Increasing penetration of electric vehicles (EVs) gives rise to the challenges in the secure operation of power systems. The EV charging loads should be distributed among charging stations in a fair and incentive-compatible manner while ensuring that power transmission and transformation facilities are not overloaded. This paper first proposes a charging right (or charging power ration) trading mechanism and model based on blockchain. Considering all kinds of random factors of charging station loads, we use Monte Carlo modeling to determine the charging demand of charging stations in the future. Based on the charging demand of charging stations, a charging station needs to submit the charging demand for a future period. The blockchain first distributes initial charging right in a just manner and ensures the security of facilities. Given that the charging urgency and elasticity differences vary by charging stations, all charging stations then proceed with double auction and peer-to-peer (P2P) transaction of charging right. Bids and offers are cleared via double auctions if bids are higher than offers. The remaining bids and offers are cleared via the P2P market. Then, this paper designs the charging right allocation and trading platform and smart contract based on the Ethernet blockchain to ensure the safety of the distribution network (DN) and the transparency and efficiency of charging right trading. Simulation results based on the Ethereum private blockchain show the fairness and efficiency of the proposed mechanism and the effectiveness of the method and the mechanism.

Orderly power utilization (OPU) is an important measure to alleviate contradiction between supply and demand in a power system peak load period. As a load management system becomes smarter, it is necessary to fully explore the interactive ability among users and make schemes for OPU more applicable. Therefore, an intelligent multi-agent apanage management system that includes a mutual aid mechanism (MAM) is proposed. In the decision-making scheme, users’ participation patterns and the potential of peak shifting and willingness are considered, as well as the interests of both power consumers and power grid are comprehensively considered. For residential users, the charging time for their electric vehicles (EVs) is managed to consume the locally distributed power generation. To fully exploit user response potential, the algorithm for improved clustering by fast search and find of density peaks (I-CFSFDP), i.e., clusters the power load curve, is proposed. To conduct electrical mutual aid among users and adjust the schemes reasonably, a multi-objective optimization model (M2OM) is established based on the cluster load curves. The objectives include the OPU control cost, the user’s electricity cost, and the consumption of distributed photovoltaic (PV). Our results of a case study show that the above method is effective and economical for improving interactive ability among users. Agents can coordinate their apanage power resources optimally. Experiments and examples verify the practicability and effectiveness of the improved algorithm proposed in this study.