Optimal placement and sizing of battery energy storage using the genetic algorithm

Abstract

Voltage drop and rise at network peak and off-peak periods are one of the major power quality problems in low voltage distribution networks. Additionally, the ever increasing demand for electricity along with the other requirements are driving modern day power systems towards more distributed generation (DG). Integration of large scale DG can be limited by these voltage variations. Therefore, it is of high interest to investigate the voltage support strategies that are able to successfully mitigate these problems. One of the solutions is to use energy storage systems (ESS). On the one hand limited amount of energy storage might not have the desired impact. On the other hand it is not possible to install large amount of energy storage as this would increase the costs substantially. Therefore it is required to optimally place and size energy storage. The purpose of this thesis is to investigate the optimal placement and sizing of battery energy storage with the integration of renewable energy sources (RES) in a low voltage distribution network. An optimization model has been developed in order to identify the potential battery size and location combinations that increase the RES hosting capacity of the distribution network. The optimization tool used for this problem is the Genetic Algorithm (GA). Among the most important features of this algorithm stands its robustness and ability to provide good results in optimization processes. The functionality and the performance of the developed model is assessed using an IEEE benchmark network that has shown to be adequate for both the dynamic and steady-state analysis. The results of this work show that the voltage problems caused by the integration of RES can be mitigated by optimal placement and sizing of battery energy storage. They also show that besides the the voltage profile improvement reduction of losses can be achieved.