Optimal consumption photovoltaic power station optimization design method and device

Abstract

This invention discloses an optimal photovoltaic power plant design method and apparatus for optimal load absorption. The design method includes the following steps: (1) Load data is collected through smart meters, the load data is preprocessed, and curve analysis is performed to obtain the load characteristics of the load curve; (2) Under the load curve characteristics obtained in step (1), combined with the photovoltaic output model, the photovoltaic power plant is optimized using a single component layout algorithm based on the PSO algorithm to obtain the designed component layout; (3) Based on the characteristics of the load curve in step (1), step (2) is repeated multiple times, and a comprehensive power plant design is performed, supplemented by the PSO algorithm for acceleration, to obtain the comprehensive power plant optimization design and achieve the load absorption target. This invention uses an algorithm strategy to design a photovoltaic power plant without relying on energy storage devices and scheduling algorithms, and accelerates the operation efficiency of the power plant design algorithm through the PSO algorithm. This can not only greatly reduce the investment cost of the power plant in the early stage, but also reduce the cost and difficulty of later operation and maintenance, providing an effective and economical solution for the sustainable construction and operation of photovoltaic power plants.

Description

Technical Field

[0001] This invention discloses an optimal design method and apparatus for photovoltaic power plants with optimal power absorption capacity, belonging to the field of photovoltaic power plant design. Background Technology

[0002] With the growth of global energy demand and the advancement of carbon neutrality goals, solar photovoltaic (PV) is playing an increasingly crucial role in the energy structure. However, the discontinuous and unstable nature of solar power generation poses challenges to grid dispatch and stable operation. In regions with well-developed solar power generation, when power generation exceeds demand, the excess electricity is difficult to utilize effectively, sometimes even leading to abnormal electricity market prices (such as negative electricity prices), which threatens the economics and sustainability of the power grid. Therefore, researchers both domestically and internationally have conducted extensive technical research on the challenge of solar power integration, seeking optimization strategies to improve the integration capacity of photovoltaic power plants.

[0003] Currently, the main research directions for solving the solar power absorption problem focus on using energy storage systems and scheduling schemes to mitigate the volatility of solar power generation. For example, Wan Yuexiang et al. proposed a method for energy storage planning in distributed photovoltaic medium-voltage distribution networks, achieving automated energy storage planning through in-depth research on energy storage technology. Jin Li et al. proposed a "generation-storage-load" coordinated scheduling strategy, considering the demand response of the load side and electrochemical energy storage system at different time scales to achieve coordinated operation of load and energy storage, thereby improving the absorption capacity of the power station. Zhang Ruixue et al. explored a precise load forecasting technology combining weather forecasting and deep learning, predicting electricity demand and solar power generation through algorithms, and designing a dynamic reconfiguration strategy for the distribution network to optimize the absorption effect of solar energy. Chen Bin et al. proposed a solar energy absorption optimization scheme suitable for AC / DC hybrid distribution areas, effectively improving the absorption performance of photovoltaic power stations by establishing a joint output model of thermal power, solar energy, and electrical equipment, and adopting an energy interaction control method.

[0004] Although many studies have proposed various strategies to solve the problem of photovoltaic power plant grid integration, there are still some shortcomings, mainly including: 1) Most current studies focus on adjusting energy dispatch strategies, which often require the configuration of energy storage facilities, and the cost of energy storage facilities is relatively high; 2) The research content has not fully considered the actual environment of photovoltaic power plant construction, resulting in greater difficulty in its engineering implementation. Summary of the Invention

[0005] This invention employs an algorithmic strategy to design photovoltaic power plants without relying on energy storage devices or scheduling algorithms, accelerating the operation efficiency of the power plant design algorithm through the Particle Swarm Optimization (PSO) algorithm. This not only significantly reduces the initial investment cost of the power plant but also reduces the cost and difficulty of subsequent operation and maintenance, providing an effective and economical solution for the sustainable construction and operation of photovoltaic power plants.

[0006] The technical solution of the present invention is as follows:

[0007] An optimal design method for photovoltaic power plants with optimal power absorption capacity includes the following steps:

[0008] (1) Load data is collected through smart meters, the load data is preprocessed, and curve analysis is performed to obtain the load characteristics of the load curve;

[0009] (2) Under the load curve characteristics obtained in step (1), combined with the photovoltaic output model, the photovoltaic power station is optimized by using the single component layout algorithm based on the PSO algorithm to obtain the designed component layout.

[0010] (3) Based on the characteristics of the load curve in step (1), repeat step (2) multiple times and carry out integrated power plant design. With the help of PSO algorithm acceleration, the integrated power plant optimization design is obtained to achieve the absorption target.

[0011] Preferably, the specific steps of step (1) above are as follows:

[0012] (1-1) Load data collection: The data is continuously monitored by smart meters over a period of time. The data is recorded in time steps of hours, minutes or seconds to capture fluctuations in load changes.

[0013] (1-2) To ensure data accuracy, the collected data needs to be preprocessed. Preprocessing steps include outlier cleaning and missing value imputation. Outlier cleaning requires identifying and removing unreasonable load data, such as transient anomalous data changes. Missing value imputation mainly involves filling in missing data points to ensure continuity; methods such as interpolation or mean shift can be used for this purpose.

[0014] (1-3) After completing data collection and preprocessing, it is necessary to extract the basic characteristics of the load curve. The main extracted characteristics include time characteristics and frequency characteristics. Time characteristics include the distribution of peak and trough periods and the specific times and values ​​of their occurrence. This characteristic can reflect the electricity demand of users during specific periods. Peak load periods usually occur in the morning and afternoon, while trough load periods usually occur late at night or early in the morning. Frequency characteristics mainly include the periodic components in the load curve, which can extract the daily, weekly, and monthly variation patterns of the load, reflect the electricity consumption habits of users, and provide a reference for periodic power dispatching and load management.

[0015] (1-4) Finally, fluctuation analysis is performed. Fluctuation analysis studies the trend of load curve changes over time, primarily aiming to analyze the magnitude of changes around peaks and troughs. The standard for fluctuation analysis is the Pearson correlation coefficient, used to measure the degree of correlation between two variables, commonly represented by the symbol r. Its value ranges from -1 to 1. When r = 1, the two variables are perfectly positively correlated, meaning there is a perfect linear relationship between them, and one variable increases as the other increases. When r = -1, the two variables are perfectly negatively correlated, meaning one variable decreases as the other increases. When r = 0, the two variables are uncorrelated, meaning the increase or decrease of one variable is unrelated to the increase or decrease of the other. The specific mathematical model of the Pearson correlation coefficient is as follows:

[0016]

[0017] In the formula: X i Let Y be the i-th sample in variable X; X is the mean of sample X; Y i Let Y be the i-th sample in variable Y; Y is the mean of sample Y.

[0018] Preferably, the specific steps of step (2) above are as follows:

[0019] (2-1) The tilt angle and azimuth angle of the component arrangement in the power plant design are used as the solution domain of the PSO algorithm, and the arrangement scheme of the power plant components is represented by the particle position.

[0020] (2-2) Constructing the objective function of the single-component layout algorithm based on the PSO algorithm: The output model of the photovoltaic power station and the Pearson coefficient are introduced to construct the objective function of the algorithm. Its mathematical expression is as follows:

[0021] f(x) = A·abs(T) energy -T grid )-Br

[0022] In the formula: A and B are empirical coefficients, with A defaulting to 2 and B defaulting to 1; T energyT represents the peak value of the photovoltaic power plant output model. grid is the peak moment of the load curve; r is the Pierce coefficient of the two curves;

[0023] (2-3) Based on the objective function and the solution domain, construct the fitness function of the PSO algorithm and optimize the PSO algorithm for single component placement.

[0024] (2-4) Then, the output of the photovoltaic power station is calculated to obtain the hourly power generation data for the whole year. Through aggregation, the hourly change data for one day is obtained and compared with the load curve.

[0025] Preferably, the specific steps of step (3) above are as follows:

[0026] (3-1) Based on the load curve characteristics, repeat step (2) multiple times to obtain the power plant design scheme for single component arrangement under different load characteristics.

[0027] (3-2) Define the number of components in the power plant design scheme with single component arrangement under different load characteristics as the solution space, where the particle position represents the number of components in each power plant design.

[0028] (3-3) Construct the objective function of the integrated power plant optimization algorithm based on the PSO algorithm, and its mathematical expression is:

[0029]

[0030] In the formula: p represents time; T MAX The maximum time; N is the number of characteristic curves, E pq,ac E represents the power generation of the optimized arrangement at time p, where q is the time increment. p,grid Let x be the load power at time p. q This represents the number of components required for the q-th optimized layout.

[0031] (3-4) Based on the power plant design plan with single component layout under various load characteristics and the corresponding number of components, a comprehensive power plant design is carried out. This result can achieve the effect of power absorption.

[0032] The photovoltaic output model steps in steps (2) and (3) above are as follows, and the flowchart is shown below. Figure 2 As shown,

[0033] (1) The horizontal irradiance model is connected to the Meteonorm meteorological database. Horizontal irradiance data were obtained.

[0034] (2) The horizontal irradiance data obtained by the model is further combined with the arrangement of the components to calculate the irradiance of the inclined surface;

[0035] In this algorithm, the irradiance model for the inclined surface is an anisotropic model, consisting of three parts: direct irradiance, diffuse irradiance, and ground-reflected irradiance, which can be expressed as:

[0036] G tilt =G tilt,b +G tilt,d +G tilt,r

[0037] G tilt,b The formula for calculating the direct irradiance on an inclined surface is:

[0038] G tilt,b =G b R b

[0039] Where: G b R represents the direct irradiance on a horizontal plane. b The ratio of direct irradiance on the inclined plane to that on the horizontal plane can be specifically expressed as:

[0040]

[0041] In the formula: θ Z θ is the solar zenith angle; θ is the solar incidence angle, and the specific formula is:

[0042] θ = arccos(cosθ) Z cosβ+sinθ Z sinβcos(γ s -γ))

[0043] In the formula: β is the tilt angle of the component's tilt surface; γ is the azimuth angle of the tilt, with due south as 0°; γ s —Sun azimuth.

[0044] G tilt,d The scattered irradiance on the inclined surface is calculated using the following formula:

[0045]

[0046] Where: G d F1 is the diffuse irradiance on the horizontal plane; F2 is the circumstellar coefficient; F2 is the horizontal brightness coefficient; a is the maximum value of 0, cosθ. Z};b is max{cos85°, cosθ Z}

[0047] G tilt,r The formula for calculating ground-reflected irradiance is:

[0048]

[0049] In the formula: G is the total horizontal irradiance; is the ground reflectivity.

[0050] (3) Combine the obtained irradiance with the performance of the photovoltaic module itself to calculate the output of the photovoltaic module;

[0051] The solar cells of the module can be represented as an equivalent single-diode circuit model, as shown in the equivalent circuit diagram. Figure 3 As shown:

[0052] The characteristic equation relating the output current and voltage of a photovoltaic cell, simulated using an ideal single-diode circuit model, i.e., the IV characteristic equation, can be expressed as:

[0053]

[0054] In the formula: I L To generate dark current for light; I o R is the saturation current of the equivalent diode. sh Rs is the parallel resistance; T is the series resistance; N is the battery temperature; s γ is the number of cells in series; γ is the diode ideality factor; k is the Boltzmann constant (1.38×10-23J / k); q is the charge (1.6×10-9C); α is the module-based modified ideality factor.

[0055] The key to solving the IV characteristic equation is to obtain I. L I o α, R s R sh The output power characteristics of the module are obtained by considering five parameters, including those listed above. These parameters can be calculated based on the nameplate data under standard test conditions for photovoltaic modules. Under different environmental conditions, these parameters need to be corrected using the following correction formula:

[0056]

[0057]

[0058] In the formula: T C E represents the component's operating temperature. g denoted as the band gap of the material; μ as the temperature coefficient of short-circuit current; and G as the surface irradiance of the module.

[0059] (4) Through the formula

[0060]

[0061] Using the above formulas and the Monte Carlo iteration method, the DC-side power generation of the photovoltaic module can be calculated. The AC-side photovoltaic output power can be obtained through the inverter model. The inverter model used in this paper is the Sundia model, a linearized model proposed by Sundia National Laboratory in the United States. The formula for calculating the AC output power of the inverter is as follows:

[0062]

[0063] In the formula: P ac For AC output power; P dc For input DC power; P aco P represents the maximum output power under standard operating conditions. dco P represents the input DC power under standard operating conditions. so The DC power used to start the inverter, i.e., the DC power lost by the inverter itself; V dc This is the DC input voltage; V dco C0-C3 are the input DC voltage under standard operating conditions; C0-C3 are empirical coefficients for the model, which are usually set to 0, but can be obtained from experimental data to improve model accuracy.

[0064] The particle swarm optimization algorithm steps in steps (2) and (3) above are as follows:

[0065] Particle Swarm Optimization (PSO) is an optimization technique that simulates swarm intelligence, inspired by the foraging behavior of birds. In this algorithm, potential solutions to the optimization problem are viewed as "particles" in the search space, each representing a possible solution, while the optimal solution in the swarm is likened to the food the flock seeks. The execution steps of PSO are as follows:

[0066] 1) Initialize the particle swarm algorithm. When the algorithm starts, the position and velocity of the particles are randomly initialized.

[0067] 2) Evaluating the Fitness of Particles: Fitness is an indicator of the quality of a particle's solution, guiding the search direction and path of the particle swarm during the optimization process. The evaluation criterion for fitness depends on the specific optimization problem and objective function. For the objective function f(x), the fitness function Fit(x) can be defined as:

[0068]

[0069] 3) Update the particle’s personal best position: Compare the fitness value calculated in the current iteration with the best fitness value in the particle’s history. If the current value is better, update the particle’s personal best position, denoted as pbest.

[0070] 4) Update the best position of the particle swarm: Compare the fitness value in the current iteration with the best fitness value in the entire history of the particle swarm. If the current value is better, update the best position of the entire particle swarm, denoted as gbest.

[0071] 5) Iteratively update the velocity and position of particles. For a particle, whose position is represented by X and velocity by V, the update formulas for velocity and position in the t-th iteration are as follows:

[0072]

[0073] In the formula: m represents the position in the first dimension of the particle search space, n represents the position in the second dimension; ω is the inertia weight, which affects the continuity of the particle's previous velocity; c1 is the individual learning factor, c2 is the group learning factor, which is usually taken as 2; r1 is the individual random factor, r2 is the group random factor, which are random numbers between [0,1] to increase randomness; The optimal position for the individual particle; The best position for the group.

[0074] For the inertia weight ω, a larger value allows the particle to explore more extensively in the search space, but may cause the particle to miss the optimal region or become unstable; a smaller value can enhance the particle's local search ability, but may cause it to get trapped in a local optimum too early, limiting the comprehensive exploration of the search space. Therefore, the inertia weight needs to strike a balance between global search and local search, which can be achieved by dynamically adjusting the value of ω. This paper adopts a linearly decreasing weight (LDW) strategy to adjust ω, that is, starting from a larger initial value w max Decrease linearly to a smaller final value w min This method helps to expand the search range in the early stages of optimization to find the global optimum, and narrow the search range in the later stages of optimization to find the optimum precisely. For the t-th iteration, the formula for the LDW strategy is as follows:

[0075] w t =(w max -w min )(t max -t) / t max +w min

[0076] In the formula: t max This represents the maximum number of iterations.

[0077] The present invention also discloses an optimal photovoltaic power plant design device for optimal power absorption, comprising a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the method as described in any of the above.

[0078] The beneficial effects of this invention are:

[0079] This invention simulates the optimized scheme and compares it with the grid absorption ratio of traditional power plant layouts. The results show that the photovoltaic power plant design scheme optimized by this algorithm has a certain load absorption capacity, significantly improving the self-consumption capability of the photovoltaic power plant. Furthermore, an economic analysis of the scheme reveals that the optimized scheme has higher economic benefits under negative electricity prices compared to the traditional layout scheme. This paper only adopts a design approach to achieve self-consumption of the photovoltaic power plant, improving the grid absorption ratio solely through redesign, without relying on intelligent dispatching or energy storage technologies. This not only significantly reduces the initial investment cost of the power plant but also reduces the cost and difficulty of subsequent operation and maintenance, providing an effective and economical solution for the sustainable construction and operation of photovoltaic power plants. Attached Figure Description

[0080] Figure 1 This is a flowchart of the design method of the present invention;

[0081] Figure 2 This is a flowchart of the photovoltaic output model;

[0082] Figure 3 It is an equivalent circuit diagram;

[0083] Figure 4 This is a flowchart of the particle swarm optimization algorithm;

[0084] Figure 5 This is a load curve diagram from an embodiment;

[0085] Figure 6 This is an optimized layout scheme based on the load curve in the embodiment, the final power plant design layout, and a specific absorption effect diagram;

[0086] Figure 7 In this embodiment, the power plant is designed to maximize the absorption ratio by using both traditional and optimized component layout methods, and an absorption assessment is conducted. The specific absorption effect diagram is shown.

[0087] Figure 8 This is a comparison chart of the heat dissipation ratio of traditional layout, the heat dissipation ratio of optimized layout, and the standard heat dissipation ratio;

[0088] Figure 9 It is an optimized layout of economic curves compared to the traditional layout of economic curves. Detailed Implementation

[0089] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0090] An optimal design method for photovoltaic power plants with optimal power absorption capacity, such as Figure 1 As shown, it includes the following steps:

[0091] (1) Load data is collected through smart meters, the load data is preprocessed, and curve analysis is performed to obtain the load characteristics of the load curve;

[0092] (2) Under the load curve characteristics obtained in step (1), combined with the photovoltaic output model, the photovoltaic power station is optimized by using the single component layout algorithm based on the PSO algorithm to obtain the designed component layout.

[0093] (3) Based on the characteristics of the load curve in step (1), repeat step (2) multiple times and carry out integrated power plant design. With the help of PSO algorithm acceleration, the integrated power plant optimization design is obtained to achieve the absorption target.

[0094] Preferably, the specific steps of step (1) above are as follows:

[0095] (1-1) Load data collection: The data is continuously monitored by smart meters over a period of time. The data is recorded in time steps of hours, minutes or seconds to capture fluctuations in load changes.

[0096] (1-2) The steps for preprocessing load data include outlier cleaning and missing value imputation. Outlier cleaning requires identifying and removing unreasonable load data; missing value imputation is to fill in missing data points to ensure its continuity, using interpolation or mean method.

[0097] (1-3) After completing data collection and preprocessing, the basic characteristics of the load curve are extracted. The extracted characteristics include time characteristics and frequency characteristics. The time characteristics include the distribution of peak and trough periods and the specific time and value of their occurrence. The frequency characteristics include the periodic components in the load curve, and the daily, weekly and monthly variation patterns of the load are extracted.

[0098] (1-4) Finally, fluctuation analysis is performed. Fluctuation analysis studies the changing trend of the load curve on the time scale. Its main purpose is to analyze the change amplitude around the peak and trough.

[0099] The metric for volatility analysis is the Pearson correlation coefficient, which measures the degree of correlation between two variables. It is denoted by the symbol r, and the specific mathematical model for the Pearson correlation coefficient is as follows:

[0100]

[0101] In the formula: X i Let Y be the i-th sample in variable X; X is the mean of sample X; Y iLet i be the i-th sample in variable Y; This represents the average value of sample Y.

[0102] Preferably, the specific steps of step (2) above are as follows:

[0103] (2-1) The tilt angle and azimuth angle of the component arrangement in the power plant design are used as the solution domain of the PSO algorithm, and the arrangement scheme of the power plant components is represented by the particle position.

[0104] (2-2) Constructing the objective function of the single-component layout algorithm based on the PSO algorithm: The output model of the photovoltaic power station and the Pearson coefficient are introduced to construct the objective function of the algorithm. Its mathematical expression is as follows:

[0105] f(x) = A·abs(T) energy -T grid )-Br

[0106] In the formula: A and B are empirical coefficients, with A defaulting to 2 and B defaulting to 1; T energy T represents the peak value of the photovoltaic power plant output model. grid is the peak moment of the load curve; r is the Pierce coefficient of the two curves;

[0107] (2-3) Based on the objective function and the solution domain, construct the fitness function of the PSO algorithm and optimize the PSO algorithm for single component placement.

[0108] (2-4) Then, the output of the photovoltaic power station is calculated to obtain the hourly power generation data for the whole year. Through aggregation, the hourly change data for one day is obtained and compared with the load curve.

[0109] Preferably, the specific steps of step (3) above are as follows:

[0110] (3-1) Based on the load curve characteristics, repeat step (2) multiple times to obtain the power plant design scheme for single component arrangement under different load characteristics.

[0111] (3-2) Define the number of components in the power plant design scheme with single component arrangement under different load characteristics as the solution domain, and represent the number of components in each power plant design by the particle position;

[0112] (3-3) Construct the objective function of the integrated power plant optimization algorithm based on the PSO algorithm, and its mathematical expression is:

[0113]

[0114] In the formula: p represents time; T MAX The maximum time; N is the number of characteristic curves, E pq,acE represents the power generation of the optimized arrangement at time p, where q is the time increment. p,grid Let x be the load power at time p. q This represents the number of components required for the q-th optimized layout.

[0115] (3-4) Based on the arrangement of individual components under each load characteristic and the corresponding number of components under that arrangement, the power plant is designed in a comprehensive manner, and the result can achieve the effect of absorption.

[0116] Example:

[0117] Electricity load data from a factory was collected using smart meters, and data cleaning and missing value handling were performed to ensure data integrity and accuracy. Given that the structure and component layout of a photovoltaic power station are fixed once built, the optimized design of the station must not only ensure high precision but also maintain good power absorption capacity during long-term operation. By aggregating hourly load data throughout the year and simplifying it into hourly variation data within a single day, the changing trends can be analyzed. The changing trends are as follows... Figure 5 As shown.

[0118] Analysis shows that the factory's load curve exhibits a bimodal shape, peaking at 10:00 AM and 3:00 PM. Given these load characteristics, traditional single-module layouts are insufficient for efficient load absorption. Therefore, this study employs a design combining multiple layout methods to achieve a balance between load and photovoltaic power generation.

[0119] Considering the bimodal characteristics of the load curve, this study selected two component layout methods adapted to different load characteristics. Using power plant absorption design optimization based on the PSO algorithm, the optimal design strategy for the power plant was derived to effectively absorb the load curve. Specifically, two component layout methods were adopted: one with an azimuth angle of approximately -85.1° and an inclination angle of 84°, and the other with an azimuth angle of approximately 131.2° and an inclination angle of 56°, arranged in a ratio of 1:1.7.

[0120] Since the photovoltaic power station cannot generate electricity at night, the grid connection period is set from 7:00 AM to 7:00 PM. Based on the optimized layout scheme according to the load curve, the specific grid connection effect of the power station is as follows: Figure 6 As shown.

[0121] With the goal of maximizing the power absorption ratio, the power plant was designed using both traditional and optimized module layout methods, and its absorption capacity was assessed. The specific absorption effects are as follows: Figure 7 As shown:

[0122] As shown in the graph, the optimized power station's power generation curve closely matches the electricity demand from 10:00 AM to 4:00 PM, demonstrating a significant absorption effect. However, during other times, due to the intermittent nature of photovoltaic power generation, the power output cannot fully meet the demand. In contrast, the traditionally arranged power station experiences a larger demand gap before 11:00 AM and after 2:00 PM. Furthermore, the electricity generated by the traditionally arranged power station during the midday period is not fully absorbed, resulting in power loss, impacting economic efficiency, and potentially damaging equipment.

[0123] To intuitively compare the absorption capacity of the optimized power plant with that of a traditional power plant, it is necessary to calculate the absorption ratio of the power generation curve. The formula is as follows:

[0124]

[0125] In the formula: E gen For self-consumption of generated electricity; E grid This refers to the electricity consumption of the load.

[0126] This formula quantifies the self-consumption capacity of a photovoltaic power station, reflecting the absorption effect of different layout schemes. Furthermore, to comprehensively evaluate the matching degree between the power generation curve and electricity demand, this paper also introduces Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) as evaluation indicators. The specific mathematical formulas are as follows:

[0127]

[0128] In the formula: n is the total number of time periods; r max This represents the maximum absorption rate, which is 100%.

[0129] Table 1 Comprehensive Error of Consumption Ratio

[0130]

[0131] As shown in the figure, the power generation curve of the optimized layout matches the standard line better than the traditional layout in the intervals of 7:00 to 10:00 in the morning and 4:00 to 7:00 in the afternoon, showing a better absorption ratio; while in the period of 10:00 to 4:00 in the morning, its absorption ratio is basically maintained near the standard line, and the absorption effect is better than the traditional layout.

[0132] The comprehensive error of the absorption ratio in Table 1 shows that the optimized layout is significantly better than the traditional layout in terms of its conformity with the standard line. Therefore, it can be concluded that the optimized power plant layout can significantly enhance the power plant's absorption capacity.

[0133] To address negative electricity prices, this study analyzes the economics of the power system under such conditions. Negative electricity prices typically occur when supply significantly exceeds demand, causing prices to fall below zero. With the increasing use of renewable energy, the pressure on the power grid to regulate energy is growing, making negative electricity prices more frequent. Therefore, assessing and optimizing the economics under negative electricity price scenarios is crucial for the robust operation and sustainable development of the power system. According to the survey, negative electricity prices often occur between 12 PM and 2 PM, with an average price of approximately €27 per megawatt-hour and a maximum of €130 per megawatt-hour. In this simulation environment, the negative electricity price is set at 0.27 yuan per kilowatt-hour. The electricity purchase price is referenced from the Jiangsu Provincial power grid price: peak consumption periods are 8:00 AM to 12:00 PM and 5:00 PM to 10:00 PM, with a price of 1.227 yuan / kilowatt-hour; the neutral consumption period is 12:00 PM to 5:00 PM, with a price of 0.734 yuan / kilowatt-hour; and the off-peak period is midnight to 8:00 AM, with a price of 0.331 yuan / kilowatt-hour. The grid-connected electricity price is 0.391 yuan / kilowatt-hour during the periods of 10:00 AM to 12:00 PM and 2:00 PM to 4:00 PM. Comparison results were obtained after calculating the electricity purchase and grid-connected prices.

[0134] according to Figure 9 The analysis results show that the optimized power plant design is significantly superior to the traditional design in terms of overall economic benefits. Especially with the increasing prevalence of negative electricity prices, the advantages of the optimized design are even more pronounced, demonstrating its superior performance in improving economic efficiency and responding to market fluctuations.

[0135] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An optimal design method for photovoltaic power plants with optimal power absorption capacity, characterized in that, The steps include the following: (1) Load data is collected through smart meters, the load data is preprocessed, and curve analysis is performed to obtain the load characteristics of the load curve; (2) Under the load curve characteristics obtained in step (1), combined with the output model of the photovoltaic power station, the single component layout algorithm based on the PSO algorithm is used to optimize the design of the photovoltaic power station and obtain the designed component layout. The specific steps of step (2) are as follows: (2-1) The tilt angle and azimuth angle of the component arrangement in the power plant design are used as the solution domain of the PSO algorithm, and the arrangement scheme of the power plant components is represented by the particle position. (2-2) Constructing the objective function of the single-component deployment algorithm based on the PSO algorithm, the objective function of the algorithm is constructed by introducing the output model of the photovoltaic power station and the Pearson correlation coefficient. Its mathematical expression is: In the formula: A and B are empirical coefficients, with A defaulting to 2 and B defaulting to 1; This represents the peak value of the photovoltaic power plant output model. is the peak time of the load curve; r is the Pearson correlation coefficient between the two curves; (2-3) Based on the objective function and the solution domain, construct the fitness function of the PSO algorithm and optimize the PSO algorithm for single component placement. (2-4) Then, the output of the photovoltaic power station is calculated to obtain the hourly power generation data for the whole year. Through aggregation, the hourly change data for one day is obtained and compared with the load curve for analysis. (3) Based on the characteristics of the load curve in step (1), repeat step (2) multiple times and carry out integrated power plant design. With the help of PSO algorithm acceleration, the integrated power plant optimization design is obtained to achieve the absorption target.

2. The optimal photovoltaic power plant design method for optimal power absorption according to claim 1, characterized in that, The specific steps of step (1) are as follows: (1-1) Load data collection: The data is continuously monitored by smart meters over a period of time. The data is recorded in time steps of hours, minutes or seconds to capture fluctuations in load changes. (1-2) Preprocess the load data. The steps include outlier cleaning and missing value imputation. Outlier cleaning requires identifying and removing unreasonable load data. Missing value imputation is to fill in missing data points to ensure their continuity. Interpolation or mean value methods are used for imputation. (1-3) After completing data collection and preprocessing, the basic characteristics of the load curve are extracted. The extracted characteristics include time characteristics and frequency characteristics. The time characteristics include the distribution of peak and trough periods and the specific time and value of their occurrence. Frequency characteristics include the periodic components in the load curve, and the daily, weekly, and monthly variation patterns of the load are extracted. (1-4) Finally, fluctuation analysis is performed. Fluctuation analysis studies the changing trend of the load curve over time. Its main purpose is to analyze the magnitude of changes around the peak and trough. The metric for volatility analysis is the Pearson correlation coefficient, which measures the degree of correlation between two variables. It is denoted by the symbol r, and the specific mathematical model for the Pearson correlation coefficient is as follows: In the formula: Let X be the i-th sample in variable X; The mean of sample X; Let i be the i-th sample in variable Y; This represents the average value of sample Y.

3. The optimal photovoltaic power plant design method for optimal power absorption according to claim 1, characterized in that, The specific steps of step (3) are as follows: (3-1) Based on the characteristics of the load curve, repeat step (2) multiple times to obtain the power plant design scheme for the arrangement of a single component under different load characteristics; (3-2) Define the number of components in the power plant design scheme with single component arrangement under different load characteristics as the solution space, where the particle position represents the number of components in each power plant design. (3-3) Construct the objective function of the integrated power plant optimization algorithm based on the PSO algorithm, and its mathematical expression is: In the formula: p represents time; The maximum value is N; N is the number of characteristic features of the load curve. Let be the power generation capacity of the optimized arrangement at time p; Let the load power be at time p. This represents the number of components required for the q-th optimized layout. (3-4) Based on the power plant design plan of the single component layout under each load characteristic and the corresponding number of components, the power plant is comprehensively designed; this result can achieve the effect of power consumption.

4. A storage medium for optimal photovoltaic power plant design, used to store computer programs, characterized in that, The computer program performs the method as described in any one of claims 1 to 3.

5. An optimal photovoltaic power plant design device for optimal power absorption, comprising a processor and the storage medium as described in claim 4, characterized in that, The processor is used to execute the computer program to perform the method as described in any one of claims 1 to 3.

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