Research on Low-Carbon Optimization Scheduling of Microgrid Based on Improved Dream Optimization Algorithm
改良型ドリーム最適化アルゴリズムに基づくマイクログリッドの低炭素最適化スケジューリングに関する研究 (AI 翻訳)
Ruiteng Shao, Ke Li, Qing Teng, Ruobing Li, Yangrui Li, Jian Zhou
🤖 gxceed AI 要約
日本語
本研究は、マイクログリッドの低炭素経済スケジューリング問題に対して、サイン・コサイン演算子を統合した改良型ドリーム最適化アルゴリズム（SCO-DOA）を提案する。太陽光、風力、ディーゼル発電機、マイクロガスタービン、蓄電池からなるマイクログリッドモデルを用いたシミュレーションでは、従来のDOAと比較して収束速度が約15%向上し、総炭素排出量が9.2%削減、系統依存度が18.1%低減した。
English
This study proposes an improved dream optimization algorithm (SCO-DOA) integrating sine and cosine operators for low-carbon economic scheduling of microgrids. Simulations on a microgrid with PV, wind, diesel, microturbine, and storage show that SCO-DOA improves convergence speed by ~15%, reduces total carbon emissions by 9.2%, and decreases grid dependence by 18.1% compared to traditional DOA.
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
本論文は、再生可能エネルギーを高比率で含むマイクログリッドの最適運用に貢献するアルゴリズムを提案しており、日本の分散型エネルギーシステム推進や2050年カーボンニュートラル達成に向けた技術的知見を提供する。
In the global GX context
This paper presents algorithmic improvements for low-carbon microgrid scheduling that are applicable globally, particularly for systems with high renewable penetration and carbon cost considerations, contributing to energy transition efforts.
👥 読者別の含意
🔬研究者:The proposed SCO-DOA algorithm offers a new method for multi-objective optimization in high-dimensional energy scheduling problems.
🏢実務担当者:The study provides an operational optimization tool that can reduce carbon emissions and grid dependence in microgrid management.
📄 Abstract（原文）
Abstract Energy optimization scheduling of microgrids is a key technology for achieving low-carbon operation of the system and enhancing the capacity to accommodate high proportions of intermittent renewable energy. Addressing the challenge of traditional optimization algorithms being prone to getting stuck in local optima and having insufficient convergence in the high-dimensional low-carbon economic scheduling problem of microgrids, this paper proposes an improved dream optimization algorithm (SCO-DOA) that integrates the sine and cosine operators. In the “forgetting-supplementing” mechanism of the dream optimization algorithm, this method introduces a probability-mixed positive and negative cosine oscillation update strategy to dynamically coordinate global exploration and local development, effectively enhancing the algorithm’s search ability and convergence accuracy in the high-dimensional solution space.To verify the algorithm’s performance, a microgrid model comprising photovoltaics, wind turbines, diesel generators, micro gas turbines, and energy storage is established and optimized by SCO-DOA for minimizing combined operational and carbon emission costs. Simulation results show SCO-DOA improves convergence speed by ∼15%, reduces total carbon emissions by 9.2%, and decreases grid dependence by 18.1% compared to traditional DOA, demonstrating superior efficiency, economy, and low-carbon performance.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1088/1742-6596/3229/1/012026first seen 2026-05-29 05:01:32 · last seen 2026-05-31 05:22:11
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