Power grid cooperative dispatching method and device, storage medium and equipment

Abstract

The application discloses a power grid cooperative scheduling method and device, a storage medium and equipment. The method comprises the following steps: obtaining historical operation data of a target power grid, wherein the historical operation data comprises energy consumption generation data, target power grid output data and electrical load data; determining a wind power output scene and an electrical load scene based on the historical operation data; determining a basic energy consumption model and an additional energy consumption model based on the historical operation data; constructing a double-layer optimization scheduling model based on the wind power output scene, the electrical load scene, the basic energy consumption model and the additional energy consumption model; and completing power grid cooperative scheduling by using the double-layer optimization scheduling model. The application solves the technical problem of serious power grid energy waste in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a power grid collaborative scheduling method and device, a storage medium and equipment. BACKGROUND

[0002] New energy power sources are gradually widely used in power systems in various countries due to their flexibility, economy, environmental protection and other characteristics, to cope with energy shortages, environmental protection and other issues. However, the volatility, randomness and intermittency of new energy power output pose risks to the safety and stability of the power system; in addition, in places where new energy is abundant, there is often a phenomenon of curtailment of wind and light, resulting in serious waste of energy. Therefore, the consumption of new energy has become the focus of attention of the academic and industrial circles at home and abroad.

[0003] For the strategy of load migration, in recent years, with the development of technology, the delay speed of load migration has been greatly reduced, so that more loads can be migrated between different geographic data centers, making full use of the energy and climate advantages of different regions, and thus realizing the geographic balance of data. Similarly, with the progress of communication data network architecture, the data center computing task queuing sequence technology is becoming mature, and more migration of tasks in different time periods in the data center will migrate a large amount of load to a low electricity price period, thereby reducing the cost output of electricity prices. However, the cooling power consumption, which accounts for a large proportion of energy consumption, is often ignored. Current researches have fully utilized the thermal energy inertia migration ability at different times according to the thermal inertia law, and realized the transfer of thermal energy at different times.

[0004] At present, there is no effective solution to the above problems. SUMMARY

[0005] The embodiments of the present application provide a power grid collaborative scheduling method, device, storage medium and equipment to at least solve the technical problem of serious waste of power grid energy in the prior art.

[0006] According to an aspect of an embodiment of the present application, a power grid collaborative scheduling method is provided, comprising: obtaining historical operation data of a target power grid, wherein the historical operation data comprises energy consumption generation data, target power grid output data and electrical load data; determining a wind power output scenario and an electrical load scenario based on the historical operation data; determining a basic energy consumption model and an additional energy consumption model based on the historical operation data; constructing a double-layer optimization scheduling model based on the wind power output scenario, the electrical load scenario, the basic energy consumption model and the additional energy consumption model; and completing power grid collaborative scheduling using the double-layer optimization scheduling model.

[0007] Optionally, the determining the wind power output scenario based on the historical operation data comprises: determining a thermal power generation cost function and a wind power generation cost function; determining a thermal power output and a wind power output based on the thermal power generation cost function and the wind power generation cost function; and determining the wind power output scenario based on the thermal power output and the wind power output.

[0008] Optionally, before the determining the wind power output scenario based on the thermal power output and the wind power output, the method further comprises: determining a thermal power generation constraint function and a wind power generation constraint function; and determining a system balance constraint based on the thermal power generation constraint function and the wind power generation constraint function.

[0009] Optionally, the determining the basic energy consumption model and the additional energy consumption model based on the historical operation data comprises: determining an electricity bidding cost and a load migration transmission cost based on the energy consumption generation data; determining the basic energy consumption model based on the electricity bidding cost; and determining the additional energy consumption model based on the load migration transmission cost.

[0010] Optionally, the determining the basic energy consumption model based on the electricity bidding cost comprises: determining an electricity demand constraint; and determining the basic energy consumption model based on the electricity bidding cost and the electricity demand constraint.

[0011] Optionally, before the determining the additional energy consumption model based on the load migration transmission cost, the method further comprises: determining a transmission cost coefficient and a transmission workload based on the energy consumption generation data; and determining the load migration transmission cost based on the transmission cost coefficient and the transmission workload.

[0012] According to another aspect of the embodiments of the present application, there is also provided a power grid coordinated dispatching device, comprising: an acquisition module configured to acquire historical operation data of a target power grid, wherein the historical operation data comprises energy consumption generation data, target power grid output data and electrical load data; a first determination module configured to determine a wind power output scenario and an electrical load scenario based on the historical operation data; a second determination module configured to determine a basic energy consumption model and an additional energy consumption model based on the historical operation data; a construction module configured to construct a double-layer optimization dispatching model based on the wind power output scenario, the electrical load scenario, the basic energy consumption model and the additional energy consumption model; and a dispatching module configured to complete power grid coordinated dispatching by using the double-layer optimization dispatching model.

[0013] According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, which stores a plurality of instructions, and the instructions are adapted to be loaded and executed by a processor to implement any of the power grid coordinated dispatching methods.

[0014] According to another aspect of the embodiments of the present application, a processor is provided for running a program, wherein the program is configured to perform any one of the power grid collaborative scheduling methods when running.

[0015] According to another aspect of the embodiments of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform any one of the power grid collaborative scheduling methods.

[0016] In the embodiments of the present application, the historical operation data of the target power grid is obtained, wherein the historical operation data comprises energy consumption generation data, target power grid output data and electrical load data; the wind power output scene and the electrical load scene are determined based on the historical operation data; the basic energy consumption model and the additional energy consumption model are determined based on the historical operation data; the double-layer optimization scheduling model is constructed based on the wind power output scene, the electrical load scene, the basic energy consumption model and the additional energy consumption model; and the power grid collaborative scheduling is completed by using the double-layer optimization scheduling model, so as to fully mobilize the time-space flexibility of the data center, thereby realizing the technical effect of absorbing the excess new energy of the system and reducing the operating cost of the enterprise, and further solving the technical problem of serious waste of power grid energy in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the present application and are incorporated in and constitute a part of this application, illustrate embodiments of the present application and together with the description serve to explain the present application. In the drawings:

[0018] Figure 1 is a flowchart of a power grid collaborative scheduling method according to an embodiment of the present application;

[0019] Figure 2 is an optional overall flowchart of a power grid collaborative scheduling according to an embodiment of the present application;

[0020] Figure 3 is a structural schematic diagram of a power grid collaborative scheduling device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", and the like in the description and claims of the application and the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] Embodiment 1

[0024] According to an embodiment of the application, a method embodiment of power grid cooperative scheduling is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0025] At present, as the infrastructure of cloud computing technology, data center is closely related to digital and informatization, and is also closely related to the development of traditional industry, and is a key technology for realizing big data processing and storage. Especially in the field related to energy. Data center can migrate load between different geographical location data centers through optical fiber, and can also migrate load in different time periods through cloud computing task sequencing mode, so as to realize real-time regulation and control of its power load, so that it can participate in power system operation as a demand side flexibility resource with great flexibility potential. Based on the ability of load migration, data center can change from a passive acceptor of electricity price to an active participant in market price regulation. Through the high and low of electricity price, the load migration strategy is guided to realize the reduction of enterprise operation cost. And for the power system, the load migration of data center can also be regulated according to the new energy output curve, so as to realize the role of fully absorbing new energy. Therefore, the participation of data center in power grid can further improve the flexibility of demand side operation of power system, and promote the high-quality development of power market and auxiliary service market.

[0026] Figure 1 The flowchart of the power grid cooperative scheduling method according to an embodiment of the application is shown in FIG. 1, which comprises the following steps: Figure 1

[0027] In step S102, the historical operation data of the target power grid is obtained, wherein the historical operation data comprises energy consumption generation data, target power grid output data and electrical load data.​

[0028] In step S104, the wind power output scenario and the electrical load scenario are determined based on the historical operation data.

[0029] In step S106, the basic energy consumption model and the additional energy consumption model are determined based on the historical operation data.

[0030] In step S108, the double-layer optimization scheduling model is constructed based on the wind power output scenario, the electrical load scenario, the basic energy consumption model and the additional energy consumption model.

[0031] In step S110, the grid collaborative scheduling is completed by using the double-layer optimization scheduling model.

[0032] In the embodiment of the present application, the execution subject of the grid collaborative scheduling method provided in steps S102 to S110 is a grid collaborative scheduling system, and the historical operation data of the target grid is obtained by using the system, wherein the historical operation data includes energy consumption generation data, target grid output data and electrical load data; the wind power output scenario and the electrical load scenario are determined based on the historical operation data; the basic energy consumption model and the additional energy consumption model are determined based on the historical operation data; the double-layer optimization scheduling model is constructed based on the wind power output scenario, the electrical load scenario, the basic energy consumption model and the additional energy consumption model; and the grid collaborative scheduling is completed by using the double-layer optimization scheduling model.

[0033] As an optional embodiment, the wind power output scenario is generated based on the wind power prediction result and the correlation coefficient of the output of each wind farm in the alliance, the initial electricity price is generated from the day-ahead market electricity price, and the load scenario is generated from the general electrical load; the data center basic energy consumption model is generated based on the energy consumption of the IT equipment of the data center processing load and the energy consumption of the refrigeration demand, the data center additional energy consumption model is generated based on the start-stop state of the server in the data center and the equipment energy consumption during the load migration process, and the network architecture is considered; according to the wind power output scenario and the initial electricity price scenario, and the data center basic energy consumption model and the additional energy consumption model, the interaction between the data center and the grid is comprehensively considered, and the double-layer optimization scheduling model considering the time and space flexible characteristics of the data center is established, which is composed of an objective function and a constraint condition.

[0034] In an optional embodiment, the wind power output scenario is determined based on the historical operation data, including: determining a thermal power generation cost function and a wind power generation cost function; determining a thermal power output and a wind power output based on the thermal power generation cost function and the wind power generation cost function; and determining the wind power output scenario based on the thermal power output and the wind power output.

[0035] Optionally, since the data center has the ability of load migration in space-time dimension, the data center can be regarded as a flexible resource with space-time flexibility. In the participation of the optimal operation of the power grid, through the reasonable setting of the data center load migration, the excess renewable energy can be accommodated, thereby generating economic benefits. The optimal bidding model considering the space-time flexible characteristics of the data center is obtained, and the double-layer optimization scheduling model of the data center accessing the power grid is as follows: the objective function of the upper model is as follows:

[0036]

[0037] The objective function mainly includes three parts, wherein the first part is the cost of the thermal power plant in the day-ahead market ; the second part is the cost of the wind power plant in the day-ahead market ; , , represents the generation cost coefficient of the thermal power unit, which is generally an empirical constant; represents the thermal power output; represents the wind power cost coefficient, which is generally determined by factors such as mechanical materials of the power plant; represents the wind power output.

[0038] As an optional embodiment, the wind power output scenario is generated based on the wind power prediction result and the correlation coefficient of the output of each wind farm in the alliance, the initial electricity price is generated based on the day-ahead market electricity price, and the load scenario is generated based on the general electrical load. The data center basic energy consumption model is generated based on the energy consumption of the IT equipment and the cooling demand energy consumption of the data center processing load, the data center additional energy consumption model is generated based on the server start-stop state in the data center and the equipment energy consumption in the load migration process, and the network architecture is considered. According to the obtained wind power output scenario and initial electricity price scenario, the obtained data center basic energy consumption model and additional energy consumption model, and the interaction between the data center and the power grid, a double-layer optimization scheduling model considering the space-time flexible characteristics of the data center is established, which consists of an objective function and constraint conditions. The objective function of the joint generation cost of the power system side controlled by the independent operator is constructed, and the expression is as follows:

[0039]

[0040] In the expression, is the thermal power generation cost, and the specific expression is as follows:

[0041]

[0042] Among them, , , represents the power generation cost coefficient of the thermal power unit, which is generally an empirical constant; represents the thermal power output.

[0043] Optionally, is the wind power generation cost, and the specific expression is as follows:

[0044]

[0045] wherein, represents the wind power cost coefficient, which is generally determined by mechanical materials and other factors of the power plant; represents the wind power output.

[0046] As an optional embodiment, before determining the wind power output scenario based on the thermal power output and the wind power output, the method further comprises: determining a thermal power generation constraint function and a wind power generation constraint function; and determining a system balance constraint based on the thermal power generation constraint function and the wind power generation constraint function.

[0047] Optionally, the constraint conditions for constructing the upper layer clearing model are as follows:

[0048] Thermal power unit constraint:

[0049]

[0050]

[0051] Optionally, refers to the minimum output of thermal power; refers to the start-up state of the thermal power unit; refers to the thermal power output at different stages; refers to the maximum output value of the thermal power unit; , refers to the minimum start-up time and shutdown time; refers to the unit start-up state variable; refers to the unit shutdown state variable; represents the initial running state of the unit; , refers to the unit up-regulation and down-regulation spinning reserve, which needs to satisfy the maximum and minimum output range of the unit and double constraints of the ramping range within the time; , refers to the start-up and shutdown time at the initial moment.

[0052] Optionally, the wind power unit constraint is:

[0053]

[0054] Optional, represents the mechanical power of wind power output; , , , respectively represent the empirical coefficient of wind power mechanical power, horizontal sweep area, and wind blade rotation speed; represents the wind turbine abandoned wind power; represents the coefficient of mechanical power and electrical power.

[0055] Optional, system balance constraints:

[0056]

[0057] Optional, represents the generator set located at node , represents the load set located at node , represents the line set ending at node , represents the line set starting at node ; represents the line transmission power, represents the load demand other than the data center.

[0058] As an optional embodiment, the above determining the basic energy consumption model and the additional energy consumption model based on the historical operation data comprises: determining the electricity bidding cost and the load migration transmission cost based on the energy consumption generation data; determining the basic energy consumption model based on the electricity bidding cost, and determining the additional energy consumption model based on the load migration transmission cost.

[0059] As an optional embodiment, the above determining the basic energy consumption model based on the electricity bidding cost comprises: determining the electricity demand constraint; determining the basic energy consumption model based on the electricity bidding cost and the electricity demand constraint.

[0060] Optionally, the target function of the data center operation cost controlled by the Internet company is constructed, and the expression is as follows:

[0061]

[0062] wherein, represents the data center electricity bidding cost, and the specific expression is as follows:

[0063]

[0064] wherein, is the bidding electricity of the aggregator in the day-ahead market, i.e., the data center is the electricity consumption of the data center at time period t after demand response. is the electricity consumption of the data center at time period t before demand response. is the node marginal price of the node where the data center is located.

[0065] Optionally, is the transmission cost of data load migration, and the specific expression is as follows:

[0066]

[0067] wherein, is a transmission cost coefficient, and respectively represent the data center working load before demand response at time period t.

[0068] As an optional embodiment, before determining the above additional energy consumption model based on the above load migration transmission cost, the method further comprises: determining a transmission cost coefficient and a transmission working load based on the energy consumption generation data; and determining the load migration transmission cost based on the transmission cost coefficient and the transmission working load.

[0069] Optionally, the constraint conditions for constructing the lower-layer data center electricity bidding model are as follows:

[0070] The data center electricity demand constraint is:

[0071]

[0072]

[0073] wherein, refers to the total energy consumption of the data center; refers to the energy consumption demand after the geographical balance means; refers to the energy consumption demand after the batch load processing means; refers to the energy consumption demand after the thermal inertia means; refers to the energy consumption of the redundant system; refers to the heat storage energy level. 、 、 、 、 、 、 、 、 、 、 、 、 、 , , , are the non-electricity parameters of the packaged data center, respectively.

[0074] Optionally, the non-electricity parameters of the packaged data center are set as follows:

[0075]

[0076]

[0077] wherein, represents the increase in the total power consumption of the data center caused by the increase in the power consumption of the IT equipment per unit of work load; represents the increase in the total power consumption of the data center caused by the increase in the power consumption of the IT equipment per unit of work load; represents that the energy storage level in two consecutive time periods is a cumulative superposition state; represents the increase in the energy storage level in time period t caused by the reduction in the power adjustment demand of the subcooling refrigeration mode; represents the increase in the total power consumption of the data center caused by the maximum delay-sensitive load processed by the server, wherein the second term represents the maximum amount of delay-sensitive load that can be processed by a server; represents the increase in the total power consumption of the data center caused by the maximum delay-tolerant load processed by the server, wherein the second term represents the increase in the power consumption of the IT equipment caused by the maximum delay-tolerant load; represents the increase in the power consumption of the refrigeration system required to increase the total power consumption of the data center by one unit; represents the increase in the total power consumption of the refrigeration system caused by the increase in the power consumption of the refrigeration equipment, which is an empirical constant of the refrigeration system. represents the baseline power consumption of the data center before participating in demand response; represents the difference between the energy storage levels in time period t and time period t-1, and the temperature in the last time period is at the indoor baseline temperature without any heat change; represents the initial energy storage level; represents the increase in the power consumption of all delay-sensitive loads after the processing load is allocated according to the computing capacity of each data center; represents the increase in the power consumption of all delay-tolerant loads after the processing load is allocated to each time period. represents the number of servers that do not participate in processing load; represents the margin of the IT equipment power, i.e., the remaining IT equipment power after meeting the processing requirements of the baseline load; formula represents the deficiency of the refrigeration power; represents the margin of the refrigeration power.

[0078] In this embodiment of the invention, the process of establishing a data center model is divided into establishing a basic model of the data center's processing load and a relational expression representing the coupling of different load migration methods. First, the processing load process of a single data center is selected for analysis, and its charging process is divided into two stages: basic energy consumption and redundant energy consumption. Then, the perspective is expanded to the interaction of multiple data centers, considering both load migration between different data centers and load migration and the thermal inertia law within a single data center, and the coupling relationship between them is analyzed.

[0079] Optional, such as Figure 2 The diagram shown illustrates the overall process of grid collaborative dispatch. In constructing the optimal bidding model for data centers, it considers both the market clearing model controlled by independent operators and the data center electricity bidding model controlled by internet companies. Through shadow price theory, it solves the marginal electricity price of the node where the data center is located, guiding the data center in the lower-level model to adjust its load migration strategy. At the same time, based on the adjusted load migration strategy, the lower-level model outputs the electricity required by the data center to the upper-level model, changing the marginal electricity price of the node output by the upper-level model. This process is repeated until both the upper and lower-level models reach their optimal state.

[0080] Through embodiments of this invention, based on the massive energy consumption and spatiotemporal flexibility of data centers, a detailed model describing data center energy consumption is established. A two-layer optimal scheduling model involving new energy sources, traditional thermal power, and data centers is proposed. The operating mechanism of data centers is analyzed, and optimal coordinated scheduling between data centers and the power grid is achieved. This can significantly reduce market operating costs, increase economic and environmental benefits, and enhance the absorption capacity of renewable energy.

[0081] Example 2

[0082] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described power grid coordinated dispatch method is also provided. Figure 3 This is a schematic diagram of the structure of a power grid coordinated dispatching device according to an embodiment of the present invention, as shown below. Figure 3 As shown, the above-mentioned device includes: an acquisition module 30, a first determination module 32, a second determination module 34, a construction module 36, and a scheduling module 38, wherein:

[0083] The acquisition module 30 is used to acquire historical operating data of the target power grid, wherein the historical operating data includes: energy generation data, power output data of the target power grid, and electrical load data;

[0084] The first determining module 32 is used to determine the wind power output scenario and the electrical load scenario based on the above-mentioned historical operating data;

[0085] The second determining module 34 is configured to determine a basic energy consumption model and an additional energy consumption model based on the historical operation data.

[0086] The constructing module 36 is configured to construct a double-layer optimization scheduling model based on the wind power output scenario, the electrical load scenario, the basic energy consumption model and the additional energy consumption model.

[0087] The scheduling module 38 is configured to complete the grid collaborative scheduling by using the double-layer optimization scheduling model.

[0088] It should be noted that the obtaining module 30, the first determining module 32, the second determining module 34, the constructing module 36 and the scheduling module 38 correspond to steps S102 to S108 in Embodiment 1, and the five modules have the same instances and application scenarios as the corresponding steps, but are not limited to the contents disclosed in Embodiment 1.

[0089] It should be noted that the preferred embodiments of the present embodiment can refer to the related description in Embodiment 1, which will not be repeated here.

[0090] According to the embodiments of the present application, an embodiment of a computer readable storage medium is also provided. Optionally, in the present embodiment, the computer readable storage medium can be used to save the program code executed by the grid collaborative scheduling method provided in Embodiment 1.

[0091] Optionally, in the present embodiment, the computer readable storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0092] Optionally, in the present embodiment, the computer readable storage medium is configured to store program code for performing the following steps: obtaining historical operation data of a target grid, wherein the historical operation data includes energy consumption generation data, target grid output data and electrical load data; determining a wind power output scenario and an electrical load scenario based on the historical operation data; determining a basic energy consumption model and an additional energy consumption model based on the historical operation data; constructing a double-layer optimization scheduling model based on the wind power output scenario, the electrical load scenario, the basic energy consumption model and the additional energy consumption model; and completing the grid collaborative scheduling by using the double-layer optimization scheduling model.

[0093] Optionally, the computer readable storage medium is configured to store program code for performing the following steps: determining a thermal power generation cost function and a wind power generation cost function; determining a thermal power output and a wind power output based on the thermal power generation cost function and the wind power generation cost function; and determining the wind power output scenario based on the thermal power output and the wind power output.

[0094] Optionally, the computer readable storage medium is configured to store program code for performing the following steps: determining a thermal power generation constraint function and a wind power generation constraint function; and determining a system balance constraint based on the thermal power generation constraint function and the wind power generation constraint function.

[0095] Optionally, the computer readable storage medium is configured to store program code for performing the following steps: determining an electricity bidding cost and a load migration transmission cost based on the energy generation data; determining the basic energy consumption model based on the electricity bidding cost, and determining the additional energy consumption model based on the load migration transmission cost.

[0096] Optionally, the computer readable storage medium is configured to store program code for performing the following steps: determining a power demand constraint; and determining the basic energy consumption model based on the electricity bidding cost and the power demand constraint.

[0097] Optionally, the computer readable storage medium is configured to store program code for performing the following steps: determining a transmission cost coefficient and a transmission workload based on the energy generation data; and determining the load migration transmission cost based on the transmission cost coefficient and the transmission workload.

[0098] According to an embodiment of the present application, a processor is also provided. Optionally, in the present embodiment, the computer readable storage medium can be used to save the program code executed by the power grid collaborative scheduling method provided in the above embodiment 1.

[0099] The present application provides an electronic device, which comprises a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining historical operation data of a target power grid, wherein the historical operation data comprises energy generation data, target power grid output data, and electrical load data; determining a wind power output scenario and an electrical load scenario based on the historical operation data; determining a basic energy consumption model and an additional energy consumption model based on the historical operation data; constructing a double-layer optimization scheduling model based on the wind power output scenario, the electrical load scenario, the basic energy consumption model, and the additional energy consumption model; and performing power grid collaborative scheduling by using the double-layer optimization scheduling model.

[0100] The application further provides a computer program product, which is suitable for executing a program for initializing the following method steps when executed on a data processing device: obtaining historical operation data of a target power grid, wherein the historical operation data comprises energy consumption generation data, target power grid output data and electrical load data; determining a wind power output scenario and an electrical load scenario based on the historical operation data; determining a basic energy consumption model and an additional energy consumption model based on the historical operation data; constructing a double-layer optimization scheduling model based on the wind power output scenario, the electrical load scenario, the basic energy consumption model and the additional energy consumption model; and performing power grid collaborative scheduling by using the double-layer optimization scheduling model.

[0101] The above-mentioned serial numbers of the embodiments of the application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0102] In the above-mentioned embodiments of the application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0103] In several embodiments provided in the present application, it should be understood that the disclosed technical contents can be implemented by other manners. Among them, the above-mentioned device embodiments are only schematic, for example, the division of the units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0104] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0105] In addition, each functional unit in each embodiment of the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0106] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0107] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A power grid coordinated dispatch method, characterized in that, include: Acquire historical operating data of the target power grid, wherein the historical operating data includes: energy generation data, target power grid output data, and electrical load data; Based on the historical operating data, wind power output scenarios and electrical load scenarios are determined. The basic energy consumption model and the additional energy consumption model are determined based on the historical operating data. Based on the wind power output scenario, the electrical load scenario, the basic energy consumption model, and the additional energy consumption model, a two-layer optimization scheduling model is constructed. The two-layer optimized scheduling model is used to complete the grid coordinated scheduling; The step of determining the basic energy consumption model and the additional energy consumption model based on the historical operating data includes: determining the electricity bidding cost and the load migration and transmission cost based on the energy consumption generation data; determining the basic energy consumption model based on the electricity bidding cost; and determining the additional energy consumption model based on the load migration and transmission cost, wherein the electricity bidding cost is calculated based on the following expression: ， in, This represents the bidding cost for the electricity volume. For data centers Electricity consumption during time period t after demand response; It is a data center The marginal electricity price at the node where it is located; The load migration transmission cost is calculated based on the following formula: ， in, It is the transmission cost coefficient. and They represent data centers For the workload before and after the demand response in time period t, both I and T are constants.

2. The method according to claim 1, characterized in that, The determination of wind power output scenarios based on the historical operating data includes: Determine the cost functions for thermal power generation and wind power generation; The output power of thermal power and the output power of wind power are determined based on the aforementioned cost functions for thermal power generation and wind power generation. Based on the thermal power output power and the wind power output power, the wind power output scenario is determined.

3. The method according to claim 2, characterized in that, Before determining the wind power output scenario based on the thermal power output power and the wind power output power, the method further includes: Determine the constraint functions for thermal power generation and wind power generation; The system balance constraints are determined based on the thermal power generation constraint function and the wind power generation constraint function.

4. The method according to claim 1, characterized in that, The determination of the basic energy consumption model based on the electricity bidding cost includes: Determine electricity demand constraints; Based on the electricity bidding cost and the electricity demand constraint, the basic energy consumption model is determined.

5. The method according to claim 1, characterized in that, Before determining the additional energy consumption model based on the load migration transmission cost, the method further includes: Based on the energy consumption data, the transmission cost coefficient and transmission workload are determined. The load migration transmission cost is determined based on the transmission cost coefficient and the transmission workload.

6. A power grid collaborative dispatching device, characterized in that, include: The acquisition module is used to acquire historical operating data of the target power grid, wherein the historical operating data includes: energy generation data, power output data of the target power grid, and electrical load data; The first determining module is used to determine the wind power output scenario and the electrical load scenario based on the historical operating data. The second determining module is used to determine the basic energy consumption model and the additional energy consumption model based on the historical operating data. The construction module is used to construct a two-layer optimized scheduling model based on the wind power output scenario, the electrical load scenario, the basic energy consumption model, and the additional energy consumption model; The scheduling module is used to complete the power grid coordinated scheduling using the two-layer optimized scheduling model. The step of determining the basic energy consumption model and the additional energy consumption model based on the historical operating data includes: determining the electricity bidding cost and the load migration and transmission cost based on the energy consumption generation data; determining the basic energy consumption model based on the electricity bidding cost; and determining the additional energy consumption model based on the load migration and transmission cost, wherein the electricity bidding cost is calculated based on the following expression: ， in, This represents the bidding cost for the electricity volume. For data centers Electricity consumption during time period t after demand response; It is a data center The marginal electricity price at the node where it is located; The load migration transmission cost is calculated based on the following formula: ， in, It is the transmission cost coefficient. and They represent data centers For the workload before and after the demand response in time period t, both I and T are constants.

7. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the power grid coordinated scheduling method according to any one of claims 1 to 5.

8. A processor, characterized in that, The processor is used to run a program, wherein the program is configured to execute the power grid coordinated scheduling method according to any one of claims 1 to 5 when running.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the power grid coordinated dispatch method according to any one of claims 1 to 5.

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