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| 考虑电储能碳足迹的园区综合能源系统电-碳协同需求响应决策优化 |
| Optimization of electricity-carbon collaborative demand response decision-making for park-level integrated energy systems considering electrical energy storage carbon footprint |
| 投稿时间：2025-10-18 修订日期：2025-11-27 |
| DOI： |
| 中文关键词: 园区综合能源系统 电-碳协同 储能碳足迹 分层优化 |
| 英文关键词: park integrated energy system electricity-carbon collabora-tion energy storage carbon footprint hierarchical optimiza-tion |
| 基金项目:国网江苏省电力有限公司科技项目 |
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| 中文摘要: |
| 目前，电-碳市场协同已成为园区低碳转型的核心手段，而园区综合能源系统（PIES）的碳排放量化精度与优化决策效率将直接影响降碳成效。针对源荷随机性加剧系统波动、系统碳特性量化精度不足导致盲目推广储能等设备造成“伪减排”、园区集中式决策效率仍有待提升等问题，本文提出了一种考虑电储能碳足迹的园区综合能源系统电-碳协同需求响应决策优化方法。首先，基于动态碳计量模型提出储能碳储放率以精准量化储能充放电碳特性；其次，构建融合电-碳市场信号的PIES优化决策模型，并提出包含“上层协同-下层执行”分层机制的模型求解办法；最后，采用实际算例验证模型有效性与可拓展性。结果表明，考虑储能碳足迹的园区综合能源系统电-碳协同需求响应决策优化方法能有效提升园区决策效率，控制决策投资成本的同时进一步促进碳减排。 |
| 英文摘要: |
| Currently, the collaboration between electricity and carbon markets has become a core means for the low-carbon trans-formation of parks. However, the carbon emission quantification accuracy and optimal decision-making efficiency of Park Integrated Energy Systems (PIES) will directly affect the effectiveness of carbon reduction. To address issues such as in-creased system fluctuations caused by source-load random-ness, "false emission reduction" resulting from the blind pro-motion of energy storage and other equipment due to insufficient quantification accuracy of system carbon characteristics, and the need for improvement in the efficiency of centralized decision-making in parks, this study proposes an optimization method for electricity-carbon collaborative demand response decision-making in PIES that considers the carbon footprint of electrical energy storage.
Firstly, based on a dynamic carbon accounting model, the energy storage carbon storage-discharge rate is proposed to accurately quantify the carbon characteristics of energy storage. Secondly, an optimized decision-making model for PIES integrating electricity-carbon market signals is constructed, and a model solution method including a hierarchical mechanism of "upper-layer collaboration - lower-layer execution" is put forward. Finally, practical calculation examples are used to verify the effectiveness and scalability of the proposed model.
The results show that the optimization method for electricity-carbon collaborative demand response decision-making in PIES, which considers the carbon footprint of energy storage, can effectively improve the decision-making efficiency of parks, control the investment cost of decisions, and further promote carbon reduction. |
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