Implementation of Analytical Method and Improved Particle Swarm Optimization Method for Optimal Sizing of a Standalone PV/Wind and Battery Energy Storage Hybrid System

Abstract

Power quality of the grid is affected due to the integration of Renewable Energy Sources (RES) such as wind and solar into the grid along with power electronic devices. As the output power of these RES is fluctuating in nature, a gap between 'power generation and load demand' always exists. This gives rise to insecurity of power system and poor reliability. Further problems in regulation of voltage and frequency occur. All these power system issues can be resolved by introduction of Energy Storage Systems (ESS), which make integration of RES smooth. Also, ESS acts as a power balancer, peak shaver and a load follower in a grid. As BESS can act as a source and sink, also it decides operating cost and losses of the system, sizing of BESS is very important and complex issue. This paper proposes a sizing method to optimize the components of a system which consists of Solar Photovoltaic (SPV), Wind and Battery. The aim of this paper is to minimize the Total Annual Cost (TAC) of the system which includes annual capital cost, annual maintenance cost and replacement cost over a 20 year life of project. In this study, Analytical Method (AM) and Improved Particle Swarm Optimization (IPSO) algorithm are used for optimal sizing of PV system, Wind System and BESS. To optimise the system, meteorological data such as solar irradiation, wind speed and typical load curve of actual site are considered. Through simulation results, it is observed that IPSO algorithm has better efficiency than AM.