Residential rooftop photovoltaic capacity optimization configuration method, device and electronic equipment

Abstract

The present invention provides a method, device and electronic equipment for optimizing the configuration of photovoltaic capacity on residential roofs. The method comprises: obtaining historical solar irradiance, historical temperature, historical target demand data and photovoltaic capacity optimization influencing factors of a target area, wherein the historical target demand data includes historical power demand data of residential buildings that need to install photovoltaics in the target area, excluding electric vehicles; based on the historical solar irradiance, historical temperature, historical target demand data and the photovoltaic capacity optimization influencing factors of the target area, with the goal of minimizing the annual total electricity cost of residential users in the target area, constructing an objective function and obtaining an optimal solution of the objective function; and configuring the photovoltaic capacity on residential roofs based on the optimal solution of the objective function, thereby providing residential users with a more accurate and efficient rooftop photovoltaic capacity configuration plan, improving photovoltaic self-sufficiency and local absorption capabilities, avoiding photovoltaic curtailment and large-scale grid access, and reducing the electricity costs of residential users.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic technology, and in particular to a method, device and electronic equipment for optimizing the configuration of photovoltaic capacity on a residential roof. Background Art

[0002] Among the related technologies, the optimal configuration method of residential rooftop photovoltaic capacity still faces multiple technical challenges, including how to accurately characterize the seasonal characteristics of solar irradiance, temperature and residential electricity demand while reducing data dimensions and improving computing efficiency; how to simulate residential electric vehicle charging, vehicle-to-home (V2H) and other load demand response behaviors and incorporate them into the optimal configuration model of residential rooftop photovoltaic capacity; how to efficiently and accurately solve the optimal configuration model of residential rooftop photovoltaic capacity, etc. Summary of the Invention

[0003] The present invention provides a method, device and electronic equipment for optimizing the configuration of residential rooftop photovoltaic capacity, which are used to provide residential users with a more accurate and efficient rooftop photovoltaic capacity configuration plan, improve photovoltaic self-sufficiency and local consumption capabilities, avoid photovoltaic curtailment and large-scale grid connection, and reduce residential users' electricity costs.

[0004] The present invention provides a method for optimizing the configuration of photovoltaic capacity on a residential roof, comprising:

[0005] Obtaining historical solar irradiance, historical temperature, and historical target demand data and photovoltaic capacity optimization influencing factors for the target area, wherein the historical target demand data includes historical electricity demand data of residences in the target area that need to install photovoltaics, excluding electric vehicles;

[0006] Based on the historical solar irradiance, historical temperature, and historical target demand data of the target area and the factors affecting photovoltaic capacity optimization, and with the goal of minimizing the annual total electricity cost for residential users in the target area, constructing an objective function to obtain the optimal solution for the photovoltaic installation capacity;

[0007] Based on the optimal solution of the objective function, the residential rooftop photovoltaic capacity is configured.

[0008] According to a method for optimizing the configuration of photovoltaic capacity on a residential roof provided by the present invention, after obtaining historical solar irradiance, historical temperature, and historical target demand data of a target area and factors affecting photovoltaic capacity optimization, the method further comprises:

[0009] historical solar irradiance, historical temperature, and historical target demand data for the target area divided by season;

[0010] Clustering the historical solar irradiance, historical temperature, and historical target demand data of the target area in each season respectively to obtain cluster center points of different categories of historical solar irradiance in each season, cluster center points of different categories of historical temperature in each season, and cluster center points of different categories of historical target demand data in each season for the target area;

[0011] Based on the number of days of historical solar irradiance in each category of each season of the target area, the number of days of historical temperature in each category of each season and the number of days of historical target demand data in each category of each season, a weighted average is performed on the cluster center points of the historical solar irradiance of different categories in each season of the target area, the cluster center points of the historical temperature of different categories in each season and the cluster center points of the historical target demand data of different categories in each season to obtain the solar irradiance of typical days in each season per hour, the temperature of typical days in each season per hour and the historical target demand data of typical days in each season per hour of the target area.

[0012] According to a residential rooftop photovoltaic capacity optimization configuration method provided by the present invention, the photovoltaic capacity optimization influencing factors include:

[0013] Photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residences in the target area where photovoltaics need to be installed, and electricity prices in the target area.

[0014] According to a method for optimizing the configuration of residential rooftop photovoltaic capacity provided by the present invention, the objective function and at least one constraint condition for solving the objective function are constructed based on the historical solar irradiance, historical temperature, and historical target demand data of the target area and the photovoltaic capacity optimization influencing factors, including:

[0015] Establishing the objective function based on the photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in the residences in the target area where photovoltaics are required to be installed, and electricity prices in the target area;

[0016] Based on one or more of the solar irradiance per hour on a typical day in each season, the temperature per hour on a typical day in each season, the historical target demand data per hour on a typical day in each season, photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residences in the target area that need to install photovoltaics, and the electricity price in the target area, constraints for solving the objective function are constructed. The constraints include one or more of the following: residential rooftop photovoltaic output constraints, residential rooftop photovoltaic grid-connected constraints, residential demand response constraints for loads other than electric vehicles, electric vehicle charging and electric vehicle power supply to residence (V2H) constraints, power balance constraints, and residential installation area constraints.

[0017] According to a residential rooftop photovoltaic capacity optimization configuration method provided by the present invention, before obtaining the optimal solution for the photovoltaic installation capacity, the method further includes:

[0018] Initializing first parameters, wherein the first parameters include at least the number of particles, inertia weight, and acceleration constant;

[0019] Randomly initialize the position and velocity of each particle;

[0020] Repeat the first process until the preset maximum number of iterations is reached;

[0021] Determine the optimal positions of all particles based on the positions of all particles in the last iteration;

[0022] The first process includes:

[0023] Based on the objective function, calculating the objective function value of each particle;

[0024] Calculating a constraint penalty function value for each particle based on the position of each particle, the number of particles, and the number of constraints for solving the objective function;

[0025] Calculating a fitness function value of each particle based on the objective function value of each particle and the constraint penalty function value of each particle;

[0026] Determine the optimal particle position. The determination rule is: if there is a particle with a constraint penalty function value of 0, then the position of the particle with the smallest fitness function value is selected from the particles with the constraint penalty function value of 0 as the optimal particle position; if the penalty function values ​​of all particles are greater than 0, then the position of the particle with the smallest fitness function value is selected from all particles as the optimal particle position;

[0027] Adopting an adaptive weight strategy to update the inertia weight;

[0028] The position and velocity of each particle are updated based on the optimal particle position, the updated inertia weight, and the acceleration constant.

[0029] According to a method for optimizing the configuration of photovoltaic capacity on residential rooftops provided by the present invention, the method aims to minimize the total annual electricity cost of residential users in the target area and obtains the optimal solution for the photovoltaic installation capacity, including:

[0030] Determining optimal values ​​of multiple variables in the objective function based on the optimal positions of all particles;

[0031] Based on the optimal values ​​of multiple variables in the objective function, the photovoltaic installation capacity corresponding to the lowest annual total electricity cost for residential users in the target area is determined as the optimal solution for the photovoltaic installation capacity.

[0032] The present invention also provides a residential rooftop photovoltaic capacity optimization configuration device, comprising:

[0033] an acquisition module, configured to acquire historical solar irradiance, historical temperature, and historical target demand data and photovoltaic capacity optimization influencing factors for a target area, wherein the historical target demand data includes historical power demand data of residences in the target area that need to install photovoltaics, excluding electric vehicles;

[0034] a construction module for constructing an objective function based on historical solar irradiance, historical temperature, and historical target demand data of the target area and the photovoltaic capacity optimization influencing factors, with the goal of minimizing the annual total electricity cost of residential users in the target area, and obtaining an optimal solution to the objective function;

[0035] A configuration module is used to configure the residential rooftop photovoltaic capacity based on the optimal solution of the objective function.

[0036] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing the configuration of photovoltaic capacity on a residential roof as described above is implemented.

[0037] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for optimizing the configuration of residential rooftop photovoltaic capacity.

[0038] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned methods for optimizing the configuration of residential rooftop photovoltaic capacity.

[0039] The method, device and electronic equipment for optimizing the configuration of residential rooftop photovoltaic capacity provided by the present invention construct an objective function and constraints for optimizing the configuration of residential rooftop photovoltaic capacity based on the precise characterization of historical solar irradiance, temperature and residential electricity demand, while taking into account the demand response behavior of electric vehicle charging, V2H and other loads in the residence. By solving the objective function with the goal of minimizing the annual total electricity cost of residential users in the target area, the optimal photovoltaic installation capacity can be obtained, providing a more accurate and efficient residential rooftop photovoltaic capacity configuration plan for the residence, improving the photovoltaic self-sufficiency and self-consumption rate, and reducing the residential electricity cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a flow chart of the method for optimizing the configuration of photovoltaic capacity on a residential roof provided by the present invention;

[0042] Figure 2 It is a schematic diagram of the process of the improved particle swarm algorithm provided by the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of the residential rooftop photovoltaic capacity optimization configuration device provided by the present invention;

[0044] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0046] First, let’s introduce the following contents:

[0047] With the growing emphasis on clean energy and sustainable development, distributed photovoltaic systems, as a key form of renewable energy, have become widely adopted in the residential sector. Proper allocation of residential rooftop photovoltaic capacity not only enhances residential electricity self-sufficiency, reduces reliance on traditional electricity, and lowers electricity costs, but also mitigates the impact of large-scale photovoltaic grid integration on the grid and reduces photovoltaic curtailment.

[0048] However, due to the intermittent output of photovoltaic systems, the volatility of residential electricity demand, and the increasing popularity of electric vehicles, accurately determining the optimal installation capacity of rooftop photovoltaics for different residential users remains complex and challenging. Furthermore, the energy storage characteristics of electric vehicles and the demand response of other residential flexible loads also provide further potential value for optimizing the configuration of residential rooftop photovoltaic capacity.

[0049] The residential rooftop photovoltaic capacity optimization configuration method in related technologies still faces multiple technical challenges, including how to accurately characterize the seasonal characteristics of solar irradiance, temperature and residential electricity demand while reducing data dimensions and improving computational efficiency; how to simulate residential electric vehicle charging, V2H and other load demand response behaviors and incorporate them into the residential rooftop photovoltaic capacity optimization configuration model; how to efficiently and accurately solve the residential rooftop photovoltaic capacity optimization configuration model, etc.

[0050] In response to the shortcomings of related technical solutions, the present invention provides a method, device and electronic equipment for optimizing the configuration of residential rooftop photovoltaic capacity. Based on the accurate characterization of solar irradiance, temperature and residential electricity demand characteristics in different seasons, a residential rooftop photovoltaic capacity optimization configuration model is constructed that takes into account the response behavior of electric vehicle charging, V2H and other loads in the residence, and an improved particle swarm algorithm is used to solve it, thereby providing residential users with a more accurate and efficient rooftop photovoltaic capacity configuration plan, improving photovoltaic self-sufficiency and local consumption capabilities, avoiding photovoltaic curtailment and large-scale grid access, and reducing residential users' electricity costs.

[0051] The following combination Figure 1-Figure 2 The present invention describes the method for optimizing the configuration of photovoltaic capacity on a residential roof.

[0052] Figure 1 This is a flow chart of the method for optimizing the configuration of residential rooftop photovoltaic capacity provided by the present invention. Figure 1 As shown, the method includes the following steps:

[0053] Step 100: Obtain historical solar irradiance, historical temperature, historical target demand data, and photovoltaic capacity optimization influencing factors for the target area, wherein the historical target demand data includes historical power demand data of residences in the target area that need to install photovoltaics, excluding electric vehicles;

[0054] Optionally, the target area may be an area where a residence requiring rooftop photovoltaic capacity optimization is located, such as a residential community, region, or city where the residence is located, etc., which is not limited in the present invention.

[0055] Alternatively, the historical solar irradiance of the target area may be the historical hourly average solar irradiance of the target area, or the historical half-hourly average solar irradiance, or the historical solar irradiance with a finer time granularity.

[0056] Optionally, the historical temperature of the target area may be the historical hourly average temperature of the target area, or the historical half-hourly average temperature, or a historical temperature with a finer time granularity.

[0057] Optionally, the historical target demand data may include historical electricity demand data of residences requiring photovoltaic installation within the target area, excluding electric vehicles.

[0058] Optionally, the electricity demand data of the residences in the target area that need to install photovoltaics, excluding the electricity demand of electric vehicles, can be the sum of the electricity demand of other electrical appliances in the residences that need to install photovoltaics, excluding the electricity demand of electric vehicles, including but not limited to the electricity demand of electrical equipment such as electric lights and televisions in the residence.

[0059] Optionally, the historical electricity demand data of the residences in the target area that need to install photovoltaics, excluding electric vehicles, may be historical hourly average electricity demand, historical half-hourly average electricity demand, or historical electricity demand with a finer time granularity.

[0060] Optionally, since the photovoltaic capacity configuration needs to consider the impact of solar irradiance, temperature and historical electricity demand of photovoltaic-installed residences other than electric vehicles, in order to construct the objective function, historical data of the target area can be obtained first, and its changing pattern can be obtained based on the historical data, such as solar irradiance, temperature and historical electricity demand of photovoltaic-installed residences other than electric vehicles in each month or season, so as to facilitate the subsequent construction of the objective function and constraints.

[0061] Optionally, in order to reduce the electricity costs of residential users, when considering the configuration of residential rooftop photovoltaic capacity, factors affecting photovoltaic capacity optimization can be considered at the same time when establishing the objective function, such as photovoltaic economic parameters, or electricity costs, etc. Therefore, it is necessary to determine the factors affecting photovoltaic capacity optimization in advance.

[0062] Step 110, based on the historical solar irradiance, historical temperature, historical target demand data of the target area and the photovoltaic capacity optimization influencing factors, with the goal of minimizing the annual total electricity cost of residential users in the target area, constructing an objective function and obtaining an optimal solution of the objective function;

[0063] Optionally, after obtaining the historical solar irradiance, historical temperature, historical target demand data and the photovoltaic capacity optimization influencing factors of the target area, an objective function and at least one constraint condition for solving the objective function can be constructed. The objective function is used to characterize the relationship between the annual total electricity cost of residential users in the target area, the photovoltaic capacity optimization configuration influencing factors and the photovoltaic installation capacity. It can be understood that in order to reduce the electricity cost of residential users, the objective function aims to make the annual total electricity cost of residential users in the target area as low as possible.

[0064] Optionally, the constraints are used to meet the basic electricity demand and electricity safety of residential users, that is, the goal of the objective function is to minimize the annual total electricity cost of residential users in the target area while meeting the constraints.

[0065] Optionally, the constraint condition may be a constraint condition related to residential rooftop photovoltaic output, or a constraint condition related to power balance, or other constraint conditions, which are not limited in the present invention.

[0066] Optionally, the constraint conditions for solving the objective function may be obtained based on historical solar irradiance, historical temperature, and historical target demand data of residences in the target area that need to install photovoltaics.

[0067] Optionally, after constructing the objective function and constraints, the photovoltaic installation capacity corresponding to the lowest annual total electricity cost for residential users of the objective function under the constraints can be obtained as the optimal solution.

[0068] Step 120: Based on the optimal solution of the objective function, configure the residential rooftop photovoltaic capacity.

[0069] Optionally, after obtaining the photovoltaic installation capacity corresponding to the lowest annual total electricity cost for residential users, that is, the optimal photovoltaic installation capacity, the optimal residential rooftop photovoltaic capacity configuration plan can be determined. The plan at least includes configuring the photovoltaic capacity of residential rooftops that need to install photovoltaics in the target area to the optimal photovoltaic installation capacity, which can improve the photovoltaic self-sufficiency and self-consumption rate and reduce residential electricity costs.

[0070] The method for optimizing the configuration of residential rooftop photovoltaic capacity provided by the present invention constructs an objective function and constraints for achieving optimal configuration of residential rooftop photovoltaic capacity based on the precise characterization of historical solar irradiance, temperature and residential electricity demand, while taking into account the demand response behavior of electric vehicle charging, V2H and other loads in the residence. By solving the objective function with the goal of minimizing the annual total electricity cost of residential users in the target area, the optimal photovoltaic installation capacity can be obtained, providing a more accurate and efficient residential rooftop photovoltaic capacity configuration plan for the residence, improving the photovoltaic self-sufficiency and self-consumption rate, and reducing the residential electricity cost.

[0071] Optionally, after obtaining historical solar irradiance, historical temperature, and historical target demand data and photovoltaic capacity optimization influencing factors of the target area, the method further includes:

[0072] historical solar irradiance, historical temperature, and historical target demand data for the target area divided by season;

[0073] Clustering the historical solar irradiance, historical temperature, and historical target demand data of the target area in each season respectively to obtain cluster center points of different categories of historical solar irradiance in each season, cluster center points of different categories of historical temperature in each season, and cluster center points of different categories of historical target demand data in each season for the target area;

[0074] Based on the number of days of historical solar irradiance in each category of each season of the target area, the number of days of historical temperature in each category of each season and the number of days of historical target demand data in each category of each season, a weighted average is performed on the cluster center points of the historical solar irradiance of different categories in each season of the target area, the cluster center points of the historical temperature of different categories in each season and the cluster center points of the historical target demand data of different categories in each season to obtain the solar irradiance of typical days in each season per hour, the temperature of typical days in each season per hour and the historical target demand data of typical days in each season per hour of the target area.

[0075] Optionally, in order to obtain the solar irradiance, historical temperature, and electricity demand data of houses requiring photovoltaic installation excluding electric vehicles on typical days in the target area, the historical data of the target area may be divided into spring, summer, autumn, and winter.

[0076] In one embodiment of the present invention, the solar irradiance, temperature and residential electricity demand data excluding electric vehicles for each hour of each day in the historical year of the target area are divided into solar irradiance, temperature and residential electricity demand data matrices excluding electric vehicles according to spring, summer, autumn and winter, respectively. The rows of the matrices represent each day of each season, and the columns of the matrices represent each hour of each day.

[0077] Optionally, after obtaining the historical data of each season in the target area, the data may be clustered. The clustering method may be clustering by a clustering algorithm. The clustering algorithm may be K-means clustering or density-based spatial clustering of applications with noise (Density-Based Spatial Clustering of Applications with Noise, DBSCAN), etc. The present invention is not limited to this.

[0078] In one embodiment of the present invention, the DBSCAN clustering method is selected from among many clustering methods to cluster the seasonal characteristics of solar irradiance, temperature and residential electricity demand in each season, and the cluster center points of different categories of data in each season are obtained, so as to achieve an optimal balance between the accurate characterization of seasonal characteristics and the subsequent computational efficiency.

[0079] In one embodiment of the present invention, after the historical annual solar irradiance, temperature and electricity demand data of residential buildings excluding electric vehicles for each hour of each day in the target area are divided into solar irradiance, temperature and electricity demand data matrices of residential buildings excluding electric vehicles according to spring, summer, autumn and winter, the 90-day × 24-hour matrix of each season can be imported into the clustering model. The result of the clustering is that each category includes electricity demand data or solar irradiance data or temperature data for each hour of the 24 hours of a day.

[0080] Optionally, after obtaining the cluster center points of different categories of data in each season, the number of days included in each category of historical solar irradiance in each season, the number of days included in each category of historical temperature in each season, and the number of days included in each category of historical target demand data in each season can be obtained.

[0081] Optionally, after obtaining the number of days of different categories of each data in each season, the cluster center points of each data can be weighted averaged to obtain the hourly solar irradiance, temperature and electricity demand data of residential buildings that need to install photovoltaics, excluding electric vehicles, on typical days in each season in the target area.

[0082] In one embodiment of the present invention, the calculation formula for weighted averaging of the cluster centers of each data is as follows:

[0083]

[0084]

[0085]

[0086] Where:

[0087] represents the solar irradiance at hour t on a typical day in season s;

[0088] represents the solar irradiance of the cluster center at hour t in category i in season s;

[0089] RD s,i represents the number of days included in the i-th type of solar irradiance in the s-th season;

[0090] represents the temperature at hour t on a typical day in season s;

[0091] represents the temperature of the cluster center at hour t in category i in season s;

[0092] TD s,i Indicates the number of days in the i-th temperature category in the s-th season;

[0093] represents the electricity demand of the residential buildings that need to install photovoltaics, excluding electric vehicles, at hour t on a typical day in season s;

[0094] represents the electricity demand of the residential buildings that need to install photovoltaics, excluding electric vehicles, at the cluster center point at the tth hour of each day in the i-th category in the s-th season;

[0095] ED s,irepresents the number of days in season s where the electricity demand of the i-th type of residential buildings that need to install photovoltaics, excluding electric vehicles, is included;

[0096] I represents the total number of categories divided in the sth season.

[0097] Optionally, after obtaining the historical solar irradiance, historical temperature, and historical target demand data of the target area, the historical solar irradiance, historical temperature, and historical target demand data of the residences in the target area that need to install photovoltaics can be divided according to season, and then the different data of each season are clustered to obtain the cluster center points of different categories. Based on the number of days of each category of different data in each season, the cluster center points of different categories are weighted averaged to obtain the solar irradiance, temperature, and electricity demand data of the residences that need to install photovoltaics, excluding electric vehicles, for each hour on a typical day in each season in the target area. This facilitates the subsequent construction of constraint conditions for solving the objective function based on the solar irradiance, temperature, and electricity demand data of the residences that need to install photovoltaics, excluding electric vehicles, for each hour on a typical day in each season in the target area, so as to obtain the optimal solution for the photovoltaic installation capacity.

[0098] The residential rooftop photovoltaic capacity optimization configuration method provided by the present invention adopts the DBSCAN clustering and weighted average method to accurately characterize the typical characteristics of solar irradiation, temperature and residential electricity demand in different seasons, while reducing the data dimension and improving the calculation efficiency. At the same time, the solar irradiance, temperature and electricity demand data of the residences that need to install photovoltaics, excluding electric vehicles, on typical days of each season in the target area are obtained every hour, which is convenient for the subsequent construction of the constraint conditions for solving the objective function.

[0099] Optionally, the photovoltaic capacity optimization influencing factors include:

[0100] Photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residences in the target area where photovoltaics need to be installed, and electricity prices in the target area.

[0101] Optionally, the photovoltaic technical parameter may be a parameter related to photovoltaic performance, such as photovoltaic life cycle or photovoltaic panel area.

[0102] Optionally, the photovoltaic economic parameter may be a parameter related to photovoltaic costs, such as photovoltaic unit capacity investment cost or discount rate.

[0103] Alternatively, the flexible load in a residence may be a load that can both use the electricity of the residence and provide electricity to the residence, such as an electric vehicle.

[0104] Optionally, the operating parameters of the flexible loads in the residences in the target area where photovoltaics need to be installed can be the electric vehicle battery capacity, maximum charging and discharging power and efficiency, departure and arrival time, energy status when leaving and arriving home, maximum and minimum energy status and maximum flexibility capacity of other loads in the residence, etc.

[0105] Optionally, the maximum flexibility capacity is the maximum power demand of the residential flexibility load participating in demand response, which is generally about 10% of the residential power demand.

[0106] Optionally, the electricity price in the target area may be the electricity purchase price for residential users in the target area or the photovoltaic grid-connected electricity price in the target area.

[0107] Optionally, after obtaining the factors affecting photovoltaic capacity optimization, namely photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residential buildings in the target area that need to install photovoltaics, and electricity prices in the target area, an objective function can be constructed based on the above parameters, with the goal of minimizing the annual total electricity cost of residential users in the target area, to obtain the optimal solution for the photovoltaic installation capacity.

[0108] The method for optimizing the configuration of residential rooftop photovoltaic capacity provided by the present invention constructs an objective function for solving the optimal solution for photovoltaic installation capacity by obtaining photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residential buildings that need to install photovoltaics in the target area, and electricity prices in the target area, thereby providing residential users with a more accurate and efficient rooftop photovoltaic capacity configuration plan, improving photovoltaic self-sufficiency and local absorption capabilities, avoiding photovoltaic curtailment and large-scale grid connection, and reducing electricity costs for residential users.

[0109] Optionally, constructing an objective function and at least one constraint condition for solving the objective function based on historical solar irradiance, historical temperature, and historical target demand data of the target area and the photovoltaic capacity optimization influencing factors includes:

[0110] Establishing the objective function based on the photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in the residences in the target area where photovoltaics are required to be installed, and electricity prices in the target area;

[0111] Based on one or more of the solar irradiance per hour on a typical day in each season, the temperature per hour on a typical day in each season, the historical target demand data per hour on a typical day in each season, photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residences in the target area that need to install photovoltaics, and the electricity price in the target area, constraints for solving the objective function are constructed. The constraints include one or more of the following: residential rooftop photovoltaic output constraints, residential rooftop photovoltaic grid-connected constraints, residential demand response constraints for loads other than electric vehicles, electric vehicle charging and electric vehicle power supply to residence (V2H) constraints, power balance constraints, and residential installation area constraints.

[0112] Optionally, an objective function may be established based on photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residential buildings in a target area where photovoltaics are required to be installed, and electricity prices in the target area. The objective function may be expressed as follows:

[0113]

[0114] Where:

[0115] TC represents the total annual electricity cost for residential users;

[0116] represents the equivalent annual investment cost of photovoltaics;

[0117] represents the annual operation and maintenance cost of photovoltaics;

[0118] C grid represents the annual electricity purchase cost for residential users;

[0119] Income from photovoltaic grid connection;

[0120] ICC pv represents the investment cost per unit capacity of photovoltaic power;

[0121] Indicates the installed capacity of photovoltaics;

[0122] O&MC pv Represents the annual operating cost of photovoltaic power per unit capacity;

[0123] r is the discount rate;

[0124] Y is the life cycle of photovoltaics;

[0125] s=1,2,3,4 represent the four seasons of spring, summer, autumn and winter respectively;

[0126] is the price of photovoltaic power grid access at hour t in season s;

[0127] p s,t is the price of electricity purchased by residential users at hour t in season s;

[0128] The amount of electricity purchased by residential users at hour t on a typical day in season s;

[0129] is the photovoltaic grid-connected power at hour t on a typical day in season s;

[0130] D s is the number of days in season s.

[0131] Optionally, based on the above objective function, the goal of the objective function is to minimize TC, that is, the annual total electricity cost of residential users, and obtain the corresponding That is, the installed capacity of photovoltaics.

[0132] Optionally, after constructing the objective function, in order to meet the basic electricity demand and electricity safety of residential users, one or more constraints for solving the objective function can be constructed, including but not limited to residential rooftop photovoltaic output constraints, residential rooftop photovoltaic grid-connected constraints, residential demand response constraints for loads other than electric vehicles, electric vehicle charging and electric vehicle power supply to residential V2H constraints, power balance constraints, and residential installation area constraints.

[0133] Alternatively, the residential rooftop photovoltaic output constraint can be expressed as follows:

[0134]

[0135] Where:

[0136] represents the output power of the photovoltaic panel at time t on a typical day in season s;

[0137] Indicates the rated power of the photovoltaic panel, that is, the photovoltaic installation capacity;

[0138] represents the solar irradiance at period t on a typical day in season s;

[0139] R ref represents the solar irradiance under reference conditions;

[0140] T ref Indicates the temperature of the PV module under reference conditions;

[0141] represents the ambient temperature at time t on a typical day in season s;

[0142] K t is the temperature coefficient of maximum power;

[0143] represents the photovoltaic power generation in the tth period of a typical day in season s;

[0144] Δt represents a time interval;

[0145] Alternatively, the reference condition may be a value under standard conditions, where the solar irradiance under the reference condition may be 1kW / m 2 , the PV module temperature under reference conditions can be taken as 25℃.

[0146] Optionally, Δt may be related to the time granularity of acquiring data, and may be 1 hour, or 0.5 hour, etc. In one embodiment of the present invention, Δt=1 hour.

[0147] Alternatively, the residential rooftop photovoltaic grid-connected constraint condition can be expressed by the following formula:

[0148]

[0149] Where:

[0150] represents the photovoltaic grid-connected power in period t on a typical day in season s;

[0151] represents the charging amount of electric vehicles in the t period on a typical day in season s;

[0152] represents the electricity demand of residential loads excluding electric vehicles after demand response in period t on a typical day in season s;

[0153] Optionally, for parameter data of non-optimized results, since it is difficult to obtain values ​​on typical days, data on typical days in each season can be obtained by surveying residential users.

[0154] Alternatively, the constraint condition for residential loads other than electric vehicles to respond to demand can be expressed by the following formula:

[0155]

[0156] Where:

[0157] FL max Indicates the maximum load transfer capacity of the residence excluding electric vehicles;

[0158] It represents the maximum allowable transfer-in amount in the tth period of a typical day in season s;

[0159] Optionally, the maximum load transfer capacity of the residence excluding electric vehicles can be taken as around 10%.

[0160] Alternatively, the constraints for EV charging and EV-to-home power supply (V2H) can be expressed by the following formula:

[0161]

[0162] Where:

[0163] EVSOC s,t+1 represents the battery energy state of the electric vehicle at time t+1 on a typical day in season s;

[0164] EVSOC s,t represents the energy state of the electric vehicle battery at time t on a typical day in season s;

[0165] EEV bat Indicates the capacity of the electric vehicle battery;

[0166] represents the charging power of electric vehicles at time t on a typical day in season s;

[0167] represents the discharge power of electric vehicles at time t on a typical day in season s;

[0168] Indicates the charging efficiency of electric vehicle batteries;

[0169] Indicates the discharge efficiency of electric vehicle batteries;

[0170] represents the charging amount of electric vehicles in the t period on a typical day in season s;

[0171] represents the amount of electricity supplied to residential loads by electric vehicles during period t on a typical day in season s;

[0172] Indicates the maximum charging power of electric vehicle batteries;

[0173] Indicates the minimum charging power of electric vehicle batteries;

[0174] Indicates the maximum discharge power of electric vehicle battery;

[0175] Indicates the minimum discharge power of electric vehicle batteries;

[0176] EVSOC ini,min Indicates the minimum initial energy state of the electric vehicle when it arrives home;

[0177] EVSOC fin,min Indicates the minimum initial energy state of the electric vehicle when it leaves home;

[0178] EVSOC max Indicates the maximum energy state of the electric vehicle battery;

[0179] EVSOC min Indicates the minimum energy state of the electric vehicle battery;

[0180] On / off binary representing electric vehicle battery charging;

[0181] On / off binary representing the discharge of an electric vehicle battery;

[0182] T a represents the time it takes for the electric vehicle to arrive at the residence;

[0183] T d Indicates the time of leaving the residence;

[0184] Alternatively, the power balance constraint can be expressed by the following formula:

[0185]

[0186] Where: represents the charging amount of electric vehicles in the t period on a typical day in season s;

[0187] represents the electricity demand of residential loads excluding electric vehicles after demand response in period t on a typical day in season s;

[0188] represents the photovoltaic grid-connected power in period t on a typical day in season s;

[0189] represents the photovoltaic power generation in the tth period of a typical day in season s;

[0190] represents the amount of electricity supplied to residential loads by electric vehicles during period t on a typical day in season s;

[0191] The amount of electricity purchased by residential users at hour t on a typical day in season s.

[0192] Optionally, in order to ensure that the optimal solution of the photovoltaic installation capacity does not exceed the residential installation area, a residential installation area constraint condition can be constructed, which can be expressed by the following formula:

[0193]

[0194] Where:

[0195] S pv Indicates the area of ​​photovoltaic panels per unit capacity;

[0196] S max Indicates the maximum available area for installing photovoltaic panels on a residence.

[0197] Optionally, after constructing the objective function, one or more constraints can be constructed for the objective function so that the optimal solution of photovoltaic installation capacity obtained by solving the problem can minimize the electricity cost of residential users while satisfying the constraints. The selection of the above-mentioned residential rooftop photovoltaic output constraints, residential rooftop photovoltaic grid-connected constraints, residential load demand response constraints other than electric vehicles, electric vehicle charging and electric vehicle power supply to residential V2H constraints, power balance constraints and residential installation area constraints maintains a low model complexity and can achieve a faster solution speed.

[0198] The residential rooftop photovoltaic capacity optimization configuration method provided by the present invention constructs more accurate and efficient objective functions and constraints by more accurately characterizing the seasonal characteristics of solar irradiation, temperature, and residential electricity demand and simulating the demand response behaviors of electric vehicle charging, V2H, and other loads in the residence. This can improve the efficiency and accuracy of solving the objective function.

[0199] Optionally, before obtaining the optimal solution for the photovoltaic installation capacity, the method further includes:

[0200] Initializing first parameters, wherein the first parameters include at least the number of particles, inertia weight, and acceleration constant;

[0201] Randomly initialize the position and velocity of each particle;

[0202] Repeat the first process until the preset maximum number of iterations is reached;

[0203] Determine the optimal positions of all particles based on the positions of all particles in the last iteration;

[0204] The first process includes:

[0205] Based on the objective function, calculating the objective function value of each particle;

[0206] Calculating a constraint penalty function value for each particle based on the position of each particle, the number of particles, and the number of constraints for solving the objective function;

[0207] Calculating a fitness function value of each particle based on the objective function value of each particle and the constraint penalty function value of each particle;

[0208] Determine the optimal particle position. The determination rule is: if there is a particle with a constraint penalty function value of 0, then the position of the particle with the smallest fitness function value is selected from the particles with the constraint penalty function value of 0 as the optimal particle position; if the penalty function values ​​of all particles are greater than 0, then the position of the particle with the smallest fitness function value is selected from all particles as the optimal particle position;

[0209] Adopting an adaptive weight strategy to update the inertia weight;

[0210] The position and velocity of each particle are updated based on the optimal particle position, the updated inertia weight, and the acceleration constant.

[0211] Optionally, after obtaining the objective function and constraints, in order to obtain the optimal solution for the photovoltaic installation capacity, an algorithm can be used to solve the optimal solution for each parameter in the objective function, such as a simulated annealing algorithm, a deep learning algorithm, etc.

[0212] Optionally, in order to solve the optimal solution of each parameter in the objective function, the present invention adopts an improved particle swarm algorithm to solve it. Compared with other algorithms, the improved particle swarm algorithm improves the search performance and convergence speed, making the objective function solution more efficient and the result more accurate.

[0213] Figure 2 This is a flow chart of the improved particle swarm algorithm provided by the present invention. Figure 2 As shown, in one embodiment of the present invention, the improved particle swarm algorithm is used to solve the optimal solution of each parameter in the objective function, including the following steps:

[0214] 1. First, you can initialize the number of particle swarms, inertia weight, acceleration constants c1 and c2 and other parameters.

[0215] 2. After initializing the first parameter, the position and velocity of each particle can be randomly initialized.

[0216] 3. Based on the objective function, calculate the objective function value TC(x i );

[0217] 4. Based on the position of each particle, the number of particles, and the number of constraints for solving the objective function, calculate the constraint penalty function value penalty(x i ), expressed as follows:

[0218]

[0219] Where:

[0220] x i represents the position of the i-th particle;

[0221] μ represents the penalty coefficient;

[0222] k represents the number of iterations;

[0223] represents the penalty function value of the i-th particle violating the constraint condition in the k-th iteration;

[0224] represents the overall penalty term;

[0225] represents the penalty term for violating the j-th constraint;

[0226] M represents the number of constraint formulas;

[0227] N represents the number of particles;

[0228] Indicates the value calculated by bringing the i-th particle of the k-th iteration into the j-th constraint formula;

[0229] L j Indicates the degree of violation of each constraint;

[0230] 5. Based on the objective function value of each particle and the constraint penalty function value of each particle, calculate the fitness function value of each particle It is expressed as the following formula:

[0231]

[0232] 6. Determine the individual optimal position and the global optimal position. The rule for determining the optimal particle position is: if there is a particle with a penalty term of 0, then select the particle position with the smallest fitness function value from the particles with a penalty term of 0 as the optimal particle position; if the penalty function values ​​of all particles are greater than 0, then select the particle position with the smallest fitness function value from all particles as the optimal particle position;

[0233] 7. Adopt the adaptive weight strategy to update the inertia weight, which is expressed as follows:

[0234]

[0235] Where:

[0236] is the inertia weight of the i-th particle at time k;

[0237] w max With w min Refers to the preset maximum and minimum inertia weights respectively;

[0238] α is a parameter that controls the speed of weight adjustment;

[0239] 8. Based on the optimal particle position, the updated inertia weight, and the acceleration constant, update the position and velocity of each particle, as expressed by the following formula:

[0240]

[0241] Where:

[0242] c1 and c2 are acceleration factors;

[0243] r1 and r2 are random numbers between (0, 1);

[0244] is the d-th dimension component of the optimal position of the i-th particle in the k-th iteration;

[0245] is the d-th dimension component in the optimal position vector of the population at the k-th iteration;

[0246] is the velocity of the d-th dimension component of the i-th particle at the k-th iteration;

[0247] is the d-th dimension component of the position of the i-th particle at the k-th iteration;

[0248] 9. Repeat steps 3 to 8 until the maximum number of iterations is reached;

[0249] Optionally, the maximum number of iterations can be determined based on the complexity of the problem and the convergence condition, and is generally set to 100-4000. If the maximum number of iterations is too small, the solution will be unstable, and if the maximum number of iterations is too large, time will be wasted.

[0250] In one embodiment of the present invention, the maximum number of iterations is 600.

[0251] 10. Save the optimal solution.

[0252] Optionally, after obtaining the optimal positions of all particles, the position of the optimal particle is the optimal result of various variables in the objective function.

[0253] Optionally, after constructing the objective function and constraints, an improved particle swarm algorithm can be used based on the objective function and constraints to obtain the optimal position of the particles as the optimal result of each variable in the objective function, so as to obtain the optimal solution for photovoltaic installation capacity with the goal of minimizing the annual total electricity cost of residential users in the target area.

[0254] The residential rooftop photovoltaic capacity optimization configuration method provided by the present invention can, after constructing the objective function and constraints, use an improved particle swarm algorithm based on the objective function and constraints to obtain the optimal position of particles as the optimal result of each variable in the objective function, so as to obtain the optimal solution of photovoltaic installation capacity with the goal of minimizing the annual total electricity cost of residential users in the target area, improve the search performance and convergence speed, and make the efficiency of solving the objective function and the accuracy of the results higher.

[0255] Optionally, the step of obtaining an optimal solution for the photovoltaic installation capacity with the goal of minimizing the total annual electricity cost for residential users in the target area includes:

[0256] Determining optimal values ​​of multiple variables in the objective function based on the optimal positions of all particles;

[0257] Based on the optimal values ​​of multiple variables in the objective function, the photovoltaic installation capacity corresponding to the lowest annual total electricity cost for residential users in the target area is determined as the optimal solution for the photovoltaic installation capacity.

[0258] Optionally, after obtaining the optimal position of the particles using the improved particle swarm algorithm, it can be used as the optimal result of each variable in the objective function. Then, under the condition that the annual total electricity cost of residential users in the target area is minimized in the objective function, the corresponding photovoltaic installation capacity is obtained as the optimal solution, and then it is determined that the optimal residential rooftop photovoltaic capacity configuration plan includes the optimal photovoltaic installation capacity solution.

[0259] The residential rooftop photovoltaic capacity optimization configuration method provided by the present invention adopts an improved particle swarm algorithm to obtain the optimal position of particles as the optimal result of each variable in the objective function, so as to obtain the optimal solution of photovoltaic installation capacity with the goal of minimizing the annual total electricity cost of residential users in the target area. The search performance and convergence speed are improved, and the efficiency of solving the objective function and the accuracy of the results are higher.

[0260] The residential rooftop photovoltaic capacity optimization configuration device provided by the present invention is described below. The residential rooftop photovoltaic capacity optimization configuration device described below and the residential rooftop photovoltaic capacity optimization configuration method described above can be referenced to each other.

[0261] Figure 3 This is a schematic diagram of the structure of the residential rooftop photovoltaic capacity optimization configuration device provided by the present invention. Figure 3 As shown, the apparatus includes an acquisition module 310, a construction module 320 and a configuration module 330, wherein:

[0262] An acquisition module 310 is configured to acquire historical solar irradiance, historical temperature, and historical target demand data and photovoltaic capacity optimization influencing factors for a target area, wherein the historical target demand data includes historical power demand data of residences in the target area that require photovoltaic installation, excluding electric vehicles;

[0263] A construction module 320 is configured to construct an objective function based on historical solar irradiance, historical temperature, and historical target demand data of the target area and the photovoltaic capacity optimization influencing factors, with the goal of minimizing the annual total electricity cost of residential users in the target area, and obtain an optimal solution to the objective function;

[0264] The configuration module 330 is used to configure the optimal residential rooftop photovoltaic capacity based on the optimal solution of the objective function.

[0265] The residential rooftop photovoltaic capacity optimization configuration device provided by the present invention constructs an objective function and constraints for achieving optimal configuration of residential rooftop photovoltaic capacity based on the accurate characterization of historical solar irradiance, temperature and residential electricity demand, while taking into account the demand response behavior of electric vehicle charging, V2H and other loads in the residence. By solving the objective function with the goal of minimizing the annual total electricity cost of residential users in the target area, the optimal photovoltaic installation capacity can be obtained, providing a more accurate and efficient residential rooftop photovoltaic capacity configuration plan for the residence, improving the photovoltaic self-sufficiency and self-consumption rate, and reducing the residential electricity cost.

[0266] It can be understood that the residential rooftop photovoltaic capacity optimization configuration device provided by the present invention corresponds to the residential rooftop photovoltaic capacity optimization configuration method provided by the above-mentioned embodiments. The relevant technical features of the residential rooftop photovoltaic capacity optimization configuration device provided by the present invention can refer to the relevant technical features of the residential rooftop photovoltaic capacity optimization configuration method provided by the above-mentioned embodiments, and will not be repeated here.

[0267] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call logic instructions in the memory 430 to execute a method for optimizing the configuration of residential rooftop photovoltaic capacity, the method comprising: obtaining historical solar irradiance, historical temperature, and historical target demand data of a target area, and factors influencing photovoltaic capacity optimization, wherein the historical target demand data includes historical power demand data of residential buildings in the target area that need to install photovoltaics, excluding electric vehicles; based on the historical solar irradiance, historical temperature, and historical target demand data of the target area and the factors influencing photovoltaic capacity optimization, constructing an objective function with the goal of minimizing the annual total electricity cost of residential users in the target area, and obtaining an optimal solution to the objective function; and configuring an optimal residential rooftop photovoltaic capacity based on the optimal solution to the objective function.

[0268] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0269] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the residential rooftop photovoltaic capacity optimization configuration method provided by the above-mentioned methods, the method including: obtaining historical solar irradiance, historical temperature and historical target demand data and photovoltaic capacity optimization influencing factors of the target area, wherein the historical target demand data includes historical electricity demand data of residences that need to install photovoltaics in the target area, excluding electric vehicles; based on the historical solar irradiance, historical temperature and historical target demand data of the target area and the photovoltaic capacity optimization influencing factors, with the goal of minimizing the annual total electricity cost of residential users in the target area, constructing an objective function and obtaining the optimal solution of the objective function; based on the optimal solution of the objective function, configuring the optimal residential rooftop photovoltaic capacity.

[0270] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the residential rooftop photovoltaic capacity optimization configuration method provided by the above-mentioned methods, the method comprising: obtaining historical solar irradiance, historical temperature, historical target demand data and photovoltaic capacity optimization influencing factors of the target area, wherein the historical target demand data includes historical electricity demand data of residences that need to install photovoltaics in the target area, excluding electric vehicles; based on the historical solar irradiance, historical temperature, historical target demand data and the photovoltaic capacity optimization influencing factors of the target area, with the goal of minimizing the annual total electricity cost of residential users in the target area, constructing an objective function and obtaining the optimal solution of the objective function; and configuring the optimal residential rooftop photovoltaic capacity based on the optimal solution of the objective function.

[0271] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0272] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0273] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing the configuration of photovoltaic capacity on residential rooftops, characterized in that: include: Obtaining historical solar irradiance, historical temperature, historical target demand data, and photovoltaic capacity optimization influencing factors for the target area, wherein the historical target demand data includes historical electricity demand data for residences in the target area that need to install photovoltaics, excluding electric vehicles; Based on the historical solar irradiance, historical temperature, historical target demand data of the target area and the factors affecting photovoltaic capacity optimization, an objective function is constructed with the goal of minimizing the annual total electricity cost of residential users in the target area, and an optimal solution of the objective function is obtained as the optimal solution for photovoltaic installation capacity; Based on the optimal solution of the objective function, configuring the residential rooftop photovoltaic capacity; The factors affecting photovoltaic capacity optimization include: Photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residences in the target area where photovoltaics are required to be installed, and electricity prices in the target area; The constructing of an objective function and at least one constraint condition for solving the objective function based on the historical solar irradiance, historical temperature, and historical target demand data of the target area and the photovoltaic capacity optimization influencing factors includes: Establishing the objective function based on the photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in the residences in the target area where photovoltaics are required to be installed, and electricity prices in the target area; Based on one or more of the solar irradiance per hour on a typical day in each season, the temperature per hour on a typical day in each season, the historical target demand data per hour on a typical day in each season, photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residences in the target area that need to install photovoltaics, and the electricity price in the target area, constraints for solving the objective function are constructed. The constraints include one or more of the following: residential rooftop photovoltaic output constraints, residential rooftop photovoltaic grid-connected constraints, residential demand response constraints for loads other than electric vehicles, electric vehicle charging and electric vehicle power supply to residence (V2H) constraints, power balance constraints, and residential installation area constraints.

2. The method for optimizing the configuration of residential rooftop photovoltaic capacity according to claim 1, characterized in that: After obtaining the historical solar irradiance, historical temperature, and historical target demand data and photovoltaic capacity optimization influencing factors of the target area, the method further includes: historical solar irradiance, historical temperature, and historical target demand data for the target area divided by season; Clustering the historical solar irradiance, historical temperature, and historical target demand data of the target area in each season respectively to obtain cluster center points of different categories of historical solar irradiance in each season, cluster center points of different categories of historical temperature in each season, and cluster center points of different categories of historical target demand data in each season for the target area; Based on the number of days of historical solar irradiance in each category of each season of the target area, the number of days of historical temperature in each category of each season and the number of days of historical target demand data in each category of each season, a weighted average is performed on the cluster center points of the historical solar irradiance of different categories in each season of the target area, the cluster center points of the historical temperature of different categories in each season and the cluster center points of the historical target demand data of different categories in each season to obtain the solar irradiance of typical days in each season per hour, the temperature of typical days in each season per hour and the historical target demand data of typical days in each season per hour of the target area.

3. The method for optimizing the configuration of residential rooftop photovoltaic capacity according to claim 1, characterized in that: Before obtaining the optimal solution for the photovoltaic installation capacity, the method further includes: Initializing first parameters, wherein the first parameters include at least the number of particles, inertia weight, and acceleration constant; Randomly initialize the position and velocity of each particle; Repeat the first process until the preset maximum number of iterations is reached; Determine the optimal positions of all particles based on the positions of all particles in the last iteration; The first process includes: Based on the objective function, calculating the objective function value of each particle; Calculating a constraint penalty function value for each particle based on the position of each particle, the number of particles, and the number of constraints for solving the objective function; Calculating a fitness function value of each particle based on the objective function value of each particle and the constraint penalty function value of each particle; Determine the optimal particle position. The determination rule is: if there is a particle with a constraint penalty function value of 0, then the position of the particle with the smallest fitness function value is selected from the particles with the constraint penalty function value of 0 as the optimal particle position; if the penalty function values ​​of all particles are greater than 0, then the position of the particle with the smallest fitness function value is selected from all particles as the optimal particle position; Adopting an adaptive weight strategy to update the inertia weight; The position and velocity of each particle are updated based on the optimal particle position, the updated inertia weight, and the acceleration constant.

4. The method for optimizing the configuration of residential rooftop photovoltaic capacity according to claim 3, characterized in that: The step of obtaining an optimal solution for the photovoltaic installation capacity with the goal of minimizing the total annual electricity cost for residential users in the target area includes: Determining optimal values ​​of multiple variables in the objective function based on the optimal positions of all particles; Based on the optimal values ​​of multiple variables in the objective function, the photovoltaic installation capacity corresponding to the lowest annual total electricity cost for residential users in the target area is determined as the optimal solution for the photovoltaic installation capacity.

5. A residential rooftop photovoltaic capacity optimization configuration device, characterized in that: The device comprises: an acquisition module, configured to acquire historical solar irradiance, historical temperature, and historical target demand data and photovoltaic capacity optimization influencing factors for a target area, wherein the historical target demand data includes historical power demand data of residences in the target area that need to install photovoltaics, excluding electric vehicles; a construction module for constructing an objective function based on historical solar irradiance, historical temperature, and historical target demand data of the target area and the photovoltaic capacity optimization influencing factors, with the goal of minimizing the annual total electricity cost of residential users in the target area, and obtaining an optimal solution to the objective function; A configuration module, configured to configure the residential rooftop photovoltaic capacity based on the optimal solution of the objective function; The factors affecting photovoltaic capacity optimization include: Photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residences in the target area where photovoltaics are required to be installed, and electricity prices in the target area; The building blocks are specifically used for: Establishing the objective function based on the photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in the residences in the target area where photovoltaics are required to be installed, and electricity prices in the target area; Based on one or more of the solar irradiance per hour on a typical day in each season, the temperature per hour on a typical day in each season, the historical target demand data per hour on a typical day in each season, photovoltaic technical parameters, photovoltaic economic parameters, operating parameters of flexible loads in residences in the target area that need to install photovoltaics, and the electricity price in the target area, constraints for solving the objective function are constructed. The constraints include one or more of the following: residential rooftop photovoltaic output constraints, residential rooftop photovoltaic grid-connected constraints, residential demand response constraints for loads other than electric vehicles, electric vehicle charging and electric vehicle power supply to residence (V2H) constraints, power balance constraints, and residential installation area constraints.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the residential rooftop photovoltaic capacity optimization configuration method as described in any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the configuration of residential rooftop photovoltaic capacity as claimed in any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing the configuration of residential rooftop photovoltaic capacity as claimed in any one of claims 1 to 4 is implemented.

Images