Optimization of Day-Ahead Energy Storage System Scheduling in Microgrid Using Genetic Algorithm and Particle Swarm Optimization

Raghavan, Ajay and Maan, Paarth and Shenoy, Ajitha K B (2020) Optimization of Day-Ahead Energy Storage System Scheduling in Microgrid Using Genetic Algorithm and Particle Swarm Optimization. IEEE Access, 8. pp. 173068-173078. ISSN 2169-3536

[img] PDF
9789.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy

Abstract

We present a day-ahead scheduling strategy for an Energy Storage System (ESS) in a microgrid using two algorithms - Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The scheduling strategy aims to minimize the cost paid by consumers in a microgrid subject to dynamic pricing. We define an objective function for the optimization problem, present its search space, and study its structural properties. We prove that the search space has a magnification of at least 50 × (Bc − Bd + 1), where Bc and Bd are the maximum depths of charge and discharge in an hour (in percentage) of the ESS respectively. In a simulation involving load, energy generation, and grid price forecasts for three microgrids of different sizes, we obtain ESS schedules that provide average cost reductions of 11.31% (using GA) and 14.31% (using PSO) over the ESS schedule obtained using Net Power Based Algorithm.

Item Type: Article
Uncontrolled Keywords: Microgrid, energy storage system, dynamic pricing, scheduling strategy, optimization, genetic algorithm, particle swarm optimization.
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 06 Mar 2021 05:24
Last Modified: 06 Mar 2021 05:24
URI: http://eprints.manipal.edu/id/eprint/156486

Actions (login required)

View Item View Item