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

Abstract

The present application relates to the technical field of energy storage. Disclosed are a multi-type source-storage configuration method and apparatus, a computer device and a storage medium. By means of a source-grid-load-storage configuration dataset and a load forecasting dataset of a regional power grid system, the present application performs demand analysis on the regional power grid system to obtain corresponding stability metrics; optionally, by means of a preset full life cycle model, corresponding economic performance metrics and carbon trading metrics can be determined separately, thus achieving multi-type source-storage configuration for the regional power grid system; the present application essentially solves the problem of the stability of regional power grids caused by the increase in the proportion of new energy supporting facilities, and moreover, by taking into consideration the economic performance metrics and carbon trading metrics, solves the problems of insufficient economic efficiency and low utilization rate of source-storage configuration.

Description

A multi-type source storage configuration method, device, computer equipment and storage medium Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a multi-type source storage configuration method, apparatus, computer equipment, and storage medium. Background Art

[0002] The benefits of rapidly promoting renewable energy are not only the requirements of decarbonization, but also the improvement of regional energy security and independence. However, the two core goals of the power system are to ensure continuous and reliable power supply and good power quality.

[0003] Current renewable energy generation primarily refers to wind power and photovoltaic power. These are fundamentally different from synchronous generators and can be collectively referred to as asynchronous generators. They are generally composed of power electronic converters, whose characteristics are shaped by the converter's control characteristics. The converters currently widely used in practical projects employ a grid-following control strategy. This involves using a phase-locked loop (PLL) to synchronize the converter with the grid and employing vector current control to control the converter's output current, thereby controlling the active and reactive power fed into the grid. Essentially, they are controlled current sources, whose primary control objective is to track the current maximum power of solar and wind energy and to convert them into electrical energy with maximum efficiency for feeding into the power system. Because this control strategy focuses on current, it cannot provide energy on demand, maintain supply-demand balance (frequency stability), or stabilize grid voltage.

[0004] Alternatively, new energy will occupy a major position in the power supply structure. As the proportion of new energy power generation devices increases to a certain level (the extreme case is 100% new energy), the synchronous generators in the system will no longer be able to achieve controlled and on-demand power balance, and stabilize the voltage of the entire power grid at a reasonable level. Such a system will not be able to operate stably and normally.

[0005] Alternatively, the existence of the above characteristics of renewable energy is related to both its own development laws and the relatively reduced voltage and frequency regulation and control capabilities of the power system after renewable energy replaces conventional power sources. The solution also needs to start from two aspects: First, iteratively upgrade the power generation characteristics of renewable energy so that it has the good characteristics of conventional hydroelectric, thermal and other synchronous generators to support the power grid and can work in conjunction with synchronous generators. This direction affects the power generation capacity of renewable energy, reduces economic efficiency, and is difficult to achieve in the short term. Second, add new equipment near the renewable energy with the voltage control and energy regulation capabilities of synchronous generators or similar synchronous generators to ensure that the renewable energy can be absorbed while eliminating the adverse effects of renewable energy on the power system and improving system stability.

[0006] However, the current requirements for configuring energy storage for new energy are all proposed from the perspective of solving the fluctuations of new energy. Simple calculations based on a certain proportion of the scale of new energy construction show that there is energy storage redundancy or waste of resources due to unconditional grid connection after new energy construction.

[0007] Summary of the Invention

[0008] In view of this, the present application provides a multi-type source storage configuration method, device, computer equipment and storage medium to solve the problem that the existing energy storage configuration causes the power grid system to be unable to operate stably and normally, and there is energy storage redundancy or unconditional grid connection after new energy construction and resource waste.

[0009] In a first aspect, the present application provides a multi-type source-storage configuration method for a regional power grid system; the method comprises:

[0010] Obtain the source-grid-load-storage configuration dataset, load forecast dataset, and multi-type energy storage characteristic dataset of the regional power grid system; analyze the regional power grid system according to preset requirements based on the source-grid-load-storage configuration dataset, load forecast dataset, and multi-type energy storage characteristic dataset to obtain stability indicators; use the preset full life cycle model to determine economic indicators and carbon trading indicators; based on the stability indicators, economic indicators, and carbon trading indicators, use the source-grid-load-storage configuration dataset and load forecast dataset to perform multi-type source-storage configuration on the regional power grid system and obtain multi-type source-storage configuration results.

[0011] The multi-type source-storage configuration method provided in this application can analyze the demand of the regional power grid system through the source-grid-load-storage configuration data set and load forecast data set of the regional power grid system, and obtain the corresponding stability index. Optionally, the corresponding economic indicators and carbon trading indicators can be determined separately by presetting the full life cycle model, thereby realizing the multi-type source-storage configuration of the regional power grid system, which essentially solves the regional power grid stability problem caused by the increase in the proportion of new energy construction, and takes into account the economic indicators and carbon trading indicators, solving the problems of insufficient economy and low call rate of source-storage configuration.

[0012] In an optional embodiment, based on the source-grid-load-storage configuration dataset, the load forecast dataset, and the multi-type energy storage characteristic dataset, the regional power grid system is analyzed according to preset requirements to obtain stability indicators, including:

[0013] Based on the source-grid-load-storage configuration dataset and the load forecast dataset, the regional power grid stability power generation model and target resource quantity are determined; based on the multi-type energy storage characteristic dataset, the stability index parameter value is determined; based on the regional power grid stability power generation model and the stability index parameter value, the target resource quantity is determined; based on the regional power grid stability power generation model and the target resource quantity, the stability index is determined.

[0014] This application can determine the corresponding stability indicators through the source-grid-load-storage configuration dataset and load forecast dataset of the regional power grid system, providing support for the subsequent multi-type source-storage configuration of the regional power grid system.

[0015] In an optional embodiment, a preset full life cycle model is used to determine economic indicators and carbon trading indicators, including:

[0016] Determine the economic indicator parameter set based on the preset full life cycle model; determine the economic indicator and carbon trading indicator based on the economic indicator parameter set; determine the stability indicator based on the multi-type energy storage characteristic data set and stability indicator parameters.

[0017] In an optional embodiment, determining the economic index and the carbon trading index based on the economic index parameter set includes:

[0018] Obtain a first economic indicator parameter subset and a second economic indicator parameter subset corresponding to the economic indicator parameter set; determine the economic indicator based on the first economic indicator parameter subset; and determine the carbon trading indicator based on the second economic indicator parameter subset.

[0019] In an optional embodiment, based on the source-grid-load-storage configuration dataset and the load forecast dataset, after processing the stability index, economic index, and carbon trading index, a multi-type source-storage configuration result is determined, including:

[0020] Based on the source-grid-load-storage configuration data set, the load forecast data set and the stability index, the first energy storage resource set is determined; based on the stability index, the economic index and the carbon trading index, the constraint condition set is determined; based on the first energy storage resource and the constraint condition set, the regional power grid system is configured with multiple types of sources and storages to obtain the multiple types of source and storage configuration results.

[0021] This application takes economic indicators and carbon trading indicators into consideration, solving the problems of insufficient economy and low call rate of source and storage configuration.

[0022] In an optional embodiment, determining the first energy storage resource set based on the source-grid-load-storage configuration dataset, the load forecast dataset, and the stability index includes:

[0023] Based on the source-grid-load-storage configuration data set and the load forecast data set, the range of energy storage resource types corresponding to the regional power grid system is determined; based on the range of energy storage resource types and stability indicators, the first energy storage resource set is determined.

[0024] In an optional embodiment, a set of constraints is determined based on the stability index, the economic index, and the carbon trading index, including:

[0025] The first constraint condition is determined based on the stability index; the second constraint condition is determined based on the economic index and the carbon trading index; and the constraint condition set is determined based on the first constraint condition and the second constraint condition.

[0026] In a second aspect, the present application provides a multi-type source-storage configuration device for a regional power grid system; the device includes:

[0027] The acquisition module is used to obtain the source-grid-load-storage configuration data set, load forecast data set and multi-type energy storage characteristic data set of the regional power grid system; the analysis module is used to analyze the regional power grid system according to preset requirements based on the source-grid-load-storage configuration data set, load forecast data set and multi-type energy storage characteristic data set to obtain stability indicators; the determination module is used to use the preset full life cycle model to determine economic indicators and carbon trading indicators; the configuration module is used to perform multi-type source-storage configuration of the regional power grid system based on stability indicators, economic indicators and carbon trading indicators using the source-grid-load-storage configuration data set and load forecast data set to obtain multi-type source-storage configuration results.

[0028] In a third aspect, the present application provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the multi-type source storage configuration method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the multi-type source-storage configuration method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0031] FIG1 is a schematic diagram of a flow chart of a multi-type source and storage configuration method according to an embodiment of the present application;

[0032] FIG2 is a schematic flow chart of another multi-type source and reservoir configuration method according to an embodiment of the present application;

[0033] FIG3 is a flow chart of another multi-type source and reservoir configuration method according to an embodiment of the present application;

[0034] FIG4 is a structural block diagram of a multi-type source storage configuration device according to an embodiment of the present application;

[0035] FIG5 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] To make the purpose, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0037] The embodiment of the present application provides a multi-type source-storage configuration method, which essentially solves the regional power grid stability problem caused by the increase in the proportion of new energy construction, and takes economic indicators and carbon trading indicators into consideration, solving the problems of insufficient economy and low call rate of source-storage configuration.

[0038] According to an embodiment of the present application, an embodiment of a multi-type source-storage configuration method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] In this embodiment, a multi-type source-storage configuration method is provided, which can be used in a regional power grid system. FIG1 is a flow chart of the multi-type source-storage configuration method according to an embodiment of the present application. As shown in FIG1 , the process includes the following steps:

[0040] Step S101: obtaining a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of a regional power grid system.

[0041] Specifically, the source-grid-load-storage configuration data set may include configuration data of different types of energy storage resources in the regional power grid system.

[0042] Optionally, the load forecast data set may include a plurality of future load forecast data of the regional power grid system.

[0043] Optionally, a multi-type energy storage characteristic dataset is used to reflect different types of new energy and energy storage technology characteristics of the regional power grid system.

[0044] Step S102 : Based on the source-grid-load-storage configuration data set, the load forecast data set, and the multi-type energy storage characteristic data set, the regional power grid system is analyzed according to preset requirements to obtain a stability index.

[0045] Specifically, by combining the source-grid-load-storage configuration dataset, load forecast dataset, and the characteristics of different types of renewable energy and energy storage technologies of the regional power grid system, a demand analysis of the regional power grid system can be carried out to obtain the stability index of the regional power grid system.

[0046] Step S103: Determine economic indicators and carbon trading indicators using a preset full life cycle model.

[0047] Among them, the preset full life cycle model is used to reflect the relevant status of the regional power grid system throughout its entire life cycle.

[0048] Specifically, by using the preset full life cycle model processing, the corresponding stability indicators, economic indicators and carbon trading indicators can be determined.

[0049] Step S104 , based on the stability index, economic index and carbon trading index, the source-grid-load-storage configuration dataset and the load forecast dataset are used to perform multi-type source-storage configuration on the regional power grid system to obtain a multi-type source-storage configuration result.

[0050] Specifically, with stability indicators, economic indicators and carbon trading indicators as constraints, combined with the source-grid-load-storage configuration dataset and load forecast dataset of the regional power grid system, multi-type source-storage configuration is performed on the regional power grid system, and the optimal multi-type source-storage configuration result can be obtained.

[0051] The multi-type source-storage configuration method provided in this embodiment uses the source-grid-load-storage configuration dataset and load forecast dataset of the regional power grid system to perform demand analysis on the regional power grid system and obtain corresponding stability index parameters. Optionally, the corresponding stability index, economic index, and carbon trading index can be determined respectively through the multi-type energy storage characteristic dataset of the regional power grid system, thereby realizing multi-type source-storage configuration of the regional power grid system, essentially solving the regional power grid stability problem caused by the increase in the proportion of new energy deployment, and taking into account the economic index and carbon trading index, solving the problems of insufficient economy and low call rate of source-storage configuration.

[0052] In this embodiment, a multi-type source-storage configuration method is provided, which can be used in a regional power grid system. FIG2 is a flow chart of the multi-type source-storage configuration method according to an embodiment of the present application. As shown in FIG2 , the flow chart includes the following steps:

[0053] Step S201: Acquire a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of a regional power grid system. For details, refer to step S101 of the embodiment shown in FIG1 , which will not be described in detail here.

[0054] Step S202 : Based on the source-grid-load-storage configuration data set, the load forecast data set, and the multi-type energy storage characteristic data set, the regional power grid system is analyzed according to preset requirements to obtain a stability index.

[0055] Specifically, the above step S202 includes:

[0056] Step S2021: Determine a regional power grid stability power generation model based on the source-grid-load-storage configuration dataset and the load forecast dataset.

[0057] Specifically, by combining the source-grid-load-storage configuration dataset and the load forecast dataset of the regional power grid system, a regional power grid stability power generation model P can be established.

[0058] Among them, the regional power grid stability power generation model P is used to represent the minimum and maximum power generation capacity of the regional power grid system, as shown in the following relationship (1): P=P1+P2+P3+……+Pn (1)

[0059] Step S2022: Determine stability index parameter values ​​based on the multi-type energy storage characteristic data sets.

[0060] The stability index parameter value R is used to indicate that the synchronous generator or a synchronous generator-like generator has the frequency regulation and voltage control capability. If the generator has this capability, the stability index parameter value R is 1; if the generator does not have this capability, the stability index parameter value R is 0.

[0061] 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.

[0062] Step S2023: Determine the target resource quantity based on the regional power grid stability power generation model and the stability index parameter value.

[0063] The target resource quantity S is used to indicate the frequency regulation and voltage control capability of a synchronous generator or a synchronous generator-like generator.

[0064] Specifically, according to the obtained regional power grid stability power generation, the quantity model P and the stability index parameter value R can be used to obtain the corresponding target resource quantity S, as shown in the following relationship (2): S = P*R1+P*R2+P*R3+……+P*Rn (2)

[0065] Step S2024: Determine a stability index based on the regional power grid stability power generation model and the target resource quantity.

[0066] Specifically, the corresponding stability index F(P, S) can be determined based on the regional power grid stability power generation model P and the target resource quantity S, as shown in the following relationship (3): F(P, S) = F(P1+P2+P3+…+Pn, P*R1+P*R2+P*R3+…+P*Rn) (3)

[0067] Step S203: Determine economic indicators and carbon trading indicators using a preset full life cycle model.

[0068] Specifically, the above step S203 includes:

[0069] Step S2031: Determine an economic indicator parameter set based on a preset full life cycle model.

[0070] Specifically, the economic indicator parameter set is used to reflect the corresponding cost, power, etc. of the regional power grid system.

[0071] Step S2032: Determine the economic index and the carbon trading index based on the economic index parameter set.

[0072] Among them, economic indicators may include unit electricity cost and unit mileage cost.

[0073] Specifically, the corresponding economic indicators and carbon trading indicators can be determined respectively according to the obtained economic indicator parameter set.

[0074] In some optional implementations, the above step S2032 includes:

[0075] Step a1: Acquire a first economic indicator parameter subset and a second economic indicator parameter subset corresponding to the economic indicator parameter set.

[0076] Step a2: determining the economic performance index based on the first economic performance index parameter.

[0077] Step a3: determining the carbon trading index based on the second economic index parameter subset.

[0078] Specifically, the first economic indicator parameter subset may include the fixed cost per unit electricity and the peak-shaving marginal cost corresponding to the regional power grid system over the entire life cycle.

[0079] Optionally, the second economic indicator parameter subset may include the power generation power and carbon emission coefficient corresponding to the regional power grid system.

[0080] Alternatively, the economic indicators can be determined using the following relationships (4) and (5): Unit electricity cost = fixed unit electricity cost over the entire life cycle + marginal cost of peak regulation (4) Unit mileage cost = fixed unit electricity cost over the entire life cycle + marginal cost of frequency regulation (5)

[0081] Alternatively, the carbon trading index is determined using the following relationship (6): Carbon trading index = total energy storage power / generated power × carbon emission coefficient (6)

[0082] In step S204, based on the stability, economic, and carbon trading indicators, the source-grid-load-storage configuration dataset and the load forecast dataset are used to perform multi-type source-storage configuration on the regional power grid system, obtaining a multi-type source-storage configuration result. For details, please refer to step S104 in the embodiment shown in Figure 1 and will not be repeated here.

[0083] The multi-type source-storage configuration method provided in this embodiment uses the source-grid-load-storage configuration dataset and load forecast dataset of the regional power grid system to perform demand analysis on the regional power grid system and obtain corresponding stability indicators. Optionally, corresponding economic indicators and carbon trading indicators can be determined separately through a preset full life cycle model, thereby realizing multi-type source-storage configuration for the regional power grid system. This essentially solves the regional power grid stability issues caused by the increase in the proportion of new energy deployment. By taking economic indicators and carbon trading indicators into consideration, it solves the problems of insufficient economic efficiency and low utilization rate of source-storage configuration.

[0084] In this embodiment, a multi-type source-storage configuration method is provided, which can be used in a regional power grid system. FIG3 is a flow chart of the multi-type source-storage configuration method according to an embodiment of the present application. As shown in FIG3 , the flow chart includes the following steps:

[0085] Step S301: Acquire a source-grid-load-storage configuration dataset, a load forecast dataset, and a multi-type energy storage characteristic dataset of a regional power grid system. For details, please refer to step S101 of the embodiment shown in FIG1 , which will not be described in detail here.

[0086] In step S302, based on the source-grid-load-storage configuration dataset, the load forecast dataset, and the multi-type energy storage characteristic dataset, the regional power grid system is analyzed according to preset requirements to obtain a stability index. For details, please refer to step S202 of the embodiment shown in FIG2 , which will not be repeated here.

[0087] Step S303: Determine the economic performance index and carbon trading index using the preset full life cycle model. For details, please refer to step S203 of the embodiment shown in FIG2 , which will not be described in detail here.

[0088] Step S304 , based on the stability index, economic index and carbon trading index, the source-grid-load-storage configuration dataset and the load forecast dataset are used to perform multi-type source-storage configuration on the regional power grid system to obtain a multi-type source-storage configuration result.

[0089] Specifically, the above step S304 includes:

[0090] Step S3041: Determine a first energy storage resource set based on a source-grid-load-storage configuration data set, a load forecast data set, and a stability index.

[0091] Specifically, the obtained 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 power grid system.

[0092] In some optional implementations, step S3041 includes:

[0093] Step b1: Determine the energy storage resource type range corresponding to the regional power grid system based on the source-grid-load-storage configuration dataset and the load forecast dataset.

[0094] Step b2: Determine a first energy storage resource set based on the energy storage resource type range and stability index.

[0095] Specifically, by combining the acquired source-grid-load-storage configuration dataset and load forecast dataset of the regional power grid system, the range of feasible new energy and energy storage types corresponding to the regional power grid system can be determined.

[0096] Optionally, the corresponding first energy storage resource set may be determined by combining the obtained energy storage resource type range and stability index:

[0097] (1) Energy storage systems with frequency regulation and voltage control capabilities similar to synchronous generators include:

[0098] (11) Mechanical energy storage (pumped storage, compressed air storage, lava heat storage, hydrogen storage, etc.);

[0099] (11) Electrochemical energy storage (lithium battery, nano battery, liquid flow, etc.) + network-type PCS;

[0100] (13) Power-type energy storage (flywheel energy storage, supercapacitor, superconducting energy storage, etc.).

[0101] (2) New energy sources with frequency regulation AGC and voltage control AVC capabilities of synchronous generators or similar synchronous generators include:

[0102] (21) Geothermal power generation system;

[0103] (22) Biomass power generation system;

[0104] (23) Solar thermal power generation system;

[0105] (24) Nuclear power system;

[0106] (25) Ocean energy power generation system;

[0107] (26) Photovoltaic + grid-connected inverter system;

[0108] (27) Wind power + grid-connected inverter system.

[0109] Step S3042: Determine a set of constraint conditions based on the stability index, economic index, and carbon trading index.

[0110] Specifically, according to the obtained stability index, economic index and carbon trading index, a corresponding set of constraint conditions can be determined.

[0111] In some optional implementations, step S3042 includes:

[0112] Step c1: determining a first constraint condition based on a stability index.

[0113] Step c2: determining a second constraint condition based on the economic index and the carbon trading index.

[0114] Step c3: determining a constraint condition set based on the first constraint condition and the second constraint condition.

[0115] Specifically, the maximum stability index is set as the first constraint condition, and the minimum economic index and carbon trading index are set as the second constraint condition.

[0116] Step S3043: Based on the first energy storage resource and the constraint condition set, multi-type source-storage configuration is performed on the regional power grid system to obtain a multi-type source-storage configuration result.

[0117] Specifically, under the first constraint of maximizing the stability index and the second constraint of minimizing the economic index and carbon trading index, the regional power grid system is configured with corresponding new energy and energy storage resources, which can obtain the optimal source and storage configuration of the regional power grid system, that is, the multi-type source and storage configuration result.

[0118] The multi-type source-storage configuration method provided in this embodiment uses the source-grid-load-storage configuration dataset and load forecast dataset of the regional power grid system to perform demand analysis on the regional power grid system and obtain corresponding stability index parameters. Optionally, the corresponding stability index, economic index, and carbon trading index can be determined respectively through the multi-type energy storage characteristic dataset of the regional power grid system, thereby realizing multi-type source-storage configuration of the regional power grid system, essentially solving the regional power grid stability problem caused by the increase in the proportion of new energy deployment, and taking into account the economic index and carbon trading index, solving the problems of insufficient economy and low call rate of source-storage configuration.

[0119] This embodiment also provides a multi-type source storage configuration device for implementing the above-mentioned embodiments and optional implementations. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0120] This embodiment provides a multi-type source-storage configuration device for a regional power grid system, as shown in FIG4 , including:

[0121] Acquisition module 401, 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 power grid system;

[0122] The analysis module 402 is used to analyze the regional power grid system according to preset requirements based on the source-grid-load-storage configuration data set, the load forecast data set and the multi-type energy storage characteristic data set to obtain stability indicators.

[0123] The determination module 403 is used to determine the economic index and the carbon trading index by using a preset full life cycle model.

[0124] Configuration module 404 is used to perform multi-type source-storage configuration on the regional power grid system based on stability indicators, economic indicators and carbon trading indicators using the source-grid-load-storage configuration dataset and the load forecast dataset to obtain multi-type source-storage configuration results.

[0125] In some optional implementations, the analysis module 402 includes:

[0126] The first determination submodule is used to determine a regional power grid stability power generation model based on a source-grid-load-storage configuration data set and a load forecast data set.

[0127] The second determination submodule is used to determine the stability index parameter value based on the multi-type energy storage characteristic data set.

[0128] The third determination submodule is used to determine the target resource amount based on the regional power grid stability power generation model and the stability index parameter value.

[0129] The fourth determination submodule is used to determine the stability index based on the regional power grid stability power generation model and the target resource quantity.

[0130] In some optional implementations, the determining module 403 includes:

[0131] The fifth determination submodule is used to determine the economic indicator parameter set based on a preset full life cycle model.

[0132] The sixth determination submodule is used to determine the economic index and the carbon trading index based on the economic index parameter set.

[0133] In some optional implementations, the sixth determining submodule includes:

[0134] The acquisition unit is used to acquire the first economic indicator parameter subset and the second economic indicator parameter subset corresponding to the economic indicator parameter set.

[0135] The first determining unit is configured to determine the economic performance index based on the first economic performance index parameter.

[0136] The second determining unit is configured to determine a carbon trading index based on a second economic index parameter subset.

[0137] In some optional implementations, the configuration module 404 includes:

[0138] The seventh determination submodule is used to determine the first energy storage resource set based on the source-grid-load-storage configuration data set, the load forecast data set and the stability index.

[0139] The eighth determination submodule is used to determine a set of constraint conditions based on the stability index, the economic index and the carbon trading index.

[0140] The configuration submodule is used to perform multi-type source-storage configuration on the regional power grid system based on the first energy storage resource and the constraint condition set, and obtain a multi-type source-storage configuration result.

[0141] In some optional implementations, the seventh determining submodule includes:

[0142] The third determining unit is used to determine the range of energy storage resource types corresponding to the regional power grid system based on the source-grid-load-storage configuration data set and the load forecast data set.

[0143] The fourth determining unit is configured to determine the first energy storage resource set based on the energy storage resource type range and the stability index.

[0144] In some optional implementations, the eighth determining submodule includes:

[0145] The fifth determining unit is configured to determine a first constraint condition based on the stability index.

[0146] The sixth determining unit is configured to determine a second constraint condition based on the economic index and the carbon trading index.

[0147] The seventh determining unit is configured to determine a constraint condition set based on the first constraint condition and the second constraint condition.

[0148] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0149] The multi-type source-storage configuration device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0150] An embodiment of the present application further provides a computer device having the multi-type source storage configuration device shown in FIG. 4 .

[0151] Please refer to Figure 5, which is a structural diagram of a computer device provided by an optional embodiment of the present application. As shown in Figure 5, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 takes a processor 10 as an example.

[0152] The processor 10 may be a central processing unit (CPU), a network processor (NPU), or a combination thereof. The processor 10 may also include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device (PLD) may be a complex programmable logic device (CPLD), a field programmable gate array (FPGA), a general purpose array logic (GAL), or any combination thereof.

[0153] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0154] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0155] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0156] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0157] The embodiments of the present application also provide a computer-readable storage medium. The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; optionally, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0158] Although the embodiments of the present application have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A multi-type source-storage configuration method for a regional power grid system; characterized in that, The method includes: Obtaining a source-network-load-storage configuration dataset, a load prediction dataset, and a multi-type energy storage characteristic dataset of the regional power grid system; Analyzing the regional power grid system according to preset requirements based on the source-network-load-storage configuration dataset, the load prediction dataset, and the multi-type energy storage characteristic dataset to obtain stability indicators; Determining economic indicators and carbon trading indicators using a preset full life cycle model; Based on the stability indicators, the economic indicators, and the carbon trading indicators, performing multi-type source-storage configuration on the regional power grid system using the source-network-load-storage configuration dataset and the load prediction dataset to obtain a multi-type source-storage configuration result.

2. The method according to claim 1, wherein Analyzing the regional power grid system according to preset requirements based on the source-network-load-storage configuration dataset, the load prediction dataset, and the multi-type energy storage characteristic dataset to obtain stability indicators, including: Determining a regional power grid stability power generation model based on the source-network-load-storage configuration dataset and the load prediction dataset; Determining stability indicator parameter values based on the multi-type energy storage characteristic dataset; Determining target resource quantities based on the regional power grid stability power generation model and the stability indicator parameter values; Determining the stability indicators based on the regional power grid stability power generation model and the target resource quantities.

3. The method according to claim 1, characterized in that Determining economic indicators and carbon trading indicators using a preset full life cycle model, including: Determining an economic indicator parameter set based on the preset full life cycle model; Determining the economic indicators and the carbon trading indicators based on the economic indicator parameter set.

4. The method according to claim 3, characterized in that, Determining the economic indicators and the carbon trading indicators based on the economic indicator parameter set, including: Obtaining a first economic indicator parameter subset and a second economic indicator parameter subset corresponding to the economic indicator parameter set; Determining the economic indicators based on the first economic indicator parameter subset; Determining the carbon trading indicators based on the second economic indicator parameter subset.

5. The method according to claim 1, characterized in that, Based on the stability indicators, the economic indicators, and the carbon trading indicators, performing multi-type source-storage configuration on the regional power grid system using the source-network-load-storage configuration dataset and the load prediction dataset to obtain a multi-type source-storage configuration result, including: Determining a first energy storage resource set based on the source-network-load-storage configuration dataset, the load prediction dataset, and the stability indicators; Determining a constraint condition set based on the stability indicators, the economic indicators, and the carbon trading indicators; Performing multi-type source-storage configuration on the regional power grid system based on the first energy storage resource and the constraint condition set to obtain the multi-type source-storage configuration result.

6. The method according to claim 5, wherein Determining a first energy storage resource set based on the source-network-load-storage configuration dataset, the load prediction dataset, and the stability indicators, including: Determining the range of energy storage resource types corresponding to the regional power grid system based on the source-network-load-storage configuration dataset and the load prediction dataset; Determining the first energy storage resource set based on the range of energy storage resource types and the stability indicators.

7. The method according to claim 5, characterized in that, Determining a constraint condition set based on the stability indicators, the economic indicators, and the carbon trading indicators, including: Determine the first constraint condition based on the stability index; Determine the second constraint condition based on the economic index and the carbon trading index; Determine the set of constraint conditions based on the first constraint condition and the second constraint condition.

8. A multi-type source-storage configuration device for a regional power grid system; characterized in that, The device includes: An acquisition module, configured to acquire the source-network-load-storage configuration data set, the load prediction data set, and the multi-type energy storage characteristic data set of the regional power grid system; An analysis module, configured to analyze the regional power grid system according to preset requirements based on the source-network-load-storage configuration data set, the load prediction data set, and the multi-type energy storage characteristic data set, and obtain a stability index; A determination module, configured to determine an economic index and a carbon trading index by using a preset full life cycle model; A configuration module, configured to, based on the stability index, the economic index, and the carbon trading index, perform multi-type source-storage configuration on the regional power grid system by using the source-network-load-storage configuration data set and the load prediction data set, and obtain a multi-type source-storage configuration result.

9. A computer device, characterized in that, It includes: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the multi-type source-storage configuration method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the multi-type source-storage configuration method according to any one of claims 1 to 7.

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