CN113569936A - Power supply control method and device of green power system and computer storage medium - Google Patents
Power supply control method and device of green power 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 electric system and a computer storage medium, wherein the power supply control method comprises the following steps: the method comprises the steps of obtaining expected power supply power of each green electric system, and obtaining expected power consumption power of each park associated with each green electric system at a time point corresponding to the expected power supply power; acquiring a power distribution coefficient of each green electricity system for each park when the total green electricity utilization rate of all parks reaches the maximum; and adjusting the power supply power of the green electric system to the park according to the power distribution coefficient of each green electric system to each park. According to the method, the distribution coefficient of each green electricity system for each park is calculated through the expected power supply power and the expected power consumption power, so that the green electricity systems are controlled to distribute electricity according to the distribution coefficient, and the utilization rate of the green electricity is improved.
Description
Technical Field
The invention relates to the technical field of power supply control, in particular to a power supply control method and device of a green power system and a computer storage medium.
Background
The power supply mode of the extensive type is often adopted by the green system when supplying power to the electric equipment in each park, 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.
But the actual power consumption demand of each garden can change, and this kind of extensive formula power supply can make the occupation ratio of green electricity among the actual power consumption of garden too little, leads to green low utilization ratio of electricity.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above 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, aiming at improving 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 for a green electrical system, including:
the method comprises the steps of obtaining expected power supply power of each green electricity system, and obtaining expected power consumption of each park associated with each green electricity system at a time point corresponding to the expected power supply power;
acquiring a power distribution coefficient of each green electricity system for each park when the total green electricity utilization rate of all the parks reaches the maximum, wherein the product of the power distribution coefficient of each green electricity system for each park and the expected power supply power of the green electricity system is acquired, the sum of the products of all the green electricity systems for all the parks is acquired, and the sum is divided by the sum of the expected power consumption powers of all the parks, so that the total green electricity utilization rate of all the parks is acquired;
and adjusting the power supply power of the green electric system to the park according to the power distribution coefficient of each green electric system to each park.
Optionally, the constraint condition for obtaining the power distribution coefficient of each green electricity system for each campus, when the total green electricity utilization of all the campuses reaches a maximum, includes:
and the sum of the expected failure probabilities of all the electric equipment in the park is smaller than a preset threshold value, wherein the sum of the products of the power distribution coefficient of all the green electric systems for the park and the expected power supply power of the green electric systems is used as the 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 incidence relation between the supplied green electric power corresponding to the park and the failure probability.
Optionally, the power supply control method of the green electrical system further includes:
obtaining a historical supplied green electric power in the historical electric power usage of the campus;
acquiring a historical failure probability of the electric devices in the campus at a time point corresponding to the historical green electric power supply;
and generating an association relation between the supplied green electric power and the fault probability corresponding to the campus according to the historical supplied green electric power and the historical fault probability, and storing the association relation.
Optionally, the step of generating an association between the supplied green electric power and the failure probability for the campus according to the historical supplied green electric power and the historical failure probability comprises:
acquiring a historical number of electric devices having a fault in the campus at a time point corresponding to the historical green electric power supply;
determining parameters of Poisson distribution according to the historical number of the electric equipment with the fault and the historical fault probability by adopting a Poisson distribution algorithm;
according to the corresponding relation between the historical supplied green electric power and the historical number of the electric equipment with faults and the parameter of the Poisson distribution, the corresponding relation between the supplied green electric power and the fault probability of the campus is used as the correlation relation between the supplied green electric power and the fault probability;
the step of acquiring the expected failure probability corresponding to the expected supplied green electric power of the campus based on the correlation between the supplied green electric power and the failure probability corresponding to the campus includes:
determining an expected number of failed electric devices corresponding to the expected supplied green electric power according to a correspondence between the historical supplied green electric power and the historical number of failed electric devices;
and determining the expected failure 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 supplied by the green electrical system to the campus according to the power distribution coefficient of each green electrical system to each campus, the method further includes:
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 categories of electric equipment;
and uniformly supplying power to the electric equipment belonging to the same category.
Optionally, the step of classifying the electric devices in each campus according to the historical electric power consumption of each electric device in each campus includes:
generating a covariance matrix based on historical electricity utilization power according to the historical electricity utilization power of the electricity utilization equipment in each park;
performing eigenvalue decomposition on the covariance matrix based on the historical power consumption to obtain eigenvalues corresponding to all the power consumption 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 electricity system comprises:
when the green electric system is a photovoltaic system, acquiring historical environmental parameters of the photovoltaic system at a time point corresponding to the historical power supply power of the green electric 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 power;
and determining the expected power supply power of the green electric system according to the expected environment parameters and the power generation prediction power model.
Optionally, the step of acquiring expected power consumption of each campus associated with each green electricity system at a time point corresponding to the expected power supply includes:
generating a load prediction model according to the historical power consumption of the park;
acquiring the current power consumption of the park;
and determining the expected power utilization of the park at the time point corresponding to the expected power supply according to the current power utilization and the load prediction model.
In order to achieve the above object, the present invention also provides a power supply control device for a green electric system, including: the power supply control program of the green electricity system comprises a memory, a processor and a power supply control program of the green electricity system, wherein the power supply control program of the green electricity system is stored on the memory and can run on the processor, and when the power supply control program of the green electricity system is executed by the processor, the steps of the power supply control method of the green electricity system are realized.
Further, to achieve the above object, the present invention provides a computer storage medium having stored thereon a power supply control program of a green electric system, the power supply control program of the green electric system realizing the steps of the power supply control method of the green electric system as described in any one of the above when executed by a processor.
The power supply control method, the power supply control device and the computer storage medium of the green electric systems provided by the embodiment of the invention are used for acquiring the expected power supply power of each green electric system and acquiring the expected power consumption power of each park associated with each green electric system at the time point corresponding to the expected power supply power; acquiring a power distribution coefficient of each green electricity system for each park when the total green electricity utilization rate of all the parks reaches the maximum, wherein the product of the power distribution coefficient of each green electricity system for each park and the expected power supply power of the green electricity system is acquired, the sum of the products of all the green electricity systems for all the parks is acquired, and the sum is divided by the sum of the expected power consumption powers of all the parks, so that the total green electricity utilization rate of all the parks is acquired; and adjusting the power supply power of the green electric system to the park according to the power distribution coefficient of each green electric system to each park. According to the method, the distribution coefficient of each green electricity system for each park is calculated through the expected power supply power and the expected power consumption power, so that the green electricity systems are controlled to distribute electricity according to the distribution coefficient, and the utilization rate of the green electricity is improved.
Drawings
Fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a power supply control method of a green power system according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a power supply control method of a green power system according to another embodiment of the present invention;
FIG. 4 is a schematic flow chart illustrating a power supply control method of a green power system according to still another embodiment of the present invention;
FIG. 5 is a schematic flow chart illustrating a power supply control method of a green power system according to another embodiment of the present invention;
FIG. 6 is a schematic diagram of a power supply and utilization relationship between the power utilization devices of the parks and the green power systems of the present invention;
fig. 7 is a flowchart illustrating a power supply control method of the green power system according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the invention provides a solution, which is characterized in that the power distribution coefficient of each green electricity system to each park is calculated according to the expected power supply power and the expected power consumption power so as to control the green electricity systems to distribute power according to the power distribution coefficient, and the utilization rate of the green electricity is improved.
As shown in fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating 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 electricity system.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, DSP, MCU, network interface 1004, user interface 1003, memory 1005, communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may comprise a Display screen (Display), an input unit such as keys, and the optional user interface 1003 may also comprise 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 non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein a network communication module, a user interface module, and a power supply control program of a green power system.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend 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 up the power supply control program of the seed green electricity system stored in the memory 1005, and perform the following operations:
the method comprises the steps of obtaining expected power supply power of each green electricity system, and obtaining expected power consumption of each park associated with each green electricity system at a time point corresponding to the expected power supply power;
acquiring a power distribution coefficient of each green electricity system for each park when the total green electricity utilization rate of all the parks reaches the maximum, wherein the product of the power distribution coefficient of each green electricity system for each park and the expected power supply power of the green electricity system is acquired, the sum of the products of all the green electricity systems for all the parks is acquired, and the sum is divided by the sum of the expected power consumption powers of all the parks, so that the total green electricity utilization rate of all the parks is acquired;
and adjusting the power supply power of the green electric system to the 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 seed green electricity system stored in the memory 1005, and also perform the following operations:
obtaining a historical supplied green electric power in the historical electric power usage of the campus;
acquiring a historical failure probability of the electric devices in the campus at a time point corresponding to the historical green electric power supply;
and generating an association relation between the supplied green electric power and the fault probability corresponding to the campus 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 seed green electricity system stored in the memory 1005, and also perform the following operations:
acquiring a historical number of electric devices having a fault in the campus at a time point corresponding to the historical green electric power supply;
determining parameters of Poisson distribution according to the historical number of the electric equipment with the fault and the historical fault probability by adopting a Poisson distribution algorithm;
according to the corresponding relation between the historical supplied green electric power and the historical number of the electric equipment with faults and the parameter of the Poisson distribution, the corresponding relation between the supplied green electric power and the fault probability of the campus is used as the correlation relation between the supplied green electric power and the fault probability;
further, the processor 1001 may call the power supply control program of the seed green electricity system stored in the memory 1005, and also perform the following operations:
determining an expected number of failed electric devices corresponding to the expected supplied green electric power according to a correspondence between the historical supplied green electric power and the historical number of failed electric devices;
and determining the expected failure 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 seed green electricity system stored in the memory 1005, and also perform the following operations:
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 categories of electric equipment;
and uniformly supplying power to the electric equipment belonging to the same category.
Further, the processor 1001 may call the power supply control program of the seed green electricity system stored in the memory 1005, and also perform the following operations:
generating a covariance matrix based on historical electricity utilization power according to the historical electricity utilization power of the electricity utilization equipment in each park;
performing eigenvalue decomposition on the covariance matrix based on the historical power consumption to obtain eigenvalues corresponding to all the power consumption 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 seed green electricity system stored in the memory 1005, and also perform the following operations:
when the green electric system is a photovoltaic system, acquiring historical environmental parameters of the photovoltaic system at a time point corresponding to the historical power supply power of the green electric 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 power;
and determining the expected power supply power of the green electric system according to the expected environment parameters and the power generation prediction power model.
Further, the processor 1001 may call the power supply control program of the seed green electricity system stored in the memory 1005, and also perform the following operations:
generating a load prediction model according to the historical power consumption of the park;
acquiring the current power consumption of the park;
and determining the expected power utilization of the park at the time point corresponding to the expected power supply according to the current power utilization and the load prediction model.
Referring to fig. 2, in an embodiment, a power supply control method of a green electricity system includes the steps of:
step S10, obtaining expected power supply power of each green electricity system, and obtaining expected power consumption of each park associated with each green electricity system at a time point corresponding to the expected power supply power;
in this embodiment, green electricity refers to that the emission of carbon dioxide is zero or approaches zero in the process of producing electricity, and the impact on the environment is lower compared to electricity produced by other methods (such as thermal power generation).
Alternatively, as shown in fig. 6, the power utilization area is divided into a plurality of parks, and a green power system and power utilization equipment may be provided in each park, and the green power system may supply power to the power utilization equipment in the park and may also supply power to the power utilization equipment in other parks related to the green power system.
Optionally, the portion of the supply power is green electrical power that is supplied when the green electrical system is supplying electrical power to electrical consumers on the campus. Alternatively, since the supplied green electric power is generally small, the electric devices in the campus may be connected to a municipal power supply network to supply the electric devices in the campus through a common municipal power supply network, and thus the electric power used in the campus is generally greater than the total electric power supplied by the green electric system.
Alternatively, the campus associated with the green electricity system refers to a campus where green electricity supply is available through the green electricity system.
Optionally, the expected power supply power of each green electricity system in a future period of time or a future moment is predicted according to the historical power supply power of each green electricity system, and the expected power consumption power of each park in the future period of time or the future moment is predicted according to the historical power consumption power of the park.
Alternatively, in predicting the expected supply power and/or predicting the expected consumption power, the implementation may be based on machine learning algorithms, e.g., may be based on machine forest algorithms, gradient boosting trees, or a combination of these algorithms.
Step S20, obtaining the power distribution coefficient of each green electricity system for each park when the total green electricity utilization rate of all the parks reaches the maximum, wherein, obtaining the product of the power distribution coefficient of each green electricity system for each park and the expected power supply power of the green electricity system, obtaining the sum of the products of all the green electricity systems for all the parks, and dividing the sum of the expected power consumption of all the parks to obtain the total green electricity utilization rate of all the parks;
in the present embodiment, the power distribution coefficient of the green electric system to the campus is a ratio of the power supply power of the green electric system to the power supply power of the campus with respect to the total power supply power that the green electric system can output.
Optionally, a lagrange dual algorithm may be used to perform mathematical calculations to obtain the power distribution coefficient of each green electricity system for each campus when the total green electricity usage of all the campuses reaches a maximum. Specifically, can convert the distribution coefficient of each green electric system to each garden into corresponding distribution coefficient matrix, for example, refer to fig. 6, fig. 6 includes garden 1, garden 2 and garden 3, all is provided with photovoltaic system, energy storage system and hydrogen manufacturing system in 3 gardens, so can convert the distribution coefficient of all photovoltaic systems in 3 gardens to each garden into corresponding distribution coefficient matrix, specifically be:
wherein a11 is the distribution coefficient of the photovoltaic system in garden 1 to garden 1, a12 is the distribution coefficient of the photovoltaic system in garden 1 to garden 2, a13 is the distribution coefficient of the photovoltaic system in garden 1 to garden 3, a21 is the distribution coefficient of the photovoltaic system in garden 2 to garden 1, a22 is the distribution coefficient of the photovoltaic system in garden 2 to garden 2, a23 is the distribution coefficient of the photovoltaic system in garden 2 to garden 3, a31 is the distribution coefficient of the photovoltaic system in garden 3 to garden 1, a32 is the distribution coefficient of the photovoltaic system in garden 3 to garden 2, a33 is the distribution coefficient of the photovoltaic system in garden 3 to garden 1.
Similarly, the distribution coefficients of all the energy storage systems in 3 parks for each park are converted into corresponding distribution coefficient matrixes, specifically:
similarly, the distribution coefficients of all hydrogen production systems in 3 parks for each park are converted into corresponding distribution coefficient matrices, 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 PVpredictThe expected power supply power of the photovoltaic system, the expected power supply power of the energy storage system, the HY expected power supply power of the hydrogen production system and the Sigma ELE-predict is the sum of the expected power consumption powers of all the parks.
And calculating the maximum value of the total green electricity utilization rate of all the parks by adopting a Lagrange dual algorithm, namely calculating the optimal solution of the dual problem. When the total green electricity utilization rate is the maximum value, the value of each distribution coefficient matrix at the moment can be determined, and the 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 the 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, the sum of the power supply power of a single green electric system for all parks can be ensured not to exceed the expected power supply power of the green electric system when the power supply power of the green electric system for the parks is adjusted according to the power distribution coefficient of each green electric system for each park.
And step S30, adjusting the power supply power of the green electric system to the 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 electricity system for each campus is determined, the power distribution coefficient is multiplied by the expected power supply power of the green electricity system to obtain the power supply power of the green electricity system for the campus, and the power supply power of the green electricity system for the campus is adjusted according to the power supply power, so that the total green electricity utilization rate of all the campuses can be ensured.
In the technical scheme disclosed in the embodiment, the power distribution coefficient of each green power system for each park is calculated through the expected power supply power and the expected power consumption power so as to control the green power system to dynamically distribute power according to the power distribution coefficient, so that the method has strong adaptability and improves the utilization rate of green power.
In another embodiment, as shown in fig. 3, based on the embodiment shown in fig. 2, the constraint condition for obtaining the power distribution coefficient of each green electricity system for each campus when the total green electricity utilization of all the campuses reaches the maximum may include: the sum of the expected failure probabilities of all the electric equipment in the park is less than a preset threshold.
In this embodiment, when the power supply power of the green electrical system to the campus is too high, the voltage of the electrical equipment in the campus may be too high, which causes equipment failure, so when determining the power distribution coefficient of each green electrical system to each campus, the expected failure probability of the electrical equipment in the campus can be calculated through the power distribution coefficient, and by limiting the sum of the expected failure probabilities of the electrical equipment in all the campuses, it is avoided that the power distribution coefficient of each green electrical system to each campus is unreasonable, which causes too high failure risk of the electrical equipment in the campus, and the reliability of the electrical equipment in the campus when the green electrical system supplies power to the campuses is ensured.
Alternatively, when calculating the expected failure probability of the electric equipment in a single park, the sum of the products of the distribution coefficients of all the green electric systems for the park and the expected power supply power of the corresponding green electric systems can be used as the expected supplied green electric power of the park (i.e. the total expected power supply power of all the 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 correlation between the supplied green electric power corresponding to the park and the failure probability.
Before step S20, the power supply control method for the green electrical system further includes:
a step S40 of acquiring a history supplied green electric power in the history electric power consumption of the park;
in this embodiment, since the campus can receive not only the power supply of the green power but also the power supply of the municipal power supply network, the historical supplied green power may be included in the historical power consumption of the campus.
A step S50 of acquiring a history failure probability of the electric devices in the campus at a time point corresponding to the history of being supplied with green electric power;
in the present embodiment, the historical number of electrical devices that have historically failed in the campus at the time point corresponding to the historical green electric power supplied is acquired, and the ratio of the historical number to the total number of electrical devices in the campus is used as the historical failure probability of the electrical devices in the campus.
And step S60, generating the association relationship between the supplied green electric power and the failure probability corresponding to the garden according to the historical supplied green electric power and the historical failure probability, and storing the association relationship.
Alternatively, the historical supplied green electric power and the historical failure probability at the time point corresponding to the historical supplied green electric power may be regarded as a set of correspondence relationships, and the association relationship between the supplied green electric power and the failure probability corresponding to the campus may be obtained from the plurality of sets of correspondence relationships.
Optionally, when generating the correlation between the supplied green electric power and the failure probability corresponding to the campus, the historical number of the electric devices having a failure in the campus at the time point corresponding to the historical supplied green electric power may also be obtained, and a poisson distribution algorithm may be used to determine the parameters of the poisson distribution according to the historical number of the electric devices having a failure 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 the electric devices with faults, the parameters of the poisson distribution are the expectation and variance λ of the poisson distribution, e is the base number of the natural logarithm 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 distribution situation of the fault probability when K takes different values.
Alternatively, the correspondence relationship between the historical supplied green electric power and the historical number of electric devices that have failed and the parameter of the poisson distribution may be taken as the correlation relationship between the supplied green electric power and the failure probability corresponding to the campus, so that, when the expected failure probability corresponding to the expected supplied green electric power of the campus is obtained from the correlation relationship between the supplied green electric power and the failure probability corresponding to the campus, the expected number of electric devices that have failed corresponding to the expected supplied green electric power may be determined first from the correspondence relationship between the historical supplied green electric power and the historical number of electric devices that have failed, and the expected failure probability p (X ═ K) may be determined from the expected number K and the parameter λ of the poisson distribution using the poisson distribution algorithm, thereby determining the expected failure probability corresponding to the expected supplied green electric power from the distribution situation of the failure probability when K takes different values, the prediction of the expected failure probability is made more accurate.
In the technical solution disclosed in the present embodiment, the correlation between the supplied green electric power and the failure probability is generated according to the historical data such as the historical supplied green electric power and the historical failure probability, so as to predict the expected failure probability of the electric device corresponding to different expected supplied green electric powers.
In yet another embodiment, as shown in fig. 4, on the basis of the embodiment shown in any one of fig. 2 to fig. 3, after the step S30, 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 types of electric equipment;
in this embodiment, after adjusting the power supply power of green electric system to the garden according to the distribution coefficient, can classify the consumer in each garden according to the historical power consumption of each consumer in each garden to divide the consumer that sets up in single garden into different categories, carry out unified power supply to the consumer that belongs to same category in single garden.
Optionally, the historical power consumption of the electric equipment in the park is the power consumption of the electric equipment in the historical time period or the historical time.
Alternatively, when the electric devices are classified according to the historical electric power, the plurality of electric devices in the campus may be classified by using a covariance matrix, for example, a covariance matrix based on the historical electric power is generated according to the historical electric power of the electric devices in the campus, 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 the variances of the historical power consumption of each electric device, and the elements on the non-diagonal line are the covariances between the historical power consumption of every two electric devices.
The covariance matrix E based on the historical power consumption satisfies the following formula:
E*ET＝UAUT
wherein, T represents the transposition of the matrix, U is the matrix formed by the eigenvector, and A is the matrix formed by the eigenvalue. And (3) decomposing the eigenvalue of the covariance matrix E based on the historical power consumption according to the formula to obtain a matrix A formed by the eigenvalue, determining the eigenvalue of each electric device in the park according to the matrix A formed by the eigenvalue, and classifying the electric devices in the park according to the eigenvalue.
Optionally, when the electric devices in the park are classified according to the characteristic values, at least one characteristic threshold may be set, and the electric devices in the park are divided into at least two categories according to the magnitude relationship between the characteristic values and the characteristic thresholds, so as to realize classification of the electric devices with different levels of electric power.
And step S80, uniformly supplying power to the electric equipment belonging to the same category.
In this embodiment, when the green power system supplies power to the electric devices in the park, the electric energy output by the green power system can be gradually distributed to the power supply branch where the corresponding electric devices are located from the power supply main circuit only by sequentially distributing power through the multiple layers of power distribution devices. When the electric equipment belonging to the same class needs to be supplied with power in a unified manner, the power distribution devices such as the power distribution cabinet can be adjusted through the power distribution device, the power supply branch circuits of the electric equipment belonging to the same class are adjusted to be connected with the same power supply main circuit, the centralized power supply of the electric equipment belonging to the same class of electric power is realized, and meanwhile, the power supply management of the electric equipment belonging to the same class of electric power is facilitated.
In the technical scheme disclosed in the embodiment, the centralized supply of energy is ensured by classifying the electric equipment using electric power at different levels in the park and performing the classified same power supply.
In another embodiment, as shown in fig. 5, on the basis of any one of the embodiments shown in fig. 2 to 4, the step of obtaining the expected power supply power of each green electricity system in step S10 includes:
step S11, when the green electric system is a photovoltaic system, acquiring historical environmental parameters of the photovoltaic system at a time point corresponding to the historical power supply power of the green electric system;
in this embodiment, when the green electrical system is a photovoltaic system, the power supply capacity of the photovoltaic system is affected by environmental factors such as solar radiation, and when the environment is different, the power supply power of the photovoltaic system is also different, so that when the expected power supply power of the photovoltaic system is predicted according to the historical power supply power of the green electrical 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 may include irradiance, ambient temperature, and the like.
Optionally, when the green electrical system is the energy storage system, the current energy storage remaining capacity electric power of the energy storage system may be obtained, and the current energy storage remaining capacity electric power is used as the expected power supply power of the energy storage system; when the green power system is the hydrogen production system, the current hydrogen amount power generation electric power of the hydrogen production system can be obtained, and the current hydrogen amount power generation electric power is used as the expected power supply power of the hydrogen production system.
Step S12, generating a power generation prediction power model according to the historical environmental parameters and the historical power supply power;
in this embodiment, a machine learning algorithm may be adopted, and a power generation prediction power model is obtained based on the historical environmental parameters and the historical power supply training, so as to predict the expected power supply power of the photovoltaic system through the power generation prediction power model.
Alternatively, the machine learning algorithm may comprise a random forest algorithm, a gradient boosting 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 power;
and step S14, determining the expected power supply power of the green electricity system according to the expected environment parameters and the power generation prediction power model.
In this embodiment, when the expected supply power of the photovoltaic system is predicted according to the predicted power generation power, the expected environmental parameter of the photovoltaic system at a time point corresponding to the expected supply power, that is, the expected environmental parameter of the photovoltaic system within a certain period of time in the future or at a certain time point in the future, may be obtained first, where the expected environmental parameter may be determined in a manner of weather information prediction.
Optionally, the expected environmental parameters are input into the power generation prediction power model, so that the expected power supply output by the power generation prediction power model can be obtained.
Optionally, when the expected power consumption of each campus associated with each green power system at the time point corresponding to the expected power supply power is obtained, a machine learning algorithm may also be used, and a load prediction model is obtained based on historical power consumption training of the campus, so as to predict the expected power consumption of the campus through the load prediction model, where the historical power consumption of the campus is a total power consumption of all power consumption devices in the campus at the historical time. Alternatively, the machine learning algorithm may comprise a random forest algorithm, a gradient boosting tree, or a combination of these algorithms.
Optionally, when the expected power consumption of the campus is predicted through the load prediction model, the current power consumption of the campus may be obtained, and the expected power consumption of the campus at a time point corresponding to the expected power supply power is predicted based on the current power consumption of the campus and the load power model, that is, the change of the current power consumption of the campus in a future period or a future time is predicted through the load power model, so as to obtain the expected power consumption of the campus.
In the technical scheme disclosed in the embodiment, a parameter basis is provided for improving the green electricity utilization rate of a park through the generated power prediction technology of the photovoltaic system.
In an exemplary explanation, referring to fig. 7, a power supply control method of a green electricity system based on the embodiment shown in any one of fig. 2 to 6 is as follows:
step S1: the data of each green electric system and each electric system and the power failure number of the independent electric equipment in each park are accessed
Each green electricity system is connected to comprise a photovoltaic system, an energy storage system and a hydrogen production system, wherein photovoltaic system data comprise photovoltaic power generation power of each park at the current time, the energy storage system data are energy storage residual electric quantity electric power, and the hydrogen production system hydrogen production electric power is connected to electricity utilization data of each park.
PV＝[pv1，pv2，pv3]，ES＝[es1，es2，es3]，HY＝[hy1，hy2，hy3]
Wherein, PV, ES, HY are the corresponding electric power data of photovoltaic, energy storage, hydrogen production of each park respectively, wherein, 1, 2, 3 are each park 1, 2, 3 in figure 6.
ELE＝[ele1，ele2，ele3]
The ELE is the electricity consumption data of each park, and ELE1, ELE2 and ELE3 are the data corresponding to each park respectively.
Accessing the fault number of the independent electric equipment in each park, wherein the fault number is N ═ N1, N2 and N3, calculating the fault occurrence probability according to Poisson distribution,
wherein p (X ═ K) is the probability of the failure of K devices, the expectation and variance of Poisson distribution are both lambda, and the failure number K ═ K of each park according to historyn1, n2, n3 and p (X ═ K) make it possible to calculate λ for each campus, i.e. λ ═ λ1，λ2，λ3]. Generally, the number of times or numbers occurring in a unit time (area or volume) in random events approximately follows poisson distribution, and in practical cases, the number of faults occurring in the equipment history is counted, so that a parameter lambda of the poisson distribution is obtained, and the parameter is used for calculating the probability of the future faults of the equipment in the future prediction optimization model.
Step S2: data pre-processing
And (4) carrying out interpolation processing on the time scale on null values and NA in the data.
Step S3: training model based on historical data
And obtaining a power generation prediction power model according to the accessed historical environment detector data and the 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 based on historical data according to a machine learning algorithm, 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 power
And predicting the power generation power and the load power in a future period of time according to the load data and the weather forecast data in the current period of time and the power generation prediction model and the load prediction model obtained above.
PVpredict＝[pv1predict，pv2predict，pv3predict]
ELEpredict＝[ele1predict，ele2predict，ele3predict]
Wherein PVpredictPredicting power for photovoltaic power generation, ELEpredictPower is predicted for the load.
Step S5: constructing a power distribution model:
the distribution coefficient matrix includes:
the failure rate of the single equipment unit for independent use in each park is P1, P2 and 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 aim of the above formula is to maximize the utilization rate of green electricity in electricity, and the constraint conditions are that the sum of photovoltaic power generation distribution coefficients, the sum of energy storage distribution coefficients and the sum of hydrogen distribution coefficients of each park are less than or equal to 1. In order to ensure the safety and stability of power supply, the constraint condition increases the total fault rate limit, namely the total fault rate limit is less than or equal to alpha, and the relation between the power supply and the fault of each park is obtained based on the relation between historical data.
Step S6: park energy distribution
And adjusting the energy distribution according to the power distribution coefficient matrix obtained by the calculation so as to meet the actual application requirement.
In order to effectively supply energy to the independent power consumption units of each park, the independent power consumption units of each park are classified and divided, so that the ordered energy supply is ensured.
Classifying the electricity utilization units according to the historical electricity utilization unit data, wherein the calculation mode is as follows:
E*ET＝UAUT
wherein A is a characteristic value and U is a characteristic vector. The characteristic value A is classified according to the threshold value, the threshold value can be adjusted and set according to actual conditions, and the same type of electric equipment units are supplied with energy in a unified mode.
The power supply control method of the green electric system provided by the exemplary illustration has the following advantages:
1. the dynamic allocation of each green power system to the power utilization system is realized, and the adaptability is strong;
2. besides the energy utilization rate, the reliability of each power utilization system is also considered;
3. the system has power generation prediction and load prediction, and is convenient to regulate and control;
4. the power supply device has a sensing function, and power utilization units are classified to carry out centralized power supply, so that centralized energy supply is guaranteed.
In addition, an embodiment of the present invention further provides a power supply control device for a green electrical system, where the power supply control device for the green electrical system includes: the power supply control program of the green electrical system is stored on the memory and can run on the processor, and when being executed by the processor, the power supply control program of the green electrical system realizes the steps of the power supply control method of the green electrical system according to the above embodiments.
In addition, an embodiment of the present invention further provides a computer storage medium, where a power supply control program of a green electrical system is stored on the computer storage medium, and the power supply control program of the green electrical system, when executed by a processor, implements the steps of the power supply control method of the green electrical system according to the above embodiments.
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 an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. 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 (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A power supply control method of a green electricity system is characterized by comprising the following steps:
the method comprises the steps of obtaining expected power supply power of each green electricity system, and obtaining expected power consumption of each park associated with each green electricity system at a time point corresponding to the expected power supply power;
acquiring a power distribution coefficient of each green electricity system for each park when the total green electricity utilization rate of all the parks reaches the maximum, wherein the product of the power distribution coefficient of each green electricity system for each park and the expected power supply power of the green electricity system is acquired, the sum of the products of all the green electricity systems for all the parks is acquired, and the sum is divided by the sum of the expected power consumption powers of all the parks, so that the total green electricity utilization rate of all the parks is acquired;
and adjusting the power supply power of the green electric system to the park according to the power distribution coefficient of each green electric system to each park.
2. The power supply control method of a green electricity system according to claim 1, wherein the obtaining of the constraint condition for the distribution coefficient of each of the green electricity systems for each of the parks when the total green electricity usage of all of the parks reaches a maximum includes:
and the sum of the expected failure probabilities of all the electric equipment in the park is smaller than a preset threshold value, wherein the sum of the products of the power distribution coefficient of all the green electric systems for the park and the expected power supply power of the green electric systems is used as the 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 incidence relation between the supplied green electric power corresponding to the park and the failure probability.
3. The power supply control method of a green electric system according to claim 2, further comprising:
obtaining a historical supplied green electric power in the historical electric power usage of the campus;
acquiring a historical failure probability of the electric devices in the campus at a time point corresponding to the historical green electric power supply;
and generating an association relation between the supplied green electric power and the fault probability corresponding to the campus according to the historical supplied green electric power and the historical fault probability, and storing the association relation.
4. The power supply control method of a green electric system according to claim 3, wherein the step of generating the association between the supplied green electric power and the failure probability corresponding to the campus based on the historical supplied green electric power and the historical failure probability comprises:
acquiring a historical number of electric devices having a fault in the campus at a time point corresponding to the historical green electric power supply;
determining parameters of Poisson distribution according to the historical number of the electric equipment with the fault and the historical fault probability by adopting a Poisson distribution algorithm;
according to the corresponding relation between the historical supplied green electric power and the historical number of the electric equipment with faults and the parameter of the Poisson distribution, the corresponding relation between the supplied green electric power and the fault probability of the campus is used as the correlation relation between the supplied green electric power and the fault probability;
the step of acquiring the expected failure probability corresponding to the expected supplied green electric power of the campus based on the correlation between the supplied green electric power and the failure probability corresponding to the campus includes:
determining an expected number of failed electric devices corresponding to the expected supplied green electric power according to a correspondence between the historical supplied green electric power and the historical number of failed electric devices;
and determining the expected failure probability according to the expected number and the parameters of the Poisson distribution by adopting a Poisson distribution algorithm.
5. The power supply control method of a green electricity system according to claim 1, wherein said step of adjusting the power supply of said green electricity system to said campus in accordance with the distribution coefficient of each said green electricity system to each said campus further comprises:
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 categories of electric equipment;
and uniformly supplying power to the electric equipment belonging to the same category.
6. The power supply control method of a green electricity system according to claim 5, wherein said step of classifying the electric power consumers in each of the parks in accordance with the historical electric power consumption of the electric power consumers in each of the parks includes:
generating a covariance matrix based on historical electricity utilization power according to the historical electricity utilization power of the electricity utilization equipment in each park;
performing eigenvalue decomposition on the covariance matrix based on the historical power consumption to obtain eigenvalues corresponding to all the power consumption equipment in the park;
and classifying the electric equipment in the park according to the characteristic value.
7. The power supply control method of a green electric system according to claim 1, wherein the step of acquiring the expected power supply of each green electric system comprises:
when the green electric system is a photovoltaic system, acquiring historical environmental parameters of the photovoltaic system at a time point corresponding to the historical power supply power of the green electric 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 power;
and determining the expected power supply power of the green electric system according to the expected environment parameters and the power generation prediction power model.
8. A power supply control method for a green electricity system according to claim 1, wherein the step of acquiring the expected power consumption of each campus associated with each green electricity system at the time point corresponding to the expected power supply comprises:
generating a load prediction model according to the historical power consumption of the park;
acquiring the current power consumption of the park;
and determining the expected power utilization of the park at the time point corresponding to the expected power supply according to the current power utilization and the load prediction model.
9. A power supply control device of a green electric system, characterized by comprising: a memory, a processor and a power supply control program of a green electricity system stored on the memory and operable on the processor, the power supply control program of the green electricity system realizing the steps of the power supply control method of the green electricity system according to any one of claims 1 to 8 when executed by the processor.
10. A computer storage medium characterized in that 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 realizes the steps of the power supply control method of the green electric system according to any one of claims 1 to 8 when executed by a processor.
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| CN110942217A (en) * | 2018-09-21 | 2020-03-31 | 潜能恒信能源技术股份有限公司 | Method and system for constructing zero-carbon green energy system |
| CN111144654A (en) * | 2019-12-27 | 2020-05-12 | 深圳供电局有限公司 | Park energy management configuration method and device based on Internet of things |
| CN112381410A (en) * | 2020-11-16 | 2021-02-19 | 国网山东省电力公司莒县供电公司 | Power management method and system based on Internet of things block chain |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| TWI795296B (en) * | 2022-05-23 | 2023-03-01 | 潔能氏新能源股份有限公司 | Green energy matching system and the method thereof |
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