Cooperative balance regulation and control method for power distribution area of distributed power system

Abstract

The invention relates to the technical field of power regulation and control, in particular to a power distribution area cooperative balance regulation and control method for a distributed power system. According to the method, a photovoltaic output curve model and an agricultural area irrigation load curve model of a current agricultural area in a target time period are constructed based on irrigation pump group power consumption data and photovoltaic power generation data in the target time period. And analyzing the correlation between the two curves to obtain a photovoltaic power supply irrigation consumption correlation coefficient. And the power utilization matching condition of the irrigation pump group and the photovoltaic power station is judged according to the photovoltaic power supply irrigation consumption correlation coefficient, and then the increase amount of the power grid voltage is determined based on the power utilization matching condition, so that the effective voltage adjustment amount is obtained, and safe and stable operation of the power grid is ensured. According to the invention, data analysis is carried out based on a historical database, and the accurate adjustment amount is analyzed according to the lower limit exceeding condition of the power grid voltage by analyzing the power utilization matching condition of the photovoltaic power station and the irrigation pump group.

Description

Technical Field

[0001] This invention relates to the field of power regulation technology, and specifically to a method for coordinated balancing regulation of distribution substations in a distributed power system. Background Technology

[0002] The power distribution network is undergoing a transformation from a traditional unidirectional power supply model to a model with a high proportion of distributed energy integration, and the installed capacity of distributed renewable energy is constantly breaking through. Agricultural distribution areas have abundant land resources, and building rooftops, courtyard vacant lots, and collectively owned idle land can all serve as support for grid-connected photovoltaic power generation. The integration of distributed energy, represented by agricultural areas, has led to changes in the energy structure of the power grid system and has also made the operation and maintenance of distribution substations more challenging.

[0003] The load conditions within the power supply area of ​​a substation vary across different seasons. During the large-scale irrigation season, centralized agricultural power consumption can cause grid line overload. In the winter heating season, the peak-to-valley load difference of the grid increases significantly, continuously raising the risk of transformer overload and greatly affecting the safety and stability of the power grid system. Due to the increasing proportion of distributed power sources, the voltage and frequency of some weak grids in some substation areas are extremely unstable. Under conditions of multiple generators operating in parallel, this can severely impact the grid lines, leading to backfeeding and voltage exceeding limits. Therefore, quantitative adjustments to the grid voltage are necessary to address voltage exceeding limits. However, for agricultural substations, due to the special characteristics of electricity consumption generated by agricultural irrigation and the impact of distributed energy on the grid, directly using the difference between real-time voltage and standard voltage for quantitative adjustment without considering these factors would lead to over-adjustment, wasting electrical energy and failing to further ensure grid voltage stability. Summary of the Invention

[0004] To address the inaccurate quantitative adjustment of over-limit accidents in distributed energy grids under agricultural distribution areas using existing technologies, the present invention aims to provide a method for coordinated balancing and control of distribution areas in distributed power systems. The specific technical solution adopted is as follows: This invention proposes a method for coordinated balancing and control of distribution substations in a distributed power system, the method comprising: Obtain electricity consumption data of farmland irrigation pump groups and photovoltaic power generation data of photovoltaic power stations within the target time period from the historical database; Based on the photovoltaic power generation data, according to the position of each moment in the photovoltaic power generation cycle and the maximum photovoltaic output power, a time-related photovoltaic output curve model is constructed based on the Gaussian function; Based on the power consumption data of the farmland irrigation pump group, an irrigation load curve model for the farmland is constructed according to the total operating time of the irrigation pump group in the target time period, the irrigation load power consumed in the target time period, and the maximum power consumption of the current transformer area at each moment. Based on the correlation of the changing trends between the photovoltaic power output curve model and the agricultural irrigation load curve model at the same moment, the correlation coefficient of photovoltaic power consumption for irrigation at each moment is obtained. In real time, if the grid voltage exceeds the lower limit, the matching of power consumption between the irrigation pump group and the photovoltaic power station is determined based on the correlation coefficient of photovoltaic power irrigation absorption, and the increase in grid voltage is determined based on the matching of power consumption; if the grid voltage exceeds the upper limit, the grid voltage is reduced by using photovoltaic power station equipment.

[0005] Furthermore, the photovoltaic output curve model is expressed as follows: ;in, A photovoltaic output curve model related to time t; Let be the photovoltaic conversion simulation coefficient at time t. It is obtained based on the time position of time t within a day; This represents the maximum photovoltaic power output. It is a natural constant. denoted as the time center point of the photovoltaic power output curve, and c as the width of the photovoltaic power output curve; where b and c are obtained by fitting the photovoltaic power generation data using the least squares method.

[0006] Furthermore, the method for obtaining the photovoltaic conversion simulation coefficient includes: For any given moment, the difference between the given moment and the sunrise moment is used as the numerator, and the time range of the daytime of that moment is used as the denominator to obtain the time weight; the product of the time weight and π is used as the independent variable, and the photovoltaic conversion simulation coefficient is obtained through sine function mapping.

[0007] Furthermore, the method for obtaining the irrigation load curve model for agricultural areas includes: Within the target time period, the percentage of the total operating time of the irrigation pump group in a single day is calculated, and the average percentage of the time in each day within the target time period is calculated to obtain the pump group operation weight. The difference between the total grid load power during the irrigation period and the total load power during the non-irrigation period within the target time period is taken as the irrigation load power consumption; the irrigation load power consumption is normalized to obtain the irrigation load power consumption weight. The pump group operation weight, irrigation load power weight, and maximum power consumption at each time point are multiplied to obtain the agricultural irrigation load data point at each time point. Based on the agricultural irrigation load data point, curve fitting is performed to obtain the agricultural irrigation load curve model.

[0008] Furthermore, the method for obtaining the correlation coefficient of photovoltaic power for irrigation includes: For any given moment, if the slopes of the tangents on the photovoltaic power output curve model and the agricultural irrigation load curve model at that moment have the same positive or negative sign, then the difference between the two tangent slopes and the Pearson correlation coefficient between the two curves within the neighborhood of that moment are obtained. The product of the difference in tangent slopes and the Pearson correlation coefficient is taken as the correlation coefficient for photovoltaic power generation for irrigation. If the slopes of the tangents on the photovoltaic power output curve model and the agricultural irrigation load curve model at that moment have different positive or negative signs, then the correlation coefficient for photovoltaic power generation for irrigation at that moment is set to 0.

[0009] Furthermore, the step of determining the power consumption matching between the irrigation pump group and the photovoltaic power station based on the correlation coefficient of photovoltaic power irrigation consumption includes: If the correlation coefficient of photovoltaic power for irrigation is 0, then the power matching situation is judged to be mismatched. If the correlation coefficient of photovoltaic power irrigation is not 0, then the power matching situation is judged to be matched.

[0010] Furthermore, increasing the grid voltage according to the power matching situation includes: If the power supply matching is mismatched, the difference between the preset standard grid voltage and the real-time grid voltage will be used as the increment. If the power supply matching is matched, the product of the operating voltage of the irrigation pump group and the correlation coefficient of the photovoltaic power supply for irrigation is used as the compensation voltage of the photovoltaic power station; the real-time grid voltage is subtracted from the preset standard grid voltage, and then the compensation voltage is subtracted to obtain the increase.

[0011] Furthermore, the method of reducing grid voltage using photovoltaic power station equipment includes: Photovoltaic inverters in photovoltaic power plants absorb reactive power, and electrical energy exceeding the power absorbed by the photovoltaic inverters is converted into other forms of energy.

[0012] Furthermore, the target time period is from May to June each year.

[0013] Furthermore, the upper and lower limits of the standard grid voltage are adjusted according to the preset alarm capacity coefficient to obtain the lower limit voltage threshold and the upper limit voltage threshold; if the grid voltage is less than the lower limit voltage threshold, a lower limit signal is fed back; if the grid voltage is greater than the upper limit voltage threshold, an upper limit signal is fed back.

[0014] The present invention has the following beneficial effects: This invention first constructs a photovoltaic power output curve model and an agricultural irrigation load curve model for the current agricultural area within the target time period, based on statistical data of irrigation pump group electricity consumption and photovoltaic power generation within a historical database. In the photovoltaic power output curve model, considering the influence of daily time variations on photovoltaic power generation, its curve should resemble a Gaussian curve; therefore, a time-dependent photovoltaic power output curve model can be constructed based on a Gaussian function. For the agricultural irrigation load curve model, a time-dependent agricultural irrigation load curve model is constructed by combining the irrigation load electricity consumption characteristics within the target time period and the maximum electricity load power at each moment. Further analysis of the correlation between the two curves reveals that a greater correlation indicates a greater ability of the photovoltaic power station to supplement the current power grid system's irrigation load. Therefore, if the grid experiences a voltage drop below the lower limit, the electricity consumption matching between the irrigation pump group and the photovoltaic power station can be determined based on the correlation coefficient of photovoltaic power generation for irrigation absorption. This allows for the determination of the increase in grid voltage based on the electricity consumption matching, thereby obtaining an effective voltage adjustment amount to ensure the safe and stable operation of the power grid. For voltage drops above the upper limit, the grid voltage can be reduced through energy conversion by the photovoltaic power station equipment, achieving effective control of grid voltage exceedance incidents. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This invention provides a method for coordinated balancing and control of distribution substations in a distributed power system, as an embodiment of the present invention. Figure 2 This is a schematic diagram of a complementary coordination and balance control method for transformer substations provided in an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a distribution substation coordinated balancing control method for a distributed power system according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a distributed power system's distribution area coordinated balance control method.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a method for coordinated balancing and control of distribution substations in a distributed power system, provided by an embodiment of the present invention. The method includes: Step S1: Obtain the electricity consumption data of farmland irrigation pump groups and the photovoltaic power generation data of photovoltaic power stations within the target time period from the historical database.

[0021] This invention first requires using historical data to statistically analyze the photovoltaic power output curve model and the agricultural irrigation load curve model for the current agricultural area within the target time period. This allows for the determination of the power consumption matching between the irrigation pump group and the photovoltaic power station at each moment, and the use of this matching information to determine the necessary actions to resolve over-limit events. Therefore, this invention statistically analyzes the electricity consumption data of the farmland irrigation pump group and the photovoltaic power generation data of the photovoltaic power station within the target time period in a historical database. The irrigation pump group electricity consumption data includes various information such as operating time and operating power; the photovoltaic power generation data includes various information such as the photovoltaic conversion simulation coefficient and maximum output power.

[0022] It should be noted that electricity load varies across different seasons and time periods. For agricultural irrigation areas, crops have different water requirements at different growth stages, and the current electricity demand is closely related to the crop growth status. To meet the water needs of crops in irrigated areas, the number and operating power of water pumps in irrigation areas continuously increase, leading to a surge in electricity consumption. Typically, large-scale irrigation periods occur in May-June and September-October. During these periods, the operation of water pumps in irrigation areas is concentrated, and the concentrated startup of irrigation pump groups can cause voltage drops. Simultaneously, from April to June, due to abundant sunshine, it is the peak period for photovoltaic power generation, with photovoltaic power plants reaching their peak output. At this time, power generation increases significantly, far exceeding the load demand under normal power system operation. The lack of synchronization between power supply and demand leads to an incoordination of the power load in the distribution area. The combined effects of large-scale photovoltaic power generation and the concentrated startup of agricultural irrigation pump groups cause changes in the grid load. Therefore, the target time period set in this embodiment of the invention is the electricity load data of the distribution area during May-June.

[0023] Step S2: Based on the photovoltaic power generation data, according to the position of each moment in the photovoltaic power generation cycle and the maximum photovoltaic output power, construct a time-related photovoltaic output curve model based on a Gaussian function.

[0024] According to existing technology, the power output efficiency of photovoltaic (PV) power generation is directly proportional to the intensity of solar radiation. Throughout the day, solar radiation intensity initially increases and then decreases, following a general trend similar to a Gaussian function, with the width of the Gaussian function varying only in different seasons and months. Therefore, for each moment, a time-dependent PV power output curve model can be constructed based on the Gaussian function, taking into account the moment's position within the PV power generation cycle and the maximum PV power output. That is, the horizontal axis of the PV power output curve model represents time, and the vertical axis represents power output efficiency. If a moment falls at the point of maximum solar radiation intensity, i.e., noon, then the closer the PV power output curve is to the center point of the model, the closer the vertical axis is to the maximum PV power output.

[0025] Preferably, in this embodiment of the invention, the photovoltaic power output curve model is represented as follows: ;in, A photovoltaic output curve model related to time t; Let be the photovoltaic conversion simulation coefficient at time t. It is obtained based on the time position of time t within a day; This represents the maximum photovoltaic power output. It is a natural constant. denoted as the time center point of the photovoltaic power output curve, and c as the width of the photovoltaic power output curve; where b and c are obtained by fitting the photovoltaic power generation data using the least squares method.

[0026] In this curve model, a photovoltaic conversion simulation coefficient is first introduced. This coefficient is related to time t; that is, the closer time t is to the point of high solar radiation intensity within a day (i.e., near noon), the larger the photovoltaic conversion simulation coefficient, and the closer this coefficient is to 1. Meanwhile, the traditional Gaussian function... The closer the value is to 1, i.e., the closer tb is to 0, the closer the overall result of the model is to the maximum photovoltaic power output efficiency within the target time period. By statistically analyzing the actual power generation efficiency at various times, we can obtain the corresponding data points for the agricultural area at the same time over the years in the coordinate system. These data points can then be substituted into the model and fitted using the least squares method to obtain the two unknown parameters b and c, thus obtaining a complete photovoltaic power output curve model.

[0027] It should be noted that the above photovoltaic output curve model can be considered as a model within a day. Therefore, the actual photovoltaic output curve model within the target time period should be the result of continuously stitching together curve models from multiple days in a coordinate system. Furthermore, since photovoltaic equipment does not generate electricity at night, the times analyzed in this embodiment of the invention should all be daytime.

[0028] Furthermore, methods for obtaining the photovoltaic conversion simulation coefficient include: For any given moment, the difference between the given moment and the sunrise moment is used as the numerator, and the time range of the daytime of the given moment is used as the denominator to obtain the time weight; the product of the time weight and π is used as the independent variable, and the photovoltaic conversion simulation coefficient is obtained through sine function mapping.

[0029] In this embodiment of the invention, the denominator of the time weight is the daytime hours, i.e., the total time with solar radiation, which can be obtained by subtracting the sunrise time from the sunset time. The numerator can be considered as using the sunrise time as a reference endpoint, and the position of the time to be analyzed relative to the endpoint is determined by the difference, thus obtaining the time weight. The closer the time weight is to half, i.e., the closer the time to be analyzed is to noon, the larger the photovoltaic conversion simulation coefficient should be. Therefore, this embodiment of the invention utilizes the trend property of the sine function, which first increases and then decreases, to obtain the photovoltaic conversion simulation coefficient, i.e., the value range of the photovoltaic conversion simulation coefficient is between 0 and 1.

[0030] Step S3: Based on the power consumption data of the farmland irrigation pump group, construct the farmland irrigation load curve model according to the total operating time of the irrigation pump group in the target time period, the irrigation load power consumed in the target time period, and the maximum power consumption of the current transformer area at each moment.

[0031] The concentrated startup of irrigation pump groups in agricultural areas can cause a sudden drop in grid voltage load, which can easily lead to overload of some lines and transformers in localized areas of the irrigation area, and excessively low voltage at the end of the lines. In order to effectively analyze the matching between agricultural irrigation load and photovoltaic power station output, it is also necessary to construct a model of the power load characteristics of the irrigation pump groups.

[0032] For irrigation pump groups, the longer they operate within the target time period, the higher their load. Similarly, the greater the combined power consumed by the irrigation pump group within the target time period, the more significant the load consumption should be within that period. Therefore, this load characteristic of the agricultural area within the target time period can be used as a coefficient, and by combining it with the maximum electrical load power at each moment, an irrigation load curve model for the agricultural area can be constructed.

[0033] Preferably, in this embodiment of the invention, the method for obtaining the irrigation load curve model in agricultural areas includes: Within the target time period, the percentage of the total operating time of the irrigation pump group in each day is calculated, and the percentage of each day within the target time period is summed to obtain the pump group operation weight.

[0034] The difference between the total grid load power during the irrigation period and the total load power during the non-irrigation period within the target time period is taken as the irrigation load power consumption; the irrigation load power consumption is normalized to obtain the irrigation load power consumption weight.

[0035] The pump group operation weight, irrigation load power weight, and maximum power consumption at each time point are multiplied to obtain the agricultural irrigation load data point at each time point. Based on the agricultural irrigation load data point, curve fitting is performed to obtain the agricultural irrigation load curve model.

[0036] As a concrete example, the irrigation load curve model for agricultural areas can be expressed by the following formula: ;in, For the photovoltaic output curve model related to time t, The number of natural days for irrigation within the target time period. Let $\frac{k}{k}$ be the runtime of the irrigation pump group during the k-th irrigation natural day. The total length of a day, This represents the total power load of the power grid in the current agricultural distribution area during the irrigation period within the target time period. This refers to the total power load of other electricity used in the agricultural distribution area during non-irrigation periods within the target time period. This can be considered as the power of the irrigation pump group during operation, i.e. This indicates that the greater the weight of the irrigation pump group load in the total load during the target time period, the more accurate the calculation can be achieved through the denominator. Normalization, i.e. Assuming a weighted load for irrigation consumption, further consider the weighted operation of the pump group and the maximum electrical load at time t. Multiplying these values ​​yields an irrigation load curve model for the agricultural area. In this model, two weights are used to assess the importance of irrigation electricity load during the current target time period, and then multiplied by the maximum electricity load to evaluate the irrigation load at each moment.

[0037] Step S4: Based on the correlation of the changing trends between the photovoltaic power output curve model and the agricultural irrigation load curve model at the same moment, obtain the correlation coefficient of photovoltaic power irrigation consumption at each moment.

[0038] In this embodiment of the invention, before executing step S4, considering that the ordinates of the two models have different dimensions, it is necessary to normalize the ordinates of the two models according to the data in their respective dimensions to facilitate subsequent correlation analysis. The specific normalization method can be range standardization, and those skilled in the art can also choose other methods for normalization, which will not be elaborated further.

[0039] Typically, irrigation for farmland is carried out between 5:00-10:00 AM and 5:00-8:00 PM on a natural day. However, during these periods, the intensity of sunlight radiation is relatively weak, and the load on the existing power grid remains relatively high. This can easily lead to voltage exceeding limits, requiring significant adjustments to the grid voltage regulation in such cases. At other times, the photovoltaic power station operates at higher efficiency. If the irrigation pump group generates a large load, causing the grid voltage to exceed limits, the photovoltaic power station can provide supplementary energy, thus requiring only minor adjustments to the grid voltage regulation.

[0040] Therefore, this embodiment of the invention further analyzes the correlation between the photovoltaic power output curve model and the agricultural irrigation load curve model at the same moment to obtain the correlation coefficient of photovoltaic power irrigation absorption at each moment. Under over-limit accidents, the power matching between the photovoltaic power station and the irrigation pump group can be evaluated based on the correlation coefficient of photovoltaic power irrigation absorption. That is, the stronger the correlation, the more the irrigation pump group generates a large load, and the photovoltaic power station can generate more electricity, thus the better the power matching.

[0041] Preferably, in this embodiment of the invention, the method for obtaining the correlation coefficient of photovoltaic power generation for irrigation includes: For any given moment, if the slopes of the tangents on the photovoltaic power output curve model and the agricultural irrigation load curve model at that moment have the same positive or negative sign, then the difference between the two tangent slopes and the Pearson correlation coefficient between the two curves within the neighborhood of that moment are obtained. The product of the difference in tangent slopes and the Pearson correlation coefficient is taken as the correlation coefficient for photovoltaic power generation for irrigation. If the slopes of the tangents on the photovoltaic power output curve model and the agricultural irrigation load curve model at that moment have different positive or negative signs, then the correlation coefficient for photovoltaic power generation for irrigation at that moment is set to 0.

[0042] It should be noted that the difference in tangent slopes is the absolute value of the difference between the two tangent slopes.

[0043] If the slopes of the photovoltaic output curve and the irrigation load power consumption curve are opposite at time point t, it indicates that the photovoltaic output and irrigation load power consumption curves are changing in opposite directions at this point. This means that the distributed photovoltaic power generation and irrigation load power in the distribution area are not compatible at this time, and the distributed photovoltaic power generation and irrigation load power consumption cannot compensate for each other. In this case, the direct source of the irrigation load power is still the existing grid load. Conversely, if the slopes are opposite, it indicates that the distributed photovoltaic output curve and the irrigation load power consumption curve are changing in a more similar manner. In this case, the calculated correlation coefficient for photovoltaic power generation in irrigation should be relatively large, and the load generated by the distributed photovoltaic power generation is used to compensate for the original irrigation load consumption.

[0044] Step S5: For real-time conditions, if the grid voltage exceeds the lower limit, the power matching between the irrigation pump group and the photovoltaic power station is determined based on the correlation coefficient of photovoltaic power irrigation absorption, and the increase in grid voltage is determined based on the power matching. If the grid voltage exceeds the upper limit, the grid voltage is reduced using photovoltaic power station equipment.

[0045] In the above steps, a photovoltaic power output curve model and an agricultural irrigation load curve model have been constructed by combining historical data of agricultural distribution areas, and the correlation coefficients of photovoltaic power irrigation absorption at each time point have been further analyzed. Regarding the voltage limit exceeding problem that occurs in real time, this embodiment of the invention divides the limit exceeding into two types: exceeding the upper limit and exceeding the lower limit. When the grid load is too high at a certain time, such as when a pump cluster starts up in a concentrated manner, the grid voltage will be too low, thus falling below the minimum voltage and causing a lower limit exceeding situation. Conversely, when distributed photovoltaic power is in a period of high photovoltaic power generation, the power generation of distributed photovoltaic power exceeds the load of the distribution area, and the power that cannot be absorbed in time will cause the grid voltage to exceed the maximum voltage, thus causing the grid to exceed the upper limit behavior, resulting in a higher failure rate of the power grid operation. Specifically, the two situations include: (1) For exceeding the lower limit, it means that the grid voltage at real time is less than the lower limit. In order to ensure the stability of the grid, the grid voltage needs to be compensated. Because of the factors of the distributed grid, i.e. the photovoltaic power station, it is necessary to first judge the power matching between the irrigation pump group and the photovoltaic power station based on the correlation coefficient of the photovoltaic power irrigation absorption. If the power matching indicates a match, it means that the output efficiency of the photovoltaic power station is good. At this time, the direct adjustment amount of the grid voltage can be set to a small amount to avoid over-adjustment. Conversely, if the power matching indicates a mismatch, it means that the photovoltaic power station cannot provide compensation to the grid at this time. At this time, the adjustment amount needs to be determined according to the actual degree of exceeding the limit.

[0046] (2) Exceeding the upper limit indicates that the grid voltage at the real time is greater than the upper limit, and the power generation of the photovoltaic power station is strong, resulting in the power generation exceeding the load of the distribution area and power being fed back to the grid. In this case, it is necessary to use the photovoltaic power station equipment to reduce the grid voltage and use energy storage or energy conversion methods to utilize the excess power.

[0047] Preferably, in one embodiment of the present invention, the method for determining the power matching situation based on the photovoltaic power irrigation absorption correlation coefficient obtained in the above steps is as follows: if the photovoltaic power irrigation absorption correlation coefficient is 0, then the power matching situation is determined to be mismatched, at which time the irrigation power demand is higher than the power generated by the distributed photovoltaic power source; if the photovoltaic power irrigation absorption correlation coefficient is not 0, then the power matching situation is determined to be matched, and the photovoltaic power station has the ability to compensate the grid.

[0048] Furthermore, increasing the grid voltage according to the power matching situation includes: If the power supply matching is mismatched, the difference between the preset standard grid voltage and the real-time grid voltage will be used as the increment. If the power supply matching is satisfactory, the product of the operating voltage of the irrigation pump group and the correlation coefficient of the photovoltaic power supply for irrigation is used as the compensation voltage of the photovoltaic power station. The incremental amount is obtained by subtracting the real-time grid voltage from the preset standard grid voltage, and then subtracting the compensation voltage. In this case, the required incremental amount is relatively small, and the photovoltaic power station can be used to handle the load of the irrigation pump group.

[0049] It should be noted that once the increase in grid voltage is determined, the reactive power provided by the photovoltaic inverter can be used as compensation to increase the grid voltage.

[0050] In this embodiment of the invention, under the condition of exceeding the upper limit accident, the irrigation pump group is in a non-operational phase in many places. The photovoltaic power station has a strong power generation capacity, resulting in photovoltaic backfeeding. The power generated by the distributed photovoltaic power source cannot be absorbed. Therefore, the photovoltaic inverter of the photovoltaic power station is used to absorb reactive power, and the electrical energy exceeding the power absorbed by the photovoltaic inverter is converted into other energy sources. In this embodiment of the invention, for the portion exceeding the power absorbed by the photovoltaic inverter, the pump group is used to raise the reservoir water level, converting the electrical energy generated by the distributed photovoltaic power source into the potential energy of the reservoir water level, thereby reducing the grid voltage.

[0051] Please see Figure 2 This illustration shows a schematic diagram of a complementary coordinated balance control method for transformer substations provided in an embodiment of the present invention. Figure 2 The distribution network in the central area is affected by backfeeding from distributed photovoltaic (PV) power sources, as well as voltage drops caused by irrigation loads. During the complementary and coordinated balancing process, PV inverters in the distributed power sources can supplement the irrigation load, and in cases of high PV generation, excess energy can be converted into potential energy through pumped storage.

[0052] It should be noted that the upper limit of the conventional power grid voltage is 242V and the lower limit is 187V. To allow for sufficient margin for voltage monitoring, this embodiment of the invention adjusts the upper and lower limits of the standard power grid voltage according to a preset alarm capacity coefficient to obtain the lower limit voltage threshold and the upper limit voltage threshold. The alarm capacity coefficient set in this invention is 0.1. The upper limit value is increased by 0.1 times to obtain the upper limit voltage threshold; the lower limit value is decreased by 0.1 times to obtain the lower limit voltage threshold. If the power grid voltage is lower than the lower limit voltage threshold, a lower limit signal is fed back; if the power grid voltage is higher than the upper limit voltage threshold, an upper limit signal is fed back.

[0053] In summary, this invention constructs a photovoltaic output curve model and an agricultural irrigation load curve model for the current agricultural area within the target time period, based on historical databases and statistically analyzed irrigation pump group power consumption data and photovoltaic power generation data for the target time period. The correlation between the two curves is analyzed to obtain the photovoltaic power irrigation absorption correlation coefficient. Based on the photovoltaic power irrigation absorption correlation coefficient, the power consumption matching between the irrigation pump group and the photovoltaic power station is determined. Then, based on the power consumption matching, the increase in grid voltage is determined, thereby obtaining an effective voltage adjustment amount to ensure the safe and stable operation of the grid. This invention uses historical databases for data analysis, and by analyzing the power consumption matching between the photovoltaic power station and the irrigation pump group, it accurately determines the adjustment amount when the grid voltage exceeds the lower limit.

[0054] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0055] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for coordinated balancing and control of distribution substations in a distributed power system, characterized in that, The method includes: Obtain electricity consumption data of farmland irrigation pump groups and photovoltaic power generation data of photovoltaic power stations within the target time period from the historical database; Based on the photovoltaic power generation data, according to the position of each moment in the photovoltaic power generation cycle and the maximum photovoltaic output power, a time-related photovoltaic output curve model is constructed based on the Gaussian function; Based on the power consumption data of the farmland irrigation pump group, an irrigation load curve model for the farmland is constructed according to the total operating time of the irrigation pump group in the target time period, the irrigation load power consumed in the target time period, and the maximum power consumption of the current transformer area at each moment. Based on the correlation of the changing trends between the photovoltaic power output curve model and the agricultural irrigation load curve model at the same moment, the correlation coefficient of photovoltaic power consumption for irrigation at each moment is obtained. In real time, if the grid voltage exceeds the lower limit, the matching of power consumption between the irrigation pump group and the photovoltaic power station is determined based on the correlation coefficient of photovoltaic power irrigation absorption, and the increase in grid voltage is determined based on the matching of power consumption; if the grid voltage exceeds the upper limit, the grid voltage is reduced by using photovoltaic power station equipment.

2. The method for coordinated balancing and control of distribution substations in a distributed power system according to claim 1, characterized in that, The photovoltaic output curve model is represented as follows: ;in, A photovoltaic output curve model related to time t; Let be the photovoltaic conversion simulation coefficient at time t. It is obtained based on the time position of time t within a day; This represents the maximum photovoltaic power output. It is a natural constant. denoted as the time center point of the photovoltaic power output curve, and c as the width of the photovoltaic power output curve; where b and c are obtained by fitting the photovoltaic power generation data using the least squares method.

3. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 2, characterized in that, The method for obtaining the photovoltaic conversion simulation coefficient includes: For any given moment, the difference between the given moment and the sunrise moment is used as the numerator, and the time range of the daytime of that moment is used as the denominator to obtain the time weight; the product of the time weight and π is used as the independent variable, and the photovoltaic conversion simulation coefficient is obtained through sine function mapping.

4. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 1, characterized in that, The method for obtaining the irrigation load curve model in the agricultural area includes: Within the target time period, the percentage of the total operating time of the irrigation pump group in a single day is calculated, and the average percentage of the time in each day within the target time period is calculated to obtain the pump group operation weight. The difference between the total grid load power during the irrigation period and the total load power during the non-irrigation period within the target time period is taken as the irrigation load power consumption; the irrigation load power consumption is normalized to obtain the irrigation load power consumption weight. The pump group operation weight, irrigation load power weight, and maximum power consumption at each time point are multiplied to obtain the agricultural irrigation load data point at each time point. Based on the agricultural irrigation load data point, curve fitting is performed to obtain the agricultural irrigation load curve model.

5. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 1, characterized in that, The method for obtaining the correlation coefficient of photovoltaic power for irrigation includes: For any given moment, if the slopes of the tangents on the photovoltaic power output curve model and the agricultural irrigation load curve model at that moment have the same positive or negative sign, then the difference between the two tangent slopes and the Pearson correlation coefficient between the two curves within the neighborhood of that moment are obtained. The product of the difference in tangent slopes and the Pearson correlation coefficient is taken as the correlation coefficient for photovoltaic power generation for irrigation. If the slopes of the tangents on the photovoltaic power output curve model and the agricultural irrigation load curve model at that moment have different positive or negative signs, then the correlation coefficient for photovoltaic power generation for irrigation at that moment is set to 0.

6. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 5, characterized in that, The method of determining the power consumption matching between the irrigation pump group and the photovoltaic power station based on the correlation coefficient of photovoltaic power irrigation consumption includes: If the correlation coefficient of photovoltaic power for irrigation is 0, then the power matching situation is judged to be mismatched. If the correlation coefficient of photovoltaic power irrigation is not 0, then the power matching situation is judged to be matched.

7. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 6, characterized in that, The method for obtaining the increased amount includes: If the power supply matching is mismatched, the difference between the preset standard grid voltage and the real-time grid voltage will be used as the increment. If the power supply matching is matched, the product of the operating voltage of the irrigation pump group and the correlation coefficient of the photovoltaic power supply for irrigation is used as the compensation voltage of the photovoltaic power station; the real-time grid voltage is subtracted from the preset standard grid voltage, and then the compensation voltage is subtracted to obtain the increase.

8. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 1, characterized in that, The method of reducing grid voltage using photovoltaic power station equipment includes: Photovoltaic inverters in photovoltaic power plants absorb reactive power, and electrical energy exceeding the power absorbed by the photovoltaic inverters is converted into other forms of energy.

9. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 1, characterized in that, The target period is from May to June each year.

10. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 1, characterized in that, The upper and lower limits of the standard grid voltage are adjusted according to the preset alarm capacity coefficient to obtain the lower limit voltage threshold and the upper limit voltage threshold. If the grid voltage is lower than the lower limit voltage threshold, a lower limit signal is fed back; if the grid voltage is higher than the upper limit voltage threshold, an upper limit signal is fed back.

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