A method, apparatus, and medium for configuring a light storage system

Abstract

The application discloses a kind of light storage system configuration method, device and medium, by obtaining historical power consumption data and environmental information;According to the configuration parameters of user demand confirmation light storage system;Set upper constraint condition and construct upper optimization model, according to configuration parameter and environmental information upper objective function is established;Set lower constraint condition and construct lower optimization model, according to configuration parameter and historical power consumption data lower objective function is established;According to the solution of upper optimization model and lower optimization model, confirm the configuration scheme of light storage system.This technical scheme, historical power consumption data can reflect the power consumption information of user, environmental information can reflect the working capacity of light storage system, by the historical power consumption data and environmental information of user, set upper optimization model and lower optimization model, according to the different needs of user set different objective function, make light storage system in the case where economic and performance are comprehensively considered make optimized configuration method, to improve performance.

Description

Technical Field

[0001] This application relates to the field of photovoltaic energy storage systems, and in particular to a method, apparatus and medium for configuring a photovoltaic energy storage system. Background Technology

[0002] Capacity configuration is the primary issue to be addressed during the design phase of a photovoltaic (PV) and energy storage system, as the quality of the configuration directly determines the safe operation and economic benefits for users. To improve the power supply reliability and PV utilization rate of independent photovoltaic systems, it is necessary to rationally configure the capacity of PV modules and energy storage systems. Optimized configuration of a PV and energy storage system refers to determining the system structure and equipment configuration based on statistical analysis of load demand and solar resources, targeting specific objectives and constraints, to optimize quantitative indicators such as economy, low carbon emissions, and energy efficiency.

[0003] In practical engineering design, the rated power of photovoltaic systems and the capacity of energy storage systems are often determined by empirical methods or the total investment amount of the user, or by directly adopting pre-matched capacity configuration schemes. Such configuration schemes make it difficult to ensure that the photovoltaic and energy storage systems are in an ideal matching state, which may lead to higher investment costs and poor performance of the entire system, failing to realize the advantages of the photovoltaic and energy storage systems.

[0004] Therefore, it is evident that providing a method for configuring a photovoltaic-storage system that balances economic efficiency and technical feasibility, improves system performance, and reduces costs is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, and medium for configuring a photovoltaic storage system, so as to balance economy and technology when configuring a photovoltaic storage system, improve system performance, and reduce costs.

[0006] To address the aforementioned technical problems, this application provides a method for configuring a photovoltaic energy storage system, characterized by comprising:

[0007] Obtain historical electricity consumption data and environmental information;

[0008] Confirm the configuration parameters of the photovoltaic storage system based on user requirements;

[0009] Set upper-level constraints and construct an upper-level optimization model. Establish an upper-level objective function based on the configuration parameters and environmental information to select the rated photovoltaic output and energy storage capacity of the photovoltaic-storage system.

[0010] Set lower-level constraints and construct a lower-level optimization model. Establish a lower-level objective function based on the configuration parameters and the historical electricity consumption data to generate the battery charging and discharging strategy.

[0011] Based on the solution results of the upper-level optimization model and the lower-level optimization model, the configuration scheme of the photovoltaic storage system is confirmed.

[0012] Preferably, the historical electricity consumption data is active power information collected by a smart meter; the environmental information includes latitude and longitude, light resources, and temperature, wherein the light resources are the total daily radiation Q per unit area of ​​the user's geographical location; further, it also includes obtaining the irradiance G at the corresponding time based on the light resources. c,t The irradiance is calculated as follows:

[0013]

[0014] Where a and b are the sunrise and sunset times, respectively;

[0015] Based on the temperature, the irradiance, and the rated power S of the photovoltaic array STC Calculate the output power S of the photovoltaic array PV,t :

[0016]

[0017] Among them, G STC and T STC The irradiance and system temperature are given under standard test conditions, where k is the power temperature coefficient, and T is the irradiance and system temperature. c，t This represents the surface temperature of the current photovoltaic power generation system.

[0018] Preferably, the configuration parameters include: the annual value cost of the user's photovoltaic system and its operation and maintenance cost C. PV Annual cost and operation and maintenance cost of energy storage systems, etc. (C) Batt The annual operating cost C′ without a photovoltaic and energy storage system Y The annual operating cost C for configuring a photovoltaic and energy storage system Y One-time subsidy for photovoltaic power generation C PV,sub One-time subsidy C for energy storage systems Batt,sub Spontaneous self-use rate R suff and self-equilibrium rate R self The self-consumption rate reflects the impact of the grid-connected microgrid on its own power generation, and the self-balance rate reflects its own power supply capacity and dependence on the grid.

[0019] Furthermore, the upper-level objective function is:

[0020] min TC=(C PV +C Batt +C Y -C′ Y -C PV,sub -C Batt,sub )×R suff ×Rself .

[0021] Preferably, the annual value cost and operation and maintenance cost of the user photovoltaic system are calculated as follows:

[0022]

[0023] The calculation of the equivalent annual cost and operation and maintenance cost of the energy storage system is as follows:

[0024]

[0025] Among them, S PV,i For photovoltaic system capacity; κ PV,i Cost per unit capacity of photovoltaic system; κ Batt,i For energy storage system capacity cost; γ PV,i and γ Batt,i The annual operation and maintenance cost per unit capacity of photovoltaic and energy storage systems; N PV and N Batt The service life of photovoltaic arrays and energy storage systems; r o This is the discount rate.

[0026] The calculation of the one-time subsidy for photovoltaic power generation is as follows:

[0027]

[0028] Among them, c PV,sub,i Subsidies for installed capacity provided to the unit;

[0029] The rated power constraint of a photovoltaic system is calculated as follows:

[0030] S PV,i,min <=S PV,i <=S PV,i,max ;

[0031] Among them, S PV,i Rated power of the photovoltaic array;

[0032] The self-consumption rate constraint is calculated as follows:

[0033]

[0034] Among them, E DG E represents the total electricity generated by distributed power sources. Out Electricity sales volume of the microgrid;

[0035] The self-balancing rate is calculated as follows:

[0036]

[0037] Among them, E total E represents the total load demand during the scheduling period.In This refers to the electricity purchased for the microgrid.

[0038] The annual operating cost is calculated as follows:

[0039] C Y =f(C o );

[0040] Among them, C o Typical daily operating costs of the system.

[0041] Preferably, the lower-level objective function is:

[0042]

[0043] Among them, C In,t and C Out,t These represent the electricity prices for electricity purchased and sold by users during the period from t to t+1; P In,t and P Out,t These represent the active power flowing into users and into the power grid during the period from t to t+1, respectively; C PV For photovoltaic power subsidies; P PV,i,t Contribute to photovoltaic power.

[0044] Preferably, the lower-level constraints include:

[0045] Backfeed and maximum power purchase constraints:

[0046]

[0047] In the formula, P In,t and P Out,t These are the purchased power and the power fed back to the grid, respectively; P In,max and P Out,max These are the maximum allowable power purchase capacity and the maximum allowable power feedback to the grid, respectively; B In,t and B Out,t Let B be a variable taking the value 0 or 1, representing the electricity purchase and sale status of the microgrid during time period t, respectively. In,t A value of 1 indicates that the microgrid is in a power purchase state, B Out,t A value of 1 indicates that the microgrid is in a state of selling electricity; if all values ​​are 0, it means that the microgrid system neither buys nor sells electricity.

[0048] The power balance equation is:

[0049]

[0050] Among them, P loss This represents the approximate loss of the photovoltaic-storage system.

[0051] The charging and discharging power constraints of the energy storage system are:

[0052]

[0053] In the formula, B d,i,t and B c,i,t Let B be a variable taking the value 0 or 1, representing the discharge and charge states of the energy storage system during time period t, respectively. d,i,t A value of 1 indicates that the energy storage system is in a discharging state, B c,i,t A value of 1 indicates that the energy storage system is in a charging state; if all values ​​are 0, it indicates that the energy storage system is in a stopped state, neither charging nor discharging; P d,i,max and P c,i,max These are the maximum discharge power and maximum charging power of the energy storage system, respectively.

[0054] The state of charge constraints of the energy storage system are:

[0055] SOC i,t =SOC i,t-1 +η c,i P c,i,t Δt / E i,max -P d,i,t Δt / η d,i E i,max ;

[0056] In the formula, SOC i,t E represents the state of charge of the energy storage system at time t. i,max This represents the maximum state of charge of the energy storage system; η c,i and η d,i These refer to the charging efficiency and discharging efficiency of the energy storage system, respectively.

[0057] The state of charge constraints of the energy storage system are:

[0058] SOC i,min ≤SOC i,t ≤SOC i,max ;

[0059] The constraint on the number of charge-discharge conversions of the energy storage system is:

[0060]

[0061] In the formula, N max This represents the maximum number of charge-discharge conversions within the energy storage system's scheduling cycle.

[0062] Preferably, the upper-layer model uses a metaheuristic algorithm or enumeration method to select the rated output of photovoltaic power and the energy storage capacity of the residential photovoltaic-storage system, and passes them to the lower-layer model. The lower-layer model uses a CBC solver to obtain user electricity bill information and passes it to the upper-layer model.

[0063] To address the aforementioned technical problems, this application also provides a photovoltaic storage system configuration device, comprising:

[0064] The acquisition module is used to acquire historical electricity consumption data and environmental information;

[0065] The configuration module is used to confirm the configuration parameters of the optical storage system according to user requirements;

[0066] The first setting module is used to set upper-level constraints and build an upper-level optimization model. It establishes an upper-level objective function based on the configuration parameters and the environmental information, which is used to select the rated output of photovoltaic power and the energy storage capacity of the photovoltaic-storage system.

[0067] The second setting module is used to set lower-level constraints and build a lower-level optimization model. It establishes a lower-level objective function based on the configuration parameters and the historical electricity consumption data to generate a battery charging and discharging strategy.

[0068] The confirmation module is used to confirm the configuration scheme of the optical storage system based on the solution results of the upper-layer optimization model and the lower-layer optimization model.

[0069] To address the aforementioned technical problems, this application also provides another optical storage system configuration device, including a memory for storing computer programs;

[0070] A processor is used to execute the computer program to implement the steps of the optical storage system configuration method described above.

[0071] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the optical storage system configuration method described above.

[0072] The photovoltaic-storage system configuration method provided in this application involves: acquiring historical electricity consumption data and environmental information; confirming the configuration parameters of the photovoltaic-storage system based on user needs; setting upper-level constraints and constructing an upper-level optimization model, establishing an upper-level objective function based on the configuration parameters and environmental information to select the rated photovoltaic output and energy storage capacity of the photovoltaic-storage system; setting lower-level constraints and constructing a lower-level optimization model, establishing a lower-level objective function based on the configuration parameters and historical electricity consumption data to generate the battery charging and discharging strategy; and confirming the configuration scheme of the photovoltaic-storage system based on the solution results of the upper-level and lower-level optimization models. Compared to current technologies where fixed configuration methods result in photovoltaic-storage systems not achieving ideal matching and exhibiting lower performance, this technical solution utilizes historical electricity consumption data to reflect user electricity usage information and environmental information to reflect the working capacity of the photovoltaic-storage system. By comprehensively considering the user's historical electricity consumption data and environmental information, setting upper-level and lower-level optimization models, and setting different objective functions according to the different needs of different users, the photovoltaic-storage system can make an optimal configuration method while comprehensively considering both economy and performance, thereby improving performance and reducing costs.

[0073] Furthermore, the optical storage system configuration device and medium provided in this application correspond to the optical storage system configuration method described above, and have the same effect. Attached Figure Description

[0074] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 A flowchart illustrating a method for configuring a photovoltaic energy storage system provided in this application embodiment;

[0076] Figure 2 A structural diagram of a photovoltaic energy storage system configuration device provided in an embodiment of this application;

[0077] Figure 3 This is a structural diagram of another optical energy storage system configuration device provided in an embodiment of this application. Detailed Implementation

[0078] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0079] The core of this application is to provide a method, apparatus, and medium for configuring a photovoltaic energy storage system, so as to balance economy and technology when configuring a photovoltaic energy storage system, improve system performance, and reduce costs.

[0080] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0081] Figure 1 A flowchart illustrating a method for configuring a photovoltaic storage system provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0082] S10: Obtain historical electricity consumption data and environmental information;

[0083] S11: Confirm the configuration parameters of the photovoltaic storage system according to user requirements;

[0084] S12: Set upper-level constraints and build an upper-level optimization model. Establish an upper-level objective function based on configuration parameters and environmental information to select the rated output of photovoltaic power and energy storage capacity of the photovoltaic-storage system.

[0085] S13: Set lower-level constraints and build a lower-level optimization model. Establish a lower-level objective function based on configuration parameters and historical power consumption data to generate battery charging and discharging strategies.

[0086] S14: Based on the solution results of the upper-level optimization model and the lower-level optimization model, confirm the configuration scheme of the photovoltaic storage system.

[0087] Optimized configuration of photovoltaic-storage systems refers to determining the system structure and equipment configuration based on statistical analysis of load demand and solar resources, targeting specific objectives and constraints, to optimize quantitative indicators such as economy, low carbon emissions, and energy efficiency. Capacity configuration is the primary issue to be addressed in the design phase of a photovoltaic-storage system, and the quality of the configuration directly determines the safe operation and economic benefits for users.

[0088] In practical engineering design, the rated power of photovoltaics and the capacity of energy storage systems are often determined by empirical methods or the total investment amount of the user, or by directly adopting a pre-matched capacity configuration scheme. This configuration scheme makes it difficult to ensure that the photovoltaic, energy storage and load are in an ideal matching state, which may lead to high investment costs and poor performance of the entire system, and fail to bring out the advantages of photovoltaic and energy storage load.

[0089] Existing methods for configuring photovoltaic and energy storage systems typically do not consider users' actual electricity consumption and weather information to recommend optimal configuration schemes. Instead, they rely solely on experience or total investment to determine the configuration scheme. This results in a lack of specificity in the configuration schemes and fails to balance economic and technical considerations. Therefore, there is an urgent need for an optimized configuration method for residential photovoltaic and energy storage systems that balances both economic efficiency and technical feasibility.

[0090] Historical electricity consumption data consists of active power information collected by smart meters; environmental information includes latitude and longitude, as well as light resources and temperature. Light resources are the total daily radiation Q per unit area of ​​the user's geographical location. Furthermore, it also includes the irradiance G at the corresponding time, obtained based on the light resources. c,t The irradiance is calculated as follows:

[0091]

[0092] Where t is the current time, and a and b are the sunrise and sunset times, respectively;

[0093] Based on temperature, irradiance, and the rated power S of the photovoltaic array STC Calculate the output power S of the photovoltaic array PV,t :

[0094]

[0095] Among them, G STC and T STC The irradiance and system temperature are given under standard test conditions, where k is the power temperature coefficient, and T is the irradiance and system temperature. c，t This represents the surface temperature of the current photovoltaic power generation system.

[0096] This embodiment also provides a photovoltaic-storage system configuration method that combines historical electricity consumption information and meteorological information to construct a two-layer optimization configuration model. This model performs data analysis and optimization calculations to recommend the best configuration scheme for the user, ensuring the scientific validity of the scheme. This embodiment balances economic and technical considerations, constructing a unified objective function that considers multiple indicators. It autonomously coordinates and balances economic and technical indicators, eliminating the need for complex multi-objective solution algorithms. It is simple, practical, and highly scalable. The entire process of optimizing the configuration of residential photovoltaic-storage capacity requires only a small amount of information such as capacity and price to obtain a recommended scheme. It does not rely on the experience of technical personnel and is convenient and quick to use.

[0097] The photovoltaic-storage system configuration method provided in this application involves: acquiring historical electricity consumption data and environmental information; confirming the configuration parameters of the photovoltaic-storage system based on user needs; setting upper-level constraints and constructing an upper-level optimization model, establishing an upper-level objective function based on the configuration parameters and environmental information to select the rated photovoltaic output and energy storage capacity of the photovoltaic-storage system; setting lower-level constraints and constructing a lower-level optimization model, establishing a lower-level objective function based on the configuration parameters and historical electricity consumption data to generate the battery charging and discharging strategy; and confirming the configuration scheme of the photovoltaic-storage system based on the solution results of the upper-level and lower-level optimization models. Compared to current technologies where fixed configuration methods result in photovoltaic-storage systems not achieving ideal matching and exhibiting lower performance, this technical solution utilizes historical electricity consumption data to reflect user electricity usage information and environmental information to reflect the working capacity of the photovoltaic-storage system. By comprehensively considering the user's historical electricity consumption data and environmental information, setting upper-level and lower-level optimization models, and setting different objective functions according to the different needs of different users, the photovoltaic-storage system can make an optimal configuration method while comprehensively considering both economy and performance, thereby improving performance and reducing costs.

[0098] Based on the above embodiments, the configuration parameters include: the annual value cost of the user's photovoltaic system and the operation and maintenance cost C. PV Annual cost and operation and maintenance cost of energy storage systems, etc. (C) Batt The annual operating cost C′ without a photovoltaic and energy storage system Y The annual operating cost C for configuring a photovoltaic and energy storage system Y One-time subsidy for photovoltaic power generation C PV,sub One-time subsidy C for energy storage systems Batt,sub Spontaneous self-use rate R suffand self-equilibrium rate R self The self-consumption rate reflects the impact of grid-connected microgrids on their own power generation, while the self-balance rate reflects their own power supply capacity and dependence on the grid.

[0099] Furthermore, the upper-level objective function is:

[0100] min TC=(C PV +C Batt +C Y -C′ Y -C PV,sub -C Batt,sub )×R suff ×R self .

[0101] The calculation of the annual value cost and operation and maintenance cost of the user's photovoltaic system is as follows:

[0102]

[0103] The annual cost and operation and maintenance costs of energy storage systems are calculated as follows:

[0104]

[0105] Among them, S PV,i For photovoltaic system capacity; κ PV,i Cost per unit capacity of photovoltaic system; κ Batt,i For energy storage system capacity cost; γ PV,i and γ Batt,i The annual operation and maintenance cost per unit capacity of photovoltaic and energy storage systems; N PV and N Batt The service life of photovoltaic arrays and energy storage systems; r o This is the discount rate.

[0106] The calculation of the one-time subsidy for photovoltaic power generation is as follows:

[0107]

[0108] Among them, c PV,sub,i Subsidies for installed capacity provided to the unit;

[0109] The rated power constraint of a photovoltaic system is calculated as follows:

[0110] S PV,i,min <=S PV,i <=S PV,i,max ;

[0111] Among them, S PV,i Rated power of the photovoltaic array;

[0112] The self-consumption rate constraint is calculated as follows:

[0113]

[0114] Among them, E DG E represents the total electricity generated by distributed power sources. Out Electricity sales volume of the microgrid;

[0115] The self-balancing rate is calculated as follows:

[0116]

[0117] Among them, E total E represents the total load demand during the scheduling period. In This refers to the electricity purchased for the microgrid.

[0118] The annual operating cost is calculated as follows:

[0119] C Y =f(C o );

[0120] Among them, C o Typical daily operating costs of the system.

[0121] The lower-level objective function is:

[0122]

[0123] Among them, C In,t and C Out,t These represent the electricity prices for electricity purchased and sold by users during the period from t to t+1; P In,t and P Out,t These represent the active power flowing into users and into the power grid during the period from t to t+1, respectively; C PV For photovoltaic power subsidies; P PV,i,t Contribute to photovoltaic power.

[0124] Lower-level constraints include:

[0125] Backfeed and maximum power purchase constraints:

[0126]

[0127] In the formula, P In,t and P Out,t These are the purchased power and the power fed back to the grid, respectively; P In,max and P Out,max These are the maximum allowable power purchase capacity and the maximum allowable power feedback to the grid, respectively; B In,t and B Out,t Let B be a variable taking the value 0 or 1, representing the electricity purchase and sale status of the microgrid during time period t, respectively. In,t A value of 1 indicates that the microgrid is in a power purchase state, BOut,t A value of 1 indicates that the microgrid is in a state of selling electricity; if all values ​​are 0, it means that the microgrid system neither buys nor sells electricity.

[0128] The power balance equation is:

[0129]

[0130] Among them, P loss This represents the approximate loss of the photovoltaic-storage system.

[0131] The charging and discharging power constraints of the energy storage system are:

[0132]

[0133] In the formula, B d,i,t and B c,i,t Let B be a variable taking the value 0 or 1, representing the discharge and charge states of the energy storage system during time period t, respectively. d,i,t A value of 1 indicates that the energy storage system is in a discharging state, B c,i,t A value of 1 indicates that the energy storage system is in a charging state; if all values ​​are 0, it indicates that the energy storage system is in a stopped state, neither charging nor discharging; P d,i,max and P c,i,max These are the maximum discharge power and maximum charging power of the energy storage system, respectively.

[0134] The state of charge constraints of the energy storage system are:

[0135] SOC i,t =SOC i,t-1 +η c,i P c,i,t Δt / E i,max -P d,i,t Δt / η d,i E i,max ;

[0136] In the formula, SOC i,t E represents the state of charge of the energy storage system at time t. i,max This represents the maximum state of charge of the energy storage system; η c,i and η d,i These refer to the charging efficiency and discharging efficiency of the energy storage system, respectively.

[0137] The state of charge constraints of the energy storage system are:

[0138] SOC i,min ≤SOC i,t ≤SOC i,max ;

[0139] The constraint on the number of charge-discharge conversions of the energy storage system is:

[0140]

[0141] In the formula, N max This represents the maximum number of charge-discharge conversions within the energy storage system's scheduling cycle.

[0142] The upper-level model uses a metaheuristic algorithm or enumeration method to select the rated output of photovoltaic power and the energy storage capacity of the residential photovoltaic-storage system, and then passes them to the lower-level model. The lower-level model uses a CBC solver to obtain the user's electricity bill information and then passes it to the upper-level model.

[0143] The above embodiments have described the configuration method for the photovoltaic storage system in detail. This application also provides embodiments corresponding to the configuration device for the photovoltaic storage system. It should be noted that this application describes the embodiments of the device part from two perspectives: one is based on the functional modules, and the other is based on the hardware.

[0144] Figure 2 A structural diagram of a photovoltaic energy storage system configuration device provided in an embodiment of this application is shown below. Figure 2 As shown, the device includes:

[0145] Module 10 is used to acquire historical electricity consumption data and environmental information;

[0146] Configuration module 11 is used to confirm the configuration parameters of the optical storage system according to user requirements;

[0147] The first setting module 12 is used to set upper-level constraints and build an upper-level optimization model. It establishes an upper-level objective function based on configuration parameters and environmental information, which is used to select the rated output of photovoltaic power and energy storage capacity of the photovoltaic-storage system.

[0148] The second setting module 13 is used to set lower-level constraints and build a lower-level optimization model. It establishes a lower-level objective function based on configuration parameters and historical power consumption data to generate a battery charging and discharging strategy.

[0149] The confirmation module 14 is used to confirm the configuration scheme of the photovoltaic storage system based on the solution results of the upper-level optimization model and the lower-level optimization model.

[0150] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0151] The photovoltaic-storage system configuration device provided in this application acquires historical electricity consumption data and environmental information; confirms the configuration parameters of the photovoltaic-storage system according to user needs; sets upper-level constraints and constructs an upper-level optimization model, establishing an upper-level objective function based on the configuration parameters and environmental information to select the rated photovoltaic output and energy storage capacity of the photovoltaic-storage system; sets lower-level constraints and constructs a lower-level optimization model, establishing a lower-level objective function based on the configuration parameters and historical electricity consumption data to generate the battery charging and discharging strategy; and confirms the configuration scheme of the photovoltaic-storage system based on the solution results of the upper-level and lower-level optimization models. Compared with the current technology, where the use of a fixed configuration method leads to the photovoltaic-storage system not being in an ideal matching state and resulting in lower performance, this technical solution uses historical electricity consumption data to reflect the user's electricity consumption information and environmental information to reflect the working capacity of the photovoltaic-storage system. By comprehensively considering the user's historical electricity consumption data and environmental information, setting upper-level and lower-level optimization models, and setting different objective functions according to the different needs of different users, the photovoltaic-storage system can make the optimal configuration method while comprehensively considering economy and performance, thereby improving performance and reducing costs.

[0152] Figure 3 A structural diagram of another optical storage system configuration device provided in the embodiments of this application is shown below. Figure 3 As shown, the device includes: a memory 20 for storing computer programs;

[0153] The processor 21 is used to execute a computer program to implement the steps of the optical storage system configuration method as described in the above embodiments.

[0154] The optical storage system configuration device provided in this embodiment may include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0155] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which is used to handle computational operations related to machine learning.

[0156] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the optical storage system configuration method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, historical electricity consumption data, configuration parameters, etc.

[0157] In some embodiments, the optical storage system configuration device may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0158] Those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the configuration of the optical storage system and may include more or fewer components than shown.

[0159] The photovoltaic-storage system configuration device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can perform the following methods: acquire historical electricity consumption data and environmental information; confirm the configuration parameters of the photovoltaic-storage system according to user requirements; set upper-level constraints and construct an upper-level optimization model, and establish an upper-level objective function based on the configuration parameters and environmental information to select the rated photovoltaic output and energy storage capacity of the photovoltaic-storage system; set lower-level constraints and construct a lower-level optimization model, and establish a lower-level objective function based on the configuration parameters and historical electricity consumption data to generate the battery charging and discharging strategy; and confirm the configuration scheme of the photovoltaic-storage system based on the solution results of the upper-level optimization model and the lower-level optimization model.

[0160] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0161] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] The foregoing has provided a detailed description of the optical storage system configuration method, apparatus, and medium provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0163] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for configuring a photovoltaic-storage system, characterized in that, include: Obtain historical electricity consumption data and environmental information; Confirm the configuration parameters of the photovoltaic storage system based on user requirements; Set upper-level constraints and construct an upper-level optimization model. Establish an upper-level objective function based on the configuration parameters and environmental information to select the rated photovoltaic output and energy storage capacity of the photovoltaic-storage system. Set lower-level constraints and construct a lower-level optimization model. Establish a lower-level objective function based on the configuration parameters and the historical power consumption data to generate the battery charging and discharging strategy. Based on the solution results of the upper-level optimization model and the lower-level optimization model, the configuration scheme of the photovoltaic storage system is confirmed; The historical electricity consumption data refers to the active power information collected by the smart meter; the environmental information includes latitude and longitude, light resources, and temperature, where light resources refer to the total daily radiation per unit area at the user's geographical location. Furthermore, it also includes obtaining the irradiance at a corresponding time based on the light resources. The irradiance is calculated as follows: ； Where t is the current time, and a and b are the sunrise and sunset times, respectively; Based on the temperature, the irradiance, and the rated power of the photovoltaic array. Calculate the output power of the photovoltaic array : ； in, and Here, represents the irradiance and system temperature under standard test conditions, and k is the power temperature coefficient. This refers to the surface temperature of the current photovoltaic power generation system. The configuration parameters include: the annual value cost of the user's photovoltaic system and its operation and maintenance costs. Annual cost and operation and maintenance costs of energy storage systems, etc. Annual operating costs without a photovoltaic and energy storage system Annual operating cost of configuring a photovoltaic storage system One-time subsidy for photovoltaic power generation One-time subsidy for energy storage systems Spontaneous self-use rate and self-balance rate The self-consumption rate reflects the impact of the grid-connected microgrid on its own power generation, and the self-balance rate reflects its own power supply capacity and dependence on the grid. Furthermore, the upper-level objective function is: ； The calculation of the annual value cost and operation and maintenance cost of the user's photovoltaic system is as follows: ； The calculation of the equivalent annual cost and operation and maintenance cost of the energy storage system is as follows: ； in, For the capacity of the photovoltaic system; Cost per unit capacity of photovoltaic system; Cost of energy storage system capacity; and The annual operation and maintenance cost per unit capacity of photovoltaic systems and energy storage systems; and The service life of photovoltaic arrays and energy storage systems; The discount rate; The calculation of the one-time subsidy for photovoltaic power generation is as follows: ； in, Subsidies for installed capacity provided to the unit; The rated power constraint of a photovoltaic system is calculated as follows: ； in, Rated power of the photovoltaic array; The self-consumption rate constraint is calculated as follows: ； in, The total electricity generated by distributed power sources; Electricity sales volume of the microgrid; The self-balancing rate is calculated as follows: ； in, This represents the total load demand during the scheduling period. Purchased electricity for the microgrid; The annual operating cost is calculated as follows: ； in, Typical daily operating costs of the system.

2. The configuration method for a photovoltaic energy storage system according to claim 1, characterized in that, The lower-level objective function is: ； in, and These are the electricity prices for electricity purchased and sold by users during the period from t to t+1, respectively. and These represent the active power flowing into users and into the power grid during the period from t to t+1, respectively. Subsidized electricity prices for photovoltaic power; Contribute to photovoltaic power.

3. The configuration method for a photovoltaic-storage system according to claim 2, characterized in that, The lower-level constraints include: Backfeed and maximum power purchase constraints: ； In the formula, and These are the purchased power and the power fed back to the grid, respectively. and These are the maximum allowable power purchase capacity and the maximum allowable power feedback to the grid, respectively. and Let be variables taking values ​​of 0 or 1, representing the electricity purchase and sale status of the microgrid during time period t, respectively. A value of 1 indicates that the microgrid is in a power purchase state. A value of 1 indicates that the microgrid is in a state of selling electricity; if all values ​​are 0, it means that the microgrid system neither buys nor sells electricity. The power balance equation is: ； in, This represents the approximate loss of the photovoltaic-storage system. The charging and discharging power constraints of the energy storage system are: ； In the formula, and Let be variables taking values ​​of 0 or 1, representing the discharge and charge states of the energy storage system during time period t, respectively. A value of 1 indicates that the energy storage system is in a discharging state. A value of 1 indicates that the energy storage system is in a charging state; if all values ​​are 0, it indicates that the energy storage system is in a stopped state, neither charging nor discharging. and These are the maximum discharge power and maximum charging power of the energy storage system, respectively. The state of charge constraints of the energy storage system are: ； In the formula, This represents the state of charge of the energy storage system at time t. This represents the maximum state of charge of the energy storage system. and These refer to the charging efficiency and discharging efficiency of the energy storage system, respectively. The state of charge constraints of the energy storage system are: ； The constraint on the number of charge-discharge conversions of the energy storage system is: ； In the formula, This represents the maximum number of charge-discharge conversions within the energy storage system's scheduling cycle.

4. The configuration method for a photovoltaic energy storage system according to claim 1, characterized in that, The upper-level model uses a metaheuristic algorithm or enumeration method to select the rated output of photovoltaic power and the energy storage capacity of the residential photovoltaic-storage system, and then passes them to the lower-level model. The lower-level model uses a CBC solver to obtain user electricity bill information and then passes it to the upper-level model.

5. A photovoltaic energy storage system configuration device, characterized in that, include: The acquisition module is used to acquire historical electricity consumption data and environmental information; The configuration module is used to confirm the configuration parameters of the optical storage system according to user requirements; The first setting module is used to set upper-level constraints and build an upper-level optimization model. It establishes an upper-level objective function based on the configuration parameters and the environmental information, which is used to select the rated output of photovoltaic power and the energy storage capacity of the photovoltaic-storage system. The second setting module is used to set lower-level constraints and build a lower-level optimization model. It establishes a lower-level objective function based on the configuration parameters and the historical electricity consumption data to generate a battery charging and discharging strategy. The confirmation module is used to confirm the configuration scheme of the optical storage system based on the solution results of the upper-layer optimization model and the lower-layer optimization model. The historical electricity consumption data refers to the active power information collected by the smart meter; the environmental information includes latitude and longitude, light resources, and temperature, where light resources refer to the total daily radiation per unit area at the user's geographical location. Furthermore, it also includes obtaining the irradiance at a corresponding time based on the light resources. The irradiance is calculated as follows: ； Where t is the current time, and a and b are the sunrise and sunset times, respectively; Based on the temperature, the irradiance, and the rated power of the photovoltaic array. Calculate the output power of the photovoltaic array : ； in, and Here, represents the irradiance and system temperature under standard test conditions, and k is the power temperature coefficient. This refers to the surface temperature of the current photovoltaic power generation system. The configuration parameters include: the annual value cost of the user's photovoltaic system and its operation and maintenance costs. Annual cost and operation and maintenance costs of energy storage systems, etc. Annual operating costs without a photovoltaic and energy storage system Annual operating cost of configuring a photovoltaic storage system One-time subsidy for photovoltaic power generation One-time subsidy for energy storage systems Spontaneous self-use rate and self-balance rate The self-consumption rate reflects the impact of the grid-connected microgrid on its own power generation, and the self-balance rate reflects its own power supply capacity and dependence on the grid. Furthermore, the upper-level objective function is: ； The calculation of the annual value cost and operation and maintenance cost of the user's photovoltaic system is as follows: ； The calculation of the equivalent annual cost and operation and maintenance cost of the energy storage system is as follows: ； in, For the capacity of the photovoltaic system; Cost per unit capacity of photovoltaic system; Cost of energy storage system capacity; and The annual operation and maintenance cost per unit capacity of photovoltaic systems and energy storage systems; and The service life of photovoltaic arrays and energy storage systems; The discount rate; The calculation of the one-time subsidy for photovoltaic power generation is as follows: ； in, Subsidies for installed capacity provided to the unit; The rated power constraint of a photovoltaic system is calculated as follows: ； in, Rated power of the photovoltaic array; The self-consumption rate constraint is calculated as follows: ； in, The total electricity generated by distributed power sources; Electricity sales volume of the microgrid; The self-balancing rate is calculated as follows: ； in, This represents the total load demand during the scheduling period. Purchased electricity for the microgrid; The annual operating cost is calculated as follows: ； in, Typical daily operating costs of the system.

6. A photovoltaic energy storage system configuration device, characterized in that, Includes memory used to store computer programs; A processor, configured to implement the steps of the optical storage system configuration method as described in any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the optical storage system configuration method as described in any one of claims 1 to 4.

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