CN117148875A - Photovoltaic panel corner control method and system based on energy storage cooperation and storage medium - Google Patents
Photovoltaic panel corner control method and system based on energy storage cooperation and storage medium Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S20/00—Supporting structures for PV modules
- H02S20/30—Supporting structures being movable or adjustable, e.g. for angle adjustment
- H02S20/32—Supporting structures being movable or adjustable, e.g. for angle adjustment specially adapted for solar tracking
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D3/00—Control of position or direction
- G05D3/12—Control of position or direction using feedback
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
- H02J2300/26—The renewable source being solar energy of photovoltaic origin involving maximum power point tracking control for photovoltaic sources
Abstract
The application discloses a photovoltaic panel corner control method, a photovoltaic panel corner control system and a storage medium based on energy storage cooperation, wherein the method comprises the following steps: constructing a minimum loss objective function based on an energy-consumption side load prediction model as an external optimization layer; constructing an internal optimization layer based on a photovoltaic module power change model and an energy storage module energy storage loss model; and constructing a double-layer optimization model by using the external optimization layer and the internal optimization layer, acquiring weather prediction data on a prediction time sequence, outputting a photovoltaic panel corner optimal control strategy on the prediction time sequence, and controlling the change of the photovoltaic panel corner by using the photovoltaic panel corner optimal control strategy. The application has the beneficial effects that: and the service life of the photovoltaic panel is prolonged while the energy storage loss is reduced.
Description
Technical Field
The application relates to the technical field of photovoltaic power generation control, in particular to a photovoltaic panel corner control method and system based on energy storage cooperation and a storage medium.
Background
Photovoltaic power generation is a technology for directly converting light energy into electric energy by utilizing photovoltaic effect of a semiconductor interface, and mainly comprises three parts of a solar panel (component), a controller and an inverter, wherein main components are composed of electronic components. The photovoltaic power generation is a power generation mode for converting solar radiation into electric energy, and has the characteristics of cleanness, reproducibility and the like. In a photovoltaic power generation system, a photovoltaic panel converts solar energy into direct current electrical energy, and then converts the direct current electrical energy into alternating current through an inverter, and the alternating current is supplied to a load or a power grid. However, due to the influence of factors such as weather, seasons, time and the like, the power supply of the photovoltaic power generation is unstable, so that a photovoltaic energy storage technology is generated, electric energy can be stored when the power demand is high through an energy storage system, and the electric energy can be charged when the demand is low, so that the load of a power grid is balanced, and the stability and the reliability of a power system are improved. Meanwhile, the energy storage system can play a role in peak clipping and valley filling in the photovoltaic power generation system, and efficiency and economy of the power system are improved.
Common energy storage technologies include battery energy storage technologies, super capacitor energy storage technologies, mechanical energy storage technologies, and the like. Among them, the battery energy storage technology is most widely used, and the energy storage device mainly comprises a battery pack and a battery management system. The battery pack is responsible for storing electric energy, and the battery management system is responsible for charge and discharge control and protection of the battery. The super capacitor energy storage technology has the characteristics of high charging speed, long service life, large capacity and the like, and is suitable for occasions with high-power transient response. The mechanical energy storage technology converts electric energy into kinetic energy or potential energy for storage, and then releases the kinetic energy or potential energy when needed.
However, due to the rapid development of the photovoltaic technology, the photovoltaic productivity is higher and higher, the supply source of the power demand side is diversified along with the development of the new energy and productivity industry, so that the electric energy demand of the power demand side is smaller than the photovoltaic energy, the photovoltaic energy is removed from the part supplied to the power demand side, and the rest of the energy is stored in the energy storage system, so that the energy storage pressure of the energy storage system is overlarge, and meanwhile, the energy storage system usually stores energy by a battery, and the energy storage loss such as battery discharge is inevitably caused in the process. In the related art, the rotation angle of the photovoltaic panel is controlled to enable the light load on the surface of the photovoltaic panel to reach the maximum value, so that the maximum power generation efficiency is realized, but the service life of the photovoltaic panel is also reduced, and the problems that energy overflows and needs to be stored in an energy storage system and the service life of the photovoltaic panel is short are caused.
Chinese patent "optimization method of inclination angle and orientation of photovoltaic module in distributed photovoltaic power station", publication No.: CN 114020047A, publication date: 2022, 02 and 08, particularly discloses a photovoltaic module inclination angle, a photovoltaic module orientation azimuth angle and a photovoltaic module installation power, wherein related data comprise meteorological data, module temperature, current, voltage and recording time of each data; according to the recording time of each data, matching the common time point, processing related data, and obtaining an empirical formula among the data through a fitting formula; and acquiring the orientation azimuth angle and the inclination angle of the photovoltaic module with highest local photovoltaic array efficiency. In the scheme, only the generated power of the photovoltaic module is considered, the azimuth angle and the inclination angle of the photovoltaic module which can achieve the highest generated power are calculated, and the energy loss caused by surplus capacity and energy storage at present is not considered.
Chinese patent (a method and a device for scheduling photovoltaic energy storage system) discloses the following steps: CN 114421530A, publication date: 2022, 04, 29, specifically discloses generating a first predicted generated power from the meteorological parameters and the photovoltaic power generation parameters, and then correcting the first predicted generated power with historical power generation data to generate a second predicted generated power; generating predicted load power according to the production plan; and generating energy storage regulating power corresponding to the scheduling moment according to the predicted load power and the second predicted generated power, and controlling the photovoltaic energy storage system by adopting the energy storage regulating power. According to the scheme, the historical power generation data corresponding to the scheduling time is added in the power generation power prediction of the photovoltaic power generation system to be corrected, so that the photovoltaic power generation power calculated based on the physical model is more in line with the historical experience data, and the problem that the scheduling power is easily deviated from reality due to model errors and other reasons in the energy storage system in the prior art, and the normal operation of the energy storage system is affected is avoided. However, the energy loss when the energy storage system stores excessive energy is not considered.
Disclosure of Invention
Aiming at the problems that excessive energy storage loss is easy to generate in time due to excessive energy consumption of photovoltaic power generation in the prior art, the application provides a photovoltaic panel corner control method based on energy storage fit, by constructing an internal optimization layer based on a photovoltaic module power change model and an energy storage module energy storage loss model and an external optimization layer based on an energy utilization side load prediction model, a minimum loss objective function is constructed with minimum running cost loss, a double-layer optimization model is constructed with the internal optimization layer and the external optimization layer, the optimal solution of the minimum loss objective function is carried out, a photovoltaic panel corner optimal control strategy for realizing minimum energy storage loss is obtained under the condition of meeting energy utilization side load requirements, the photovoltaic panel corner change is controlled by the photovoltaic panel corner optimal control strategy, so that the photovoltaic panel corner is adjusted to realize maximum power generation when the energy storage energy is insufficient to meet the energy utilization side load requirements, when the energy storage energy is far more than the energy utilization side load requirements, the photovoltaic panel corner change enables the photovoltaic panel to not generate power or to carry out low power, the break of a photovoltaic panel PN junction when the energy storage capacity is sufficient, the service life of the photovoltaic panel is prolonged, and the energy storage loss in the energy storage module is reduced.
In order to achieve the technical purpose, the technical scheme provided by the application is that the photovoltaic panel corner control method based on energy storage cooperation comprises the following steps: acquiring the illuminated intensity, the output power, the photovoltaic panel corner and the photovoltaic panel area of a historical photovoltaic module on a long-time sequence, and constructing a photovoltaic module power change model; acquiring energy storage energy, energy storage loss rate and energy storage loss of a historical energy storage component on a long-time sequence, and constructing an energy storage loss model of the energy storage component; acquiring historical energy utilization side load data and weather data on a long-time sequence, and constructing an energy utilization side load prediction model; constructing a minimum loss objective function based on an energy-consumption side load prediction model as an external optimization layer; constructing an internal optimization layer based on a photovoltaic module power change model and an energy storage module energy storage loss model; and constructing a double-layer optimization model by using the external optimization layer and the internal optimization layer, acquiring weather prediction data on a prediction time sequence, outputting a photovoltaic panel corner optimal control strategy on the prediction time sequence, and controlling the change of the photovoltaic panel corner by using the photovoltaic panel corner optimal control strategy.
Further, the illumination intensity, the output power, the photovoltaic panel rotation angle and the photovoltaic panel area of the historical photovoltaic module on the long-time sequence are obtained, and a photovoltaic module power change model is constructed as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Output power for photovoltaic module, < >>For photovoltaic panel area, +.>Is illuminated with light>Is a corner of a photovoltaic panel>Is the photoelectric conversion efficiency of the photovoltaic panel.
Further, the energy storage energy, the energy storage loss rate and the energy storage loss of the historical energy storage component on the long-time sequence are obtained, and an energy storage loss model of the energy storage component is constructed as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For energy storage loss->For energy storage->Is the energy storage loss rate.
Further, constructing a minimum loss objective function based on the energy-consumption side load prediction model as an external optimization layer comprises the following steps: constructing a minimum loss objective function with minimum running cost loss; taking a load predicted value output by the energy-consumption side load prediction model as a constraint condition; and taking the minimum loss objective function and the constraint condition as an external optimization layer.
Further, constructing a minimum loss objective function with minimum running cost loss is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the purpose of operating cost loss>For the energy storage loss of the energy storage component at time t, < >>Maintenance loss for photovoltaic system at time t, < >>For the energy-storage assembly at time tEnergy storage capacity->And the energy storage loss rate of the energy storage component at the time T is the predicted time sequence.
Further, the construction of the internal optimization layer based on the photovoltaic module power change model and the energy storage module energy storage loss model comprises the following steps: constructing a correlation function of the output power of the photovoltaic module and the energy storage energy of the energy storage module; and fusing the correlation function with a photovoltaic module power change model and an energy storage module energy storage loss model, and constructing an internal objective function with minimum energy storage loss as an internal optimization layer.
Further, the internal objective function is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The photovoltaic module is subjected to illumination intensity at the moment t, < >>The corner of the photovoltaic panel of the photovoltaic module at the moment t is represented by S, wherein S is the area of the photovoltaic panel, and +.>The photoelectric conversion efficiency of the photovoltaic panel is shown, and T is a predicted time sequence.
Further, the minimum loss objective function is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>For the purpose of operating cost loss>For the energy storage loss of the energy storage component at time t, < >>Is light ofLoss of equipment maintenance of the volt-age equipment at time t, < >>For the energy storage of the energy storage component at time t, < >>For the energy storage loss rate of the energy storage component at the time T, T is a predicted time sequence, and +.>The loss cost of the photovoltaic panel at the time t is defined as R, and the fixed maintenance cost of the photovoltaic equipment is defined as R.
The application provides another technical scheme that the photovoltaic panel corner control system based on energy storage cooperation is used for realizing the photovoltaic panel corner control method based on energy storage cooperation, and comprises the following steps: the data acquisition module is used for acquiring historical data on a long time sequence and weather prediction data on a prediction time sequence; the data modeling module is used for constructing a double-layer optimization model according to the data acquired by the data acquisition module; the data interaction module is used for receiving a control demand and outputting an optimal control strategy of the photovoltaic panel rotation angle;
and the control execution module is used for executing corner control on the photovoltaic panel according to the optimal control strategy of the corner of the photovoltaic panel.
The application provides another technical scheme that the photovoltaic panel corner control method based on energy storage cooperation is realized by a computer readable storage medium, wherein the computer readable storage medium is used for storing a computer program or instructions, and when the computer program or instructions are executed by processing equipment.
The application has the beneficial effects that: the photovoltaic panel corner optimal control strategy for realizing the minimum energy storage loss is obtained under the condition of meeting the energy utilization side load demand, the photovoltaic panel corner optimal control strategy is used for controlling the photovoltaic panel corner change, so that the photovoltaic panel corner is adjusted to realize the maximum power generation when the energy storage energy is insufficient to meet the energy utilization side load demand, and the photovoltaic panel corner change is used for enabling the photovoltaic panel to not generate power or generate low power when the energy storage energy far exceeds the energy utilization side load demand, so that the breaking of PN junctions of the photovoltaic panel when the energy storage energy is sufficient is reduced, the service life of the photovoltaic panel is prolonged, and the energy storage energy of the energy storage system is reduced under the condition of the energy storage energy abundance due to the fact that the energy storage loss of the energy storage system is increased along with the increase of the energy storage energy.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of a photovoltaic panel corner control method based on energy storage cooperation.
Fig. 2 is a schematic structural diagram of a photovoltaic panel corner control system based on energy storage cooperation according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings and examples, it being understood that the detailed description herein is merely a preferred embodiment of the present application, which is intended to illustrate the present application, and not to limit the scope of the application, as all other embodiments obtained by those skilled in the art without making any inventive effort fall within the scope of the present application.
As shown in fig. 1, as a first embodiment of the present application, a photovoltaic panel corner control method based on energy storage cooperation includes the following steps:
acquiring the illuminated intensity, the output power, the photovoltaic panel corner and the photovoltaic panel area of a historical photovoltaic module on a long-time sequence, and constructing a photovoltaic module power change model;
acquiring energy storage energy, energy storage loss rate and energy storage loss of a historical energy storage component on a long-time sequence, and constructing an energy storage loss model of the energy storage component;
acquiring historical energy utilization side load data and weather data on a long-time sequence, and constructing an energy utilization side load prediction model;
constructing a minimum loss objective function based on an energy-consumption side load prediction model as an external optimization layer;
constructing an internal optimization layer based on a photovoltaic module power change model and an energy storage module energy storage loss model;
and constructing a double-layer optimization model by using the external optimization layer and the internal optimization layer, acquiring weather prediction data on a prediction time sequence, outputting a photovoltaic panel corner optimal control strategy on the prediction time sequence, and controlling the change of the photovoltaic panel corner by using the photovoltaic panel corner optimal control strategy.
In this embodiment, a photovoltaic module power change model is constructed according to the influence of the illuminated intensity, the photovoltaic panel rotation angle and the photovoltaic panel area of the photovoltaic module on the output power of the photovoltaic module, an energy storage loss model is constructed according to the influence of the energy storage energy and the energy storage loss rate of the energy storage module on the energy storage loss, and an energy consumption side load prediction model is constructed according to the influence of weather change on the energy consumption side load data change. And constructing a minimum loss objective function based on the energy consumption side load prediction model as an external optimization layer by taking the energy consumption side load requirement as a constraint and the minimum loss in the operation process as a target. The method comprises the steps that an internal optimization layer based on a photovoltaic module power change model and an energy storage module energy storage loss model is constructed according to the mutual influence relation of energy storage energy influence energy storage loss and energy storage module output power influence energy storage energy, the loss in the operation process at least comprises the energy storage loss of the energy storage module, the energy storage energy corresponding to the minimum energy storage loss under the condition that the requirement of a user side is met is calculated, the photovoltaic module output power corresponding to the energy storage energy is obtained, an optimal control strategy of a photovoltaic panel corner is obtained through calculation by using the photovoltaic module output power, the photovoltaic panel corner change is controlled by using the optimal control strategy of the photovoltaic panel corner, so that the energy storage loss caused by excessive energy storage energy is reduced, meanwhile, the photovoltaic panel light receiving change is caused due to the photovoltaic panel corner change, the electricity production quantity of the photovoltaic panel is reduced, and the service life of the photovoltaic panel is prolonged.
Specifically, the illumination intensity, the output power, the photovoltaic panel rotation angle and the photovoltaic panel area of the historical photovoltaic module on the long-time sequence are obtained, and a photovoltaic module power change model is constructed as follows:
；
wherein,output power for photovoltaic module, < >>For photovoltaic panel area, +.>Is illuminated with light>Is a corner of a photovoltaic panel>Is the photoelectric conversion efficiency of the photovoltaic panel.
Acquiring the energy storage energy, the energy storage loss rate and the energy storage loss of the historical energy storage component on a long-time sequence, and constructing an energy storage loss model of the energy storage component as follows:
；
wherein,for energy storage loss->For energy storage->Is the energy storage loss rate.
In the present embodiment, the photoelectric conversion efficiency of the photovoltaic panelAnd energy storage loss rate->Can be calculated from historical data over a long period of time.
For the construction of the energy-consumption side load prediction model, a deep learning algorithm in a neural network can be adopted, and the construction of the energy-consumption side load prediction model can be carried out through a decision tree. In this embodiment, an LSTM-attribute is used to construct an energy-consumption-side load prediction model, a random forest algorithm, a self-adaptive integration algorithm and a gradient lifting tree algorithm are used to perform preliminary fitting prediction on historical energy-consumption-side load data and weather data, a fitting result is extracted to obtain a correlation coefficient of weather feature quantity and load size, a comprehensive correlation coefficient is established, feature quantity with smaller correlation coefficient in the weather feature quantity is removed according to the size of the comprehensive correlation coefficient, the remaining feature quantity and the historical load size data are combined to form a new data set, and the LSTM-attribute prediction model is constructed to be the energy-consumption-side load prediction model. It will be appreciated that in this embodiment only the influence of weather factors on the energy usage side load is considered, and in other embodiments the characteristic quantity also includes other energy usage side load influencing factors, such as activity influence and the like.
Constructing a minimum loss objective function based on an energy-consumption side load prediction model as an external optimization layer, wherein the minimum loss objective function comprises the following steps of:
constructing a minimum loss objective function with minimum running cost loss;
taking a load predicted value output by the energy-consumption side load prediction model as a constraint condition;
and taking the minimum loss objective function and the constraint condition as an external optimization layer.
The minimum loss objective function is constructed with the minimum running cost loss as follows:
；
wherein,for the purpose of operating cost loss>For the energy storage loss of the energy storage component at time t, < >>The equipment maintenance loss for photovoltaic equipment is +.>Energy storage of the energy storage component at time t +.>And the energy storage loss rate of the energy storage component at the time T is the predicted time sequence.
The constraint condition of the load predicted value output by the energy-consumption side load prediction model is as follows:
；
the load predicted value of the energy utilization side at the time t is the output of the energy utilization side load prediction model.
In this embodiment, the equipment maintenance loss of the photovoltaic equipment at least includes the maintenance loss of the photovoltaic module and the maintenance loss of the energy storage module, and the maintenance cost of the photovoltaic module or the energy storage module in the set time is considered to be the equipment maintenance loss. And counting historical equipment maintenance loss in the long-time sequence, calculating an equipment maintenance loss sharing value every day, and calculating the equipment maintenance loss on the predicted time sequence according to the equipment maintenance loss sharing value every day and the predicted time sequence. I.e. long time seriesThe maintenance loss of the medium history equipment is->The daily device maintains the loss sharing value +.>The method comprises the following steps:
；
predicting time seriesThe following equipment maintenance loss is:
；
the minimum loss objective function is thus:
。
further, the construction of the internal optimization layer based on the photovoltaic module power change model and the energy storage module energy storage loss model comprises the following steps:
constructing a correlation function of the output power of the photovoltaic module and the energy storage energy of the energy storage module;
and fusing the correlation function with a photovoltaic module power change model and an energy storage module energy storage loss model, and constructing an internal objective function with minimum energy storage loss as an internal optimization layer.
Because the output power of the photovoltaic module is related to the generated energy of the photovoltaic module, and the generated energy of the photovoltaic module is related to the stored energy of the energy storage module, a correlation function of the output power of the photovoltaic module and the stored energy of the energy storage module is constructed according to the two correlations:
；
wherein,the output power of the photovoltaic module at the time t is obtained.
The correlation function is used for fusing a photovoltaic module power change model and an energy storage module energy storage loss model, and an internal objective function with minimum energy storage loss as a target is constructed as follows:
；
and the internal objective function can be obtained according to the photovoltaic module power change model as follows:
；
wherein,the photovoltaic module is subjected to illumination intensity at the moment t, < >>The corner of the photovoltaic panel of the photovoltaic module at the moment t.
In this embodiment, the illumination intensity of the photovoltaic module at time t is obtained by weather forecast data, the illumination intensity of the photovoltaic module at time t is used as input, a photovoltaic panel corner corresponding to the minimum energy storage loss when the energy storage energy is larger than the corresponding load forecast value is calculated and obtained at each time, and the photovoltaic panel corner is used as a photovoltaic panel corner control strategy, so that the control adjustment of the photovoltaic panel corner is performed according to the photovoltaic panel corner control strategy, and the purposes of reducing the energy storage loss and prolonging the service life of the photovoltaic module while meeting the load demand of a user side are achieved.
As a second embodiment of the application, the photovoltaic panel is a key element of photovoltaic power generation, and the output power of the photovoltaic panel continuously decreases along with the increase of illumination time, namely the power of the photovoltaic panel is attenuated, and the photovoltaic module manufactured by p-type (boron doped) crystal silicon wafers is illuminated, so that boron and oxygen in the silicon wafers generate a complex, and the service life of the photovoltaic panel is reduced. In the process of generating power by the photovoltaic panel, the PN junction of the photovoltaic panel can be continuously broken, so that the generated power of the photovoltaic panel is continuously reduced, and the service life of the photovoltaic panel is reduced. Therefore, the photoelectric conversion efficiency of the photovoltaic module is also related to the generation time of the photovoltaic module, namely
；
For the photoelectric conversion efficiency of the photovoltaic module at time t, < >>For the theoretical photoelectric conversion efficiency of the photovoltaic module, < +.>The power attenuation ratio of the photovoltaic module at the time t is obtained.
And acquiring the illuminated time, the illuminated intensity and the output power of the historical photovoltaic module on the long-time sequence, and calculating the power attenuation proportion of the photovoltaic module on different using time lengths. In this embodiment, only the influence of the illumination intensity on the output power is considered, and in other embodiments, other factors, such as the temperature of the photovoltaic panel, of the historical photovoltaic module, which have influence on the output power, are obtained over a long time sequence, so that the interference of the other influencing factors is eliminated, and the power attenuation condition of the output power under the long change of the use time is calculated.
In this embodiment, the internal objective function is:
；
the output power of the photovoltaic module is calculated according to the photoelectric conversion efficiency of the photovoltaic module at different moments, the accuracy of a calculation result is improved, the optimal control strategy of the photovoltaic panel rotation angle is more in line with the actual capacity condition, and the energy loss is further reduced.
In this embodiment, the device maintenance loss of the photovoltaic device also includes the loss of the photovoltaic panel due to the excessively long service time, that is, the minimum loss objective function is:
；
wherein,and maintaining the loss for the photovoltaic equipment at the time t.
The loss of the photovoltaic panel is related to the time of use, and therefore:
；
where R is the fixed maintenance cost, such as the timing trimming cost,is the loss cost of the photovoltaic panel at the time t. In this embodiment, with the normal lifetime of the photovoltaic panel being 25 years, the replacement cost of the photovoltaic panel is w:
；
thus, the update minimum loss objective function is:
；
and establishing a double-layer optimization model according to the internal objective function and the minimum loss objective function, taking weather prediction data as input, namely taking the illumination intensity as input, and outputting the minimum loss photovoltaic panel corner and the change time when the energy consumption side load requirement is met, so as to serve as a photovoltaic panel corner optimal control strategy. I.e. when illuminated with light intensityWhen changing, according to photoelectric conversion efficiencyCalculate->The sum of the energy storage loss and the loss of the photovoltaic panel is minimized, the energy loss is reduced, and meanwhile, when the energy storage is sufficient, unnecessary use of the photovoltaic panel is avoided by changing the corner of the photovoltaic panel, so that the service life of the photovoltaic panel is prolonged.
As a third embodiment of the present application, constructing a correlation function of output power of a photovoltaic module and energy stored by an energy storage module includes:
and acquiring a historical energy storage input value of the energy storage component on a long-time sequence, and constructing a correlation function of the output power of the photovoltaic component and the energy storage energy of the energy storage component.
Acquiring a historical energy storage input value of an energy storage component on a long-time sequence and a historical output power of a photovoltaic component on the long-time sequence, and calculating the transportation loss rate:
；
for the transport loss rate at time t, +.>For the output power of the photovoltaic module at time t, < >>And the energy storage input value of the energy storage component at the time t is obtained.
Obtaining historical weather data on a long-time sequence to construct a transportation loss rate prediction model, wherein a correlation function of the output power of the photovoltaic module and the energy storage energy of the energy storage module is as follows:
；
and the predicted transportation loss rate is output by a transportation loss rate prediction model according to weather prediction data.
In this embodiment, further comprising:
acquiring an energy storage input value of an energy storage component and output power of a photovoltaic component in a previous time sequence, calculating the actual transportation loss rate of the previous time sequence, and acquiring the predicted transportation loss rate of the previous time sequence;
calculating a corrected fluctuation value according to the actual transportation loss rate and the predicted transportation loss rate of the previous time sequence;
correcting the transportation loss rate prediction model by using the corrected fluctuation value;
and constructing a correlation function of the output power of the photovoltaic module and the energy storage energy of the energy storage module.
The corrected fluctuation value is:
；
wherein,for correction of the fluctuation value->For the actual transport loss rate of the previous time sequence, < >>The transport loss rate is predicted for the previous time series.
The relevance function of the output power of the photovoltaic module and the energy stored by the energy storage module is as follows:
；
the fluctuation value is corrected through the ratio of the actual transportation loss rate to the predicted transportation loss rate of the previous time sequence, so that the predicted difference caused by unquantifiable factors is avoided, the final result is more accurate, the energy storage loss is reduced as much as possible, the energy utilization rate is improved, the capacity of the photovoltaic panel in an unnecessary time period is reduced, and the service life of the photovoltaic panel is prolonged.
As shown in fig. 2, as a fourth embodiment of the present application, a photovoltaic panel corner control system based on energy storage cooperation is used to implement the above method, which includes:
the data acquisition module is used for acquiring historical data on a long time sequence and weather prediction data on a prediction time sequence;
the data modeling module is used for constructing a double-layer optimization model according to the data acquired by the data acquisition module;
the data interaction module is used for receiving a control demand and outputting an optimal control strategy of the photovoltaic panel rotation angle;
and the control execution module is used for executing corner control on the photovoltaic panel according to the optimal control strategy of the corner of the photovoltaic panel.
Specifically, the data acquisition module acquires historical data on a long-time sequence and transmits the historical data to the data modeling module, the data modeling module constructs a double-layer optimization model according to the data acquired by the data acquisition module, the data interaction module receives control demands from users and sends data acquisition signals to the data acquisition module, the data acquisition module is connected with a weather bureau network to acquire weather prediction data on a prediction time sequence and transmits the weather prediction data to the data interaction module, the data interaction module invokes the double-layer optimization model to output a photovoltaic panel corner optimal control strategy and sends the photovoltaic panel corner optimal control strategy to the control execution module, and the control execution module receives the photovoltaic panel corner optimal control strategy and executes control on the photovoltaic panel corner.
The data modeling module at least comprises a first modeling unit for constructing a power change model of the photovoltaic module, a second modeling unit for constructing an energy storage loss model of the energy storage module, a third modeling unit for constructing an energy side load prediction model and a fourth modeling unit for constructing a double-layer optimization model.
The first modeling unit constructs a photovoltaic module power change model according to the illuminated intensity, the output power, the photovoltaic panel rotation angle and the photovoltaic panel area of the historical photovoltaic module on the long-time sequence:
；
wherein,output power for photovoltaic module, < >>For photovoltaic panel area, +.>Is illuminated with light>Is a corner of a photovoltaic panel>Is the photoelectric conversion efficiency of the photovoltaic panel.
The second modeling unit builds an energy storage loss model of the energy storage component according to the energy storage energy, the energy storage time and the energy storage loss of the historical energy storage component in the long-time sequence:
；
wherein,for energy storage loss->For energy storage->Is the energy storage loss rate.
The third modeling unit constructs an energy consumption side load prediction model according to the historical energy consumption side load data and weather data in a long time sequence.
The fourth modeling unit receives the photovoltaic module power change model from the first modeling unit, the energy storage module energy storage loss model of the second modeling unit and the energy utilization side load prediction model of the third modeling unit, and performs model fusion construction to obtain a double-layer optimization model:
；
；
wherein,for the purpose of operating cost loss>For the energy storage loss of the energy storage component at time t, < >>Maintenance loss for photovoltaic system at time t, < >>For the energy storage of the energy storage component at time t, < >>The energy storage loss rate of the energy storage component at the moment T is the predicted time sequence; />The photovoltaic module is subjected to illumination intensity at the moment t, < >>The corner of the photovoltaic panel of the photovoltaic module at the moment t.
In other embodiments, the data modeling module further includes a fifth modeling unit that constructs a transportation loss rate prediction model, where the fifth modeling unit constructs the transportation loss rate prediction model based on the historical energy storage input values of the energy storage components over the long time sequence, the historical output power of the photovoltaic components over the long time sequence, and the historical weather data over the long time sequence. The fourth modeling unit receives the photovoltaic module power change model from the first modeling unit, the energy storage module energy storage loss model of the second modeling unit, the energy utilization side load prediction model of the third modeling unit and the transportation loss rate prediction model of the fifth modeling unit, and performs model fusion construction to obtain a double-layer optimization model.
As a fifth embodiment of the present application, a computer-readable storage medium is configured to store a computer program or instructions, which when executed by a processing device, implement the above-described photovoltaic panel rotation angle control method based on energy storage cooperation. Computer readable storage media can be any available media that can be stored by a computing device or data storage device such as a data center containing one or more available media. Usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tape), optical media (e.g., DVD), or semiconductor media (e.g., solid state disk), among others.
The above embodiments are preferred embodiments of the method, system and storage medium for controlling the rotation angle of a photovoltaic panel based on energy storage cooperation according to the present application, and are not limited to the specific embodiments, but the scope of the application includes equivalent changes made in shape and structure according to the present application.
Claims (10)
1. The photovoltaic panel corner control method based on energy storage cooperation is characterized by comprising the following steps of: the method comprises the following steps:
acquiring the illuminated intensity, the output power, the photovoltaic panel corner and the photovoltaic panel area of a historical photovoltaic module on a long-time sequence, and constructing a photovoltaic module power change model;
acquiring energy storage energy, energy storage loss rate and energy storage loss of a historical energy storage component on a long-time sequence, and constructing an energy storage loss model of the energy storage component;
acquiring historical energy utilization side load data and weather data on a long-time sequence, and constructing an energy utilization side load prediction model;
constructing a minimum loss objective function based on an energy-consumption side load prediction model as an external optimization layer;
constructing an internal optimization layer based on a photovoltaic module power change model and an energy storage module energy storage loss model;
and constructing a double-layer optimization model by using the external optimization layer and the internal optimization layer, acquiring weather prediction data on a prediction time sequence, outputting a photovoltaic panel corner optimal control strategy on the prediction time sequence, and controlling the change of the photovoltaic panel corner by using the photovoltaic panel corner optimal control strategy.
2. The energy storage fit-based photovoltaic panel corner control method as claimed in claim 1, wherein:
the method comprises the steps of obtaining the illuminated intensity, the output power, the photovoltaic panel corner and the photovoltaic panel area of a historical photovoltaic module on a long-time sequence, and constructing a photovoltaic module power change model as follows:
；
wherein,output power for photovoltaic module, < >>For photovoltaic panel area, +.>Is illuminated with light>Is a corner of a photovoltaic panel>Is the photoelectric conversion efficiency of the photovoltaic panel.
3. The energy storage fit-based photovoltaic panel corner control method as claimed in claim 1, wherein:
acquiring the energy storage energy, the energy storage loss rate and the energy storage loss of the historical energy storage component on a long-time sequence, and constructing an energy storage loss model of the energy storage component as follows:
；
wherein,for energy storage loss->For energy storage->Is the energy storage loss rate.
4. The energy storage fit-based photovoltaic panel corner control method as claimed in claim 1, wherein:
constructing a minimum loss objective function based on an energy-consumption side load prediction model as an external optimization layer, wherein the minimum loss objective function comprises the following steps of:
constructing a minimum loss objective function with minimum running cost loss;
taking a load predicted value output by the energy-consumption side load prediction model as a constraint condition;
and taking the minimum loss objective function and the constraint condition as an external optimization layer.
5. The energy storage fit-based photovoltaic panel corner control method as claimed in claim 4, wherein:
the minimum loss objective function is constructed with the minimum running cost loss as follows:
；
wherein,for the purpose of operating cost loss>For the energy storage loss of the energy storage component at time t, < >>Maintenance loss for photovoltaic systems, +.>For the energy storage of the energy storage component at time t, < >>And the energy storage loss rate of the energy storage component at the time T is the predicted time sequence.
6. The energy storage fit-based photovoltaic panel corner control method as claimed in claim 1, wherein:
the method for constructing the internal optimization layer based on the photovoltaic module power change model and the energy storage module energy storage loss model comprises the following steps of:
constructing a correlation function of the output power of the photovoltaic module and the energy storage energy of the energy storage module;
and fusing the correlation function with a photovoltaic module power change model and an energy storage module energy storage loss model, and constructing an internal objective function with minimum energy storage loss as an internal optimization layer.
7. The energy storage fit-based photovoltaic panel corner control method as claimed in claim 6, wherein:
the internal objective function is:
；
wherein,the photovoltaic module is subjected to illumination intensity at the moment t, < >>Corner of photovoltaic panel at time t for photovoltaic module>For photovoltaic panel area, +.>The photoelectric conversion efficiency of the photovoltaic panel is shown, and T is a predicted time sequence.
8. The energy storage fit-based photovoltaic panel corner control method as claimed in claim 1, wherein:
the minimum loss objective function is:
；
wherein,for the purpose of operating cost loss>For the energy storage loss of the energy storage component at time t, < >>Maintenance loss for photovoltaic system at time t, < >>For the energy storage of the energy storage component at time t, < >>For the energy storage loss rate of the energy storage component at the time T, T is a predicted time sequence, and +.>The loss cost of the photovoltaic panel at the time t is defined as R, and the fixed maintenance cost of the photovoltaic equipment is defined as R.
9. The photovoltaic panel corner control system based on energy storage cooperation is used for realizing the photovoltaic panel corner control method based on energy storage cooperation as claimed in any one of claims 1 to 8, and is characterized in that: comprising the following steps:
the data acquisition module is used for acquiring historical data on a long time sequence and weather prediction data on a prediction time sequence;
the data modeling module is used for constructing a double-layer optimization model according to the data acquired by the data acquisition module;
the data interaction module is used for receiving a control demand and outputting an optimal control strategy of the photovoltaic panel rotation angle;
and the control execution module is used for executing corner control on the photovoltaic panel according to the optimal control strategy of the corner of the photovoltaic panel.
10. A computer-readable storage medium, characterized in that: for storing a computer program or instructions which, when executed by a processing device, implement the energy storage fit-based photovoltaic panel corner control method of any one of claims 1 to 8.
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