CN-121998279-A - Electric automobile traffic-electric power collaborative optimization method, device, equipment and medium

CN121998279ACN 121998279 ACN121998279 ACN 121998279ACN-121998279-A

Abstract

The application relates to the technical field of traffic-power optimal scheduling. The method comprises the steps of constructing a battery capacity and energy consumption model of an electric vehicle along with temperature change, constructing a road traffic capacity and running speed correction model along with weather state change, constructing a charging efficiency model of the electric vehicle battery along with temperature change, constructing a traffic-electric double-layer optimization model according to the battery capacity and energy consumption model, the road traffic capacity and running speed correction model and the charging efficiency model, wherein the traffic-electric double-layer optimization model comprises an upper-layer dynamic traffic distribution model and a lower-layer power distribution network direct current optimal power flow model, and solving the traffic-electric double-layer optimization model based on a preset solving model to obtain the traffic flow space-time distribution of the electric vehicle. The method can realize the mutual feedback regulation of traffic and a power grid under a space-time dynamic frame, reduce estimation deviation and effectively reflect the system behavior under extreme weather in winter.

Inventors

LIU HANGYU
SHI KEJIAN
CHEN QIANG
HU XIAOXIAO
GU TAIYU
Ji Xinzhe
WANG ZIANG
BO WEN
CHEN YUSHU
ZHANG XINYU
MA NING
ZHANG ZHI
LIU HAOYU
LIU XIMU
CHEN YUQI
SUN JIAZHENG
Bian Gechen
WANG SHANSHAN
ZHU YIDONG
TIAN YE
YE YUJIAN
HE JINSONG

Assignees

国网辽宁省电力有限公司电力科学研究院
国网辽宁省电力有限公司本溪供电公司
东南大学

Dates

Publication Date: 20260508
Application Date: 20251203

Claims (10)

1. The electric automobile traffic-electric power collaborative optimization method is characterized by comprising the following steps of: Constructing a battery capacity and energy consumption model of the electric automobile along with temperature change; constructing a road traffic capacity and driving speed correction model which changes along with weather conditions; constructing a charging efficiency model of the battery of the electric automobile along with the temperature change; Constructing a traffic-power double-layer optimization model according to the battery capacity and energy consumption model, the road traffic capacity and running speed correction model and the charging efficiency model, wherein the traffic-power double-layer optimization model comprises an upper-layer dynamic traffic distribution model and a lower-layer power distribution network direct current optimal power flow model; And solving the traffic-electric power double-layer optimization model based on a preset solving model to obtain the space-time distribution of the electric vehicle traffic flow.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises, The method for constructing the battery capacity and energy consumption model of the electric automobile along with the temperature change comprises the following steps: Constructing a capacity retention factor of the battery of the electric automobile along with the temperature change; determining a battery capacity model according to the capacity retention factor and the nominal battery capacity; Constructing a driving efficiency factor, carriage heating power and battery thermal management consumption power of the electric automobile along with temperature change; And determining an electric automobile energy consumption model according to the driving efficiency factor, the carriage heating power and the battery thermal management consumption power.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises, The method for constructing the road traffic capacity and driving speed correction model changing along with the weather state comprises the following steps: constructing a road capacity reduction coefficient and a speed maintenance factor which change along with weather conditions; Determining a road traffic capacity correction model according to the road capacity reduction coefficient; And determining a driving speed correction model according to the speed maintenance factor.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises, The method for constructing the charging efficiency model of the electric automobile battery along with the temperature change comprises the following steps: constructing a charging efficiency factor and battery preheating power which change along with temperature; Determining an actual effective charging efficiency according to the charging efficiency factor and the nominal charging efficiency; And determining a charging efficiency model according to the actual effective charging efficiency and the battery preheating power.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises, Constructing a traffic-power double-layer optimization model according to the battery capacity and energy consumption model, the road traffic capacity and running speed correction model and the charging efficiency model, wherein the traffic-power double-layer optimization model comprises an upper-layer dynamic traffic distribution model and a lower-layer power distribution network direct current optimal power flow model, and the traffic-power double-layer optimization model comprises: constructing a dynamic road model according to the road traffic capacity and the driving speed correction model; Constructing a dynamic charging station model according to the charging efficiency model; Constructing an energy constraint condition of the electric automobile according to the battery capacity and energy consumption model; constructing a driving demand distribution constraint condition; And constructing an upper-layer dynamic traffic distribution model according to the dynamic road model and the charging station model, wherein the upper-layer dynamic traffic distribution model takes the travel cost of all electric vehicles as an optimization target, and constraint conditions of the upper-layer dynamic traffic distribution model comprise the electric vehicle energy constraint conditions and the driving demand distribution constraint conditions.
6. The method of claim 1, wherein the step of determining the position of the substrate comprises, The lower-layer power distribution network direct-current optimal power flow model takes the power generation cost of the power distribution network as an optimization target; the constraint conditions of the lower-layer power distribution network direct current optimal power flow model comprise power balance constraint, phase angle constraint, power flow constraint and power generation output constraint.
7. The method of claim 1, wherein the step of determining the position of the substrate comprises, Solving the traffic-electric power double-layer optimization model based on a preset solving model to obtain the space-time distribution of the electric vehicle traffic flow, wherein the method comprises the following steps: Converting the lower-layer power distribution network direct current optimal power flow model into a group of constraints by using KKT conditions, and converting the traffic-power double-layer optimization model into a single-layer optimization model; and solving the single-layer optimization model based on a preset solving model to obtain the space-time distribution of the electric vehicle traffic flow-power distribution network.
8. An electric vehicle traffic-electric power collaborative optimization device, characterized by comprising: the first construction module is used for constructing a battery capacity and energy consumption model of the electric automobile along with temperature change; The second construction module is used for constructing a road traffic capacity and driving speed correction model which changes along with the weather state; the third construction module is used for constructing a charging efficiency model of the electric automobile battery along with the temperature change; The optimization module is used for constructing a traffic-power double-layer optimization model according to the battery capacity and energy consumption model, the road traffic capacity and running speed correction model and the charging efficiency model, wherein the traffic-power double-layer optimization model comprises an upper-layer dynamic traffic distribution model and a lower-layer power distribution network direct current optimal power flow model; and the solving module is used for solving the traffic-electric power double-layer optimization model based on a preset solving model to obtain the space-time distribution of the electric vehicle traffic flow.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the electric vehicle traffic-power co-optimization method according to any one of claims 1 to 7 when the computer program is executed.
10. A computer-readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the electric vehicle traffic-power co-optimization method according to any one of claims 1 to 7.

Description

Electric automobile traffic-electric power collaborative optimization method, device, equipment and medium Technical Field The application relates to the technical field of traffic-power optimization scheduling, in particular to a method, a device, equipment and a medium for electric vehicle traffic-power collaborative optimization. Background With the popularization of electric vehicles in cold areas, the traveling of the electric vehicles and the running of a power system under low-temperature conditions face new challenges. The existing traffic-power coupling optimization model is mostly based on ideal climate assumption, and only energy consumption parameters or battery capacity proportion are adjusted in the model, so that estimation deviation of travel rules, charging loads and distribution network safety margin of the electric vehicle is larger in cold climate. Moreover, existing traffic-power coupling optimization models often employ static traffic allocation or simplified charging logic, which is difficult to reflect system behavior in extreme weather in winter. Therefore, there is a need to find an electric vehicle traffic-power collaborative optimization method that can reduce estimation bias, effectively reflect system behavior in extreme weather in winter. Disclosure of Invention In view of the above, the embodiments of the present application provide a method, an apparatus, a device, and a medium for collaborative optimization of electric vehicle traffic-power, which aim to solve the above problems or at least partially solve the above problems. In a first aspect, the present application provides a traffic-power collaborative optimization method for an electric vehicle, including: Constructing a battery capacity and energy consumption model of the electric automobile along with temperature change; constructing a road traffic capacity and driving speed correction model which changes along with weather conditions; constructing a charging efficiency model of the battery of the electric automobile along with the temperature change; Constructing a traffic-power double-layer optimization model according to the battery capacity and energy consumption model, the road traffic capacity and running speed correction model and the charging efficiency model, wherein the traffic-power double-layer optimization model comprises an upper-layer dynamic traffic distribution model and a lower-layer power distribution network direct current optimal power flow model; And solving the traffic-electric power double-layer optimization model based on a preset solving model to obtain the space-time distribution of the electric vehicle traffic flow. Exemplary, constructing a battery capacity and energy consumption model of an electric vehicle along with temperature change includes: Constructing a capacity retention factor of the battery of the electric automobile along with the temperature change; determining a battery capacity model according to the capacity retention factor and the nominal battery capacity; Constructing a driving efficiency factor, carriage heating power and battery thermal management consumption power of the electric automobile along with temperature change; And determining an electric automobile energy consumption model according to the driving efficiency factor, the carriage heating power and the battery thermal management consumption power. Illustratively, constructing a road traffic capacity and driving speed correction model according to weather conditions includes: constructing a road capacity reduction coefficient and a speed maintenance factor which change along with weather conditions; Determining a road traffic capacity correction model according to the road capacity reduction coefficient; And determining a driving speed correction model according to the speed maintenance factor. Exemplary, constructing a charging efficiency model of an electric vehicle battery according to temperature change includes: constructing a charging efficiency factor and battery preheating power which change along with temperature; Determining an actual effective charging efficiency according to the charging efficiency factor and the nominal charging efficiency; And determining a charging efficiency model according to the actual effective charging efficiency and the battery preheating power. Exemplary, a traffic-power double-layer optimization model is constructed according to the battery capacity and energy consumption model, the road traffic capacity and driving speed correction model and the charging efficiency model, the traffic-power double-layer optimization model comprises an upper-layer dynamic traffic distribution model and a lower-layer power distribution network direct current optimal power flow model, and the method comprises the following steps: constructing a dynamic road model according to the road traffic capacity and the driving speed correction model; Constructing a dynamic charging station model ac