A method, device and electronic equipment for capacity configuration

Abstract

The present invention provides a method, device and electronic device for capacity configuration, wherein the method includes: obtaining multiple solar radiation data at the location of the BIPV system; clustering the multiple solar radiation data to determine the typical solar radiation data and photovoltaic output in each category; setting multiple capacity configuration schemes for the BIPV system; determining the investment payback period of the BIPV system corresponding to each capacity configuration scheme based on the photovoltaic output of multiple categories; and configuring the capacity of the BIPV system based on the capacity configuration scheme with the shortest investment payback period. The present invention can determine the optimal capacity configuration scheme when the energy storage capacity and the photovoltaic capacity influence each other, and can ensure the maximization of the benefits of the entire system; taking into account the differences in the four seasons and the adverse climate, etc., it is convenient to realize the coordinated configuration analysis of the photovoltaic-energy storage capacity, and taking the investment payback period as a single optimization target, it is convenient to solve, and can more accurately evaluate the advantages and disadvantages of the capacity configuration scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic building integration, and in particular to a capacity configuration method, device, electronic equipment and computer-readable storage medium. Background Art

[0002] With social progress and economic development, while pursuing a more comfortable building environment, the proportion of building energy consumption in the terminal electricity consumption has increased year by year. According to the "China Building Energy Consumption Research Report (2020)", in 2018, the energy consumption of buildings in the operation stage was 1 billion tce, accounting for 21.7% of the total energy consumption in the country. In order to reduce building energy consumption and achieve sustainable development of environmental energy, the application of renewable energy in buildings has become a focus of attention, among which distributed photovoltaic systems have huge development potential and wide applicability. Building Integrated Photovoltaic (BIPV) transforms buildings from energy consumers to distributed energy suppliers, with many advantages such as high efficiency, economy, and environmental protection, and can provide support for carbon neutrality. The photovoltaic industry continues to innovate technology and reduce costs. The cost of photovoltaic electricity has achieved parity between the power generation and electricity consumption sides, and has an economic basis for large-scale application. For a BIPV system, making full use of natural resources, coordinating the configuration of energy storage and photovoltaic capacity to meet load demand and benefit goals, formulating reasonable and economical energy regulation strategies during operation, and ensuring system power supply security and a high return on investment have become the core issues in the system construction and operation stages.

[0003] At present, the energy storage capacity is mainly optimized in distributed photovoltaic-energy storage systems; however, BIPV systems are different from general photovoltaic-energy storage systems. Under the premise of known building energy consumption, photovoltaic capacity should be used together with energy storage capacity as optimization parameters, coordinated and configured to participate in the optimization process, otherwise it will lead to additional construction costs and waste of resources. There are few studies on the integrated analysis of economic capacity configuration and operation strategy of BIPV systems. Some scholars have proposed schemes to study energy storage capacity configuration and energy scheduling strategies for distributed photovoltaic-energy storage systems with economy as the goal. The main purpose of this scheme is to solve the single-objective optimization problem by taking energy storage capacity as the optimization parameter and economic cost as the optimization goal when determining the energy regulation strategy (for example, photovoltaic priority for self-use and surplus power for grid access).

[0004] However, for BIPV systems, the energy storage capacity and photovoltaic capacity configuration results affect each other, and only optimizing the configuration of energy storage capacity cannot guarantee the maximization of the benefits of the entire system. Summary of the invention

[0005] In order to solve the technical problem that the existing capacity configuration scheme is not suitable for BIPV systems, the embodiments of the present invention provide a capacity configuration method, device, electronic device and computer-readable storage medium.

[0006] In a first aspect, an embodiment of the present invention provides a method for capacity configuration, including:

[0007] Acquire a plurality of solar radiation data at a location of the BIPV system, wherein the solar radiation data includes solar radiation of a plurality of unit durations within a preset time period;

[0008] Performing clustering processing on the plurality of solar radiation data, determining typical solar radiation data in each category, and determining the photovoltaic output of each category according to each of the typical solar radiation data;

[0009] Setting a plurality of capacity configuration schemes of the BIPV system, wherein the capacity configuration schemes include photovoltaic area and energy storage capacity;

[0010] Determine the investment payback period of the BIPV system corresponding to each of the capacity configuration schemes based on multiple categories of photovoltaic output;

[0011] The capacity of the BIPV system is configured based on a capacity configuration scheme with the shortest investment payback period.

[0012] In a second aspect, an embodiment of the present invention further provides a device for capacity configuration, including:

[0013] An acquisition module, used to acquire a plurality of solar radiation data at a location of the BIPV system, wherein the solar radiation data includes solar radiation of a plurality of unit durations within a preset time period;

[0014] A clustering module, used for clustering the plurality of solar radiation data, determining typical solar radiation data in each category, and determining the photovoltaic output of each category according to each of the typical solar radiation data;

[0015] A setting module, used to set a plurality of capacity configuration schemes of the BIPV system, wherein the capacity configuration schemes include photovoltaic area and energy storage capacity;

[0016] A processing module, configured to determine the payback period of the BIPV system corresponding to each of the capacity configuration schemes based on a plurality of categories of photovoltaic outputs;

[0017] The capacity configuration module is used to configure the capacity of the BIPV system based on the capacity configuration scheme with the shortest investment payback period.

[0018] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps in any one of the above-described capacity configuration methods.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in any one of the above-mentioned capacity configuration methods.

[0020] The capacity configuration method, device, electronic device and computer-readable storage medium provided in the embodiment of the present invention determine the typical solar radiation data of each category through clustering processing, and then determine the photovoltaic output corresponding to each category; and the capacity configuration scheme uses both photovoltaic area and energy storage capacity as optimization parameters, and takes the shortest investment payback period as the goal, and then the optimal capacity configuration scheme can be selected. This method uses both photovoltaic area and energy storage capacity as optimization parameters, and can determine the optimal capacity configuration scheme when the energy storage capacity and photovoltaic capacity influence each other, which can ensure the maximum benefit of the entire BIPV system; clustering multiple solar radiation data, and performing capacity configuration based on the photovoltaic output corresponding to each category, which can determine the solar radiation distribution characteristics throughout the year, taking into account the differences in the four seasons and severe climate, etc., to facilitate the coordinated configuration analysis of the photovoltaic-energy storage capacity of the BIPV system, as well as the study of more flexible and changeable energy scheduling strategies; and taking the investment payback period as a single optimization target, it is convenient to solve, and can more accurately evaluate the advantages and disadvantages of the capacity configuration scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0022] Figure 1 A flow chart of a capacity configuration method provided by an embodiment of the present invention is shown;

[0023] Figure 2 A schematic diagram showing a clustering processing result in the capacity configuration method provided in an embodiment of the present invention;

[0024] Figure 3 A flow chart showing another capacity configuration method provided by an embodiment of the present invention;

[0025] Figure 4 A schematic diagram showing the location of an example update using the particle swarm optimization algorithm;

[0026] Figure 5 A schematic diagram showing the convergence comparison between the solution provided by the embodiment of the present invention and the traditional PSO algorithm is shown;

[0027] Figure 6 A schematic diagram showing the structure of a device for capacity configuration provided by an embodiment of the present invention is shown;

[0028] Figure 7 A schematic structural diagram of an electronic device for executing a capacity configuration method provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0029] The embodiments of the present invention are described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0030] A capacity configuration method provided in an embodiment of the present invention is used to provide a suitable capacity configuration scheme for a BIPV system to determine the photovoltaic area and energy storage capacity of the BIPV system. Figure 1 FIG. 1 is a flow chart showing a method for capacity configuration provided by an embodiment of the present invention. Figure 1 As shown, the method includes:

[0031] Step 101: Acquire a plurality of solar radiation data at the location of the BIPV system, wherein the solar radiation data includes solar radiation of a plurality of unit durations within a preset time period.

[0032] In an embodiment of the present invention, the location of the BIPV system refers to the location where the BIPV system is installed. After determining the location of the BIPV system, the solar radiation of multiple unit durations within a preset time period can be determined, and then the solar radiation data is generated; in this embodiment, multiple different solar radiation data are required, that is, solar radiation data of multiple different preset time periods. For example, the preset time period is 1 day, and the unit duration is 1 hour, then the solar radiation data includes the solar radiation of 24 hours in 1 day, and the solar radiation data can be represented by a 24-dimensional array. In order to better analyze the solar radiation conditions at the location of the BIPV system, the embodiment of the present invention obtains solar radiation data within one year. Among them, the solar radiation refers to the solar radiation per unit area.

[0033] Step 102: clustering the plurality of solar radiation data, determining typical solar radiation data in each category, and determining the photovoltaic output of each category according to each typical solar radiation data.

[0034] At present, the average value of solar radiation is generally used to achieve capacity configuration, which does not take into account the situation where the solar radiation changes due to weather changes and the like. The embodiment of the present invention determines the solar radiation distribution characteristics based on clustering processing to achieve capacity configuration. Specifically, by clustering multiple solar radiation data, the multiple solar radiation data can be divided into multiple categories, such as k categories; for each category of solar radiation data, typical solar radiation data is extracted. For example, the average of the multiple solar radiation data in each category can be taken, and the average value can be used as the typical solar radiation data of the category; or, the median value of the multiple solar radiation data in the category can be used as the typical solar radiation data; or, the maximum value or minimum value of the multiple solar radiation data in the category can be used as the typical solar radiation data, which is not limited in this embodiment.

[0035] Among them, since the selection of category k value will affect the clustering effect, the k value can be determined based on the elbow method, the silhouette coefficient method, etc., and then the multiple solar radiation data can be divided into k categories through the clustering algorithm; for example, the clustering processing can be implemented through the K-Means clustering algorithm. After determining the typical solar radiation data of each category, the photovoltaic output of the category can be determined based on the typical solar radiation data. The photovoltaic output refers to the photovoltaic output per unit area. For example, the photovoltaic output per unit area can be calculated based on the typical solar radiation data and the photoelectric conversion rate of the photovoltaic system in the BIPV system, or other mature technologies can be used to determine the photovoltaic output, which is not described in detail in this embodiment.

[0036] For example, taking the solar radiation data including the solar radiation for 24 hours in a day as an example, the solar radiation data for 365 days in a year can be obtained, with a total of 365 samples. The k value is determined to be 5 by the elbow method and the silhouette coefficient method, and the 5 typical solar radiation data finally determined are shown in Figure 2 As shown; Figure 2 The horizontal axis represents time, and the vertical axis represents the solar radiation per unit area (unit: W / m 2 ), each curve in the figure represents a typical solar radiation data of a category. After that, the photovoltaic output of each category can be calculated.

[0037] Step 103: Set multiple capacity configuration schemes for the BIPV system, where the capacity configuration schemes include photovoltaic area and energy storage capacity.

[0038] In the embodiment of the present invention, the BIPV system includes two parts: a photovoltaic system and an energy storage system. The photovoltaic area of ​​the photovoltaic system can represent the photovoltaic capacity of the photovoltaic system, and the capacity of the energy storage battery in the energy storage system can represent the energy storage capacity of the BIPV system. This embodiment pre-sets multiple candidate capacity configuration schemes; wherein, in addition to the energy storage capacity, the capacity configuration scheme also includes the photovoltaic area, that is, this embodiment uses the photovoltaic capacity and the energy storage capacity as optimization parameters, and can determine the optimal capacity configuration scheme. For example, the roof area is 160m 2 , N types of photovoltaic areas [20, 30, 40…, 160] can be set, and M types of energy storage capacities [1kWh, 2kWh, ..., 10kWh] can be set, and a total of N×M capacity configuration schemes can be determined. Among them, step 103 can also be executed before step 101 or step 102, and the execution order thereof is not limited in this embodiment.

[0039] Step 104: Determine the payback period of the BIPV system corresponding to each capacity configuration scheme based on multiple categories of photovoltaic output.

[0040] In the embodiment of the present invention, the payback period of the BIPV system refers to the length of time it takes to recover the investment in building the BIPV system. The shorter the payback period, the more beneficial it is to the long-term development of the BIPV system and the better the effect. Among them, the payback period is related to the local solar radiation, that is, it is related to the photovoltaic output, and it is also related to the photovoltaic area and energy storage capacity of the BIPV system, that is, it is related to the capacity configuration plan. In the embodiment of the present invention, for each capacity configuration plan, the payback period under different categories of photovoltaic output is determined, and the payback period of the BIPV system corresponding to the capacity configuration plan is represented by multiple payback periods or a range of payback periods. For example, a total of k types of photovoltaic output are determined. For a certain capacity configuration plan, the payback period T corresponding to each type of photovoltaic output can be determined. i , i = 1, 2, ..., k, then the investment payback period of the capacity configuration party can be {T1, T2, ..., T k}, or the time range of the investment payback period: [min(T i ),max(T i )].

[0041] Optionally, in the embodiment of the present invention, the following formula (1) represents the investment payback period, that is, each investment payback period of the capacity configuration solution satisfies the following formula (1):

[0042]

[0043] Among them, C pv represents the construction and operation and maintenance cost of photovoltaic modules, E sub represents subsidy income, E carbon represents environmental benefits, Erec represents the recycling benefit, E elec Represents the electricity price income, E pv_syn represents the income from the photovoltaic surplus power grid connection, C b Represents the depreciation cost of charging and discharging the energy storage battery. This embodiment uses this to determine the investment payback period, and can pre-formulate an energy scheduling strategy based on policies and local time-of-use electricity price characteristics in order to achieve good economic benefits; and can effectively respond to changes in system parameters in the short term in real time, and can make full use of the advantages of "peak-valley arbitrage" of the energy storage system, which is beneficial to the investment recovery of the system and can ensure good economic benefits during the operation phase of the BIPV system.

[0044] Step 105: Capacity configuration of the BIPV system is performed based on the capacity configuration solution with the shortest investment payback period.

[0045] In an embodiment of the present invention, for each capacity configuration scheme, the investment payback period of the BIPV system corresponding to the capacity configuration scheme can be determined. In an embodiment of the present invention, the capacity configuration scheme with the shortest investment payback period of the BIPV system is used as the optimal capacity configuration scheme, and the capacity of the BIPV system is configured with the optimal capacity configuration scheme, that is, the photovoltaic area of ​​the BIPV system and the energy storage capacity of the energy storage battery are configured. In an embodiment of the present invention, since there are multiple types of photovoltaic outputs, the investment payback period of the BIPV system corresponding to a capacity configuration scheme includes multiple payback periods or includes a payback period range. This embodiment can compare the mean of multiple payback periods or the median of the payback period range to determine which capacity configuration scheme has the shortest investment payback period, thereby determining the optimal capacity configuration scheme. In addition, if the BIPV system is configured with the optimal capacity configuration scheme, the investment payback period range of the BIPV system at this time can be determined (that is, the payback period range of the optimal capacity configuration scheme, not just a single investment payback period), which is convenient for more effectively evaluating the investment payback period of the BIPV system based on the investment payback period range when using the BIPV system.

[0046] A capacity configuration method provided by an embodiment of the present invention determines the typical solar radiation data of each category through clustering processing, and then determines the photovoltaic output corresponding to each category; and the capacity configuration plan uses both photovoltaic area and energy storage capacity as optimization parameters, and takes the shortest investment payback period as the goal, and then the optimal capacity configuration plan can be selected. This method uses both photovoltaic area and energy storage capacity as optimization parameters, and can determine the optimal capacity configuration plan when the energy storage capacity and photovoltaic capacity influence each other, which can ensure the maximum benefit of the entire BIPV system; clustering multiple solar radiation data, and performing capacity configuration based on the photovoltaic output corresponding to each category, which can determine the solar radiation distribution characteristics throughout the year, taking into account the differences in the four seasons and severe climate, etc., to facilitate the coordinated configuration analysis of the photovoltaic-energy storage capacity of the BIPV system, as well as the study of more flexible and changeable energy scheduling strategies; and using the investment payback period as a single optimization target is convenient for solution, and can more accurately evaluate the advantages and disadvantages of the capacity configuration plan.

[0047] Alternatively, if Figure 3 As shown, the above step 104 "determining the investment payback period of the BIPV system corresponding to each capacity configuration scheme based on multiple categories of photovoltaic output" includes step 1041 and step 1042:

[0048] Step 1041: For each category of photovoltaic output, a process of determining the investment payback period of the capacity configuration solution is performed.

[0049] Step 1042: Determine the payback period of the BIPV system based on multiple payback periods of the capacity configuration scheme.

[0050] The process of "determining the investment payback period of the capacity configuration solution" in step 1041 includes:

[0051] Step A1: The state of charge sequence of the energy storage battery of the BIPV system is used as a particle, and the investment payback period of the capacity configuration scheme is used as a fitness function. The state of charge sequence includes multiple charge states of the energy storage battery within a preset time period.

[0052] Step A2: Iterate based on the particle swarm optimization algorithm to determine the optimal particle corresponding to convergence, and use the investment payback period corresponding to the optimal particle as an investment payback period of the capacity configuration solution.

[0053] The embodiment of the present invention realizes the capacity configuration of the BIPV system by a double-layer coupling model. The double-layer coupling model includes an outer layer model and an inner layer model; Figure 3As shown, the inner model is used to execute the process of "determining the investment payback period of the capacity configuration scheme", that is, the inner model is used to execute the above steps A1-A2; the outer model is used to provide the inner model with the data required by the inner model (for example, the photovoltaic output of each category, the current capacity configuration scheme, etc.), and realize the capacity configuration based on the output of the inner model (including the investment payback period of the current capacity configuration scheme). The inner model is embedded in the outer model to realize the double-layer coupling model.

[0054] Specifically, for each capacity configuration scheme, the inner model calculates the payback period of the capacity configuration scheme under each category of photovoltaic output, so that multiple payback periods of the capacity configuration scheme can be determined; as shown above, this embodiment represents the payback period of the BIPV system with the multiple payback periods or payback period ranges.

[0055] In an embodiment of the present invention, the inner model determines a shortest payback period for a capacity configuration scheme based on a particle swarm optimization (PSO) algorithm. As shown in the above steps A1-A2, in an embodiment of the present invention, the state of charge sequence of the energy storage battery of the BIPV system is used as a particle; wherein the state of charge sequence includes multiple states of charge (SOC) of the energy storage battery within a preset time period, which is used to indicate the change of the state of charge within the preset time period; for example, the preset time period is 1 day, and the state of charge of the energy storage battery can be determined every hour, then a 24-dimensional state of charge sequence can be determined, that is, the particle is 24-dimensional.

[0056] Moreover, the fitness function used by the particle swarm optimization algorithm is the investment payback period of the capacity configuration scheme, that is, the fitness value represents the investment payback period, and the smaller the fitness value, the better the particle. Optionally, the investment payback period of the capacity configuration scheme can be determined based on the above formula (1), and the fitness function can be expressed as minimizing the investment payback period, that is:

[0057]

[0058] Among them, the particle swarm optimization algorithm uses multiple rounds of iterations to determine the optimal solution, that is, the optimal state of charge sequence; in each round of iteration, the position and speed of the particle are updated based on the particle's own historical best position and the population's best position. The update method of each particle's position and speed is:

[0059]

[0060] Among them, if the current round is the tth round, V t id , X t id Represent the current speed and current position respectively, V t+1 id, X t+1 id Respectively represent the speed and position after the next round of update; c1 and c2 are learning factors, r1 and r2 are random numbers in the range of [0,1]. t id is the inertia part, which reflects the influence of the particle’s current velocity. t id -X t id ) is the cognitive part, which indicates the influence of its own historical best position on the trend of particle position change. t gd -X t id ) is the social part, which indicates the influence of the optimal position of the group on the trend of particle position change. t id represents the best historical position of the particle that can be determined at present, p t gd Indicates the optimal position of the group that can be determined at present. The particle position update method can be found in Figure 4 As shown. Among them, p t id 、p t gd It is determined based on the fitness function, and the two have the shortest payback period.

[0061] When optimizing the energy storage capacity, the existing scheme is optimized under the condition of determining the energy regulation strategy. Take the energy regulation strategy of "photovoltaic priority for self-use, surplus power to the grid" as an example. At this time, photovoltaic power generation first meets the building load, then provides energy storage, and finally the surplus power is connected to the grid. In this case, the energy storage state of charge (SOC) is determined by the photovoltaic power generation data and the building load. There is no possibility of using it as an optimization parameter, and the best economic benefits cannot be guaranteed. The embodiment of the present invention optimizes the state of charge when the energy storage capacity and photovoltaic area are fixed to ensure the best economic benefits. Specifically, the embodiment of the present invention can quickly solve the capacity configuration of the BIPV system using a double-layer coupling model; the state of charge sequence is used as a particle and the payback period is used as a fitness function for solution. Not only can the state of charge be used as an optimization parameter to ensure economic benefits and solve the problem of setting the operation strategy of the BIPV system, but the payback period of the BIPV system can also be determined, which is convenient for the subsequent determination of the optimal capacity configuration plan.

[0062] Optionally, the above step A2 "iterating based on the particle swarm optimization algorithm to determine the optimal particle corresponding to convergence" includes:

[0063] Step A21: In the case of non-convergence, update the velocity and position of the current-round particles based on the particle swarm optimization algorithm to determine the current velocity and current position of the current-round particles.

[0064] Among them, the velocity and position of the particles can be updated based on the above formula (3), which will not be elaborated in this embodiment.

[0065] Step A22: Add a constraint condition to the current position determined based on the particle swarm optimization algorithm, and use the current position with the added constraint condition as the input for the next round; the current position with the added constraint condition satisfies:

[0066]

[0067] where x' id represents the current position with the added constraint condition, x id represents the current position determined based on the particle swarm optimization algorithm, represents the floor function, a represents the adjustment coefficient, and 0 < a < 1; for example, a = 0.5 or 0.6, etc.

[0068] In the embodiment of the present invention, the particle is a sequence of charge states composed of multiple charge states, for example, a 24-dimensional vector; since this sequence belongs to a complex optimization problem with a high dimension and multiple local extreme points, the convergence speed of the traditional particle swarm optimization algorithm is relatively slow. In the embodiment of the present invention, the charge state is between 0 and 1, and a charge state of 0 means that the energy storage battery is fully discharged, and a charge state of 1 means that the energy storage battery is fully charged. Therefore, the value range of each element in this particle is [0, 1]. In this embodiment, the floor function is used to add a constraint condition to the position of the particle to accelerate the convergence speed and reduce the program running time. Specifically, after determining the current position x id based on the above formula (3), the following formula (4) is used to further update this position x id to determine the further updated position x' id , and the subsequent iterative process is executed with this position x' id .

[0069]

[0070] Compared with the traditional classic PSO algorithm, the PSO algorithm provided by the embodiment of the present invention has a faster convergence speed. For the comparison of the convergence of the two, see Figure 5 as shown, Figure 5 in which curve 1 represents the PSO algorithm provided by the embodiment of the present invention, and curve 2 represents the classic PSO algorithm. Under the same set parameters (such as: population size, space dimension, maximum number of iterations, initial position, etc.), as Figure 5As shown, the PSO algorithm provided by the embodiment of the present invention has converged when the number of iterations is less than 100, while the classic PSO algorithm needs more than 300 iterations to converge, and the optimization results of the two are consistent when converged, which clearly reflects the advantages of the PSO algorithm provided by the embodiment of the present invention.

[0071] In addition, optionally, for the fitness function of the embodiment of the present invention, the photovoltaic module construction and operation and maintenance cost C pv satisfy:

[0072]

[0073] Among them, C pv_dev is the construction cost of photovoltaic modules (for example, in yuan), C pv_ops is the operation and maintenance cost of PV panels (for example, in yuan), r y is the annual discount rate, and N is the life of the BIPV system (e.g., in years).

[0074] Subsidy income E sub satisfy:

[0075]

[0076] Among them, S sub is the subsidy standard (for example, the unit is: yuan / kWh), H is the total annual solar radiation (e.g., in kJ / m 2 ), A is the photovoltaic area (for example, the unit is: m 2 ), η is the photoelectric conversion efficiency, and K is the efficiency of the photovoltaic system. Among them, the photovoltaic area A is the photovoltaic area in the capacity configuration scheme.

[0077] Environmental Benefits carbon satisfy:

[0078]

[0079] Among them, S carbon It is the unit emission reduction benefit (for example, the unit is: yuan / kWh).

[0080] Recycling income E rec satisfy:

[0081]

[0082] Among them, E rec_net It is the net recycling income of photovoltaic modules (for example, the unit is: Yuan).

[0083] Electricity price income E elec satisfy:

[0084]

[0085] Photovoltaic surplus power grid-connected income E pv_syn satisfy:

[0086]

[0087] Among them, P load t is the building load in the tth time period (for example, in kW), P grid t is the power taken from the grid in the tth time period (for example, in kW), x g t is the real-time electricity price of the power grid (for example, the unit is: yuan / kWh), r m is the monthly discount rate, Δt is the unit time, and T is the number of unit time in the preset time period; P pv_syn t is the photovoltaic power connected to the grid in the tth time period, that is, the power generated when connected to the grid (for example, in kW). In the embodiment of the present invention, the preset time period is 1 day, and 30 days per month is taken as an example. If the unit time is 1 hour, then T = 24. Among them, when the photovoltaic power is not enough to carry the building load, it is necessary to draw power from the grid. At this time, the photovoltaic power connected to the grid P pv_syn t 0; if it is not necessary to draw power from the grid, it can be connected to the grid for power generation or charge the energy storage battery. At this time, the grid power P grid t is 0, that is, P grid t ≥0, P pv_syn t ≥0.

[0088] Energy storage battery charging and discharging depreciation cost C b satisfy:

[0089]

[0090] Among them, P b t is the charge and discharge power in the tth time period (for example, in kW), X B is the unit cycle cost of the battery, which is the initial investment converted into the expenditure per unit complete cycle (for example, in RMB / time), SoC B max SoC is the maximum value of the state of charge of the energy storage battery. B min is the minimum value of the state of charge of the energy storage battery. After the optimal state of charge sequence is determined based on the PSO algorithm, the corresponding charge and discharge power P in each time period can be determined based on the optimal state of charge sequence.b t In the embodiment of the present invention, the state of charge sequence may include T states of charge, that is, it is T-dimensional. When the energy storage battery is discharged, P b t >0; When the energy storage battery is charging, P b t <0.

[0091] In the embodiment of the present invention, in each time period t, the building load of the BIPV system is P load t . Since the photovoltaic module may or may not generate electricity, and may not be able to meet the requirements of the building load when generating electricity, or there is excess electricity, it can be connected to the grid for power generation; and the energy storage battery can be charged, or the energy storage battery can be discharged to provide electricity to the building load. In the embodiment of the present invention, the "photovoltaic output of each category" determined in the above step 102 includes the photovoltaic output per unit area of ​​multiple unit time lengths corresponding to each category within a preset time period; in this embodiment, each unit time length corresponds to a time period, if the photovoltaic output per unit area of ​​the i-th time period is p t , the photovoltaic area is A, then the photovoltaic power generation power P in the t time period pv t Satisfaction: P pv t =p t ×A. In addition, the various powers of the BIPV system meet the following requirements:

[0092] P load t +P pv_syn t =P grid t +P pv t +P b t (12)

[0093] For a certain capacity configuration scheme, the photovoltaic area and energy storage capacity are fixed, so the photovoltaic power generation power P pv t and the charging and discharging power P of the energy storage battery b t Both have upper and lower limits, namely:

[0094] P b min ≤P b t ≤P b max ,P pv min ≤P pv t≤P pv max (13)

[0095] Among them, P b max , P b min are the maximum and minimum values ​​of the charging and discharging power of the energy storage battery, P pv max , P pv min are the maximum and minimum values ​​of photovoltaic power generation respectively. In general, the minimum value of photovoltaic power generation P pv min is 0.

[0096] Moreover, the charge state of the energy storage battery is related to the charge and discharge power P b t Satisfy between:

[0097]

[0098] Where 0≤SoC B min ≤SoC B (t)≤SoC B max ≤1, SoC B (t) is the state of charge of the energy storage battery in the tth time period, t0 is the initial time period, E rate SoC is the rated capacity of the energy storage battery; B max 、SoC B min are the maximum and minimum state of charge of the energy storage battery, respectively. In the embodiment of the present invention, in the process of determining the optimal state of charge sequence based on the particle swarm optimization algorithm, the conditions that the state of charge corresponding to the above formula (14) needs to satisfy can be used as constraints. Alternatively, a more complete constraint can be determined based on the above formulas (12), (13), and (14), that is, the constraint includes:

[0099]

[0100] In the embodiment of the present invention, the particle swarm optimization algorithm is executed with the constraint condition represented by formula (15), so that the optimal particle can be determined more accurately, and then the appropriate investment payback period can be determined.

[0101] Optionally, the method further comprises:

[0102] Step B1: Determine the evaluation index of the BIPV system, the evaluation index including at least one of: power supply safety duration, charging and discharging time distribution, and electricity price revenue ratio;

[0103] Among them, the power supply safety duration meets the following requirements:

[0104] The proportion of electricity price revenue meets the following requirements:

[0105] Among them, T safe For power supply safety duration, E rate is the rated capacity of the energy storage battery, ρ d is the discharge efficiency, ρ1 is the conversion efficiency of the converter of the BIPV system, P load t is the building load in the tth time period.

[0106] In the embodiment of the present invention, in order to comprehensively evaluate the capacity configuration and operation strategy results of the BIPV system, an evaluation index system is established. The indicators used in the evaluation index system include the above-mentioned power supply safety duration, charging and discharging time period distribution, electricity price revenue ratio, etc.; wherein, the charging and discharging time period distribution indicates which time periods are charged and which time periods are discharged within the preset time period. In addition, the BIPV system can also be evaluated based on indicators such as photovoltaic output intensity, photovoltaic grid connection rate, energy storage action duration, etc., which reflect the internal energy scheduling of the system, as well as economic indicators such as net present value and investment payback period.

[0107] Among them, after determining the optimal capacity configuration plan of the BIPV system, the BIPV system can be evaluated based on the above step B1 during the use of the BIPV system; or, it can be combined with step B1 to determine which capacity configuration plan is the optimal capacity configuration plan, that is, the optimal capacity configuration plan has a shorter investment recovery period, a longer power supply safety time, a better electricity price benefit ratio, etc. This embodiment does not limit this.

[0108] A capacity configuration method provided by an embodiment of the present invention determines the typical solar radiation data of each category through clustering processing, and then determines the photovoltaic output corresponding to each category; and the capacity configuration plan uses both photovoltaic area and energy storage capacity as optimization parameters, and takes the shortest investment payback period as the goal, and then the optimal capacity configuration plan can be selected. This method uses both photovoltaic area and energy storage capacity as optimization parameters, and can determine the optimal capacity configuration plan when the energy storage capacity and photovoltaic capacity influence each other, which can ensure the maximum benefit of the entire BIPV system; clustering multiple solar radiation data, and performing capacity configuration based on the photovoltaic output corresponding to each category, which can determine the solar radiation distribution characteristics throughout the year, taking into account the differences in the four seasons and severe climate, etc., to facilitate the coordinated configuration analysis of the photovoltaic-energy storage capacity of the BIPV system, as well as the study of more flexible and changeable energy scheduling strategies; and using the investment payback period as a single optimization target is convenient for solution, and can more accurately evaluate the advantages and disadvantages of the capacity configuration plan. Taking the state of charge of the energy storage battery as the optimization variable, the improved PSO algorithm is used to solve the non-preset energy scheduling strategy, which can flexibly respond to changes in BIPV system parameters and has wide applicability and portability.

[0109] Combination of the above Figures 1 to 4 , describes in detail the method for capacity configuration provided by an embodiment of the present invention, which method can also be implemented by a corresponding device. The device for capacity configuration provided by an embodiment of the present invention is described in detail below.

[0110] Figure 6 FIG. 1 is a schematic diagram showing a structure of a capacity configuration device provided by an embodiment of the present invention. Figure 6 As shown, the device of the capacity configuration includes:

[0111] An acquisition module 61 is used to acquire a plurality of solar radiation data at a location of the BIPV system, wherein the solar radiation data includes solar radiation of a plurality of unit durations within a preset time period;

[0112] A clustering module 62, configured to perform clustering processing on the plurality of solar radiation data, determine typical solar radiation data in each category, and determine the photovoltaic output of each category according to each of the typical solar radiation data;

[0113] A setting module 63, used to set a plurality of capacity configuration schemes of the BIPV system, wherein the capacity configuration schemes include photovoltaic area and energy storage capacity;

[0114] A processing module 64 is used to determine the investment payback period of the BIPV system corresponding to each of the capacity configuration schemes based on multiple categories of photovoltaic output;

[0115] The capacity configuration module 65 is used to configure the capacity of the BIPV system based on the capacity configuration scheme with the shortest investment payback period.

[0116] In a possible implementation, the processing module 64 includes:

[0117] an optimization unit, configured to execute a process of determining a payback period of the capacity configuration scheme for each category of photovoltaic output; and

[0118] A determination unit, configured to determine the payback period of the BIPV system based on a plurality of payback periods of the capacity configuration scheme;

[0119] The process of determining the investment payback period of the capacity configuration solution by the optimization unit includes:

[0120] Taking the state of charge sequence of the energy storage battery of the BIPV system as a particle and the investment payback period of the capacity configuration scheme as a fitness function, the state of charge sequence includes a plurality of states of charge of the energy storage battery within the preset time period;

[0121] Iteration is performed based on a particle swarm optimization algorithm to determine an optimal particle corresponding to convergence, and the investment payback period corresponding to the optimal particle is used as an investment payback period of the capacity configuration solution.

[0122] In a possible implementation, the optimization unit iterates based on a particle swarm optimization algorithm to determine the optimal particle corresponding to convergence, including:

[0123] In the case of non-convergence, the speed and position of the current round particle are updated based on the particle swarm optimization algorithm to determine the current speed and current position of the current round particle;

[0124] Add restrictions to the current position determined based on the particle swarm optimization algorithm, and use the current position with the restrictions as the input for the next round; the current position with the restrictions satisfies:

[0125]

[0126] Among them, x' id Indicates the current position of the increase or decrease constraint condition, x id Indicates that the current position is determined based on the particle swarm optimization algorithm. represents the floor function, a represents the adjustment coefficient, and 0 <a<1。

[0127] In a possible implementation, the fitness function includes:

[0128]

[0129] Among them, C pv represents the construction and operation and maintenance cost of photovoltaic modules, E sub represents subsidy income, E carbon represents environmental benefits, E rec represents the recycling benefit, E elec Represents the electricity price income, E pv_syn represents the income from the photovoltaic surplus power grid connection, C b Represents the depreciation cost of charging and discharging energy storage batteries.

[0130] In one possible implementation, C pv satisfy:

[0131] E sub satisfy:

[0132] E carbon satisfy:

[0133] E rec satisfy:

[0134] E elec satisfy:

[0135] E pv_syn satisfy:

[0136] C b satisfy:

[0137] Among them, C pv_dev is the construction cost of photovoltaic modules, C pv_ops The operation and maintenance cost of photovoltaic modules;

[0138] S sub is the subsidy standard, r y is the annual discount rate, N is the life of the BIPV system, H is the total annual solar radiation, A is the photovoltaic area, η is the photoelectric conversion efficiency, and K is the efficiency of the photovoltaic system; S carbon is the unit emission reduction benefit; E rec_net P is the net recycling income of photovoltaic modules; load t is the building load in the tth time period, P grid t is the power taken from the grid in the tth time period, x g t is the real-time electricity price of the power grid, r m is the monthly discount rate, Δt is the unit duration, and T is the number of the unit durations in the preset time period; Ppv_syn t is the photovoltaic grid-connected power in the tth time period; P b t is the charge and discharge power in the tth time period, X B SoC is the unit cycle cost of the battery B max SoC is the maximum value of the state of charge of the energy storage battery. B min It is the minimum value of the state of charge of the energy storage battery.

[0139] In a possible implementation, the device further includes:

[0140] An evaluation module, used to determine the evaluation index of the BIPV system, wherein the evaluation index includes at least one of: power supply safety duration, charge and discharge time distribution, and electricity price revenue ratio;

[0141] The power supply safety duration satisfies:

[0142] The electricity price revenue ratio meets the following requirements:

[0143] Among them, T safe For power supply safety duration, E rate is the rated capacity of the energy storage battery, ρ d is the discharge efficiency, ρ1 is the conversion efficiency of the converter of the BIPV system, P load t is the building load in the tth time period.

[0144] In a possible implementation, the constraints of the particle swarm optimization algorithm include:

[0145]

[0146] Among them, P load t is the building load in the tth time period, P grid t is the power taken from the grid in the tth time period, P pv t is the photovoltaic power generation power in the tth time period, P b t is the charge and discharge power in the tth time period, SoC B (t) is the state of charge of the energy storage battery in the tth time period, t0 is the initial time period, E rate SoC is the rated capacity of the energy storage battery; B max 、SoC B minare the maximum and minimum state of charge of the energy storage battery, P b max , P b min are the maximum and minimum values ​​of the charging and discharging power of the energy storage battery, P pv max , P pv min are the maximum and minimum values ​​of photovoltaic power generation respectively.

[0147] In addition, an embodiment of the present invention further provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned capacity configuration method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0148] For details, see Figure 7 As shown, an embodiment of the present invention further provides an electronic device, which includes a bus 1110 , a processor 1120 , a transceiver 1130 , a bus interface 1140 , a memory 1150 and a user interface 1160 .

[0149] In the embodiment of the present invention, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120, and when the computer program is executed by the processor 1120, each process of the above-mentioned capacity configuration method embodiment is implemented.

[0150] The transceiver 1130 is configured to receive and send data under the control of the processor 1120 .

[0151] In an embodiment of the present invention, the bus architecture (represented by bus 1110), bus 1110 may include any number of interconnected buses and bridges, and bus 1110 connects various circuits including one or more processors represented by processor 1120 and a memory represented by memory 1150.

[0152] Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an Accelerate Graphical Port (AGP), a processor, or a local bus using any of a variety of bus architectures. By way of example and not limitation, such architectures include: Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA), Peripheral Component Interconnect (PCI) bus.

[0153] The processor 1120 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment may be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processors include: a general-purpose processor, a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a complex programmable logic device (CPLD), a programmable logic array (PLA), a microcontroller unit (MCU) or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention may be implemented or executed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated into a single chip or located in multiple different chips.

[0154] Processor 1120 can be a microprocessor or any conventional processor. The method steps disclosed in conjunction with the embodiments of the present invention can be directly executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a readable storage medium known in the art, such as a random access memory (RAM), a flash memory (FlashMemory), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a register, etc. The readable storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware.

[0155] The bus 1110 may also connect various other circuits such as peripheral devices, voltage regulators or power management circuits, and the bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130, which are well known in the art. Therefore, the embodiments of the present invention will not be further described.

[0156] The transceiver 1130 may be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on a transmission medium. For example, the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as a touch screen, a physical keyboard, a display, a mouse, a speaker, a microphone, a trackball, a joystick, and a stylus.

[0157] It should be understood that in an embodiment of the present invention, the memory 1150 may further include a memory remotely arranged relative to the processor 1120, and these remotely arranged memories may be connected to the server through a network. One or more parts of the above-mentioned network may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless local area network (WLAN), a wide area network (WAN), a wireless wide area network (WWAN), a metropolitan area network (MAN), the Internet, a public switched telephone network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless network, a wireless fidelity (Wi-Fi) network, and a combination of two or more of the above-mentioned networks. For example, the cellular telephone network and the wireless network can be a Global System for Mobile Communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, a LTE Frequency Division Duplex (FDD) system, a LTE Time Division Duplex (TDD) system, an Advanced Long Term Evolution (LTE-A) system, a Universal Mobile Telecommunications (UMTS) system, an Enhanced Mobile Broadband (Enhance Mobile Broadband, eMBB) system, a massive Machine Type of Communication (mMTC) system, an Ultra-Reliable Low Latency Communications (UltraReliable Low Latency Communications, uRLLC) system, etc.

[0158] It should be understood that the memory 1150 in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory includes: a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0159] Volatile memory includes: Random Access Memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as: Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM) and Direct Rambus RAM (DRRAM). The memory 1150 of the electronic device described in the embodiment of the present invention includes but is not limited to the above and any other suitable types of memory.

[0160] In the embodiment of the present invention, the memory 1150 stores the following elements of the operating system 1151 and the application program 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0161] Specifically, the operating system 1151 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 1152 includes various applications, such as a media player (Media Player) and a browser (Browser), which are used to implement various application services. The program for implementing the method of the embodiment of the present invention may be included in the application 1152. The application 1152 includes applets, objects, components, logic, data structures, and other computer system executable instructions that perform specific tasks or implement specific abstract data types.

[0162] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned capacity configuration method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0163] Computer readable storage media include: permanent and non-permanent, removable and non-removable media, which are tangible devices that can retain and store instructions for use by instruction execution devices. Computer readable storage media include: electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, and any suitable combination of the above. Computer readable storage media include: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassette storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanical encoding devices (such as punched cards or raised structures in grooves with instructions recorded thereon) or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined in the embodiments of the present invention, computer-readable storage media do not include temporary signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (such as light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0164] In the several embodiments provided in the present application, it should be understood that the disclosed devices, electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0165] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one location or distributed on multiple network units. Some or all of the units may be selected according to actual needs to solve the problem to be solved by the embodiments of the present invention.

[0166] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0167] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention is essentially or part of the contribution to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (including: a personal computer, a server, a data center or other network device) to perform all or part of the steps of the method described in each embodiment of the present invention. The above-mentioned storage medium includes various media that can store program codes as listed above.

[0168] In the description of the embodiments of the present invention, those skilled in the art should know that the embodiments of the present invention can be implemented as methods, devices, electronic devices and computer-readable storage media. Therefore, the embodiments of the present invention can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the embodiments of the present invention can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0169] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM), flash memories, optical fibers, compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices or any combination of the above. In an embodiment of the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device, or device.

[0170] The computer program code contained in the above-mentioned computer-readable storage medium can be transmitted by any appropriate medium, including: wireless, wire, optical cable, radio frequency (RF) or any suitable combination thereof.

[0171] The computer program code for performing the operation of the embodiments of the present invention can be written in assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or in one or more programming languages ​​or a combination thereof, wherein the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as C language or a similar programming language. The computer program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, and completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer or to an external computer through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0172] The embodiments of the present invention describe the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0173] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0174] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0175] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0176] The above is only a specific implementation of the embodiment of the present invention, but the protection scope of the embodiment of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the embodiment of the present invention, which should be included in the protection scope of the embodiment of the present invention. Therefore, the protection scope of the embodiment of the present invention should be based on the protection scope of the claims.

Claims

1. A capacity configuration method, characterized in that: include: Acquire a plurality of solar radiation data at a location of the BIPV system, wherein the solar radiation data includes solar radiation of a plurality of unit durations within a preset time period; Performing clustering processing on the plurality of solar radiation data, determining typical solar radiation data in each category, and determining the photovoltaic output of each category according to each of the typical solar radiation data; Setting a plurality of capacity configuration schemes of the BIPV system, wherein the capacity configuration schemes include photovoltaic area and energy storage capacity; Determine the payback period of the BIPV system corresponding to each of the capacity configuration schemes based on multiple categories of photovoltaic output; Capacity configuration of the BIPV system is performed based on a capacity configuration scheme with the shortest payback period; The step of determining the investment payback period of the BIPV system corresponding to each of the capacity configuration schemes based on the photovoltaic outputs of the multiple categories includes: For each category of photovoltaic output, performing a process of determining a payback period for the capacity configuration solution; and Determining the payback period of the BIPV system based on multiple payback periods of the capacity configuration scheme; The process of determining the investment payback period of the capacity configuration solution includes: Taking the state of charge sequence of the energy storage battery of the BIPV system as a particle and the investment payback period of the capacity configuration scheme as a fitness function, the state of charge sequence includes a plurality of states of charge of the energy storage battery within the preset time period; Iterating based on the particle swarm optimization algorithm to determine the optimal particle corresponding to the convergence, and using the investment payback period corresponding to the optimal particle as an investment payback period of the capacity configuration solution; iterating based on the particle swarm optimization algorithm to determine the optimal particle corresponding to the convergence includes: In the case of non-convergence, the speed and position of the current round particle are updated based on the particle swarm optimization algorithm to determine the current speed and current position of the current round particle; Add restrictions to the current position determined based on the particle swarm optimization algorithm, and use the current position with the restrictions as the input for the next round; the current position with the restrictions satisfies: Among them, x' id represents the current position of the increase and decrease limit condition, and x id represents the current position determined based on the particle swarm optimization algorithm. represents the floor function, a represents the adjustment coefficient, and 0 < a < 1; the fitness function includes: Among them, C pv represents the construction and operation and maintenance cost of photovoltaic modules, E sub represents subsidy income, E carbon represents environmental benefits, E rec represents the recycling benefit, E elec Represents the electricity price income, E pv_syn represents the income from the photovoltaic surplus power grid connection, C b Represents the depreciation cost of charging and discharging energy storage batteries; C pv satisfy: E sub satisfy: E carbon satisfy: E rec satisfy: E elec satisfy: E pv_syn satisfy: C b satisfy: Among them, C pv_dev is the construction cost of photovoltaic modules, C pv_ops The operation and maintenance cost of photovoltaic modules; S sub is the subsidy standard, r y is the annual discount rate, N is the life of the BIPV system, H is the total annual solar radiation, A is the photovoltaic area, η is the photoelectric conversion efficiency, and K is the efficiency of the photovoltaic system; S carbon is the unit emission reduction benefit; E rec_net P is the net recycling income of photovoltaic modules; load t is the building load in the tth time period, P grid t is the power taken from the grid in the tth time period, x g t is the real-time electricity price of the power grid, r m is the monthly discount rate, Δt is the unit duration, and T is the number of the unit durations in the preset time period; P pv_syn t P is the photovoltaic grid-connected power in the tth time period; b t is the charge and discharge power in the tth time period, X B SoC is the unit cycle cost of the battery B max SoC is the maximum value of the state of charge of the energy storage battery. B min is the minimum value of the state of charge of the energy storage battery; Also includes: Determine the evaluation index of the BIPV system, wherein the evaluation index includes at least one of: power supply safety duration, charging and discharging time period distribution, and electricity price revenue ratio; The power supply safety duration satisfies: The electricity price revenue ratio meets the following requirements: Among them, T safe For power supply safety duration, E rate is the rated capacity of the energy storage battery, ρ d is the discharge efficiency, ρ1 is the conversion efficiency of the converter of the BIPV system, P load t is the building load in the tth time period; the constraints of the particle swarm optimization algorithm include: Among them, P load t is the building load in the tth time period, P grid t is the power taken from the grid in the tth time period, P pv t is the photovoltaic power generation power in the tth time period, P b t is the charge and discharge power in the tth time period, SoC B (t) is the state of charge of the energy storage battery in the tth time period, t0 is the initial time period, E rate SoC is the rated capacity of the energy storage battery; B max 、SoC B min are the maximum and minimum state of charge of the energy storage battery, P b max , P b min are the maximum and minimum values ​​of the charging and discharging power of the energy storage battery, P pv max , P pv min are the maximum and minimum values ​​of photovoltaic power generation respectively.

2. A device for capacity configuration, characterized in that: The method for executing the capacity configuration proposed in claim 1 above comprises: An acquisition module, used to acquire a plurality of solar radiation data at a location of the BIPV system, wherein the solar radiation data includes solar radiation of a plurality of unit durations within a preset time period; A clustering module, used for clustering the plurality of solar radiation data, determining typical solar radiation data in each category, and determining the photovoltaic output of each category according to each of the typical solar radiation data; A setting module, used to set a plurality of capacity configuration schemes of the BIPV system, wherein the capacity configuration schemes include photovoltaic area and energy storage capacity; A processing module, configured to determine the payback period of the BIPV system corresponding to each of the capacity configuration schemes based on a plurality of categories of photovoltaic outputs; The capacity configuration module is used to configure the capacity of the BIPV system based on the capacity configuration scheme with the shortest investment payback period.

3. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps in the capacity configuration method according to claim 1 are implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the capacity configuration method according to claim 1 are implemented.

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