Wind-solar hydrogen storage equipment installation strategy prediction method and device based on urban space

Abstract

The invention discloses a wind and light hydrogen storage equipment installation strategy prediction method and device based on an urban space. The method comprises the following steps: predicting land utilization rasterized data evolved by a future urban space land utilization condition; determining a total power load value corresponding to the second land utilization rasterized data; establishing a target optimization function of the installation strategy of the wind and light hydrogen storage equipment; establishing constraint conditions; and combining the target optimization function with the constraint condition, and resolving to obtain an installation strategy of the wind and light hydrogen storage equipment which enables the target optimization function to be optimal and meets the constraint condition. According to the method, the future urban space land utilization evolution condition can be predicted, the target optimization function and the constraint condition for establishing the installation strategy of the wind and light hydrogen storage equipment are determined, and the installation strategy of the wind and light hydrogen storage equipment which enables the target optimization function to be optimal and meets the constraint condition is obtained through calculation. The obtained installation strategy can be associated with future urban space land utilization evolution conditions, and the advantage of determining the optimal effect and newly-added installed capacity in the actual environment is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of smart city and new power system planning, and in particular relates to a method and device for predicting the installation strategy of wind, solar and hydrogen storage equipment based on urban space. Background Art

[0002] Under existing technical conditions, power planning generally suffers from the problem of being out of touch with dynamic urban construction. For example, power load forecasts are not correlated with the evolution of land use types. The expansion of existing power systems and the installation strategies of wind, solar, and hydrogen storage equipment usually rely on experience, and the prediction of the evolution of urban land use is obviously insufficient. Therefore, there is a problem that the installation strategies of wind, solar, and hydrogen storage equipment based on experience cannot achieve the optimal installed capacity in the actual environment. Summary of the Invention

[0003] In view of this, the present invention aims to overcome the defects in the prior art and proposes a method and device for predicting the installation strategy of wind-solar-hydrogen storage equipment based on urban space.

[0004] To achieve the above object, the technical solution of the present invention is achieved as follows: In a first aspect, the present invention discloses a method for predicting the installation strategy of wind-solar-hydrogen storage equipment based on urban space, comprising: inputting historical data of first land use rasterized data, driving factor rasterized data, and neighborhood factor rasterized data of the same area into a neural network model to obtain second land use rasterized data for predicting the future evolution of land use, wherein the first land use rasterized data is data for characterizing the distribution of land use types, the driving factor rasterized data is data for characterizing the influence on the evolution of land use, and the neighborhood factor rasterized data is data for characterizing the distribution of land use types within a set neighborhood range of each cell in the grid; Based on the second land use rasterized data, according to the power load index value corresponding to each land use type, a total power load value corresponding to the second land use rasterized data is obtained; Based on the total power load, determine the required number of substations and the power supply area of ​​each substation, and obtain the maximum area that can be occupied by the planned photovoltaic equipment, wind power equipment, energy storage equipment, and hydrogen production equipment in each power supply area; Establish a target optimization function for the installation strategy of wind-solar-hydrogen storage equipment, where the target optimization function is used to minimize the sum of the installed investment cost and operation and maintenance cost of the wind-solar-hydrogen storage equipment; Establishing constraints, including: constraints based on the maximum area that can be occupied by wind power equipment, photovoltaic equipment, energy storage equipment, and hydrogen production equipment planned for the power supply area; The objective optimization function and constraints are combined to obtain the installation strategy of wind-solar-hydrogen storage equipment that makes the objective optimization function optimal and satisfies the constraints.

[0005] In one embodiment of the present invention, it also includes: using historical data of the first land use rasterized data, driving factor rasterized data and neighborhood factor rasterized data for several years as input, and then historical data of the first land use rasterized data for at least one year as output to train the neural network model.

[0006] In one embodiment of the present invention, based on the second land use rasterized data, according to the power load index value corresponding to each land use type, the total power load value corresponding to the second land use rasterized data is obtained, including: the total power load value is based on the second land use rasterized data, and the sum of the products of the area of ​​each land use type and the corresponding power load index value is calculated.

[0007] In one embodiment of the present invention, the required number of substations and the power supply area of ​​each substation are determined based on the total power load value, including: obtaining the capacity of the current power facilities, calculating the capacity-load ratio based on the total power load value and the capacity, and if the capacity-load ratio does not meet the design standard, adding at least one substation in the public facility land to make the capacity-load ratio meet the design standard.

[0008] In one embodiment of the present invention, the installed capacity investment cost includes: the sum of the wind power equipment investment cost, the photovoltaic equipment investment cost, the energy storage equipment investment cost and the hydrogen production equipment investment cost.

[0009] In one embodiment of the present invention, the constraints further include: energy storage constraints, hydrogen production constraints, regional external power purchase constraint kits, power balance constraints, and substation voltage and current constraints.

[0010] In a second aspect, the present invention discloses a device for predicting the installation strategy of wind-solar hydrogen storage equipment based on urban space, the device comprising: A prediction module is used to input historical data of first land use rasterized data, driving factor rasterized data, and neighborhood factor rasterized data of the same area into a neural network model to obtain second land use rasterized data for predicting future land use evolution, wherein the first land use rasterized data is data for characterizing the distribution of land use types, the driving factor rasterized data is data for characterizing the influence on the evolution of land use, and the neighborhood factor rasterized data is data for characterizing the distribution of land use types within a set neighborhood range of each cell in the grid; A first determining module is configured to obtain, based on the second land use rasterized data and according to the power load index value corresponding to each land use type, a total power load value corresponding to the second land use rasterized data; The second determination module is used to determine the required number of substations and the power supply area of ​​each substation based on the total power load value, and obtain the maximum area that can be occupied by the planned photovoltaic equipment, wind power equipment, energy storage equipment, and hydrogen production equipment in each power supply area; A target optimization function determination module is used to establish a target optimization function for the installation strategy of wind-solar-hydrogen storage equipment, wherein the target optimization function is used to characterize the minimum sum of the installation investment cost and operation and maintenance cost of the wind-solar-hydrogen storage equipment; Establishing a constraint condition module, used to establish constraint conditions, including: constraints based on the maximum area that can be occupied by wind power equipment, photovoltaic equipment, energy storage equipment, and hydrogen production equipment planned in the power supply area; The installation strategy determination module is used to jointly establish the objective optimization function and the constraints, and calculate the installation strategy of the wind, solar and hydrogen storage equipment that optimizes the objective optimization function and satisfies the constraints.

[0011] In a third aspect, the present invention discloses an electronic device comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0012] In a fourth aspect, the present invention discloses a computer-readable storage medium having a computer program stored thereon, which implements the above method when executed by a processor.

[0013] In a fifth aspect, the present invention discloses a computer program product, comprising a computer program, which implements the above method when executed by a processor.

[0014] Compared with the prior art, the present invention has the following advantages: The present invention discloses a method and device for predicting the installation strategy of wind-solar hydrogen storage equipment based on urban space, comprising: predicting land use rasterized data of the future evolution of urban space land use; determining the total power load value corresponding to the second land use rasterized data; establishing a target optimization function for the installation strategy of wind-solar hydrogen storage equipment; establishing constraints; combining the target optimization function and the constraints, and solving to obtain the installation strategy of wind-solar hydrogen storage equipment that makes the target optimization function optimal and satisfies the constraints. The present invention discloses a method and device for predicting the installation strategy of wind-solar hydrogen storage equipment based on urban space, which can predict the future evolution of land use, and on this basis, determine the target optimization function and constraints for establishing the installation strategy of wind-solar hydrogen storage equipment, and solve to obtain the installation strategy of wind-solar hydrogen storage equipment that makes the target optimization function optimal and satisfies the constraints. The obtained installation strategy can be associated with the future evolution of land use, and achieve the advantage of determining the optimal effect of newly added installed capacity in an actual environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0016] In the attached figure: Figure 1 This is a schematic diagram of a method for predicting wind-solar hydrogen storage equipment installation strategy based on urban space according to an embodiment of the present invention; Figure 2 This is a schematic diagram of constraints for a method for predicting wind-solar hydrogen storage equipment installation strategies based on urban space according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a device for predicting wind-solar hydrogen storage equipment installation strategies based on urban space according to an embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device for predicting the installation strategy of wind-solar hydrogen storage equipment based on urban space in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0018] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0019] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0020] In the description of the present invention, it should be further clarified that the terms "first," "second," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first," "second," etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0021] Under existing technical conditions, there is a common problem of disconnection between power planning and dynamic urban construction. Therefore, the expansion of the power system and the installation strategy of wind, solar and hydrogen storage equipment usually rely on experience to make judgments, and the prediction of the evolution of urban land use is obviously insufficient. There is a problem that the installation strategy of wind, solar and hydrogen storage equipment based on experience cannot achieve the optimal installed capacity in the actual environment. The present invention discloses a method and device for predicting the installation strategy of wind, solar and hydrogen storage equipment based on urban space, which can predict the future evolution of land use, determine the target optimization function and constraints for establishing the installation strategy of wind, solar and hydrogen storage equipment, and solve the installation strategy of wind, solar and hydrogen storage equipment that optimizes the target optimization function and satisfies the constraints, thereby achieving the advantage of determining the optimal installed capacity in the actual environment.

[0022] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] In one embodiment of the present invention, Figure 1 As shown, a method for predicting the installation strategy of wind-solar-hydrogen storage equipment based on urban space includes: Step S101: Inputting historical data of first land use rasterized data, driving factor rasterized data, and neighborhood factor rasterized data of the same region into a neural network model to obtain second land use rasterized data for predicting future land use evolution, wherein the first land use rasterized data is data for characterizing the distribution of land use types, the driving factor rasterized data is data for characterizing the influence on the evolution of land use, and the neighborhood factor rasterized data is data for characterizing the distribution of land use types within a set neighborhood range of each cell in the grid; In this embodiment, a database of rasterized data is established; For example, the grid resolution is 30m×30m, and these grids serve as basic cells for predicting the future evolution of land use.

[0024] In this embodiment, the neural network model is trained using several years of historical data of first land use rasterized data, driving factor rasterized data, and neighborhood factor rasterized data as input, and then at least one year of historical data of first land use rasterized data as output.

[0025] For example, the rasterized data of driving factors include: land resources, road traffic, supporting facilities, natural factors (elevation), current space (enterprise distribution), transportation convenience (highway entrances and exits, road accessibility), municipal infrastructure (energy infrastructure, water supply and drainage infrastructure), social environment (current land allocation, planned land type) and other key driving factors; Exemplarily, the neural network model may be a long short-term memory (LSTM) neural network model.

[0026] For example, the input data includes the first land use rasterized data, driving factor rasterized data, and neighborhood factor rasterized data from 2018 to 2023; the output data is the second land use rasterized data for each of the next Y years; Using the trained neural network model, a simulation is performed on future land use. By inputting historical data such as rasterized data of the first land use, rasterized data of driving factors, and rasterized data of neighborhood factors over several years, the neural network model can output land use forecasts for future years. During the simulation process, planning guidance information, such as urban planning policies and land use plans, is added. This information can serve as an additional input layer or constraint, influencing the model's output and thus enabling the rational planning of the development and construction schedule of future land parcels.

[0027] In this embodiment, the neighborhood factor rasterized data calculates the area of ​​each land use category within the neighborhood range of each cell. For example, the neighborhood range is 5×5 grids to capture local spatial relationships and reflect the spatial distribution characteristics of local land use.

[0028] Step S102, based on the second land use rasterized data, obtaining a total power load value corresponding to the second land use rasterized data according to the power load index value corresponding to each land use type; In this embodiment, land use types include eight categories: residential land, commercial service facility land, public management and public service facility land, industrial land, logistics and warehousing land, road and transportation facility land, public utility land, and green space and square land. In this embodiment, the total power load value is calculated based on the second land use rasterized data, and the sum of the products of the area of ​​each land use type and the corresponding power load index value is calculated.

[0029] Step S103: Based on the total power load value, determine the required number of substations and the power supply area of ​​each substation, and obtain the maximum area that can be occupied by the planned photovoltaic equipment, wind power equipment, energy storage equipment, and hydrogen production equipment in each power supply area; In this embodiment, based on the total power load value, the required number of substations and the power supply area of ​​each substation are determined, including: obtaining the capacity of the current power facilities, combining with the total power load value, calculating the capacity-load ratio; if the capacity-load ratio does not meet the design standard, then adding at least one substation in the public facility land so that the capacity-load ratio meets the design standard.

[0030] For example, the first phase of the neural network model prediction, that is, the second land use rasterized data for the first year in the future, is combined to determine the corresponding total power load value and the capacity of the power facilities, calculate the capacity load ratio of the first phase, and determine whether the capacity load ratio of the first phase meets the requirements of relevant specifications and standards. If so, the newly added power load value planned in the first phase will be borne by the existing substation. If not, a new substation facility will be added to the public utility land, and the corresponding capacity load ratio will continue to be calculated. If it still does not meet the requirements, substations will continue to be added until the corresponding capacity load ratio is within a reasonable range. Similarly, the second stage predicted by the neural network model, that is, the second land use rasterized data for the second year in the future, is combined to determine the corresponding total power load value and the capacity of the known power facilities, calculate the capacity load ratio of the second stage, and determine whether the capacity load ratio of the second stage meets the requirements of the specification standards. If it does, the substation with surplus capacity in the first stage will bear the additional power load of the second stage. If it does not meet the requirements, a new substation will be added until the capacity load ratio is within a reasonable range. This is analogous to the third and fourth stages predicted by the neural network model. Further, based on each stage, the power supply zone corresponding to each substation is determined, and the maximum area that can be occupied by the planned wind power equipment, photovoltaic equipment, energy storage equipment, and hydrogen production equipment in each power supply zone is counted, which are expressed as follows: 、 、 as well as , where n represents the corresponding n-th substation.

[0031] In this embodiment, the yth stage represents the second land use rasterized data for the future yth year predicted by the neural network model, thereby realizing the prediction of the evolution of land use.

[0032] Step S104, establishing a target optimization function for the installation strategy of the wind-solar-hydrogen storage equipment, wherein the target optimization function is used to characterize the minimum sum of the installation investment cost and the operation and maintenance cost of the wind-solar-hydrogen storage equipment; Exemplarily, the installed capacity investment cost includes the sum of the wind power equipment investment cost, the photovoltaic equipment investment cost, the energy storage equipment investment cost, and the hydrogen production equipment investment cost.

[0033] For example, the objective optimization function is expressed as : ; in, The installed investment cost of the wind-solar-hydrogen storage project within the power supply range of the nth substation in phase y; The operation and maintenance cost of the wind-solar-hydrogen storage project within the power supply range of the nth substation in phase y; Further: ; Among them, the unit investment cost of photovoltaic equipment is expressed as , the unit investment cost of wind power equipment is expressed as , the unit investment cost of hydrogen production equipment is expressed as , the unit capacity investment cost of energy storage equipment is expressed as , Investment cost per unit power of energy storage equipment ; No. The newly installed capacity of photovoltaic equipment within the power supply range of the nth substation in the stage is expressed as , No. The newly installed capacity of wind power equipment within the power supply range of the nth substation in the stage is expressed as , No. The newly installed capacity of energy storage equipment within the power supply range of the nth substation in the stage is expressed as , No. The newly installed capacity of hydrogen production equipment within the power supply range of the nth substation in the stage is expressed as , No. The newly installed power of energy storage equipment within the power supply range of the nth substation in the stage is expressed as , represents the discount rate; Further: ; in, and are the electricity purchase and sales prices of the interconnection lines with other regions in period t, respectively; is the unit power generation cost of the power plant; is the selling price per kilogram of hydrogen; The carbon emission cost per kilowatt-hour of electricity generated by traditional power plants in the region; and They are the power purchased and sold through the tie lines with external areas in the power supply range of the nth substation in the yth stage and the tth period respectively; The power generated by the traditional power plant in the power supply range of the nth substation in the yth period during the tth period; The weight of hydrogen produced in the t period of the wind-solar-hydrogen storage project within the power supply range of the nth substation in the yth phase; 、 、 and They are the unit installed capacity operating costs of photovoltaic equipment, wind power equipment, energy storage equipment, and hydrogen production equipment; 、 、 and They represent the installed capacity of photovoltaic equipment, wind power equipment, energy storage equipment and hydrogen production equipment within the power supply range of the nth substation in the yth stage, which is equal to the installed capacity of the previous stage plus 、 、 as well as ; and The photovoltaic and wind power generation power in the power supply range of the nth substation in the yth period during the tth period.

[0034] Step S105: establishing constraint conditions, which include: constraint conditions based on the maximum area that can be occupied by the wind power equipment, photovoltaic equipment, energy storage equipment, and hydrogen production equipment planned in the power supply area; For example, the constraints established based on the maximum land area of ​​each of the planned wind power equipment, photovoltaic equipment, energy storage equipment, and hydrogen production equipment in the power supply area are planned land use constraints, which are expressed as follows: ; in, 、 、 as well as Respectively represent the floor space per unit installed capacity of photovoltaic equipment, per unit installed capacity of wind power equipment, per unit installed capacity of energy storage equipment, and per unit installed capacity of hydrogen production equipment; Indicates that substation n is y Stage commissioning flag, 1 for commissioned, 0 for not commissioned; In step S106 , the objective optimization function and the constraints are combined to obtain an installation strategy for the wind-solar-hydrogen storage device that optimizes the objective optimization function and satisfies the constraints.

[0035] In this embodiment, for example, a particle swarm algorithm or a genetic algorithm can be used to solve the installation strategy of the wind-solar hydrogen storage equipment that optimizes the target optimization function and satisfies the constraints. The installation strategy is the newly installed capacity of the substation in the yth stage. 、 、 as well as ,according to 、 、 as well as Install wind and solar hydrogen storage equipment.

[0036] The equivalent capacity of photovoltaic equipment, wind power equipment, energy storage equipment and hydrogen production equipment is expressed as follows: ; in, They represent the equivalent capacities of photovoltaic equipment, wind power equipment, energy storage equipment, and hydrogen production equipment within the power supply range of the nth substation in the yth stage respectively; 、 、 and They are the annual capacity attenuation of photovoltaic equipment, wind power equipment, energy storage equipment and hydrogen production equipment.

[0037] Furthermore, the energy storage mathematical model and hydrogen production mathematical model are expressed as: Energy storage mathematical model: ; in, The energy storage charge state at the end of period t within the power supply range of the nth substation in stage y; The energy storage capacity within the power supply range of the nth substation in the yth stage at the end of the tth period; represents the equivalent capacity of the energy storage equipment within the power supply range of the nth substation in the yth stage; Charging efficiency for energy storage; is the energy storage discharge efficiency; The energy storage charging power at the end of period t within the power supply range of the nth substation in stage y; The energy storage discharge power at the end of time period t within the power supply range of the nth substation in stage y; Represents time; Mathematical model of hydrogen production: ;

[0038] in, The weight of hydrogen produced by the hydrogen production equipment within the power supply range of the nth substation in the yth stage during the tth period; is the weight of hydrogen produced per unit power; The hydrogen production power of the hydrogen production equipment within the power supply range of the nth substation in the yth stage during period t, Indicates time; On the basis of the above embodiment, in another embodiment of the present invention, as Figure 2 As shown, the constraints also include: energy storage constraints, hydrogen production constraints, regional external power purchase constraints, power balance constraints, and substation voltage and current constraints.

[0039] Example: The energy storage constraints are expressed as follows: ; in, and They are respectively the energy storage charging state bit and discharging state bit within the power supply range of the nth substation in the yth stage; is the maximum charge and discharge power of energy storage; and They are the lower and upper limits of the energy storage charge state within the power supply range of the nth substation in the yth stage respectively; The energy storage charge state at the end of period t within the power supply range of the nth substation in stage y; The energy storage charging power at the end of period t within the power supply range of the nth substation in stage y; The energy storage discharge power within the power supply range of the nth substation in the yth stage at the end of period t.

[0040] The hydrogen production constraints are expressed as follows: ; in, and The power consumption and standby power of the hydrogen production equipment in the power supply range of the nth substation in the yth stage during period t, is a constant; and They are the hydrogen production and standby states of the hydrogen production equipment within the power supply range of the nth substation in the yth stage respectively; The percentage of the lower limit of hydrogen production power of the hydrogen production equipment within the power supply range of the nth substation in the yth stage; The hydrogen production power of the hydrogen production equipment within the power supply range of the nth substation in the yth stage during period t; represents the equivalent capacity of hydrogen production equipment within the power supply range of the nth substation in stage y; ; in, Indicates the annual capacity attenuation of hydrogen production equipment; The constraints on regional electricity purchase are as follows: ; in, and They are the status of purchasing electricity from outside the region and the status of selling electricity within the power supply range of the nth substation in the yth stage; and are the maximum active power purchased and sold outside the region within the power supply range of the nth substation in the yth stage; further, and They are the power purchased and sold through the tie lines with external areas in the power supply range of the nth substation in the yth stage and the tth period respectively; ; in, and The reactive power status bit provided to the outside of the area and the reactive power status bit absorbed by the n-th substation in the y-th phase are respectively; and They are the upper limit of reactive power absorbed and provided outside the area within the power supply range of the nth substation in the yth stage respectively; and They are the reactive power absorbed and reactive power provided outside the area within the power supply range of the nth substation in the yth stage; The power balance constraint is expressed as follows: ; in, , , is the power consumption of the hydrogen production equipment within the power supply range of the nth substation in the yth stage during period t; The energy storage charging power at the end of period t within the power supply range of the nth substation in stage y; The energy storage discharge power at the end of time period t within the power supply range of the nth substation in stage y; and The photovoltaic and wind power generation power within the power supply range of the nth substation in the yth period in the tth period; 、 、 、 、 、 、 、 、 They represent the active power absorbed by other loads in the region, the active power emitted by traditional power plants, the reactive power emitted by wind power, the reactive power emitted by photovoltaic power, the reactive power absorbed by hydrogen production loads, the reactive power absorbed by energy storage, the reactive power emitted by energy storage, the reactive power absorbed by other loads in the region, and the reactive power emitted by traditional power plants within the power supply range of the nth substation in the yth stage during period t.

[0041] The voltage and current constraints of the substation are expressed as follows: ; in, represents the voltage of the nth substation in the yth phase and the tth period, represents the line current between two substations in period t during stage y, and Respectively represent the lower and upper limits of voltage; and They represent the lower limit and upper limit of the line current between the two substations in stage y respectively.

[0042] like Figure 3 As shown, the present invention also discloses a device for predicting the installation strategy of wind-solar hydrogen storage equipment based on urban space, comprising: The prediction module 301 is configured to input historical data of first land use rasterized data, driving factor rasterized data, and neighborhood factor rasterized data of the same region into a neural network model to obtain second land use rasterized data for predicting future land use evolution, wherein the first land use rasterized data is data for characterizing the distribution of land use types, the driving factor rasterized data is data for characterizing the influence on the evolution of land use, and the neighborhood factor rasterized data is data for characterizing the distribution of land use types within a set neighborhood range of each cell in the grid; A first determining module 302 is configured to obtain a total power load value corresponding to the second land use rasterized data based on the second land use rasterized data and according to the power load index value corresponding to each land use type; The second determination module 303 is configured to determine the required number of substations and the power supply area of ​​each substation based on the total power load value, and obtain the maximum area that can be occupied by the planned photovoltaic equipment, wind power equipment, energy storage equipment, and hydrogen production equipment in each power supply area; The target optimization function determination module 304 is used to establish a target optimization function for the installation strategy of the wind-solar hydrogen storage device, wherein the target optimization function is used to characterize the minimum sum of the installation investment cost and the operation and maintenance cost of the wind-solar hydrogen storage device; Establishing constraint condition module 305, for establishing constraint conditions, the constraint conditions including: constraint conditions established based on the maximum area that can be occupied by wind power equipment, photovoltaic equipment, energy storage equipment and hydrogen production equipment planned in the power supply area; The installation strategy determination module 306 is used to jointly determine the target optimization function and the constraints, and calculate the installation strategy of the wind-solar-hydrogen storage equipment that optimizes the target optimization function and satisfies the constraints.

[0043] The present invention also discloses an electronic device, such as Figure 4 As shown, an embodiment is disclosed, which is a block diagram of an electronic device suitable for the installation strategy of the above-mentioned wind-solar hydrogen storage equipment.

[0044] The electronic device 40 of this embodiment includes a processor 401, which can perform various appropriate actions and processes according to the program stored in the ROM 402 or the program loaded from the storage part 408 into the RAM 403. The processor 401 may include, for example, a general-purpose microprocessor, an instruction set processor and / or a related chipset and / or a dedicated microprocessor, etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present invention.

[0045] RAM 403 stores various programs and data required for the operation of electronic device 40. Processor 401, ROM 402, and RAM 403 are connected to each other via bus 404. Processor 401 executes the programs in ROM 402 and / or RAM 403 to perform various operations according to the method flow of the embodiment of the present invention. It should be noted that the programs may also be stored in one or more memories other than ROM 402 and RAM 403, and processor 401 may also execute the programs stored in one or more memories to perform various operations according to the method flow of the embodiment of the present invention.

[0046] According to an embodiment of the present invention, electronic device 40 may further include an I / O interface 405, which is also connected to bus 404. Electronic device 40 may further include one or more of the following components connected to I / O interface 405: an input unit 406 including a keyboard, mouse, etc.; an output unit 407 including a cathode ray tube, liquid crystal display, and speaker; a storage unit 408 including a hard disk; and a communication unit 409 including a network interface card such as a LAN card or modem. Communication unit 409 performs communication processing via a network such as the Internet. A drive 4010 is also connected to I / O interface 405 as needed. Removable media 4011, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 4010 as needed, so that computer programs read therefrom can be installed into storage unit 408 as needed.

[0047] The present invention also provides a computer-readable storage medium.

[0048] The computer-readable storage medium may be included in the electronic device / device system described in the above embodiments, or may exist independently and not be incorporated into the electronic device / device. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of the present invention.

[0049] According to an embodiment of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, a portable compact disk read-only memory (CDROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0050] Embodiments of the present invention also include a computer program product.

[0051] The computer program product includes a computer program, which contains program code for executing the method provided by the embodiment of the present invention. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the method provided by the embodiment of the present invention.

[0052] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal over a network medium. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0053] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written by any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages. Programming languages ​​include, but are not limited to, Java, C++, Python, C language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device.

[0054] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, as well as the combination of boxes in the block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or may be implemented using a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments and / or claims of the present invention may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments and / or claims of the present invention may be combined and / or coupled in various ways, and all such combinations and / or couplings fall within the scope of the present invention.

[0055] The embodiments of the present invention have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described above separately, this does not mean that the measures in each embodiment cannot be advantageously used in combination. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for predicting the installation strategy of wind and solar hydrogen storage equipment based on urban space, characterized in that: The method comprises: inputting historical data of first land use rasterized data, driving factor rasterized data, and neighborhood factor rasterized data of the same area into a neural network model to obtain second land use rasterized data for predicting future land use evolution, wherein the first land use rasterized data is data for characterizing the distribution of land use types, the driving factor rasterized data is data for characterizing the influence on the evolution of land use, and the neighborhood factor rasterized data is data for characterizing the distribution of land use types within a set neighborhood range of each cell in the grid; Based on the second land use rasterized data, obtaining a total power load value corresponding to the second land use rasterized data according to the power load index value corresponding to each land use type; Based on the total power load value, determine the required number of substations and the power supply area of ​​each substation, and obtain the maximum area that can be occupied by the planned photovoltaic equipment, wind power equipment, energy storage equipment, and hydrogen production equipment in each power supply area; Establishing a target optimization function for the installation strategy of the wind-solar hydrogen storage device, wherein the target optimization function is used to characterize the minimum sum of the installation investment cost and the operation and maintenance cost of the wind-solar hydrogen storage device; Establishing constraint conditions, wherein the constraint conditions include: constraint conditions established based on the maximum area that can be occupied by the wind power equipment, photovoltaic equipment, energy storage equipment, and hydrogen production equipment planned in the power supply area; The objective optimization function and the constraint conditions are combined to obtain an installation strategy for the wind-solar hydrogen storage device that optimizes the objective optimization function and satisfies the constraint conditions.

2. The method for predicting the installation strategy of wind-solar hydrogen storage equipment based on urban space according to claim 1 is characterized in that: The method also includes: using several years of historical data of the first land use rasterized data, the driving factor rasterized data and the neighborhood factor rasterized data as input, and then at least one year of historical data of the first land use rasterized data as output to train the neural network model.

3. The method for predicting wind-solar hydrogen storage equipment installation strategy based on urban space according to claim 1 is characterized in that: The method of obtaining a total power load value corresponding to the second land use rasterized data based on the second land use rasterized data and the power load index value corresponding to each land use type includes: the total power load value is calculated based on the second land use rasterized data, and the sum of the products of the area of ​​each land use type and the corresponding power load index value.

4. The method for predicting wind-solar hydrogen storage equipment installation strategy based on urban space according to claim 1 is characterized in that: The method of determining the required number of substations and the power supply area of ​​each substation based on the total power load value includes: obtaining the capacity of the current power facility, calculating the capacity-load ratio based on the total power load value and the capacity, and if the capacity-load ratio does not meet the design standard, adding at least one substation in the public facility land to make the capacity-load ratio meet the design standard.

5. The method for predicting wind-solar hydrogen storage equipment installation strategy based on urban space according to claim 1 is characterized in that: The installed capacity investment cost includes the sum of the investment cost of wind power equipment, photovoltaic equipment, energy storage equipment and hydrogen production equipment.

6. The method for predicting wind-solar hydrogen storage equipment installation strategy based on urban space according to claim 1 is characterized in that: The constraints also include: energy storage constraints, hydrogen production constraints, regional external power purchase constraints, power balance constraints, and substation voltage and current constraints.

7. A device for predicting the installation strategy of wind and solar hydrogen storage equipment based on urban space, characterized by: The device comprises: A prediction module is used to input historical data of first land use rasterized data, driving factor rasterized data, and neighborhood factor rasterized data of the same area into a neural network model to obtain second land use rasterized data for predicting future land use evolution, wherein the first land use rasterized data is data for characterizing the distribution of land use types, the driving factor rasterized data is data for characterizing the influence on the evolution of land use, and the neighborhood factor rasterized data is data for characterizing the distribution of land use types within a set neighborhood range of each cell in the grid; A first determining module is configured to obtain, based on the second land use rasterized data and according to the power load index value corresponding to each land use type, a total power load value corresponding to the second land use rasterized data; A second determination module is configured to determine the required number of substations and the power supply area of ​​each substation based on the total power load value, and obtain the maximum area that can be occupied by the photovoltaic equipment, wind power equipment, energy storage equipment, and hydrogen production equipment planned for each power supply area; A target optimization function determination module is used to establish a target optimization function for the installation strategy of the wind-solar hydrogen storage device, wherein the target optimization function is used to characterize the minimum sum of the installation investment cost and the operation and maintenance cost of the wind-solar hydrogen storage device; Establishing a constraint condition module, used to establish constraint conditions, wherein the constraint conditions include: constraint conditions established based on the maximum area that can be occupied by the wind power equipment, photovoltaic equipment, energy storage equipment and hydrogen production equipment planned in the power supply area; The installation strategy determination module is used to jointly establish the objective optimization function and the constraints, and solve the installation strategy of the wind-solar hydrogen storage equipment that optimizes the objective optimization function and satisfies the constraints.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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