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CN-121998282-A - Source network load storage cooperative scheduling method and related equipment

CN121998282ACN 121998282 ACN121998282 ACN 121998282ACN-121998282-A

Abstract

The application provides a source network load storage collaborative scheduling method and related equipment. The method comprises the steps of obtaining flexible load data and electric vehicle data, carrying out first scheduling processing on the flexible load based on a scheduling strategy preset by the flexible load data, carrying out second scheduling processing on the electric vehicle based on a scheduling strategy preset by the electric vehicle data, determining an objective function value based on the first scheduling processing and the second scheduling processing, determining first scheduling processing and the second scheduling processing corresponding to the minimum value of the objective function value, carrying out scheduling processing on the flexible load based on the first scheduling processing, and carrying out scheduling processing on the electric vehicle based on the second scheduling processing. The embodiment of the application can realize multi-energy optimal scheduling and improve the stability of the power grid and the utilization efficiency of new energy.

Inventors

  • LIU HAIHAN
  • SU LIANCAI
  • YU QIAN
  • LIU XIAODAN
  • DU LISHI
  • YU LONG
  • GAN LIQING

Assignees

  • 北京中电飞华通信有限公司

Dates

Publication Date
20260508
Application Date
20251205

Claims (10)

  1. 1. The source network load storage collaborative scheduling method is characterized by comprising the following steps of: acquiring flexible load data and electric vehicle data; performing first scheduling processing on the flexible load based on a scheduling strategy preset by the flexible load data; Performing second scheduling processing on the electric automobile based on a scheduling strategy preset by the electric automobile data; determining an objective function value based on the first scheduling process and the second scheduling process; Determining the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function value; Scheduling the flexible load based on the first scheduling process; and carrying out scheduling processing on the electric automobile based on the second scheduling processing.
  2. 2. The method of claim 1, wherein the scheduled power of the flexible load is represented by: ; Wherein, the The amount of load compensation is indicated and, 、 、 Representing the power variation factor of the power, Representing the power at which the load can be transferred, Representing the power of the translatable load, Indicating that the power of the load can be cut down, Representing a transferable period of time interval, Representing the period of the translatable load, Showing the period during which the load can be cut.
  3. 3. The method according to claim 2, wherein the constraint of the scheduling policy preset by the flexible load data is represented by the following formula: ; Wherein, the The state of the transition is indicated and, A state of being translatable is indicated, Indicating a schedulable state, with 1 occurring, 0 not occurring, The time of the translation is indicated and, The time for the reduction is indicated as such, The number of times of reduction is indicated, Representing the minimum value of the transferable load power, Representing the maximum value of the transferable load power, Representing the minimum value of translatable load power, Representing the maximum value of translatable load power, Representing the minimum value at which the load power can be cut, Representing the maximum value of the load power that can be cut down.
  4. 4. A method according to claim 3, characterized in that the method further comprises: Compensating in response to scheduling the flexible load; The compensation is represented by the following formula: ; Wherein, the Representing a compensation of the transferable load, Representing a compensation of the translatable load, Showing the compensation that the load can be cut down, 、 、 Representing the compensation coefficient.
  5. 5. The method of claim 1, wherein the constraint condition of the scheduling policy preset by the electric vehicle data is represented by the following formula: ; Wherein, the Indicating the charge level of the lowest state of the battery, Indicating the current state of charge of the battery of the electric vehicle at time t, Indicating the charge level of the highest state of the battery, Representing the minimum value of the grid node voltage, Representing the maximum value of the grid node voltage, The grid-connected state at the moment t is represented, 1 represents grid-connected, otherwise 0, Represents the minimum energy capacity of the battery of the electric vehicle, Indicating the maximum energy capacity of the battery of the electric vehicle, Indicating the energy level of the battery of the electric vehicle at time t, Indicating the charging efficiency of the battery, Indicating the efficiency of the discharge, The charging power of the electric automobile at the time t is shown, The discharge power of the electric automobile at the time t is shown, Representing a time interval.
  6. 6. The method of claim 1, wherein the objective function is represented by: ; Wherein, the Represents the charge-discharge operation and maintenance cost of the energy storage facility, The new energy source duty ratio is represented, Represents the wind-abandoning and light-abandoning punishment cost, Representing the carbon trade and the benefits of the carbon capture system, Representing the voltage deviation.
  7. 7. The utility model provides a source network lotus stores up cooperative scheduling device which characterized in that includes: the acquisition module is configured to acquire flexible load data and electric vehicle data; The first scheduling module is configured to perform first scheduling processing on the flexible load based on a scheduling strategy preset by the flexible load data; the second scheduling module is configured to perform second scheduling processing on the electric automobile based on a scheduling strategy preset by the electric automobile data; An objective function calculation module configured to determine an objective function value based on the first scheduling process and the second scheduling process; a determining module configured to determine the first scheduling process and the second scheduling process corresponding to a minimum value of the objective function values; a first processing module configured to schedule the flexible load based on the first scheduling process; And the second processing module is configured to schedule the electric automobile based on the second scheduling process.
  8. 8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 6 when the computer program is executed by the processor.
  9. 9. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 6.
  10. 10. A computer program product comprising computer program instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 6.

Description

Source network load storage cooperative scheduling method and related equipment Technical Field The application relates to the technical field of resource scheduling, in particular to a source network load storage collaborative scheduling method and related equipment. Background In the prior art, flexible interaction among power generation resources, load resources and a dispatching system is gradually realized by coordinating power generation side resources and demand side resources. Based on Yun Bian cooperative scheduling frames, the upper layer scheduling is responsible for overall optimization scheduling of the multi-energy system, and the lower layer scheduling transmits demand information to the upper layer scheduling center through comprehensive management of load side controllable equipment, so that wide cooperative interaction between source network and load storage is realized. Compared with the traditional system, the dispatching mode can integrate the power generation side and load side resources, and the upper dispatching center transmits price signals to the lower equipment through a market means, so that the operation of the lower equipment is indirectly controlled, and the safety, economy and environmental friendliness of the operation of the power grid are improved. However, the prior art has some drawbacks in specific applications. Because the new energy has strong randomness and volatility, and the load distribution at the demand side is more dispersed, the information acquisition and analysis capability is insufficient when the prior art processes large-scale data, and the user behavior characteristics are difficult to extract and screen efficiently. In addition, the existing network load interaction mode has higher requirements on response speed and capacity, and the existing scheduling technology still has defects in terms of instantaneity and dynamic coordination capacity, so that the stability of the power grid under the new energy grid connection condition is difficult to meet the requirements. Especially, with the popularization of new energy automobiles, the interaction of the automobile network is used as flexible resources to participate in the power grid dispatching, but the prior art cannot effectively formulate a strategy to fully exert the bidirectional effect of the electric automobile, and the maximization technical effect of the collaborative dispatching of the source network and the charge storage is difficult to realize. Disclosure of Invention Therefore, the present application aims to provide a source network load storage cooperative scheduling method and related equipment. Based on the above purpose, the application provides a source network load storage collaborative scheduling method, which comprises the following steps: acquiring flexible load data and electric vehicle data; performing first scheduling processing on the flexible load based on a scheduling strategy preset by the flexible load data; Performing second scheduling processing on the electric automobile based on a scheduling strategy preset by the electric automobile data; determining an objective function value based on the first scheduling process and the second scheduling process; Determining the first scheduling process and the second scheduling process corresponding to the minimum value of the objective function value; Scheduling the flexible load based on the first scheduling process; and carrying out scheduling processing on the electric automobile based on the second scheduling processing. In one possible implementation, the scheduled power of the flexible load is represented by: Wherein, the The amount of load compensation is indicated and,、、Representing the power variation factor of the power,Representing the power at which the load can be transferred,Representing the power of the translatable load,Indicating that the power of the load can be cut down,Representing a transferable period of time interval,Representing the period of the translatable load,Showing the period during which the load can be cut. In one possible implementation manner, the constraint condition of the scheduling policy preset by the flexible load data is represented by the following formula: Wherein, the The state of the transition is indicated and,A state of being translatable is indicated,Indicating a schedulable state, with 1 occurring, 0 not occurring,The time of the translation is indicated and,The time for the reduction is indicated as such,The number of times of reduction is indicated,Representing the minimum value of the transferable load power,Representing the maximum value of the transferable load power,Representing the minimum value of translatable load power,Representing the maximum value of translatable load power,Representing the minimum value at which the load power can be cut,Representing the maximum value of the load power that can be cut down. In one possible implementation, the method further includes: