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考虑可再生能源发电不确定性的多能微网P2P电碳交易分布式优化

已有 581 次阅读 2024-4-16 15:52 |系统分类:博客资讯

近期,团队博士研究生王灼撰写的学术论文Distributed optimization for joint peer-to-peer electricity and carbon trading among multi-energy microgrids considering renewable generation uncertainty在《IET Energy Conversion and Economics》期刊上发表

原文链接:http://doi.org/10.1049/enc2.12110

作者信息:

Hui Hou 1,2, Zhuo Wang 1,2,*, Bo Zhao 3, Leiqi Zhang 3, Yin Shi 1,2, Changjun Xie 1,2, Zhao Yang Dong 4, Keren Yu 5

1. School of Automation, Wuhan University of Technology, Wuhan, China

2. Shenzhen Research Institute, Wuhan University of Technology, Shenzhen, China

3. State Grid Zhejiang Electric Power Research Institute, Hangzhou, China

4. School of Electrical and Electronics Engineering, Nanyang Technological University, Jurong West, Singapore

5. ESG Future Foundation, Level 15, 350 Collins Street, Melbourne, Vic 3000, Australia

摘要

可再生能源渗透率的不断提高和电碳市场的进一步耦合,为实现新型电力系统的高效低碳转型带来了巨大障碍。本文研究了考虑分布式可再生能源发电不确定性的多能微网点对点(peer-to-peer, P2P)电碳联合交易优化问题。首先,以最小化运营成本、潜在风险损失和可再生能源弃用为目标,建立多能微网P2P电碳联合交易模型。其次,采用随机规划方法并引入条件价值风险技术,量化不确定性带来的潜在风险损失。第三,提出了基于交替方向乘子法的分布式优化方法,以保持个体多能微网决策的独立性和隐私性。在电碳交易过程中,采用拉格朗日乘子作为价格信号,保证了最优交易方案的公平性。引入并行求解机制,以最小的计算开销提高整体运行效率。最后,仿真结果表明,该方法可以有效降低系统运行成本和碳排放,避免大量的可再生能源弃用。

Abstract: The increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low-carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer-to-peer (P2P) electricity and carbon trading in multi-energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value-at-risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision-making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy wastage.

多能微网系统框架如图1所示。

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多能微网P2P电碳联合交易示意图如图2所示。

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风光随机场景示意图如图3所示。

image.png

不同随机场景下的多能微网P2P电碳交易如图4所示。

image.png

不同随机场景下的多能微网P2P电碳交易影子价格如图5所示。

image.png

作者简介:王灼,武汉理工大学2022级博士研究生,硕士阶段就读于济南大学,主要研究方向为多能微网规划运行、分布式优化及可再生能源整合等。多次在学年综测中排名第一并获一等奖学金,目前以第一作者及通信作者在Applied EnergyInternational Journal of Electrical Power & Energy SystemsIET Energy Conversion and Economic等期刊发表多篇论文。

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