Distributed operation optimization of active distribution network with P2P electricity trading in blockchain environment

With the increasing integration of distributed generators and the rapid development of electricity markets, active distribution networks (ADNs) confront new challenges in operation optimization. Centralized optimization methods usually aggravate the computational burden when applied to large-scale distribution networks (DNs). A distributed operation optimization model incorporating peer-to-peer (P2P) electricity trading in a blockchain environment is proposed in this paper, where network usage fees considering electrical distance are specially addressed. Meanwhile, the private information security of prosumers is ensured by the Proof-of-Authority (PoA) consensus blockchain during P2P electricity trading. The trading information of adjustable prosumers should be sent to the proxy entity authorized by the DN using the blockchain platform. In addition, a network partitioning method via dynamic reconfiguration considering multi-level switching is offered to adapt to the required distributed manner. The operation network, as well as the corresponding market structure can be automatically updated according to the results of dynamic reconfiguration. Moreover, the unit price of network usage fees can be adjusted to derive a higher amount of P2P electricity trading with security constraints. Case studies on a practical 148-bus system and a 297-bus system verify that the proposed model maximizes the profits and reduces the computational time in the distributed operation optimization.

Automated summary

A distributed operation optimization model incorporating peer-to-peer (P2P) electricity trading in a blockchain environment is proposed in this paper, considering network usage fees considering electrical distance and ensuring the private information security of prosumers. A network partitioning method via dynamic reconfiguration is offered to adapt to the required distributed manner, and the operation network and market structure can be automatically updated according to the results of dynamic reconfiguration. Case studies verify the effectiveness of the proposed model.