Multi-Objective Optimization Algorithms for Energy Management Systems in Microgrids: A Control Strategy Based on a PHIL System

TL;DR

This paper presents a real-time adaptive multi-objective optimization method for microgrid energy management using hardware-in-the-loop testing, balancing multiple factors like fuel use, power quality, and renewable energy utilization.

Contribution

It introduces a novel adaptive weighted optimization approach integrated with hardware-in-the-loop testing for real-time microgrid control, addressing multiple complex objectives.

Findings

Enhanced photovoltaic energy utilization

Reduced power mismatch and load imbalance

Improved battery management under various conditions

Abstract

In this research a real time power hardware in loop configuration has been implemented for an microgrid with the combination of distribution energy resources such as photovoltaic, grid tied inverter, battery, utility grid, and a diesel generator. This paper introduces an unique adaptive multi-objective optimization approach that employs weighted optimization techniques for real-time microgrid systems. The aim is to effectively balance various factors including fuel consumption, load mismatch, power quality, battery degradation, and the utilization of renewable energy sources. A real time experimental data from power hardware in loop system has been used for dynamically updating system states. The adaptive preference-based selection method are adjusted based on state of battery charging thresholds. The technique has been integrated with six technical objectives and complex constraints. This approach helps to practical microgrid decision making and optimization of dynamic energy systems. The energy management process were also able to maximize photovoltaic production where minimizing power mismatch, stabilizing battery state of charge under different condition. The research results were also compared with the baseline system without optimization techniques, and a reliable outcome was found.