Deep Reinforcement Learning-Based Multi-Objective Coupling Optimization of Economy, Low-Carbon and Energy Efficiency for Integrated Energy Systems

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🔬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。

全球能源转型与清洁能源技术的快速推进，使得综合能源系统（IES）必须逐步向经济、低碳、高效运行的方向发展。 为此，本文提出一种基于深度强化学习（DRL）的IES多目标耦合优化策略，以经济性、低碳足迹和㶲效率（EE）为目标。该策略引入电-冷-气需求响应（DR）模型以优化负荷曲线，并引入阶梯式碳交易（SCT）机制。此外，利用㶲效率系数法构建了综合㶲效率模型。最后，开发了一个集成系统经济成本、低碳运行和综合㶲效率的IES多目标优化框架。 案例研究表明，所提出的优化调度方案在提升系统运行经济性的同时，兼顾了低碳与高效运行性能。 所提出的基于DRL的多目标耦合优化模型显著降低了总运行成本（TOC），有效削减了整体碳排放（CE），并提高了综合能源利用效率，从而在确保高运行效率的同时实现低碳经济运行。通过引入电-冷-气需求响应模型进行负荷优化，并引入阶梯式碳定价机制，同时利用㶲效率系数法建立综合㶲效率模型，本研究实现了供需协同优化，并显著提升了㶲效率。 所采用的深度确定性策略梯度（DDPG）算法有效应对了IES的高度非线性、多约束和强耦合复杂性，成功在经济成本、CE和系统效率这三个相互依赖的目标之间识别出均衡的最优解，展示了其在解决此类多目标协同优化问题方面的强大潜力和有效性。

AI 翻訳（deepseek-v4-flash）。 正確を期す場合は下の原文を参照してください。

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.

• semanticscholar https://doi.org/10.2174/0123520965441803260112095913first seen 2026-05-15 17:11:46 · last seen 2026-08-16 05:30:07