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

Saiful Islam, Sanaz Mostaghim, Michael Hartmann

TL;DR

This paper tackles real-time energy management in microgrids by integrating a PHIL-based experimental setup with a NSGA-III-driven multi-objective optimization framework. It defines six objectives, including fuel consumption, power mismatch, frequency deviation, PF penalty, battery degradation, and PV utilization, and employs adaptive weights tied to the battery state of charge to balance trade-offs in real time. The approach demonstrates that MOO-driven EMS can significantly reduce or eliminate diesel use, improve PV utilization, and stabilize power balance under dynamic conditions, albeit within the 5 kW PHIL hardware constraint. The work offers a practical path toward renewable-dominant, robust EMS for microgrids and points to extensions with higher capacity and surrogate-model integration for broader applicability.

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.

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

TL;DR

This paper tackles real-time energy management in microgrids by integrating a PHIL-based experimental setup with a NSGA-III-driven multi-objective optimization framework. It defines six objectives, including fuel consumption, power mismatch, frequency deviation, PF penalty, battery degradation, and PV utilization, and employs adaptive weights tied to the battery state of charge to balance trade-offs in real time. The approach demonstrates that MOO-driven EMS can significantly reduce or eliminate diesel use, improve PV utilization, and stabilize power balance under dynamic conditions, albeit within the 5 kW PHIL hardware constraint. The work offers a practical path toward renewable-dominant, robust EMS for microgrids and points to extensions with higher capacity and surrogate-model integration for broader applicability.

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.

Paper Structure

This paper contains 8 sections, 16 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Technical setup for the experiment
  3. MOO in Energy Management System
  4. Objective functions and constraints
  5. Energy management process
  6. Performance Indicators
  7. Results and Critical discussions
  8. Conclusion

Figures (6)

  • Figure 1: PHIL Integration with RT Lab and Lucas Nülle test bench
  • Figure 2: Application Flow Diagram for Multi-Objective Optimization in PHIL Systems
  • Figure 3: Pareto optimal solution plot with best solution in 3D
  • Figure 4: Visualization of a 3D Pareto Front with Diversity Index Analysis
  • Figure 5: Incorporating PV Usage as a Diversity Metric in Pareto Front Analysis
  • ...and 1 more figures