| 引用本文: | 周 玮,高 垚,彭飞翔,等.基于模型预测控制的能源产消者端对端交易策略[J].电力系统保护与控制,2022,50(9):1-9.[点击复制] |
| ZHOU Wei,GAO Yao,PENG Feixiang,et al.Peer-to-peer energy trading strategy for prosumers based on model predictive control[J].Power System Protection and Control,2022,50(9):1-9[点击复制] |
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| 摘要: |
| 新能源政策市场化无补贴发展背景下，为保障分布式光伏资源的消纳，须探索有效的市场机制，发挥用户调动灵活性资源的主动性。端对端(Peer-to-Peer, P2P)能源交易作为本地电力消费者和生产者间直接能源交易模式，有利于局部区域功率平衡。采用连续双向拍卖市场机制，提出基于模型预测控制的P2P市场交易策略。针对储能参与后的产消者电量投标决策问题，利用模型预测控制滚动优化储能的充放电功率，指导市场主体的电量投标。在此基础上，将具有学习能力的增强零信息策略作为报价方法，实现各主体的自主决策。辽西某区域电网算例结果表明：所提方法能够有效调动市场主体的积极性，指导其投标行为，进一步增加个体收益，提高配网接纳分布式光伏的能力。 |
| 关键词: 分布式光伏 P2P能源交易 连续双向拍卖 模型预测控制 灵活性资源 |
| DOI：DOI: 10.19783/j.cnki.pspc.211078 |
| 投稿时间：2021-08-12修订日期：2021-11-11 |
| 基金项目:国家自然科学基金项目资助(61873048) |
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| Peer-to-peer energy trading strategy for prosumers based on model predictive control |
| ZHOU Wei,GAO Yao,PENG Feixiang,WU Jianzhong,DANG Wei,LIU Ying,WANG Zhonghui |
| (1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;
2. Economic and Technological Research Institute of State Grid Jilin Electric Power Co., Ltd., Changchun 130000, China;
3. School of Engineering, Cardiff University, Cardiff CF24 3AA, United Kingdom;
4. State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China) |
| Abstract: |
| Under the market development of new energy without subsidies, an effective market mechanism is necessary for ensuring the accommodation of distributed PV. It can also promote the initiative of participants. Peer-to-peer energy trading is a method, and it can make electricity consumers and producers trade directly and then facilitate local power balance. This paper proposes a P2P trading strategy based on a model predictive control method and continuous double auction. The model predictive control is used to optimize the charge and discharge power of energy storage in order to guide the electricity bidding of market players. To realize the autonomous decision-making of each participant, a zero-intelligence-plus strategy with learning ability is adopted as the bidding price method. The calculation results of a regional power grid in west Liaoning province show that the trading strategy of distributed P2P market based on model predictive control can guide the bidding behavior of users. This strategy can further increase the benefits of prosumers and improve distributed photovoltaic hosting capacity in distribution networks.
This work is supported by the National Natural Sciences Foundation of China (No. 61873048). |
| Key words: Peer-to-peer energy trading strategy for prosumers based on model predictive control |
