Active power distribution network optimization scheduling method for large-scale electric vehicle access

Abstract

The invention discloses an active power distribution network optimization scheduling method for large-scale electric vehicle access, and particularly relates to the technical field of power distribution network optimization scheduling, and the method specifically comprises the following steps: S1, a first layer is two-stage optimization scheduling implemented by DEMS; S2, a second layer is real-time scheduling executed by the regional EV-EMS; and S3, optimized scheduling models in each stage are established and solving methods are adopted. According to the invention, the scheduling strategy isoptimized through three stages; day-ahead scheduling and intra-day correction of controllable resources of the power distribution network and real-time control of EVs charging are cooperatively realized; the influence of uncertainty factors such as RES power generation and EVs charging behavior randomness is reduced; by adopting a 'centralized+distributed 'hybrid computing framework, the computing time is greatly reduced, the problem of dimensionality disasters is solved, in addition, the adverse effect of local communication failure on overall optimal scheduling is also reduced, and the requirement on the reliability of a communication system is reduced.

Description

technical field [0001] The invention relates to the technical field of distribution network optimization dispatching, and more specifically, the invention relates to an active distribution network optimization dispatching method for large-scale electric vehicle access. Background technique [0002] Electric vehicles have good energy saving, environmental protection and low emission characteristics, so vigorously developing electric vehicles has become an important means to effectively alleviate the energy crisis and pollution problems. However, the randomness of electric vehicles in time and space is large. Once connected to the grid on a large scale, it will not only trigger a new round of power load growth, but also pose new challenges to the complete, stable, economical and efficient operation of the grid. . Relevant studies have shown that if the charging behavior of electric vehicles is not controlled, it will lead to an increase in peak load, overload of power supply ...

AI Technical Summary

Problems solved by technology

At the level of the distribution network, there are three control methods: 1) The distribution network directly dispatches each EV, which strengthens the connection between the EV and the power grid, but the optimal dispatch time is long and the actual operability is relatively poor

2) Based on the control method of the centralized control system, an intermediary system is established between the regional electric vehicle group and the distribution network. The distribution network does not directly participate in the charging control, but only issues scheduling optimization commands to the centralized control system. This method can It greatly simplifies the management of EVs by the power grid, and also considers the interests of users. However, when the scale of EVs is very large, it will cause a disaster of dimensionality to the optimal control of the centralized control system.

3) In view of the possible problems in the centralized control system, some scholars have proposed a dispatching method of partition control, which divides the distribution system into several regions according to the region. For each region, the distribution system dispatching organization or a third-party electric vehicle agent ( EVAggregators) are responsible for the coordinated scheduling of EVs in the region. For the third scheduling method, a two-layer model is generally established and a centralized optimization algorithm is used for calculation, which requires a relatively large amount of information storage and calculation, and is prone to the curse of dimensionality. The communication requirements are high. In addition, the influence of uncertainties such as load and renewable energy (RES) power generation is not considered. From the perspective of time scale, EV charging control is divided into day-ahead scheduling and real-time scheduling. The existing literature focuses on Due to the independent research of the two, there is a lack of research on the synergy between the two

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