Connecting large numbers of electric vehicles to the power grid creates challenges for the operation of the power distribution network, but also provides a new method for supporting grid operation. This paper considers the trip patterns of electric vehicle users in China, including their trip starting time, traffic congestion, vehicle energy consumption, and other factors. We develop a charging–discharging operation strategy for electric vehicles in different functional areas with the goal of minimizing the cost of distribution network, which considers the distribution patterns of electric vehicles in different functional areas. As different types of cities in China have different proportions of electric vehicle users who follow different travel chains, we provide multiple examples showing the effectiveness of our proposed V2G method in different cities.

In order to deal with the fluctuation of the renewable energy, this paper proposes rolling scheduling strategy taking into account the capacity of load-side resources. By considering the energy characteristics of shiftable loads, an improved rolling scheduling model is proposed by adopting a full-cycle scheduling and adding periodic power constraints. By this way, the accuracy of the scheduling can be improved. The testing examples verified that the proposed rolling scheduling method can reflect the long-time benefit and therefore result in better performance.

New energy connected to the grid reduces the environmental pollution caused by the combustion of traditional fuels, so it is of great practical significance to vigorously develop renewable energy for the development of a distribution network. The system composition and operation structure of the active distribution network and the traditional distribution network are roughly the same. The main difference lies in the control mode of the two. By changing different control modes to adjust the operation mode, the main task of the active distribution network optimal dispatch is to improve its economy and power quality, but it is easy to fall into local optimum and slow convergence. In this paper, the traditional genetic algorithm is improved. The multi-objective co-generation system is mathematically modeled to construct the optimization objectives. The economic benefit of the co-generation system is maximized. The fluctuation of the output power following the load is minimized, and the requirement of carbon emission minimization is met. Through optimal control technology, the reasonable action of a variety of control equipment can be achieved. The voltage can be prevented from exceeding the limit. The multi-objective coordinated regulation ability can be improved, which is significant in practical engineering and intelligent control.