CN117200280B

CN117200280B - Photovoltaic power station power control method and related equipment

Info

Publication number: CN117200280B
Application number: CN202311477699.6A
Authority: CN
Inventors: 李春阳; 高丽媛; 刘迪; 李温静; 明萌; 崔明涛; 张沛尧; 马红月; 刘玉民; 尹玉
Original assignee: State Grid Information and Telecommunication Co Ltd
Current assignee: State Grid Information and Telecommunication Co Ltd
Priority date: 2023-11-08
Filing date: 2023-11-08
Publication date: 2024-02-09
Anticipated expiration: 2043-11-08
Also published as: CN117200280A

Abstract

The utility model provides a photovoltaic power station power control method and related equipment, obtain the power forecast deviation of photovoltaic power station, when confirming that the power forecast deviation is greater than the power deviation threshold value of predetermineeing, it is great to indicate that the power forecast deviation needs to adjust the power of photovoltaic power station, through obtaining the energy storage income of photovoltaic power station and the power deviation rewards of photovoltaic power station, confirm photovoltaic power station income function based on energy storage income and power deviation rewards, utilize particle swarm optimization to solve the photovoltaic power station income function and handle, obtain the target distribution power of energy storage equipment, with the target distribution power of energy storage equipment as the basis, adjust the power of photovoltaic power station with the help of energy storage equipment, make the power of photovoltaic power station can accord with the operation demand of distribution network, and then can avoid leading to the reverse and voltage of distribution network to fluctuate problem by a wide margin along with photovoltaic output power, ensure the safe operation of distribution network.

Description

Photovoltaic power station power control method and related equipment

Technical Field

The application relates to the technical field of photovoltaic power station control, in particular to a photovoltaic power station power control method and related equipment.

Background

As the situation of energy shortage is increasingly severe, new energy technologies such as photovoltaic and the like are rapidly developed, and the replacement of traditional energy with clean energy represented by distributed photovoltaic is gradually a trend of development in the power grid field. Photovoltaic power plants that access the distribution grid typically provide power forecast data on the previous day, and the power grid performs day-to-day dispatch balancing.

However, sudden factors such as faults and extreme weather can cause larger deviation between actual power data and power forecast data of the photovoltaic power station, and at the moment, if scheduling is carried out according to the power forecast data, problems of power flow reversal of the power distribution network and great fluctuation of voltage along with photovoltaic output power can be caused, so that safe operation of the power distribution network cannot be guaranteed.

Disclosure of Invention

In view of the foregoing, an objective of the present application is to provide a power control method and related apparatus for a photovoltaic power station, which are used for solving the above-mentioned technical problems.

In view of the above object, a first aspect of the present application provides a photovoltaic power plant power control method applied to a photovoltaic power plant power control system, the system comprising at least one preset power supply area comprising at least one photovoltaic power plant and at least one energy storage device, the method comprising:

Acquiring power prediction deviation of a photovoltaic power station, and acquiring energy storage income of the photovoltaic power station and power deviation rewards of the photovoltaic power station when the power prediction deviation is determined to be larger than a preset power deviation threshold value;

determining a photovoltaic power plant revenue function based on the energy storage revenue and the power deviation rewards;

and solving the gain function of the photovoltaic power station by using a particle swarm algorithm to obtain the target distribution power of the energy storage equipment, and adjusting the power of the photovoltaic power station according to the target distribution power.

Optionally, the obtaining the energy storage benefit of the photovoltaic power station includes:

obtaining constant power, charge and discharge time of the energy storage device, income of the energy storage device in the charge and discharge time, and charge and discharge cost of the energy storage device in the charge and discharge time;

determining charging and discharging power by using constant power of the energy storage device and the charging and discharging time length;

performing difference solving processing according to the benefits of the energy storage equipment in the charge-discharge time period and the charge-discharge cost of the energy storage equipment in the charge-discharge time period to obtain benefit differences;

and carrying out product processing by using the charge and discharge power and the gain difference to obtain the energy storage gain of the photovoltaic power station.

Optionally, the obtaining the power deviation rewards of the photovoltaic power station includes:

acquiring a reward coefficient and a power deviation value of a position where a preset power supply area is located;

and performing product processing by using the power deviation value and the rewarding coefficient at the position of the preset power supply area to obtain the power deviation rewarding of the photovoltaic power station.

Optionally, the determining a photovoltaic power plant benefit function based on the energy storage benefit and the power deviation reward includes:

and carrying out difference processing by utilizing the energy storage benefit and the power deviation rewards to determine a photovoltaic power station benefit function.

Optionally, the solving the photovoltaic power station profit function by using a particle swarm algorithm to obtain the target distributed power of the energy storage device includes:

acquiring preset initial distribution power of energy storage equipment, and taking the preset initial distribution power as an initial solution set;

performing maximum value solving processing on the photovoltaic power station gain function based on the initial solution set to obtain a first maximum photovoltaic power station gain, and obtaining an initial solution corresponding to the first maximum photovoltaic power station gain in the initial solution set as a first optimal solution;

updating the initial solution set to obtain a first updated solution set, carrying out maximum value solving processing on the photovoltaic power station gain function based on the first updated solution set to obtain second maximum photovoltaic power station gain, and obtaining a first updated solution corresponding to the second maximum photovoltaic power station gain in the first updated solution set as a second optimal solution;

Selecting the maximum value in the first optimal solution and the second optimal solution as a first current optimal solution, updating the first updated solution set to obtain a second updated solution set, carrying out maximum value solving processing on the photovoltaic power station gain function based on the second updated solution set to obtain a third maximum photovoltaic power station gain, and obtaining a second updated solution corresponding to the third maximum photovoltaic power station gain in the second updated solution set as a third optimal solution;

and selecting the maximum value in the first current optimal solution and the third optimal solution as a second current optimal solution, repeatedly executing the above process until the preset iteration times are reached, obtaining a final current optimal solution, and taking the final current optimal solution as the target distributed power of the energy storage equipment.

Optionally, the obtaining the power prediction bias of the photovoltaic power station includes:

acquiring power prediction data of a photovoltaic power station and actual power data corresponding to the power prediction data;

and carrying out difference solving processing by utilizing the power prediction data and the actual power data to obtain the power prediction deviation of the photovoltaic power station.

Optionally, the adjusting the power of the photovoltaic power station according to the target distributed power includes:

In response to determining that the power prediction data is greater than the actual power data, reducing the power of the photovoltaic power plant according to a target allocated power of the energy storage device; or,

and in response to determining that the power prediction data is smaller than the actual power data, controlling the energy storage equipment to cooperatively execute a power supply process with the photovoltaic power station according to the target distributed power.

Based on the same inventive concept, a second aspect of the present application provides a photovoltaic power plant power control apparatus, the apparatus being arranged in a photovoltaic power plant power control system, the system comprising at least one preset power supply area comprising at least one photovoltaic power plant and at least one energy storage device, the apparatus comprising:

the acquisition module is configured to acquire power prediction deviation of the photovoltaic power station, and acquire energy storage income of the photovoltaic power station and power deviation rewards of the photovoltaic power station when the power prediction deviation is determined to be larger than a preset power deviation threshold value;

a function construction module configured to determine a photovoltaic power plant revenue function based on the energy storage revenue and the power deviation rewards;

and the power adjusting module is configured to solve the gain function of the photovoltaic power station by utilizing a particle swarm algorithm to obtain the target distribution power of the energy storage equipment, and adjust the power of the photovoltaic power station according to the target distribution power.

Based on the same inventive concept, a third aspect of the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, the processor implementing the method of the first aspect when executing the computer program.

Based on the same inventive concept, a fourth aspect of the present application provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of the first aspect.

From the above, it can be seen that, according to the photovoltaic power station power control method and related equipment provided by the application, the power prediction deviation of the photovoltaic power station is obtained, when the power prediction deviation is determined to be greater than the preset power deviation threshold, the power prediction deviation is larger, the power of the photovoltaic power station needs to be adjusted, the energy storage gain of the photovoltaic power station and the power deviation rewards of the photovoltaic power station are obtained, the photovoltaic power station gain function is determined based on the energy storage gain and the power deviation rewards, the particle swarm algorithm is utilized to solve the photovoltaic power station gain function, the target distribution power of the energy storage equipment is obtained, the target distribution power of the energy storage equipment is used as a basis, and the power of the photovoltaic power station is adjusted by means of the energy storage equipment, so that the power of the photovoltaic power station can meet the operation requirement of the power distribution network, and further, the problems that the power flow of the power distribution network is reversed and the voltage fluctuates greatly along with the photovoltaic output power can be avoided, and the safe operation of the power distribution network is ensured.

Drawings

In order to more clearly illustrate the technical solutions of the present application or related art, the drawings that are required to be used in the description of the embodiments or related art will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to those of ordinary skill in the art.

Fig. 1 is a flowchart of a photovoltaic power plant power control method according to an embodiment of the present application;

fig. 2A is an application scenario schematic diagram of a photovoltaic power station power control method according to an embodiment of the present application;

fig. 2B is a schematic diagram of a photovoltaic power plant power control flow according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a photovoltaic power station power control device according to an embodiment of the present application;

fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings.

It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present application should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "first," "second," and the like, as used in embodiments of the present application, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.

It can be appreciated that before using the technical solutions of the embodiments in the present application, the user is informed about the type, the use range, the use scenario, etc. of the related personal information in an appropriate manner, and the authorization of the user is obtained.

For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Therefore, the user can select whether to provide personal information to the software or hardware such as the electronic equipment, the application program, the server or the storage medium for executing the operation of the technical scheme according to the prompt information.

As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.

It will be appreciated that the above-described notification and user authorization acquisition process is merely illustrative, and not limiting of the implementation of the present application, and that other ways of satisfying relevant legal regulations may be applied to the implementation of the present application.

In the related technology, new energy technologies such as photovoltaic and the like are rapidly developed due to the increasingly severe situation of energy shortage, and the replacement of the traditional energy with clean energy represented by distributed photovoltaic is gradually a trend of development in the power grid field. Photovoltaic power plants that access the distribution grid typically provide power forecast data on the previous day, and the power grid performs day-to-day dispatch balancing.

In addition, in the related art, the centralized control of the master station in the power system is adopted, when the master station is in face of faults or extreme weather and other influences, only a single station area (namely a preset power supply area) is often influenced, if a regulation strategy can only be provided from the angle of the master station, the required calculation resources and data volume are huge, and the regulation and control cannot be realized for the single station area.

The embodiment of the application provides a photovoltaic power station power control method, which is used for determining a photovoltaic power station profit function based on energy storage profit and power deviation rewards, solving the photovoltaic power station profit function by utilizing a particle swarm algorithm to obtain target distribution power of energy storage equipment, and adjusting the power of the photovoltaic power station by means of the energy storage equipment by taking the target distribution power of the energy storage equipment as a basis, so that the power of the photovoltaic power station can meet the operation requirement of a power distribution network, further, the problems of power flow reversal of the power distribution network and great fluctuation of voltage along with photovoltaic output power can be avoided, and the safe operation of the power distribution network is ensured.

As shown in fig. 1, the present embodiment is applied to a photovoltaic power plant power control system, the system including at least one preset power supply area, the preset power supply area including at least one photovoltaic power plant and at least one energy storage device, the method including:

step 101, obtaining power prediction deviation of a photovoltaic power station, and obtaining energy storage income of the photovoltaic power station and power deviation rewards of the photovoltaic power station when the power prediction deviation is larger than a preset power deviation threshold value.

In the step, the power prediction deviation of the photovoltaic power station connected to the power distribution network is obtained, when the power prediction deviation is larger than a preset power deviation threshold, the power prediction deviation is larger, the power of the photovoltaic power station needs to be regulated, the energy storage income of the photovoltaic power station and the power deviation rewards of the photovoltaic power station are obtained, the energy storage income and the power deviation rewards are comprehensively considered, the energy storage income and the power deviation rewards are used as the basis for regulating the power of the photovoltaic power station, and the power regulation of the photovoltaic power station can be more accurate.

Step 102, determining a photovoltaic power station benefit function based on the energy storage benefit and the power deviation reward.

In the step, the energy storage benefits are used for measuring the energy efficiency conditions of power supply and distribution of the photovoltaic power stations corresponding to the preset power supply areas, and the power deviation rewards are used for measuring the power distribution deviation degree of the photovoltaic power stations corresponding to the preset power supply areas.

On the basis of energy storage income, the income function of the photovoltaic power station is determined by combining the power deviation rewards, so that the income function of the photovoltaic power station introduces the influence caused by different power distribution deviation degrees, and the income function of the photovoltaic power station can be ensured to more accurately represent the energy efficiency condition of power supply and distribution.

And 103, solving the gain function of the photovoltaic power station by using a particle swarm algorithm to obtain the target distribution power of the energy storage equipment, and adjusting the power of the photovoltaic power station according to the target distribution power.

In the step, a photovoltaic power station profit function is solved through a particle swarm optimization (Particle Swarm Optimization, PSO), target distribution power of energy storage equipment corresponding to the photovoltaic power station profit result corresponding to the photovoltaic power station profit function is determined, the target distribution power of the energy storage equipment is taken as a basis, and the power of the photovoltaic power station is regulated by means of the energy storage equipment, so that the power of the photovoltaic power station can meet the operation requirement of a power distribution network, further, the problems that the power flow of the power distribution network is reversed and the voltage fluctuates greatly along with the photovoltaic output power can be avoided, and the safe operation of the power distribution network is ensured.

In addition, in the process of solving the photovoltaic power station yield function by using the particle swarm algorithm, preset constraint conditions are required to be met, so that the target distribution power of the obtained energy storage equipment can meet the requirement of a power distribution network.

The power regulation mode of the corresponding photovoltaic power station is determined from the angle of the preset power supply area with the power prediction deviation larger than the preset power deviation threshold value, a large amount of calculation resources are not needed, and timely regulation and control can be achieved.

The preset constraint conditions comprise the following steps:

the voltage value at the grid-connected point of each preset power supply area is equal to the voltage value at the position of the preset power supply area where the power prediction deviation is larger than the preset power deviation threshold value, and the voltage value of the voltage value is larger than or equal to a preset first voltage threshold value and smaller than or equal to a preset second voltage threshold value, wherein the first voltage threshold value is preferably 198V, and the second voltage threshold value is preferably 235.4V, and the voltage value can be expressed as follows:

wherein,representing the voltage value at the grid connection point of the respective preset power supply area +.>A preset power supply area +_representing the presence of a power prediction deviation greater than a preset power deviation threshold >Voltage values at the locations.

The power of the photovoltaic power station is greater than or equal to a preset first power threshold and less than or equal to a preset second power threshold, which can be expressed as follows:

wherein,representation storeThe power supply is set to be a power supply for the power supply in the case of a power prediction deviation greater than a preset power deviation threshold value>Power of individual photovoltaic power station, +.>Representation and->Representation and->First power threshold value corresponding to the respective photovoltaic power station, < >>Representation and->A second power threshold corresponding to the respective photovoltaic power plant,

the storage capacity of the energy storage device is greater than or equal to a preset first capacity threshold and less than or equal to a preset second capacity threshold.

Wherein,indicate->Storage capacity of the individual energy storage devices, +.>Representation and->A first capacity threshold value corresponding to the energy storage device, < >>Representation and the first/>And a second capacity threshold corresponding to each energy storage device.

According to the scheme, the power prediction deviation of the photovoltaic power station is obtained, when the power prediction deviation is determined to be larger than the preset power deviation threshold value, the fact that the power prediction deviation is larger is indicated, the power of the photovoltaic power station needs to be adjusted, the energy storage income of the photovoltaic power station and the power deviation rewards of the photovoltaic power station are obtained, the profit function of the photovoltaic power station is determined based on the energy storage income and the power deviation rewards, the profit function of the photovoltaic power station is solved by using a particle swarm algorithm, the target distribution power of the energy storage equipment is obtained, the target distribution power of the energy storage equipment is used as a basis, the power of the photovoltaic power station is adjusted by means of the energy storage equipment, the power of the photovoltaic power station can meet the operation requirement of a power distribution network, the problem that the power flow of the power distribution network is reversed and the voltage fluctuates greatly along with the photovoltaic output power can be avoided, and safe operation of the power distribution network is guaranteed.

In some embodiments, in step 101, the obtaining the energy storage benefit of the photovoltaic power station includes:

step A1, constant power, charge and discharge time of the energy storage device, benefits of the energy storage device in the charge and discharge time and charge and discharge cost of the energy storage device in the charge and discharge time are obtained.

And step A2, determining the charge and discharge power by utilizing the constant power of the energy storage device and the charge and discharge time length.

And step A3, carrying out difference solving processing according to the benefits of the energy storage equipment in the charge-discharge time period and the charge-discharge cost of the energy storage equipment in the charge-discharge time period, so as to obtain the benefits difference.

And step A4, performing product processing by using the charge and discharge power and the gain difference to obtain energy storage gain of the photovoltaic power station.

In the above scheme, for each energy storage device under the preset power supply area, the corresponding energy storage income can be obtained through the following process:

and performing product processing by using constant power of the energy storage device and the charge and discharge time length to obtain charge and discharge power, performing difference processing according to the benefits of the energy storage device in the charge and discharge time length and the charge and discharge cost of the energy storage device in the charge and discharge time length to obtain benefit difference, and performing product processing by using the charge and discharge power and the benefit difference to obtain the energy storage benefits of the photovoltaic power station.

The method can be expressed as follows:

wherein,indicate->Constant power of the individual energy storage devices, +.>Indicates charge and discharge time length, < >>Indicating the benefit of the energy storage device during the charge and discharge period, < + >>Indicating the +.>Charge and discharge costs of the individual energy storage devices +.>Indicating the number of energy storage devices.

The constant power, the charge and discharge time length and the benefit of the energy storage equipment in the charge and discharge time length of the energy storage equipment are comprehensively considered, and the charge and discharge cost of the energy storage equipment in the charge and discharge time length of the energy storage equipment are comprehensively considered, so that the obtained energy storage benefit of the photovoltaic power station is more accurate.

In some embodiments, in step 101, the obtaining a power deviation reward of the photovoltaic power plant includes:

and step B1, acquiring a reward coefficient and a power deviation value of a position where a preset power supply area is located.

And step B2, performing product processing by using the power deviation value at the position of the preset power supply area and the rewarding coefficient to obtain the power deviation rewards of the photovoltaic power station.

In the above scheme, the product processing is performed by using the power deviation value and the reward coefficient at the position of the preset power supply area, so as to obtain the power deviation reward of the photovoltaic power station, which can be expressed as:wherein->Representing a reward factor- >Representing a preset power supply area +.>Power offset value at the location.

And on the basis of the power deviation value, the estimation of the power distribution deviation degree is realized by combining the reward coefficient, so that the power deviation reward can be more accurate.

In some embodiments, in step 102, the determining a photovoltaic power plant revenue function based on the energy storage revenue and the power bias rewards includes:

In the above scheme, for the energy storage benefit corresponding to each energy storage device in the preset power supply area, the difference processing is performed with the corresponding power deviation rewards, so as to determine the corresponding photovoltaic power station benefit function, and when the photovoltaic power station benefit value corresponding to the final photovoltaic power station benefit function is calculated, the photovoltaic power station benefit value corresponding to each energy storage device is summed, so as to obtain the photovoltaic power station benefit value corresponding to the final photovoltaic power station benefit function, which can be expressed as follows:

wherein,representing a photovoltaic power plant benefit value corresponding to the final photovoltaic power plant benefit function, +.>Representing photovoltaic power plant yield,/->Indicate->Constant power of the individual energy storage devices, +. >Indicates charge and discharge time length, < >>Indicating the benefit of the energy storage device during the charge and discharge period, < + >>Indicating the +.>Charge and discharge costs of the individual energy storage devices +.>Representing the number of energy storage devices->Representing a reward factor->Representing a preset power supply area +.>Power offset value at the location.

In some embodiments, in step 103, the solving the photovoltaic power station profit function by using a particle swarm algorithm to obtain the target distributed power of the energy storage device includes:

step C1, acquiring preset initial distribution power of the energy storage equipment, and taking the preset initial distribution power as an initial solution set.

And C2, carrying out maximum value solving processing on the photovoltaic power station gain function based on the initial solution set to obtain a first maximum photovoltaic power station gain, and obtaining an initial solution corresponding to the first maximum photovoltaic power station gain in the initial solution set as a first optimal solution.

And C3, updating the initial solution set to obtain a first updated solution set, carrying out maximum processing on the photovoltaic power station gain function based on the first updated solution set to obtain second maximum photovoltaic power station gain, and obtaining a first updated solution corresponding to the second maximum photovoltaic power station gain in the first updated solution set as a second optimal solution.

And C4, selecting the maximum value in the first optimal solution and the second optimal solution as a first current optimal solution, updating the first updating solution set to obtain a second updating solution set, carrying out maximum value solving processing on the photovoltaic power station gain function based on the second updating solution set to obtain a third maximum photovoltaic power station gain, and obtaining a second updating solution corresponding to the third maximum photovoltaic power station gain in the second updating solution set as a third optimal solution.

And C5, selecting the maximum value in the first current optimal solution and the third optimal solution as a second current optimal solution, repeatedly executing the above process until the preset iteration times are reached, obtaining a final current optimal solution, and taking the final current optimal solution as the target distribution power of the energy storage equipment.

In the scheme, under the preset constraint condition, the particle swarm algorithm is utilized to solve the photovoltaic power station gain function, and when the photovoltaic power station gain value corresponding to the photovoltaic power station gain function is maximum, the target distribution power of the corresponding energy storage equipment is obtained.

The preset constraint conditions comprise the following steps:

Wherein,representing the voltage value at the grid connection point of the respective preset power supply area +.>A preset power supply area +_representing the presence of a power prediction deviation greater than a preset power deviation threshold>Voltage values at the locations.

wherein,a power supply region of a power supply, which is a power supply region in which a power prediction deviation is greater than a power deviation threshold value>Power of individual photovoltaic power station, +.>Representation and->Representation and->First power threshold value corresponding to the respective photovoltaic power station, < >>Representation and->A second power threshold corresponding to the respective photovoltaic power plant,

Wherein,indicate->Storage capacity of the individual energy storage devices, +.>Representation and->A first capacity threshold value corresponding to the energy storage device, < >>Representation and->And a second capacity threshold corresponding to each energy storage device.

When solving by using a particle swarm algorithm, taking the preset initial distribution power of the energy storage equipment as an initial solution setThe average distribution power of each energy storage device can be used as the initial solution set +. >Presetting the iteration times->。

And carrying out maximum value solving processing on the photovoltaic power station profit function based on the initial solution set to obtain a first maximum photovoltaic power station profit, and obtaining an initial solution corresponding to the first maximum photovoltaic power station profit in the initial solution set as a first optimal solution.

And then updating the initial solution set based on a particle swarm algorithm to obtain a first updated solution set, carrying out maximum value solving processing on the photovoltaic power station gain function based on the first updated solution set to obtain second maximum photovoltaic power station gain, and obtaining a first updated solution corresponding to the second maximum photovoltaic power station gain in the first updated solution set as a second optimal solution.

And selecting the maximum value in the first optimal solution and the second optimal solution as a first current optimal solution, updating the first updating solution set based on a particle swarm algorithm to obtain a second updating solution set, carrying out maximum value solving processing on a photovoltaic power station gain function based on the second updating solution set to obtain a third maximum photovoltaic power station gain, acquiring a second updating solution corresponding to the third maximum photovoltaic power station gain in the second updating solution set as a third optimal solution, and selecting the maximum value in the first current optimal solution and the third optimal solution as a second current optimal solution.

Repeatedly executing the above process until reaching the preset iteration timesAnd acquiring a final current optimal solution, and taking the final current optimal solution as a target distributed power of the energy storage equipment.

And solving the photovoltaic power station profit function by using a particle swarm algorithm, so that the target distribution power of the energy storage equipment can be rapidly obtained.

In some embodiments, in step 101, the obtaining a power prediction bias of the photovoltaic power plant includes:

and D1, acquiring power prediction data of the photovoltaic power station and actual power data corresponding to the power prediction data.

And D2, performing difference processing by using the power prediction data and the actual power data to obtain the power prediction deviation of the photovoltaic power station.

In the above scheme, the power prediction data may be a real-time power prediction data curve under the corresponding time, and at this time, the actual power data corresponding to the power prediction data is also a real-time actual power curve, so that the situation that the power prediction deviation is large can be found in time, and the effect of timely regulation and control is achieved.

In some embodiments, in step 103, the adjusting the power of the photovoltaic power plant according to the target allocated power includes:

And E1, in response to determining that the power prediction data is larger than the actual power data, reducing the power of the photovoltaic power station according to the target distribution power of the energy storage equipment. Or,

and E2, controlling the energy storage equipment to cooperatively execute a power supply process with the photovoltaic power station according to the target distributed power in response to the fact that the power prediction data is smaller than the actual power data.

In the above scheme, when the power prediction data is greater than the actual power data, it is indicated that the power distribution is performed according to the power prediction data, the power distribution of the photovoltaic power station is more, and at this time, the power of the photovoltaic power station is reduced according to the target distribution power of the energy storage device, and the redundant power is distributed from the photovoltaic power station to the energy storage device. Or when the power prediction data is smaller than the actual power data, the power distribution of the photovoltaic power station is less when the power distribution is performed according to the power prediction data, and in order to avoid the problem that the normal operation of the photovoltaic power station is affected due to frequent adjustment of the photovoltaic power station, the energy storage equipment is controlled to cooperatively execute the power supply process according to the target distribution power and the photovoltaic power station.

Based on the same inventive concept, an application scenario corresponding to the photovoltaic power station power control method of the above embodiment is specifically described, as shown in fig. 2A, specifically as follows:

The method comprises the steps that a plurality of transformer areas (namely preset power supply areas) are arranged, the transformer areas are connected to the connecting positions of the power grids through distribution network access points, the distribution network access points represent the connecting positions of the transformer areas, a plurality of distributed power generation photovoltaic power stations (comprising photovoltaic power stations and energy storage equipment) are managed under the transformer areas, operation information of the photovoltaic power stations under the transformer areas can be collected and sent to transformer area sides, and meanwhile, transformer area side instructions can be received and sent to the photovoltaic power stations, the energy storage equipment and other equipment.

As shown in fig. 2B, day-ahead prediction data (i.e., power prediction data), and real-time zone operation data (i.e., actual power data) are acquired, a power prediction bias is determined based on the day-ahead prediction data and the real-time zone operation data, and whether the power prediction bias is less than or equal to(i.e., power deviation threshold), if yes, no adjustment is required, and if no, adjustment is required.

And solving a determined objective function (namely, a photovoltaic power station gain function) under a preset constraint condition, acquiring a power distribution instruction (namely, target distribution power) of the energy storage equipment corresponding to the maximum value obtained by the objective function, issuing the instruction, executing the instruction, and adjusting the power of the photovoltaic power station according to the power distribution instruction of the energy storage equipment.

It should be noted that, the method of the embodiments of the present application may be performed by a single device, for example, a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of embodiments of the present application, and the devices may interact with each other to complete the methods.

It should be noted that some embodiments of the present application are described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Based on the same inventive concept, the application also provides a photovoltaic power station power control device corresponding to the method of any embodiment.

Referring to fig. 3, the photovoltaic power plant power control apparatus is disposed in a photovoltaic power plant power control system, the system including at least one preset power supply area including at least one photovoltaic power plant and at least one energy storage device, the apparatus comprising:

the obtaining module 301 is configured to obtain a power prediction deviation of the photovoltaic power station, and when the power prediction deviation is determined to be greater than a preset power deviation threshold, obtain energy storage benefits of the photovoltaic power station and a power deviation reward of the photovoltaic power station;

a function construction module 302 configured to determine a photovoltaic power plant revenue function based on the energy storage revenue and the power deviation rewards;

the power adjustment module 303 is configured to solve the photovoltaic power station profit function by using a particle swarm algorithm to obtain a target distribution power of the energy storage device, and adjust the power of the photovoltaic power station according to the target distribution power.

In some embodiments, the acquisition module 301 is specifically configured to:

In some embodiments, the function construction module 302 is specifically configured to:

In some embodiments, the power adjustment module 303 is specifically configured to:

In some embodiments, the acquisition module 301 is specifically configured to:

For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.

The device of the foregoing embodiment is used for implementing the corresponding photovoltaic power station power control method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.

Based on the same inventive concept, the application also provides an electronic device corresponding to the method of any embodiment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the photovoltaic power station power control method of any embodiment when executing the program.

Fig. 4 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 401, a memory 402, an input/output interface 403, a communication interface 404, and a bus 405. Wherein the processor 401, the memory 402, the input/output interface 403 and the communication interface 404 are in communication connection with each other inside the device via a bus 405.

The processor 401 may be implemented by a general purpose CPU (Central Processing Unit ), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.

The Memory 402 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 402 may store an operating system and other application programs, and when implementing the solutions provided by the embodiments of the present specification by software or firmware, the relevant program code is stored in memory 402 and invoked for execution by processor 401.

The input/output interface 403 is used to connect with an input/output module to realize information input and output. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.

The communication interface 404 is used to connect a communication module (not shown in the figure) to enable communication interaction between the present device and other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).

Bus 405 includes a path to transfer information between components of the device (e.g., processor 401, memory 402, input/output interface 403, and communication interface 404).

It should be noted that, although the above device only shows the processor 401, the memory 402, the input/output interface 403, the communication interface 404, and the bus 405, in the implementation, the device may further include other components necessary for realizing normal operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.

The electronic device of the foregoing embodiment is configured to implement the corresponding photovoltaic power station power control method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.

Based on the same inventive concept, corresponding to any of the above embodiments of the method, the present application further provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the photovoltaic power plant power control method according to any of the above embodiments.

The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.

The computer instructions stored in the storage medium of the foregoing embodiments are used to make the computer execute the photovoltaic power station power control method according to any one of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which are not described herein.

Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the present application, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.

Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present application. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present application, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform on which the embodiments of the present application are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.

While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalents, improvements and/or the like which are within the spirit and principles of the embodiments are intended to be included within the scope of the present application.

Claims

1. A photovoltaic power plant power control method, characterized by being applied to a photovoltaic power plant power control system, the system comprising at least one preset power supply area comprising at least one photovoltaic power plant and at least one energy storage device, the method comprising:

acquiring power prediction deviation of a photovoltaic power station, and acquiring energy storage benefits of the photovoltaic power station and power deviation rewards of the photovoltaic power station when the power prediction deviation is larger than a preset power deviation threshold value, wherein the energy storage benefits are used for measuring energy efficiency conditions of power supply and distribution of the photovoltaic power station corresponding to a preset power supply area, the power deviation rewards are used for measuring power distribution deviation degree of the photovoltaic power station corresponding to the preset power supply area, and the acquiring of the energy storage benefits of the photovoltaic power station comprises the following steps:

performing product processing by using the charge and discharge power and the gain difference to obtain energy storage gain of the photovoltaic power station;

the obtaining the power deviation rewards of the photovoltaic power station comprises the following steps:

performing product processing by using a power deviation value and a reward coefficient at the position of a preset power supply area to obtain power deviation rewards of the photovoltaic power station;

determining a photovoltaic power plant revenue function based on the energy storage revenue and the power bias rewards, the determining a photovoltaic power plant revenue function based on the energy storage revenue and the power bias rewards comprising:

performing difference solving processing by utilizing the energy storage benefit and the power deviation rewards to determine a photovoltaic power station benefit function;

Solving the photovoltaic power station profit function by utilizing a particle swarm algorithm based on a preset constraint condition to obtain target distribution power of energy storage equipment, and adjusting the power of the photovoltaic power station according to the target distribution power, wherein the preset constraint condition comprises: the voltage value at the grid-connected point of each preset power supply area is equal to the voltage value at the position of the preset power supply area where the power prediction deviation is larger than the preset power deviation threshold value, and is larger than or equal to a preset first voltage threshold value and smaller than or equal to a preset second voltage threshold value, wherein the first voltage threshold value is 198V, and the second voltage threshold value is 235.4V, and specifically:

wherein,representing the voltage value at the grid connection point of the respective preset power supply area +.>A preset power supply area +_representing the presence of a power prediction deviation greater than a preset power deviation threshold>Voltage value at the location;

the power of the photovoltaic power station is larger than or equal to a preset first power threshold value and smaller than or equal to a preset second power threshold value, and specifically comprises the following steps:

wherein,indicating that the power prediction deviation is greater than the preset power deviation threshold value in the preset power supply area Power of individual photovoltaic power station, +.>Representation and->Representation and->First power threshold value corresponding to the respective photovoltaic power station, < >>Representation and->A second power threshold corresponding to the respective photovoltaic power plant,

the storage capacity of the energy storage device is greater than or equal to a preset first capacity threshold value and less than or equal to a preset second capacity threshold value, specifically:

wherein,indicate->Storage capacity of the individual energy storage devices, +.>Representation and->A first capacity threshold value corresponding to the energy storage device, < >>Representation and->A second capacity threshold corresponding to the energy storage device;

the method for solving the photovoltaic power station profit function based on the preset constraint condition by utilizing a particle swarm algorithm to obtain the target distribution power of the energy storage equipment comprises the following steps:

selecting the maximum value in the first current optimal solution and the third optimal solution as a second current optimal solution, repeatedly executing the above process until the preset iteration times are reached, obtaining a final current optimal solution, and taking the final current optimal solution as the target distribution power of the energy storage equipment;

the obtaining the power prediction deviation of the photovoltaic power station comprises the following steps:

performing difference solving processing by using the power prediction data and the actual power data to obtain a power prediction deviation of the photovoltaic power station;

the adjusting the power of the photovoltaic power station according to the target distribution power comprises the following steps:

2. A photovoltaic power plant power control apparatus, the apparatus being disposed in a photovoltaic power plant power control system, the system comprising at least one preset power supply area comprising at least one photovoltaic power plant and at least one energy storage device, the apparatus comprising:

the acquisition module is configured to acquire power prediction deviation of the photovoltaic power station, and acquire energy storage benefits of the photovoltaic power station and power deviation rewards of the photovoltaic power station when the power prediction deviation is determined to be larger than a preset power deviation threshold, wherein the energy storage benefits are used for measuring energy efficiency conditions of power supply and distribution of the photovoltaic power station corresponding to a preset power supply area, the power deviation rewards are used for measuring power distribution deviation degree of the photovoltaic power station corresponding to the preset power supply area, and the acquisition of the energy storage benefits of the photovoltaic power station comprises the following steps:

a function construction module configured to determine a photovoltaic power plant revenue function based on the energy storage revenue and the power deviation reward, the determining a photovoltaic power plant revenue function based on the energy storage revenue and the power deviation reward comprising:

The power adjustment module is configured to solve the photovoltaic power station profit function by utilizing a particle swarm algorithm based on a preset constraint condition to obtain target distribution power of energy storage equipment, and adjust the power of the photovoltaic power station according to the target distribution power, wherein the preset constraint condition comprises: the voltage value at the grid-connected point of each preset power supply area is equal to the voltage value at the position of the preset power supply area where the power prediction deviation is larger than the preset power deviation threshold value, and is larger than or equal to a preset first voltage threshold value and smaller than or equal to a preset second voltage threshold value, wherein the first voltage threshold value is 198V, and the second voltage threshold value is 235.4V, and specifically:

3. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of claim 1 when executing the program.

4. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of claim 1.