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
Abstract
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).