Toward Carbon-Neutral Energy Transition: A Multiobjective Optimization Framework for Hydrogen-Integrated Demand-Side Management
カーボンニュートラルなエネルギー転換に向けて: 水素統合デマンドサイド管理のための多目的最適化フレームワーク (AI 翻訳)
Sampatirao Nanibabu, Shakila Baskaran, P. Marimuthu
🤖 gxceed AI 要約
日本語
本論文は、水素エネルギーシステムを統合した需要側管理（DSM）のための多目的最適化フレームワーク（MOGAO）を提案。ピーク負荷38.36%削減、コスト42.69%削減、排出量67.56%削減を達成し、カーボンニュートラルなスマートグリッド実現に貢献する。
English
This paper proposes a multiobjective green anaconda optimization (MOGAO) framework for demand-side management integrating hydrogen storage, renewables, and EVs. It achieves significant peak load (38.36%), cost (42.69%), and emission (67.56%) reductions, offering a scalable path to carbon-neutral smart grids.
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
日本は水素社会実現に向けた基本戦略を掲げており、本フレームワークは再生可能エネルギーと水素を組み合わせた次世代電力システムの設計に直接貢献する。SSBJやGX政策とも整合する技術的基盤となる。
In the global GX context
Globally, hydrogen is a key pillar of decarbonization strategies. This framework's integration of hydrogen storage with demand-side management aligns with IEA and IRENA roadmaps, providing a practical optimization method for smart grids aiming for carbon neutrality.
👥 読者別の含意
🔬研究者:The MOGAO algorithm and its Pareto optimization approach provide a novel method for multiobjective DSM with hydrogen that can be extended or compared with other techniques.
🏢実務担当者:Utilities and grid operators can use this framework to design DSM programs that reduce peak load and costs while incorporating hydrogen storage.
🏛政策担当者:The results support policies that promote hydrogen infrastructure and demand response as cost-effective emission reduction tools.
📄 Abstract（原文）
The rapid integration of renewable energy sources, electric vehicles (EVs), and flexible demand technologies has made demand-side management (DSM) a vital element of modern smart grid operation. However, achieving cost-effective, emission-conscious, and consumer-friendly scheduling remains challenging due to the unpredictable nature of renewable energy production and consumer habits. Although hydrogen is considered a green and sustainable energy carrier, most global production still depends on fossil-based sources. This limits its decarbonization potential, emphasizing the need to promote renewable-based hydrogen generation. This article proposes a novel multiobjective green anaconda optimization (MOGAO) framework for optimizing DSM in a renewable-integrated microgrid. The framework simultaneously minimizes peak load, operational cost, and carbon emissions while maximizing consumer incentives, thereby achieving a balanced tradeoff between economic, environmental, and social objectives. The proposed system integrates photovoltaic and wind generation, a battery energy storage system, EVs with vehicle-to-grid functionality, and a green hydrogen energy system for long-duration energy storage. The MOGAO algorithm employs adaptive Pareto optimization to coordinate electricity pricing, hydrogen production, and degradation-aware dispatch under a 24-h time-of-use tariff structure. Simulation results demonstrate significant improvements over benchmark methods (PSO, WOA, and green anaconda optimization), achieving 38.36% peak load reduction, 42.69% cost savings, and 67.56% emission reduction. Additionally, the incentive–comfort satisfaction ratio (1.5685) indicates enhanced user participation and fairness. Overall, the proposed MOGAO-based DSM framework offers a scalable, hydrogen-supported, and carbon-neutral solution for next-generation smart grids, enabling sustainable and resilient energy transition pathways.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/jestie.2026.3687039first seen 2026-07-13 06:54:48
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