基于区块链含电动汽车的微电网组合出力优化策略

文章针对电动汽车充放电交易中车主与服务商之间缺乏信任,大量电动汽车无序充放电造成电网峰上加峰的问题,提出基于电动汽车联盟链的互信交易架构和调度策略。首先,引入区块链中白盒密码、智能合约、阻塞管理对电动汽车链上交易机制执行过程进行分析;然后,搭建了电动汽车双层优化调度模型,外层针对鼓励低电价充电时产生的新充电高峰问题,建立基于区块链的电动汽车出行诚信度模型,综合考虑负荷方差和用户诚信度,优化分时电价定价策略,并将优化后的电价曲线输出到内层模型;内层针对偏差电量消除成本较高的问题,利用电动汽车的移动储能特性,实现供求自平衡。最终采用含激励机制的差分进化演化博弈组合算法进行求解,通过算例仿真验证了所提策略的有效性。

Combined Output Optimization Based on Blockchain for Micro-grid with Electric Vehicles

Blockchain technology has the characteristics of decentralization, non-tampering, multi-party data sharing and common maintenance, which enables two parties that do not understand and trust each other to achieve credible and equivalent power transactions. In view of the lack of trust between vehicle owners and service providers in the charge and discharge of electric vehicles, the disorder charge and discharge of a large number of electric vehicles will cause peaks on the grids’ peak load. A mutual trust transaction architecture and scheduling strategy based on the electric vehicle alliance chain is proposed. Firstly, the white box password, smart contract, and congestion management in the blockchain are introduced to analyze the execution process of the transaction mechanism on the electric vehicle chain; Secondly, a double-layer optimal scheduling model for electric vehicles is built. The outer layer aims at encouraging the new charging peak problem aroused by encouraging to charge at low electricity prices, comprehensively considering the load variance and user creditability, optimizing the time-of-use electricity pricing strategy, and optimizing the electricity price curve export to the inner model. The inner layer aims to eliminate the problem of higher cost for the deviation of electricity, and uses the mobile energy storage characteristics of electric vehicles to achieve self-balance of power supply and demand. Finally, the differential evolutionary game combination algorithm with incentive mechanism is used to solve the problem, and the effectiveness of the proposed strategy is verified by simulation.