科研之友
微信
新浪微博
Facebook
分享链接
摘要
Aiming at the overload risk caused by large-scale charging of electric vehicles (EV) connected to the power grid, a real-time optimization strategy of EV considering the difference between predicted load and user demand is proposed. First of all, according to the signing or not, the EV connected to the grid is divided into signed users and non-signed users. By analyzing the actual situation, the non-signed users are included as demand response objects, and they are divided into different categories according to the difference of users’ charging requirements and the corresponding models are established. Secondly, the relationship model between the response probability of non-contracted users and the compensation electricity price is introduced, the subsidy mechanism of aggregators and users is established, and the real-time dispatching scheme of each period is formulated according to the demand of power grid and the potential of various users. Then, with the control target power of distribution grid as the constraint, and the profit increase ratio of aggregate quotient and the average profit increase ratio of users as the comprehensive optimization objectives, the particle swarm optimization algorithm is used to solve the EV charging power participating in demand response in different periods. Finally, through several groups of simulation analysis, it is proved that the proposed optimization strategy can not only reduce the peak load, but also take into account the benefits of aggregators and users, which verifies the effectiveness and applicability of the proposed real-time scheduling strategy. ? 2022, Global Energy Interconnection Development and Cooperation Organization.
- 单位
