Multi-Objective Optimization Algorithms for Energy Management Systems in Microgrids: A Control Strategy Based on a PHIL System
🤖 AI Summary
Addressing the multi-objective optimization challenge in microgrids—encompassing fuel consumption, load matching, power quality, battery degradation, and renewable energy integration—this paper develops a real-time power hardware-in-the-loop (PHIL)-based experimental microgrid platform integrating photovoltaic (PV) generation, battery storage, diesel generators, and the main grid. We propose a novel preference-weighted multi-objective optimization method featuring dynamically adjusted state-of-charge (SOC) thresholds, jointly optimizing six technical objectives under stringent operational constraints to enable adaptive energy management. Key innovations include integrated PHIL validation, real-time state feedback, battery health-aware control, and maximum renewable dispatch. Experimental results demonstrate significant improvements over the baseline: 18.7% reduction in fuel consumption, 32.4% decrease in active power deviation, 41.2% suppression of SOC fluctuation, and 26.5% increase in PV utilization rate—collectively enhancing system economy, reliability, and robustness.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Optimize real-time microgrid energy management with multiple objectives
Balance fuel consumption, load mismatch, and renewable energy utilization
Enhance power quality and battery lifespan in dynamic microgrid systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Real-time power hardware in loop configuration
Adaptive multi-objective optimization approach
Dynamic updating with experimental PHIL data
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