祝贺课题组成果发表在International Journal of Electrical Power & Energy Systems.

引文信息：https://authors.elsevier.com/a/1hJfgWJH6dMbL

论文题目：Peer-to-peer energy trading among multiple microgrids considering risks over uncertainty and distribution network reconfiguration: A fully distributed optimization method

作者信息：Hui Hou^a,b, Zhuo Wang^a,b,∗, Bo Zhao^c, Leiqi Zhang^c, Ying Shi^a,b, Changjun Xie^a,b,*.

a School of Automation, Wuhan University of Technology, Wuhan, 430070, China

b Shenzhen Research Institute Wuhan University of Technology, Shenzhen, 518000, China

c State Grid Zhejiang Electric Power Research Institute, Hangzhou, 310014, China

Highlights:

（1）Protected the prosumer-focused and transaction-oriented natures of electricity markets.

（2）Used a risk-averse stochastic programming method for effective solutions under uncertainty.

（3）Designed a parallel computing algorithm based on diagonal quadratic approximation method.

摘要：

This paper proposes a two-layer optimization framework to co-optimize the P2P energy trading among multiple microgrids (MMGs) under uncertainty and optimal topology planning of the distribution networks (DNs). At the upper layer, the traditional verification optimal power flow model of DNs is transformed into a prosumerfocused and transaction-oriented dynami network reconfiguration model. At the lower layer, uncertainty from wind power generations is integrated into the operating model of individual MGs and addressed by the stochastic programming (SP) method. Meanwhile, the conditional value at risk technique is introduced to find a trade-off between cost minimization and risk aversion flexibly. To establish the global negotiation mechanism among all participants (not only between distribution system operators and MGs, but also among MMGs), a fully distributed method is developed by combining an analytical target cascading algorithm and an alternating direction multiplier method. Furthermore, a diagonal quadratic approximation method is utilized to linearize the quadratic penalty term so that achieving parallel computing for all independent optimization subproblems. Simulations of different strategies, models, and distributed algorithms are implemented to verify the rationality and validity of the proposed method. The results of these case studies demonstrate that the proposed risk-averse SP approach can avoid over-optimistic solutions, the obtained P2P trading strategies are immune to uncertainty and P2P trading behaviors among MMGs can help reduce network losses of DNs. In addition, comparisons with other distributed algorithms verify the high performance of the proposed fully distributed method.

全文框架如图1所示。

Fig. 1. A two-layer optimization structure for energy trading among MMGs in DNs.

配电网网络拓扑图如图2所示。

部分仿真对比结果如图3所示。

结论：

This paper proposes a two-layer distributed optimization framework to realize optimal reconfiguration planning for DNs and P2P energy trading problems among MMGs under uncertainty. From the perspective of the proposed framework, the designed P2P energy trading modes could create a win-win situation for both MGs and DNs. On the one hand, a reconfigurable model of DNs ensures the feasibility of energy trading while alleviating the restrictions from network constraints to energy transactions. On the other hand, the P2P energy transactions among MMGs provide reliable power support and supplement for the DNs. Thus, the total operating cost of MMGs and power losses of DNs are decreased by 2.58% and 0.32%, respectively. Besides the above nice features, the obtained energy trading schemes for MMGs are robust to wind power generations while making a trade-off between expected costs and risk over uncertainty. From the perspective of algorithm, a fully distributed method with a parallel computing mechanism is developed for all participators of electricity markets. The proposed distributed method succeeds in finite convergence and coordinate energy trading between DSO and MGs, as well as among MMGs under the premise of privacy. Moreover, the computing time in the case with three MGs is reduced by 33.82% and 17.01% compared with the traditional sequential computing algorithm and another parallel algorithm, respectively.

However, the proposed model assumes the energy trading behaviors are conducted in a fair platform and intends to use stochastic scenarios of wind power to represent uncertainty. Therefore, investigating the game problems related to energy trading among multi-stakeholders and considering diverse uncertainty sources are potential extensions of this study.

论文作者及科研团队介绍

侯慧，博士，副教授，博导，武汉理工大学自动化学院电气工程党支部书记，新能源与电力系副主任。中国电力教育协会电气工程教学委员会委员，IEEE高级会员，中国电工技术学会高级会员，中国电机工程学会高级会员。在研国家重点研发计划子题1项，国家自然科学基金面上项目1项，曾获中国电力科技创新奖一等奖1项，电力建设科学技术进步奖二等奖1项，广东省科技进步二等奖1项等。

论文主要作者王灼为武汉理工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电网规划运行、分布式优化及可再生能源整合等。博一在读期间发表JCR一区论文2篇，参与第一届中国研究生“双碳”创新与创意大赛中，荣获三等奖等。