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A rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic
Saif Jamal1, Jagadeesh Pasupuleti2, Janaka Ekanayake3
1Department of Electrical and Electronics Engineering, College of Engineering, Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia. pe20936@student.uniten.edu.my.
Scientific Reports
|February 28, 2024
Summary
The Genetic Algorithm (GA) optimization technique significantly reduces costs in Nanogrid (NG) systems by 40%. This method offers a cost-effective solution for managing hybrid renewable energy sources (HRESs) and energy storage systems (ESSs).
Area of Science:
- Electrical Engineering
- Renewable Energy Systems
- Optimization Techniques
Background:
- Nanogrids (NGs) integrate Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) but face challenges from load variability and intermittent generation.
- Existing Energy Management Systems (EMSs) and ESSs have been optimized, but cost-effectiveness remains a key consideration for NG operational performance.
- EMS plays a crucial role in managing power generation, usage, distribution, and pricing within NGs.
Purpose of the Study:
- To develop and evaluate an effective Energy Management System (EMS) for a grid-connected Nanogrid (NG) using MATLAB Simulink.
- To reduce the overall operational costs of the NG system by integrating Hybrid Renewable Energy Sources (HRESs) and Battery Storage Devices (BSDs).
- To compare the cost-effectiveness of a Rule-Based EMS (RB-EMS) with optimization algorithms like Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA).
Main Methods:
- Modeling a grid-connected NG with HRES (wind and PV) and three Battery Storage Devices (BSDs) in MATLAB Simulink.
- Developing a Rule-Based EMS (RB-EMS) using State Flow (SF) for safe and reliable power flow management.
- Implementing and comparing a Genetic Algorithm (GA)-based optimization and a Simulated Annealing optimization Algorithm (SAA) for cost reduction.
Main Results:
- The Genetic Algorithm (GA) optimization achieved a significant 40% cost saving compared to the Rule-Based EMS (RB-EMS).
- The Simulated Annealing Algorithm (SAA) demonstrated a 19.3% cost saving relative to the RB-EMS.
- The GA-based optimization proved highly cost-effective, exhibiting rapid convergence, a simple design, and minimal control factors.
Conclusions:
- The Genetic Algorithm (GA) is a superior optimization technique for Nanogrid (NG) energy management, offering substantial cost reductions.
- GA-based optimization provides a cost-effective and efficient solution for managing HRES and ESS in NGs.
- The study highlights the potential of advanced optimization algorithms to enhance the economic viability of Nanogrid systems.
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