Multi-type source storage configuration method, device, computer device, 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 more particularly to a multi-type source storage configuration method, apparatus, computer device and storage medium. [Background technology]

[0002] The benefits of rapidly deploying renewable energy are not only the requirement for low carbonization, but also the ability to improve regional energy security and independence. However, the two core goals of the power system are to ensure a sustainable and reliable power supply and to ensure good electrical energy quality.

[0003] Current technology for new energy generation primarily refers to wind power and solar photovoltaic power, which are essentially different from synchronous generators and can be collectively referred to as asynchronous power sources. These power sources are generally comprised of power electronic converters, whose performance is determined by the converter's control characteristics. Currently, converters widely used in practical engineering employ a grid-following control strategy, i.e., they synchronize the converter with the grid using a phase-locked loop and use vector current control to control the converter's output current, thereby controlling the active and reactive power supplied to the grid. They are essentially controlled current sources, whose primary control goal is to track the current maximum power of solar and wind energy and convert it into electrical energy with maximum efficiency for supply to the power system. Because this control strategy targets current, it cannot supply energy according to demand, achieving a balance between supply and demand (stable frequency) and stabilizing grid voltage.

[0004] Selectively, new energy occupies a major position in the power supply structure, and when the proportion of new energy power generation equipment increases to a certain extent (in the extreme case, new energy becomes 100%), the synchronous generators in the system will no longer be able to be controlled to balance the power according to demand and stabilize the voltage of the entire grid to a reasonable level, resulting in a system unable to operate stably and normally.

[0005] The above characteristics of new energy sources not only affect their own developmental laws, but also lead to a relative decline in the voltage and frequency regulation and control capabilities of the power system after new energy sources replace traditional power sources. Solutions must be considered from two perspectives: 1. By repeatedly upgrading the characteristics of new energy generation, we can give traditional synchronous generators, such as water and fire, better grid-supporting characteristics and enable them to operate in conjunction with synchronous generators. This approach affects the amount of new energy generated, reduces economic efficiency, and is difficult to achieve in the short term. 2. By adding new equipment with voltage control and energy regulation capabilities similar to or similar to synchronous generators near new energy sources, we can not only consume new energy sources but also eliminate their negative impact on the power system and ensure system stability.

[0006] However, the current requirements for energy storage in new energy configurations are proposed from the perspective of solving the fluctuations of new energy. A simple estimation based on a certain proportion of the construction scale of new energy shows that there is a redundancy in energy storage or a resource waste phenomenon due to unconditional grid connection after the construction of new energy. Summary of the Invention [Problem to be solved by the invention]

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

[0008] In a first aspect, the present application provides a multi-type source storage configuration method applied to a regional grid system, comprising: The present invention provides a multi-type source storage configuration method, including the steps of: acquiring a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of 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 multi-type energy storage characteristic dataset, to obtain a stability index; determining an economic index and a carbon trading index using a predetermined full life cycle model; and performing a multi-type 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, the economic index, and the carbon trading index, to obtain a multi-type source storage configuration result.

[0009] The multi-type source storage configuration method according to the present application can obtain the corresponding stability index 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, and optionally determine the corresponding economic indicator and carbon trading indicator 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 economic efficiency and low call rate of the source storage configuration by taking into account the economic indicator and carbon trading indicator.

[0010] In one alternative embodiment, the step of analyzing the regional grid system according to the predetermined demand based on the source-grid-load-storage configuration dataset, the load forecast dataset, and the multi-type energy storage characteristic dataset to obtain a stability index includes: The method includes determining a regional grid stability power generation model and a target resource amount based on a source-grid-load-storage configuration dataset and a load forecast dataset; determining a stability index parameter value based on a multi-type energy storage characteristic dataset; determining a target resource amount based on the regional grid stability power generation model and the stability index parameter value; and determining a stability index based on the regional grid stability power generation model and the target resource amount.

[0011] The present application can determine the corresponding stability index according to the source-grid-load-storage configuration dataset and load forecast dataset of the regional grid system, and supports the subsequent multi-type source-storage configuration of the regional grid system.

[0012] In one alternative embodiment, the step of determining the economic indicators and the carbon trading indicators using a preset full life cycle model includes: The method includes a step of determining an economic performance index parameter set based on a preset full life cycle model, a step of determining an economic performance index and a carbon trading index based on the economic performance index parameter set, and a step of determining a stability index based on the multi-type energy storage characteristic dataset and stability index parameters.

[0013] In one alternative embodiment, the step of determining the economic performance index and the carbon trading index based on the economic performance index parameter set includes: The method includes steps of obtaining a first economic indicator parameter subset and a second economic indicator parameter subset corresponding to the 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 alternative embodiment, the step of determining a multi-type source storage configuration result based on the source-grid-load-storage configuration dataset and the load forecast dataset through processing a stability index, an economic index, and a carbon trading index includes: The method includes determining a first energy storage resource set based on the source-grid-load-storage configuration dataset, the load forecast dataset, and a stability index; determining a set of constraints based on the stability index, the economic index, and the carbon trading index; and performing a multi-type source storage configuration for the regional grid system based on the first energy storage resource set and the set of constraints, and obtaining a multi-type source storage configuration result.

[0015] This application takes into account economic indicators and carbon trading indicators to solve the problems of insufficient economic efficiency and low call rates of source storage configurations.

[0016] In one alternative embodiment, determining the first set of energy storage resources based on the source-grid-load-storage configuration dataset, the load forecasting dataset, and the stability index comprises: The method includes determining an energy storage resource type range corresponding to the regional grid system based on the source-grid-load-storage configuration dataset and the load forecast dataset, and determining a first energy storage resource set based on the energy storage resource type range and the stability index.

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

[0018] In a second aspect, the present application provides a multi-type source storage configuration device applied to a regional grid system, comprising: The present invention provides a multi-type source storage configuration device, including: an acquisition module for acquiring a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of a regional grid system; an analysis module for analyzing the regional grid system according to a preset demand based on the source-grid-load-storage configuration dataset, the load forecast dataset, and the multi-type energy storage characteristic dataset, and obtaining a stability index; a determination module for determining an economic index and a carbon trading index using a preset full life cycle model; and a configuration module for performing a multi-type 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, the economic index, and the carbon trading index, and obtaining a multi-type source storage configuration result.

[0019] In a third aspect, the present application provides a computer apparatus including a memory and a processor communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the multi-type source storage configuration method of the first aspect above or any of the corresponding embodiments thereof.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the multi-type source storage configuration method of the first aspect or any of the corresponding embodiments thereof.

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

[0022] [Figure 1] 2 is a flowchart of a multi-type source storage configuration method according to an embodiment of the present application; [Figure 2] 4 is a flowchart of another multi-type source storage configuration method according to an embodiment of the present application; [Figure 3] 10 is a flowchart of yet another multi-type source storage configuration method according to an embodiment of the present application; [Figure 4] FIG. 2 is a structural block diagram of a multi-type source storage configuration device according to an embodiment of the present application; [Figure 5] FIG. 2 is a hardware structure schematic diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0023] In order to clarify the objectives, technical solutions and advantages of the embodiments of the present application, the following will clearly and completely describe them with reference to the drawings of the embodiments and the technical solutions of the embodiments of the present application, and it is obvious that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments that a person skilled in the art can obtain based on the embodiments of the present application without any creative work fall within the scope of protection of the present application.

[0024] The embodiments of this application provide a multi-type source storage configuration method, 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 economic efficiency and low call rate of source storage configuration by taking into account economic indicators and carbon trading indicators.

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

[0026] In this embodiment, a multi-type source storage configuration method is provided, which can be applied to a regional grid system. FIG. 1 is a flowchart of the multi-type source storage configuration method according to the embodiment of the present application. As shown in FIG. 1, the process includes the following steps:

[0027] Step S101: Obtain a source-grid-load-storage configuration dataset, a load forecast dataset and a multi-type energy storage characteristic dataset of a 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 forecast data set may include a plurality of future load forecast data for the regional grid system.

[0030] Optionally, a multi-type energy storage characteristic data set is used to reflect the characteristics of different types of new energy and energy storage technologies in the local grid system.

[0031] Step S102: Based on the source-grid-load-storage configuration dataset, the load forecast dataset and the multi-type energy storage characteristic dataset, analyze the regional grid system according to the preset demand, and obtain a stability index.

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

[0033] Step S103: Determine the economic indicators and carbon trading indicators using a preset full life cycle model.

[0034] A pre-defined full life cycle model is used to reflect the relevant states in the full life cycle of the regional grid system.

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

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

[0037] Specifically, by using stability indexes, economic indexes, and carbon trading indexes as constraints, and combining the source-grid-load-storage configuration dataset and load forecast dataset of the regional grid system, we can perform a multi-type source storage configuration for the regional grid system, thereby obtaining the optimal multi-type source storage configuration result.

[0038] The multi-type source storage configuration method according to this embodiment uses the source-grid-load-storage configuration dataset and load forecast dataset of the local grid system to perform demand analysis on the local grid system, thereby obtaining corresponding stability index parameters. Optionally, the corresponding stability index, economic efficiency index, and carbon trading index can be determined respectively using the multi-type energy storage characteristic dataset of the local grid system, thereby realizing a multi-type source storage configuration for the local 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 economic efficiency and low call rate of the source storage configuration by taking into account the economic efficiency index and carbon trading index.

[0039] In this embodiment, a multi-type source storage configuration method is provided, which can be applied to a regional grid system. FIG. 2 is a flowchart of the multi-type source storage configuration method according to the embodiment of the present application. As shown in FIG. 2, the process includes the following steps:

[0040] Step S201: obtain a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of a regional grid system. For details, refer to step S101 in the embodiment shown in FIG. 1, and redundant description will be omitted here.

[0041] Step S202: Based on the source-grid-load-storage configuration dataset, the load forecast dataset and the multi-type energy storage characteristic dataset, analyze the regional grid system according to the preset demand, and obtain a stability index.

[0042] Specifically, the above step S202 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 forecast dataset.

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

[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 a stability index parameter value based on the multi-type energy storage characteristic data set.

[0048] The stability index parameter value R is used to indicate whether the generator has the frequency regulation and voltage control capabilities of a synchronous generator or similar to a synchronous generator. If the generator has these capabilities, the stability index parameter value R is 1; if not, the stability index parameter value R is 0.

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

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

[0051] The target resource amount S is used to represent the ability of a synchronous generator or similar to a synchronous generator to regulate frequency and control voltage.

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

[0053] S=P*R1+P*R2+P*R3+……+P*Rn (2)

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

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

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

[0057] Step S203: Determine the economic indicators and carbon trading indicators using a pre-defined full life cycle model.

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

[0059] Step S2031: Determine an economic index parameter set based on a preset full life cycle model.

[0060] Specifically, the economic index parameter set is used to reflect the cost, power, etc. associated with the regional grid system.

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

[0062] Economic indicators may include cost per unit of power and cost per mileage.

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

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

[0065] Step a1: Obtain a first economic index parameter subset and a second economic index parameter subset corresponding to the economic index parameter set.

[0066] Step a2: determining an economic performance index based on the first economic performance index parameter subset;

[0067] Step a3: determining a carbon trading index based on the second economic index parameter subset;

[0068] Specifically, the first subset of economic index parameters may include a fixed cost per unit of electricity over a full life cycle corresponding to the local grid system and a peak shift marginal cost.

[0069] Optionally, the second subset of economic indicator parameters may include generated power and carbon emission factors corresponding to the local grid system.

[0070] Optionally, the following relations (4) and (5) are used to determine the economic indicators:

[0071] Cost per unit of energy = Fixed cost per unit of energy over the full life cycle + Peak shift marginal cost (4) Cost per mileage = fixed cost per unit of electricity over the full life cycle + marginal frequency regulation costs (5)

[0072] Alternatively, the carbon trading index can be determined using the following equation (6):

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

[0074] Step S204: Based on the stability index, the economic index, and the carbon trading index, perform a multi-type source storage configuration for the regional grid system using the source-grid-load-storage configuration dataset and the load forecast dataset, and obtain a multi-type source storage configuration result. For details, refer to step S104 in the embodiment shown in Figure 1, and redundant explanations will be omitted here.

[0075] The multi-type source storage configuration method according to this embodiment uses the source-grid-load-storage configuration dataset and load forecast dataset of the regional grid system to perform demand analysis on the regional grid system to obtain corresponding stability indicators, and optionally determines corresponding economic indicators and carbon trading indicators 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 economic efficiency and low call rate of the source storage configuration by taking into account the economic indicators and carbon trading indicators.

[0076] In this embodiment, a multi-type source storage configuration method is provided, which can be applied to a regional grid system. FIG. 3 is a flowchart of the multi-type source storage configuration method according to the embodiment of the present application. As shown in FIG. 3, the process includes the following steps:

[0077] Step S301: obtain a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of a regional grid system. For details, refer to step S101 in the embodiment shown in FIG. 1, and redundant description will be omitted here.

[0078] Step S302: Based on the source-grid-load-storage configuration dataset, the load forecast dataset, and the multi-type energy storage characteristic dataset, analyze the regional grid system according to the preset demand and obtain a stability index. For details, refer to step S202 in the embodiment shown in Figure 2, and redundant description will be omitted here.

[0079] Step S303: Determine the economic indicators and carbon trading indicators using a preset full life cycle model. For details, refer to step S203 in the embodiment shown in Figure 2, and redundant explanations will be omitted here.

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

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

[0082] Step S3041: Determine a first energy storage resource set based on the source-grid-load-storage configuration dataset, the load forecast dataset and the stability index.

[0083] Specifically, the resulting source-grid-load-storage configuration dataset, load forecast dataset, and stability index can be combined to determine multiple energy storage resources corresponding to the regional grid system.

[0084] In some alternative embodiments, step S3041 includes steps b1 and b2.

[0085] Step b1: Determine an energy storage resource type range corresponding to the regional grid system based on the source-grid-load-storage configuration dataset and the load forecast dataset.

[0086] Step b2: Determine a first set of energy storage resources according to the energy storage resource type range and the stability index.

[0087] Specifically, the obtained source-grid-load-storage configuration dataset and load forecast dataset of the regional grid system can be combined to determine a range of viable new energy and energy storage types corresponding to the regional grid system.

[0088] Optionally, the obtained energy storage resource type range and stability index may be combined to determine a corresponding first set of energy storage resources.

[0089] (1) Energy storage with frequency regulation and voltage control capabilities of or similar to a synchronous generator, as follows: (11) Mechanical energy storage (pumped hydro energy storage, compressed air energy storage, lava heat storage, hydrogen energy storage, etc.) (11) Electrochemical energy storage (lithium batteries, sodium ion batteries, redox flow batteries, etc.) + grid-forming PCS (13) Electric energy storage (flywheel energy storage, supercapacitor, superconducting energy storage, etc.)

[0090] (2) New energy sources with the capabilities of frequency regulation AGC and voltage control AVC of synchronous generators or similar to synchronous generators are as follows: (21) Geothermal power generation system (22) Biomass power generation system (23) Solar thermal power generation system (24) Nuclear Power Generation System (25) Ocean Energy Power Generation System (26) Solar power generation + grid-forming inverter system (27) Wind power generation + grid-forming inverter system

[0091] Step S3042: A set of constraints is determined based on the stability index, the economic index, and the carbon trading index.

[0092] Specifically, a set of corresponding constraints can be determined according to the obtained stability index, economic index, and carbon trading index.

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

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

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

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

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

[0098] Step S3043: Based on the first set of energy storage resources and the set of constraints, perform a multi-type source storage configuration for the regional grid system, and obtain a multi-type source storage configuration result.

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

[0100] The multi-type source storage configuration method according to this embodiment uses the source-grid-load-storage configuration dataset and load forecast dataset of the local grid system to perform demand analysis on the local grid system, thereby obtaining corresponding stability index parameters. Optionally, the corresponding stability index, economic efficiency index, and carbon trading index can be determined respectively using the multi-type energy storage characteristic dataset of the local grid system, thereby realizing a multi-type source storage configuration for the local 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 economic efficiency and low call rate of the source storage configuration by taking into account the economic efficiency index and carbon trading index.

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

[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 determination module 403, and a configuration module 404, as shown in FIG.

[0103] The acquisition module 401 is used to acquire a source-grid-load-storage configuration dataset, a load forecast dataset and a multi-type energy storage characteristic dataset of a regional grid system.

[0104] The analysis module 402 is used to analyze the regional grid system according to the predetermined demand based on the source-grid-load-storage configuration dataset, the load forecast dataset and the multi-type energy storage characteristic dataset, and obtain a stability index.

[0105] The determination module 403 is used to determine the economic indicators and the carbon trading indicators using a preset full life cycle model.

[0106] The configuration module 404 is used to perform multi-type 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, the economic index, and the carbon trading index, and obtain a multi-type source storage configuration result.

[0107] In some alternative embodiments, the analysis module 402 includes a first determination sub-module, a second determination sub-module, a third determination sub-module, and a fourth determination sub-module.

[0108] The first determination sub-module is used to determine a regional grid stability generation model based on the source-grid-load-storage configuration dataset and the load forecast dataset.

[0109] The second determination sub-module is used for determining a stability index parameter value based on the multi-type energy storage characteristic data set.

[0110] The third determination sub-module is used for determining a target resource amount based on the regional grid stability power generation model and the stability index parameter value.

[0111] The fourth determination sub-module is used for determining a stability index based on the regional grid stability power generation model and the target resource amount.

[0112] In some alternative embodiments, the decision module 403 includes a fifth decision sub-module and a sixth decision sub-module.

[0113] The fifth determination sub-module is used for determining an economic index parameter set based on a preset full life cycle model.

[0114] The sixth determination sub-module is used to determine the economic indicator and the carbon trading indicator based on the economic indicator parameter set.

[0115] In some alternative embodiments, the sixth determining sub-module includes an obtaining unit, a first determining unit, and a second determining unit.

[0116] The obtaining unit is used for obtaining a first economic index parameter subset and a second economic index parameter subset corresponding to the economic index parameter set.

[0117] The first determining unit is used for determining an economic index based on the first economic index parameter subset.

[0118] The second determination unit is used for determining a carbon trading index based on the second economic index parameter subset.

[0119] In some alternative embodiments, the configuration module 404 includes a seventh determination sub-module, an eighth determination sub-module, and a configuration sub-module.

[0120] The seventh determination sub-module is used for determining a first set of energy storage resources based on the source-grid-load-storage configuration dataset, the load forecast dataset and the stability index.

[0121] The eighth determination sub-module is used to determine a set of constraints based on the stability index, the economic index, and the carbon trading index.

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

[0123] In some alternative embodiments, the seventh determining sub-module includes a third determining unit and a fourth determining unit.

[0124] The third determining unit is used for determining an energy storage resource type range corresponding to the regional grid system based on the source-grid-load-storage configuration dataset and the load forecast dataset.

[0125] The fourth determining unit is used for determining the first energy storage resource set according to the energy storage resource type range and the stability index.

[0126] In some alternative embodiments, the eighth determination sub-module includes a fifth determination unit, a sixth determination unit, and a seventh determination unit.

[0127] The fifth determining unit is used for determining the first constraint based on the stability index.

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

[0129] The seventh determining unit is used for determining a set of constraints according to the first constraint and the second constraint.

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

[0131] The multi-type source storage configuration device in this embodiment is represented in the form of a functional unit, where unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory executing one or more software or fixed programs, and / or other device capable of providing the above functionality.

[0132] An embodiment of the present application further provides a computer device, which comprises the multi-type source storage configuration device shown in FIG.

[0133] Referring to FIG. 5, FIG. 5 is a structural diagram of a computer device according to an alternative embodiment of the present application. As shown in FIG. 5, the computer device includes one or more processors 10, memory 20, and interfaces, including high-speed and low-speed interfaces, for connecting each component. The components are communicatively connected to each other via different buses and may be mounted on a common motherboard or otherwise attached as needed. The processor can process instructions executed within the computer device, including instructions stored in or in memory for displaying GUI graphic information on an external input / output device (e.g., a display device coupled to the interface). In some alternative embodiments, multiple processors and / or multiple buses may be used along with multiple memories as needed. Similarly, multiple computer devices may be connected, each performing a portion of the required operations (e.g., functioning as a server array, a set of blade servers, or a multiprocessor system). FIG. 5 illustrates 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 a hardware chip. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable logic gate array, a generic array logic, or any combination thereof.

[0135] The memory 20 stores instructions executable by the at least one processor 10, causing the at least one processor 10 to execute and implement the methods shown in the above embodiments.

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

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

[0138] The computing device further includes a communications interface 30 that allows the computing device to communicate with other devices or communications networks.

[0139] The present embodiment further provides a computer-readable storage medium, and the methods according to the embodiments of the present application may be implemented in hardware, firmware, recordable on a storage medium, or as computer code downloaded over a network, originally stored on a remote storage medium or a non-transitory machine-readable storage medium, but stored on a local storage medium, whereby the methods described herein may be processed by software stored on a storage medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware. The storage medium may be a magnetic disk, optical disk, read-only memory, random-access memory, flash memory, hard disk, solid-state drive, etc., and optionally, the storage medium may include a combination of the above types of memory. As will be understood, a computer, processor, microprocessor controller, or programmable hardware may include a storage component capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described in the above embodiments.

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

Claims

1. A multi-type source storage configuration method applied to a regional grid system, comprising: obtaining a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of the 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 multi-type energy storage characteristic dataset to obtain a stability index; determining economic indicators and carbon trading indicators using a preset full life cycle model; and performing a multi-type 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, the economic efficiency index, and the carbon trading index, and obtaining a multi-type source storage configuration result.

2. 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 multi-type energy storage characteristic dataset to obtain a stability index; determining a regional grid stability generation model based on the source-grid-load-storage configuration dataset and the load forecast dataset; determining a stability index parameter value based on the multi-type energy storage characteristic data set; determining a target resource amount based on the regional grid stability generation model and the stability index parameter value; and determining the stability index based on the regional grid stability generation model and the target resource amount.

3. The step of determining an economic indicator and a carbon trading indicator using a preset full life cycle model includes: determining an economic index parameter set based on the preset full life cycle model; and determining the economic performance index and the carbon trading index based on the economic performance index parameter set.

4. The step of determining the economic index and the carbon trading index based on the economic index parameter set includes: obtaining a first economic index parameter subset and a second economic index parameter subset corresponding to the economic index parameter set; determining the economic performance index based on the first economic performance index parameter subset; and determining the carbon trading index based on the second subset of economic index parameters.

5. performing a multi-type 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, the economic efficiency index, and the carbon trading index, and obtaining a multi-type source storage configuration result, determining a first set of energy storage resources based on the source-grid-load-storage configuration dataset, the load forecasting dataset, and the stability index; determining a set of constraints based on the stability index, the economic efficiency index, and the carbon trading index; 2. The method of claim 1, further comprising: performing a multi-type source storage configuration for the 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.

6. determining a first set of energy storage resources based on the source-grid-load-storage configuration dataset, the load forecasting dataset, and the stability index; determining a 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; and determining the first set of energy storage resources based on the energy storage resource type range and the stability index.

7. The step of determining a set of constraint conditions based on the stability index, the economic efficiency index, and the carbon trading index includes: determining a first constraint based on the stability index; determining a second constraint based on the economic indicator and the carbon trading indicator; and determining the set of constraints based on the first constraint and the second constraint.

8. A multi-type source storage configuration device applied to a regional grid system, comprising: an acquisition module for acquiring a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of the regional grid system; an analysis module for 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 multi-type energy storage characteristic dataset, and obtaining a stability index; a determination module for determining economic indicators and carbon trading indicators using a preset full life cycle model; a configuration module for performing a multi-type 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, the economic efficiency index, and the carbon trading index, and obtaining a multi-type source storage configuration result.

9. A computer device comprising: A computer device comprising a memory and a processor 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 any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the multi-type source storage configuration method according to any one of claims 1 to 7.

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