改善された混沌進化最適化アルゴリズムに基づく風光発電貯蔵ハイブリッドシステムの最適な容量構成
Yingchao Dong1, Xiang Zhou2, Xiguo Cao2
1School of Energy Engineering, Xinjiang Institute of Engineering, Urumqi, 830023, China. dycxju@163.com.
Scientific reports
|February 20, 2026
まとめ
この研究は,高い再生可能エネルギーを持つグリッドのための風光発電貯蔵システム (WPS) を最適化します. 改善された混沌進化アルゴリズムは,容量計画を強化し,費用対効果とシステムの堅実性を向上させます.
科学分野:
- 再生可能エネルギーシステム工学 再生可能エネルギーシステム工学
- オプティマイゼーション アルゴリズム
- ネットワーク・インテグレーション
背景:
- 高度な再生可能エネルギーの普及は,グリッドの安定性や経済的生存可能性に課題をもたらします.
- 風光発電貯蔵システム (WPS) は有望なソリューションですが,最適な容量構成が必要です.
- 既存の計画モデルは,複雑な非線形的な制約と経済的要求と闘っています.
研究 の 目的:
- 風光発電貯蔵システム (WPS) の最適な容量構成モデルを開発する.
- 高度再生可能エネルギーネットワークにおける複雑な非線形的制約と経済的要因に対処する.
- WPSの容量計画における費用対効果と堅実性を高めること.
主な方法:
- マルチエネルギーコラボレーティブ・キャパシティ・プランニング・モデルを開発しました.
- 風力,太陽光,貯蔵の相互依存性を捉えるエネルギー管理戦略を策定しました.
- 改善されたカオス進化最適化アルゴリズム (ICEO) を提案し,自己学習の混乱と適応的なローカル検索を備えた.
主要な成果:
- ICEOは,ベンチマーク関数に関する最先端のメタヒューリスティックと比較して,優れたソリューション品質と堅実性を実証しました.
- 実践的なWPSケーススタディのシミュレーションにより,アルゴリズムの有効性が検証されました.
- 提案された方法は,WPSの容量計画における費用対効果を大幅に改善しました.
結論:
- 開発された ICEO アルゴリズムは,WPS 容量計画における複雑な最適化問題を効果的に解決します.
- 統合的アプローチは,高い再生可能エネルギー比率を持つネットワークの経済的可行性と信頼性を高めます.
- この研究は,ハイブリッド再生可能エネルギーシステムの最適化のための堅固な枠組みを提供します.
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