location process. In the first stage, the optimal size of the BESS is evaluated using the RBFNN based on frequency and voltage control so that frequency and voltage of the microgrid can return to nominal values under the sudden changes in the microgrid. In the second stage, the optimal location of the BESS is determined using the RBFNN based on minimizing power losses in the microgrid. With a suitable RBFNN training, the results indicate that the proposed RBFNN method can achieve the superb performances in predicting the optimum size and location of the BESS with a minor change compared to the measured data based on the simulation. Therefore, it is obvious that if BESS is located at the optimum location and has the optimum size, it can save an enormous amount of power in a microgrid and can avoid the microgrid from instability and system collapse.