Method for determining photovoltaic power generation consumption rate

Abstract

The invention relates to the field of photovoltaic power generation, and discloses a photovoltaic power generation consumption rate determination method. The method comprises the following steps: acquiring the monthly average maximum illumination intensity and the monthly daily average sunshine time interval of a target area to construct an illumination intensity function, and constructing a photovoltaic power generation power function based on the illumination intensity function; obtaining a monthly typical workday electrical load curve and a monthly typical rest day electrical load curve to calculate an actual workday electrical load curve and an actual rest day electrical load curve; and calculating the monthly consumption rate based on the photovoltaic power generation power function, the actual working day electrical load curve and the actual rest day electrical load curve. According to the method, the actual workday power consumption load curve and the actual rest day power consumption load curve which accord with the actual situation are obtained through calculation, so that the actual power generation situation of the photovoltaic power generation system and the power consumption situation of the user are reflected more accurately, and the accuracy of consumption proportion calculation can be improved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic power generation, and in particular to a method for determining a photovoltaic power generation consumption rate. Background Art

[0002] With the transformation of the global energy structure and the emphasis on renewable energy, distributed photovoltaic power generation has developed rapidly as a clean and environmentally friendly form of energy. Distributed photovoltaic power generation systems usually adopt the mode of "self-generation for self-use, surplus power to the grid", that is, the power generated by the system first meets its own power demand, and the surplus power is transmitted to the grid. In this mode, the economic benefits of the photovoltaic power generation system depend not only on its power generation, but also on the power price policy. Since it is usually stipulated that photovoltaic power must be connected to the grid at parity and no additional subsidies are provided, when the photovoltaic power generation exceeds the enterprise's usage, the electricity price of this part of "surplus power to the grid" will follow the standard electricity price of coal-fired power generation in each province. In contrast, the electricity price of "self-generation for self-use" by enterprises can be set based on the enterprise's own electricity cost, which is usually much higher than the grid-connected electricity price. Therefore, accurately calculating the ratio of self-generation and self-use to total power generation (i.e., the absorption rate) is crucial to evaluating the economic benefits of distributed photovoltaic power stations.

[0003] However, existing estimates of distributed photovoltaic power generation consumption mostly rely on engineering experience or are based on limited typical data. They cannot reflect the actual operation of the photovoltaic power generation system in real time, and thus cannot obtain an accurate consumption rate, nor can they accurately predict its economic benefits based on the consumption rate. This limits the investment decision-making and operation management of distributed photovoltaic power generation projects. Summary of the invention

[0004] In view of this, in order to solve the technical problem of low accuracy in calculation of the absorption rate in the prior art, the present application provides a method for determining the absorption rate of photovoltaic power generation.

[0005] In a first aspect, the present application provides a method for determining a photovoltaic power generation consumption rate, comprising:

[0006] Obtain the monthly average maximum light intensity and the monthly average daily sunshine time interval of the target area to construct a light intensity function, and construct a photovoltaic power generation function based on the light intensity function;

[0007] Obtaining a typical working day electricity load curve and a typical rest day electricity load curve for a month to calculate an actual working day electricity load curve and an actual rest day electricity load curve;

[0008] The monthly consumption rate is calculated based on the photovoltaic power generation function, the actual working day electricity load curve and the actual rest day electricity load curve.

[0009] In a second aspect, the present application provides a method for determining a comprehensive electricity price, comprising:

[0010] The monthly consumption rate is determined by adopting the above-mentioned method for determining the photovoltaic power generation consumption rate, and the comprehensive electricity price in the target time period is calculated based on the monthly consumption rate.

[0011] In a third aspect, the present application provides a method for scheduling electric equipment, including:

[0012] Obtaining electricity consumption data of each user in the target area, wherein the electricity consumption data includes electricity consumption time, electricity consumption equipment and electricity consumption;

[0013] Analyze the electricity consumption data to determine the types of electricity consumption equipment of each user in different electricity consumption time periods; the types of electricity consumption equipment include dispatchable types and non-dispatched types;

[0014] The above-mentioned method for determining the photovoltaic power consumption rate is used to determine the consumption rates in different time periods. Based on the consumption rates, electricity prices and power generation in different time periods, the operating time of the target type of electrical equipment is adjusted.

[0015] In a fourth aspect, the present application provides a computer device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the method for determining the photovoltaic power generation absorption rate of the first aspect, or the method for determining the comprehensive electricity price of the second aspect, or the method for scheduling power equipment of the third aspect.

[0016] In a fifth aspect, the present application provides a computer storage medium storing a computer program, which, when executed on a processor, implements the method for determining the photovoltaic power generation absorption rate according to the first aspect, or the method for determining the comprehensive electricity price according to the second aspect, or the method for scheduling power equipment according to the third aspect.

[0017] The embodiments of the present application have the following beneficial effects:

[0018] The embodiment of the present application provides a method for determining the photovoltaic power generation absorption rate, the method comprising: obtaining the monthly average maximum light intensity and the monthly average daily sunshine time interval of the target area to construct a light intensity function, and based on the light intensity function, constructing a photovoltaic power generation power function; obtaining the monthly typical working day electricity load curve and the monthly typical rest day electricity load curve to calculate the actual working day electricity load curve and the actual rest day electricity load curve; based on the photovoltaic power generation power function, the actual working day electricity load curve and the actual rest day electricity load curve, calculate the monthly absorption rate. The present application calculates the actual working day electricity load curve and the actual rest day electricity load curve that are more in line with the actual situation, and then calculates the absorption ratio through the actual working day electricity load curve and the actual rest day electricity load curve, so that the calculated absorption ratio can be more in line with the actual situation, thereby improving the calculation accuracy of the absorption ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and should not be regarded as limiting the scope of protection of the present application. For ordinary technicians in this field, other related drawings can also be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic diagram of a process for determining a photovoltaic power generation consumption rate in an embodiment of the present application is shown;

[0021] Figure 2 A fitting schematic diagram of the light intensity function in an embodiment of the present application is shown;

[0022] Figure 3 A second flow chart of the method for determining the photovoltaic power generation consumption rate in the embodiment of the present application is shown;

[0023] Figure 4 A schematic diagram of time interval division for characterizing time periods of different electricity price standards and light time periods in an embodiment of the present application is shown;

[0024] Figure 5 A schematic diagram of fitting the power load curve in the embodiment of the present application is shown;

[0025] Figure 6 A third flow chart of the method for determining the photovoltaic power generation consumption rate in the embodiment of the present application is shown;

[0026] Figure 7 A fourth flow chart of the method for determining the photovoltaic power generation consumption rate in the embodiment of the present application is shown;

[0027] Figure 8 A schematic diagram for indicating the amount of absorption in an embodiment of the present application is shown;

[0028] Fig. 9 A schematic diagram of a process of determining a comprehensive electricity price in an embodiment of the present application is shown;

[0029] Fig.10 A flow chart of a method for scheduling electric equipment in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0031] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0032] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present application, are intended only to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations of the foregoing items.

[0033] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0034] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.

[0035] The following is combined with Figure 1-Figure 10 , some implementation methods of the present application are described in detail. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0036] Absorption rate: the proportion of renewable energy electricity that can actually be absorbed and utilized in the power grid to the total electricity.

[0037] As the global economy continues to grow, electricity demand continues to increase, and the traditional single-grid power supply model is difficult to meet the growing demand. The power system needs to maintain a balance between supply and demand in real time, and the intermittent and volatile nature of photovoltaic power generation has brought new challenges to the power grid. During peak periods of electricity consumption, the power grid needs additional power generation capacity to meet demand, and photovoltaic power generation can provide additional power support during the day. The combined power supply mode of photovoltaic power generation and grid power generation can not only effectively utilize renewable energy and reduce environmental pollution, but also improve the reliability and flexibility of the power system.

[0038] Among them, by calculating the photovoltaic absorption ratio, the grid's absorption capacity for photovoltaic power generation can be effectively evaluated and optimized based on the calculation results. In addition, increasing the photovoltaic absorption ratio will help increase the proportion of clean energy, reduce the use of fossil fuels, thereby reducing greenhouse gas emissions and combating climate change. It can also reasonably plan resources such as energy storage equipment and peak-shaving units to enhance the flexibility and stability of the power system. For investors and power companies, accurately calculating the photovoltaic absorption ratio will help evaluate the economic feasibility of the project and determine the best grid-connected strategy. Therefore, improving the accuracy of the calculation of the photovoltaic absorption ratio is an extremely important issue.

[0039] Based on this, the embodiment of the present application provides a method for determining the photovoltaic power generation absorption rate, by fitting the light intensity function, the monthly typical working day electricity load curve and the monthly typical rest day electricity load curve, so as to obtain the actual working day electricity load curve and the actual rest day electricity load curve that are more in line with the actual situation. The actual working day electricity load curve and the actual rest day electricity load curve can more accurately reflect the actual power generation of the photovoltaic power generation system and the user's electricity consumption. Then, when the absorption ratio is calculated by the actual working day electricity load curve and the actual rest day electricity load curve, the calculated absorption ratio can be made more in line with the actual situation, thereby improving the calculation accuracy of the absorption ratio. Then, the accurate absorption ratio can be used to help evaluate the actual effect of photovoltaic power generation on improving local air quality and reducing pollutant emissions, thereby providing reliable data support for environmental protection; and according to the absorption ratio that fits the actual situation, a more reasonable energy policy can be formulated, such as setting an upper limit on photovoltaic installed capacity, promoting distributed photovoltaic projects and other measures to promote the healthy development of the photovoltaic industry.

[0040] For reference, Figure 1 As shown, the method for determining the photovoltaic power generation absorption rate is illustrated below with an example.

[0041] S110, obtaining the monthly average maximum light intensity and the monthly average daily sunshine time interval of the target area to construct a light intensity function, and constructing a photovoltaic power generation function based on the light intensity function.

[0042] In this embodiment, the target area is the area where the distributed photovoltaic power generation system is located. The distributed photovoltaic power generation system is a small, decentralized photovoltaic power generation facility built near the user's site, with the operation mode of self-generation and self-use on the user side, excess electricity connected to the Internet, and balanced regulation in the distribution system; it is usually installed on the user's roof, parking lot, open space or other available space to meet local power needs or be connected to the power grid. In this embodiment, the distributed photovoltaic power generation system can be set in a site space such as a residential area, that is, the target area can be a residential area or other areas.

[0043] The monthly average maximum light intensity usually refers to the maximum value of the daily light intensity within a month; the monthly average daily sunshine time range refers to the average value of the daily sunshine time within a month.

[0044] Among them, data such as the daylight intensity of the target area, such as the light intensity at each moment, the daily light time, the monthly light time, the monthly average maximum light intensity and the monthly average daily sunshine time interval, can be obtained through meteorological stations, satellite remote sensing or other sensor equipment.

[0045] When constructing the light intensity function, this embodiment selects multiple time points from the monthly average daily sunshine time interval, and determines the light intensity corresponding to each time point. Each time point and its corresponding light intensity are taken as a group of discrete points, thereby obtaining multiple groups of discrete points, and data fitting is performed on the third discrete point of each group to obtain the light intensity function.

[0046] Among them, this embodiment can use any one of the fitting algorithms including polynomial fitting, least squares method, nonlinear fitting, genetic algorithm, etc. to realize data fitting, and the fitting results obtained by using different fitting algorithms are similar. Therefore, the corresponding fitting algorithm can be selected for data fitting according to actual needs, and this embodiment is not limited to this.

[0047] In addition, in this embodiment, each group of third discrete points may be selected according to actual needs, and the selection criteria and the number of selected third discrete points are not specifically limited.

[0048] Exemplarily, this embodiment takes a polynomial fitting algorithm as an example. The polynomial fitting algorithm is a method for finding the most suitable polynomial function based on multiple discrete points. This polynomial function can be used to approximate these data points, so that new data can be predicted or analyzed. The order of the polynomial usually depends on the number of data points and the characteristics of the data.

[0049] like Figure 2 As shown, if the average daily sunshine time interval of the month is [t A ,t B ], and five groups of discrete points are selected according to the average daily sunshine time interval of the month to perform data fitting; for example, the discrete points are set as: (t A ,0), (t B ,0).

[0050] After fitting the data of the five discrete points selected above, the light intensity function (denoted as U t );Among them, the light intensity function represents the correspondence between each time point in the monthly average sunshine time interval and different light intensity values, that is, the light intensity corresponding to any time point in the monthly average sunshine time interval can be determined through the light intensity function.

[0051] In some examples, a polynomial fitting algorithm is used to implement data fitting to construct a polynomial function (i.e., a light intensity function). For the five groups of discrete points mentioned above, a quartic polynomial can be selected for data fitting, because an n-order polynomial requires (n+1) discrete points to be fully determined, so five groups of discrete points are sufficient to determine a quartic polynomial (i.e., n=4). The number of coefficients (i.e., a0, a1, a2, a3, a4) in the polynomial is related to the number of discrete points, i.e., the number of coefficients is less than or equal to the number of discrete points.

[0052] Furthermore, the illumination intensity function (U t ) is:

[0053]

[0054] Where t represents any time point within the monthly average daily sunshine time interval, and coefficients a0, a1, a2, a3, and a4 are all unknowns to be solved.

[0055] Furthermore, by substituting the above five groups of discrete points into the light intensity function in the form of the above polynomial function, the value of each coefficient can be solved to obtain the final light intensity function expression.

[0056] Furthermore, after obtaining the light intensity function, a photovoltaic power generation function can be constructed based on the light intensity function. Specifically, the target information of the photovoltaic panels in the distributed photovoltaic power generation system is first obtained, and then the photovoltaic power generation function (denoted as P) is constructed based on the target information and the light intensity function. t ). Among them, the target information of the photovoltaic panel includes the conversion efficiency of the photovoltaic panel in the distributed photovoltaic power generation system, the effective area of ​​a single photovoltaic panel and the total number of photovoltaic panels.

[0057] In this embodiment, the product of the conversion efficiency of the photovoltaic panel, the effective area of ​​a single photovoltaic panel, the total number of photovoltaic panels and the light intensity can be used as the photovoltaic power generation power corresponding to any time point within the monthly average daily sunshine time interval.

[0058] Based on this, the photovoltaic power generation function (P t ) can be set as:

[0059]

[0060] In the formula, λ represents the conversion efficiency of the photovoltaic panel, A represents the effective area of ​​a single photovoltaic panel, and N a Represents the total number of photovoltaic panels.

[0061] S120, obtaining a monthly typical working day electricity load curve and a monthly typical rest day electricity load curve to calculate an actual working day electricity load curve and an actual rest day electricity load curve.

[0062] In this embodiment, corresponding data points can be selected from the monthly typical working day electricity load curve and the monthly typical rest day electricity load curve to perform corresponding data fitting, so as to obtain the actual working day electricity load curve and the actual rest day electricity load curve that are more in line with the actual situation, and then the monthly consumption rate can be calculated through the actual working day electricity load curve and the actual rest day electricity load curve.

[0063] The typical working day electricity load curve of the month is used to describe the electricity usage of a typical working day in a month, that is, to describe the trend of electricity consumption over time on each working day; further, the typical working day electricity load curve of the month represents the functional correspondence between each time point and the amount of electricity consumption in a working day. The time parameter item in the typical working day electricity load curve of the month represents the number of hours in a working day (for example, from 0:00 am to 24:00 midnight), and the electricity consumption parameter item represents the amount of electricity consumption (which may be kilowatt-hours or other units).

[0064] Similarly, the typical rest day electricity load curve of the month is used to describe the electricity usage on typical rest days (such as weekends or statutory holidays) within a month, that is, to describe the trend of electricity consumption over time during these non-working days. In other words, the typical rest day electricity load curve of the month represents the functional correspondence between each time point and the amount of electricity consumption on a non-working day.

[0065] For reference, in this embodiment, if Figure 3 As shown in Figure 1, the process of calculating the actual working day power load curve includes the following steps:

[0066] S310, obtaining a monthly power grid electricity price, and dividing a day into a plurality of different first time intervals according to time periods corresponding to different peak and valley electricity price standards of the monthly power grid electricity price within a day.

[0067] In this embodiment, the time interval corresponding to a day is [0:00, 24:00], and then the interval [0:00, 24:00] is divided into several different first time intervals according to the peak and valley periods of the monthly grid electricity price; that is, each first time interval is a part of the interval [0:00, 24:00].

[0068] It should be noted that the local power company or relevant policies determine the different electricity price standards corresponding to the peak, flat and valley sections of the power grid. Figure 4 As shown, the interval [t0, t1] is the valley section of electricity price, where the time period corresponding to the valley section is the low electricity consumption period and the electricity price is the lowest; the interval [t1, t2] is the flat section of electricity price, and the electricity price of the flat section is between the peak section and the valley section; the interval [t2, t3] is the sharp section, the interval [t3, t4] is the flat section, and the interval [t4, t5] is the peak section, where the sharp section and the peak section are the peak electricity consumption periods, and the electricity price is the highest compared to other sections, and the electricity price of the sharp section is higher than that of the peak section; the interval [t5, t6] is the flat section.

[0069] In this embodiment, when dividing the time intervals of a day according to the time periods corresponding to the peak and valley electricity price standards in the power grid electricity price within a day, each of the divided first time intervals corresponds to an electricity price standard.

[0070] That is, the first time intervals obtained by division are respectively: [t0, t1], [t1, t2], [t2, t3], [t3, t4], [t4, t5], and [t5, t6].

[0071] S320: Select any one or more data points from a plurality of data points corresponding to the monthly typical working day power load curve in each first time interval as the first discrete point.

[0072] In this embodiment, the electricity load curve of a typical working day of the month represents the functional correspondence between each time point in a working day and the amount of electricity consumption. The electricity load curve of a typical working day of the month can be used to determine the electricity consumption corresponding to each time point in a working day; that is, each data point on the electricity load curve of a typical working day of the month is used to describe any time point and the electricity consumption corresponding to that time point.

[0073] It can be understood that the first discrete point on the monthly typical working day power load curve includes any time point in any first time interval on the monthly typical working day power load curve and the power consumption value corresponding to the time point. Among them, the selection criteria and number of the first discrete points can be set according to actual needs and are not limited here. Figure 5 As shown, a plurality of marked points in any first time interval in the diagram can be regarded as first discrete points.

[0074] S330, performing data fitting on each first discrete point to construct an actual working day electricity load curve.

[0075] In the process of data fitting for each first discrete point, this embodiment makes the integral value of the fitted function in each first time interval equal to the power consumption of the monthly working day in the same first time interval, so that the final fitted function is used as the expression of the actual working day power load curve.

[0076] That is, the actual working day electricity load curve satisfies: the integral value in the first time interval is equal to the corresponding electricity consumption in the same first time interval on the working day.

[0077] As a feasible implementation method, Figure 6 As shown, the process of constructing the actual working day power load curve by performing data fitting on the first discrete point in the above S330 may include the following steps:

[0078] S610, performing data fitting on the first discrete point using a preset data fitting algorithm to obtain a first function.

[0079] S620, respectively calculating a first integral value of the first function in each first time interval, and calculating a first power consumption in each first time interval on a working day of the month.

[0080] S630, determining whether the first integral value corresponding to the same time interval is equal to the first power consumption.

[0081] S640: If the first integral value corresponding to the same time interval is equal to the first power consumption, the first function at this time is used as an expression of the actual working day power load curve.

[0082] S650, if the first integral value corresponding to the same time interval is not equal to the first power consumption, return to execute the aforementioned step of fitting the data of the first discrete point using a preset data fitting algorithm until the first integral value corresponding to the fitted first function is equal to the first power consumption.

[0083] In this embodiment, the preset data fitting algorithm includes any one of the fitting algorithms such as polynomial fitting, least squares method, nonlinear fitting, genetic algorithm, etc., and the fitting results obtained using different fitting algorithms are similar. Therefore, the corresponding fitting algorithm can be selected for data fitting according to actual needs, and this embodiment is not limited to this.

[0084] For example, this embodiment takes the polynomial fitting algorithm as an example. For each of the above first discrete points, a k-order polynomial can be selected for data fitting, where k>1, to obtain the first function (denoted as G t ), wherein the expression of the first function is as follows:

[0085] G i (t) = b0 + b1t + b2t 2 …b k t k ;t i-1 ≤t≤t i ;

[0086] Where b0, b1, b2, ..., b k is the polynomial coefficient to be solved in the first function, i is the i-th first time interval, i≥1.

[0087] Then, in the process of fitting the above-mentioned first function, the values ​​of each first discrete point are substituted into the above-mentioned first function to solve each coefficient, and the first integral value of the solved first function in each first time interval is equal to the first power consumption in the same first time interval on the working day of the month, and when it is determined that the first integral value is equal to the value of the first power consumption, the first function at this time is used as the expression of the actual working day power load curve.

[0088] In some examples, the process of calculating the first power consumption of a monthly working day in each first time interval may be to calculate the total power consumption of the monthly working day based on the monthly typical working day power load curve, and then calculate the average power consumption of the monthly working day based on the total power consumption of the monthly working day. Furthermore, based on the average power consumption of the monthly working day, calculate the first power consumption of the monthly working day in each first time interval.

[0089] In this embodiment, the total power consumption of the monthly working days can be determined by combining the photovoltaic power generation power function.

[0090] Exemplarily, this embodiment first divides the monthly average sunshine time interval into a plurality of different second time intervals according to the time periods corresponding to the peak and valley electricity price standards of the monthly grid electricity price within a day.

[0091] In this embodiment, firstly, the monthly average sunshine time interval [tA ,t B ] is divided into a plurality of different second time intervals. Among them, the second time intervals do not overlap, and the time periods of different peak and valley electricity price standards do not overlap.

[0092] Based on this, Figure 4 As shown, in this embodiment, the monthly average sunshine time interval [t A ,t B ] The multiple second time intervals obtained by dividing are [t A ,t1], [t1,t2], [t2,t3], [t3,t4], [t4,t B ], a total of five second time intervals.

[0093] In this embodiment, the corresponding power generation of the distributed photovoltaic power generation system in each second time interval is calculated according to the photovoltaic power generation power function.

[0094] Then, according to the aforementioned photovoltaic power generation function (P t ) can calculate the corresponding power generation of the distributed photovoltaic power generation system in each second time interval (denoted as S i ), where the calculation expression of the power generation is as follows:

[0095]

[0096] In the formula, i represents the i-th interval in multiple second time intervals, and i>1.

[0097] Similarly, if we calculate the power generation of the first interval (i=1) in multiple second time intervals, that is, calculate [t A ,t1], the corresponding power generation calculation expression is as follows:

[0098]

[0099] If the power generation of the last interval (i=5) in multiple second time intervals is calculated, that is, [t4,t B ], the corresponding power generation calculation expression is as follows:

[0100]

[0101] Furthermore, the total monthly electricity consumption of the target area is obtained, and a set of equations is constructed based on the total monthly electricity consumption, the monthly typical working day electricity load curve, and the power generation in each second time interval as parameter items to solve the total monthly working day electricity consumption.

[0102] It is worth noting that the total electricity consumption ratio of the total electricity consumption on working days of the month to the total electricity consumption on rest days of the month in the target area, as well as the integral ratio of the monthly typical working day electricity load curve within one day to the integral ratio of the monthly typical rest day electricity load curve within one day, these two values ​​(i.e., the total electricity consumption ratio and the integral ratio) are numerically equal.

[0103] It can be understood that this embodiment can construct a set of equations by using the total electricity consumption of the monthly electricity bill, the monthly typical working day electricity load curve, the monthly typical rest day electricity load curve and the power generation in the second time interval as parameter items to solve the total electricity consumption on the monthly working days and the total electricity consumption on the monthly rest days.

[0104] Among them, this embodiment first obtains the monthly typical working day power load curve of the target area (denoted as P w ), monthly typical rest day power load curve (denoted as P r ) and the total electricity consumption on the monthly electricity bill (denoted as E).

[0105] If the total electricity consumption on working days in a month is E w , the total electricity consumption on rest days in a month is E r According to the total power consumption on working days and rest days of the month, the total power consumption on working days of the month (denoted as E w ), the total electricity consumption on rest days of the month (denoted as E r ). The total power consumption of working days in a month is the sum of power consumption of all working days in a month, and the total power consumption of rest days in a month is the sum of power consumption of all rest days in a month. The calculation expressions of the corresponding equations are as follows:

[0106]

[0107] In the formula, T is the actual sampling interval of the power sensor for the electric load, that is, the sampling time interval (ie, sampling period) for sampling the power for the electric load within a day, N is the number of samplings, and i represents the sampling number.

[0108] Furthermore, after the total power consumption of the monthly working days is obtained by solving the above equation group, the average power consumption of the monthly working days can be calculated based on the total power consumption of the monthly working days.

[0109] That is, the average power consumption of monthly working days can be calculated based on the total number of working days in a month and the total power consumption of monthly working days; wherein, the average power consumption of monthly working days=(total power consumption of monthly working days / total number of monthly working days).

[0110] Furthermore, based on the average power consumption on the monthly working days, the first power consumption on the monthly working days in each first time interval is calculated.

[0111] Among them, first calculate the monthly typical working day power load curve Pw The second integral value in each first time interval; then, respectively calculate the ratio between each second integral value and the average power consumption on the monthly working days to obtain the first power consumption ratio on the monthly working days in each first time interval; then, determine the first power consumption on the monthly working days in each first time interval according to the first power consumption ratio and the average power consumption on the monthly working days.

[0112] It should be noted that the first electricity consumption proportion is the proportion of the integral of the monthly typical working day electricity load curve in each first time interval to the corresponding monthly average electricity consumption on working days.

[0113] Furthermore, the average power consumption of monthly working days can be calculated based on the first power consumption proportion, and the first power consumption corresponding to each first time interval within the monthly working day can be calculated based on the average power consumption of the monthly working day.

[0114] Secondly, determine whether the corresponding first integral value and the first power consumption in the same time interval are equal; if the first integral value and the first power consumption are equal, use the first function as the expression of the actual working day power load curve; if the first integral value and the first power consumption are not equal, return to execute the step of performing data fitting on the first discrete point using a preset data fitting algorithm until the first integral value corresponding to the fitted first function is equal to the first power consumption.

[0115] It can be understood that in this embodiment, the monthly typical working day power load curve P w Data fitting is performed at multiple first discrete points in the corresponding first time interval so that the first integral value of the fitted first function in the first time interval is equal to the first power consumption in the same first time interval, and the obtained first function is used as the expression of the actual monthly working day power load curve.

[0116] Among them, in the expression of the first function above, G i The highest order term k of (t) is equal to the sampling point N of the monthly typical working day electricity load curve in the i-th time interval in each first time interval i , where the sampling point N i It can be any time point in the ith time interval; furthermore, the actual working day power load curve (denoted as G t ) is as follows:

[0117]

[0118] In this embodiment, the actual rest day power load curve P r The calculation process is similar to the actual working day power load curve P w The process is similar.

[0119] It can be understood that in this embodiment, the typical rest day power load curve P r Any one or more data points are selected from the multiple data points corresponding to the different first time intervals as the second discrete points, and then the second discrete points in each first time interval are fitted to construct an actual rest day power load curve (denoted as H) that is more in line with the actual situation. t ).

[0120] For example, Figure 7 As shown in Figure 1, the process of calculating the actual rest day power load curve includes the following steps:

[0121] S710, dividing a day into a plurality of different first time intervals according to time periods corresponding to different peak and valley electricity price standards of the monthly power grid electricity price within a day.

[0122] S720: Select any one or more data points from the multiple data points corresponding to the monthly typical rest day power load curve in each first time interval as the second discrete point.

[0123] S730: Perform data fitting on each second discrete point to construct an actual rest day electricity load curve.

[0124] The process of step S710 is consistent with the process of step S610, so it will not be described here in detail.

[0125] In this embodiment, the typical rest day power load curve P r The functional correspondence between each time point and the power consumption on a non-working day (such as a statutory holiday) is characterized. The power consumption corresponding to each time point on a rest day can be determined through the power load curve of a typical rest day of the month; that is, each data point on the power load curve of a typical rest day of the month is used to describe any time point and the power consumption corresponding to that time point.

[0126] It can be understood that the second discrete point on the monthly typical rest day power load curve includes any time point in any first time interval on the monthly typical rest day power load curve and the power consumption value corresponding to the time point. Among them, the selection criteria and number of the second discrete points can be set according to actual needs and are not limited here.

[0127] In the process of data fitting for each second discrete point, this embodiment makes the integral value of the fitted second function in each first time interval equal to the power consumption on the monthly rest day in the same first time interval, so that the last fitted second function is used as the expression of the actual rest day power load curve.

[0128] Similar to the above process of constructing the actual working day electricity load curve, the process of constructing the actual rest day electricity load curve by performing data fitting on the second discrete points can be to use a preset data fitting algorithm to perform data fitting on the second discrete points to obtain a second function.

[0129] Then, the third integral value of the second function in each first time interval is calculated respectively, and the second power consumption in each first time interval on a monthly rest day is calculated.

[0130] In some examples, the process of calculating the second power consumption on the monthly rest day in each first time interval is: first, based on the monthly typical rest day power load curve, calculate the total power consumption on the monthly rest day, then calculate the average power consumption on the monthly rest day based on the total power consumption on the monthly rest day. And then, based on the average power consumption on the monthly rest day, calculate the second power consumption on the monthly rest day in each first time interval.

[0131] Among them, this embodiment can also combine the photovoltaic power generation power function to determine the total power consumption on the rest days of the month.

[0132] Exemplarily, this embodiment first divides the monthly average sunshine time interval into a plurality of different second time intervals according to the time periods corresponding to the peak and valley electricity price standards of the monthly grid electricity price in one day. The second time interval in this step is the [t A ,t1], [t1,t2], [t2,t3], [t3,t4], [t4,t B ], there are five second time intervals in total, so I will not go into details here.

[0133] Then, according to the photovoltaic power generation function, the corresponding power generation of the distributed photovoltaic power generation system in each second time interval is calculated; wherein the power generation in this step (represented as S i ) is the same as the power generation (S i ) is consistent with the expression of , so it will not be repeated here.

[0134] Then, the total electricity consumption of the monthly electricity bill in the target area is obtained, and the total electricity consumption of the monthly electricity bill, the monthly typical rest day electricity load curve, and the power generation in each second time interval are used as parameter items to construct a system of equations to solve the total electricity consumption of the monthly rest days. Among them, the solution process of the total electricity consumption of the rest days of the month is the same as the solution process of the total electricity consumption of the working days of the month, so it will not be repeated here.

[0135] Furthermore, the average power consumption on the monthly rest days is calculated based on the total power consumption on the monthly rest days; and then, based on the average power consumption on the monthly rest days, the second power consumption on the monthly rest days in each first time interval is calculated.

[0136] Among them, the fourth integral value of the monthly typical rest day power load curve in each first time interval is calculated respectively; then, the ratio between each fourth integral value and the average power consumption on the monthly rest day is calculated respectively to obtain the second power consumption proportion on the monthly rest day in each first time interval; then, according to the second power consumption proportion and the average power consumption on the monthly rest day, the second power consumption on the monthly rest day in each first time interval is determined.

[0137] It should be noted that the second electricity consumption proportion is the proportion of the integral of the monthly typical rest day electricity load curve in each first time interval to the corresponding monthly average electricity consumption on rest days.

[0138] Furthermore, the average power consumption on the monthly rest days can be calculated based on the second power consumption proportion, and the second power consumption corresponding to each first time interval on the monthly rest days can be calculated based on the average power consumption on the monthly rest days.

[0139] The calculation process of the second power consumption is the same as the calculation process of the first power consumption, and will not be elaborated here.

[0140] Secondly, determine whether the corresponding third integral value in the same time interval is equal to the second power consumption; if they are equal, use the second function as the expression of the actual rest day power load curve; if they are not equal, return to execute the step of fitting the second discrete points using a preset data fitting algorithm until the third integral value corresponding to the fitted second function is equal to the second power consumption.

[0141] That is, the actual rest day electricity load curve (H t ) satisfies: the third integral value in the first time interval is equal to the second power consumption corresponding to the same first time interval on the rest day.

[0142] It can be understood that in this embodiment, the typical rest day power load curve P r Data fitting is performed at multiple second discrete points within the corresponding first time interval so that the third integral value of the fitted second function within the first time interval is equal to the second power consumption within the same first time interval, and the obtained second function is used as the expression of the actual monthly rest day power load curve.

[0143] Among them, with G i (t) function is similar, H i The highest order term k of (t) is equal to the sampling point N of the typical rest day electricity load curve in the i-th time interval in each first time interval. i ; Furthermore, the actual rest day power load curve (H t ) is as follows:

[0144]

[0145] It is worth noting that the existing technology usually only chooses to calculate the photovoltaic absorption rate through the typical daily load curve or the monthly electricity bill, without combining the change trend of the typical daily load curve with the accurate power consumption of the monthly electricity bill, and thus cannot fully reflect the actual photovoltaic power generation and power consumption, resulting in a large error between the calculation result of the absorption rate and the actual situation. In addition, the existing technology generally averages the power consumption of the monthly electricity bill to obtain the power load curve, but the actual power load curve fluctuates significantly, and thus the power load power curve calculated in the existing technology has a large error with the actual power load power curve.

[0146] The present embodiment selects typical discrete points and uses a data fitting algorithm to establish a light intensity function and a photovoltaic power generation function, thereby being able to quickly and accurately describe the power generation characteristics of the photovoltaic system; then, the typical daily variation trend and the accurate electricity consumption of the monthly electricity bill are combined, and the electricity consumption on working days and rest days is obtained based on the discrete data of the electricity load curves of typical working days and typical rest days. The discrete points of typical working days and typical rest days are used as fitting points, and the actual working day electricity load curve and rest day electricity load curve are obtained through data fitting, so that the actual working day electricity load curve and the actual rest day electricity load curve can more accurately reflect the actual electricity consumption of the photovoltaic power generation system.

[0147] Furthermore, when the absorption ratio (i.e., absorption rate) is calculated by the functional relationship between the actual working day electricity load curve and the actual rest day electricity load curve, the calculation accuracy of the absorption ratio can be improved, thereby improving the accuracy of the electricity price calculated by the absorption ratio, and improving the evaluation accuracy of the absorption situation of distributed photovoltaic power stations through the absorption ratio. In this way, accurate absorption evaluation results can provide a more scientific basis for the planning and operation of photovoltaic power generation projects, help optimize investment decisions and improve the economic efficiency of projects, thereby promoting the popularization and application of distributed photovoltaic power generation technology and supporting the development of renewable energy.

[0148] S130, calculating a monthly consumption rate based on the photovoltaic power generation power function, an actual working day electricity load curve, and an actual rest day electricity load curve.

[0149] In this embodiment, based on the photovoltaic power generation power function and the actual working day electricity load curve, the consumption amount in each second time interval of the monthly working day can be calculated, and then the monthly working day consumption rate can be calculated based on the consumption amount.

[0150] Similarly, based on the photovoltaic power generation power function and the actual rest day electricity load curve, the consumption amount in each second time interval on the monthly rest day can be calculated, and then the monthly rest day consumption rate can be calculated based on the consumption amount.

[0151] Then, combining the consumption rate on working days and rest days of the month, the monthly consumption rate can be calculated.

[0152] In this embodiment, when calculating the monthly working day consumption rate, the monthly average sunshine time interval can be divided into a plurality of different second time intervals according to the time periods corresponding to the peak and valley electricity price standards of the monthly power grid electricity price in one day; each second time interval is as follows: Figure 4 As shown in [t A ,t1], [t1,t2], [t2,t3], [t3,t4], [t4,t B ].

[0153] Then, according to the photovoltaic power generation function, the corresponding power generation of the distributed photovoltaic power generation system in each second time interval is calculated. The expression of the power generation is the S in the above step. i , so I won’t go into details here.

[0154] Next, based on the photovoltaic power generation power function and the actual working day electricity load curve, the first consumption amount in each second time interval on the working day of the month is calculated.

[0155] Among them, the integral value of the minimum value of the photovoltaic power generation function and the actual working day power load curve in the same second time interval can be regarded as the absorption capacity. Figure 8 As shown, the area of ​​the shaded portion in each second time interval in the diagram represents the consumption amount in the corresponding second time interval on the working days of the month.

[0156] Furthermore, if we assume that the consumption of the working day of the jth month in any second time interval is recorded as A i , then the corresponding calculation expression of the absorption amount is as follows:

[0157]

[0158] By analogy, the consumption A1 of the first second time interval of the monthly working day is:

[0159]

[0160] And, the consumption A in the nth second time interval of the working day of the month n That is:

[0161]

[0162] Then, based on the first consumption and power generation corresponding to the monthly working days in each second time interval, the monthly working day consumption rate is calculated; wherein the monthly working day consumption rate is equal to the ratio between the sum of the first consumption in each second time interval of the monthly working days and the sum of the power generation in each second time interval of the monthly working days.

[0163] For example, the consumption rate α on working days in the jth month j The calculation expression is as follows:

[0164]

[0165] Furthermore, this embodiment can also calculate the second consumption amount (denoted as B) in each second time interval on the rest day of the month based on the photovoltaic power generation power function and the actual rest day power load curve. i ); Then, based on the second consumption and power generation corresponding to each second time interval, the monthly rest day consumption rate (denoted as β j ).

[0166] Among them, the calculation process of the second consumption amount and the monthly rest day consumption rate is the same as the calculation process of the first consumption amount and the monthly working day consumption rate mentioned above, so they will not be repeated here.

[0167] For example, the consumption rate of rest days in the jth month is β j The calculation expression is as follows:

[0168]

[0169] In this embodiment, the monthly consumption rate (i.e., the total consumption rate of a single month) can be calculated based on the monthly working day consumption rate and the monthly rest day consumption rate. The product of the monthly working day consumption rate and the number of working days in the month is first calculated, and the product of the monthly rest day consumption rate and the number of rest days in the month is calculated, and then the sum of the two product values ​​is calculated, and the quotient between the sum and the number of days in the month is used as the monthly consumption rate.

[0170] For example, the photovoltaic power generation consumption rate in the jth month (denoted as η j ):

[0171]

[0172] According to the monthly photovoltaic power generation consumption rate, the annual consumption rate (denoted as η) can be calculated:

[0173]

[0174] It is worth noting that the existing technology usually compares the photovoltaic power generation and power consumption within a certain hour to determine the absorption ratio, but the power consumption within the time period is not evenly distributed, and there is an error with the actual situation. In addition, the existing technology often integrates the average load power of the working day of the month and the average load power of the rest day of the month into a power curve by weighted average, and then compares the size with the photovoltaic power generation power function, and calculates the absorption rate by integrating the smaller one. However, the actual situation is that working days and rest days are separated, and the absorption rate obtained in the existing technology is greatly different from the actual situation, and cannot accurately reflect the actual absorption situation.

[0175] Based on this, this embodiment divides the time intervals into different time intervals, and calculates the absorption ratio by comparing the photovoltaic power generation power function and the working day electricity load curve and the rest day electricity load curve in different time intervals, and then obtains the monthly absorption rate by weighted average. Finally, the annual absorption rate is obtained based on the average absorption rate of each month, so that the absorption rate can accurately reflect the actual situation of self-generation and self-use of photovoltaic power generation, so as to improve the accuracy of absorption rate calculation.

[0176] In this embodiment, data fitting is performed by combining the photovoltaic power generation power function, the monthly typical working day electricity load curve, and the monthly typical rest day electricity load curve to obtain the actual working day electricity load curve and the actual rest day electricity load curve that are more in line with the actual situation. Based on the actual working day electricity load curve and the actual rest day electricity load curve, the actual power generation of the photovoltaic power generation system and the user's electricity consumption can be more accurately reflected, thereby improving the accuracy of the calculation of the absorption rate. In addition, an accurate absorption rate can provide a more scientific basis for the planning and operation of photovoltaic power generation projects, help optimize investment decisions and improve the economic efficiency of projects, thereby promoting the popularization and application of distributed photovoltaic power generation technology and supporting the development of renewable energy.

[0177] Furthermore, after calculating the absorption rate, the comprehensive electricity price can be calculated based on the absorption rate, where the comprehensive electricity price is the overall electricity cost calculated by combining multiple factors such as self-generation, purchase of electricity from the power grid and sale of electricity to the power grid when the user uses the distributed photovoltaic power generation system. Then, the economic benefits of the distributed photovoltaic power station can be evaluated based on the absorption rate and the comprehensive electricity price.

[0178] Based on this, Fig. 9 As shown, this embodiment also provides a method for determining a comprehensive electricity price, wherein this embodiment calculates the comprehensive electricity price through the accurate consumption rate obtained in the above embodiment, so as to obtain a relatively accurate electricity price calculation result, thereby effectively evaluating the economic benefits of distributed photovoltaic power stations and improving the accuracy of the evaluation.

[0179] For reference, the comprehensive electricity price determination method includes the following steps:

[0180] S910, adopt the method for determining the photovoltaic power generation absorption rate to determine the monthly absorption rate.

[0181] S920: Calculate the electricity price in the target time period according to the monthly consumption rate.

[0182] It can be understood that in this embodiment, the comprehensive electricity price in different time periods, such as the monthly comprehensive electricity price, the annual comprehensive electricity price, etc., can be calculated by the consumption rate obtained in the aforementioned steps.

[0183] In this embodiment, since the user's electricity is supplied by two power sources, namely the grid power supply and the photovoltaic power supply, when calculating the electricity price, it is necessary to comprehensively calculate the electricity price of the grid power supply and the photovoltaic power supply, and the electricity price can also be calculated separately for working days and rest days.

[0184] Furthermore, by taking a weighted average of the comprehensive electricity prices of monthly working days and monthly rest days, the monthly comprehensive electricity price and the annual comprehensive electricity price can be obtained successively.

[0185] For example, if the target time period is a single month, when calculating the monthly comprehensive electricity price, the consumption rate of the monthly working day in each second time interval is first calculated based on the consumption amount in each second time interval on the monthly working day, and then the monthly working day consumption rate is calculated based on the consumption rate in each second interval; then, the grid power supply prices and photovoltaic power supply prices corresponding to different peak and valley electricity price standards in one day in the monthly grid electricity price of the target area are obtained, so as to calculate the monthly working day comprehensive electricity price based on the grid power supply price, photovoltaic power supply price and the monthly working day consumption rate.

[0186] Then, the consumption rate of the monthly rest day in each second time interval is calculated according to the consumption amount in each second time interval on the monthly rest day, and then the monthly rest day consumption rate is calculated according to the consumption rate in each second interval; then, the power grid power supply prices and photovoltaic power supply prices corresponding to different peak and valley power price standards in one day in the monthly power grid electricity price of the target area are obtained, and the monthly working day comprehensive electricity price is calculated according to the power grid power supply price, photovoltaic power supply price and the monthly rest day consumption rate.

[0187] Among them, this embodiment can determine the monthly consumption rate based on the method for determining the photovoltaic power generation consumption rate in the above embodiment, and the monthly consumption rate includes the monthly working day consumption rate and the monthly rest day consumption rate.

[0188] Among them, the consumption rate of the working day of the jth month in each second time interval (denoted as α ji ) is calculated as follows:

[0189]

[0190] Similarly, the consumption rate of the rest day in the jth month in each second time interval (denoted as β ji ) is calculated as follows:

[0191]

[0192] Furthermore, if the grid power supply price corresponding to different peak and valley power price standards in one day is set as m i (where i represents the i-th interval in multiple second time intervals) and the price of photovoltaic power supply is The monthly comprehensive electricity price for working days and the monthly comprehensive electricity price for rest days can be calculated separately according to the consumption rate in each second time interval.

[0193] For example, the comprehensive electricity price on the working day of the jth month (denoted as m wj ) is calculated as follows:

[0194]

[0195] By analogy, the comprehensive electricity price on the rest day of the jth month (denoted as m rj ) is calculated as follows:

[0196]

[0197] Then, the monthly comprehensive electricity price is calculated by weighted average of the monthly working day comprehensive electricity price and the monthly rest day comprehensive electricity price. Then, the comprehensive electricity price of the jth month (denoted as m j ) is calculated as follows:

[0198]

[0199] Finally, the annual comprehensive electricity price is calculated based on the monthly comprehensive electricity price. The sum of the monthly comprehensive electricity prices for each month in the whole year can be calculated to take the sum as the annual comprehensive electricity price. Therefore, the calculation expression of the annual comprehensive electricity price (denoted as m) is as follows:

[0200]

[0201] In this embodiment, the absorption rate of different peak and valley periods on working days and rest days is determined according to the method of the above embodiment, and the comprehensive electricity price on working days and the comprehensive electricity price on rest days are calculated respectively according to the absorption rate, and then the monthly comprehensive electricity price is obtained by weighted average, and finally the annual comprehensive electricity price can be obtained according to the comprehensive electricity price of each month. Among them, since the absorption rate can accurately reflect the actual situation of self-generation and self-use of photovoltaic power generation, and then the comprehensive electricity price is calculated by the accurate absorption rate, the accuracy of the comprehensive electricity price can be improved, and the economic effect of the photovoltaic power station can be effectively evaluated through the accurate comprehensive electricity price in the future, which provides a scientific basis for the planning and operation of photovoltaic power generation projects, and helps to optimize investment decisions and improve the economic efficiency of projects.

[0202] Furthermore, this embodiment can also analyze the electricity consumption data according to the absorption rate determined in the above-mentioned embodiment, so as to optimize the electricity consumption strategy by adjusting the operation time of the electrical equipment, thereby saving the user's electricity cost.

[0203] Based on this, this embodiment also provides a method for scheduling power consumption equipment. By analyzing the power consumption data and the absorption rate, the power consumption strategy can be optimized to reasonably schedule the power consumption time of the power consumption equipment, thereby optimizing energy utilization efficiency and reducing operating costs, so as to improve the energy utilization rate of the entire photovoltaic power generation system.

[0204] For example, Fig.10 As shown, the electric equipment scheduling method may include the following steps:

[0205] S1010, obtaining power consumption data of each user in the target area, and analyzing the power consumption data to determine the type of power consumption equipment of each user in different power consumption time periods.

[0206] S1020, adopting the method for determining the photovoltaic power generation absorption rate, determine the absorption rate in different time periods, and adjust the operating time of the target type of electrical equipment based on the absorption rate, electricity price and power generation in different time periods.

[0207] Among them, this embodiment can collect users' electricity consumption data through various types of electricity consumption data collection devices such as smart meters installed in the target area; the electricity consumption data includes data such as electricity consumption time, electricity consumption equipment and electricity consumption.

[0208] In one example, the smart meter and other electricity consumption data collection devices can transmit the collected electricity consumption data to a processing end such as a host computer, cloud or server for performing operations such as data storage or processing through communication methods such as the Internet of Things (IoT) or a wired network or a wireless network.

[0209] The processing end receives and stores electricity consumption data transmitted from various smart meters and other electricity consumption data collection devices, and processes it (such as cleaning data, removing abnormal values, missing values ​​and other irregular data, etc., to ensure data quality) and stores it.

[0210] In addition, the electricity consumption data may also be analyzed to determine the type of electricity consumption equipment of each user in different electricity consumption time periods; wherein the type of electricity consumption equipment includes a dispatchable type and a non-dispatched type.

[0211] In this embodiment, the schedulable type of equipment is an electrical equipment whose operating time can be adjusted, such as a washing machine, a dryer, an electric vehicle charger, etc.; the non-schedulable type of equipment is an electrical equipment whose operating time cannot be adjusted, such as a refrigerator, etc.

[0212] The processing end can divide the electricity consumption data into electricity consumption time periods according to the electricity consumption time and electricity consumption in the electricity consumption data. For example, the electricity consumption time period can be divided into peak time, off-peak time and valley time of the day.

[0213] Furthermore, this embodiment can determine the absorption rate, power generation and other parameters of different time periods in a day according to the method for determining the photovoltaic power generation absorption rate of the above embodiment, and can determine the comprehensive electricity price of different time periods in a day according to the method for determining the comprehensive electricity price of the above embodiment. For example, the different time periods can be time periods corresponding to different peak and valley electricity price standards in the monthly power grid electricity price.

[0214] Then, based on the consumption rate, comprehensive electricity price and power generation in different time periods of the day, the operating time of the target type of power equipment is adjusted to save electricity costs. That is, the operating time arrangement of different equipment in a day is optimized to adjust the dispatchable equipment to operate when the grid load is lower, so as to balance the grid load and minimize the electricity cost.

[0215] In one example, this embodiment can set the scheduling priority for each time period within a day according to the consumption rate, comprehensive electricity price and power generation in different time periods; and schedule the target type of power-consuming equipment to operate in the target time period according to the scheduling priority from high to low. Among them, the time period with low consumption rate, low comprehensive electricity price and high power generation is the time period with the highest scheduling priority. That is, this embodiment can preferentially schedule the dispatchable equipment to the time period with low consumption rate, low comprehensive electricity price and high power generation.

[0216] For reference, the scheduling priority of each time period in a day can be set as follows: valley section > flat section > peak section > sharp section; and the scheduling priority of dispatchable equipment is: equipment operating in sharp section > equipment operating in peak section > equipment operating in flat section > equipment operating in valley section.

[0217] It can be understood that the dispatchable equipment operating in the peak period can adjust its operating time preferentially, and the target time period to which it is adjusted is preferably the time period corresponding to the valley period when the electricity price standard is the valley period.

[0218] For example, if a user's washing machine (dispatched equipment) consumes more electricity between 7pm and 9pm, and the comprehensive electricity price and consumption rate data of the target area where the user is located show that the comprehensive electricity price and consumption rate of the time period from 10pm to 2am the next day are lower, then the user can be advised to adjust the operation time of the washing machine to 10pm to 2am the next day to reduce electricity expenses and help balance the load of the power grid.

[0219] In some examples, this embodiment can also execute the above-mentioned power equipment scheduling strategy through an automated control system (such as a smart home system) to automatically adjust the operating time of each power equipment; and the system can also monitor power consumption data in real time to dynamically adjust the operating time of power equipment according to changes in grid conditions to maximize cost savings and improve grid stability.

[0220] This embodiment manages the load of the power system through the determined absorption rate and power consumption data, so as to reasonably schedule the operation time of the power equipment, and then through intelligent scheduling of the power equipment, energy can be stored in advance when the power supply is sufficient, and released during the peak demand period, thereby smoothing the power load curve and improving the energy utilization rate of the entire photovoltaic power generation system. In addition, by reasonably scheduling the working time of each power equipment, it is possible to avoid using high-energy consumption equipment during peak electricity price periods, so that the power supply can be adjusted according to the real-time power demand, which helps to relieve the pressure on the power grid, prevent power outages caused by excessive local loads, improve power supply reliability and stability, and effectively reduce electricity costs. In addition, additional benefits can be obtained by participating in the peak load regulation service of the power grid.

[0221] The embodiment of the present application also provides a device for determining a photovoltaic power generation consumption rate, the device comprising:

[0222] A construction module is used to obtain the monthly average maximum light intensity and the monthly average sunshine time interval of the target area to construct a light intensity function, and to construct a photovoltaic power generation function based on the light intensity function;

[0223] The first calculation module is used to obtain a typical working day electricity load curve and a typical rest day electricity load curve of the month to calculate an actual working day electricity load curve and an actual rest day electricity load curve;

[0224] The second calculation module is used to calculate the monthly consumption rate based on the photovoltaic power generation power function, the actual working day electricity load curve and the actual rest day electricity load curve.

[0225] It can be understood that the device for determining the photovoltaic power generation absorption rate of this embodiment corresponds to the method for determining the photovoltaic power generation absorption rate of the above embodiment, and the optional items in the above embodiment are also applicable to this embodiment, so they will not be repeated here.

[0226] The embodiment of the present application also provides a device for determining a comprehensive electricity price, the device comprising:

[0227] A determination module, used to determine the monthly consumption rate by using a method for determining the photovoltaic power generation consumption rate;

[0228] A calculation module is used to calculate the comprehensive electricity price in the target time period according to the monthly consumption rate.

[0229] It can be understood that the comprehensive electricity price determination device of this embodiment corresponds to the comprehensive electricity price determination method of the above embodiment, and the optional items in the above embodiment are also applicable to this embodiment, so they will not be repeated here.

[0230] The present application also provides a device for scheduling electric equipment, the device comprising:

[0231] The acquisition module is used to acquire the power consumption data of each user in the target area, wherein the power consumption data includes data such as power consumption time, power consumption equipment and power consumption.

[0232] The analysis module is used to analyze the electricity consumption data to determine the type of electricity consumption equipment of each user in different electricity consumption time periods; the electricity consumption equipment types include schedulable types and non-schedulable types.

[0233] The scheduling module is used to adopt the method for determining the photovoltaic power generation absorption rate to determine the absorption rate in different time periods, and adjust the operating time of the target type of power equipment based on the absorption rate, electricity price and power generation in different time periods.

[0234] It can be understood that the power equipment scheduling device of this embodiment corresponds to the power equipment scheduling method of the above embodiment, and the optional items in the above embodiment are also applicable to this embodiment, so they will not be described repeatedly here.

[0235] The present application also provides a computer device. Exemplarily, the computer device includes a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program to enable the computer device to execute the above-mentioned method for determining the photovoltaic power generation absorption rate, or the method for determining the comprehensive electricity price, or the method for scheduling power equipment.

[0236] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU) and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or at least one of other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.

[0237] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory is used to store a computer program, and the processor may execute the computer program accordingly after receiving an execution instruction.

[0238] The present application also provides a computer storage medium for storing the computer program used in the above-mentioned computer device. The computer storage medium may be a readable storage medium, or a non-volatile storage medium or a volatile storage medium. For example, the computer storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0239] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0240] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0241] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.

[0242] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for determining photovoltaic power generation consumption rate, characterized in that: include: Obtain the monthly average maximum light intensity and the monthly average daily sunshine time interval of the target area to construct a light intensity function, and construct a photovoltaic power generation function based on the light intensity function; Obtaining a typical working day electricity load curve and a typical rest day electricity load curve for a month to calculate an actual working day electricity load curve and an actual rest day electricity load curve; The monthly consumption rate is calculated based on the photovoltaic power generation function, the actual working day electricity load curve and the actual rest day electricity load curve.

2. The method for determining the photovoltaic power generation consumption rate according to claim 1, characterized in that: The calculation of the actual working day electricity load curve and the actual rest day electricity load curve includes: Obtaining a monthly power grid electricity price, and dividing a day into a plurality of different first time intervals according to time periods corresponding to different peak and valley electricity price standards of the monthly power grid electricity price within a day; Select any one or more data points from the plurality of data points corresponding to the monthly typical working day electricity load curve in each of the first time intervals as the first discrete points; Performing data fitting on the first discrete points to construct an actual working day electricity load curve; Select any one or more data points from the plurality of data points corresponding to the monthly typical rest day electricity load curve in each of the first time intervals as second discrete points; Data fitting is performed on the second discrete points to construct an actual rest day electricity load curve.

3. The method for determining the photovoltaic power generation consumption rate according to claim 2, characterized in that: The performing data fitting on the first discrete points to construct an actual working day electricity load curve includes: Using a preset data fitting algorithm to perform data fitting on the first discrete points to obtain a first function; respectively calculating a first integral value of the first function in each of the first time intervals, and calculating a first power consumption in each of the first time intervals on a working day of a month; Determining whether the first integral value corresponding to the same time interval is equal to the first power consumption; If yes, then the first function is used as the expression of the actual working day electricity load curve; If not, the process returns to executing the step of performing data fitting on the first discrete points using a preset data fitting algorithm until the first integral value is equal to the first power consumption.

4. The method for determining the photovoltaic power generation consumption rate according to claim 3, characterized in that: The calculating of the first power consumption in each of the first time intervals on a monthly working day includes: Based on the monthly typical working day electricity load curve, calculate the monthly working day total electricity consumption; Calculate the average power consumption on working days of the month based on the total power consumption on working days of the month; Based on the average power consumption on the monthly working days, the first power consumption on the monthly working days in each of the first time intervals is calculated.

5. The method for determining the photovoltaic power generation consumption rate according to claim 4, characterized in that: The calculating of the total power consumption of the working days of the month based on the power load curve of the typical working days of the month includes: Dividing the monthly average sunshine time interval into a plurality of different second time intervals according to time periods corresponding to different peak and valley electricity price standards of the monthly grid electricity price within a day; Calculating the power generation of the distributed photovoltaic power generation system in each of the second time intervals according to the photovoltaic power generation function; The monthly total electricity consumption of the target area is obtained, and a set of equations is constructed based on the monthly total electricity consumption, the monthly typical working day electricity load curve, and the power generation in each of the second time intervals as parameter items to solve the monthly total working day electricity consumption.

6. The method for determining the photovoltaic power generation consumption rate according to claim 4, characterized in that: The calculating, based on the average power consumption of the monthly working days, the first power consumption in each of the first time intervals of the monthly working days includes: Calculating the second integral value of the monthly typical working day power load curve in each of the first time intervals respectively; Calculate the ratio between each of the second integral values ​​and the average power consumption on the monthly working days respectively, and obtain the first power consumption ratio on the monthly working days in each of the first time intervals; The first power consumption of the monthly working day in each of the first time intervals is determined according to the first power consumption proportion and the average power consumption of the monthly working day.

7. The method for determining the photovoltaic power generation consumption rate according to claim 2, characterized in that: The performing data fitting on the second discrete points to construct an actual rest day electricity load curve includes: Using a preset data fitting algorithm to perform data fitting on the second discrete points to obtain a second function; respectively calculating the third integral value of the second function in each of the first time intervals, and calculating the second power consumption in each of the first time intervals on a monthly rest day; Determining whether the third integral value corresponding to the same time interval is equal to the second power consumption; If yes, then the second function is used as the expression of the actual rest day electricity load curve; If not, return to the step of performing data fitting on the second discrete points using a preset data fitting algorithm until the third integral value is equal to the second power consumption.

8. The method for determining the photovoltaic power generation consumption rate according to claim 7, characterized in that: The calculating of the second power consumption in each of the first time intervals on the monthly rest days includes: Calculate the total power consumption on the monthly rest days based on the monthly typical rest day power load curve; Calculate the average power consumption on the rest days of the month based on the total power consumption on the rest days of the month; Based on the average power consumption on the monthly rest days, a second power consumption on the monthly rest days in each of the first time intervals is calculated.

9. The method for determining the photovoltaic power generation consumption rate according to claim 8, characterized in that: The calculating of the total power consumption on the monthly rest days based on the monthly typical rest day power load curve includes: Dividing the monthly average sunshine time interval into a plurality of different second time intervals according to time periods corresponding to different peak and valley electricity price standards of the monthly grid electricity price within a day; Calculating the power generation of the distributed photovoltaic power generation system in each of the second time intervals according to the photovoltaic power generation function; The monthly total electricity consumption of the target area is obtained, and a set of equations is constructed based on the monthly total electricity consumption, the monthly typical rest day electricity load curve, and the power generation in each of the second time intervals as parameter items to solve the monthly total rest day electricity consumption.

10. The method for determining the photovoltaic power generation consumption rate according to claim 8, characterized in that: The calculating, based on the average power consumption on the monthly rest days, the second power consumption on the monthly rest days in each of the first time intervals, comprises: Calculating respectively the fourth integral value of the monthly typical rest day power load curve in each of the first time intervals; Calculate the ratio between each of the fourth integral values ​​and the average power consumption on the monthly rest days respectively, and obtain the second power consumption ratio on the monthly rest days in each of the first time intervals; The second power consumption on the monthly rest day in each of the first time intervals is determined according to the second power consumption proportion and the average power consumption on the monthly rest day.

11. The method for determining the photovoltaic power generation consumption rate according to claim 1, characterized in that: The calculating of the monthly consumption rate based on the photovoltaic power generation function, the actual working day electricity load curve and the actual rest day electricity load curve comprises: Dividing the monthly average sunshine time interval into a plurality of different second time intervals according to time periods corresponding to different peak and valley electricity price standards of the monthly grid electricity price within a day; Calculating the power generation of the distributed photovoltaic power generation system in each of the second time intervals according to the photovoltaic power generation function; Based on the photovoltaic power generation function and the actual working day electricity load curve, calculate the first consumption amount in each second time interval on the working day of the month; Based on the photovoltaic power generation function and the actual rest day electricity load curve, calculate the second consumption amount in each second time interval on the rest day of the month; Based on the first consumption amount and the power generation amount corresponding to each of the second time intervals, a monthly working day consumption rate is calculated; Based on the second consumption amount and the power generation amount corresponding to each of the second time intervals, a consumption rate on a rest day of a month is calculated; The monthly consumption rate is calculated based on the monthly working day consumption rate and the monthly rest day consumption rate.

12. The method for determining the photovoltaic power generation consumption rate according to claim 1, characterized in that: The step of obtaining the monthly average maximum light intensity and the monthly average sunshine time interval of the target area to construct a light intensity function includes: Taking a plurality of time points from the monthly average daily sunshine time interval, and determining the light intensity corresponding to each of the time points; Taking each of the time points and the corresponding light intensity as a group of third discrete points, respectively, to obtain multiple groups of third discrete points; Data fitting is performed on each group of the third discrete points to obtain a light intensity function.

13. The method for determining the photovoltaic power generation consumption rate according to claim 1, characterized in that: The step of constructing a photovoltaic power generation function according to the light intensity function comprises: The target information of photovoltaic panels in the distributed photovoltaic power generation system is obtained, and a photovoltaic power generation function is constructed according to the target information and the light intensity function; the target information includes the conversion efficiency of the photovoltaic panels, the effective area of ​​a single photovoltaic panel and the total number of photovoltaic panels.

14. The method for determining the photovoltaic power generation consumption rate according to claim 2, 3 or 7, characterized in that: In the process of data fitting, a preset fitting algorithm is used for data fitting, wherein the preset fitting algorithm includes any one of fitting algorithms such as polynomial fitting, least squares method, nonlinear fitting, genetic algorithm, etc.

15. A method for determining a comprehensive electricity price, characterized in that: include: The method for determining the photovoltaic power generation absorption rate as described in any one of claims 1 to 14 is used to determine the monthly absorption rate, and the comprehensive electricity price in the target time period is calculated based on the monthly absorption rate.

16. The comprehensive electricity price determination method according to claim 15, characterized in that: If the target time period is a single month, the calculation of the comprehensive electricity price for the target time period includes: Obtaining the grid power supply price and the photovoltaic power supply price; wherein the grid power supply price includes the grid power supply price corresponding to different peak and valley power price standards in a day in the monthly grid power price; calculating the monthly working day comprehensive power price based on the monthly working day consumption rate in the monthly consumption rate, the grid power supply price and the photovoltaic power supply price; calculating the monthly rest day comprehensive power price based on the monthly rest day consumption rate in the monthly consumption rate, the grid power supply price and the photovoltaic power supply price; calculating the monthly comprehensive power price based on the monthly working day comprehensive power price and the monthly rest day comprehensive power price; or, If the target time period is the whole year, the calculation of the comprehensive electricity price of the target time period includes: The sum of the monthly comprehensive electricity prices for each month throughout the year is calculated to obtain the annual comprehensive electricity price.

17. A method for dispatching electric equipment, characterized in that: include: Obtaining electricity consumption data of each user in the target area, wherein the electricity consumption data includes electricity consumption time, electricity consumption equipment and electricity consumption; Analyze the power consumption data to determine the types of power consumption equipment of each user in different power consumption time periods; the types of power consumption equipment include dispatchable types and non-dispatched types; The method for determining the photovoltaic power generation absorption rate as described in any one of claims 1 to 14 is used to determine the absorption rates in different time periods, and the operating time of the target type of electrical equipment is adjusted based on the absorption rates, electricity prices and power generation in different time periods.

18. The method for dispatching electric equipment according to claim 17, characterized in that: The operation time of the target type of electrical equipment is adjusted based on the consumption rate, electricity price and power generation in different time periods, including: According to the consumption rate, electricity price and power generation in different time periods, set the dispatch priority for each time period; The target type of electrical equipment is scheduled to operate in the target time period according to the scheduling priority from high to low, wherein the time period with low consumption rate, low electricity price and high power generation is the time period with the highest scheduling priority.

19. A computer device, characterized in that: The computer device comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the method according to any one of claims 1 to 18.

20. A computer storage medium, characterized in that The device stores a computer program, which, when executed on a processor, implements the method according to any one of claims 1 to 18.

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