A method, device and electronic device for generating a capacity configuration result of a power generation device

Abstract

The present disclosure provides a method, apparatus, electronic device, and storage medium for generating a capacity configuration result of a power generation device, relating to the field of energy and power. The specific implementation solution is as follows: predicting the energy storage charge and discharge power of the power generation device; determining the purchased power according to the energy storage charge and discharge power; determining the levelized cost of electricity according to the purchased power; generating a capacity configuration result of the power generation device based on the energy storage charge and discharge power, the purchased power, and the levelized cost of electricity, where the capacity configuration result of the power generation device is used to configure at least one type of power generation device to operate. The present disclosure can improve the allocation effect of the installed capacity of power generation devices in new energy projects.

Description

Technical Field

[0001] The present invention relates to the field of energy and power, and particularly to a method for generating a capacity configuration result of a power generation device. Background Art

[0002] The relationship between global climate change and human activities has become a current international focus issue. It is urgent to address climate change, and carbon peak and carbon neutrality have become the goals under the construction of a new power system. Among them, new energy sources such as wind power and photovoltaic power generation will enter a new round of doubling stage. In the prior art, capacity configuration is only carried out for power generation projects of a single energy form, resulting in poor allocation effects of the installed capacity of power generation equipment in new energy projects. Summary of the Invention

[0003] The present disclosure provides a method, device, electronic device and storage medium for generating a capacity configuration result of a power generation device to solve the problem of poor allocation effects of the installed capacity of power generation equipment in new energy projects.

[0004] According to one aspect of the present disclosure, a method for generating a capacity configuration result of a power generation device is provided, including:

[0005] Predicting the energy storage charge and discharge power of the power generation device;

[0006] Determining the purchased power according to the energy storage charge and discharge power;

[0007] Determining the levelized cost of electricity according to the purchased power;

[0008] Generating a capacity configuration result of the power generation device based on the energy storage charge and discharge power, the purchased power and the levelized cost of electricity, and the capacity configuration result of the power generation device is used to configure at least one type of power generation device to work.

[0009] According to another aspect of the present disclosure, a device for generating a capacity configuration result of a power generation device is provided, including:

[0010] A first prediction module for predicting the energy storage charge and discharge power of the power generation device;

[0011] A first generation module for determining the purchased power according to the energy storage charge and discharge power;

[0012] A second generation module for determining the levelized cost of electricity according to the purchased power;

[0013] A third generation module for generating a capacity configuration result of the power generation device based on the energy storage charge and discharge power, the purchased power and the levelized cost of electricity, and the capacity configuration result of the power generation device is used to configure at least one type of power generation device to work.

[0014] According to another aspect of the present disclosure, there is provided an electronic device, including:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for generating a power generation device capacity configuration result provided by the present disclosure.

[0018] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method for generating a power generation device capacity configuration result provided by the present disclosure.

[0019] In the present disclosure, by first predicting the energy storage charge and discharge power of the power generation device, determining the externally purchased electric power according to the energy storage charge and discharge power, then obtaining the externally purchased electricity quantity and electricity cost according to the externally purchased electric power, determining the levelized cost of electricity, and finally generating a power generation device capacity configuration result based on the energy storage charge and discharge power, the externally purchased electric power, and the levelized cost of electricity, the power generation device capacity configuration result is used to configure at least one type of power generation device to work, and the power generation device capacity configuration result includes the capacity combination of the power generation devices in the system or park, thereby obtaining the investment scale of each power generation device and energy storage charge and discharge device in the system or park, and obtaining the power generation device capacity configuration result, which improves the allocation effect of the installed capacity of the power generation devices in the new energy project.

[0020] It should be understood that the content described in this part is not intended to represent the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. Among them:

[0022] Figure 1 is a flowchart of a method for generating a power generation device capacity configuration result provided by the present disclosure;

[0023] Figure 2 is a scenario diagram based on the method for generating a power generation device capacity configuration result provided by the present disclosure;

[0024] Figure 3 is a structural diagram of a device for generating a power generation device capacity configuration result provided by the present disclosure;

[0025] Figure 4It is a block diagram of an electronic device for implementing the method for generating the capacity configuration result of the power generation device according to the embodiments of the present disclosure. Detailed implementation manners

[0026] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, descriptions of well-known functions and structures are omitted below for clarity and conciseness.

[0027] Please refer to Figure 1 , Figure 1 It is a flowchart of a method for generating the capacity configuration result of a power generation device provided by the present disclosure. As Figure 1 shown, it includes the following steps:

[0028] Step S101: Predict the energy storage charge and discharge power of the power generation device.

[0029] The above-mentioned power generation device can be a photovoltaic power generation device, a wind power generation device, an electrochemical energy storage device, etc. Among them, the energy storage charge and discharge power of the power generation device can be the charge and discharge power of the electrochemical energy storage device. Based on the setting of the above-mentioned power generation device, the power generation devices in the system can be used in combination with different types. For example, the power generation devices in the system can be used in combination with a photovoltaic power generation device, a wind power generation device, and an electrochemical energy storage device. In addition, when the power generation power of the power generation devices in the system does not meet the power consumption demand, it is necessary to purchase external power.

[0030] The determination of the above-mentioned energy storage charge and discharge power can be based on the actual working conditions of the above-mentioned energy storage device. For example, the determination of the above-mentioned energy storage charge and discharge power needs to be based on the discharge depth limit of the above-mentioned energy storage device, the charge and discharge efficiency loss, and the performance attenuation of the above-mentioned energy storage device.

[0031] Step S102: Determine the external power purchase based on the energy storage charge and discharge power.

[0032] The above-mentioned external power purchase is the power grid purchase power when the total power generation of other power generation devices is still insufficient to meet the power consumption demand. Among them, the determination of the above-mentioned external power purchase can be determined by the power generation power of the known power generation devices. For example, given the power generation power of other power generation devices and the total power generation in the system, obtain the energy storage charge and discharge power of the power generation device from the above step S101, and determine the above-mentioned external power purchase according to the sum of the power generation power of the power generation device and the external power purchase being equal to the total power generation in the whole area.

[0033] Step S103: Determine the levelized cost of electricity based on the external power purchase.

[0034] The above - mentioned levelized cost of electricity can be obtained according to the following formula

[0035]

[0036] where I0 is the project capital, including the construction investment capital and the self - owned working capital; V R is the residual value of fixed assets; Q 1n and Q 2n are the electricity capacity charge and the electricity quantity charge purchased externally in the nth year respectively; O n , D n , I n , Tax n are the other operating costs except the externally - purchased electricity cost, the repayment of loan principal, the interest, the sales tax and surcharges, and the enterprise income tax in the nth year respectively; Y n is the electricity supply quantity in the nth year; i is the benchmark internal rate of return of the capital; N is the calculation period.

[0037] In addition, adjust the levelized cost of electricity according to the inflow and outflow of each cash flow, or factors such as the deduction of construction - period value - added tax, income - tax preference, value - added tax refund, and short - term loan. Based on the levelized cost of electricity obtained from the above formula, the financial evaluation method of the spreadsheet and the cash - flow statement can be used for calculation, and then the levelized cost of electricity based on the input data, parameters, and a certain specific capacity combination can be obtained.

[0038] Step S104: Generate a power - generation equipment capacity configuration result based on the energy - storage charge - discharge power, the externally - purchased power, and the levelized cost of electricity. The power - generation equipment capacity configuration result is used to configure at least one type of power - generation equipment to operate.

[0039] The above - mentioned power - generation equipment capacity configuration result can be the installed capacity of each power - generation equipment in the system. For example, the above - mentioned installed capacity can be divided into the installed capacity of photovoltaic power - generation equipment, the installed capacity of wind - power generation equipment, and the installed capacity of electrochemical energy - storage equipment. Among them, the installed capacity is the rated active power of the generator set actually installed in the system.

[0040] In this embodiment, the charging and discharging power of the energy storage of the power generation equipment is obtained through prediction and used as the basis for determining the externally purchased electric power. Further, the levelized cost of electricity is determined based on the obtained data and parameters. Finally, the capacity configuration result of the power generation equipment is generated from the determined objective function and constraint conditions. In addition, relevant data and parameters need to be obtained in advance and then the capacity configuration result of the power generation equipment is generated through the above steps. Among them, the relevant data and parameters may include: the unit construction cost levels of wind power, photovoltaic power, and energy storage; the wind power and photovoltaic resources and output curves; the load curve of the project power supply system; the capacity price and energy price of externally purchased electricity; the depth-of-discharge limit of energy storage, charge-discharge efficiency loss, and performance degradation; the benchmark rate of return, residual value rate, depreciation life, interest rate, calculation period, tax rate, and various operating costs. The capacity configuration result of the power generation equipment in the system or park is generated through the above steps, thereby improving the allocation effect of the installed capacity of the power generation equipment in the new energy project.

[0041] Please refer to Figure 2 , Figure 2 which is a scenario diagram based on a method for generating a capacity configuration result of a power generation equipment provided by the present disclosure. Among them, Figure 2 the power generation equipment in Figure 2 includes photovoltaic power generation equipment, wind power generation equipment, and electrochemical energy storage equipment. When

[0042] the power generation equipment in

[0043] does not meet the electricity demand, it is necessary to purchase electricity from outside to meet the electricity demand of the system or park.

[0044] As an alternative embodiment, determining the levelized cost of electricity based on the externally purchased electric power includes: obtaining the levelized cost of electricity through a first prediction model with the externally purchased electric power as the input.

[0045] In this embodiment, the first prediction model is used, and the levelized cost of electricity (LCOE) is obtained by taking the purchased electricity power and other parameters as inputs. The above-mentioned LCOE is the calculation basis for obtaining the capacity configuration result of the power generation equipment, so as to obtain the capacity configuration result of the power generation equipment, improving the allocation effect of the installed capacity of the power generation equipment in the new energy project.

[0046] As an alternative embodiment, determining the purchased electricity power according to the energy storage charge-discharge power includes: determining the purchased electricity power through the following formula

[0047]

[0048] wherein, the sum of the total power of two types of power generation equipment at time t, the total power of the energy storage charge-discharge equipment at time t, and the total power of the purchased electricity at time t is equal to the total electricity consumption power at time t, where P 1,t and P 2,t are respectively the powers of two types of power generation equipment at time t, P 3,t is the energy storage charge-discharge power at time t, and P 4,t is the purchased electricity power.

[0049] The determination of the above-mentioned purchased electricity power needs to be based on the above-mentioned energy storage charge-discharge power and the powers of other power generation equipment. As shown in the above formula, P 1,t and P 2,t are respectively the powers of two types of power generation equipment at time t, P 3,t is the energy storage charge-discharge power at time t, and P 4,t is the purchased electricity power. Therefore, the sum of the power generation power of other power generation equipment at time t, the charge-discharge power of the energy storage charge-discharge equipment at time t, and the purchased electricity power at time t is equal to the total electricity consumption power at time t. The power generation power of other power generation equipment at time t, the charge-discharge power of the energy storage charge-discharge equipment at time t, and the total power generation power at time t are known, so the purchased electricity power at time t can be obtained.

[0050] In this embodiment, the purchased electricity power is determined, and then the electricity quantity and electricity charge of the purchased electricity analyzed from the purchased electricity power are obtained. Thus, based on this, the prediction calculation of the levelized cost of electricity is carried out, and then the capacity configuration result of the power generation equipment is obtained, improving the allocation effect of the installed capacity of the power generation equipment in the new energy project.

[0051] It should be noted that the above-mentioned energy storage charge-discharge power has positive and negative values. Among them, when the electric power of the energy storage charge-discharge equipment is positive, the energy storage charge-discharge equipment is charging at this time; when the electric power of the energy storage charge-discharge equipment is negative, the energy storage charge-discharge equipment is discharging at this time.

[0052] As an alternative implementation, generating the power generation equipment capacity configuration result based on the energy storage charge and discharge power, the externally purchased power, and the levelized cost of electricity includes: obtaining the power generation equipment capacity configuration result through a second prediction model with the energy storage charge and discharge power, the externally purchased power, and the levelized cost of electricity as inputs.

[0053] The above-mentioned second prediction model is used to calculate and predict the above-mentioned power generation equipment capacity configuration result. Among them, obtaining the power generation equipment capacity configuration result through the second prediction model with the energy storage charge and discharge power, the externally purchased power, and the levelized cost of electricity as inputs mainly determines the objective function as the calculation basis for obtaining the power generation equipment capacity configuration result based on the above-mentioned levelized cost of electricity as a quantitative indicator for measuring the capacity configuration result.

[0054] The above-mentioned second prediction model also includes a constraint condition for restricting the objective function. This constraint condition is based on the output situation of the power generation equipment itself as a constraint. Under this constraint condition, the value of the objective function is determined, and then the result of the power generation equipment capacity configuration is obtained, improving the allocation effect of the installed capacity of the power generation equipment in the new energy project.

[0055] It should be noted that in the above-mentioned second prediction model, the higher the levelized cost of electricity, the weaker the cost competitiveness of the power generation equipment project in the system or park, and the lower the levelized cost of electricity, the stronger the cost competitiveness of the power generation equipment project in the system or park.

[0056] Optionally, the objective function of the second prediction model is min c1,c2,c3 LCOE, where LCOE is the levelized cost of electricity, and C1, C2, and C3 are the installed capacities of two types of power generation equipment and energy storage equipment respectively. This function represents the installed capacity of each power generation equipment when the levelized cost of electricity takes the minimum value;

[0057] The first constraint condition of the second prediction model is expressed as Where P k,t represents the power of the power generation equipment at time t, represents the power limit P of the power generation equipment 3,t is the energy storage charge and discharge power at time t, P 3 represents the energy storage charging power limit, represents the energy storage discharge power limit;

[0058] The second constraint condition of the second prediction model is expressed as Among them, the sum of the total power of the power generation equipment at time t, the total power of the energy storage at time t, and the total power of the externally purchased power at time t is equal to the total power consumption at time t. Among them, P 1,t and P2,t are the outputs of two types of power generation equipment at time t, P 3,t is the energy storage charge and discharge power at time t, P 4,t is the purchased electricity power, D t is the sum of the powers of all power generation equipment and purchased electricity at time t.

[0059] The objective function of the above second prediction model is min c1,c2,c3 LCOE, representing the installed capacity of the power generation equipment in the system or park when the levelized cost of electricity takes the minimum value. Among them, for the solution of the objective function, an algorithm for solving unconstrained nonlinear programming problems can be used. For example, for the equality condition of the above constraint conditions, through equality transformation, the operation can be converted into an unconstrained problem, and an unconstrained nonlinear programming problem such as the step acceleration method is used for solution, and iteration is continuously carried out in the direction of lower levelized cost of electricity to obtain the capacity combination, and this capacity combination is the capacity combination of the power generation equipment in the system or park, until the optimal capacity combination of the power generation equipment is obtained to meet the accuracy requirements.

[0060] The above first constraint condition means that the power generation power requirement of other power generation equipment at time t is less than or equal to the charging limit of its own equipment and greater than or equal to 0, while the power requirement of the energy storage charge and discharge equipment at time t is greater than or equal to the energy storage charging limit, and the power requirement of the energy storage charge and discharge equipment at time t is less than or equal to the energy storage discharge limit.

[0061] The above second constraint condition means that the sum of the powers of all power generation equipment and energy storage charge and discharge equipment in the system or park at time t is equal to the total power of the system or park.

[0062] In this embodiment, through the above second prediction model, the capacity combination of the power generation equipment and energy storage charge and discharge equipment in the system or park in the optimal case is obtained. Furthermore, the investment scale of each power generation equipment and energy storage charge and discharge equipment in the system or park is obtained, and the capacity allocation result of the power generation equipment is obtained, improving the allocation effect of the installed capacity of the power generation equipment in the new energy project.

[0063] As an alternative embodiment, the energy storage charge and discharge power is:

[0064]

[0065] Where P 1,t and P 2,t are the powers of two types of power generation equipment at time t respectively, P 3,t is the energy storage charge and discharge power at time t, P 3 represents the energy storage charging power limit, represents the energy storage discharge power limit, D tIt is the sum of the powers of all power generation equipment and purchased electricity at time t.

[0066] The determination of the above energy storage charge and discharge power needs to be based on the power of other power generation equipment at time t. Among them, taking the above formula as an example, P 1,t and P 2,t are the powers of two types of power generation equipment at time t respectively, that is, P1 and P2 represent other power generation equipment in the system or park. For example: when the sum of the energy storage charging power limit and the power generation power of other power generation equipment is greater than or equal to the sum of the powers of all power generation equipment and purchased electricity, the energy storage charge and discharge power takes the energy storage charging power limit; when the sum of the powers of all power generation equipment and purchased electricity at the above time t is greater than the sum of the energy storage charging power limit and the power generation power of other power generation equipment at time t and less than or equal to the sum of the powers of other power generation equipment at time t, the energy storage charge and discharge power at time t takes the opposite number of the difference between the sum of the powers of other power generation equipment at time t and the sum of the powers of all power generation equipment and purchased electricity at time t; when the sum of the powers of all power generation equipment and purchased electricity at the above time t is greater than the sum of the powers of other power generation equipment at time t and less than or equal to the sum of the powers of other power generation equipment at time t plus the energy storage discharge power limit, the energy storage charge and discharge power at time t takes the difference between the sum of the powers of all power generation equipment and purchased electricity at time t and the sum of the powers of other power generation equipment at time t; when the sum of the powers of all power generation equipment and purchased electricity at the above time t is greater than the sum of the powers of other power generation equipment at time t plus the energy storage discharge power limit, the energy storage charge and discharge power at time t takes the energy storage discharge power limit.

[0067] In this embodiment, the energy storage charge and discharge power is determined, which improves the operation effects of the above first prediction model and the above second prediction model on the levelized cost of electricity and the power generation equipment capacity configuration result, and further improves the allocation effect of the installed capacity of power generation equipment in the new energy project.

[0068] Please refer to Figure 3 , Figure 3 which is a power generation equipment capacity configuration result generation device provided by the present disclosure. As Figure 3 shown, the power generation equipment capacity configuration result generation device 300 includes:

[0069] A first prediction module 301, configured to predict the energy storage charge and discharge power of the power generation equipment;

[0070] A first generation module 302, configured to determine the purchased electricity power according to the energy storage charge and discharge power;

[0071] A second generation module 303, configured to determine the levelized cost of electricity according to the purchased electricity power;

[0072] The third generation module 304 is configured to generate a power generation equipment capacity configuration result based on the energy storage charge-discharge power, the externally purchased power, and the levelized cost of electricity, and the power generation equipment capacity configuration result is used to configure at least one type of power generation equipment to operate.

[0073] Optionally, determining the levelized cost of electricity based on the externally purchased power includes: obtaining the levelized cost of electricity through a first prediction model with the externally purchased power as the input.

[0074] Optionally, determining the externally purchased power based on the energy storage charge-discharge power includes: determining the externally purchased power through the following formula

[0075]

[0076] wherein, the sum of the total power of two types of power generation equipment at time t, the total power of the energy storage charge-discharge equipment at time t, and the total power of the externally purchased power at time t is equal to the total power consumption at time t, where P 1,t and P 2,t are respectively the power of two types of power generation equipment at time t, P 3,t is the energy storage charge-discharge power at time t, and P 4,t is the externally purchased power.

[0077] Optionally, generating the power generation equipment capacity configuration result based on the energy storage charge-discharge power, the externally purchased power, and the levelized cost of electricity includes: obtaining the power generation equipment capacity configuration result through a second prediction model with the energy storage charge-discharge power, the externally purchased power, and the levelized cost of electricity as the input.

[0078] Optionally, the objective function of the second prediction model is min c1,c2,c3 LCOE, where LCOE is the levelized cost of electricity, and C1, C2, and C3 are respectively the installed capacities of two types of power generation equipment and the energy storage equipment. This function represents the installed capacity of each power generation equipment when the levelized cost of electricity takes the minimum value; the first constraint condition of the second prediction model is expressed as where P k,t represents the power of the power generation equipment at time t, represents the power limit of the power generation equipment P 3,t is the energy storage charge-discharge power at time t, P 3 represents the energy storage charging power limit, represents the energy storage discharging power limit; the second constraint condition of the second prediction model is expressed as Among them, the sum of the total power of the power generation equipment at time t, the total power of the energy storage at time t, and the total power of the purchased electricity at time t is equal to the total power consumption at time t, where P 1,t and P 2,t are the outputs of two types of power generation equipment at time t respectively, P 3,t is the charge-discharge power of the energy storage at time t, P 4,t is the purchased electricity power, and D t is the sum of the powers of all power generation equipment and purchased electricity at time t.

[0079] Optionally, the charge-discharge power of the energy storage is:

[0080]

[0081] Among them, P 1,t and P 2,t are the powers of two types of power generation equipment at time t respectively, P 3,t is the charge-discharge power of the energy storage at time t, P3 represents the energy storage charging power limit, represents the energy storage discharging power limit, and D t is the sum of the powers of all power generation equipment and purchased electricity at time t.

[0082] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0083] Figure 4 FIG. shows a schematic block diagram of an exemplary electronic device 400 that can be used to implement the embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0084] As Figure 4 shown, the device 400 includes a computing unit 401, which can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 402 or the computer program loaded from the storage unit 408 into the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0085] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as a keyboard, mouse, etc.; output unit 407, such as various types of displays, speakers, etc.; storage unit 408, such as a disk, optical disc, etc.; and communication unit 409, such as a network card, modem, wireless communication transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0086] Computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 401 executes the various methods and processes described above, such as the power generation device capacity configuration result generation method.

[0087] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0088] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0089] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0090] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0091] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0092] A computer system can include a client and a server. The client and the server are generally far apart from each other and typically interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, can also be a server of a distributed system, or a server incorporating a blockchain.

[0093] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0094] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for generating the capacity configuration result of a power generation device, characterized in that The method includes: Predicting the energy storage charge and discharge power of the power generation equipment; Determining the purchased power according to the energy storage charge and discharge power; Determining the levelized cost of electricity according to the purchased power; Generating a power generation equipment capacity configuration result based on the energy storage charge and discharge power, the purchased power, and the levelized cost of electricity, where the power generation equipment capacity configuration result is used to configure at least one type of power generation equipment to operate; The determining the levelized cost of electricity according to the purchased power includes: Obtaining the levelized cost of electricity by using the purchased power as an input through a first prediction model; The determining the purchased power according to the energy storage charge and discharge power includes: Determining the purchased power through the following formula ； Among them, the sum of the total power of two types of power generation equipment at time t, the total power of the energy storage charge and discharge equipment at time t, and the total power of the purchased electricity at time t is equal to the total electricity consumption power at time t, where, P 1,t and P 2,t are the powers of two types of power generation equipment at time t respectively, P 3,t is the energy storage charge and discharge power at time t, P 4,t is the purchased electricity power; The generating the power generation equipment capacity configuration result based on the energy storage charge and discharge power, the purchased power, and the levelized cost of electricity includes: Obtaining the power generation equipment capacity configuration result by using the energy storage charge and discharge power, the purchased power, and the levelized cost of electricity as inputs through a second prediction model; The objective function of the second prediction model is , where LCOE is the levelized cost of electricity, C 1 、C 2 and C 3 are the installed capacities of two types of power generation equipment and energy storage equipment respectively. This function represents the installed capacity of each power generation equipment when the levelized cost of electricity takes the minimum value; The first constraint condition of the second prediction model is expressed as , , where P k,t represents the power of the power generation equipment at time t, represents the power limit of the power generation equipment P 3,t is the charge and discharge power of the energy storage at time t, represents the energy storage charging power limit, represents the energy storage discharge power limit; The second constraint condition of the second prediction model is expressed as , where the sum of the total power of the power generation equipment, the total power of the energy storage, and the total power of the purchased electricity at time t is equal to the total power consumption at time t, where P 1,t and P 2,t are the outputs of two types of power generation equipment at time t, P 3,t is the charging and discharging power of the energy storage at time t, P 4,t is the purchased electricity power, D t is the sum of the powers of all power generation equipment and purchased electricity at time t.

2. The method for generating the power generation equipment capacity configuration result according to claim 1, wherein The energy storage charge and discharge power is: ； Among them, P 1,t and P 2,t are the powers of two types of power generation equipment at time t respectively, P 3,t is the charge and discharge power of energy storage at time t, represents the energy storage charging power limit, represents the energy storage discharging power limit, D t is the sum of the powers of all power generation equipment and purchased electricity at time t.

3. A generating equipment capacity configuration result generating device, characterized in that The device is used to execute the power generation equipment capacity configuration result generation method according to any one of claims 1 and 2, and the device includes: A first prediction unit for predicting the energy storage charge and discharge power of the power generation equipment; A first generation unit for determining the purchased power according to the energy storage charge and discharge power; A second generation unit for determining the levelized cost of electricity according to the purchased power; A third generation unit for generating a power generation equipment capacity configuration result based on the energy storage charge and discharge power, the purchased power, and the levelized cost of electricity, where the power generation equipment capacity configuration result is used to configure at least one type of power generation equipment to operate.

4. An electronic device, including: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 and 2.

5. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the method according to any one of claims 1 and 2.

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