CN107194620B - Linear point value Zadeh fuzzy calculation method and device for daily power generation amount of photovoltaic power generation - Google Patents

Linear point value Zadeh fuzzy calculation method and device for daily power generation amount of photovoltaic power generation Download PDF

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CN107194620B
CN107194620B CN201710566332.XA CN201710566332A CN107194620B CN 107194620 B CN107194620 B CN 107194620B CN 201710566332 A CN201710566332 A CN 201710566332A CN 107194620 B CN107194620 B CN 107194620B
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吴杰康
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Abstract

The embodiment of the invention discloses a linear point value Zadeh fuzzy calculation method and device for daily generated energy of photovoltaic power generation, which are used for solving the technical problems that uncertainty and randomness of influence factors are not fully considered in a daily generated energy calculation method of a distributed photovoltaic power generation system in the prior art, and the applicability, the practicability and the applicability of the calculation method are difficult to meet. The method provided by the embodiment of the invention comprises the following steps: the solar power generation method is characterized in that the sunshine intensity, the sunshine time, the sunshine shadow and the sunshine deflection angle are considered, a dimensional trapezoid fuzzy set concept and a calculation method thereof are introduced, the solar power generation amount of the distributed photovoltaic power generation system is calculated on the basis of fuzzy probability analysis based on the parameters of the sunshine intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle and the like and the user battery energy storage charging event all obey the Zadeh fuzzy distribution rule, and necessary technical support is provided for distributed new energy power generation and intelligent power grid dispatching operation.

Description

Linear point value Zadeh fuzzy calculation method and device for daily power generation amount of photovoltaic power generation
Technical Field
The invention relates to the technical field of power systems and automation thereof, in particular to a linear point value Zadeh fuzzy calculation method and device for daily power generation amount of photovoltaic power generation.
Background
The development of a solar distributed power generation system is the development trend of smart cities, and photovoltaic power generation and photo-thermal power generation are two different forms of solar power generation. In recent years, a photovoltaic-photothermal integrated distributed power generation system becomes a mainstream development direction and a subject of research hot spot.
The principle of photovoltaic power generation is that solar heat energy is directly converted into electric energy by utilizing the temperature difference of semiconductor or metal materials such as vacuum devices, alkali metals, magnetic fluids and the like, so as to realize power generation. The principle of photo-thermal power generation is that working media such as water and the like are heated to high-temperature and high-pressure steam in a light-gathering and heat-collecting mode, the high-temperature and high-pressure steam drives a heat engine such as a steam turbine and the like, the heat engine drives a generator set to generate power, and the power generation is realized by utilizing conversion of various energy sources such as sunlight, heat, a machine and electricity.
At present, photovoltaic power generation has become a very mature technology, and the power generation cost thereof has been reduced to the level of 7000 ten thousand yuan/ten thousand kilowatts. The photothermal power generation mainly comprises four types, namely tower type, groove type, disc type and Fresnel type. The principle of the trough type solar photo-thermal power generation system is that a plurality of series-parallel trough type paraboloid concentrating collectors are used for concentrating solar heat, a working medium is heated to high-temperature high-pressure steam, and then a steam turbine generator set is driven to generate power. The power generation principle of the disc type solar photo-thermal power generation system is that a parabolic reflector is composed of a plurality of mirrors, solar light is focused on the focus of the parabolic reflector, working media in a parabolic receiver are enabled to heat the working media to high-temperature high-pressure steam, and an engine is driven to generate power. The Fresnel type photo-thermal power generation system has the power generation principle that a condenser with a Fresnel structure is adopted to collect solar heat, heat working media to high-temperature high-pressure steam, and drive a steam turbine generator set to generate power, and the Fresnel type photo-thermal power generation system is low in power generation efficiency, simple in structure and low in construction and maintenance cost. The principle of the tower type solar thermal power generation system is that an absorber on the top of a central absorption tower collects solar heat, a working medium is heated to high-temperature high-pressure steam, a steam turbine generator set is driven to generate power, a plurality of heliostats are arranged around the tower, sunlight is collected to a cavity of a receiver on the top of the tower through the heliostats to generate high temperature, and then the working medium of the absorber is heated to generate high-temperature steam to drive a steam turbine to generate power. The photo-thermal power generation modes are all that the steam turbine is driven to generate power by converting light into heat and then generating steam.
The radiation intensity and the sunshine time of sunlight in different areas have great difference, the sunshine intensity in different time and space also has great difference, randomness and ambiguity due to the fact that the cloud layer shields to form a shadow in the same place, and the uncertain characteristic determines that the output of the photovoltaic and photo-thermal power generation system also has great difference, randomness and ambiguity. Therefore, to determine the output power of the photovoltaic and photothermal power generation system, the solar radiation intensity and the sunshine duration in the area need to be subjected to probability analysis or fuzzy analysis and probability fuzzy analysis, and the sunshine intensity at different time and space needs to be subjected to probability analysis or fuzzy analysis and probability fuzzy analysis.
By utilizing the continuous power generation principle of battery energy storage, the photovoltaic power generation system can continuously generate power or continuously generate power in cloudy days or at night. But the continuous power generation or continuous power generation capacity depends on factors such as the energy storage capacity, the efficiency, the control mode and the like of the battery, and the factors influence the output power level of the battery energy storage continuous power generation system. The continuous power generation or the continuous power generation can be realized by utilizing the fused salt energy storage when the photo-thermal power generation is carried out in the cloudy day or at night. Like photovoltaic power generation, the continuous power generation or continuous power generation capacity of the photo-thermal power generation system depends on factors such as fused salt energy storage capacity, energy conversion efficiency, flexible control mode and the like, and the output power level of the photo-thermal power generation system has high randomness and ambiguity due to the influence of various uncertain factors.
Renewable energy sources of all countries around the world have a rapid growth trend in recent years in power grid access. The photovoltaic power generation access is the fastest to increase, and the annual growth rate is 60 percent; secondly, wind power generation and biofuel power generation are carried out, and the annual growth rate is respectively 27% and 18%. The department of industry and informatization predicts that the nationwide electric automobile reserves will reach 6000 million in 2030 years, the peak charging power will reach 0.42TW, accounting for 18% of the expected total installed capacity of 2.32 TW. Therefore, the large-scale access of distributed power generation, energy storage and electric vehicle charging systems to urban power distribution networks is a necessary trend. With the interactive support and promotion of national policies and industrial development, in space, such as small users like urban residents and large user groups like commercial buildings, communities and industrial areas, the distributed photovoltaic power generation system tends to develop rapidly, and the photovoltaic and photothermal power generation integrated system also shows a strong development situation. The distributed energy storage system is a distributed system with fixed access voltage level and access point, and comprises compressed hydrogen energy storage, battery energy storage, super capacitor energy storage and the like, and the energy storage power is flexible and controllable; the distributed charging system of the electric automobile is a distributed system with variable access voltage levels and access points, the charging power can be flexibly controlled, and the randomness is extremely high. The distributed generation volatility, the intermittency, the randomness and the charging uncertainty of the electric automobile enable the single distributed generation, the power utilization and the charging to have more randomness, and the randomness and the ambiguity of the distributed output power can be further increased by the interaction relationship of small users such as urban residents and the like and large user groups such as commercial buildings, communities and industrial areas for distributed generation, energy storage and electric automobile charging systems.
For random uncertainty, probability statistics theory is conventionally used to analyze and process information of random uncertainty, such as constructing a probability model of an uncertainty event or parameter by using a probability density function and a probability distribution function with mean and variance as characteristic values, and describing occurrence probability characteristics of the uncertainty event and fluctuation characteristics of uncertainty parameters such as power, voltage and current.
For ambiguity uncertainty, it is conventional to use ambiguity analysis methods to analyze and process the ambiguity uncertainty information. Simulating and describing inaccurate information of the fuzzy uncertainty event or parameter by adopting a Zadeh fuzzy set or TYPE1 fuzzy set, and mainly simulating the fuzzy uncertainty event or parameter by using a single-layer membership function method to describe the fuzzy uncertainty event or parameter by using a membership value. In an actual application system, uncertain events become more and more complex, the number of uncertain parameters is huge, the relation is complex, the fuzzy degrees of the events or the parameters and the mutual information are greatly increased, a single-layer membership function method based on a Zadeh fuzzy set and a TYPE1 fuzzy set is obviously insufficient, and the fuzzy uncertain events or the parameters are difficult to analyze and process in direct simulation information. Based on the TYPE1 fuzzy set, Zadeh proposes a TYPE2 fuzzy set based on two layers of membership functions, and further enhances the processing capacity of fuzzy uncertain events or parameters.
In a practical application system, random and fuzzy uncertain events or parameters exist simultaneously, and interact and are superposed with each other. The traditional probability analysis method and the fuzzy analysis method are limited by the mechanism thereof, and have obvious defects when the random and fuzzy uncertain events or parameters of the system are processed, and the analysis effect cannot approach to the actual situation. Therefore, in recent years, the fuzzy theory and the probability theory are fused to form a development direction and a trend, and an attractive idea and a method for understanding the problem of uncertainty are provided. One is to introduce fuzzy theory into traditional probability theory, such as random set, fuzzy random variable; another is to introduce probability theory into fuzzy theory, such as unstable fuzzy set, probability set and probabilistic fuzzy set. The method is characterized in that on the basis of the TYPE2 fuzzy set, the probability fuzzy set introduces a random theory into the traditional fuzzy theory, describes the random characteristics of uncertain random and fuzzy events or parameters by fuzzy membership, and forms a fuzzy set form of an n-dimensional membership function.
Distributed photovoltaic power generation systems for small users such as urban residents and large user groups such as commercial buildings, communities and industrial areas are systems with random and fuzzy uncertain events or parameters which have complex relationships and interaction. Under the influence of various uncertain random and fuzzy events or parameters, the daily generated energy of new energy small users such as urban residents and the like and new energy large user groups such as commercial buildings, communities and industrial areas with distributed photovoltaic power generation systems becomes more random and fuzzy. The daily generated energy of the traditional distributed photovoltaic power generation system usually adopts a deterministic calculation method, and some systems also adopt an uncertain calculation method of probability analysis. The deterministic calculation method is generally used for calculating the daily generated energy of the distributed photovoltaic power generation system under the condition that the solar radiation intensity, the sunshine time, the sunshine intensity, the sunshine shadow and the sunshine deflection angle of a user location in different time and space are all determined in an assumed region, the influences of factors such as the battery energy storage capacity of the photovoltaic power generation system used for continuous power generation or the fused salt energy storage installed capacity, the energy storage state, the energy conversion efficiency, the power distribution network voltage regulation requirement, the flexible control mode and the like of the photo-thermal power generation system are not considered, and the calculation result is unique and deterministic and can not reflect the actual condition of the daily generated energy of the distributed photovoltaic power generation system. In the calculation method of probability analysis, the daily power generation amount of the distributed photovoltaic power generation system is usually calculated under the condition that only single factors such as sunlight intensity are assumed as uncertainty factors, and the calculation result is a probability value with a confidence level. Actually, the daily power generation amount of the distributed photovoltaic power generation system is determined by the solar radiation intensity, the sunshine duration and the probability or ambiguity thereof in the region, the sunshine intensity, the sunshine duration, the sunshine shadow, the sunshine deflection angle and the probability or ambiguity thereof in different time and space at the user location, and also depends on the battery energy storage capacity of the photovoltaic power generation system used for continuous power generation or the fuse salt energy storage installed capacity, the energy storage state, the energy conversion efficiency, the power distribution network voltage regulation requirement, the flexible control mode and other factors of the photo-thermal power generation system. Moreover, these influencing factors are typically random uncertainties or fuzzy uncertainties, or they are random and fuzzy uncertainties, often present as random and fuzzy uncertainty events or quantities. Therefore, the uncertainty and randomness of the influence factors are not considered comprehensively in the prior art of the calculation of the daily generated energy of the distributed photovoltaic power generation system, and the applicability, the practicability and the applicability of the calculation method are difficult to meet.
Disclosure of Invention
The embodiment of the invention provides a linear point value Zadeh fuzzy calculation method and device for daily generated energy of photovoltaic power generation, and solves the technical problems that uncertainty and randomness of influence factors are not considered comprehensively in a daily generated energy calculation method of a distributed photovoltaic power generation system in the prior art, and the applicability, the practicability and the applicability of the calculation method are difficult to meet.
The embodiment of the invention provides a fuzzy calculation method and a fuzzy calculation device for a linear point value Zadeh of daily power generation of photovoltaic power generation, which comprises the following steps:
calculating a first one-to-one point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation of the photovoltaic power generation system according to the acquired daily power generation data of the photovoltaic power generation system of the user;
acquiring sunshine intensity data of N time periods in the daytime of a user location, and calculating a second type point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine intensity in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine intensity;
acquiring sunshine time data of N time periods in the daytime of a user location, and calculating and determining a third type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine time in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine time;
acquiring sunshine shadow data of N time periods in the daytime of a user location, and calculating and determining a fourth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine shadow in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine shadow;
acquiring sunshine deflection angle data of N time periods in the daytime of a user location, and calculating and determining a fifth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine deflection angle in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine deflection angle;
acquiring environmental temperature rise data caused by sunshine in N time periods in the daytime of a user location, and calculating a sixth type point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise;
and calculating the daily power generation amount of the photovoltaic power generation system according to the first type point value Zadeh fuzzy set, the second type point value Zadeh fuzzy set, the third type point value Zadeh fuzzy set, the fourth type point value Zadeh fuzzy set, the fifth type point value Zadeh fuzzy set and the sixth type point value Zadeh fuzzy set by a fuzzy analysis method.
Optionally, calculating a first one-to-one point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation amount of the photovoltaic power generation system according to the acquired daily power generation amount data of the photovoltaic power generation system of the user includes:
according to the photovoltaic power generation system daily power generation data of a user acquired from a local monitoring center, a first one-type point value Zadeh fuzzy set for determining fuzzy uncertainty of the photovoltaic power generation system daily power generation is calculated through a daily power generation fuzzy set determination formula, wherein the daily power generation fuzzy set determination formula specifically comprises the following steps:
Figure BDA0001348462950000051
wherein E isHiFor the ith day power generation amount type point value Zadeh fuzzy set, EHiV1、EHiV2、...、EHiVnAnd muHiV1、μHiV2、...、μHiVnRespectively sent from the ith day1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of extremely low, medium, high, extremely high electric quantity fuzzy uncertainty values.
Optionally, the method includes the steps of acquiring sunshine intensity data of N time periods in the daytime of a place where a user is located, and calculating a second type point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between output power of the photovoltaic power generation system and sunshine intensity in the N time periods according to a relation between the output power of the photovoltaic power generation system and the sunshine intensity, and the second type point value Zadeh fuzzy set includes:
obtaining daytime N of user location in one daySHThe sunshine intensity in each period is N values, and according to the relation between the output power of the photovoltaic power generation system and the sunshine intensity, the N value is determined by calculation through a sunshine intensity fuzzy set determination formulaSHIn each period, a second type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight intensity is defined as follows:
Figure BDA0001348462950000061
wherein the content of the first and second substances,
Figure BDA0001348462950000069
is a time period t, t 1,2SHThe solar intensity-type point value Zadeh fuzzy set,
Figure BDA0001348462950000062
Figure BDA0001348462950000063
and
Figure BDA0001348462950000064
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunlight intensity fuzzy uncertainty value of the time period t.
Optionally, the method includes acquiring sunshine time data of N time periods in the daytime of a location where a user is located, and calculating a third type point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine time in the N time periods according to a relation between the output power of the photovoltaic power generation system and the sunshine time, and includes:
obtaining daytime N of user location in one daySHThe sunshine time in each period is N data, and according to the relation between the output power of the photovoltaic power generation system and the sunshine time, the sunshine time fuzzy set determination formula is used for calculating and determining NSHIn each period, a third type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine duration, wherein the sunshine duration fuzzy set determination formula specifically comprises:
Figure BDA0001348462950000065
wherein the content of the first and second substances,
Figure BDA0001348462950000066
is a sunshine time-type point value Zadeh fuzzy set of a time period t,
Figure BDA0001348462950000067
and
Figure BDA0001348462950000068
respectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine time fuzzy uncertainty value of the time period t.
Optionally, the method includes the steps of acquiring sunshine shadow data of N time periods in the daytime of a location where a user is located, and calculating a fourth type point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine shadow in the N time periods according to a relation between the output power of the photovoltaic power generation system and the sunshine shadow, where the fourth type point value Zadeh fuzzy set includes:
obtaining daytime N of user location in one daySHThe sun shadow of each time period is n values of data, and the data is determined by determining a formula through a sun shadow fuzzy set according to the relation between the output power of the photovoltaic power generation system and the sun shadowDetermining NSHA fourth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight shadow in each period, wherein the sunlight shadow fuzzy set determination formula specifically comprises:
Figure BDA0001348462950000071
wherein the content of the first and second substances,
Figure BDA0001348462950000072
a sun shadow type point value Zadeh fuzzy set for time period t,
Figure BDA0001348462950000073
and
Figure BDA0001348462950000074
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine shadow fuzzy uncertainty value of the time period t.
Optionally, the method includes the steps of acquiring sunshine deflection angle data of N time periods in the daytime of a location where a user is located, and calculating a fifth type point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine deflection angle in the N time periods according to a relation between the output power of the photovoltaic power generation system and the sunshine deflection angle, and includes:
obtaining daytime N of user location in one daySHThe sunshine declination angle in each period is N values, and according to the relation between the output power of the photovoltaic power generation system and the sunshine declination angle, the N is determined by calculation through a sunshine declination angle fuzzy set determination formulaSHIn each period, a fifth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight deflection angle is defined as follows:
Figure BDA0001348462950000075
wherein the content of the first and second substances,
Figure BDA0001348462950000076
is a sunshine deflection angle type point value Zadeh fuzzy set of a time period t,
Figure BDA0001348462950000077
and
Figure BDA0001348462950000078
respectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine deflection angle fuzzy uncertainty value of the time period t.
Optionally, the method for determining the fuzzy uncertainty relationship between the output power of the photovoltaic power generation system and the environmental temperature rise in the N periods includes the steps of obtaining environmental temperature rise data caused by sunshine in the N periods of the day where the user is located, and calculating a sixth type point value Zadeh fuzzy set for determining the fuzzy uncertainty relationship between the output power of the photovoltaic power generation system and the environmental temperature rise in the N periods according to the relationship between the output power of the photovoltaic power generation system and the environmental temperature rise, and includes:
obtaining daytime N of user location in one daySHThe environmental temperature rise caused by sunshine in each period is N values of data, and according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise, the N value is determined through calculation of a sunshine environmental temperature rise fuzzy set determination formulaSHIn each period, a sixth type point value Zadeh fuzzy set of the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise is specifically defined as follows:
Figure BDA0001348462950000081
wherein the content of the first and second substances,
Figure BDA0001348462950000082
a type point value Zadeh fuzzy set for the temperature rise of the sunshine environment in the time period t,
Figure BDA0001348462950000083
Figure BDA0001348462950000084
and
Figure BDA0001348462950000085
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine environment temperature rise fuzzy uncertainty value of the time period t.
Optionally, calculating the daily power generation amount of the photovoltaic power generation system according to the first type point value Zadeh fuzzy set, the second type point value Zadeh fuzzy set, the third type point value Zadeh fuzzy set, the fourth type point value Zadeh fuzzy set, the fifth type point value Zadeh fuzzy set and the sixth type point value Zadeh fuzzy set by the fuzzy analysis method comprises:
calculating the daily power generation amount of the photovoltaic power generation system according to a daily power generation amount calculation formula by using a fuzzy analysis method according to a first type point value Zadeh fuzzy set, a second type point value Zadeh fuzzy set, a third type point value Zadeh fuzzy set, a fourth type point value Zadeh fuzzy set, a fifth type point value Zadeh fuzzy set and a sixth type point value Zadeh fuzzy set, wherein the daily power generation amount calculation formula specifically comprises the following steps:
Figure BDA00013484629500000812
wherein k isPVEIs a photoelectric conversion coefficient of the photovoltaic power generation panel,
Figure BDA0001348462950000086
the probability of occurrence of b (b 1, 2.. multidot.q) fuzzy numbers in the period with the sunshine intensity in the historical data,
Figure BDA0001348462950000087
is the probability of occurrence of the b < th > fuzzy number in the historical data and the sunshine temperature rise in the time period t,
Figure BDA0001348462950000088
as the probability of occurrence of the b-th fuzzy number with the sunshine shading in the history data in the time period t,
Figure BDA0001348462950000089
as historical dataThe probability of occurrence of the b-th blur number at time period t of the medium to sun bias angle,
Figure BDA00013484629500000810
is the probability of occurrence of the b-th fuzzy number in the historical data and the sunshine duration in the time period t.
Figure BDA00013484629500000811
Represents NSHThe union of the fuzzy sets is set as,
Figure BDA00013484629500000813
representing a union of fuzzy sets.
The invention provides a linear point value Zadeh fuzzy calculation device for daily power generation amount of photovoltaic power generation, which is characterized by comprising the following components:
the first calculation module is used for calculating a first one-type point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation of the photovoltaic power generation system according to the acquired daily power generation data of the photovoltaic power generation system of the user;
the second calculation module is used for acquiring sunshine intensity data of N time periods in the daytime of the location of the user, and calculating a second linear point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine intensity in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine intensity;
the third calculation module is used for acquiring sunshine time data of N time periods in the daytime of the location of the user, and calculating a third linear point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine time in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine time;
the fourth calculation module is used for acquiring sunshine shadow data of N time periods in the daytime of the location of the user, and calculating a fourth linear point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine shadow in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine shadow;
the fifth calculation module is used for acquiring sunshine deflection angle data of N time intervals in the daytime of the location of a user, and calculating a fifth type point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine deflection angle in the N time intervals according to the relation between the output power of the photovoltaic power generation system and the sunshine deflection angle;
the sixth calculation module is used for acquiring environmental temperature rise data caused by sunshine in N time periods in the daytime of the location of the user, and calculating a sixth type point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise;
and the seventh calculation module is used for calculating the daily power generation amount of the photovoltaic power generation system according to the first type point value Zadeh fuzzy set, the second type point value Zadeh fuzzy set, the third type point value Zadeh fuzzy set, the fourth type point value Zadeh fuzzy set, the fifth type point value Zadeh fuzzy set and the sixth type point value Zadeh fuzzy set by a fuzzy analysis method.
Optionally, the first computing module specifically includes:
the calculation unit is used for calculating a first one-dimensional point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation of the photovoltaic power generation system through a daily power generation fuzzy set determination formula according to daily power generation data of the photovoltaic power generation system of a user acquired from the local monitoring center, wherein the daily power generation fuzzy set determination formula specifically comprises the following steps:
Figure BDA0001348462950000091
wherein E isHiFor the ith day power generation amount type point value Zadeh fuzzy set, EHiV1、EHiV2、...、EHiVnAnd muHiV1、μHiV2、...、μHiVnRespectively determining 1 to n linear point value Zadeh fuzzy numbers and the slavery thereof in the interval range of the fuzzy uncertainty value of the ith day power generation amount from very low, medium, high, very high and very highAnd (4) attribute value.
According to the technical scheme, the embodiment of the invention has the following advantages:
the embodiment of the invention provides a linear point value Zadeh fuzzy calculation method and device for daily power generation amount of photovoltaic power generation, comprising the following steps: calculating a linear point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation of the photovoltaic power generation system according to the acquired daily power generation data of the photovoltaic power generation system of the user; acquiring sunshine intensity data of N time periods in the daytime of a user location, and calculating a point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine intensity in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine intensity; acquiring sunshine time data of N time periods in the daytime of a user location, and calculating a point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of a photovoltaic power generation system and sunshine time in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine time; acquiring sunshine shadow data of N time periods in the daytime of a user location, and calculating a point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of a photovoltaic power generation system and the sunshine shadow in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine shadow; acquiring sunshine deflection angle data of N time periods in the daytime of a user location, and calculating a type-I point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine deflection angle in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine deflection angle; acquiring environmental temperature rise data caused by sunshine in N time periods in the daytime of a user location, and calculating and determining a point value Zadeh fuzzy set of a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise; the embodiment of the invention calculates the daily generated energy of the photovoltaic power generation system according to a type point value Zadeh fuzzy set of the output power of the photovoltaic power generation system by a fuzzy analysis method, simultaneously considers the random and fuzzy uncertainty influencing the daily generated energy of the distributed photovoltaic power generation system, mainly considers the random and fuzzy uncertainty factors such as the solar radiation intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle and the like of the area, and the like of the user location in different time and space, mainly introduces a dimensional trapezoid fuzzy set concept and a calculation method thereof when considering the sunshine intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle and the like, assumes that the parameters such as the sunshine intensity, the sunshine shadow, the sunshine deflection angle and the like and the user battery energy storage charging event all obey the Zadeh fuzzy distribution rule, and calculates the daily generated energy of the distributed photovoltaic power generation system on the basis of fuzzy probability analysis, the method provides necessary technical support for distributed new energy power generation and intelligent power grid dispatching operation, and solves the technical problems that uncertainty and randomness of influence factors are not considered comprehensively in the distributed photovoltaic power generation system daily power generation amount calculation method in the prior art, and the calculation method is difficult to meet the applicability, the practicability and the applicability.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a first-type point value Zadeh fuzzy calculation method for daily power generation amount of photovoltaic power generation according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a point value Zadeh fuzzy calculation device for daily power generation amount of photovoltaic power generation according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a linear point value Zadeh fuzzy calculation method and device for daily power generation amount of photovoltaic power generation, which are used for solving the technical problems that uncertainty and randomness of influence factors are not considered comprehensively in a daily power generation amount calculation method of a distributed photovoltaic power generation system in the prior art, and the applicability, the practicability and the applicability of the calculation method are difficult to meet.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a first-type point value Zadeh fuzzy calculation method for daily power generation amount of photovoltaic power generation according to an embodiment of the present invention includes:
101. according to the photovoltaic power generation system daily power generation data of a user acquired from a local monitoring center, a first one-type point value Zadeh fuzzy set for determining fuzzy uncertainty of the photovoltaic power generation system daily power generation is calculated through a daily power generation fuzzy set determination formula, wherein the daily power generation fuzzy set determination formula specifically comprises the following steps:
Figure BDA0001348462950000111
wherein E isHiFor the ith day power generation amount type point value Zadeh fuzzy set, EHiV1、EHiV2、...、EHiVnAnd muHiV1、μHiV2、...、μHiVnRespectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the i-th day power generation fuzzy uncertainty value, namely the extremely low, the very low, the medium, the high and the extremely high.
Firstly, obtaining relevant data information of daily generated energy of the distributed photovoltaic power generation system from a local monitoring data center, and providing a type-point value Zadeh fuzzy set with 9 fuzzy uncertainties for calculating and determining that the daily generated energy of the photovoltaic power generation system of a user is extremely low, very low, medium, high and extremely high by adopting a statistical analysis method.
102Acquiring daytime N of user location in one daySHThe sunshine intensity in each period is N values, and according to the relation between the output power of the photovoltaic power generation system and the sunshine intensity, the N value is determined by calculation through a sunshine intensity fuzzy set determination formulaSHIn each period, a second type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight intensity is defined as follows:
Figure BDA0001348462950000121
wherein the content of the first and second substances,
Figure BDA0001348462950000122
is a time period t, t 1,2SHThe solar intensity-type point value Zadeh fuzzy set,
Figure BDA0001348462950000123
Figure BDA0001348462950000124
and
Figure BDA0001348462950000125
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunlight intensity fuzzy uncertainty value of the time period t.
Obtaining daytime N of user location in one day from public meteorological data platformSHAnd (4) sunshine intensity related data information of each time interval, wherein the number of the data information is n. According to the characteristic relation between the output power of the photovoltaic power generation system and the sunshine intensity, a statistical analysis method is adopted to calculate and determine a type point value Zadeh fuzzy set of n numerical fuzzy uncertainty relations between the output power of the photovoltaic power generation system and the sunshine intensity in a user time period t.
103. Obtaining daytime N of user location in one daySHThe sunshine time in each period is n data, and the sunshine time is determined through calculation of a sunshine time fuzzy set determination formula according to the relation between the output power of the photovoltaic power generation system and the sunshine timeNSHIn each period, a third type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine duration, wherein the sunshine duration fuzzy set determination formula specifically comprises:
Figure BDA0001348462950000126
wherein the content of the first and second substances,
Figure BDA0001348462950000127
is a sunshine time-type point value Zadeh fuzzy set of a time period t,
Figure BDA0001348462950000128
and
Figure BDA0001348462950000129
respectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine time fuzzy uncertainty value of the time period t.
Obtaining daytime N of user location in one day from public meteorological data platformSHAnd (3) sunshine duration related data information of each time interval, wherein the number of the data information is n. According to the characteristic relation between the output power of the photovoltaic power generation system and the sunshine time, a statistical analysis method is adopted to calculate and determine a linear point value Zadeh fuzzy set of n numerical value fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine time in a user time period t
104. Obtaining daytime N of user location in one daySHThe sun shadow of each time interval is N values of data, and according to the relation between the output power of the photovoltaic power generation system and the sun shadow, the N is determined by calculation through a sun shadow fuzzy set determination formulaSHA fourth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight shadow in each period, wherein the sunlight shadow fuzzy set determination formula specifically comprises:
Figure BDA0001348462950000131
wherein the content of the first and second substances,
Figure BDA0001348462950000132
a sun shadow type point value Zadeh fuzzy set for time period t,
Figure BDA0001348462950000133
and
Figure BDA0001348462950000134
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine shadow fuzzy uncertainty value of the time period t.
Obtaining daytime N of user location in one day from public meteorological data platformSHAnd (3) sunshine shadow related data information of each time interval, wherein the number of the data information is n. According to the characteristic relation between the output power of the photovoltaic power generation system and the sunlight shadow, a statistical analysis method is adopted to calculate and determine a type point value Zadeh fuzzy set of n numerical value fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight shadow in a user time period t.
105. Obtaining daytime N of user location in one daySHThe sunshine declination angle in each period is N values, and according to the relation between the output power of the photovoltaic power generation system and the sunshine declination angle, the N is determined by calculation through a sunshine declination angle fuzzy set determination formulaSHIn each period, a fifth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight deflection angle is defined as follows:
Figure BDA0001348462950000135
wherein the content of the first and second substances,
Figure BDA0001348462950000136
is a sunshine deflection angle type point value Zadeh fuzzy set of a time period t,
Figure BDA0001348462950000137
and
Figure BDA0001348462950000138
respectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine deflection angle fuzzy uncertainty value of the time period t.
Obtaining daytime N of user location in one day from public meteorological data platformSHAnd (4) sunshine declination related data information of each time period, wherein the number of the sunshine declination related data information is n. According to the characteristic relation between the output power of the photovoltaic power generation system and the sunlight deflection angle, a statistical analysis method is adopted to calculate and determine a point value Zadeh fuzzy set of n numerical fuzzy uncertainty relations between the output power of the photovoltaic power generation system and the sunlight deflection angle in a user time period t.
106. Acquiring N values of data of environmental temperature rise caused by sunshine in N time periods in the daytime of a user location, and according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise, calculating and determining a sixth type point value Zadeh fuzzy set of the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods through a sunshine environmental temperature rise fuzzy set determination formula, wherein the sunshine environmental temperature rise fuzzy set determination formula specifically comprises the following steps:
Figure BDA0001348462950000141
wherein the content of the first and second substances,
Figure BDA0001348462950000142
a type point value Zadeh fuzzy set for the temperature rise of the sunshine environment in the time period t,
Figure BDA0001348462950000143
Figure BDA0001348462950000144
and
Figure BDA0001348462950000145
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine environment temperature rise fuzzy uncertainty value of the time period t.
Obtaining daytime N of user location in one day from public meteorological data platformSHThe data information related to the rise amplitude value of the ambient temperature caused by sunshine in each time interval has n values. According to the characteristic relation between the output power of the photovoltaic power generation system and the environmental temperature rise, a statistical analysis method is adopted to calculate and determine a type point value Zadeh fuzzy set of n numerical fuzzy uncertainty relations between the output power of the photovoltaic power generation system and the environmental temperature rise in a user time period t.
107. Calculating the daily power generation amount of the photovoltaic power generation system according to a daily power generation amount calculation formula by using a fuzzy analysis method according to a first type point value Zadeh fuzzy set, a second type point value Zadeh fuzzy set, a third type point value Zadeh fuzzy set, a fourth type point value Zadeh fuzzy set, a fifth type point value Zadeh fuzzy set and a sixth type point value Zadeh fuzzy set, wherein the daily power generation amount calculation formula specifically comprises the following steps:
Figure BDA0001348462950000146
wherein k isPVEIs a photoelectric conversion coefficient of the photovoltaic power generation panel,
Figure BDA0001348462950000147
the probability of occurrence of b (b 1, 2.. multidot.q) fuzzy numbers in the period with the sunshine intensity in the historical data,
Figure BDA0001348462950000148
is the probability of occurrence of the b < th > fuzzy number in the historical data and the sunshine temperature rise in the time period t,
Figure BDA0001348462950000149
as the probability of occurrence of the b-th fuzzy number with the sunshine shading in the history data in the time period t,
Figure BDA00013484629500001410
is the probability of occurrence of the b-th fuzzy number in the historical data at the time interval t from the sunshine deviation angle,
Figure BDA00013484629500001411
is the probability of occurrence of the b-th fuzzy number in the historical data and the sunshine duration in the time period t.
Figure BDA00013484629500001412
Represents NSHThe union of the fuzzy sets is set as,
Figure BDA00013484629500001413
representing a union of fuzzy sets.
Finally, calculating the installed capacity P by adopting a fuzzy analysis methodPVN(to generate electric power at a reference temperature, PPVNR1) is used.
It should be noted that the basic principle of the embodiment of the present invention is to consider the random and fuzzy uncertainty affecting the daily power generation amount of the distributed photovoltaic power generation system, mainly consider the random and fuzzy uncertainty factors such as the solar radiation intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle, etc. of the user location in the area, and obtain the daytime N of the user location in one day through the public meteorological data platformSHThe method comprises the steps that relevant data information of maximum values, average values and minimum values of parameters such as the sunshine intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle and the like in each period is obtained through a local monitoring data center, relevant data information of period generated energy of a distributed photovoltaic power generation system is obtained through a power grid energy management system EMS, the running data of a power grid is obtained through the power grid energy management system, a dimensional trapezoid fuzzy set concept and a calculation method thereof are mainly introduced when the sunshine intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle and the like are considered, and the daily generated energy of the distributed photovoltaic power generation system is calculated on the basis of fuzzy probability analysis on the assumption that parameters such as the sunshine intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle and the like and a user battery energy storage charging event all obey a Zadeh fuzzy distribution.
The embodiment of the invention provides a linear point value Zadeh fuzzy calculation method for daily power generation amount of photovoltaic power generation, which comprises the following steps: calculating a linear point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation of the photovoltaic power generation system according to the acquired daily power generation data of the photovoltaic power generation system of the user; acquiring sunshine intensity data of N time periods in the daytime of a user location, and calculating a point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine intensity in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine intensity; acquiring sunshine time data of N time periods in the daytime of a user location, and calculating a point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of a photovoltaic power generation system and sunshine time in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine time; acquiring sunshine shadow data of N time periods in the daytime of a user location, and calculating a point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of a photovoltaic power generation system and the sunshine shadow in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine shadow; acquiring sunshine deflection angle data of N time periods in the daytime of a user location, and calculating a type-I point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine deflection angle in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine deflection angle; acquiring environmental temperature rise data caused by sunshine in N time periods in the daytime of a user location, and calculating and determining a point value Zadeh fuzzy set of a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise; the embodiment of the invention calculates the daily generated energy of the photovoltaic power generation system according to a type point value Zadeh fuzzy set of the output power of the photovoltaic power generation system by a fuzzy analysis method, simultaneously considers the random and fuzzy uncertainty influencing the daily generated energy of the distributed photovoltaic power generation system, mainly considers the random and fuzzy uncertainty factors such as the solar radiation intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle and the like of the area, and the like of the user location in different time and space, mainly introduces a dimensional trapezoid fuzzy set concept and a calculation method thereof when considering the sunshine intensity, the sunshine time, the sunshine shadow, the sunshine deflection angle and the like, assumes that the parameters such as the sunshine intensity, the sunshine shadow, the sunshine deflection angle and the like and the user battery energy storage charging event all obey the Zadeh fuzzy distribution rule, and calculates the daily generated energy of the distributed photovoltaic power generation system on the basis of fuzzy probability analysis, the method provides necessary technical support for distributed new energy power generation and intelligent power grid dispatching operation, and solves the technical problems that uncertainty and randomness of influence factors are not considered comprehensively in the distributed photovoltaic power generation system daily power generation amount calculation method in the prior art, and the calculation method is difficult to meet the applicability, the practicability and the applicability.
In the above, a detailed description is given of a first-type point value Zadeh fuzzy calculation method for daily power generation amount of photovoltaic power generation provided by the embodiment of the present invention, and a detailed description is given of a first-type point value Zadeh fuzzy calculation device for daily power generation amount of photovoltaic power generation provided by the embodiment of the present invention.
Referring to fig. 2, an embodiment of the invention provides a linear point value Zadeh fuzzy calculation apparatus for daily power generation amount of photovoltaic power generation, including:
the first calculation module 201 is configured to calculate a first one-to-one point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation amount of the photovoltaic power generation system according to the acquired daily power generation amount data of the photovoltaic power generation system of the user;
the second calculation module 202 is configured to acquire sunshine intensity data of N time periods in the daytime of a location where a user is located, and calculate a second linear point value Zadeh fuzzy set for determining a fuzzy uncertainty relationship between output power of the photovoltaic power generation system and sunshine intensity in the N time periods according to a relationship between the output power of the photovoltaic power generation system and the sunshine intensity;
the third calculation module 203 is configured to acquire sunshine time data of N time periods in the daytime of a place where a user is located, and calculate a third linear point value Zadeh fuzzy set for determining a fuzzy uncertainty relationship between the output power of the photovoltaic power generation system and sunshine time in the N time periods according to a relationship between the output power of the photovoltaic power generation system and sunshine time;
the fourth calculation module 204 is configured to acquire sunshine shadow data of N time periods in the daytime of a location where a user is located, and calculate a fourth linear point value Zadeh fuzzy set for determining a fuzzy uncertainty relationship between the output power of the photovoltaic power generation system and the sunshine shadow in the N time periods according to a relationship between the output power of the photovoltaic power generation system and the sunshine shadow;
the fifth calculation module 205 is configured to acquire sunshine deflection angle data of N time periods in the daytime of a location where a user is located, and calculate a fifth type point value Zadeh fuzzy set for determining a fuzzy uncertainty relationship between the output power of the photovoltaic power generation system and the sunshine deflection angle in the N time periods according to a relationship between the output power of the photovoltaic power generation system and the sunshine deflection angle;
the sixth calculation module 206 is configured to acquire environmental temperature rise data caused by sunlight in N time periods in the daytime of a location where the user is located, and calculate a sixth type point value Zadeh fuzzy set for determining a fuzzy uncertainty relationship between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods according to a relationship between the output power of the photovoltaic power generation system and the environmental temperature rise;
and a seventh calculating module 207, configured to calculate the daily power generation amount of the photovoltaic power generation system according to the first type point value Zadeh fuzzy set, the second type point value Zadeh fuzzy set, the third type point value Zadeh fuzzy set, the fourth type point value Zadeh fuzzy set, the fifth type point value Zadeh fuzzy set, and the sixth type point value Zadeh fuzzy set by using a fuzzy analysis method.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. A type-I point value Zadeh fuzzy calculation method of daily power generation amount of photovoltaic power generation is characterized by comprising the following steps:
calculating a first one-type point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation of the photovoltaic power generation system according to the acquired daily power generation data of the photovoltaic power generation system of the user;
acquiring sunshine intensity data of N time periods in the daytime of a user location, and calculating and determining a second type-I point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine intensity in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine intensity;
acquiring sunshine time data of N time periods in the daytime of a user location, and calculating and determining a third type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and sunshine time in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine time;
acquiring sunshine shadow data of N time periods in the daytime of a user location, and calculating and determining a fourth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine shadow in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine shadow;
acquiring sunshine deflection angle data of N time periods in the daytime of a user location, and calculating and determining a fifth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine deflection angle in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine deflection angle;
acquiring environmental temperature rise data caused by sunshine in N time periods in the daytime of a user location, and calculating a sixth type point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise;
calculating the daily power generation amount of the photovoltaic power generation system according to the first type point value Zadeh fuzzy set, the second type point value Zadeh fuzzy set, the third type point value Zadeh fuzzy set, the fourth type point value Zadeh fuzzy set, the fifth type point value Zadeh fuzzy set and the sixth type point value Zadeh fuzzy set by a fuzzy analysis method;
the first one-type point value Zadeh fuzzy set for calculating and determining the fuzzy uncertainty of the daily power generation amount of the photovoltaic power generation system according to the acquired daily power generation amount data of the photovoltaic power generation system of the user comprises the following steps:
according to the photovoltaic power generation system daily power generation data of a user acquired from a local monitoring center, a first one-type point value Zadeh fuzzy set of fuzzy uncertainty of the photovoltaic power generation system daily power generation is determined through a daily power generation fuzzy set determination formula, wherein the daily power generation fuzzy set determination formula specifically comprises the following steps:
Figure FDA0002732794110000021
wherein E isHiFor the ith day power generation amount type point value Zadeh fuzzy set, EHiV1、EHiV2、…、EHiVnAnd muHiV1、μHiV2、…、μHiVnRespectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the i-th day power generation fuzzy uncertainty value, namely the extremely low, the very low, the medium, the high and the extremely high.
2. The method for fuzzy calculation of a point value Zadeh of photovoltaic power generation daily power generation according to claim 1, wherein the step of obtaining sunshine intensity data of N time periods in the daytime of a location where a user is located, and calculating a second fuzzy set of point values Zadeh for determining fuzzy uncertainty relationship between output power and sunshine intensity of the photovoltaic power generation system in the N time periods according to the relationship between output power and sunshine intensity of the photovoltaic power generation system comprises:
obtaining daytime N of user location in one daySHThe sunshine intensity in each period is N values, and according to the relation between the output power of the photovoltaic power generation system and the sunshine intensity, the N is determined by calculation through a sunshine intensity fuzzy set determination formulaSHIn each period, a second type-I point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight intensity is defined as follows:
Figure FDA0002732794110000022
wherein the content of the first and second substances,
Figure FDA0002732794110000023
is a time period t, t 1,2SHThe solar intensity-type point value Zadeh fuzzy set,
Figure FDA0002732794110000024
Figure FDA0002732794110000025
and
Figure FDA0002732794110000026
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunlight intensity fuzzy uncertainty value of the time period t.
3. The method for fuzzy calculation of a point value Zadeh of photovoltaic power generation daily power generation according to claim 2, wherein the step of obtaining sunshine time data of N periods of daytime in a day where a user is located, and calculating a third fuzzy set of point values Zadeh for determining fuzzy uncertainty relationship between output power of the photovoltaic power generation system and sunshine time in the N periods according to the relationship between output power of the photovoltaic power generation system and sunshine time comprises:
obtaining daytime N of user location in one daySHThe sunshine time in each period is N data, and according to the relation between the output power of the photovoltaic power generation system and the sunshine time, the sunshine time fuzzy set determination formula is used for calculating and determining NSHIn each period, a third linear point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine duration, wherein the sunshine duration fuzzy set determination formula specifically comprises:
Figure FDA0002732794110000031
wherein the content of the first and second substances,
Figure FDA0002732794110000032
is a sunshine time-type point value Zadeh fuzzy set of a time period t,
Figure FDA0002732794110000033
and
Figure FDA0002732794110000034
respectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine time fuzzy uncertainty value of the time period t.
4. The method for fuzzy calculation of a point value Zadeh of photovoltaic power generation daily power generation according to claim 3, wherein the step of obtaining sunshine shadow data of N time periods in the daytime of a day where a user is located, and calculating a fourth point value Zadeh fuzzy set for determining fuzzy uncertainty relation between output power of the photovoltaic power generation system and sunshine shadow in the N time periods according to the relation between output power of the photovoltaic power generation system and sunshine shadow comprises:
obtaining the location of a userDay N of the earthSHThe sun shadow of each time period is N values, and according to the relation between the output power of the photovoltaic power generation system and the sun shadow, the N is determined by calculation of a sun shadow fuzzy set determination formulaSHIn each period, a fourth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunlight shadow, wherein the sunlight shadow fuzzy set determination formula specifically comprises:
Figure FDA0002732794110000035
wherein the content of the first and second substances,
Figure FDA0002732794110000036
a sun shadow type point value Zadeh fuzzy set for time period t,
Figure FDA0002732794110000037
and
Figure FDA0002732794110000038
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine shadow fuzzy uncertainty value of the time period t.
5. The method for fuzzy calculation of a point value Zadeh of daily power generation amount of photovoltaic power generation according to claim 4, wherein the step of obtaining sunshine declination data of N time intervals in the daytime of a place where a user is located, and calculating a fifth type point value Zadeh fuzzy set for determining fuzzy uncertainty relation between output power of the photovoltaic power generation system and the sunshine declination in the N time intervals according to the relation between the output power of the photovoltaic power generation system and the sunshine declination comprises:
obtaining daytime N of user location in one daySHThe sunshine declination angle in each period is N values, and according to the relation between the output power of the photovoltaic power generation system and the sunshine declination angle, the N is determined by calculation through a sunshine declination angle fuzzy set determination formulaSHIn each period, a fifth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the solar declination angle, wherein the determination formula of the solar declination fuzzy set is as follows:
Figure FDA0002732794110000041
wherein the content of the first and second substances,
Figure FDA0002732794110000042
is a sunshine deflection angle type point value Zadeh fuzzy set of a time period t,
Figure FDA0002732794110000043
and
Figure FDA0002732794110000044
respectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine deflection angle fuzzy uncertainty value of the time period t.
6. The method for fuzzy calculation of a point value Zadeh of daily power generation amount of photovoltaic power generation according to claim 5, wherein the step of obtaining environmental temperature rise data caused by sunshine in N time periods of a day where a user is located, and calculating a sixth point value Zadeh fuzzy set for determining a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise comprises:
obtaining daytime N of user location in one daySHThe method comprises the steps of obtaining N values of environmental temperature rise caused by sunshine in each time period, and calculating and determining N through a sunshine environmental temperature rise fuzzy set determination formula according to the relation between the output power of the photovoltaic power generation system and the environmental temperature riseSHA sixth type point value Zadeh fuzzy set of fuzzy uncertainty relation between output power of the photovoltaic power generation system and environmental temperature rise in each period, wherein the sunshine environmental temperature rise fuzzy set determination formulaThe method specifically comprises the following steps:
Figure FDA0002732794110000045
wherein the content of the first and second substances,
Figure FDA0002732794110000046
a type point value Zadeh fuzzy set for the temperature rise of the sunshine environment in the time period t,
Figure FDA0002732794110000047
Figure FDA0002732794110000048
and
Figure FDA0002732794110000049
respectively are 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the sunshine environment temperature rise fuzzy uncertainty value of the time period t.
7. The method for fuzzy calculation of a point value Zadeh of daily generated power of photovoltaic power generation according to claim 6, wherein said calculating daily generated power of said photovoltaic power generation system from said first, second, third, fourth, fifth, and sixth fuzzy sets of point values Zadeh by fuzzy analysis comprises:
calculating the daily power generation amount of the photovoltaic power generation system according to the first type point value Zadeh fuzzy set, the second type point value Zadeh fuzzy set, the third type point value Zadeh fuzzy set, the fourth type point value Zadeh fuzzy set, the fifth type point value Zadeh fuzzy set and the sixth type point value Zadeh fuzzy set by a daily power generation amount calculation formula through a fuzzy analysis method, wherein the daily power generation amount calculation formula specifically comprises the following steps:
Figure FDA0002732794110000051
wherein k isPVEIs a photoelectric conversion coefficient of the photovoltaic power generation panel,
Figure FDA0002732794110000052
the probability of occurrence of the b-th fuzzy number in the period with the sunshine intensity in the historical data,
Figure FDA0002732794110000053
is the probability of occurrence of the b < th > fuzzy number in the historical data and the sunshine temperature rise in the time period t,
Figure FDA0002732794110000054
as the probability of occurrence of the b-th fuzzy number with the sunshine shading in the history data in the time period t,
Figure FDA0002732794110000055
is the probability of occurrence of the b-th fuzzy number in the historical data at the time interval t from the sunshine deviation angle,
Figure FDA0002732794110000056
is the probability of occurrence of the b-th fuzzy number in the historical data and the sunshine duration in the time period t,
Figure FDA0002732794110000057
represents NSHA union of fuzzy sets, a V-shaped cluster represents the union of fuzzy sets, and b is 1, 2.
8. The utility model provides a fuzzy accounting device of a type point value Zadeh of photovoltaic power generation daily generated energy which characterized in that includes:
the first calculation module is used for calculating a first one-model point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation of the photovoltaic power generation system according to the acquired daily power generation data of the photovoltaic power generation system of the user;
the second calculation module is used for acquiring sunshine intensity data of N time periods in the daytime of the location of a user, and calculating a second linear point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power and the sunshine intensity of the photovoltaic power generation system in the N time periods according to the relation between the output power and the sunshine intensity of the photovoltaic power generation system;
the third calculation module is used for acquiring sunshine time data of N time periods in the daytime of the location of a user, and calculating a third linear point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine time in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine time;
the fourth calculation module is used for acquiring sunshine shadow data of N time periods in the daytime of the location of a user, and calculating a fourth linear point value Zadeh fuzzy set for determining the fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine shadow in the N time periods according to the relation between the output power of the photovoltaic power generation system and the sunshine shadow;
the fifth calculation module is used for acquiring sunshine deflection angle data of N time intervals in the daytime of the location of a user, and calculating and determining a fifth type point value Zadeh fuzzy set of fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the sunshine deflection angle in the N time intervals according to the relation between the output power of the photovoltaic power generation system and the sunshine deflection angle;
the sixth calculation module is used for acquiring environmental temperature rise data caused by sunshine in N time periods in the daytime of the location of a user, and calculating and determining a sixth type point value Zadeh fuzzy set of a fuzzy uncertainty relation between the output power of the photovoltaic power generation system and the environmental temperature rise in the N time periods according to the relation between the output power of the photovoltaic power generation system and the environmental temperature rise;
a seventh calculating module, configured to calculate, by a fuzzy analysis method, a daily power generation amount of the photovoltaic power generation system according to the first type point value Zadeh fuzzy set, the second type point value Zadeh fuzzy set, the third type point value Zadeh fuzzy set, the fourth type point value Zadeh fuzzy set, the fifth type point value Zadeh fuzzy set, and the sixth type point value Zadeh fuzzy set;
the first calculation module specifically includes:
the calculation unit is used for calculating a first one-to-one point value Zadeh fuzzy set for determining fuzzy uncertainty of daily power generation of the photovoltaic power generation system through a daily power generation fuzzy set determination formula according to daily power generation data of the photovoltaic power generation system of a user acquired from a local monitoring center, wherein the daily power generation fuzzy set determination formula specifically comprises the following steps:
Figure FDA0002732794110000061
wherein E isHiFor the ith day power generation amount type point value Zadeh fuzzy set, EHiV1、EHiV2、…、EHiVnAnd muHiV1、μHiV2、…、μHiVnRespectively 1 to n linear point value Zadeh fuzzy numbers and membership values thereof determined in the interval range of the i-th day power generation fuzzy uncertainty value, namely the extremely low, the very low, the medium, the high and the extremely high.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103362507A (en) * 2013-07-09 2013-10-23 中国矿业大学 Method for improving memory cutting execution precision of coal mining machine
CN106300423A (en) * 2016-09-07 2017-01-04 广东工业大学 Based on the trapezoidal fuzzy method and device determining photovoltaic generation daily generation of three-dimensional
CN106383937A (en) * 2016-09-07 2017-02-08 广东工业大学 Method and system for calculating output power of water cooling photovoltaic-solar thermal power generation system

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US7580817B2 (en) * 2003-08-20 2009-08-25 New Energy Options, Inc. Method and system for predicting solar energy production

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103362507A (en) * 2013-07-09 2013-10-23 中国矿业大学 Method for improving memory cutting execution precision of coal mining machine
CN106300423A (en) * 2016-09-07 2017-01-04 广东工业大学 Based on the trapezoidal fuzzy method and device determining photovoltaic generation daily generation of three-dimensional
CN106383937A (en) * 2016-09-07 2017-02-08 广东工业大学 Method and system for calculating output power of water cooling photovoltaic-solar thermal power generation system

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