多目的アントライオン最適化アルゴリズムに基づくマルチヘッド自己注意機構付き全結合深層ニューラルネットワークによる低炭素経済負荷配分
A Fully Connected Deep Neural Network with Multi-Head Self-Attention Mechanisms Based on the Multi-Objective Ant-Lion Optimization Algorithm for Low-Carbon Economic Dispatch (原題)
Feiwei Li, Dexing Sun, Junwei Zhang, Pei Liu, Xiaoshun Zhang, Haoxia Jiang
🤖 gxceed AI 要約
日本語
多目的アントライオン最適化（MALO）とマルチヘッド自己注意機構付き深層ニューラルネットワークを組み合わせ、電力系統の低炭素経済負荷配分を高速・高精度に解く手法（FMM）を提案。IEEE 118・300バス系統でCO2排出を最低1.02%、コストを最低0.64%削減し、計算時間も17.11%以上短縮、パレート前沿への収束性も改善した。
English
This study proposes FMM, combining multi-objective ant-lion optimization with a multi-head self-attention deep neural network, to solve low-carbon economic dispatch in power systems. On IEEE 118- and 300-bus systems it cuts CO2 emissions by at least 1.02% and cost by 0.64%, reduces computation time by over 17.11%, and yields solutions closer to the Pareto frontier.
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
電力システムの脱炭素化は第7次エネルギー基本計画やGX推進戦略の中核であり、AIによる経済負荷配分の効率化は系統運用・再エネ大量導入下での運用コストと排出削減の両立に直結する。SSBJ開示とは直接関係しないが、電力会社の脱炭素実務・Scope2削減の技術的裏付けとして参考になる。
In the global GX context
Low-carbon economic dispatch sits at the operational core of power-sector decarbonization, relevant to grid operators managing high renewable penetration. While not a disclosure paper, it offers quantitative evidence on how AI optimization can jointly reduce emissions and cost, informing transition-planning assumptions behind TCFD/ISSB climate targets.
👥 読者別の含意
🔬研究者:AI最適化と電力系統運用の融合手法として、収束性・計算効率の改善を定量的に示す参考になる。
🏢実務担当者:系統運用・需給計画における排出とコストの同時最適化にAIを活用する際の実装可能性を示す。
🏛政策担当者:電力部門の脱炭素と経済性の両立に向けたAI活用の政策的ポテンシャルを示唆する。
📄 Abstract（原文）
As global climate change intensifies, decarbonizing the power system is key to achieving carbon-reduction targets. Inspired by the multi-objective ant-lion optimization (MALO) method and deep neural networks (DNNs), this study proposes an innovative approach to address the complex carbon reduction challenges faced by energy-consuming enterprises. The proposed fully connected deep neural networks with multi-head self-attention mechanisms based on the multi-objective ant-lion optimization algorithm (FCDNN-MHSAM-MALO, FMM) integrate the advantages of MALO and DNN. MALO plays a key role in optimizing the decision variables in economic scheduling. By contrast, DNN predicts the search direction of the optimal solution by learning historical optimization results. This reduces the number of iterations, improves computational efficiency, and speeds up the solution process. The multi-head self-attention mechanism calculates the importance of various input features, enabling the model to focus on factors that significantly impact the scheduling solution. This attention-driven approach improves prediction accuracy and enables MALO to optimize from an earlier starting point, thus achieving global convergence more efficiently. Compared with several state-of-the-art algorithms on IEEE 118- and IEEE 300-bus systems, the simulation results show that (1) both carbon dioxide emissions and costs can be reduced: carbon emissions are reduced by at least 1.02% and the cost is lowered by at least 0.64% when the FMM algorithm is adopted; (2) better real-time performance: an at least 17.11% reduction in computation time is achieved using FMM; and (3) better stability performance: the curves obtained by FMM for the two cases are closer to the Pareto frontier.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/en19184389first seen 2026-09-18 04:42:56
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