Paper Title
Genetic Algorithm for Optimal Battery Energy Storage Systems Capacity and Site Selection for a Distribution Network with a High Penetration of Wind Energy

Distributed generation (DG) sources, e.g. wind turbines (WT) and photovoltaics (PV) are becoming more common in distribution network systems (DNS) and their presence can affect DNS performance. To improve DNS reliability and performance, incorporating energy storage systems (ESS) is becoming increasingly important. ESS can help facilitate the incorporation of DG sources and to resolve technical challenges, e.g. transient stability. Power generated from DG sources is variable, particularly in the case of WT, and as power variability affects the DNS stability, ESS have been used to regulate the demand from the supply network. Given the high cost of mass battery energy storage systems (BESS), finding a method to determine the optimum size of BESS for a given DNS would assist operators. In DNS, voltage and power losses of the network are considered to be significant matters. BESS location affects system operation and power flow within the network, so there is a clear need to find the optimal BESS location in DNS. In this paper, a methodology for optimal siting and sizing of a BESS in DNS with a high integration of energy from WT is presented. The proposed technique is based on genetic algorithms (GA) with power flow (PF) to identify the best placement and capacity in order to achieve optimal DNS performance. The general principle of operation is to charge the BESS through unused energy or off-peak duration and discharge during peak time or low energy generation. The suggested method is verified on a typical UK DNS to validate the performance and effectiveness of the technique. Keywords - Distributed generation (DG), Distribution systems, battery energy storage system, optimal allocation and Genetic algorithms (GA).