Comparative evaluation and architectural enhancement of a genetic algorithm-tuned fuzzy logic battery control in microgrid energy management
マイクログリッドエネルギー管理における遺伝的アルゴリズムチューニングファジィ論理バッテリ制御の比較評価とアーキテクチャ改善 (AI 翻訳)
Bouzek, Karel, Meryem, MELIANI, Paušová, Šárka
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギーと蓄電池を統合したマイクログリッドにおいて、遺伝的アルゴリズム（GA）で調整したファジィ論理制御器によるバッテリ管理手法を提案し、従来手法と比較。フェーズ1ではGA調整の有効性を確認、フェーズ2では制御アーキテクチャを拡張し、より効率的なエネルギー管理と不要なバッテリ充放電の抑制を実証した。
English
This paper proposes a genetic algorithm-tuned fuzzy logic battery controller for a hybrid microgrid with PV, wind, and battery storage. Phase 1 compares GA tuning with PSO and BSA, showing comparable regulation. Phase 2 enhances the controller architecture by embedding charge/discharge decisions, achieving better renewable utilization and reduced battery cycling.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では電力システムの分散化とレジリエンス強化が進む中、本研究成果は自家消費型マイクログリッドや地域エネルギー管理システムの効率改善に示唆を与える。特に、制御アーキテクチャの重要性を強調しており、実装段階での設計指針として有用。
In the global GX context
Globally, as microgrids and renewable integration expand, this study highlights the impact of controller architecture over tuning methods for battery management. The findings support practical implementations for reducing battery degradation and maximizing renewable self-consumption in distributed energy systems.
👥 読者別の含意
🔬研究者:Provides a comparative evaluation of optimization algorithms and demonstrates that controller architecture significantly affects microgrid energy management performance.
🏢実務担当者:Offers insights into designing fuzzy logic controllers for battery storage that reduce cycling and improve renewable utilization in microgrids.
🏛政策担当者:Supports policies promoting advanced energy management systems for grid stability and renewable integration.
📄 Abstract（原文）
Efficient battery energy management remains a key challenge in renewable-integrated microgrids due to nonlinear battery dynamics, intermittent generation, and variable load demand. This paper investigates a fuzzy logic-based battery control strategy for a hybrid microgrid comprising photovoltaic generation, wind energy, battery energy storage, and a bidirectional grid interface. A two-phase control framework is adopted. In Phase 1, a genetic algorithm (GA) is used to tune an established fuzzy logic controller under identical operating conditions previously reported for particle swarm optimization (PSO) and backtracking search algorithm (BSA) approaches, enabling a fair comparative evaluation. In Phase 2, the controller architecture is enhanced by embedding battery charge-discharge and grid import-export decisions directly within the fuzzy inference system using real-time power imbalance and state-of-charge as inputs. Simulation results show that, while GA converges more slowly than BSA, all tuning methods achieve comparable battery current and state-of-charge regulation in Phase 1. In contrast, the enhanced control architecture in Phase 2 yields more coherent energy management behavior, improved utilization of renewable energy, reduced unnecessary battery cycling, and stable electrical performance. The results demonstrate that controller architecture has a greater impact on practical microgrid energy management performance than tuning strategy alone.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20628747first seen 2026-06-11 04:32:18
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