Multi-type source storage configuration method, apparatus, computer equipment, and storage medium

Abstract

This application relates to the technical field of energy storage and discloses a multi-type source storage configuration method, device, computer device, and storage medium. This application uses a source-grid-load-storage configuration dataset and a load forecast dataset of a regional grid system to perform demand analysis on the regional grid system and obtain corresponding stability indicators. Optionally, corresponding economic indicators and carbon trading indicators are determined respectively through a preset full life cycle model, thereby realizing a multi-type source storage configuration for the regional grid system, which essentially solves the regional grid stability problem caused by the increase in the construction rate of new energy, and also solves the problems of insufficient economics and low call rate of the source storage configuration by taking into account the economic indicators and carbon trading indicators.

Description

[Technical Field]

[0001] The present application relates to the technical field of energy storage, and specifically to a multi-type source-storage configuration method, an apparatus, a computer device and a storage medium. [Background Art]

[0002] The rapid popularization of renewable energy not only meets the requirements for low-carbon development, but also can improve regional energy security and self-sufficiency. However, the two core goals of a power system are to ensure a sustainable and reliable power supply and to ensure good electrical energy quality.

[0003] New energy power generation in the current technology mainly refers to wind power generation and photovoltaic power generation. These are essentially different from synchronous generators and can be collectively referred to as asynchronous power sources. They are generally composed of power electronic converters, and their characteristics are determined by the control characteristics of the converters. At present, converters widely used in practical engineering adopt a grid-following control strategy, that is, synchronization between the converter and the grid is realized through a phase-locked loop, and vector current control is adopted to control the output current of the converter, thereby controlling the active / reactive power supplied to the grid. It is essentially a controlled current source, and its main control objective is to track the current maximum power of solar energy and wind energy, convert solar energy and wind energy into electrical energy with maximum efficiency and supply it to the power system. Since this control strategy takes current as the control target, it cannot supply energy according to demand to balance supply and demand (to stabilize frequency) and stabilize grid voltage.

[0004] As renewable energy sources become more readily available and occupy a dominant position in the power supply structure, if the proportion of renewable energy generators increases to a certain extent (in extreme cases, if renewable energy accounts for 100%), the synchronous generators in the system will no longer be able to control and balance power according to demand, stabilizing the overall grid voltage to a reasonable level. As a result, one system will no longer be able to operate stably and normally.

[0005] The existence of the above characteristics of new energy sources, while potentially selectable, is related not only to the problems of their own development laws but also to the relative decrease in the voltage and frequency regulation control capabilities of the power system after replacing conventional power sources with new energy. Solutions also need to be considered from two perspectives: 1. Repeatedly upgrading the characteristics of new energy generation to provide good characteristics that support the grid of conventional synchronous generators such as water and fire, and enabling operation in conjunction with synchronous generators. This direction will affect the amount of new energy generated, reduce economic viability, and is difficult to achieve in the short term. 2. Adding new equipment with voltage control and energy regulation capabilities similar to or equivalent to synchronous generators near new energy sources will not only allow for the consumption of new energy but also ensure that the adverse effects of new energy on the power system are eliminated and system stability is provided.

[0006] However, the current requirements for energy storage in new energy sources are proposed from the perspective of resolving fluctuations in new energy sources. A simple estimation based on a certain proportion of the scale of new energy construction reveals that there will be redundancy in energy storage or resource waste due to unconditional grid connection after the construction of new energy sources. [Overview of the project] [Problems that the invention aims to solve]

[0007] In view of the above circumstances, the present invention provides a multi-type source storage configuration method, apparatus, computer equipment, and storage medium that solve the problem of conventional energy storage configurations preventing grid systems from operating stably and normally, resulting in redundancy in energy storage or resource waste due to unconditional grid connection after the construction of new energy sources. [Means for solving the problem]

[0008] In the first aspect, the present application relates to a multi-type source storage configuration method applicable to a regional grid system, The present invention provides a method for configuring multitype source storage, comprising the steps of: obtaining a source-grid-load-storage configuration dataset, a load forecast dataset, and a multitype energy storage characteristics dataset for a regional grid system; analyzing the regional grid system according to a predetermined demand based on the source-grid-load-storage configuration dataset, the load forecast dataset, and the multitype energy storage characteristics dataset to obtain a stability index; determining an economic index and a carbon trading index using a predetermined full-lifecycle model; and performing a multitype source storage configuration for the regional grid system using the source-grid-load-storage configuration dataset and the load forecast dataset, based on the stability index, economic index, and carbon trading index, to obtain a multitype source storage configuration result.

[0009] The multi-type source storage configuration method according to this application allows for the acquisition of corresponding stability indicators by performing a demand analysis on a regional grid system using a source-grid-load-storage configuration dataset and a load forecast dataset of the regional grid system. By selectively determining corresponding economic indicators and carbon trading indicators using a pre-configured full-lifecycle model, it is possible to realize a multi-type source storage configuration for the regional grid system, thereby essentially solving the stability problem of the regional grid due to the increase in the proportion of new energy construction, and by taking economic indicators and carbon trading indicators into consideration, it solves the problems of insufficient economics and low call rates in source storage configurations.

[0010] In one selectable embodiment, the step of analyzing a regional grid system according to a preset demand and obtaining stability indicators based on a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristics dataset is: The process includes the steps of determining a regional grid stability power generation model and target resource quantity based on a source-grid-load-storage configuration dataset and a load prediction dataset; determining stability index parameter values ​​based on a multi-type energy storage characteristics dataset; determining target resource quantity based on the regional grid stability power generation model and stability index parameter values; and determining stability indicators based on the regional grid stability power generation model and target resource quantity.

[0011] This invention enables the determination of corresponding stability indicators using source-grid-load-storage configuration datasets and load prediction datasets of a regional grid system, and supports subsequent multi-type source-storage configurations of regional grid systems.

[0012] In one selectable embodiment, the step of determining economic indicators and carbon trading indicators using a pre-configured full-lifecycle model is: The method includes the steps of determining a set of economic indicator parameters based on a pre-configured full-lifecycle model, determining economic indicators and carbon trading indicators based on the set of economic indicator parameters, and determining stability indicators based on a multi-type energy storage characteristics dataset and stability indicator parameters.

[0013] In one selectable embodiment, the step of determining economic indicators and carbon trading indicators based on an economic indicator parameter set is: The method includes the steps of obtaining a first economic indicator parameter subset and a second economic indicator parameter subset corresponding to an economic indicator parameter set; determining an economic indicator based on the first economic indicator parameter subset; and determining a carbon trading indicator based on the second economic indicator parameter subset.

[0014] In one selectable embodiment, the step of determining a multi-type source storage configuration result based on a source-grid-load-storage configuration dataset and a load forecast dataset, after processing stability indicators, economic indicators and carbon trading indicators, is: The process includes the steps of determining a first set of energy storage resources based on a source-grid-load-storage configuration dataset, a load forecast dataset, and stability indicators; determining a set of constraints based on stability indicators, economic indicators, and carbon trading indicators; and performing a multi-type source storage configuration for a regional grid system based on the first set of energy storage resources and the set of constraints, and obtaining the multi-type source storage configuration result.

[0015] This invention addresses the problems of insufficient economics and low call rates in source storage configurations by taking into account economic indicators and carbon trading indicators.

[0016] In one selectable embodiment, the step of determining a first set of energy storage resources based on a source-grid-load-storage configuration dataset, a load prediction dataset, and stability indicators is: The process includes the steps of determining a range of energy storage resource types corresponding to a regional grid system based on a source-grid-load-storage configuration dataset and a load forecast dataset, and determining a first set of energy storage resources based on the energy storage resource type range and stability indicators.

[0017] In one selectable embodiment, the step of determining a set of constraints based on a stability index, an economic index, and a carbon trading index is: The method includes the steps of determining a first constraint based on stability indicators, determining a second constraint based on economic indicators and carbon trading indicators, and determining a set of constraints based on the first and second constraints.

[0018] In a second aspect, the present invention relates to a multi-type source storage configuration device applicable to a regional grid system, The present invention provides a multi-type source storage configuration device, comprising: an acquisition module for acquiring a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristics dataset of a regional grid system; an analysis module for analyzing the regional grid system according to a pre-set demand based on the source-grid-load-storage configuration dataset, the load forecast dataset, and the multi-type energy storage characteristics dataset, and obtaining stability indicators; a determination module for determining economic indicators and carbon trading indicators using a pre-set full-lifecycle model; and a configuration module for performing a multi-type source storage configuration on the regional grid system using the source-grid-load-storage configuration dataset and the load forecast dataset, based on stability indicators, economic indicators, and carbon trading indicators, and obtaining multi-type source storage configuration results.

[0019] In a third aspect, the present invention provides a computer device that includes a memory and a processor that are connected to each other in communication, wherein the memory stores computer instructions, and the processor executes the computer instructions to perform the multi-type source storage configuration method of the first aspect or any of the corresponding embodiments.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium that stores computer instructions for causing a computer to execute a multi-type source storage configuration method according to either the first aspect or a corresponding embodiment.

[0021] To more clearly describe specific embodiments of the present application or technical solutions of the prior art, the drawings necessary for describing specific embodiments or the prior art will be briefly described below. As will be clear, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings based on these without any creative work. [Brief explanation of the drawing]

[0022] [Figure 1] It is a flowchart of a multi-type source-storage configuration method according to an embodiment of the present application. [Figure 2] It is a flowchart of another multi-type source-storage configuration method according to an embodiment of the present application. [Figure 3] It is a flowchart of still another multi-type source-storage configuration method according to an embodiment of the present application. [Figure 4] It is a structural block diagram of a multi-type source-storage configuration apparatus according to an embodiment of the present application. [Figure 5] It is a schematic diagram of a hardware structure of a computer device in an embodiment of the present application. MODE FOR CARRYING OUT THE INVENTION

[0023] In order to more clearly clarify the objects, technical solutions and advantages of the embodiments of the present application, the following will clearly and completely describe the embodiments of the present application with reference to the drawings of the embodiments of the present application and the technical solutions of the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by those skilled in the art without creative efforts based on the embodiments of the present application belong to the protection scope of the present application.

[0024] Embodiments of the present application provide a multi-type source-storage configuration method, which essentially solves the stability problem of regional power grids caused by the increase in the construction proportion of new energy, and by taking economic indicators and carbon trading indicators into consideration, solves the problems of insufficient economic efficiency and low utilization rate of source-storage configuration.

[0025] According to an embodiment of the present application, an embodiment of a multi-type source-storage configuration method is provided. Note that the steps shown in the flowchart in the drawings may be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from the order herein.

[0026] This embodiment provides a multi-type source storage configuration method that can be applied to a regional grid system. Figure 1 is a flowchart of the multi-type source storage configuration method according to an embodiment of the present invention, and as shown in Figure 1, the process includes the following steps.

[0027] Step S101: Obtain the source-grid-load-storage configuration dataset, load forecast dataset, and multi-type energy storage characteristics dataset for the regional grid system.

[0028] Specifically, the source-grid-load-storage configuration dataset may include configuration data for different types of energy storage resources in a regional grid system.

[0029] Optionally, the load prediction dataset may include multiple future load prediction data for the regional grid system.

[0030] Selectively, the multi-type energy storage characteristics dataset can be used to reflect the characteristics of different types of new energy and energy storage technologies in a regional grid system.

[0031] Step S102: Based on the source-grid-load-storage configuration dataset, load forecast dataset, and multi-type energy storage characteristics dataset, the regional grid system is analyzed according to a predetermined demand to obtain stability indicators.

[0032] Specifically, by combining a source-grid-load-storage configuration dataset, a load forecast dataset, and the characteristics of different types of new energy and energy storage technologies, a demand analysis of the regional grid system can be performed to obtain a stability index for the regional grid system.

[0033] Step S103: Determine economic indicators and carbon trading indicators using a pre-configured full-lifecycle model.

[0034] The pre-configured full lifecycle model is used to reflect the relevant states throughout the full lifecycle of the regional grid system.

[0035] Specifically, by processing using a pre-defined full-lifecycle model, corresponding stability indicators, economic indicators, and carbon trading indicators can be determined.

[0036] Step S104: Based on stability indicators, economic indicators, and carbon trading indicators, a multi-type source storage configuration is performed for the regional grid system using the source-grid-load-storage configuration dataset and the load prediction dataset, and the multi-type source storage configuration results are obtained.

[0037] Specifically, by using stability indicators, economic indicators, and carbon trading indicators as constraints, and combining source-grid-load-storage configuration datasets and load prediction datasets of regional grid systems, an optimal multi-type source storage configuration can be obtained for the regional grid system.

[0038] The multi-type source storage configuration method according to this embodiment allows for the acquisition of corresponding stability indicator parameters by performing a demand analysis on the regional grid system using the source-grid-load-storage configuration dataset and load forecast dataset of the regional grid system. Selectively, the corresponding stability indicator, economic indicator, and carbon trading indicator can be determined using the multi-type energy storage characteristics dataset of the regional grid system, thereby realizing a multi-type source storage configuration for the regional grid system. This essentially solves the stability problem of the regional grid due to the increased proportion of new energy construction, and by taking economic indicators and carbon trading indicators into consideration, it solves the problems of insufficient economics and low call rates in source storage configurations.

[0039] This embodiment provides a multi-type source storage configuration method that can be applied to a regional grid system. Figure 2 is a flowchart of the multi-type source storage configuration method according to this embodiment, and as shown in Figure 2, the process includes the following steps.

[0040] Step S201: Obtain the source-grid-load-storage configuration dataset, load prediction dataset, and multi-type energy storage characteristics dataset for the regional grid system. For details, refer to step S101 of the embodiment shown in Figure 1, and a redundant explanation will be omitted here.

[0041] Step S202: Based on the source-grid-load-storage configuration dataset, load forecast dataset, and multi-type energy storage characteristics dataset, the regional grid system is analyzed according to a predetermined demand to obtain stability indicators.

[0042] Specifically, step S202 above includes steps S2021 to S2024.

[0043] Step S2021: Determine a regional grid stability power generation model based on the source-grid-load-storage configuration dataset and the load prediction dataset.

[0044] Specifically, by combining the source-grid-load-storage configuration dataset and load prediction dataset of the regional grid system, a regional grid stability power generation model P can be established.

[0045] The regional grid stability power generation model P is used to represent the minimum and maximum power generation capacity of the regional grid system, and is shown in the following relation (1).

[0046] P = P1 + P2 + P3 + ... + Pn (1)

[0047] Step S2022: Determine the stability index parameter values ​​based on the multi-type energy storage characteristics dataset.

[0048] The stability index parameter value R is used to indicate whether a synchronous generator or a synchronous generator has the capability for frequency regulation and voltage control. If such capability is present, the stability index parameter value R is 1; if it is not present, the stability index parameter value R is 0.

[0049] Specifically, the corresponding stability index parameter value R can be determined according to the characteristics of different types of new energy and energy storage technologies.

[0050] Step S2023: Determine the target resource amount based on the regional grid stability power generation model and stability index parameter values.

[0051] The target resource quantity S is used to indicate that the system possesses the capability for frequency regulation and voltage control of a synchronous generator or a system similar to a synchronous generator.

[0052] Specifically, the corresponding target resource amount S can be obtained according to the obtained regional grid stability power generation model P and stability index parameter value R, as shown in the following relation (2).

[0053] S=P*R1+P*R2+P*R3+……+P*Rn (2)

[0054] Step S2024: Determine stability indicators based on the regional grid stability power generation model and target resource levels.

[0055] Specifically, the corresponding stability index F(P,S) can be determined according to the regional grid stability power generation model P and the target resource amount S, as shown in the following relationship equation (3).

[0056] F(P,S)=F(P1+P2+P3+……+Pn,P*R1+P*R2+P*R3+……+P*Rn) (3)

[0057] Step S203: Determine economic indicators and carbon trading indicators using a pre-configured full lifecycle model.

[0058] Specifically, step S203 above includes steps S2031 to S2032.

[0059] Step S2031: Determine the set of economic indicator parameters based on a pre-configured full lifecycle model.

[0060] Specifically, the economic indicator parameter set is used to reflect costs, electricity, and other factors corresponding to the regional grid system.

[0061] Step S2032: Determine economic indicators and carbon trading indicators based on the economic indicator parameter set.

[0062] Economic indicators may include cost per unit of electricity and cost per mile.

[0063] Specifically, the corresponding economic indicators and carbon trading indicators can be determined according to the obtained set of economic indicator parameters.

[0064] In some selectable embodiments, step S2032 includes steps a1 to a3.

[0065] Step a1: Obtain the first and second subsets of economic indicator parameters corresponding to the economic indicator parameter set.

[0066] Step a2: Determine the economic indicators based on the first economic indicator parameter subset.

[0067] Step a3: Determine the carbon trading index based on the second economic indicator parameter subset.

[0068] Specifically, the first economic indicator parameter subset may include the fixed cost per unit of electricity over the full lifecycle corresponding to the regional grid system, as well as the peak shift marginal cost.

[0069] Optionally, the second set of economic indicator parameters may include power generation and carbon emission factors corresponding to the regional grid system.

[0070] The economic indicators are determined using the following relationships (4) and (5) for selection purposes.

[0071] Cost per unit of electricity = Fixed cost per unit of electricity over the full lifecycle + Peak shift marginal cost (4) Cost per mile = Fixed cost per unit of electricity over the full lifecycle + Frequency regulation marginal cost (5)

[0072] The carbon trading index is determined using the following relational equation (6) for selection.

[0073] Carbon Trading Index = Total Energy Storage Power / Power Generation Power × Carbon Emission Factor (6)

[0074] Step S204: Based on stability indicators, economic indicators, and carbon trading indicators, a multi-type source storage configuration is performed for the regional grid system using the source-grid-load-storage configuration dataset and the load prediction dataset, and the multi-type source storage configuration result is obtained. For details, please refer to step S104 of the embodiment shown in Figure 1, and a redundant explanation will be omitted here.

[0075] The multi-type source storage configuration method according to this embodiment allows for the acquisition of corresponding stability indicators by performing a demand analysis on the regional grid system using the source-grid-load-storage configuration dataset and load forecast dataset of the regional grid system. By selectively determining corresponding economic indicators and carbon trading indicators using a pre-configured full lifecycle model, it is possible to realize a multi-type source storage configuration for the regional grid system, essentially solving the stability problem of the regional grid due to the increase in the proportion of new energy construction, and by taking economic indicators and carbon trading indicators into consideration, it solves the problems of insufficient economics and low call rates in source storage configurations.

[0076] This embodiment provides a multi-type source storage configuration method that can be applied to a regional grid system. Figure 3 is a flowchart of the multi-type source storage configuration method according to this embodiment, and as shown in Figure 3, the process includes the following steps.

[0077] Step S301: Obtain the source-grid-load-storage configuration dataset, load prediction dataset, and multi-type energy storage characteristics dataset for the regional grid system. For details, refer to step S101 of the embodiment shown in Figure 1, and a redundant explanation will be omitted here.

[0078] Step S302: Based on the source-grid-load-storage configuration dataset, load forecast dataset, and multi-type energy storage characteristics dataset, the regional grid system is analyzed according to a predetermined demand to obtain stability indicators. For details, refer to step S202 of the embodiment shown in Figure 2, and a redundant explanation is omitted here.

[0079] Step S303: Determining economic indicators and carbon trading indicators using a pre-configured full-lifecycle model. For details, refer to step S203 of the embodiment shown in Figure 2, and a redundant explanation will be omitted here.

[0080] Step S304: Based on stability indicators, economic indicators, and carbon trading indicators, a multi-type source storage configuration is performed for the regional grid system using the source-grid-load-storage configuration dataset and the load prediction dataset, and the multi-type source storage configuration results are obtained.

[0081] Specifically, step S304 above includes steps S3041 to S3043.

[0082] Step S3041: Determine the first set of energy storage resources based on the source-grid-load-storage configuration dataset, load prediction dataset, and stability indicators.

[0083] Specifically, by combining the obtained source-grid-load-storage configuration dataset, load prediction dataset, and stability indicators, multiple energy storage resources corresponding to a regional grid system can be determined.

[0084] In some selectable embodiments, step S3041 includes steps b1 to b2.

[0085] Step b1: Based on the source-grid-load-storage configuration dataset and load forecast dataset, determine the range of energy storage resource types corresponding to the regional grid system.

[0086] Step b2: Determine the first set of energy storage resources based on the range of energy storage resource types and stability indicators.

[0087] Specifically, by combining the acquired source-grid-load-storage configuration dataset and load prediction dataset of the regional grid system, the feasible range of new energy and energy storage types corresponding to that regional grid system can be determined.

[0088] The obtained range of energy storage resource types and stability indicators can be selected and combined to determine the corresponding first set of energy storage resources.

[0089] (1) Energy storage systems equipped with the capability of a synchronous generator or a similar synchronous generator for frequency regulation and voltage control are as follows: (11) Mechanical energy storage (e.g., pumped water energy storage, compressed air energy storage, lava heat storage, hydrogen energy storage) (11) Electrochemical energy storage (lithium batteries, sodium-ion batteries, redox flow batteries, etc.) + grid forming type PCS (13) Electrical energy storage (flywheel energy storage, supercapacitors, superconducting energy storage, etc.)

[0090] (2) The following are new energy sources that have the capabilities of a synchronous generator or a synchronous generator with frequency control AGC and voltage control AVC: (21) Geothermal power generation system (22) Biomass power generation system (23) Solar thermal power generation system (24) Nuclear power generation system (25) Ocean energy generation systems (26) Solar power generation + grid forming inverter system (27) Wind power generation + grid forming inverter system

[0091] Step S3042: Determine the set of constraints based on stability indicators, economic indicators, and carbon trading indicators.

[0092] Specifically, a corresponding set of constraints can be determined based on the obtained stability indicators, economic indicators, and carbon trading indicators.

[0093] In some selectable embodiments, step S3042 includes steps c1 to c3.

[0094] Step c1: Determine the first constraint based on the stability index.

[0095] Step c2: Determine the second constraint based on economic indicators and carbon trading indicators.

[0096] Step c3: Determine the set of constraints based on the first and second constraints.

[0097] Specifically, the first constraint is that the stability index is maximized, and the second constraint is that the economic indicator and the carbon trading indicator are minimized.

[0098] Step S3043: Based on the first energy storage resource set and constraint set, a multi-type source storage configuration is performed for the regional grid system, and the multi-type source storage configuration result is obtained.

[0099] Specifically, if the first constraint, that the stability index is maximized, and the second constraint, that the economic efficiency index and carbon trading index are minimized, is satisfied, then by configuring the corresponding new energy and energy storage resource configuration for the regional grid system, the optimal source storage configuration for that regional grid system, i.e., a multi-type source storage configuration result, can be obtained.

[0100] The multi-type source storage configuration method according to this embodiment allows for the acquisition of corresponding stability indicator parameters by performing a demand analysis on the regional grid system using the source-grid-load-storage configuration dataset and load forecast dataset of the regional grid system. Selectively, the corresponding stability indicator, economic indicator, and carbon trading indicator can be determined using the multi-type energy storage characteristics dataset of the regional grid system, thereby realizing a multi-type source storage configuration for the regional grid system. This essentially solves the stability problem of the regional grid due to the increased proportion of new energy construction, and by taking economic indicators and carbon trading indicators into consideration, it solves the problems of insufficient economics and low call rates in source storage configurations.

[0101] This embodiment further provides a multi-type source storage configuration device used to implement the above embodiment and selectable embodiments, and redundant explanations of parts that have already been described are omitted. As used below, the term "module" can implement a combination of software and / or hardware for a given function, and the devices described in the following embodiments are preferably implemented in software, but can also be implemented in hardware, or a combination of software and hardware, and are conceived.

[0102] This embodiment provides a multi-type source storage configuration device, which is applied to a regional grid system and includes an acquisition module 401, an analysis module 402, a decision module 403, and a configuration module 404, as shown in Figure 4.

[0103] The acquisition module 401 is used to acquire source-grid-load-storage configuration datasets, load prediction datasets, and multi-type energy storage characteristics datasets for the regional grid system.

[0104] Analysis module 402 is used to analyze a regional grid system according to predefined demands and obtain stability indicators based on source-grid-load-storage configuration datasets, load forecast datasets, and multi-type energy storage characteristics datasets.

[0105] The decision module 403 is used to determine economic indicators and carbon trading indicators using a pre-configured full-lifecycle model.

[0106] Configuration module 404 is used to perform a multi-type source storage configuration on a regional grid system and obtain the multi-type source storage configuration result, using a source-grid-load-storage configuration dataset and a load prediction dataset, based on stability indicators, economic indicators, and carbon trading indicators.

[0107] In some selectable embodiments, the analysis module 402 includes a first decision submodule, a second decision submodule, a third decision submodule, and a fourth decision submodule.

[0108] The first decision submodule is used to determine a regional grid stability power generation model based on the source-grid-load-storage configuration dataset and the load prediction dataset.

[0109] The second decision submodule is used to determine stability index parameter values ​​based on a multi-type energy storage characteristics dataset.

[0110] The third decision submodule is used to determine the target resource amount based on the regional grid stability power generation model and stability index parameter values.

[0111] The fourth decision submodule is used to determine stability indicators based on the regional grid stability power generation model and target resource amounts.

[0112] In some selectable embodiments, the decision module 403 includes a fifth decision submodule and a sixth decision submodule.

[0113] The fifth decision submodule is used to determine the set of economic indicator parameters based on a pre-defined full-lifecycle model.

[0114] The sixth decision submodule is used to determine economic indicators and carbon trading indicators based on the economic indicator parameter set.

[0115] In some selectable embodiments, the sixth decision submodule includes an acquisition unit, a first decision unit, and a second decision unit.

[0116] The acquisition unit is used to acquire the first and second subsets of economic indicator parameters, which correspond to the economic indicator parameter set.

[0117] The first decision unit is used to determine economic indicators based on a first economic indicator parameter subset.

[0118] The second decision unit is used to determine the carbon trading index based on a subset of second economic indicator parameters.

[0119] In some selectable embodiments, the configuration module 404 includes a seventh decision submodule, an eighth decision submodule, and a configuration submodule.

[0120] The seventh decision submodule is used to determine the first energy storage resource set based on the source-grid-load-storage configuration dataset, load prediction dataset, and stability indicators.

[0121] The eighth decision submodule is used to determine the set of constraints based on stability indicators, economic indicators, and carbon trading indicators.

[0122] The configuration submodule is used to perform a multi-type source storage configuration for a regional grid system based on a first energy storage resource set and a constraint set, and to obtain the multi-type source storage configuration result.

[0123] In some selectable embodiments, the seventh decision submodule includes a third decision unit and a fourth decision unit.

[0124] The third decision unit is used to determine the range of energy storage resource types corresponding to the regional grid system, based on the source-grid-load-storage configuration dataset and the load forecast dataset.

[0125] The fourth decision unit is used to determine the first set of energy storage resources based on the range of energy storage resource types and stability indicators.

[0126] In some selectable embodiments, the eighth decision submodule includes a fifth decision unit, a sixth decision unit, and a seventh decision unit.

[0127] The fifth decision unit is used to determine the first constraint condition based on the stability index.

[0128] The sixth decision unit is used to determine the second constraint based on economic indicators and carbon trading indicators.

[0129] The seventh decision unit is used to determine the set of constraints based on the first and second constraints.

[0130] Further functional descriptions of each of the above modules and units are the same as those in the corresponding embodiments described above, and therefore, redundant explanations are omitted here.

[0131] In this embodiment, the multi-type source storage configuration device is represented in the form of a functional unit, where a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0132] The embodiment of the present invention further provides computer equipment comprising a multi-type source storage configuration device as shown in Figure 4 above.

[0133] As shown in Figure 5, Figure 5 is a schematic diagram of the structure of a computer device according to an optional embodiment of the present invention, which, as shown in Figure 5, includes one or more processors 10, memory 20, and interfaces for connecting each component, including high-speed and low-speed interfaces. Each component communicates with one another via different buses and may be mounted on a common motherboard or otherwise mounted as needed. The processors can process instructions executed within the computer device, including instructions in or stored in memory for displaying GUI graphic information on an external input / output device (e.g., a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses may be used together with multiple memories as needed. Similarly, multiple computer devices may be connected, each providing a portion of the required operations (e.g., functioning as a server array, a set of blade servers, or a multiprocessor system). Figure 5 takes one processor 10 as an example.

[0134] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include hardware chips. The hardware chips may be application-specific integrated circuits, programmable logic devices, or a combination thereof. The programmable logic devices may be complex programmable logic devices, field programmable logic gate arrays, generic array logic, or any combination thereof.

[0135] The memory 20 stores instructions that can be executed by at least one processor 10, thereby enabling at least one processor 10 to execute and implement the method shown in the above embodiment.

[0136] Memory 20 may include an operating system, a program storage area for storing application programs required for at least one function, and a data storage area for storing data created in accordance with the use of the computer equipment. Memory 20 may also include high-speed random-access memory and may further include non-temporary memory such as at least one disk storage device, flash memory device, or other non-temporary solid-state storage device. In some selectable embodiments, memory 20 may optionally include memory remotely installed relative to the processor 10, and these remote memories may be connected to the computer equipment via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0137] Memory 20 may include volatile memory such as random access memory, or non-volatile memory such as flash memory, hard disk, or solid-state drive, and memory 20 may include a combination of the above types of memory.

[0138] The computer equipment further includes a communication interface 30 for the computer equipment to communicate with other equipment or a communication network.

[0139] Embodiments of the present application further provide a computer-readable storage medium, and the methods according to the embodiments of the present application may be implemented in hardware, firmware, or in a recordable manner on a storage medium, or as computer code downloaded over a network, originally stored on a remote storage medium or a non-temporary machine-readable storage medium but stored on a local storage medium, thereby enabling the methods described herein to be processed by a general-purpose computer, a dedicated processor, or software stored on a storage medium using programmable or dedicated hardware. The storage medium may be a magnetic disk, an optical disk, read-only memory, random access memory, flash memory, a hard disk, or a solid-state drive, and optionally, the storage medium may include a combination of the above types of memory. As understood, the computer, processor, microprocessor controller, or programmable hardware includes a storage component capable of storing or receiving software or computer code, and when the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the embodiments are implemented.

[0140] While embodiments of the present application have been described with reference to the drawings, those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, and such changes and modifications all fall within the scope defined in the attached claims.

Claims

1. A multi-type source storage configuration method applicable to a regional grid system, wherein the configuration method is performed by computer equipment, The steps include obtaining a source-grid-load-storage configuration dataset, a load prediction dataset, and a multi-type energy storage characteristics dataset of the aforementioned regional grid system, The steps include: analyzing the regional grid system according to a predetermined demand based on the source-grid-load-storage configuration dataset, the load prediction dataset, and the multi-type energy storage characteristics dataset, and obtaining a stability index; The steps include determining economic indicators and carbon trading indicators using a pre-defined full-lifecycle model, The steps include: performing a multi-type source storage configuration for the regional grid system using the source-grid-load-storage configuration dataset and the load prediction dataset based on the stability indicator, the economic indicator, and the carbon trading indicator; obtaining a multi-type source storage configuration result; and setting the multi-type source storage configuration result as the optimal source storage configuration for the regional grid system. Of these, the step of analyzing the regional grid system according to a predetermined demand based on the source-grid-load-storage configuration dataset, the load prediction dataset, and the multi-type energy storage characteristics dataset, and obtaining a stability index, is: Based on the source-grid-load-storage configuration dataset and the load prediction dataset, a regional grid stability power generation model is determined, and the regional grid stability power generation model represents the minimum and maximum power generation capacity of the regional grid system, and includes steps: Steps include: determining a stability index parameter value based on the multi-type energy storage characteristics dataset, wherein the stability index parameter value is a parameter of 1 or 0 indicating whether or not a synchronous generator or a synchronous generator has frequency regulation and voltage control capabilities; Steps include: determining a target resource amount based on the regional grid stability power generation model and the stability index parameter values, wherein the target resource amount represents the total power generation capacity of the equipment equipped with the frequency regulation and voltage control capabilities; The step of determining the stability index based on the regional grid stability power generation model and the target resource amount is included, Of these, the step of performing a multi-type source storage configuration for the regional grid system using the source-grid-load-storage configuration dataset and the load prediction dataset, based on the stability index, the economic index, and the carbon trading index, and obtaining the multi-type source storage configuration result is: A step of determining a first energy storage resource set based on the source-grid-load-storage configuration dataset, the load prediction dataset, and the stability index, The steps include determining a set of constraints based on the stability indicator, the economic indicator, and the carbon trading indicator, A method for configuring multi-type source storage, comprising the steps of: configuring a multi-type source storage configuration for the regional grid system based on the first energy storage resource and the set of constraints, and obtaining the result of the multi-type source storage configuration.

2. The step of determining economic indicators and carbon trading indicators using a pre-defined full-life cycle model is: The steps include determining a set of economic indicator parameters based on the aforementioned pre-configured full-lifecycle model, The method according to claim 1, comprising the step of determining the economic indicator and the carbon trading indicator based on the economic indicator parameter set.

3. The step of determining the economic indicators and the carbon trading indicators based on the economic indicator parameter set is: The steps include obtaining a first economic indicator parameter subset and a second economic indicator parameter subset corresponding to the aforementioned economic indicator parameter set, The steps include determining the economic indicator based on the first economic indicator parameter subset, The method according to 2, comprising the step of determining the carbon trading index based on the second economic indicator parameter subset.

4. The step of determining a first energy storage resource set based on the source-grid-load-storage configuration dataset, the load prediction dataset, and the stability index is as follows: The steps include determining the range of energy storage resource types corresponding to the regional grid system based on the source-grid-load-storage configuration dataset and the load prediction dataset, The method according to claim 1, comprising the step of determining the first set of energy storage resources based on the range of energy storage resource types and the stability index.

5. The step of determining a set of constraints based on the stability indicator, the economic indicator, and the carbon trading indicator is: A step of determining a first constraint condition based on the stability index, The steps include determining a second constraint based on the aforementioned economic indicator and the aforementioned carbon trading indicator, The method according to claim 1, comprising the step of determining the set of constraints based on the first constraint and the second constraint.

6. A multi-type source storage configuration device applicable to a regional grid system, An acquisition module for acquiring the source-grid-load-storage configuration dataset, load prediction dataset, and multi-type energy storage characteristics dataset of the aforementioned regional grid system, An analysis module for analyzing the regional grid system according to a preset demand and obtaining stability indicators based on the source-grid-load-storage configuration dataset, the load prediction dataset, and the multi-type energy storage characteristics dataset, A decision module for determining economic indicators and carbon trading indicators using a pre-configured full-lifecycle model, A configuration module is included for performing a multi-type source storage configuration for the regional grid system using the source-grid-load-storage configuration dataset and the load prediction dataset based on the stability index, the economic index, and the carbon trading index, obtaining a multi-type source storage configuration result, and optimizing the multi-type source storage configuration result for the regional grid system. Specifically, the analysis module is configured to be used to determine a regional grid stability power generation model based on the source-grid-load-storage configuration dataset and the load prediction dataset, to determine stability index parameter values ​​based on the multi-type energy storage characteristics dataset, to determine target resource quantities based on the regional grid stability power generation model and the stability index parameter values, and to determine the stability index based on the regional grid stability power generation model and the target resource quantities. The regional grid stability power generation model represents the minimum and maximum power generation capacity of the regional grid system, the stability index parameter value is a parameter of 1 or 0 indicating whether or not it has the capability of a synchronous generator or a synchronous generator-like frequency regulation and voltage control, and the target resource amount represents the total power generation capacity of the equipment having the capability of frequency regulation and voltage control. The configuration module is specifically used to determine a first energy storage resource set based on the source-grid-load-storage configuration dataset, the load prediction dataset, and the stability index; used to determine a constraint set based on the stability index, the economic index, and the carbon trading index; used to perform a multi-type source storage configuration for the regional grid system based on the first energy storage resources and the constraint set, to obtain the multi-type source storage configuration result; and configured to make the multi-type source storage configuration result the optimal source storage configuration for the regional grid system.

7. Computer equipment, A computer device comprising a memory and a processor that are connected to each other in communication, wherein the memory stores computer instructions, and the processor executes the multi-type source storage configuration method described in any one of claims 1 to 5 by executing the computer instructions.

8. A computer-readable storage medium characterized in that it stores computer instructions for causing a computer to execute the multi-type source storage configuration method described in any one of claims 1 to 5.

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