Power distribution substation collaborative balance regulation method for distributed power system

Abstract

The present application relates to the technical field of power regulation, in particular to a power distribution area collaborative balance regulation method for a distributed power system. The method is based on the statistical irrigation pump group power consumption data and photovoltaic power generation data in a target time period to construct a photovoltaic output curve model and an agricultural area irrigation load curve model in the target time period of the current agricultural area. The correlation between the two curves is analyzed to obtain the photovoltaic power source irrigation consumption correlation coefficient. According to the photovoltaic power source irrigation consumption correlation coefficient, the power consumption matching condition of the irrigation pump group and the photovoltaic power station is determined, and then the voltage increase amount of the power grid is determined based on the power consumption matching condition, so as to obtain an effective voltage adjustment amount and ensure the safe and stable operation of the power grid. The present application analyzes the data based on the historical database, analyzes the power consumption matching condition of the photovoltaic power station and the irrigation pump group, and analyzes the accurate adjustment amount for the power grid voltage lower limit condition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power regulation, in particular to a power distribution substation collaborative balance regulation method for a distributed power system. BACKGROUND

[0002] The power distribution network is in the process of changing from the traditional unidirectional power supply mode to the high proportion of distributed energy access mode, and the installed capacity of distributed new energy is constantly breaking through. Agricultural substation has abundant land resources, building roofs, courtyard empty land and idle land can be used as the support of photovoltaic power generation of the power grid. The access of distributed energy represented by agricultural areas has changed the energy structure of the power grid system, and also makes the operation and regulation of the power distribution substation more difficult.

[0003] The load conditions in the power supply range of the substation transformer in different seasons are different. In the large-area irrigation season, concentrated agricultural electricity consumption will cause the overload of the power grid line, and in the winter heating period, the peak-valley difference of the power grid load increases significantly, the overload risk of the substation transformer load is constantly rising, and the safety and stability of the power grid system are greatly affected. Due to the increasing proportion of distributed power access, the voltage and frequency of the weak power grid in some substations are extremely unstable, and under the condition of multi-machine parallel connection, it will seriously affect the power grid line, cause the reverse sending of the power grid, and cause voltage out-of-limit accidents. Therefore, for voltage out-of-limit accidents, the power grid voltage needs to be quantitatively adjusted, but for agricultural substations, due to the special type of electricity consumption characteristics generated by agricultural irrigation and the influence of distributed energy on the power grid, if these factors are not considered and the difference between the real-time voltage and the standard voltage is directly used for quantitative adjustment, it will lead to over-adjustment, waste of power energy and inability to further ensure the stability of the power grid voltage. SUMMARY

[0004] In order to solve the technical problem of inaccurate quantitative adjustment of the out-of-limit accident of the distributed energy power grid in the agricultural substation in the prior art, the purpose of the present application is to provide a power distribution substation collaborative balance regulation method for a distributed power system, and the technical solution adopted is as follows:

[0005] The present application provides a power distribution substation collaborative balance regulation method for a distributed power system, which comprises:

[0006] In the historical database, the agricultural field irrigation pump group electricity consumption data and the photovoltaic power generation data of the photovoltaic power station in the target time period are obtained;

[0007] Based on the photovoltaic power generation data, according to the position of each time in the photovoltaic power generation cycle and the maximum output power of photovoltaic, a time-dependent photovoltaic output curve model is constructed based on the Gaussian function;

[0008] Based on the farmland irrigation pump group electricity data, according to the total irrigation pump group operation time length in the target time period, the irrigation consumption load power in the target time period, and the maximum electricity load power of the current area at each time, an agricultural area irrigation load curve model is constructed;

[0009] According to the correlation between the change trends of the photovoltaic output curve model and the agricultural area irrigation load curve model at the same time, the photovoltaic power irrigation consumption correlation coefficient at each time is obtained.

[0010] For the real-time time, if the grid generates a voltage lower limit, then according to the photovoltaic power irrigation consumption correlation coefficient, the electricity matching situation of the irrigation pump group and the photovoltaic power station is judged, and the increase amount of the grid voltage is determined according to the electricity matching situation; if the grid generates a voltage upper limit, then the photovoltaic power station equipment is used to reduce the grid voltage.

[0011] Further, the photovoltaic output curve model is represented as:

[0012] ; wherein, is the photovoltaic output curve model related to time t; is the photovoltaic conversion simulation coefficient of time t, is obtained according to the time position of time t in a day; is the maximum photovoltaic output power; is a natural constant, is the photovoltaic output curve time center point, and c is the photovoltaic output curve width; wherein b and c are obtained by fitting the photovoltaic power generation data using the least squares method.

[0013] Further, the method for obtaining the photovoltaic conversion simulation coefficient comprises:

[0014] For any time, the difference between the time and the sunrise time is taken as the numerator, and the time range of the day of the time is taken as the denominator to obtain the time weight; the product of the time weight and π is taken as the independent variable, and the photovoltaic conversion simulation coefficient is obtained by mapping through the sine function.

[0015] Further, the method for obtaining the agricultural area irrigation load curve model comprises:

[0016] In each day of the target time period, the time proportion of the total irrigation pump group operation time in a day is counted, and the time proportion of each day in the target time period is averaged to obtain the pump group operation weight;

[0017] The difference between the total load power of the grid in the irrigation time period and the total load power in the non-irrigation time period in the target time period is taken as the irrigation consumption load power; the irrigation consumption load power is normalized to obtain the irrigation consumption load power weight;

[0018] The pump group operation weight, the irrigation consumption load power weight, and the maximum power load at each time are multiplied to obtain an agricultural area irrigation load data point at each time, and a curve fitting is performed according to the agricultural area irrigation load data point to obtain the agricultural area irrigation load curve model.

[0019] Further, the method for obtaining the photovoltaic power irrigation consumption correlation coefficient comprises:

[0020] For any time, if the tangent slopes of the photovoltaic output curve model and the agricultural area irrigation load curve model at the time are of the same positive or negative sign, the difference between the two tangent slopes and the Pearson correlation coefficient between the two curves in the neighborhood range of the time are obtained, and the product of the difference between the tangent slopes and the Pearson correlation coefficient is taken as the photovoltaic power irrigation consumption correlation coefficient; if the tangent slopes of the photovoltaic output curve model and the agricultural area irrigation load curve model at the time are of different positive or negative signs, the photovoltaic power irrigation consumption correlation coefficient at the time is set to 0.

[0021] Further, the method for judging the power matching condition of the irrigation pump group and the photovoltaic power station according to the photovoltaic power irrigation consumption correlation coefficient comprises:

[0022] If the photovoltaic power irrigation consumption correlation coefficient is 0, it is judged that the power matching condition is not matched;

[0023] If the photovoltaic power irrigation consumption correlation coefficient is not 0, it is judged that the power matching condition is matched.

[0024] Further, the method for increasing the grid voltage according to the power matching condition comprises:

[0025] If the power matching condition is not matched, the difference between the preset standard grid voltage and the grid voltage at the real time is taken as the increase amount;

[0026] If the power matching condition is matched, the product of the irrigation pump group operation voltage and the photovoltaic power irrigation consumption correlation coefficient is taken as the compensation voltage of the photovoltaic power station; the preset standard grid voltage is subtracted from the grid voltage at the real time, and then the compensation voltage is subtracted to obtain the increase amount.

[0027] Further, the method for reducing the grid voltage by using the photovoltaic power station equipment comprises:

[0028] The photovoltaic inverter of the photovoltaic power station absorbs the reactive power, and converts the electric energy exceeding the absorption power of the photovoltaic inverter to other energy.

[0029] Further, the target time period is from May to June of each year.

[0030] Further, the upper limit value and the lower limit value of the standard power grid voltage are adjusted according to a preset alarm capacity coefficient to obtain an over-lower-limit voltage threshold and an over-upper-limit voltage threshold; if the power grid voltage is less than the over-lower-limit voltage threshold, an over-lower-limit signal is fed back; and if the power grid voltage is greater than the over-upper-limit voltage threshold, an over-upper-limit signal is fed back.

[0031] The present application has the following advantages:

[0032] Firstly, in the historical database, based on the statistical irrigation pump group power consumption data and photovoltaic power generation data in the target time period, the photovoltaic output curve model and the agricultural area irrigation load curve model in the target time period of the current agricultural station area are constructed. In the photovoltaic output curve model, considering that photovoltaic power generation is affected by the time change in a day, its curve should be similar to the Gaussian curve, so a time-related photovoltaic output curve model can be constructed based on the Gaussian function. For the agricultural area irrigation load curve model, combined with the irrigation load power consumption characteristics in the target time period and the maximum power consumption load at each time, a time-related agricultural area irrigation load curve model is also constructed. Further analysis of the correlation between the two curves shows that the greater the correlation, the greater the ability of the photovoltaic power station to supplement the irrigation load of the current power grid system, so if the power grid generates an over-lower-limit voltage, the matching of the irrigation pump group and the photovoltaic power station can be judged according to the photovoltaic power consumption matching coefficient, and then the increase in the power grid voltage is determined based on the power consumption matching, so as to obtain an effective voltage adjustment amount, ensuring the safe and stable operation of the power grid. For the over-upper-limit voltage, the photovoltaic power station equipment can reduce the power grid voltage through energy conversion and other ways to effectively regulate and control the over-limit accident of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0034] Figure 1 A power distribution station area collaborative balance regulation and control method for a distributed power system is provided for an embodiment of the present application.

[0035] Figure 2 A station area complementary collaborative balance regulation and control method schematic diagram is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined inventive purpose, the specific implementation, structure, features and effects of a power distribution area collaborative balance regulation method for a distributed power system according to the present application are described in detail as follows in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0037] 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 the present application belongs.

[0038] The specific scheme of the power distribution area collaborative balance regulation method for a distributed power system provided by the present application is specifically described below in combination with the drawings.

[0039] Please refer to Figure 1 , which shows a flow chart of a power distribution area collaborative balance regulation method for a distributed power system provided by an embodiment of the present application. The method comprises:

[0040] Step S1: In the historical database, obtain the farmland irrigation pump group power consumption data and the photovoltaic power generation data of the photovoltaic power station in the target time period.

[0041] The embodiment of the present application first needs to statistically obtain the photovoltaic output curve model and the farmland irrigation load curve model of the current agricultural area in the target time period by using the historical data, and then determine the power consumption matching condition of the irrigation pump group and the photovoltaic power station at each time, and determine what operation is needed to solve the overrun event by using the power consumption matching condition. Therefore, the embodiment of the present application statistically obtains the farmland irrigation pump group power consumption data and the photovoltaic power generation data of the photovoltaic power station in the target time period in the historical database. The farmland irrigation pump group power consumption data includes running time, running power and other information; the photovoltaic power generation data includes photovoltaic conversion simulation coefficient, maximum output power and other information.

[0042] It should be noted that the power load in different seasonal time periods will change, and for agricultural irrigation areas, the water requirement of crops in different growth stages will be different, and the power demand in the current time period will be closely related to the growth state of crops. In order to meet the water requirement of crops in the irrigation area, the number and operating power of the water pump in the irrigation area are increasing, and the power consumption will also increase sharply. Generally, according to the time dimension, the large-area irrigation period generally occurs in May-June and September-October, and in this period, the water pump in the irrigation area is in operation, and the centralized start of the irrigation pump group will cause voltage sag. At the same time, from April to June, due to sufficient sunlight, it is the main period of photovoltaic generation, and the power generation of the photovoltaic power station reaches the peak, and the power generation increases significantly, far exceeding the load demand in the normal operation state of the power system. The power supply end and the demand end do not grow synchronously, resulting in the uncoordinated power load of the transformer area. The centralized start of the photovoltaic generation and the irrigation pump group in the agricultural area will cause the change of the power grid load, so the target time period set in the embodiment of the present application is the power load data of the transformer area in May-June.

[0043] Step S2: Based on the photovoltaic power generation data, according to the position of each time in the photovoltaic power generation period and the maximum photovoltaic output power, a time-dependent photovoltaic output curve model is constructed based on the Gaussian function.

[0044] According to the prior art, the output efficiency of photovoltaic power generation is proportional to the solar radiation intensity, and the solar radiation intensity in a day should first increase and then decrease, and the overall trend is similar to the trend of the Gaussian function, and the width of the Gaussian function is different in different seasons. Therefore, for each time, a time-dependent photovoltaic output curve model can be constructed based on the Gaussian function according to the position of the time in the photovoltaic power generation period and the maximum photovoltaic output power. That is, the abscissa of the photovoltaic output curve model is time, and the ordinate is output efficiency. If a certain time is at the time of maximum solar radiation intensity, that is, at noon, then in the photovoltaic output curve model, the closer to the center point of the model, the closer to the maximum photovoltaic output power.

[0045] Preferably, in the embodiment of the present application, the photovoltaic output curve model is represented as:

[0046] ; wherein, is the photovoltaic output curve model related to time t; is the photovoltaic conversion simulation coefficient of time t, is obtained according to the time position of time t in a day; is the maximum photovoltaic output power; is a natural constant, b is a time center of the photovoltaic output curve, and c is a width of the photovoltaic output curve; wherein b and c are obtained by fitting photovoltaic power generation data by using a least square method.

[0047] In the curve model, a photovoltaic conversion simulation coefficient is first introduced, which is related to time t, that is, the closer the time t is to the position of high solar radiation intensity within a day, that is, the closer it is to the noon stage, the larger the photovoltaic conversion simulation coefficient is, and the closer the coefficient is to 1, and the closer the traditional Gaussian function The closer t-b is to 0, the closer the overall result of the model is to the maximum photovoltaic output efficiency in the target time period. By statistically analyzing the actual power generation efficiency at each time, the corresponding data points of the agricultural station area at the same time in the past years can be obtained in the coordinate system, and then substituted into the model to fit by using the least square method, so that the two unknown parameters b and c can be obtained, and the complete photovoltaic output curve model can be obtained.

[0048] It should be noted that the above photovoltaic output curve model can be regarded as a model within a day, and the actual photovoltaic output curve model in the target time period should be the result of continuously splicing the curve models of multiple days in the coordinate system. And because photovoltaic equipment does not generate power at night, the time analyzed by the embodiment of the present application should be during the day.

[0049] Further, the method for obtaining the photovoltaic conversion simulation coefficient comprises:

[0050] For any time, the difference between the time and the sunrise time is taken as the numerator, and the time range of the day of the time is taken as the denominator to obtain a time weight; the product of the time weight and π is taken as the independent variable, and the photovoltaic conversion simulation coefficient is obtained by mapping through a sine function.

[0051] In the embodiment of the present application, the denominator of the time weight is the daylight time within a day, that is, the total time of the existence of solar radiation, which can be obtained by subtracting the sunrise time from the sunset time. The numerator can be regarded as determining the position of the time to be analyzed relative to the endpoint through the difference form by taking the sunrise time as the reference endpoint, and then obtaining the time weight. The closer the time to be analyzed is to the noon time, the closer the time weight is to one-half, and the larger the photovoltaic conversion simulation coefficient should be. Therefore, the embodiment of the present application maps the photovoltaic conversion simulation coefficient by using the trend property of the sine function that first increases and then decreases, that is, the value range of the photovoltaic conversion simulation coefficient is between 0 and 1.

[0052] Step S3: based on the power consumption data of the farmland irrigation pump group, constructing a farmland irrigation load curve model according to the total running time of the irrigation pump group in the target time period, the irrigation consumption load power in the target time period, and the maximum power consumption load power of the current station area at each time.

[0053] The centralized starting of the irrigation pump group in the agricultural area can cause the voltage load of the power grid to suddenly drop, which can easily cause the overload of some lines and transformers in the local area of the irrigation substation and the excessively low voltage at the end of the line. In order to effectively analyze the matching between the irrigation load in the agricultural area and the output of the photovoltaic power station, the load characteristics of the irrigation pump group also need to be modeled.

[0054] For the irrigation pump group, the longer the running time in the target time period, the higher the load. Similarly, the greater the composite power consumed by the irrigation pump group in the target time period also indicates that there should be more obvious load consumption in the target time period. Therefore, the load characteristics of the agricultural area in the target time period can be used as a coefficient to further combine the maximum power consumption at each time to construct the irrigation load curve model of the agricultural area.

[0055] Preferably, in the embodiment of the present application, the method for obtaining the irrigation load curve model of the agricultural area comprises:

[0056] In each day of the target time period, the time proportion of the total running time of the irrigation pump group in a day is counted, the time proportions of each day in the target time period are accumulated, and the pump group running weight is obtained.

[0057] The difference between the total load power of the power grid in the irrigation time period and the total load power in the non-irrigation time period in the target time period is taken as the irrigation consumption load power, and the irrigation consumption load power is normalized to obtain the irrigation consumption load power weight.

[0058] The pump group running weight, the irrigation consumption load power weight, and the maximum power consumption at each time are multiplied to obtain the agricultural irrigation load data point at each time, and the agricultural irrigation load curve model is obtained by curve fitting according to the agricultural irrigation load data point.

[0059] As a specific example, the irrigation load curve model of the agricultural area is expressed by the formula:

[0060] ; wherein, is the photovoltaic output curve model related to time t, is the number of irrigation natural days in the target time period, is the running time of the irrigation pump group in the kth irrigation natural day, is the total length of a day, is the total load power of the power grid in the irrigation time period of the current agricultural substation in the target time period, is the total load power of other power consumption in the non-irrigation time period of the current agricultural substation in the target time period, and can be regarded as the power of the running of the irrigation pump group, that is, The greater the weight of the irrigation pump group load in the target time period accounts for all loads, so the denominator is further divided by normalization, that is the weight of the irrigation consumption load power, further combined with the pump group operation weight and the maximum power consumption at time t multiplication, to obtain the agricultural irrigation load curve model. In this model, the importance of the irrigation power consumption load at the current target time period is evaluated by two weights, and then multiplied by the maximum power consumption to evaluate the irrigation load at each time.

[0061] Step S4: According to the correlation between the change trend of the photovoltaic output curve model and the agricultural irrigation load curve model at the same time, obtain the photovoltaic power irrigation consumption correlation coefficient at each time.

[0062] In the embodiment of the present application, before step S4 is performed, considering that the ordinate dimensions of the two models are different, the ordinate of the two models needs to be normalized according to the data in the respective dimensions for subsequent correlation analysis. The specific normalization method can use range standardization, and those skilled in the art can also use other methods for normalization, which will not be described in detail.

[0063] Generally, the farmland is irrigated from 5:00 to 10:00 in the morning and from 17:00 to 20:00 in the evening within a natural day, but the light radiation intensity in this time period is relatively weak, and the load bearing pressure of the original power distribution network is still relatively large, at this time, voltage out-of-limit adverse effects are prone to occur, and the voltage regulation of the grid voltage accident will be regulated by a large amplitude. In other time periods, the output efficiency of the photovoltaic power station is high, and if the irrigation pump group generates a large load, the grid voltage will be out of limit, at this time, the photovoltaic power station has the function of energy compensation, so the regulation of the grid voltage only needs small amplitude regulation.

[0064] Therefore, the embodiment of the present application further analyzes the correlation between the change trend of the photovoltaic output curve model and the agricultural irrigation load curve model at the same time, to obtain the photovoltaic power irrigation consumption correlation coefficient at each time. In the out-of-limit accident, the photovoltaic power irrigation consumption correlation coefficient can be used to evaluate the power matching between the photovoltaic power station and the irrigation pump group, that is, the stronger the correlation, the more power the photovoltaic power station can generate when the irrigation pump group generates a large load, and the more matched the power matching is.

[0065] Preferably, in the embodiment of the present application, the method for obtaining the photovoltaic power irrigation consumption correlation coefficient comprises:

[0066] For any moment, if the tangent slope of the moment on the photovoltaic output curve model and the agricultural irrigation load curve model is the same positive or negative sign, the difference of the two tangent slopes is obtained, and the Pearson correlation coefficient between the two curves in the neighborhood of the moment is obtained, and the product of the difference of the tangent slope and the Pearson correlation coefficient is taken as the photovoltaic power irrigation consumption correlation coefficient; if the tangent slope of the moment on the photovoltaic output curve model and the agricultural irrigation load curve model is not the same positive or negative sign, the photovoltaic power irrigation consumption correlation coefficient at the moment is set to 0.

[0067] It should be noted that the difference of the tangent slope is the absolute value of the difference of the two tangent slopes.

[0068] The slope signs of the photovoltaic output curve and the irrigation consumption load power curve at the time point t are opposite, which indicates that the photovoltaic output condition and the change state of the irrigation consumption load power curve are opposite at this time, and the distributed photovoltaic power and the irrigation load power condition are opposite at this time. The distributed photovoltaic power and the irrigation load consumption power cannot be compensated and consumed at this time. At this time, the direct source of the irrigation load power is still the original power grid load; otherwise, it indicates that the change of the distributed photovoltaic output curve and the irrigation load consumption power curve is relatively close, and the value of the photovoltaic power irrigation consumption correlation coefficient calculated at this time should be relatively large. At this time, the load generated by the distributed photovoltaic power is used to compensate the original irrigation load consumption.

[0069] Step S5: For the real-time moment, if the voltage exceeds the lower limit, the use of the photovoltaic power irrigation consumption correlation coefficient is used to judge the use matching of the irrigation pump group and the photovoltaic power station, and the increase amount of the grid voltage is determined according to the use matching; if the voltage exceeds the upper limit, the photovoltaic power station equipment is used to reduce the grid voltage.

[0070] In the above steps, the photovoltaic output curve model and the agricultural irrigation load curve model have been constructed by combining the historical data of the agricultural station area, and the photovoltaic power irrigation consumption correlation coefficient corresponding to each moment is further analyzed. For the voltage out-of-limit problem of the real-time moment, the out-of-limit is divided into two kinds of over-limit and under-limit, when the grid operating load is too high at a certain moment, such as the start of the pump group, it will cause the grid voltage to be too low, thereby causing the under-limit of the minimum voltage. On the contrary, when the distributed photovoltaic power is in the photovoltaic high production period, the power generation of the distributed photovoltaic power exceeds the station area load, and the power that cannot be consumed in time will cause the grid to exceed the maximum voltage, and further cause the over-limit of the grid, so that the power grid operation failure rate is high. For the two cases, specifically including:

[0071] (1) For the lower limit, it indicates that the real-time grid voltage is less than the lower limit, and compensation needs to be made to the grid voltage to ensure the stability of the grid. Because of the storage of the distributed grid, i.e. the photovoltaic power station, first, the irrigation pump group and the photovoltaic power station need to be matched according to the irrigation consumption correlation coefficient of the photovoltaic power supply. If the matching condition is represented, it indicates that the output efficiency of the photovoltaic power station is good, and the direct adjustment amount of the grid voltage can be set to a small amount to avoid excessive adjustment. Conversely, if the matching condition is not represented, it indicates that the photovoltaic power station cannot provide compensation to the grid at this time, and the adjustment amount needs to be determined according to the actual limit.

[0072] (2) For the upper limit, it indicates that the real-time grid voltage is greater than the upper limit, and the power generation of the photovoltaic power station is strong, which leads to the power generation exceeding the load of the transformer area, and the power is sent back to the grid. At this time, the photovoltaic power station equipment needs to be used to reduce the grid voltage, and methods such as energy storage or energy conversion are used to utilize the excess power.

[0073] Preferably, in an embodiment of the present application, based on the photovoltaic power supply irrigation consumption correlation coefficient obtained by the above steps, the method for judging the power matching condition is: if the photovoltaic power supply irrigation consumption correlation coefficient is 0, it is judged that the power matching condition is not matched, and at this time the irrigation demand power is higher than the distributed photovoltaic power generation power; if the photovoltaic power supply irrigation consumption correlation coefficient is not 0, it is judged that the power matching condition is matched, and the photovoltaic power station has the ability to compensate the grid.

[0074] Further, the increasing of the grid voltage according to the power matching condition comprises:

[0075] If the power matching condition is not matched, the difference between the preset standard grid voltage and the real-time grid voltage is taken as the increasing amount;

[0076] If the power matching condition is matched, the product of the irrigation pump group operating voltage and the photovoltaic power supply irrigation consumption correlation coefficient is taken as the compensation voltage of the photovoltaic power station; the preset standard grid voltage is subtracted from the real-time grid voltage, and then the compensation voltage is subtracted to obtain the increasing amount. That is, the required increasing amount is small in this case, and the photovoltaic power station can be used to bear the load of the irrigation pump group.

[0077] It should be noted that after the grid voltage increasing amount is determined, the photovoltaic inverter can be used to provide reactive power as compensation to improve the grid voltage.

[0078] In the embodiment of the present application, in the over-limit accident, the irrigation pump group is in a non-operation stage in many places, the photovoltaic power station has strong power generation capacity, the photovoltaic reverse sending phenomenon occurs, and the power generated by the distributed photovoltaic power source cannot be absorbed, so the photovoltaic inverter of the photovoltaic power station is used to absorb the reactive power, and the power exceeding the absorption power of the photovoltaic inverter is converted to other energy. In the embodiment of the present application, the part of the power exceeding the absorption power of the photovoltaic inverter is used to raise the water level of the pump group, the power generated by the distributed photovoltaic power source is converted into the water level potential energy of the reservoir, so as to reduce the grid voltage.

[0079] Please refer to Figure 2 which shows a kind of table area complementary coordination balance regulation and control method schematic diagram provided by an embodiment of the present application.In Figure 2 The table area distribution network can be affected by the photovoltaic reverse sending of distributed power source, and also can be affected by the voltage reduction caused by irrigation load. In the complementary coordination balance process, the photovoltaic inverter in the distributed power source can supply energy to the irrigation load, and in the case of large photovoltaic, the excess energy can be converted into water level potential energy by pumped storage.

[0080] It should be noted that the upper limit of the conventional grid voltage is 242V, and the lower limit is 187V. In order to leave a margin for voltage monitoring, the upper limit value and the lower limit value of the standard grid voltage are adjusted according to the preset alarm capacity coefficient in the embodiment of the present application, to obtain the over-limit voltage threshold and the over-limit voltage threshold. The alarm capacity coefficient set by the present application is 0.1, and the upper limit value is increased by 0.1 times as the over-limit voltage threshold. The lower limit value is reduced by 0.1 times as the over-limit voltage threshold. If the grid voltage is less than the over-limit voltage threshold, the over-limit signal is fed back. If the grid voltage is greater than the over-limit voltage threshold, the over-limit signal is fed back.

[0081] In summary, in the historical database, based on the irrigation pump group power consumption data and photovoltaic power generation data in the target time period, the photovoltaic output curve model and the agricultural area irrigation load curve model in the target time period of the current agricultural table area are constructed. The correlation between the two curves is analyzed to obtain the photovoltaic power source irrigation consumption correlation coefficient. According to the photovoltaic power source irrigation consumption correlation coefficient, the power consumption matching of the irrigation pump group and the photovoltaic power station is determined, and then the increase amount of the grid voltage is determined based on the power consumption matching, so as to obtain the effective voltage adjustment amount, and ensure the safe and stable operation of the grid. The present application analyzes the power consumption matching of the photovoltaic power station and the irrigation pump group based on the historical database, and analyzes the accurate adjustment amount for the over-limit of the grid voltage.

[0082] It is to be noted that the sequential order of the above-described embodiments of the present application only for the purpose of description, but not the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0083] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference 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 electricity 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 during the target time period, the irrigation load power consumed during the target time period, and the maximum electricity load power 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. For real-time situations, 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. Methods for obtaining irrigation load curve models in agricultural areas include: 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 consumption load power weight, and maximum power consumption at each time are multiplied to obtain the agricultural irrigation load data point at each time. Based on the agricultural irrigation load data point, curve fitting is performed to obtain the agricultural irrigation load curve model. 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.

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 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.

5. A method for coordinated balancing and control of distribution substations in a distributed power system according to claim 4, 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.

6. 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.

7. 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.

8. 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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