Optimization of day-ahead energy storage system scheduling in microgrid using genetic algorithm and particle swarm optimization

Ajay Raghavan, Paarth Maan, Ajitha K.B. Shenoy

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 Bcand Bdare 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.

Original languageEnglish
Article number3025673
Pages (from-to)173068-173078
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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