Generating capacity intelligent regulation and control system

Abstract

The invention provides a generating capacity intelligent regulation and control system, and relates to the technical field of photovoltaic power generation systems, and the system comprises a data collection module which is used for data collection; the power consumption data analysis module is used for determining a power consumption quantitative relation coefficient corresponding to the random power consumption influence factor; the disposable electric quantity prediction module is used for predicting a disposable electric quantity value of the next regulation and control period; the influence factor statistics module is used for performing statistics on all fixed power consumption influence factors and random power consumption influence factors of the next regulation and control period of the power consumption area and the action duration of the fixed power consumption influence factors and the random power consumption influence factors; the electricity consumption prediction module is used for calculating the electricity consumption of the electricity consumption area in the next regulation and control period; and the generating capacity regulation and control module is used for regulating and controlling the generating capacity of the photovoltaic system. By predicting the electricity consumption and the generating capacity and adjusting the generating capacity in time, the stable power grid voltage and frequency are ensured, the generating capacity meets the requirement in the peak period of electricity consumption, the generating capacity is reduced in time in the valley period of electricity consumption, electricity waste is avoided, and balance of electricity supply and demand is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation systems, and in particular relates to an intelligent power generation control system. Background Art

[0002] A photovoltaic power generation system refers to a power generation system that uses the photovoltaic effect of photovoltaic cells to directly convert solar radiation energy into electrical energy. Traditional photovoltaic power generation systems do not have the function of intelligently regulating power generation. During peak power consumption periods, if power generation is insufficient to meet demand, it may lead to power shortages and affect users' normal power consumption. During low power consumption periods, power generation may exceed demand, resulting in power waste and an imbalance between power supply and demand. Summary of the invention

[0003] The present invention provides an intelligent power generation control system to solve at least one of the above-mentioned technical problems.

[0004] In order to solve the above technical problems, the present invention discloses an intelligent power generation control system, comprising:

[0005] The data collection module is used to collect the historical annual power generation data of the photovoltaic system, the historical annual power consumption data of all power consumption areas, and the power storage data of the photovoltaic energy storage system, and form the historical power consumption change curve of all power consumption areas based on the historical annual power consumption data of all power consumption areas;

[0006] The power consumption data analysis module is used to determine the historical power consumption change curve corresponding to each fixed power consumption influencing factor, the historical power consumption change curve corresponding to all random power consumption influencing factors, and the quantitative relationship between each random power consumption influencing factor and power consumption based on the historical power consumption change curves of all power consumption areas. The quantitative relationship between each random power consumption influencing factor and power consumption is the power consumption quantitative relationship coefficient corresponding to the random power consumption influencing factor;

[0007] The available power forecasting module is used to construct a photovoltaic system power generation forecasting matrix based on the historical annual power generation data of the photovoltaic system, to construct a photovoltaic energy storage system storage capacity forecasting matrix based on the power storage data of the photovoltaic energy storage system, and to obtain an available power forecasting matrix based on the photovoltaic system power generation forecasting matrix and the photovoltaic energy storage system storage capacity forecasting matrix, and to predict the available power value of the next regulation cycle based on the available power forecasting matrix;

[0008] The influencing factor statistics module is used to count all the fixed power consumption influencing factors and random power consumption influencing factors of the power consumption area in the next regulation cycle and their effect duration;

[0009] The power consumption prediction module is used to calculate the power consumption of the power consumption area in the next regulation cycle based on all fixed power consumption influencing factors and random power consumption influencing factors in the next regulation cycle and their action duration, the historical power consumption change curve corresponding to each fixed power consumption influencing factor and the power consumption quantitative relationship coefficient corresponding to each random power consumption influencing factor;

[0010] The power generation control module is used to control the power generation of the photovoltaic system based on the available power value of the next control cycle and the power consumption of the power consumption area in the next control cycle.

[0011] Preferably, the electricity consumption data analysis module includes:

[0012] A first data determination submodule is used to determine all fixed power consumption influencing factors of all power consumption areas, and based on all fixed power consumption influencing factors of all power consumption areas and the historical power consumption change curves of all power consumption areas, obtain the historical power consumption change curve corresponding to each fixed power consumption influencing factor;

[0013] The second data determination submodule is used to determine the historical power consumption change curves corresponding to all random power consumption influencing factors based on the historical power consumption change curves of all power consumption areas and the historical power consumption change curves corresponding to each fixed power consumption influencing factor;

[0014] The quantitative analysis submodule is used to determine all random power consumption influencing factors and their effective time periods in all power consumption areas, and match all random power consumption influencing factors with the historical power consumption change curves corresponding to all random power consumption influencing factors based on the time series, and determine the quantitative relationship between each random power consumption influencing factor and power consumption.

[0015] Preferably, the quantitative analysis submodule includes:

[0016] A random power consumption influencing factor determination unit, used to determine all random power consumption influencing factors and their effective time periods in all power consumption areas;

[0017] A time series matching unit, used for matching all random power consumption influencing factors with historical power consumption change curves corresponding to all random power consumption influencing factors based on the time series;

[0018] The quantitative relationship determination unit is used to find out any time period in which each random electricity consumption influencing factor acts alone in the historical electricity consumption change curve corresponding to all random electricity consumption influencing factors, and to count the change in the vertical coordinate in the historical electricity consumption change curve corresponding to all random electricity consumption influencing factors within the time period in which each random electricity consumption influencing factor acts alone, and use the quotient of the change in the vertical coordinate and the change in the corresponding horizontal coordinate as the electricity consumption quantitative relationship coefficient corresponding to the random electricity consumption influencing factor.

[0019] Preferably, the available power forecasting module includes:

[0020] The prediction matrix construction submodule 1 is used to construct a photovoltaic system power generation prediction matrix based on the power generation of the photovoltaic system in each control cycle in the historical annual power generation data of the photovoltaic system;

[0021] The second prediction matrix construction submodule is used to construct a photovoltaic energy storage system power storage prediction matrix based on the power storage data of the photovoltaic energy storage system in each control cycle;

[0022] The prediction matrix construction sub-module three is used to obtain the available power prediction matrix based on the photovoltaic system power generation prediction matrix and the photovoltaic energy storage system storage power prediction matrix, and predict the available power value of the next control cycle based on the available power prediction matrix.

[0023] Preferably, based on the power generation of the photovoltaic system in each regulation cycle in the historical annual power generation data of the photovoltaic system, constructing the photovoltaic system power generation prediction matrix includes:

[0024] The power generation of the photovoltaic system in the i-th regulation cycle and each regulation cycle before the i-th regulation cycle in the historical annual power generation data of the photovoltaic system is arranged in time series to form a power generation series of the photovoltaic system in the regulation cycle based on the time series;

[0025] Based on the power generation series of the photovoltaic system during the regulation period, the current photovoltaic system power generation prediction matrix after the end of the i-th regulation period is constructed:

[0026] Among them, A i is the prediction matrix of the current photovoltaic system power generation after the end of the i-th regulation cycle, a1 is the power generation of the photovoltaic system in the first regulation cycle, a2 is the power generation of the photovoltaic system in the second regulation cycle, a3 is the power generation of the photovoltaic system in the third regulation cycle, a4 is the power generation of the photovoltaic system in the fourth regulation cycle, and a i-1 is the power generation of the photovoltaic system in the i-1th regulation cycle, a i is the power generation of the photovoltaic system in the i-th regulation cycle.

[0027] Preferably, based on the power storage data of the photovoltaic energy storage system, the power storage prediction matrix of the photovoltaic energy storage system is constructed, which includes:

[0028] Arrange the power storage data of the photovoltaic energy storage system in the i-th regulation cycle and each regulation cycle before the i-th regulation cycle in a time series to form a time series of the power storage data of the photovoltaic energy storage system;

[0029] Based on the storage capacity series of the photovoltaic energy storage system during the regulation period, the current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation period is constructed:

[0030] Among them, B i is the storage capacity prediction matrix of the current photovoltaic energy storage system after the end of the i-th regulation cycle, b1 is the storage capacity of the photovoltaic energy storage system in the first regulation cycle, b2 is the storage capacity of the photovoltaic energy storage system in the second regulation cycle, b3 is the storage capacity of the photovoltaic energy storage system in the third regulation cycle, b4 is the storage capacity of the photovoltaic energy storage system in the fourth regulation cycle, and b i-1 is the storage capacity of the photovoltaic energy storage system in the i-1th regulation cycle, b i is the storage capacity of the photovoltaic energy storage system in the i-th regulation cycle.

[0031] Preferably, obtaining an available power forecast matrix based on the photovoltaic system power generation forecast matrix and the photovoltaic energy storage system power storage forecast matrix, and predicting the available power value of the next regulation cycle based on the available power forecast matrix includes:

[0032] Based on the current photovoltaic system power generation prediction matrix after the end of the i-th regulation cycle and the current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation cycle, the current available power prediction matrix after the end of the i-th regulation cycle is constructed:

[0033] C i =A i +B i (3); where C i is the current available power forecast matrix after the end of the i-th regulation cycle, A i is the current photovoltaic system power generation prediction matrix after the end of the i-th regulation cycle, B i The current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation cycle;

[0034] The mean of all matrix elements in any row except the first row in the current available power forecast matrix after the end of the i-th regulation cycle is obtained, and the value of any column in the current available power forecast matrix after the end of the i-th regulation cycle is set to a value equal to the mean of all matrix elements in any row except the first row, thereby obtaining a new matrix, and the rank of the new matrix is ​​regarded as the available power value of the i+1-th regulation cycle.

[0035] Preferably, based on all fixed power consumption influencing factors and random power consumption influencing factors of the next regulation cycle and their action duration, the historical power consumption change curve corresponding to each fixed power consumption influencing factor and the power consumption quantitative relationship coefficient corresponding to each random power consumption influencing factor, the power consumption of the power consumption area in the next regulation cycle is calculated:

[0036] Among them, u ij It represents the change value of electricity consumption in unit time when the jth fixed electricity consumption influencing factor acts based on the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, It represents the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, U j is the ordinate of the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, that is, the historical electricity consumption corresponding to the jth fixed electricity consumption influencing factor, is the horizontal coordinate of the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, that is, the moment when the jth fixed electricity consumption influencing factor takes effect;

[0037] Among them, Q i+1 is the power consumption of the power consumption area in the i+1th regulation cycle, n is the number of types of all fixed power consumption influencing factors in the i+1th regulation cycle, is the duration of action of the jth fixed power consumption influencing factor in the i+1th regulation cycle, m is the number of types of all random power consumption influencing factors in the i+1th regulation cycle, V k represents the quantitative relationship coefficient of power consumption corresponding to the kth random power consumption influencing factor, t V(i+1)k It is the duration of action of the kth random power consumption influencing factor in the i+1th regulation cycle.

[0038] Preferably, the power generation control module includes:

[0039] A difference determination unit, used to calculate the difference between the available power value in the next regulation cycle and the power consumption of the power consumption area in the next regulation cycle;

[0040] A level evaluation unit, used to calculate the response coefficient of the photovoltaic system based on the value of the available power in the next regulation cycle and the difference between the power consumption of the power consumption area in the next regulation cycle;

[0041] The photovoltaic system control unit is used to regulate the power generation of the photovoltaic system based on the response coefficient of the photovoltaic system.

[0042] Preferably, the response coefficient of the photovoltaic system is calculated based on the difference between the available power value in the next regulation cycle and the power consumption in the power consumption area in the next regulation cycle:

[0043] Where, is the response coefficient of the photovoltaic system, P i+1 is the available power value of the i+1th regulation cycle, Q i+1is the electricity consumption of the electricity consumption area in the i+1th regulation cycle, e is a natural number, and its value is 2.71.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention accurately predicts electricity consumption and power generation, and timely adjusts power generation to ensure stable grid voltage and frequency, ensuring that power generation meets demand during peak power consumption periods and that power generation is timely reduced during low power consumption periods to avoid power waste and ensure a balance between power supply and demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 This is a schematic diagram of an intelligent power generation control system of the present invention. DETAILED DESCRIPTION

[0048] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0049] In addition, in the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes, and do not specifically refer to the order or sequence, nor are they used to limit the present invention. They are only used to distinguish components or operations described with the same technical terms, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions and technical features between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0050] The present invention provides the following embodiments

[0051] Example 1

[0052] The embodiment of the present invention provides a power generation intelligent control system, such as Figure 1 As shown, including:

[0053] The data collection module is used to collect the historical annual power generation data of the photovoltaic system, the historical annual power consumption data of all power consumption areas, and the power storage data of the photovoltaic energy storage system, and form the historical power consumption change curve of all power consumption areas based on the historical annual power consumption data of all power consumption areas;

[0054] The power consumption data analysis module is used to determine the historical power consumption change curve corresponding to each fixed power consumption influencing factor, the historical power consumption change curve corresponding to all random power consumption influencing factors, and the quantitative relationship between each random power consumption influencing factor and power consumption based on the historical power consumption change curves of all power consumption areas. The quantitative relationship between each random power consumption influencing factor and power consumption is the power consumption quantitative relationship coefficient corresponding to the random power consumption influencing factor;

[0055] The available power forecasting module is used to construct a photovoltaic system power generation forecasting matrix based on the historical annual power generation data of the photovoltaic system, to construct a photovoltaic energy storage system storage capacity forecasting matrix based on the power storage data of the photovoltaic energy storage system, and to obtain an available power forecasting matrix based on the photovoltaic system power generation forecasting matrix and the photovoltaic energy storage system storage capacity forecasting matrix, and to predict the available power value of the next regulation cycle based on the available power forecasting matrix;

[0056] The influencing factor statistics module is used to count all the fixed power consumption influencing factors and random power consumption influencing factors of the power consumption area in the next regulation cycle and their effect duration;

[0057] The power consumption prediction module is used to calculate the power consumption of the power consumption area in the next regulation cycle based on all fixed power consumption influencing factors and random power consumption influencing factors in the next regulation cycle and their action duration, the historical power consumption change curve corresponding to each fixed power consumption influencing factor and the power consumption quantitative relationship coefficient corresponding to each random power consumption influencing factor;

[0058] The power generation control module is used to control the power generation of the photovoltaic system based on the available power value of the next control cycle and the power consumption of the power consumption area in the next control cycle.

[0059] In this embodiment, the historical annual power generation data of the photovoltaic system is data consisting of the power generation of the photovoltaic system at every moment in the past years and the corresponding moment.

[0060] In this embodiment, the historical annual electricity consumption data of all electricity consumption areas is data consisting of the electricity consumption of the electricity consumption areas at every moment in the past years and the corresponding time.

[0061] In this embodiment, the power storage data of the photovoltaic energy storage system is data consisting of the power storage of the photovoltaic energy storage system at every moment over the years and the corresponding time.

[0062] In this embodiment, the historical electricity consumption change curve of all electricity consumption areas is a curve composed of the electricity consumption at each moment in the historical annual electricity consumption data of all electricity consumption areas as the vertical coordinate and the time corresponding to the electricity consumption at each moment as the horizontal coordinate.

[0063] In this embodiment, the factors affecting fixed power consumption include public lighting factors in the power consumption area, public heating factors in the power consumption area, public transportation factors in the power consumption area, and power consumption of permanent residents / shopping malls.

[0064] In this embodiment, the random electricity consumption influencing factors are the output change index of the factories in the power consumption area, the scale of the newly added construction sites in the power consumption area, and the number of new residents moving into the power consumption area.

[0065] In this embodiment, the historical electricity consumption change curve corresponding to the fixed electricity consumption influencing factors: all fixed electricity consumption influencing factors are time-matched with the historical electricity consumption change curves of all electricity consumption areas, and the mean of the vertical coordinates of the historical electricity consumption change curves of all electricity consumption areas corresponding to all the same fixed electricity consumption influencing factors acting alone is calculated, the mean of the vertical coordinates is used as the electricity consumption corresponding to the fixed electricity consumption influencing factors, the vertical coordinates of the time periods in which all fixed electricity consumption influencing factors appear are set to the mean, and the left and right coordinate points are connected to form a curve that is the historical electricity consumption change curve corresponding to the fixed electricity consumption influencing factors.

[0066] In this embodiment, the historical electricity consumption change curve corresponding to the random electricity consumption influencing factors: the historical electricity consumption change curves of all electricity consumption areas and the historical electricity consumption change curves corresponding to each fixed electricity consumption influencing factor are matched one by one in time series, and the difference between each vertical coordinate of the historical electricity consumption change curves of all electricity consumption areas and the corresponding coordinate value of the historical electricity consumption change curves corresponding to the fixed electricity consumption influencing factors is used as the vertical coordinate of the historical electricity consumption change curves corresponding to all random electricity consumption influencing factors, and all vertical coordinates are connected to form the historical electricity consumption change curves corresponding to all random electricity consumption influencing factors.

[0067] In this embodiment, the power consumption quantification relationship coefficient corresponding to the random power consumption influencing factor represents the degree of change of power consumption under the influence of the random power consumption influencing factor within a unit time.

[0068] In this embodiment, the power generation of the photovoltaic system is regulated based on the available power value of the next regulation cycle and the power consumption of the power consumption area in the next regulation cycle, including adjusting the maximum power tracking algorithm of the photovoltaic array to ensure that the photovoltaic system is always in the highest power generation efficiency state, adjusting the output characteristics of the inverter to adapt to different power grid requirements, cleaning the surface of the photovoltaic components, adjusting the power generation power of the photovoltaic system, adjusting the charging and discharging power of the energy storage system, and performing load transfer (reducing the use of non-critical loads during peak power consumption periods, transferring loads to off-peak periods, and encouraging users to use electricity during off-peak periods by setting time-of-use electricity prices).

[0069] The working principle and beneficial effects of the above technical solution are as follows: the present invention accurately predicts electricity consumption and power generation, and timely adjusts power generation to ensure stable grid voltage and frequency, ensuring that power generation meets demand during peak power consumption periods, and that power generation is timely reduced during low power consumption periods to avoid power waste and ensure a balance between power supply and demand.

[0070] Example 2

[0071] On the basis of Example 1, the first data determination submodule is used to determine all fixed power consumption influencing factors of all power consumption areas, and based on all fixed power consumption influencing factors of all power consumption areas and the historical power consumption change curves of all power consumption areas, obtain the historical power consumption change curve corresponding to each fixed power consumption influencing factor;

[0072] The second data determination submodule is used to determine the historical power consumption change curves corresponding to all random power consumption influencing factors based on the historical power consumption change curves of all power consumption areas and the historical power consumption change curves corresponding to each fixed power consumption influencing factor;

[0073] The quantitative analysis submodule is used to determine all random power consumption influencing factors and their effective time periods in all power consumption areas, and match all random power consumption influencing factors with the historical power consumption change curves corresponding to all random power consumption influencing factors based on the time series, and determine the quantitative relationship between each random power consumption influencing factor and power consumption.

[0074] The working principle and beneficial effects of the above technical solution are: by refining the factors affecting fixed and random power consumption, the accuracy of the prediction is improved, making the prediction result closer to the actual power consumption situation.

[0075] Example 3

[0076] Based on Example 2, the quantitative analysis submodule includes:

[0077] A random power consumption influencing factor determination unit, used to determine all random power consumption influencing factors and their effective time periods in all power consumption areas;

[0078] A time series matching unit, used for matching all random power consumption influencing factors with historical power consumption change curves corresponding to all random power consumption influencing factors based on the time series;

[0079] The quantitative relationship determination unit is used to find out any time period in which each random electricity consumption influencing factor acts alone in the historical electricity consumption change curve corresponding to all random electricity consumption influencing factors, and to count the change in the vertical coordinate in the historical electricity consumption change curve corresponding to all random electricity consumption influencing factors within the time period in which each random electricity consumption influencing factor acts alone, and use the quotient of the change in the vertical coordinate and the change in the corresponding horizontal coordinate as the electricity consumption quantitative relationship coefficient corresponding to the random electricity consumption influencing factor.

[0080] The working principle and beneficial effects of the above technical solution are as follows: through detailed statistical analysis, the accuracy of the quantitative relationship coefficient of electricity consumption is improved, and the introduction of the quantitative relationship coefficient makes the prediction model more scientific and reasonable, and enhances the credibility of the prediction.

[0081] Example 4

[0082] Based on Example 1, the available power prediction module includes:

[0083] The prediction matrix construction submodule 1 is used to construct a photovoltaic system power generation prediction matrix based on the power generation of the photovoltaic system in each control cycle in the historical annual power generation data of the photovoltaic system;

[0084] The second prediction matrix construction submodule is used to construct a photovoltaic energy storage system power storage prediction matrix based on the power storage data of the photovoltaic energy storage system in each control cycle;

[0085] The prediction matrix construction sub-module three is used to obtain the available power prediction matrix based on the photovoltaic system power generation prediction matrix and the photovoltaic energy storage system storage power prediction matrix, and predict the available power value of the next control cycle based on the available power prediction matrix.

[0086] The working principle and beneficial effects of the above technical solution are as follows: By constructing a detailed prediction matrix, the prediction accuracy of available power is improved. Based on the accurate prediction results, power regulation can be carried out more effectively and power waste can be reduced.

[0087] Example 5

[0088] On the basis of Example 4, based on the power generation of the photovoltaic system in each control cycle in the historical annual power generation data of the photovoltaic system, constructing the photovoltaic system power generation prediction matrix includes:

[0089] The power generation of the photovoltaic system in the i-th regulation cycle and each regulation cycle before the i-th regulation cycle in the historical annual power generation data of the photovoltaic system is arranged in time series to form a power generation series of the photovoltaic system in the regulation cycle based on the time series;

[0090] Based on the power generation series of the photovoltaic system during the regulation period, the current photovoltaic system power generation prediction matrix after the end of the i-th regulation period is constructed:

[0091] Among them, A i is the prediction matrix of the current photovoltaic system power generation after the end of the i-th regulation cycle, a1 is the power generation of the photovoltaic system in the first regulation cycle, a2 is the power generation of the photovoltaic system in the second regulation cycle, a3 is the power generation of the photovoltaic system in the third regulation cycle, a4 is the power generation of the photovoltaic system in the fourth regulation cycle, and a i-1 is the power generation of the photovoltaic system in the i-1th regulation cycle, a i is the power generation of the photovoltaic system in the i-th regulation cycle.

[0092] The working principle and beneficial effects of the above technical solution are as follows: through time series arrangement, the data processing process is simplified, the work efficiency is improved, and based on detailed time series data, the accuracy of the current photovoltaic system power generation prediction matrix after the end of the i-th control cycle is improved.

[0093] Example 6

[0094] On the basis of Example 4, based on the power storage data of the photovoltaic energy storage system in each control cycle, constructing a photovoltaic energy storage system power storage prediction matrix includes:

[0095] Arrange the power storage data of the photovoltaic energy storage system in the i-th regulation cycle and each regulation cycle before the i-th regulation cycle in a time series to form a time series of the power storage data of the photovoltaic energy storage system;

[0096] Based on the storage capacity series of the photovoltaic energy storage system during the regulation period, the current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation period is constructed:

[0097] Among them, B i is the storage capacity prediction matrix of the current photovoltaic energy storage system after the end of the i-th regulation cycle, b1 is the storage capacity of the photovoltaic energy storage system in the first regulation cycle, b2 is the storage capacity of the photovoltaic energy storage system in the second regulation cycle, b3 is the storage capacity of the photovoltaic energy storage system in the third regulation cycle, b4 is the storage capacity of the photovoltaic energy storage system in the fourth regulation cycle, and b i-1 is the storage capacity of the photovoltaic energy storage system in the i-1th regulation cycle, b i is the storage capacity of the photovoltaic energy storage system in the i-th regulation cycle.

[0098] The working principle and beneficial effects of the above technical solution are as follows: through time series arrangement, the data processing process is simplified, the work efficiency is improved, and based on detailed time series data, the current photovoltaic energy storage system storage capacity prediction matrix is ​​improved after the end of the i-th control cycle.

[0099] Example 7

[0100] On the basis of Example 4, the available power prediction matrix is ​​obtained based on the photovoltaic system power generation prediction matrix and the photovoltaic energy storage system storage power prediction matrix, and the available power value of the next regulation cycle is predicted based on the available power prediction matrix, including:

[0101] Based on the current photovoltaic system power generation prediction matrix after the end of the i-th regulation cycle and the current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation cycle, the current available power prediction matrix after the end of the i-th regulation cycle is constructed:

[0102] C i =A i +B i (3); where C i is the current available power forecast matrix after the end of the i-th regulation cycle, A i is the current photovoltaic system power generation prediction matrix after the end of the i-th regulation cycle, B i The current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation cycle;

[0103] The mean of all matrix elements in any row except the first row in the current available power forecast matrix after the end of the i-th regulation cycle is obtained, and the value of any column in the current available power forecast matrix after the end of the i-th regulation cycle is set to a value equal to the mean of all matrix elements in any row except the first row, thereby obtaining a new matrix, and the rank of the new matrix is ​​regarded as the available power value of the i+1-th regulation cycle.

[0104] The working principle and beneficial effects of the above technical solution are as follows: by generating a detailed available power forecast matrix, the accuracy of the forecast is improved, and based on the accurate forecast results, power regulation can be carried out more effectively.

[0105] Example 8

[0106] On the basis of Example 1, based on all fixed power consumption influencing factors and random power consumption influencing factors of the next regulation cycle and their action duration, the historical power consumption change curve corresponding to each fixed power consumption influencing factor and the power consumption quantitative relationship coefficient corresponding to each random power consumption influencing factor, the power consumption of the power consumption area in the next regulation cycle is calculated:

[0107] Among them, u ijIt represents the change value of electricity consumption in unit time when the jth fixed electricity consumption influencing factor acts based on the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, It represents the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, U j is the ordinate of the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, that is, the historical electricity consumption corresponding to the jth fixed electricity consumption influencing factor, is the horizontal coordinate of the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, that is, the moment when the jth fixed electricity consumption influencing factor takes effect;

[0108] Among them, Q i+1 is the power consumption of the power consumption area in the i+1th regulation cycle, n is the number of types of all fixed power consumption influencing factors in the i+1th regulation cycle, is the duration of action of the jth fixed power consumption influencing factor in the i+1th regulation cycle, m is the number of types of all random power consumption influencing factors in the i+1th regulation cycle, V k represents the quantitative relationship coefficient of power consumption corresponding to the kth random power consumption influencing factor, t V(i+1)k It is the duration of action of the kth random power consumption influencing factor in the i+1th regulation cycle.

[0109] The working principle and beneficial effects of the above technical solution are as follows: the power consumption of the power consumption area in the i+1th regulation cycle is calculated by the power consumption quantification relationship coefficient corresponding to each random power consumption influencing factor, thereby ensuring the reliability of the prediction result.

[0110] Example 9

[0111] Based on Example 1, the power generation control module includes:

[0112] A difference determination unit, used to calculate the difference between the available power value in the next regulation cycle and the power consumption of the power consumption area in the next regulation cycle;

[0113] A level evaluation unit, used to calculate the response coefficient of the photovoltaic system based on the value of the available power in the next regulation cycle and the difference between the power consumption of the power consumption area in the next regulation cycle;

[0114] A photovoltaic system control unit, used to regulate the power generation of the photovoltaic system based on the response coefficient of the photovoltaic system;

[0115] The response coefficient of the photovoltaic system is calculated based on the difference between the available power value in the next regulation cycle and the power consumption in the power consumption area in the next regulation cycle:

[0116] Where, is the response coefficient of the photovoltaic system, P i+1 is the available power value of the i+1th regulation cycle, Q i+1 is the electricity consumption of the electricity consumption area in the i+1th regulation cycle, e is a natural number, and its value is 2.71.

[0117] The working principle and beneficial effects of the above technical solution are as follows: the present invention improves the accuracy of power generation regulation through detailed difference and response coefficient calculation, and based on precise control strategies, can more effectively regulate power and reduce power waste.

[0118] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent power generation control system, characterized in that: include: The data collection module is used to collect the historical annual power generation data of the photovoltaic system, the historical annual power consumption data of all power consumption areas, and the power storage data of the photovoltaic energy storage system, and form the historical power consumption change curve of all power consumption areas based on the historical annual power consumption data of all power consumption areas; The power consumption data analysis module is used to determine the historical power consumption change curve corresponding to each fixed power consumption influencing factor, the historical power consumption change curve corresponding to all random power consumption influencing factors, and the quantitative relationship between each random power consumption influencing factor and power consumption based on the historical power consumption change curves of all power consumption areas. The quantitative relationship between each random power consumption influencing factor and power consumption is the power consumption quantitative relationship coefficient corresponding to the random power consumption influencing factor; The available power forecasting module is used to construct a photovoltaic system power generation forecasting matrix based on the historical annual power generation data of the photovoltaic system, to construct a photovoltaic energy storage system storage capacity forecasting matrix based on the power storage data of the photovoltaic energy storage system, and to obtain an available power forecasting matrix based on the photovoltaic system power generation forecasting matrix and the photovoltaic energy storage system storage capacity forecasting matrix, and to predict the available power value of the next regulation cycle based on the available power forecasting matrix; The influencing factor statistics module is used to count all the fixed power consumption influencing factors and random power consumption influencing factors of the power consumption area in the next regulation cycle and their effect duration; The power consumption prediction module is used to calculate the power consumption of the power consumption area in the next regulation cycle based on all fixed power consumption influencing factors and random power consumption influencing factors in the next regulation cycle and their action duration, the historical power consumption change curve corresponding to each fixed power consumption influencing factor and the power consumption quantitative relationship coefficient corresponding to each random power consumption influencing factor; The power generation control module is used to control the power generation of the photovoltaic system based on the available power value of the next control cycle and the power consumption of the power consumption area in the next control cycle.

2. The intelligent power generation control system according to claim 1, characterized in that: The electricity consumption data analysis module includes: A first data determination submodule is used to determine all fixed power consumption influencing factors of all power consumption areas, and based on all fixed power consumption influencing factors of all power consumption areas and the historical power consumption change curves of all power consumption areas, obtain the historical power consumption change curve corresponding to each fixed power consumption influencing factor; The second data determination submodule is used to determine the historical power consumption change curves corresponding to all random power consumption influencing factors based on the historical power consumption change curves of all power consumption areas and the historical power consumption change curves corresponding to each fixed power consumption influencing factor; The quantitative analysis submodule is used to determine all random power consumption influencing factors and their effective time periods in all power consumption areas, and match all random power consumption influencing factors with the historical power consumption change curves corresponding to all random power consumption influencing factors based on the time series, and determine the quantitative relationship between each random power consumption influencing factor and power consumption.

3. The intelligent power generation control system according to claim 2, characterized in that: The quantitative analysis submodules include: A random power consumption influencing factor determination unit, used to determine all random power consumption influencing factors and their effective time periods in all power consumption areas; A time series matching unit, used for matching all random power consumption influencing factors with historical power consumption change curves corresponding to all random power consumption influencing factors based on the time series; The quantitative relationship determination unit is used to find out any time period in which each random electricity consumption influencing factor acts alone in the historical electricity consumption change curve corresponding to all random electricity consumption influencing factors, and to count the change in the vertical coordinate in the historical electricity consumption change curve corresponding to all random electricity consumption influencing factors within the time period in which each random electricity consumption influencing factor acts alone, and use the quotient of the change in the vertical coordinate and the change in the corresponding horizontal coordinate as the electricity consumption quantitative relationship coefficient corresponding to the random electricity consumption influencing factor.

4. The power generation intelligent control system according to claim 1, characterized in that: The available power forecast module includes: The prediction matrix construction submodule 1 is used to construct a photovoltaic system power generation prediction matrix based on the power generation of the photovoltaic system in each control cycle in the historical annual power generation data of the photovoltaic system; The second prediction matrix construction submodule is used to construct a photovoltaic energy storage system power storage prediction matrix based on the power storage data of the photovoltaic energy storage system in each control cycle; The prediction matrix construction sub-module three is used to obtain the available power prediction matrix based on the photovoltaic system power generation prediction matrix and the photovoltaic energy storage system storage power prediction matrix, and predict the available power value of the next control cycle based on the available power prediction matrix.

5. The intelligent power generation control system according to claim 4, characterized in that: Based on the power generation of the photovoltaic system in each regulation cycle in the historical annual power generation data of the photovoltaic system, the photovoltaic system power generation prediction matrix is ​​constructed, including: The power generation of the photovoltaic system in the i-th regulation cycle and each regulation cycle before the i-th regulation cycle in the historical annual power generation data of the photovoltaic system is arranged in time series to form a power generation series of the photovoltaic system in the regulation cycle based on the time series; Based on the power generation series of the photovoltaic system during the regulation period, the current photovoltaic system power generation prediction matrix after the end of the i-th regulation period is constructed: Among them, A i is the prediction matrix of the current photovoltaic system power generation after the end of the i-th regulation cycle, a1 is the power generation of the photovoltaic system in the first regulation cycle, a2 is the power generation of the photovoltaic system in the second regulation cycle, a3 is the power generation of the photovoltaic system in the third regulation cycle, a4 is the power generation of the photovoltaic system in the fourth regulation cycle, and a i-1 is the power generation of the photovoltaic system in the i-1th regulation cycle, a i is the power generation of the photovoltaic system in the i-th regulation cycle.

6. The intelligent power generation control system according to claim 4, characterized in that: Based on the storage capacity of the photovoltaic energy storage system in each control cycle in the power storage data of the photovoltaic energy storage system, the photovoltaic energy storage system storage capacity prediction matrix is ​​constructed, including: Arrange the power storage data of the photovoltaic energy storage system in the i-th regulation cycle and each regulation cycle before the i-th regulation cycle in a time series to form a time series of the power storage data of the photovoltaic energy storage system; Based on the storage capacity series of the photovoltaic energy storage system during the regulation period, the current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation period is constructed: Among them, B i is the storage capacity prediction matrix of the current photovoltaic energy storage system after the end of the i-th regulation cycle, b1 is the storage capacity of the photovoltaic energy storage system in the first regulation cycle, b2 is the storage capacity of the photovoltaic energy storage system in the second regulation cycle, b3 is the storage capacity of the photovoltaic energy storage system in the third regulation cycle, b4 is the storage capacity of the photovoltaic energy storage system in the fourth regulation cycle, and b i-1 is the storage capacity of the photovoltaic energy storage system in the i-1th regulation cycle, b i is the storage capacity of the photovoltaic energy storage system in the i-th regulation cycle.

7. The intelligent power generation control system according to claim 4, characterized in that: Based on the photovoltaic system power generation prediction matrix and the photovoltaic energy storage system storage capacity prediction matrix, the available power prediction matrix is ​​obtained, and the available power value of the next regulation cycle is predicted based on the available power prediction matrix, including: Based on the current photovoltaic system power generation prediction matrix after the end of the i-th regulation cycle and the current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation cycle, the current available power prediction matrix after the end of the i-th regulation cycle is constructed: C i =A i +B i (3); where C i is the current available power forecast matrix after the end of the i-th regulation cycle, A i is the current photovoltaic system power generation prediction matrix after the end of the i-th regulation cycle, B i The current photovoltaic energy storage system storage capacity prediction matrix after the end of the i-th regulation cycle; The mean of all matrix elements in any row except the first row in the current available power forecast matrix after the end of the i-th regulation cycle is obtained, and the value of any column in the current available power forecast matrix after the end of the i-th regulation cycle is set to a value equal to the mean of all matrix elements in any row except the first row, thereby obtaining a new matrix, and the rank of the new matrix is ​​regarded as the available power value of the i+1-th regulation cycle.

8. The intelligent power generation control system according to claim 1, characterized in that: Based on all fixed power consumption influencing factors and random power consumption influencing factors of the next regulation cycle and their action duration, the historical power consumption change curve corresponding to each fixed power consumption influencing factor and the power consumption quantitative relationship coefficient corresponding to each random power consumption influencing factor, the power consumption of the power consumption area in the next regulation cycle is calculated: Among them, u ij It represents the change value of electricity consumption in unit time when the jth fixed electricity consumption influencing factor acts based on the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, It represents the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, U j is the ordinate of the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, that is, the historical electricity consumption corresponding to the jth fixed electricity consumption influencing factor, is the horizontal coordinate of the historical electricity consumption change curve corresponding to the jth fixed electricity consumption influencing factor before the end of the i-th regulation cycle, that is, the moment when the jth fixed electricity consumption influencing factor takes effect; Among them, Q i+1 is the power consumption of the power consumption area in the i+1th regulation cycle, n is the number of types of all fixed power consumption influencing factors in the i+1th regulation cycle, is the duration of action of the jth fixed power consumption influencing factor in the i+1th regulation cycle, m is the number of types of all random power consumption influencing factors in the i+1th regulation cycle, V k represents the quantitative relationship coefficient of power consumption corresponding to the kth random power consumption influencing factor, t V(i+1)k It is the duration of action of the kth random power consumption influencing factor in the i+1th regulation cycle.

9. The power generation intelligent control system according to claim 1, characterized in that: The power generation control module includes: A difference determination unit, used to calculate the difference between the available power value in the next regulation cycle and the power consumption of the power consumption area in the next regulation cycle; A level evaluation unit, used to calculate the response coefficient of the photovoltaic system based on the value of the available power in the next regulation cycle and the difference between the power consumption of the power consumption area in the next regulation cycle; The photovoltaic system control unit is used to regulate the power generation of the photovoltaic system based on the response coefficient of the photovoltaic system.

10. The intelligent power generation control system according to claim 9, characterized in that: The response coefficient of the photovoltaic system is calculated based on the difference between the available power value in the next regulation cycle and the power consumption in the power consumption area in the next regulation cycle: Where, is the response coefficient of the photovoltaic system, P i+1 is the available power value of the i+1th regulation cycle, Q i+1 is the electricity consumption of the electricity consumption area in the i+1th regulation cycle, e is a natural number, and its value is 2.71.

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