Reinforcement Learning-Based Energy Management for Sustainable Electrified Urban Transportation with Renewable Energy Integration: A Case Study of Alexandria, Egypt
再生可能エネルギー統合を伴う持続可能な電化都市交通のための強化学習ベースのエネルギー管理：エジプト、アレクサンドリアのケーススタディ (AI 翻訳)
Amany El-Zonkoly
🤖 gxceed AI 要約
日本語
本論文は、エジプト・アレクサンドリアにおける電化都市交通システムに太陽光発電とグリーン水素を統合する最適エネルギー管理戦略を提案する。需要側管理と不確実性に対応するため、改良型マルチエージェント強化学習を導入。シミュレーションの結果、平均日次エネルギー消費コストが40.2%削減されることを示した。
English
This paper proposes an optimal energy management strategy integrating PV systems and green hydrogen into the electrified urban transportation system of Alexandria, Egypt. A modified multi-agent reinforcement learning approach handles demand-side management and uncertainty. Simulation results show a 40.2% reduction in average daily energy consumption cost.
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
本論文はエジプトの事例だが、日本でも電気バスや再生可能エネルギーの統合に向けたエネルギー管理手法として、強化学習の応用可能性を示す。特に、不確実性下での需要側管理とマルチエージェントアプローチは、日本の都市交通システムの最適化にも示唆を与える。
In the global GX context
This study demonstrates a reinforcement learning-based approach for integrating renewables into electrified urban transport, addressing demand-side management and uncertainty. It contributes to the growing literature on AI-driven energy management for sustainable transportation, relevant for cities globally pursuing zero-emission mobility.
👥 読者別の含意
🔬研究者:Provides a novel application of multi-agent reinforcement learning for energy management in electrified transportation with renewable integration.
🏢実務担当者:The method can be adapted for real-time energy scheduling of EV fleets and renewable sources in urban transit systems.
🏛政策担当者:Highlights the economic viability of combining PV, hydrogen, and electrified transportation through advanced energy management, informing infrastructure planning.
📄 Abstract（原文）
To enhance access to efficient and low-carbon public transportation, the city of Alexandria, Egypt, has introduced a fleet of electric buses. Additionally, an ongoing project aims to upgrade and electrify the existing urban railway system, which is expected to alleviate traffic congestion in this densely populated city. The implementation of electric vehicle (EV) parking facilities is also under consideration. This paper investigates the integration of photovoltaic (PV) systems and green hydrogen-powered gas turbines as components of the integrated energy system (IES). An optimal energy management strategy is proposed to maximize the benefits of incorporating renewable energy sources into the urban transportation system (UTS). The proposed energy management algorithm incorporates demand-side management (DSM) for UTS loads and EVs, increasing the complexity of the decision-making process due to the high uncertainty of decision variables. To address this challenge, a modified multi-agent reinforcement learning (MRL) approach is employed, in which uncertainty is incorporated through stochastic environment sampling. Simulation results demonstrate the economic potential of integrating renewable and sustainable energy resources into the IES of the electrified urban transportation system, achieving a 40.2% reduction in the average daily energy consumption cost.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18052352first seen 2026-05-15 19:42:40
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