A comparative study of advanced multi-objective meta-heuristics for microgrid energy management
マイクログリッドエネルギー管理のための高度な多目的メタヒューリスティックの比較研究 (AI 翻訳)
Ding Feng, Dengao Li, Xiaojie An, Yu Zhou
🤖 gxceed AI 要約
日本語
本論文は、脱炭素化とデジタル化に向けたエネルギー移行を背景に、マイクログリッドの最適運用における多目的最適化問題を扱う。経済コスト、炭素排出、系統連系線電力変動の3次元目的関数を構築し、ベースライン割当とラダー型炭素取引メカニズムを統合。汎用多目的ベンチマークフレームワーク（GMO-BF）を提案し、モンテカルロシミュレーションでアルゴリズムのロバスト性を評価した。結果は、提案フレームワークが公平な比較を支援し、経済性と低炭素性のトレードオフを達成可能であることを示す。
English
This paper addresses multi-objective optimization in microgrid energy management amid the global energy transition. It constructs a three-dimensional objective function covering economic cost, carbon emissions, and tie-line power fluctuations, integrating baseline-based quota and ladder-type carbon trading. A General Multi-Objective Benchmarking Framework (GMO-BF) is proposed, and Monte Carlo simulations assess algorithm robustness. Results show fair algorithm comparison and effective trade-offs among objectives, supporting economical, low-carbon microgrid operation.
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、再生可能エネルギーの大量導入に伴うマイクログリッドの運用最適化が重要課題である。本論文の炭素取引メカニズムを組み込んだ多目的最適化フレームワークは、日本の地域エネルギー管理やカーボンプライシング政策に示唆を与える。
In the global GX context
This research contributes to the global discourse on microgrid energy management by integrating carbon trading mechanisms into multi-objective optimization, addressing the need for standardized benchmarking in the energy transition context.
👥 読者別の含意
🔬研究者:Provides a standardized benchmarking framework (GMO-BF) for comparing multi-objective meta-heuristics in microgrid scheduling.
🏢実務担当者:Offers a practical optimization approach that balances cost, carbon emissions, and grid stability for microgrid operators.
🏛政策担当者:Demonstrates how carbon pricing mechanisms can be integrated into microgrid dispatch models to support decarbonization targets.
📄 Abstract（原文）
Abstract Against the backdrop of the global energy transition toward decarbonization and digitalization, the high penetration of renewable distributed energy resources has made microgrid optimal scheduling face complex challenges such as multi-objective conflicts and source-load variability. To address issues in existing research, such as non-standardized algorithm benchmarking and models detached from engineering practice, this paper conducts a comparative study of multi-objective meta-heuristic algorithms spanning a diverse set of representative and state-of-the-art methods. A three-dimensional objective function including economic cost, carbon emissions, and tie-line power fluctuations is constructed, integrating a baseline-based quota and ladder-type carbon trading mechanism. Considering physical device constraints, a high-dimensional microgrid dispatch model is established. A test suite covering various meta-heuristic algorithms is designed, and a General Multi-Objective Benchmarking Framework (GMO-BF) is proposed to unify constraint handling, archive management, and performance evaluation standards. Furthermore, the robustness and sensitivity of algorithm performance are evaluated under multiple uncertainty levels through Monte Carlo simulations. Simulation results show that the proposed framework can effectively support fair comparison of algorithm performance, and the selected optimization algorithms can achieve a good trade-off among multiple objectives. While ensuring the economical and efficient operation of microgrids, it maintains acceptable carbon emission levels and grid stability, providing reliable technical support for microgrid energy management.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1088/2631-8695/ae696ffirst seen 2026-05-17 07:11:34 · last seen 2026-05-20 05:16:09
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