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

doi:10.1109/access.2020.3025673

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 × (B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> - B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> + 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.

Highlights

A power grid is an interconnected network that transmits electricity from producers to consumers
We study the search space’s structural properties in depth and prove its magnification to be at least 50 · (Bc − Bd + 1), where Bc and Bd are the maximum depths of charge and discharge of the Energy Storage System (ESS) respectively
Genetic Algorithm (GA) AND Particle Swarm Optimization (PSO) FOR OPTIMIZING A DAY-AHEAD ESS SCHEDULE we describe GA and PSO for the problem defined in Section II, using the search space defined in Definition 3

Summary

INTRODUCTION

A power grid is an interconnected network that transmits electricity from producers to consumers. This requires an optimal scheduling of the ESS, i.e., determining when the ESS charges or discharges Another way to take advantage of dynamic pricing is to shift certain loads to a different time period within a day, usually to a time interval when the grid price is low. We have applied Genetic Algorithm (GA) [11] and Particle Swarm Optimization (PSO) [12] to optimize a dayahead ESS schedule in a microgrid that’s connected to a traditional one-way-power-flow grid that imposes dynamic pricing on the microgrid This optimization problem requires a day-ahead hourly forecast of the load, generated power, and grid price.

CONTRIBUTIONS The main contributions of this article are as follows:

PROBLEM DEFINITION AND SEARCH SPACE

5: Pick the fittest ns individuals from population

RESULTS AND DISCUSSION

30: Set globalBest to index of current particle

CONCLUSION