User side energy storage capacity configuration method, system and device and medium

Abstract

The invention discloses a user side energy storage capacity configuration method, system and device and a medium. The method adopted by the invention comprises the following steps: acquiring annual daily electricity consumption and solar photovoltaic power generation capacity of a user; constructing a typical day selection model based on the energy balance degree and daily volatility, and selecting a day with the highest comprehensive score as a typical day according to the typical day selection model; acquiring hourly electricity consumption and hourly photovoltaic generating capacity in 24 hours of a typical day; calculating a difference value between the hourly photovoltaic generating capacity and the hourly electricity consumption; obtaining an energy storage capacity calculation value, and determining an energy storage power design limitation set; and checking an energy storage capacity design value and an energy storage power design value. According to the method, the typical day for energy storage capacity configuration can be selected based on definite mathematical reasoning, and energy storage capacity configuration design is carried out according to the typical daily electricity consumption-photovoltaic generating capacity curve.

Description

Technical Field

[0001] This invention belongs to the field of user-side energy storage capacity configuration technology, and in particular, it is a user-side energy storage capacity configuration method, system, equipment and medium based on balancing typical daily electricity consumption-photovoltaic curves. Background Technology

[0002] Traditional building energy systems, acting as consumers, rely on external energy inputs to meet their needs. However, in the context of the dual-carbon era, managing and distributing the large influx of new distributed energy sources has become an unavoidable social issue. Building energy systems with flexible regulation potential, such as photovoltaic-energy storage-direct current and flexibility (PEDF), have emerged to address this need. PEDF refers to a new type of building power distribution system that utilizes renewable energy sources like photovoltaics for power generation, energy storage, DC power distribution, and flexible energy consumption to meet carbon neutrality goals. The economical and efficient operation of PEDF building energy systems involves the selection and capacity configuration of key equipment such as photovoltaics and energy storage. Among these, the capacity configuration of energy storage equipment has become a focus of industry attention. Insufficient energy storage capacity cannot meet the system's requirements for charge and discharge regulation capabilities; excessive energy storage capacity, on the other hand, increases equipment costs and leads to unnecessary resource waste.

[0003] Currently, most methods for configuring user-side energy storage capacity in buildings are based on the energy balance of typical days. The selection of typical days is a key step in configuring energy storage capacity for "photovoltaic-storage-direct-flexible" building energy systems. Whether it is the traditional selection of the summer solstice or the winter solstice or the assessment of the worst-case scenario, there is a lack of rigorous mathematical reasoning and quantitative proof, and there is a certain degree of subjectivity in the selection.

[0004] Therefore, it is necessary to propose a user-side energy storage configuration scheme based on explicit mathematical reasoning to select typical days, so as to achieve the highest cost-effectiveness of energy storage battery configuration. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a user-side energy storage capacity configuration method, system, equipment and medium based on the balanced typical daily electricity consumption-photovoltaic curve, so as to solve the problems of non-standard selection of typical days, strong subjectivity and generalization in the existing energy storage capacity configuration method.

[0006] Therefore, the present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a user-side energy storage capacity configuration method, comprising: Step 1: Obtain the user's daily electricity consumption Q for the whole year. i Daily photovoltaic power generation Pi i is 1-365; Step 2: Construct a typical day selection model based on energy balance and daily fluctuations, and select the day with the highest comprehensive score as the typical day according to the typical day selection model; Step 3: Obtain hourly electricity consumption DQ for a typical 24-hour period. j and hourly photovoltaic power generation DP j j is 1-24; Step 4: Calculate hourly photovoltaic power generation (DP) j With hourly electricity consumption DQ j The difference DV j ; Step 5: Calculate DV j Calculate DV by taking the absolute value of the cumulative positive values ​​DV+. j The absolute value of the cumulative total of all negative values, DV-; Step 6: Obtain the calculated energy storage capacity value C1 = Min(DV+, DV-). If DV+ is smaller than DV-, then DV... j The set of absolute values ​​of all positive values ​​is the energy storage power design constraint set M. If DV- is smaller than DV+, then DV j The set of absolute values ​​of all negative values ​​is the set of energy storage power design constraints M; Step 7: Calculate the energy storage capacity design value C=F×C1 / U, where F is the correction coefficient for the charge and discharge efficiency of the energy storage battery, and U is the depth of discharge of the energy storage battery. Step 8: Verification of energy storage capacity design value and energy storage power design value: The energy storage power design value is designed according to 1 / 2 of the energy storage capacity design value, and it is necessary to ensure that the energy storage power design value is greater than or equal to the maximum value Ma in the limit set M.

[0008] Furthermore, in step 2, the construction steps of the typical day selection model include: 21) Calculate the daily total balance deviation index E i E i =|Q i -P i | / ((Q i +P i This index uses a relative value, eliminating the bias caused by different absolute electricity levels, and can more universally measure the degree of total balance on different days. 22) Calculate the correlation coefficient of intraday curve fluctuation pattern. ', =Cov(DQ, DP) / ( × ), where Cov(DQ, DP) is the covariance between the hourly electricity consumption sequence and the hourly photovoltaic power generation sequence corresponding to day i. and , respectively, represent the standard deviations of the hourly electricity consumption series and the hourly photovoltaic power generation series; DQ is the hourly electricity consumption series, and DP is the hourly photovoltaic power generation series; this coefficient reflects the degree of temporal matching between electricity consumption and power generation. The closer it is to -1, the stronger the complementarity between the two (when one is high, the other is low), and the higher the demand for energy storage regulation. 23) Construct a typical day selection model, S i =0.5×(1-E i ')+0.5×(1- '), E i 'and 'They are E' i and After normalization to the maximum and minimum values, S i This represents the overall score. The typical day selection model ensures that the selected typical days not only have a balanced total daily energy, but also reflect the typical characteristics of light-load interaction to the greatest extent in terms of time sequence.

[0009] Furthermore, the user-side energy storage capacity configuration method further includes: step 9, rounding up the energy storage capacity design value and the energy storage power design value.

[0010] Furthermore, in steps 1 and 3, the acquisition refers to collection and / or prediction.

[0011] Furthermore, in step 7, the correction factor F for the charging and discharging efficiency of the energy storage battery is set to 1.04-1.06.

[0012] Furthermore, in step 7, the depth of discharge U of the energy storage battery is set to 0.5-0.8.

[0013] Furthermore, in step 8, if C / 2 is greater than or equal to the maximum value Ma in the constraint set M, then the energy storage power design value is C / 2; if C / 2 is less than the maximum value Ma in the constraint set M, then the energy storage power design value is Ma.

[0014] In a second aspect, the present invention provides a user-side energy storage capacity configuration system, comprising: User daily electricity consumption acquisition unit: Used to acquire the user's daily electricity consumption Q throughout the year. i Daily photovoltaic power generation P i i is 1-365; Typical day selection unit: Construct a typical day selection model based on energy balance and daily fluctuation, and select the day with the highest comprehensive score as the typical day according to the typical day selection model; Typical Daily Hourly Energy Acquisition Unit: Acquires hourly energy consumption DQ over a typical 24-hour period. j and hourly photovoltaic power generation DP jj is 1-24; Hourly Energy Difference Calculation Unit: Used to calculate hourly photovoltaic power generation (DP) j With hourly electricity consumption DQ j The difference DV j ; Hourly cumulative energy consumption calculation unit: used to calculate DV j Calculate DV by taking the absolute value of the cumulative positive values ​​DV+. j The absolute value of the cumulative total of all negative values, DV-; Energy storage capacity calculation value: used to obtain the energy storage capacity calculation value C1=Min(DV+, DV-); Restricting the set acquisition unit: If DV+ is smaller than DV-, then DV j The set of absolute values ​​of all positive values ​​is the energy storage power design constraint set M. If DV- is smaller than DV+, then DV j The set of absolute values ​​of all negative values ​​is the set of energy storage power design constraints M; Energy storage capacity design value calculation unit: used to calculate the energy storage capacity design value C=F×C1 / U, where F is the correction coefficient for the charge and discharge efficiency of the energy storage battery, and U is the depth of discharge of the energy storage battery; Design value verification unit: The energy storage power design value is designed according to 1 / 2 of the energy storage capacity design value, and it is necessary to ensure that the energy storage power design value is greater than or equal to the maximum value Ma in the limit set M.

[0015] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0017] The beneficial effects of this invention are as follows: This invention calculates and quantifies the selection of typical days for energy storage configuration, and based on this, utilizes the electricity consumption-photovoltaic curve to configure user-side energy storage capacity with the goal of maximizing renewable energy consumption, resulting in the highest cost-effectiveness of energy storage battery configuration. This invention solves the problem that existing user-side energy storage configuration methods lack rigorous mathematical reasoning and quantitative proof, and suffer from a certain degree of subjectivity in selection. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a user-side energy storage capacity configuration method according to the present invention; Figure 2 This is a daily electricity consumption curve for the whole year in Embodiment 1 of the present invention; Figure 3 This is a daily photovoltaic power generation curve for the whole year in Embodiment 1 of the present invention; Figure 4 This is a graph showing the overall score for the entire year in Embodiment 1 of the present invention; Figure 5 This is a typical daily electricity consumption-photovoltaic power generation curve diagram in Embodiment 1 of the present invention; Figure 6 This is a structural diagram of a user-side energy storage capacity configuration system according to the present invention; Figure 7 This is a schematic diagram of the logical structure of a computer device provided in Embodiment 4 of the present invention. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the embodiments and accompanying drawings, but the implementation of the present invention is not limited thereto.

[0021] Example 1 This embodiment describes a method for configuring user-side energy storage capacity, such as... Figure 1 As shown, the steps are as follows: Step 1: Collect and / or predict the user's daily electricity consumption Q for the whole year. i Daily photovoltaic power generation P i , i is 1-365.

[0022] Step 2: Construct a typical day selection model based on energy balance and daily fluctuations, and select the day with the highest comprehensive score as the typical day according to the typical day selection model.

[0023] The construction steps of a typical day selection model are as follows: 21) Calculate the daily total balance deviation index E i E i =|Q i -P i | / ((Q i +P iThis index uses a relative value, eliminating the bias caused by different absolute electricity levels, and can more universally measure the degree of total balance on different days. 22) Calculate the correlation coefficient of intraday curve fluctuation pattern. ', =Cov(DQ, DP) / ( × ), where Cov(DQ, DP) is the covariance between the hourly electricity consumption sequence and the hourly photovoltaic power generation sequence corresponding to day i. and , respectively, represent the standard deviations of the hourly electricity consumption series and the hourly photovoltaic power generation series; DQ is the hourly electricity consumption series, and DP is the hourly photovoltaic power generation series; this coefficient reflects the degree of temporal matching between electricity consumption and power generation. The closer it is to -1, the stronger the complementarity between the two (when one is high, the other is low), and the higher the demand for energy storage regulation. 23) Construct a typical day selection model, S i =0.5×(1-E i ')+0.5×(1- '), E i 'and 'They are E' i and After normalization to the maximum and minimum values, S i This represents the overall score. The typical day selection model ensures that the selected typical days not only have a balanced total daily energy, but also reflect the typical characteristics of light-load interaction to the greatest extent in terms of time sequence.

[0024] Step 3: Collect and / or predict hourly electricity consumption (DQ) for a typical 24-hour day. j and hourly photovoltaic power generation DP j j is 1-24.

[0025] Step 4: Calculate hourly photovoltaic power generation (DP) j With hourly electricity consumption DQ j The difference DV j .

[0026] Step 5: Calculate DV j Calculate DV by taking the absolute value of the cumulative positive values ​​DV+. j The absolute value of the cumulative total of all negative values, DV-.

[0027] Step 6: Obtain the calculated energy storage capacity value C1 = Min(DV+, DV-). If DV+ is smaller than DV-, then DV... j The set of absolute values ​​of all positive values ​​is the energy storage power design constraint set M. If DV- is smaller than DV+, then DV jThe set of absolute values ​​of all negative values ​​is the set M of energy storage power design constraints.

[0028] Step 7: Calculate the energy storage capacity design value C=F×C1 / U, where F is the correction coefficient for the charge and discharge efficiency of the energy storage battery, and U is the depth of discharge of the energy storage battery; the correction coefficient F for the charge and discharge efficiency of the energy storage battery is 1.04-1.06; the depth of discharge U of the energy storage battery is 0.5-0.8.

[0029] Step 8: Verification of energy storage capacity design value and energy storage power design value: The energy storage power design value is designed according to 1 / 2 of the energy storage capacity design value. It is necessary to ensure that the energy storage power design value is greater than or equal to the maximum value Ma in the limit set M. If C / 2 is greater than or equal to the maximum value Ma in the limit set M, then the energy storage power design value is C / 2. If C / 2 is less than the maximum value Ma in the limit set M, then the energy storage power design value is Ma.

[0030] Step 9: Round up the design values ​​of energy storage capacity and energy storage power.

[0031] Taking the photovoltaic energy consumption data of a specific office building as an example, the photovoltaic installed capacity of this office building is 20.16 kWp. Using the energy storage capacity configuration method described in this invention, the calculation process is as follows: 1. Collect and calculate the daily electricity consumption data for this case throughout the year, such as... Figure 2 .

[0032] 2. Collect and calculate the daily electricity consumption data for this case throughout the year, such as... Figure 3 .

[0033] 3. Based on a typical daily selection model considering energy balance and diurnal volatility, calculate the daily comprehensive score, such as... Figure 4 As shown. The day with the highest overall score was selected as the typical day, which was the 34th day, February 3 of that year. The building's electricity consumption was 39.57 kWh, and the photovoltaic power generation was 34.15 kWh.

[0034] 4. Collect hourly electricity consumption data for a typical day, such as... Figure 5 .

[0035] 5. Collect hourly photovoltaic power generation data for a typical day, such as... Figure 5 .

[0036] 6. Calculate the difference between hourly photovoltaic power generation and hourly electricity consumption, as shown in the table below.

[0037]

[0038] 7. The absolute value of the cumulative positive value of the difference between hourly photovoltaic power generation and hourly electricity consumption is 23.85 kWh, and the absolute value of the cumulative negative value is 29.26 kWh.

[0039] 8. If the absolute value of the cumulative positive value is small, then the set of all the absolute values ​​of the positive values ​​of the hourly difference between photovoltaic power generation and electricity consumption is the energy storage power design constraint set M.

[0040] 9. The correction factor for the charge and discharge efficiency of the energy storage battery is taken as 1.05, and the depth of discharge of the energy storage battery is taken as 0.7. Then the calculated design value of the energy storage capacity is 35.8kWh.

[0041] 10. Energy storage power verification: The energy storage power is designed based on 1 / 2 of the energy storage capacity, with an initial design of 17.9kW. This power is greater than all values ​​in set M, so the energy storage capacity design value is 35.8kWh and the energy storage power design value is 17.9kW.

[0042] 11. The results after rounding up the design values ​​of energy storage capacity and energy storage power are: the design value of energy storage capacity is 36kWh and the design value of energy storage power is 18kW.

[0043] Example 2 This embodiment provides a user-side energy storage capacity configuration system for implementing the user-side energy storage capacity configuration method described in Embodiment 1, such as... Figure 6 As shown, it consists of a user daily power consumption acquisition unit, a typical day selection unit, a typical day hourly power consumption acquisition unit, an hourly power consumption difference calculation unit, an hourly power consumption cumulative value calculation unit, an energy storage capacity calculation unit, a limit set acquisition unit, an energy storage capacity design value calculation unit, and a design value verification unit.

[0044] User daily electricity consumption acquisition unit: Used to acquire the user's daily electricity consumption Q throughout the year. i Daily photovoltaic power generation P i i is 1-365; Typical day selection unit: Construct a typical day selection model based on energy balance and daily fluctuation, and select the day with the highest comprehensive score as the typical day according to the typical day selection model; Typical Daily Hourly Energy Acquisition Unit: Acquires hourly energy consumption DQ over a typical 24-hour period. j and hourly photovoltaic power generation DP j j is 1-24; Hourly Energy Difference Calculation Unit: Used to calculate hourly photovoltaic power generation (DP) j With hourly electricity consumption DQ j The difference DV j ; Hourly cumulative energy consumption calculation unit: used to calculate DV jCalculate DV by taking the absolute value of the cumulative positive values ​​DV+. j The absolute value of the cumulative total of all negative values, DV-; Energy storage capacity calculation value: used to obtain the energy storage capacity calculation value C1=Min(DV+, DV-); Restricting the set acquisition unit: If DV+ is smaller than DV-, then DV j The set of absolute values ​​of all positive values ​​is the energy storage power design constraint set M. If DV- is smaller than DV+, then DV j The set of absolute values ​​of all negative values ​​is the set of energy storage power design constraints M; Energy storage capacity design value calculation unit: used to calculate the energy storage capacity design value C=F×C1 / U, where F is the correction coefficient for the charge and discharge efficiency of the energy storage battery, and U is the depth of discharge of the energy storage battery; Design value verification unit: The energy storage power design value is designed according to 1 / 2 of the energy storage capacity design value, and it is necessary to ensure that the energy storage power design value is greater than or equal to the maximum value Ma in the limit set M.

[0045] It should be noted that each unit in the aforementioned user-side energy storage capacity configuration system can be implemented entirely or partially through software, hardware, or a combination thereof. These units can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each unit. For specific limitations regarding the user-side energy storage capacity configuration system, please refer to the limitations of the user-side energy storage capacity configuration method (i.e., Embodiment 1) above; both have the same function and role, and will not be repeated here.

[0046] Example 3 This embodiment provides a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method according to Embodiment 1 of the present invention.

[0047] Example 4 This embodiment provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the method according to Embodiment 1 of the present invention.

[0048] refer to Figure 7The present invention will now be described in the form of a structural block diagram of an electronic device 400 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, 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 processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0049] like Figure 7 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0050] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, output unit 407, storage unit 408, and communication unit 409. Input unit 406 can be any type of device capable of inputting information to electronic device 400. Input unit 406 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 407 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 408 may include, but is not limited to, disks and optical discs. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0051] The computing unit 401 can be a variety of general-purpose and / or dedicated processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated 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. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the aforementioned user-side energy storage capacity configuration method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. In some embodiments, the computing unit 401 can be configured to perform the aforementioned user-side energy storage capacity configuration method by any other suitable means (e.g., by means of firmware).

[0052] The program code used to implement the methods of the present invention 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, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0053] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction 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. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0054] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0055] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0056] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0057] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0058] The above description of the embodiments is provided to enable those skilled in the art to understand and apply the present invention. It will be apparent to those skilled in the art that various modifications can be made to the above embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made to the present invention by those skilled in the art based on the disclosure thereof should be within the scope of protection of the present invention.

Claims

1. A method for configuring user-side energy storage capacity, characterized in that, include: Step 1: Obtain the user's daily electricity consumption Q for the whole year. i Daily photovoltaic power generation P i i is 1-365; Step 2: Construct a typical day selection model based on energy balance and daily fluctuations, and select the day with the highest comprehensive score as the typical day according to the typical day selection model; Step 3: Obtain hourly electricity consumption DQ for a typical 24-hour period. j and hourly photovoltaic power generation (DP) j j is 1-24; Step 4: Calculate hourly photovoltaic power generation (DP) j With hourly electricity consumption DQ j The difference DV j ; Step 5: Calculate DV j Calculate DV by taking the absolute value of the cumulative positive values ​​DV+. j The absolute value of the cumulative total of all negative values, DV-; Step 6: Obtain the calculated energy storage capacity value C1 = Min(DV+, DV-). If DV+ is smaller than DV-, then DV... j The set of absolute values ​​of all positive values ​​is the energy storage power design constraint set M. If DV- is smaller than DV+, then DV j The set of absolute values ​​of all negative values ​​is the set of energy storage power design constraints M; Step 7: Calculate the energy storage capacity design value C=F×C1 / U, where F is the correction coefficient for the charge and discharge efficiency of the energy storage battery, and U is the depth of discharge of the energy storage battery. Step 8: Verification of energy storage capacity design value and energy storage power design value: The energy storage power design value is designed according to 1 / 2 of the energy storage capacity design value, and it is necessary to ensure that the energy storage power design value is greater than or equal to the maximum value Ma in the limit set M.

2. The user-side energy storage capacity configuration method according to claim 1, characterized in that, Step 2, the construction steps of the typical day selection model include: 21) Calculate the daily total balance deviation index E i E i =|Q i -P i | / ((Q i +P i ) / 2); 22) Calculate the correlation coefficient of intraday curve fluctuation pattern. ', =Cov(DQ, DP) / ( × ), where Cov(DQ, DP) is the covariance between the hourly electricity consumption sequence and the hourly photovoltaic power generation sequence corresponding to day i. and , respectively, are the standard deviations of the hourly electricity consumption series and the hourly photovoltaic power generation series; DQ is the hourly electricity consumption series, and DP is the hourly photovoltaic power generation series; 23) Construct a typical day selection model, S i =0.5×(1-E i ')+0.5×(1- '), E i 'and 'They are E' i and After normalization to the maximum and minimum values, S i This indicates the overall score.

3. The user-side energy storage capacity configuration method according to claim 1, characterized in that, Also includes: Step 9: Round up the design values ​​of energy storage capacity and energy storage power.

4. The user-side energy storage capacity configuration method according to claim 1, characterized in that, In steps 1 and 3, the acquisition refers to collection and / or prediction.

5. The user-side energy storage capacity configuration method according to claim 1, characterized in that, In step 7, the correction factor F for the charging and discharging efficiency of the energy storage battery is set to 1.04-1.

06.

6. The user-side energy storage capacity configuration method according to claim 1, characterized in that, In step 7, the depth of discharge U of the energy storage battery is set to 0.5-0.

8.

7. The user-side energy storage capacity configuration method according to claim 1, characterized in that, In step 8, if C / 2 is greater than or equal to the maximum value Ma in the constraint set M, then the energy storage power design value is C / 2; if C / 2 is less than the maximum value Ma in the constraint set M, then the energy storage power design value is Ma.

8. A user-side energy storage capacity configuration system, characterized in that, include: User daily electricity consumption acquisition unit: Used to acquire the user's daily electricity consumption Q throughout the year. i Daily photovoltaic power generation P i i is 1-365; Typical day selection unit: Construct a typical day selection model based on energy balance and daily fluctuation, and select the day with the highest comprehensive score as the typical day according to the typical day selection model; Typical Daily Hourly Energy Acquisition Unit: Acquires hourly energy consumption DQ over a typical 24-hour period. j and hourly photovoltaic power generation (DP) j j is 1-24; Hourly Energy Difference Calculation Unit: Used to calculate hourly photovoltaic power generation (DP) j With hourly electricity consumption DQ j The difference DV j ; Hourly cumulative energy consumption calculation unit: used to calculate DV j Calculate DV by taking the absolute value of the cumulative positive values ​​DV+. j The absolute value of the cumulative total of all negative values, DV-; Energy storage capacity calculation value: used to obtain the energy storage capacity calculation value C1=Min(DV+, DV-); Restricting the set acquisition unit: If DV+ is smaller than DV-, then DV j The set of absolute values ​​of all positive values ​​is the energy storage power design constraint set M. If DV- is smaller than DV+, then DV j The set of absolute values ​​of all negative values ​​is the set of energy storage power design constraints M; Energy storage capacity design value calculation unit: used to calculate the energy storage capacity design value C=F×C1 / U, where F is the correction coefficient for the charge and discharge efficiency of the energy storage battery, and U is the depth of discharge of the energy storage battery; Design value verification unit: The energy storage power design value is designed according to 1 / 2 of the energy storage capacity design value, and it is necessary to ensure that the energy storage power design value is greater than or equal to the maximum value Ma in the limit set M.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. 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 steps of the method according to any one of claims 1 to 7.

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