Deep Reinforcement Learning-Based Multi-Objective Coupling Optimization of Economy, Low-Carbon and Energy Efficiency for Integrated Energy Systems
統合エネルギーシステムの経済性・低炭素・エネルギー効率の多目的最適化のための深層強化学習に基づく結合最適化 (AI 翻訳)
Hongyin Chen, Songcen Wang, Xiaoqiang Jia, Jingshuai Pang, Yi Guo, Manzheng Zhang, Zheng Wu, Jianfeng Li
🤖 gxceed AI 要約
日本語
本論文は、統合エネルギーシステム（IES）の経済性、低炭素、効率（エクセルギー効率）を対象とした多目的結合最適化戦略を提案する。深層強化学習（DRL）を用い、電気・冷熱・ガスの需要応答モデルと段階的炭素取引メカニズムを導入する。ケーススタディにより、総運用コスト削減、炭素排出削減、エネルギー効率向上を実証した。
English
This paper proposes a multi-objective coupling optimization strategy for integrated energy systems (IES) targeting economy, low-carbon, and exergy efficiency using deep reinforcement learning. It incorporates demand response models for electricity, cooling, and gas, and a stepped carbon trading mechanism. Case studies show reduced total operating costs, lower carbon emissions, and improved overall energy efficiency.
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、日本のGX推進におけるエネルギーシステムの最適運用に示唆を与える。特に、需要応答や炭素価格メカニズムを組み込んだ多目的最適化は、日本の統合エネルギーシステム設計や運用に応用可能である。
In the global GX context
This paper contributes to global GX by demonstrating how deep reinforcement learning can optimize integrated energy systems for economic, low-carbon, and efficiency goals. The inclusion of demand response and carbon trading aligns with global trends in energy system decarbonization and smart grid optimization.
👥 読者別の含意
🔬研究者:本研究では、深層強化学習を用いた多目的最適化手法を提案しており、エネルギーシステムの研究者は手法の応用可能性に注目すべきである。
🏢実務担当者:企業のエネルギー管理部門は、本手法を自社のエネルギーシステム最適化に応用し、コスト削減と低炭素化を同時に達成する参考となる。
🏛政策担当者:政策立案者は、段階的炭素取引メカニズムの有効性を実証した点を参考に、炭素価格政策の設計に活用できる。
📄 Abstract（原文）
With the rapid global energy transition and advancement of clean energy technologies, integrated energy systems (IES) must progressively evolve towards economic, low-carbon, and high-efficiency operation. To achieve this, this paper proposes a multi-objective coupled optimization strategy for IES, targeting economy, low-carbon footprint, and exergy efficiency (EE), based on deep reinforcement learning (DRL). The strategy incorporates an electricity-cooling-gas demand response (DR) model to optimize the load profile and introduces a stepped carbon trading (SCT) mechanism. Furthermore, a model for comprehensive EE is constructed using the EE coefficient method. Finally, a multi-objective optimization framework for IES is developed, integrating system economic cost, low-carbon operation, and comprehensive EE. Case studies demonstrate that the proposed optimal scheduling scheme enhances the system's operational economy while simultaneously balancing low-carbon and high-efficiency performance. The proposed DRL-based multi-objective coupled optimization model significantly reduces total operating costs (TOC), effectively cuts overall carbon emissions (CE), and improves comprehensive energy utilization efficiency, thereby achieving low-carbon and economic operation while ensuring high operational efficiency. By incorporating electricity-cooling-gas demand response models for load optimization and introducing a stepped carbon pricing mechanism, along with establishing an integrated EE model using the EE coefficient method, the study enables synergistic supply-demand optimization and substantially enhances EE. The adopted Deep Deterministic Policy Gradient (DDPG) algorithm effectively addresses the highly nonlinear, multi-constrained, and strongly coupled complexities of IES, successfully identifying balanced optimal solutions among the three interdependent objectives—economic cost, CE, and system efficiency—demonstrating its strong potential and effectiveness in solving such multi-objective collaborative optimization problems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.2174/0123520965441803260112095913first seen 2026-05-15 17:11:46
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