CN113569936B - Power supply control method and device for green electricity system and computer storage medium - Google Patents
Power supply control method and device for green electricity system and computer storage medium Download PDFInfo
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Abstract
The invention discloses a power supply control method and device of a green electricity system and a computer storage medium, wherein the method comprises the following steps: acquiring expected power supply of each green power system, and acquiring expected power consumption of each park associated with each green power system at a time point corresponding to the expected power supply; acquiring the power distribution coefficient of each green electric system to each park when the total green electric utilization rate of all parks reaches the maximum; and adjusting the power supply power of the green electric system to each park according to the power distribution coefficient of each green electric system to each park. According to the invention, the power distribution coefficient of each green electric system to each park is calculated through the expected power supply and the expected power consumption, so that the green electric system is controlled to distribute power according to the power distribution coefficient, and the utilization rate of green electricity is improved.
Description
Technical Field
The present invention relates to the field of power supply control technologies, and in particular, to a power supply control method and apparatus for a green power system, and a computer storage medium.
Background
When the green electricity system supplies power to electric equipment in each park, a rough power supply mode is often adopted, namely, fixed power distribution coefficients are set for each park, and power is supplied to the corresponding park according to the fixed power distribution coefficients.
However, the actual electricity demand of each park is changed, and the rough power supply can cause the green electricity to be too small in the actual electricity consumption of the park, so that the green electricity utilization rate is low.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a power supply control method and device of a green electricity system and a computer storage medium, and aims to improve the utilization rate of green electricity in actual electricity utilization of a park.
In order to achieve the above object, the present invention provides a power supply control method of a green power system, the power supply control method of the green power system comprising the steps of:
acquiring expected power supply of each green power system, and acquiring expected power consumption of each park associated with each green power system at a time point corresponding to the expected power supply;
Obtaining power distribution coefficients of all green electric systems for all parks when the total green electric utilization rate of all parks reaches the maximum, wherein the power distribution coefficients of all green electric systems for all parks are obtained, the product of the power distribution coefficients of all green electric systems for all parks and the expected power supply power of the green electric systems is obtained, the sum of the products of all green electric systems for all parks is obtained, and the sum of the expected power supply power of all parks is divided, so that the total green electric utilization rate of all parks is obtained;
And adjusting the power supply of the green electric system to each park according to the power distribution coefficient of each green electric system to each park.
Optionally, obtaining constraints on the distribution coefficients of each green system for each of the parks when the total green utilization of all of the parks is maximized includes:
And taking the sum of the power distribution coefficients of all the green electric systems for the parks and the product of the expected power supply powers of the green electric systems as the expected supplied green electric power of the parks, and acquiring the expected failure probability corresponding to the expected supplied green electric power of the parks according to the association relation between the supplied green electric power corresponding to the parks and the failure probability.
Optionally, the power supply control method of the green power system further includes:
Acquiring a history of the historical power used in the park to be supplied with green power;
acquiring historical fault probability of electric equipment in the park at a time point corresponding to the historical supplied green electric power;
And generating an association relation between the supplied green electric power corresponding to the park and the fault probability according to the historical supplied green electric power and the historical fault probability, and storing the association relation.
Optionally, the step of generating the association relationship between the supplied green electric power corresponding to the campus and the fault probability according to the historical supplied green electric power and the historical fault probability includes:
Acquiring the historical quantity of electric equipment which fails in the park at the time point corresponding to the historical supplied green electric power;
a Poisson distribution algorithm is adopted, and parameters of Poisson distribution are determined according to the historical number of the failed electric equipment, the historical failure probability and the historical failure probability;
Taking the corresponding relation between the supplied green electric power and the failure probability corresponding to the park as the parameter of the Poisson distribution according to the corresponding relation between the supplied green electric power and the historical number of the failed electric equipment;
The step of obtaining the expected failure probability corresponding to the expected supplied green electric power of the park according to the association relation between the supplied green electric power corresponding to the park and the failure probability comprises:
Determining an expected number of failed electrical devices corresponding to the expected supplied green electrical power according to a correspondence between the historical supplied green electrical power and the historical number of failed electrical devices;
And determining the expected fault probability according to the expected number and the parameters of the poisson distribution by adopting a poisson distribution algorithm.
Optionally, after the step of adjusting the power supply of the green electric system to the campus according to the power distribution coefficient of each green electric system to each campus, the method further includes:
classifying the electric equipment in each park according to the historical electric power of each electric equipment in each park to obtain a plurality of classes of electric equipment;
and uniformly supplying power to the electric equipment belonging to the same class.
Optionally, the step of classifying the electric devices in each park according to the historical electric power of the electric devices in each park includes:
Generating a covariance matrix based on the historical power consumption according to the historical power consumption of the electric equipment in each park;
Performing eigenvalue decomposition on the covariance matrix based on the historical power consumption to obtain eigenvalues corresponding to electric equipment in the park;
And classifying the electric equipment in the park according to the characteristic value.
Optionally, the step of obtaining the expected power supply of each green power system includes:
When the green power system is a photovoltaic system, acquiring historical environment parameters of the photovoltaic system at a time point corresponding to the historical power supply power of the green power system;
Generating a power generation prediction power model according to the historical environment parameters and the historical power supply power;
acquiring expected environmental parameters of the photovoltaic system at a time point corresponding to the expected power supply;
and determining the expected power supply power of the green power system according to the expected environmental parameters and the power generation predicted power model.
Optionally, the step of obtaining the expected power of each park associated with each green electric system at the point in time corresponding to the expected power of electricity comprises:
generating a load prediction model according to historical electric power of the park;
Acquiring the current power consumption of the park;
and determining the expected electric power of the park at the time point corresponding to the expected electric power according to the current electric power and the load prediction model.
In addition, to achieve the above object, the present invention also provides a power supply control device of a green power system, including: the power supply control method of the green power system comprises a memory, a processor and a power supply control program of the green power system, wherein the power supply control program of the green power system is stored in the memory and can run on the processor, and the power supply control program of the green power system is executed by the processor to realize the steps of the power supply control method of the green power system.
In addition, in order to achieve the above object, the present invention also provides a computer storage medium having stored thereon a power supply control program of a green electric system, which when executed by a processor, implements the steps of the power supply control method of the green electric system as described above.
The power supply control method, the power supply control device and the computer storage medium of the green power systems acquire the expected power supply power of each green power system, and acquire the expected power consumption of each park associated with each green power system at the time point corresponding to the expected power supply power; obtaining power distribution coefficients of all green electric systems for all parks when the total green electric utilization rate of all parks reaches the maximum, wherein the power distribution coefficients of all green electric systems for all parks are obtained, the product of the power distribution coefficients of all green electric systems for all parks and the expected power supply power of the green electric systems is obtained, the sum of the products of all green electric systems for all parks is obtained, and the sum of the expected power supply power of all parks is divided, so that the total green electric utilization rate of all parks is obtained; and adjusting the power supply of the green electric system to each park according to the power distribution coefficient of each green electric system to each park. According to the invention, the power distribution coefficient of each green electric system to each park is calculated through the expected power supply and the expected power consumption, so that the green electric system is controlled to distribute power according to the power distribution coefficient, and the utilization rate of green electricity is improved.
Drawings
FIG. 1 is a schematic diagram of a terminal structure of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of an embodiment of a power control method of a green electric system according to the present invention;
FIG. 3 is a flowchart of another embodiment of a power control method of a green electric system according to the present invention;
FIG. 4 is a flowchart illustrating a power control method of a green electric system according to another embodiment of the present invention;
FIG. 5 is a flowchart of a power control method of a green electric system according to another embodiment of the present invention;
FIG. 6 is a schematic diagram of a power supply and utilization relationship between each green electricity system and consumers in each campus in accordance with the present invention;
fig. 7 is a flowchart illustrating an exemplary power supply control method of the green electric system according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a solution, and the power distribution coefficient of each green electric system to each park is calculated through the expected power supply and the expected power consumption so as to control the green electric system to distribute power according to the power distribution coefficient, thereby improving the utilization rate of green electricity.
As shown in fig. 1, fig. 1 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention is a power supply control device of a green power system.
As shown in fig. 1, the terminal may include: a processor 1001, for example CPU, DSP, MCU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as keys, and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the terminal structure shown in fig. 1 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, a memory 1005 as a computer storage medium may include a network communication module, a user interface module, and a power supply control program of the green electric system.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call a power supply control program of the green-power-generation system stored in the memory 1005, and perform the following operations:
acquiring expected power supply of each green power system, and acquiring expected power consumption of each park associated with each green power system at a time point corresponding to the expected power supply;
Obtaining power distribution coefficients of all green electric systems for all parks when the total green electric utilization rate of all parks reaches the maximum, wherein the power distribution coefficients of all green electric systems for all parks are obtained, the product of the power distribution coefficients of all green electric systems for all parks and the expected power supply power of the green electric systems is obtained, the sum of the products of all green electric systems for all parks is obtained, and the sum of the expected power supply power of all parks is divided, so that the total green electric utilization rate of all parks is obtained;
And adjusting the power supply of the green electric system to each park according to the power distribution coefficient of each green electric system to each park.
Further, the processor 1001 may call the power supply control program of the green-power-generation system stored in the memory 1005, and further perform the following operations:
Acquiring a history of the historical power used in the park to be supplied with green power;
acquiring historical fault probability of electric equipment in the park at a time point corresponding to the historical supplied green electric power;
And generating an association relation between the supplied green electric power corresponding to the park and the fault probability according to the historical supplied green electric power and the historical fault probability, and storing the association relation.
Further, the processor 1001 may call the power supply control program of the green-power-generation system stored in the memory 1005, and further perform the following operations:
Acquiring the historical quantity of electric equipment which fails in the park at the time point corresponding to the historical supplied green electric power;
a Poisson distribution algorithm is adopted, and parameters of Poisson distribution are determined according to the historical number of the failed electric equipment, the historical failure probability and the historical failure probability;
Taking the corresponding relation between the supplied green electric power and the failure probability corresponding to the park as the parameter of the Poisson distribution according to the corresponding relation between the supplied green electric power and the historical number of the failed electric equipment;
further, the processor 1001 may call the power supply control program of the green-power-generation system stored in the memory 1005, and further perform the following operations:
Determining an expected number of failed electrical devices corresponding to the expected supplied green electrical power according to a correspondence between the historical supplied green electrical power and the historical number of failed electrical devices;
And determining the expected fault probability according to the expected number and the parameters of the poisson distribution by adopting a poisson distribution algorithm.
Further, the processor 1001 may call the power supply control program of the green-power-generation system stored in the memory 1005, and further perform the following operations:
classifying the electric equipment in each park according to the historical electric power of each electric equipment in each park to obtain a plurality of classes of electric equipment;
and uniformly supplying power to the electric equipment belonging to the same class.
Further, the processor 1001 may call the power supply control program of the green-power-generation system stored in the memory 1005, and further perform the following operations:
Generating a covariance matrix based on the historical power consumption according to the historical power consumption of the electric equipment in each park;
Performing eigenvalue decomposition on the covariance matrix based on the historical power consumption to obtain eigenvalues corresponding to electric equipment in the park;
And classifying the electric equipment in the park according to the characteristic value.
Further, the processor 1001 may call the power supply control program of the green-power-generation system stored in the memory 1005, and further perform the following operations:
When the green power system is a photovoltaic system, acquiring historical environment parameters of the photovoltaic system at a time point corresponding to the historical power supply power of the green power system;
Generating a power generation prediction power model according to the historical environment parameters and the historical power supply power;
acquiring expected environmental parameters of the photovoltaic system at a time point corresponding to the expected power supply;
and determining the expected power supply power of the green power system according to the expected environmental parameters and the power generation predicted power model.
Further, the processor 1001 may call the power supply control program of the green-power-generation system stored in the memory 1005, and further perform the following operations:
generating a load prediction model according to historical electric power of the park;
Acquiring the current power consumption of the park;
and determining the expected electric power of the park at the time point corresponding to the expected electric power according to the current electric power and the load prediction model.
Referring to fig. 2, in an embodiment, a power supply control method of a green power system includes the steps of:
step S10, obtaining expected power supply power of each green electric system, and obtaining expected power consumption of each park associated with each green electric system at a time point corresponding to the expected power supply power;
In this embodiment, green electricity refers to that in the process of producing electricity, the carbon dioxide emission amount is zero or approaches zero, and compared with the electricity produced by other modes (such as thermal power generation), the environmental impact effect is lower, and the green electricity system is used for providing electric energy for electric equipment in the corresponding park, and generally comprises a photovoltaic system, an energy storage system, a hydrogen production system and the like.
Optionally, as shown in fig. 6, the electricity consumption area is divided into a plurality of parks, and a green electricity system and electric equipment can be arranged in each park, and the green electricity system can supply power for the electric equipment in the park and can also supply power for the electric equipment in other parks associated with the green electricity system.
Optionally, when the green electric system supplies electric power to electric equipment in the campus, the partial supply power is supplied green electric power. Alternatively, since the green electric power supplied is typically small, the electric devices on the campus may also be connected to a municipal power supply network to supply electric devices on the campus via a common municipal power supply network, so that the power used on the campus is typically greater than the total power supplied by the green electric system.
Alternatively, a green electricity system-associated campus refers to a campus where green electricity supply may be performed through the green electricity system.
Optionally, the expected power supply of the green electric system in a future period of time or a future time is predicted according to the historical power supply of each green electric system, and the expected power supply of each park in the future period of time or the future time is predicted according to the historical power supply of the park.
Alternatively, the predicted expected power supply and/or the predicted expected power consumption may be implemented based on a machine learning algorithm, for example, based on a machine forest algorithm, a gradient boosting tree, or a combination of these algorithms.
Step S20, obtaining the power distribution coefficient of each green electric system for each park when the total green electric utilization rate of all parks reaches the maximum, wherein the product of the power distribution coefficient of each green electric system for each park and the expected power supply power of the green electric system is obtained, the sum of the products of all green electric systems for all parks is obtained, and the sum of the products of all green electric systems and the expected power supply power of all parks is divided to obtain the total green electric utilization rate of all parks;
In this embodiment, the distribution coefficient of the green electric system to the campus is the duty cycle of the power supplied by the green electric system to the campus relative to the total power that the green electric system can output.
Alternatively, a Lagrangian dual algorithm may be used to perform mathematical calculations to obtain the power distribution coefficients for each green system for each campus while maximizing the total green usage for all parks. Specifically, the power distribution coefficients of each green system for each park may be converted into a corresponding power distribution coefficient matrix, for example, referring to fig. 6, fig. 6 includes park 1, park 2, and park 3, where all of the photovoltaic systems, the energy storage systems, and the hydrogen production systems are disposed in each of the 3 parks, and then all of the photovoltaic systems in the 3 parks may be converted into corresponding power distribution coefficient matrices for each park, specifically:
Wherein a11 is the power distribution coefficient of the photovoltaic system in the park 1 to the park 1, a12 is the power distribution coefficient of the photovoltaic system in the park 1 to the park 2, a13 is the power distribution coefficient of the photovoltaic system in the park 1 to the park 3, a21 is the power distribution coefficient of the photovoltaic system in the park 2 to the park 1, a22 is the power distribution coefficient of the photovoltaic system in the park 2 to the park 2, a23 is the power distribution coefficient of the photovoltaic system in the park 2 to the park 3, a31 is the power distribution coefficient of the photovoltaic system in the park 3 to the park 1, a32 is the power distribution coefficient of the photovoltaic system in the park 3 to the park 2, and a33 is the power distribution coefficient of the photovoltaic system in the park 3 to the park 1.
Similarly, the distribution coefficients of all energy storage systems in 3 parks for each park are converted into a corresponding distribution coefficient matrix, specifically:
similarly, the distribution coefficients of all hydrogen production systems in 3 parks for each park are converted into a corresponding distribution coefficient matrix, specifically:
Alternatively, taking fig. 6 as an example, the calculation formula of the total green electricity usage rate of all parks is as follows:
wherein, PV predict is the expected power of the photovoltaic system, ES is the expected power of the energy storage system, HY is the expected power of the hydrogen production system, ΣELE-predict is the sum of the expected power of all parks.
And calculating the maximum value of the total green electricity utilization rate of all parks by adopting a Lagrange dual algorithm, namely calculating the optimal solution of the dual problem. When the total green electricity utilization rate reaches the maximum value, the value of each power distribution coefficient matrix at the moment can be determined, and the power distribution coefficient of each green electricity system to each park is obtained.
Optionally, the constraint condition of the lagrangian dual algorithm in calculating the maximum value of the total green electricity usage of all parks may include:
a11+a12+a13≤1
a21+a22+a23≤1
a31+a32+a33≤1
b11+b12+b13≤1
b21+b22+b23≤1
b31+b32+b33≤1
c11+c12+c13≤1
c21+c22+c23≤1
c31+c32+c33≤1
by limiting the constraint conditions, it can be ensured that the sum of the power supplied by a single green system to all parks does not exceed the expected power supplied by the green system when the power supplied by the green system to each park is adjusted according to the power distribution coefficient of each green system to each park.
And step S30, adjusting the power supply of the green electric system to each park according to the power distribution coefficient of each green electric system to each park.
In this embodiment, after the power distribution coefficient of each green electric system for each park is determined, the power distribution coefficient is multiplied by the expected power supply power of the green electric system to obtain the power supply power of the green electric system for the park, and the power supply power of the green electric system for the park is adjusted according to the power supply power, so that the total green electric utilization rate of all parks can be ensured.
In the technical scheme disclosed by the embodiment, the power distribution coefficient of each green electric system to each park is calculated through the expected power supply and the expected power consumption, so that the green electric system is controlled to dynamically distribute power according to the power distribution coefficient, the power distribution system has strong adaptability, and the utilization rate of green electricity is improved.
In another embodiment, as shown in fig. 3, the constraint condition for obtaining the power distribution coefficient of each green system for each park when the total green power usage of all parks is maximized may include: the sum of expected failure probabilities of the electrical devices in all parks is less than a preset threshold.
In this embodiment, when the power supply of the green electric system to the campus is too high, the voltage of the electric equipment in the campus may be too high, resulting in equipment failure, so when the power distribution coefficients of the green electric systems to the parks are determined, the expected failure probability of the electric equipment in the campus is calculated through the power distribution coefficients, and the limitation of the sum of the expected failure probabilities of the electric equipment in all the parks is avoided, the failure risk of the electric equipment in the campus caused by unreasonable power distribution coefficients of the green electric systems to the parks is avoided, and the reliability of the electric equipment in the park when the green electric system supplies power to the parks is ensured.
Alternatively, when calculating the expected failure probability of the electric equipment in a single park, the sum of the products of the power distribution coefficients of all green electric systems for the park and the expected power supply powers of the corresponding green electric systems can be used as the expected supplied green electric power of the park (namely, the total expected power supply power of all green electric systems for the park), and the expected failure probability corresponding to the expected supplied green electric power of the park can be determined according to the association relation between the supplied green electric power corresponding to the park and the failure probability.
Before step S20, the power supply control method of the green power system further includes:
Step S40, acquiring the green electric power supplied by histories in the historic electric power used by the park;
In this embodiment, since the campus may receive power not only from green electricity but also from municipal power supply network, the historical power of the campus may include the historical green power.
Step S50, obtaining the historical fault probability of the electric equipment in the park at the time point corresponding to the historical supplied green electric power;
In this embodiment, the historical number of electric devices that have failed in the park at the time point corresponding to the time point where the green electric power is supplied in the history is obtained, and the ratio of the historical number to the total number of electric devices in the park is used as the historical failure probability of the electric devices in the park.
And step S60, generating an association relation between the supplied green electric power corresponding to the park and the fault probability according to the historical supplied green electric power and the historical fault probability, and storing the association relation.
Alternatively, the history of the supplied green electric power and the history of the time points at which the history of the supplied green electric power corresponds may be used as a set of correspondence, and the association between the supplied green electric power and the failure probability corresponding to the campus may be obtained from the sets of correspondence.
Optionally, when generating the association relationship between the supplied green electric power and the failure probability corresponding to the park, the historical number of the failed electric devices in the time point park corresponding to the historical supplied green electric power may be obtained, and a poisson distribution algorithm is adopted to determine the parameters of poisson distribution according to the historical number of the failed electric devices and the historical failure probability, where the poisson distribution algorithm has the following formula:
Wherein p (x=k) is the fault probability, K is the number of faulty electric devices, the parameter of the poisson distribution is the expected sum variance λ of the poisson distribution, e is the base of the natural logarithmic function, the parameter λ of the poisson distribution can be calculated according to the above formula, and the parameter λ of the poisson distribution represents the fault probability distribution when K takes different values.
Alternatively, the correspondence between the historical supplied green electric power and the historical number of failed electric devices and the parameter of poisson distribution may be used as the association between the supplied green electric power corresponding to the park and the failure probability, so that when the expected failure probability corresponding to the expected supplied green electric power of the park is obtained according to the association between the supplied green electric power corresponding to the park and the failure probability, the expected number of failed electric devices corresponding to the expected supplied green electric power may be determined according to the correspondence between the historical supplied green electric power and the historical number of failed electric devices, and the poisson distribution algorithm is adopted, and the expected failure probability p (x=k) is determined according to the expected number K and the parameter λ of poisson distribution, so that the expected failure probability corresponding to the expected supplied green electric power is determined from the distribution of the failure probabilities when K takes different values, so that the prediction of the expected failure probability is more accurate.
In the technical scheme disclosed in the embodiment, the association relationship between the supplied green electric power and the fault probability is generated according to the historical data such as the historical supplied green electric power, the historical fault probability and the like, so that the expected fault probabilities of the electric equipment corresponding to different expected supplied green electric powers can be predicted.
In yet another embodiment, as shown in fig. 4, after step S30, on the basis of the embodiment shown in any one of fig. 2 to 3, the method further includes:
Step S70, classifying the electric equipment in each park according to the historical electric power of the electric equipment in each park to obtain a plurality of classes of electric equipment;
In this embodiment, after adjusting the power supply of the green power system to the parks according to the power distribution coefficient, the power consumption devices in each park can be classified according to the historical power consumption of each power consumption device in each park, so that the power consumption devices set in a single park are divided into different categories, and unified power supply is performed on the power consumption devices belonging to the same category in the single park.
Optionally, the historical power of the electric device in the campus is power of the electric device in a historical period or a historical moment.
Alternatively, when classifying the electric devices according to the historical electric power, the covariance matrix may be used to classify the electric devices in the park, for example, the covariance matrix based on the historical electric power is generated according to the historical electric power of the electric devices in the park, and the covariance matrix E based on the historical electric power is specifically as follows:
The elements on the diagonal line in the covariance matrix E are variances of the historical power consumption of each electric equipment, and the elements on the non-diagonal line are covariance between the historical power consumption of every two electric equipment.
The covariance matrix E based on the historical electric power satisfies the following formula:
E*ET＝UAUT
Where T represents the transpose of the matrix, U is the matrix of eigenvectors, and a is the matrix of eigenvalues. And decomposing the characteristic value of the covariance matrix E based on the historical electric power according to the formula to obtain a matrix A formed by the characteristic values, determining the characteristic value of each electric equipment in the park according to the matrix A formed by the characteristic values, and classifying the electric equipment in the park according to the characteristic values.
Optionally, when classifying the electric equipment in the park according to the characteristic value, at least one characteristic threshold value can be set, and the electric equipment in the park is classified into at least two categories according to the magnitude relation between the characteristic value and the characteristic threshold value, so that the classification of the electric equipment with different grades of electric power is realized.
And S80, uniformly supplying power to electric equipment belonging to the same class.
In this embodiment, when the green electric system supplies power to the electric equipment in the park, the power output by the green electric system can be gradually distributed from the power supply main circuit to the power supply branch where the corresponding electric equipment is located only by sequentially distributing power through the multi-layer power distribution device. When the electric equipment belonging to the same class is required to be uniformly supplied with power, the power supply branches of the electric equipment belonging to the same class are regulated to be connected with the same power supply main circuit by regulating the power distribution devices such as the power distribution cabinet, so that the centralized power supply of the electric equipment belonging to the same class of electric power is realized, and the power supply management of the electric equipment belonging to the same class of electric power is also facilitated.
In the technical scheme disclosed by the embodiment, the electric equipment of the electric power used at different levels in the park is classified, and the same power supply is classified, so that the centralized supply of energy is ensured.
In yet another embodiment, as shown in fig. 5, the step of obtaining the expected power supply of each green power system in step S10 includes, on the basis of the embodiment shown in any one of fig. 2 to 4:
Step S11, when the green power system is a photovoltaic system, acquiring historical environment parameters of the photovoltaic system at a time point corresponding to the historical power supply of the green power system;
in this embodiment, when the green power system is a photovoltaic system, the power supply capability of the photovoltaic system is affected by environmental factors such as solar irradiation, and when the environments are different, the power supply powers of the photovoltaic system are also different, so that the expected power supply power of the photovoltaic system is predicted according to the historical power supply power of the green power system, the historical environmental parameters of the photovoltaic system at the time point corresponding to the historical power supply power can be obtained, and the historical environmental parameters can include irradiance, environmental temperature, and the like.
Optionally, when the green electric system is an energy storage system, the current energy storage residual electric quantity electric power of the energy storage system can be obtained, and the current energy storage residual electric quantity electric power is used as the expected power supply power of the energy storage system; when the green electric system is a hydrogen production system, the current hydrogen mass production electric power of the hydrogen production system can be obtained and used as the expected power supply of the hydrogen production system.
Step S12, generating a power generation prediction power model according to the historical environment parameters and the historical power supply power;
In this embodiment, a machine learning algorithm may be used to train a power generation prediction power model based on historical environmental parameters and historical power supply power to predict the expected power supply of the photovoltaic system through the power generation prediction power model.
Alternatively, the machine learning algorithm may include a random forest algorithm, a gradient-lifting tree, or a combination of these algorithms.
Step S13, acquiring expected environmental parameters of the photovoltaic system at a time point corresponding to the expected power supply;
and step S14, determining the expected power supply power of the green power system according to the expected environmental parameters and the power generation predicted power model.
In this embodiment, when the expected power supply of the photovoltaic system is predicted according to the power generation predicted power, the expected environmental parameter of the photovoltaic system at the time point corresponding to the expected power supply, that is, the expected environmental parameter of the photovoltaic system in a certain period of time in the future or a certain time point in the future, may be obtained first, where the expected environmental parameter may be determined by means of weather information prediction.
Optionally, the expected environmental parameter is input into the power generation predictive power model, so that the expected power supply power output by the power generation predictive power model can be obtained.
Optionally, when the expected power of each park associated with each green system at the time point corresponding to the expected power supply is obtained, a machine learning algorithm may also be adopted, and a load prediction model is obtained based on historical power training of the park, so as to predict the expected power of the park through the load prediction model, where the historical power of the park is the total power of all electric equipment in the park at the historical moment. Alternatively, the machine learning algorithm may include a random forest algorithm, a gradient-lifting tree, or a combination of these algorithms.
Alternatively, when the expected power of the park is predicted by the load prediction model, the current power of the park may be obtained, and the expected power of the park at the time point corresponding to the expected power may be predicted based on the current power of the park and the load power model, that is, the change condition of the current power of the park in a future period of time or a future period of time may be predicted by the load power model, so as to obtain the expected power of the park.
In the technical scheme disclosed by the embodiment, a parameter basis is provided for improving the green electricity utilization rate of a park through the generation power prediction technology of the photovoltaic system.
In an exemplary description, referring to fig. 7, on the basis of any one of the embodiments shown in fig. 2 to 6, a power supply control method of the green power system is exemplified as follows:
Step S1: accessing data of green power systems and power utilization systems and power failure numbers of independent electric equipment in park
And accessing each green electric system comprises a photovoltaic system, an energy storage system and a hydrogen production system, wherein the photovoltaic system data comprise photovoltaic power generation power at the current time of each park, the energy storage system data are energy storage residual electric quantity electric power, the hydrogen production system hydrogen mass production electric power, and the electricity utilization data of each park are accessed.
PV＝[pv1，pv2，pv3]，ES＝[es1，es2，es3]，HY＝[hy1，hy2，hy3]
The PV, ES and HY are respectively the electric power data corresponding to the photovoltaic, energy storage and hydrogen production of each park, wherein 1,2 and 3 are the parks 1,2 and 3 in the figure 6.
ELE＝[ele1，ele2，ele3]
The above ELE is the electricity consumption data of each park, and ELE1, ELE2 and ELE3 are the corresponding data of each park.
The fault number of the independent electric equipment in each park is accessed, the fault number is N= [ N1, N2, N3], the fault occurrence probability is calculated according to the Poisson distribution,
Where p (x=k) is the probability of K devices failing, the expected and variance of poisson distribution are λ, and from the historical failure numbers k=n1, n2, n3 and p (x=k) of each park, λ of each park can be calculated, i.e., λ= [ λ 1,λ2,λ3 ]. The number or number of occurrences in a unit time (area or volume) generally follows the poisson distribution approximately in a random event, and in a practical case, the number of occurrence faults in the history of the device is counted, so as to obtain a parameter lambda of the poisson distribution, and the parameter lambda is used for calculating the probability of occurrence faults in the future of the device when predicting an optimization model in the future.
Step S2: data preprocessing
And performing interpolation processing on the time scale on null values and NA in the data.
Step S3: training a model based on historical data
And obtaining a power generation prediction power model according to the accessed historical environment detector data and photovoltaic power generation power data, and obtaining a load prediction model according to the historical load power consumption data.
The model acquisition can be obtained by training a machine learning algorithm based on historical data, and the specific algorithm can be a random forest algorithm, a gradient lifting tree or a combination of the algorithms.
Step S4: predicting power generation and load
And predicting the generated power and the load power in a future period according to the load data and the weather forecast data in the current period and the power generation prediction model and the load prediction model obtained above.
PVpredict＝[pv1predict,pv2predict,pv3predict]
ELEpredict＝[ele1predict,ele2predict,ele3predict]
Wherein, PV predict is the photovoltaic power generation predicted power, and ELE predict is the load predicted power.
Step S5: building a power distribution model:
the distribution coefficient matrix includes:
/>
the failure rate of each park with single equipment unit is P1, P2, P3
The constraint conditions are as follows:
a11+a12+a13≤1
a21+a22+a23≤1
a31+a32+a33≤1
b11+b12+b13≤1
b21+b22+b23≤1
b31+b32+b33≤1
c11+c12+c13≤1
c21+c22+c23≤1
c31+c32+c33≤1
P1+P2+P3≤α
The distribution coefficient matrix can be obtained by converting the optimization problem into a mathematical dual and Lagrangian problem.
The goal of the above formula is to maximize the utilization rate of green electricity in electricity consumption, and the constraint condition is that the sum of photovoltaic power generation and distribution coefficients, the sum of energy storage and distribution coefficients and the sum of hydrogen distribution coefficients of each park are all less than or equal to 1. In order to ensure the safety and stability of power supply, the constraint condition increases the limit of the total fault rate, namely alpha or less, and the relation between power supply and faults of each park is obtained based on the relation between historical data.
Step S6: park energy distribution
And adjusting energy distribution according to the distribution coefficient matrix calculated by the method so as to meet the actual application requirements.
In order to effectively supply energy to the independent power utilization units of each park, the independent power utilization units of each park are classified and divided, and accordingly energy orderly supply is guaranteed.
Classifying the electricity utilization units according to historical electricity utilization unit data, wherein the calculation mode is as follows:
E*ET＝UAUT
Wherein A is a eigenvalue, and U is a eigenvector. The characteristic value A is classified according to the threshold value, the threshold value can be adjusted and set according to actual conditions, and unified energy supply is carried out on the electric equipment units of the same class.
The power supply control method of the green power system provided by the present exemplary description has the following advantages:
1. The dynamic allocation of the green power systems to the power utilization systems is realized, and the method has strong adaptability;
2. In addition to the energy utilization, the reliability of each electrical system is also considered;
3. The system with power generation prediction and load prediction is convenient to regulate and control;
4. The intelligent power supply system has a sensing function, classifies the power utilization units, and supplies power intensively, so that the centralized energy supply is ensured.
In addition, the embodiment of the invention also provides a power supply control device of the green power system, which comprises: the power supply control program of the green power system is executed by the processor to realize the steps of the power supply control method of the green power system according to the above embodiments.
In addition, the embodiment of the invention also provides a computer storage medium, wherein the computer storage medium stores a power supply control program of the green power system, and the power supply control program of the green power system realizes the steps of the power supply control method of the green power system in each embodiment when being executed by a processor.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (7)
1. The power supply control method of the green power system is characterized by comprising the following steps of:
acquiring expected power supply of each green power system, and acquiring expected power consumption of each park associated with each green power system at a time point corresponding to the expected power supply;
Obtaining power distribution coefficients of all green electric systems for all parks when the total green electric utilization rate of all parks reaches the maximum, wherein the power distribution coefficients of all green electric systems for all parks are obtained, the product of the power distribution coefficients of all green electric systems for all parks and the expected power supply power of the green electric systems is obtained, the sum of the products of all green electric systems for all parks is obtained, and the sum of the expected power supply power of all parks is divided, so that the total green electric utilization rate of all parks is obtained;
Adjusting the power supply of the green electric system to each park according to the power distribution coefficient of each green electric system to each park;
The constraint condition for obtaining the distribution coefficient of each green system for each park when the total green utilization rate of all parks reaches the maximum comprises the following steps:
The sum of expected failure probabilities of all electric equipment in the park is smaller than a preset threshold, wherein the sum of products of power distribution coefficients of all green electric systems for the park and expected power supply powers of the green electric systems is used as expected supplied green electric power of the park, and the expected failure probability corresponding to the expected supplied green electric power of the park is obtained according to the association relation between the supplied green electric power corresponding to the park and the failure probability;
the step of obtaining the expected power supply of each green power system comprises the following steps:
When the green power system is a photovoltaic system, acquiring historical environment parameters of the photovoltaic system at a time point corresponding to the historical power supply power of the green power system;
Generating a power generation prediction power model according to the historical environment parameters and the historical power supply power;
acquiring expected environmental parameters of the photovoltaic system at a time point corresponding to the expected power supply;
determining the expected power supply power of the green power system according to the expected environmental parameter and the power generation predicted power model;
the step of obtaining the expected electric power of each park associated with each green electric system at the time point corresponding to the expected electric power comprises the following steps:
generating a load prediction model according to historical electric power of the park;
Acquiring the current power consumption of the park;
and determining the expected electric power of the park at the time point corresponding to the expected electric power according to the current electric power and the load prediction model.
2. The power supply control method of a green electric system according to claim 1, characterized in that the power supply control method of a green electric system further comprises:
Acquiring a history of the historical power used in the park to be supplied with green power;
acquiring historical fault probability of electric equipment in the park at a time point corresponding to the historical supplied green electric power;
And generating an association relation between the supplied green electric power corresponding to the park and the fault probability according to the historical supplied green electric power and the historical fault probability, and storing the association relation.
3. The power supply control method of a green electric system according to claim 2, wherein the step of generating an association relationship between the supplied green electric power corresponding to the campus and the failure probability from the historic supplied green electric power and the historic failure probability comprises:
Acquiring the historical quantity of electric equipment which fails in the park at the time point corresponding to the historical supplied green electric power;
a Poisson distribution algorithm is adopted, and parameters of Poisson distribution are determined according to the historical number of the failed electric equipment, the historical failure probability and the historical failure probability;
Taking the corresponding relation between the supplied green electric power and the failure probability corresponding to the park as the parameter of the Poisson distribution according to the corresponding relation between the supplied green electric power and the historical number of the failed electric equipment;
The step of obtaining the expected failure probability corresponding to the expected supplied green electric power of the park according to the association relation between the supplied green electric power corresponding to the park and the failure probability comprises:
Determining an expected number of failed electrical devices corresponding to the expected supplied green electrical power according to a correspondence between the historical supplied green electrical power and the historical number of failed electrical devices;
And determining the expected fault probability according to the expected number and the parameters of the poisson distribution by adopting a poisson distribution algorithm.
4. The method of controlling power supplied to a green electric system according to claim 1, wherein after the step of adjusting the power supplied to the green electric system to each of the parks in accordance with the power distribution coefficient of each of the green electric systems to each of the parks, further comprising:
classifying the electric equipment in each park according to the historical electric power of each electric equipment in each park to obtain a plurality of classes of electric equipment;
and uniformly supplying power to the electric equipment belonging to the same class.
5. The method of power control of a green electricity system according to claim 4, wherein the step of classifying the electricity devices in each of the parks according to the historical electricity power of the electricity devices in each of the parks includes:
Generating a covariance matrix based on the historical power consumption according to the historical power consumption of the electric equipment in each park;
Performing eigenvalue decomposition on the covariance matrix based on the historical power consumption to obtain eigenvalues corresponding to electric equipment in the park;
And classifying the electric equipment in the park according to the characteristic value.
6. A power supply control device of a green electric system, characterized in that the power supply control device of the green electric system comprises: a memory, a processor and a power supply control program of a green electric system stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the power supply control method of a green electric system as claimed in any one of claims 1 to 5.
7. A computer storage medium, wherein a power supply control program of a green electric system is stored on the computer storage medium, and the power supply control program of the green electric system, when executed by a processor, implements the steps of the power supply control method of the green electric system according to any one of claims 1 to 5.
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