CN-121984047-A - Energy storage cooperation method, device and system of energy system

CN121984047ACN 121984047 ACN121984047 ACN 121984047ACN-121984047-A

Abstract

The invention relates to the technical field of energy system optimization control, and discloses an energy storage cooperation method, device and system of an energy system. The method comprises the steps of obtaining energy state data and corresponding system state data of an energy system, carrying out data fusion on the energy state data and the corresponding system state data to obtain system situation data of the energy system, and carrying out energy scheduling on the energy system based on a preset scheduling mechanism according to the system situation data. The method and the device improve the new energy consumption rate and realize global collaborative optimization of the multi-energy system.

Inventors

HUANG ZHEFENG
LI YUXUAN
Wen Qiangyu
RUAN HUIFENG
XIE YURONG
LIU RUNBAO
ZHANG HAIZHEN
LUO CHENGXIN
ZHENG MENGCHAO
ZHAO DAZHOU
HUANG RONG
ZHANG KAI

Assignees

华电电力科学研究院有限公司

Dates

Publication Date: 20260505
Application Date: 20251216

Claims (10)

1. An energy storage synergy method of an energy system, the method comprising: Acquiring energy state data and corresponding system state data of an energy system, wherein the energy state data is used for representing an energy supply and demand state of the energy system, and the system state data is used for representing an operation state of the energy system; Performing data fusion on the energy state data and corresponding system state data to obtain system situation data of the energy system, wherein the system situation data is used for representing potential output capacity of the energy system; and carrying out energy scheduling on the energy system based on a preset scheduling mechanism according to the system situation data.
2. The method of claim 1, wherein the energy scheduling the energy system based on a preset scheduling mechanism according to the system situation data comprises: Based on a deep learning model, carrying out situation prediction on the energy system to obtain a situation prediction result; Based on a dynamic game model, acquiring corresponding energy states under different preset strategies in a preset period according to the situation prediction result, wherein the preset strategies are used for representing the relationship among the maximization of income, the minimum power fluctuation, the minimum carbon emission and the minimum equipment damage; And determining one preset strategy according to a preset rule, and controlling the energy state of the energy system according to the determined preset strategy.
3. The method of claim 2, wherein the preset policies include aggressive policies, robust policies, and friendly policies, The step of obtaining the corresponding energy states under different preset strategies in a preset period according to the situation prediction result comprises the following steps: And respectively acquiring the energy states under the aggressive strategy, the steady strategy and the friendly strategy according to the preset strategy.
4. The method of claim 1, wherein the energy system comprises a photovoltaic array subsystem, a wind turbine subsystem, a molten salt heat storage subsystem, an electrochemical energy storage subsystem, and a grid dispatching subsystem; the data fusion is performed on the energy state data and the corresponding system state data to obtain the system situation data of the energy system, including: fusing the power generation characteristic curve data and the thermal imaging data of the photovoltaic array subsystem to obtain situation data of the photovoltaic array subsystem; Fusing vibration data and wind speed prediction data of the wind turbine subsystem to obtain situation data of the wind turbine subsystem; Fusing the temperature field data and the molten salt flow data of the molten salt heat storage subsystem to obtain situation data of the molten salt heat storage subsystem; Fusing the state of charge data and the health data of the electrochemical energy storage subsystem to obtain situation data of the electrochemical energy storage subsystem; And fusing the electricity price data and the carbon intensity data of the power grid dispatching subsystem to obtain situation data of the power grid dispatching subsystem, wherein the carbon intensity data comprises carbon dioxide emission corresponding to unit generated energy of the power grid dispatching subsystem.
5. The method according to claim 2, wherein the method further comprises: According to the energy scheduling information, acquiring carbon emission information of the energy system by using a carbon metering sensor network; And optimizing the energy state based on a preset carbon emission optimization strategy according to the association relation between the carbon emission information and a preset carbon emission target.
6. The method of claim 5, wherein the obtaining carbon emission information of the energy system using the carbon meter sensor network according to the energy scheduling information comprises: acquiring a basic carbon emission factor according to the energy type and the carbon emission node information in the energy system; correcting the basic carbon emission factor according to the equipment operation parameters in the energy system to obtain a comprehensive carbon emission factor; and acquiring carbon emission information of the energy system through the operation relation between the comprehensive carbon emission factors and the carbon emission nodes.
7. The method of claim 5, wherein optimizing the energy state based on a preset carbon emission optimization strategy according to the association of the carbon emission information with a preset carbon emission target comprises: customizing a carbon emission objective function according to the carbon emission information, the carbon price information and the energy price information of the energy system; and acquiring a carbon emission optimization strategy according to the carbon emission objective function and the corresponding constraint conditions.
8. The method of claim 7, wherein the method further comprises: Comparing the system state data with a corresponding threshold value, and/or analyzing the system state data based on a deep learning model to acquire fault information of the energy system; performing association verification on the fault information to obtain fault attributes, wherein the fault attributes are used for representing the occurrence probability of fault reasons; And according to the fault attribute, performing isolation detection on the energy system, and performing grid connection recovery after the fault is removed.
9. An energy storage co-device for an energy system, the device comprising: the system comprises a data acquisition unit, a control unit and a control unit, wherein the data acquisition unit is used for acquiring energy state data of an energy system and corresponding system state data, the energy state data are used for representing the energy supply and demand state of the energy system, and the system state data are used for representing the running state of the energy system: The data fusion unit is used for carrying out data fusion on the energy state data and the corresponding system state data to acquire system situation data of the energy system, wherein the system situation data is used for representing potential output capacity of the energy system; and the information acquisition unit is used for carrying out energy scheduling on the energy system based on a preset scheduling mechanism according to the system situation data.
10. An energy storage co-system, comprising: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the energy storage co-method of the energy system of any one of claims 1 to 8.

Description

Energy storage cooperation method, device and system of energy system Technical Field The disclosure relates to the technical field of energy system optimization control, in particular to an energy storage cooperation method, device and system of an energy system. Background At present, the realization of zero carbon emission has become an important goal pursued in various parks and industrial sites. The energy system of the traditional park is difficult to adapt to complex and changeable energy demands, and coordination optimization among multiple energy systems is difficult to realize. In the related technical scheme, the zero-carbon park comprehensive energy system has the following problems that energy scheduling optimization is insufficient, links are independent, global optimization is lacked, prediction accuracy and generalization capability are insufficient, and the zero-carbon park comprehensive energy system is difficult to adapt to nonlinear and dynamic complex scenes of the zero-carbon park. The energy storage function is single, and is not enough in coordination, is difficult to consider the demand of long-time energy supply and short-time regulation. Disclosure of Invention The disclosure provides an energy storage collaborative method of an energy system, which comprises the steps of obtaining energy state data of the energy system and corresponding system state data, wherein the energy state data are used for representing energy supply and demand states of the energy system, the system state data are used for representing operation states of the energy system, carrying out data fusion on the energy state data and the corresponding system state data, obtaining system situation data of the energy system, wherein the system situation data are used for representing potential output capacity of the energy system, and carrying out energy scheduling on the energy system based on a preset scheduling mechanism according to the system situation data. The energy storage cooperative device comprises a data acquisition unit and an information acquisition unit, wherein the data acquisition unit is used for acquiring energy state data and corresponding system state data of the energy system, the energy state data are used for representing energy supply and demand states of the energy system, the system state data are used for representing operation states of the energy system, the data fusion unit is used for carrying out data fusion on the energy state data and the corresponding system state data to acquire system situation data of the energy system, the system situation data are used for representing potential output capacity of the energy system, and the information acquisition unit is used for carrying out energy scheduling on the energy system based on a preset scheduling mechanism according to the system situation data. In a third aspect, the present disclosure provides an energy storage collaboration system, including a memory and a processor, where the memory and the processor are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions, thereby executing the energy storage collaboration method of the energy system. According to the energy storage cooperation method, device and system of the energy system, system situation data are built by fusing multi-source data, multi-target optimization scheduling is achieved based on a dynamic game model, the problems of low new energy consumption rate and insufficient multi-energy cooperation are solved, the new energy consumption rate is improved, and global cooperation optimization of the multi-energy system is achieved. Drawings In order to more clearly illustrate the embodiments of the present disclosure or the prior art, the drawings that are required in the detailed description or the prior art will be briefly described, it will be apparent that the drawings in the following description are some embodiments of the present disclosure, and other drawings may be obtained according to the drawings without inventive effort for a person of ordinary skill in the art. FIG. 1 is a topology of an energy system according to an embodiment of the present application; FIG. 2 is a first flow chart of a method of energy storage synergy according to an embodiment of the application; FIG. 3 is a second flow chart of a method of energy storage synergy according to an embodiment of the application; FIG. 4 is a schematic flow chart of control logic of an energy storage synergy method according to an embodiment of the application; FIG. 5 is a block diagram of an energy storage cooperating apparatus in accordance with an embodiment of the present application; fig. 6 is a schematic diagram of a hardware architecture of an energy storage coordination system according to an embodiment of the present application. Detailed Description For the purposes of making the objects, technical solutions and a