| 引用本文: | 赵 琛,叶金池,和 萍,等.考虑源荷多重不确定性的园区综合能源系统优化策略[J].电力系统保护与控制,2025,53(4):148-164.[点击复制] |
| ZHAO Chen,YE Jinchi,HE Ping,et al.A PIES optimization strategy considering multiple uncertainties in source and load[J].Power System Protection and Control,2025,53(4):148-164[点击复制] |
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| 摘要: |
| 削弱源荷不确定性，兼顾经济性与低碳性成为园区综合能源系统(park-level integrated energy system, PIES)优化调度的重点，为此提出了预测-调节-决策一体化框架。首先，构建了配置热电联产机组、电转气与碳捕集的园区综合能源系统。其次，提出霜冰算法优化卷积-支持向量机(rime-convolutional neural network-support vector machine, RIME-CNN-SVM)的数据预测方法，并利用信息间隙决策理论(information gap decision theory, IGDT)描述概率分布未知的源荷严重不确定性。最后，建立了考虑源荷不确定性、阶梯式碳交易机制和弃风弃光惩罚的PIES低碳优化调度策略。通过算例仿真验证了模型和方法的合理性和有效性，并表明所提方法在提高PIES调度准确性的同时兼顾了运行的经济性与低碳性。 |
| 关键词: RIME-CNN-SVM IGDT 阶梯式碳交易 电转气与碳捕集 PIES |
| DOI：10.19783/j.cnki.pspc.240322 |
| 投稿时间：2024-03-21修订日期：2024-07-22 |
| 基金项目:国家自然科学基金项目资助(62203401)；河南省科技攻关研究项目资助(232102241043) |
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| A PIES optimization strategy considering multiple uncertainties in source and load |
| ZHAO Chen1,YE Jinchi1,HE Ping1,LI Qiuyan2,WANG Shiqian2 |
| (1. School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China;
2. State Grid Henan Electric Power Company Economic and Technological Research Institute, Zhengzhou 450052, China) |
| Abstract: |
| Reducing source-load uncertainty while balancing economic efficiency and low carbon emissions has become the focus of optimizing the scheduling of park-level integrated energy systems (PIES). To this end, an integrated framework of prediction, regulation, and decision-making is proposed. Firstly, a PIES incorporating combined heat and power (CHP), power to gas (P2G), and carbon capture and storage (CCS) is constructed. Secondly, a data prediction method based on the rime algorithm optimized convolutional neural network-support vector machine (RIME-CNN-SVM) is proposed, and the information gap decision theory (IGDT) is used to account for severe source-load uncertainties with unknown probability distribution. Finally, a low-carbon optimization scheduling strategy for PIES is established, considering source-load uncertainties, a tiered carbon trading mechanism, and penalties for abandoning wind and solar power. Through numerical analysis, the rationality and effectiveness of the proposed model are verified, demonstrating that the proposed method improves the accuracy of PIES scheduling while balancing economic efficiency and low-carbon emissions. |
| Key words: RIME-CNN-SVM IGDT stepped carbon trading P2G and CCS PIES |
