Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid. Issue 3 (1st February 2016)

• Record Type: Journal Article
• Authors: Sharma, Sharmistha; Bhattacharjee, Subhadeep; Bhattacharya, Aniruddha
• Is Part Of: IET generation, transmission & distribution. Volume 10:Issue 3(2016)
• Page Start: 625
• Page End: 637
• Publication Date: 2016-02-01
• Subjects: distributed power generation -- power generation planning -- power grids -- energy management systems -- battery management systems -- particle swarm optimisation -- renewable energy sources -- cost reduction -- minimisation; optimal operational planning -- microgrid -- battery energy storage device -- grey wolf optimisation -- optimal sizing -- energy cost minimisation -- renewable energy source -- power grid -- energy management system -- RES -- MG -- BES -- operation cost minimisation -- cost‐based formulation -- GWO; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2015.0429
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Nowadays, optimal operational planning of micro‐grid (MG) with regard to energy costs minimisation of MG and better utilisation of renewable energy sources (RES) such as solar and wind energy systems, has become the head of concern of modern power grids and energy management systems. Due to large integration of RES into the MG, the necessity of battery energy storage (BES) has increased rapidly. Size of BES plays an important role in the operation cost minimisation of MG. A cost‐based formulation has been performed in this study to determine the optimal size of BES in the operation cost minimisation problem of MG under various constraints, such as power capacity of distributed generators (DGs), power and energy capacity of BES, charge/discharge efficiency of BES, operating reserve and load demand satisfaction. A recently developed optimisation technique known as grey wolf optimisation (GWO) has been applied here to solve the problem. The proposed algorithm is tested on a typical MG. Simulation results establish that the proposed approach outperforms several existing optimisation techniques such as genetic algorithm, particle swarm optimisation, tabu search, differential evolution, biogeography‐based optimisation, teaching–learning‐based optimisation, bat algorithm (BA) and improved BA in terms of quality of solution obtained and computational efficiency.