| 引用本文: | 裴 林,黄 成,杨 啸,等.考虑隐私保护和去中心化的分布式能源交易模式研究[J].电力系统保护与控制,2024,52(2):143-154.[点击复制] |
| PEI Lin,HUANG Cheng,YANG Xiao,et al.A distributed energy trading model considering privacy protection and decentralization[J].Power System Protection and Control,2024,52(2):143-154[点击复制] |
|
| 摘要: |
| 为了缓解配电网分布式能源交易市场存在的计算压力大、个人隐私难以保护和抗干扰能力弱等问题，设计了一个考虑隐私保护和去中心化的分布式能源交易模式。首先，以社会福利最大化为目标构建了一个集中式的统一优化模型。然后，基于梯度上升和对偶分解方法对集中式模型进行分解，得到了相应的分布式的优化模型。进一步采用扩散策略，提出了一种基于组合适应的共识算法，旨在不引入市场组织者的情况下，仅通过主体间传递交易电价和供需不平衡电量等部分信息来实现市场的最优出清，并同时保护市场主体的个人隐私和减轻计算压力。最后，算例分析验证了该交易模式的有效性，以及在新能源出力不确定、市场主体加入退出等因素影响下的鲁棒性和可拓展性。 |
| 关键词: 分布式能源交易 去中心化 隐私保护 扩散策略 共识算法 |
| DOI：10.19783/j.cnki.pspc.230549 |
| 投稿时间：2023-05-12修订日期：2023-11-24 |
| 基金项目:国网总部科技项目资助(1400-202099523A-0-0-00) |
|
| A distributed energy trading model considering privacy protection and decentralization |
| PEI Lin1,HUANG Cheng2,YANG Xiao1,ZHAO Yong1,DING Qiang3,YU Yaowen1 |
| (1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;
2. State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103, China;
3. China Electric Power Research Institute, Beijing 100192, China) |
| Abstract: |
| To alleviate the problems of high computational burden, difficulties in privacy protection, and weak anti-interference ability in the distributed energy trading market on the distribution network, this paper designs a distributed energy trading model that considers privacy protection and decentralization. First, it constructs a centralized unified optimization model with the goal of maximizing social welfare. Then, the centralized model is decomposed into the corresponding distributed optimization model based on the gradient rise method and dual decomposition. Further, a consensus algorithm based on the combined adaptation is proposed by employing a diffusion strategy. The aim is to achieve optimal market clearing by only transmitting information such as the electricity price and supply-demand imbalance among entities without introducing market organizers, while protecting the personal privacy of market entities and alleviating computational burden. Finally, case studies are conducted to validate the effectiveness of the proposed trading model, as well as its robustness under factors such as the uncertainty of new energy power, market entities joining and exiting, and its scalability. |
| Key words: distributed energy trading decentralization privacy protection diffusion strategy consensus algorithm |
