Photovoltaic power prediction method and discharge control method of energy storage system and energy storage system

Abstract

The invention discloses a photovoltaic power prediction method and a discharge control method of an energy storage system, and the energy storage system. The method comprises the steps of obtaining geographic information of an area where photovoltaic power generation equipment is located, meteorological data of the area where the photovoltaic power generation equipment is located in a first time period and attribute information of the photovoltaic power generation equipment; and determining a photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period according to the geographic information, the meteorological data and the attribute information. According to the embodiment of the invention, the geographic information, the meteorological data and the attribute information are acquired, photovoltaic power prediction is carried out based on multi-source data fusion, and the photovoltaic power can be predicted more accurately.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a photovoltaic power prediction method, a discharge control method and an energy storage system. Background Art

[0002] The integrated photovoltaic, energy storage and charging power station is a comprehensive energy management solution that integrates photovoltaic power generation, energy storage and charging. Photovoltaic power generation is responsible for converting light energy into electrical energy, energy storage equipment is used to store electrical energy, and the charging station provides charging services for electrical equipment.

[0003] At present, in the operation of integrated photovoltaic storage and charging power stations, in order to improve the utilization rate of light energy, operators will predict the photovoltaic power generation power based on weather conditions and subjective experience, and adjust the charging output power according to the size of the photovoltaic power generation power.

[0004] However, the prediction method of photovoltaic power generation in the prior art has poor accuracy. Summary of the invention

[0005] The present application provides a photovoltaic power prediction method, a discharge control method and an energy storage system of an energy storage system, which can realize accurate prediction of the photovoltaic power of a photovoltaic power generation device.

[0006] In a first aspect, the present application provides a photovoltaic power prediction method for an energy storage system, wherein the energy storage system includes a photovoltaic power generation device and an energy storage device, and the method includes: Acquire geographic information of the region where the photovoltaic power generation equipment is located, meteorological data of the region where the photovoltaic power generation equipment is located within a first time period, and attribute information of the photovoltaic power generation equipment; A photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period is determined according to the geographic information, the meteorological data and the attribute information.

[0007] In the embodiment of the present application, by acquiring geographic information, meteorological data and attribute information, photovoltaic power prediction is based on multi-source data fusion. Compared with the traditional technology of simply predicting photovoltaic power through experience or based on historical data, this solution has a higher prediction accuracy for photovoltaic power.

[0008] In a possible implementation manner of the first aspect, determining the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period according to the geographic information, the meteorological data, and the attribute information includes: determining a first fluctuation condition of each element in the meteorological data according to the meteorological data of the first time period and the meteorological data of a third time period before the first time period; Determining a second fluctuation of the photovoltaic power of the photovoltaic power generation device according to the actual photovoltaic power in the first time period and the actual photovoltaic power in the third time period; determining a target element associated with photovoltaic power from the meteorological data according to the first fluctuation condition and the second fluctuation condition; A photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period is determined according to the geographic information, the target element and the attribute information.

[0009] In the embodiment of the present application, by screening the meteorological data, extracting the target elements related to the photovoltaic power, and then predicting the photovoltaic power based on these target elements, the prediction accuracy can be further improved.

[0010] In a possible implementation manner of the first aspect, determining the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period according to the geographic information, the meteorological data, and the attribute information includes: Acquire a first power prediction value according to the meteorological data and the first power prediction model; Acquire a second power prediction value according to the geographic information and the second power prediction model; Acquire a third power prediction value according to the attribute information and a third power prediction model; The photovoltaic power prediction value is determined according to the first power prediction value, the second power prediction value and the third power prediction value.

[0011] In the embodiment of the present application, by using the photovoltaic power prediction model, combined with multi-source meteorological data, attribute information and geographic information, the photovoltaic power is predicted, which can further improve the prediction accuracy and reduce the error caused by a single data source. At the same time, the adaptability of photovoltaic power prediction in different regions is enhanced to better meet the energy management needs of different regions.

[0012] In a possible implementation manner of the first aspect, the method further includes: Acquiring meteorological data of the second time period and actual photovoltaic power of the photovoltaic power generation equipment in the second time period; The network parameters in the first power prediction model are updated according to the meteorological data of the second time period and the actual photovoltaic power.

[0013] In an embodiment of the present application, after the first power prediction value of the second time period is predicted by the first power prediction model, the network parameters in the first power prediction model are subsequently updated by the actual photovoltaic power in the second time period, which can further improve the prediction accuracy and adaptability of the first power prediction model.

[0014] In a second aspect, the present application provides a discharge control method for an energy storage system, wherein the energy storage system includes a photovoltaic power generation device and an energy storage device, and the method includes: Acquire geographical information of the region where the photovoltaic power generation equipment is located, meteorological data of the region where the photovoltaic power generation equipment is located in a first time period, attribute information of the photovoltaic power generation equipment, and power demand of electrical equipment in the region; Determining a photovoltaic power prediction value of the photovoltaic power generation equipment in a second time period according to the geographic information, the meteorological data and the attribute information; Determining the discharge power of the energy storage device in the second time period according to the required power and the photovoltaic power prediction value; The energy storage system is controlled to discharge according to the photovoltaic power prediction value and the discharge power.

[0015] In the embodiment of the present application, by optimizing the discharge power of the energy storage device using the demand power and the photovoltaic power prediction value, the service life of the energy storage battery can be extended. At the same time, the energy storage device is controlled to charge the user's electrical equipment according to the photovoltaic power prediction value and the discharge power, which can ensure the user's electricity demand, and achieve efficient use of energy and reduce energy waste by reasonably allocating charging resources. In addition, based on photovoltaic power prediction and energy storage discharge power adjustment, collaborative optimization can be achieved to fully meet user needs. When the user's electricity consumption is at its peak, the energy storage system can provide sufficient power support in a timely manner.

[0016] In a possible implementation of the second aspect, determining the discharge power of the energy storage device in the second time period according to the required power and the photovoltaic power prediction value includes: Obtaining a difference between the photovoltaic power prediction value and the required power; When the difference is greater than a set threshold, adjusting the discharge power in the second time period to the first discharge power; When the difference is smaller than a set threshold, the discharge power in the second time period is adjusted to a second discharge power, and the first discharge power is smaller than the second discharge power.

[0017] In an embodiment of the present application, by utilizing the difference between the photovoltaic power prediction value and the required power, the discharge power of the energy storage device is adjusted, the discharge power of the energy storage device in different time periods is flexibly controlled, and the applicability of the energy storage device in different power demand scenarios is improved. This can achieve effective utilization of light energy and avoid wasting resources. On the other hand, it can also avoid excessive consumption of stored electrical energy in the energy storage device, extend the service life of the energy storage battery, and ensure the normal operation of the integrated photovoltaic storage and charging site.

[0018] In a possible implementation manner of the second aspect, controlling the energy storage system to discharge includes: When the second time period is the first target time period, the discharge power is adjusted to a preset lower limit of the discharge power, and the photovoltaic power generation equipment and / or the external power grid are controlled to discharge to the power consumption equipment; When the second time period is a second target time period, the discharge power is adjusted to a preset discharge power upper limit value, and the energy storage device is controlled to discharge to the power consuming device.

[0019] In the embodiment of the present application, by using photovoltaic power generation equipment and / or an external power grid to supply power to power-consuming equipment during the day, the amount of electricity stored in the energy storage device can be retained to the maximum extent. When the photovoltaic power generation equipment generates very little electricity at night, the amount of electricity stored in the energy storage device can be used to supply power to the power-consuming equipment to ensure electricity demand.

[0020] In a possible implementation manner of the second aspect, determining the discharge power of the energy storage device in the second time period includes: Obtain the preset discharge power corresponding to each time period, where different time periods correspond to different preset discharge powers; According to the preset discharge power corresponding to each time period, the target preset discharge power corresponding to the second time period is determined as the discharge power of the energy storage device in the second time period.

[0021] In the embodiment of the present application, by configuring different discharge powers for different time periods, it is possible to achieve refined control of the discharge power of the energy storage device, ensure the balance between power supply and demand, avoid excessive or insufficient discharge, and improve the energy efficiency of the energy storage device. In addition, reasonable control of the discharge power can help reduce the wear and aging speed of the battery and extend the service life of the energy storage battery.

[0022] In a possible implementation manner of the second aspect, the method further includes: When the photovoltaic power prediction value is greater than a preset value and the amount of electricity stored in the energy storage device is greater than an electricity threshold, controlling the photovoltaic power generation device to supplement the energy storage device with electricity; When the photovoltaic power prediction value is less than or equal to the preset value, controlling the photovoltaic power generation equipment and the external power grid to jointly supplement the energy storage device with electricity; When the amount of electricity stored in the energy storage device is less than or equal to the electricity threshold, the photovoltaic power generation device and the external power grid are controlled to jointly supplement electricity for the energy storage device.

[0023] In the embodiment of the present application, when the photovoltaic power prediction value is high, photovoltaic power generation can be used to supplement the energy storage device, thereby improving the utilization rate of light energy resources and reducing dependence on the external power grid. In addition, when the photovoltaic power prediction value is low or the power of the energy storage device is low, supplementing power through the external power grid can avoid excessive discharge of the energy storage device, thereby extending the service life of the energy storage device.

[0024] In a possible implementation manner of the second aspect, the method further includes: Obtaining operating status parameters of the photovoltaic power generation equipment; According to the operating status parameters, it is detected whether the photovoltaic power generation equipment has any abnormality.

[0025] In the embodiment of the present application, by monitoring the operating status parameters of the photovoltaic power generation equipment, the normal operation of the photovoltaic module can be ensured, and the reliability and stability of the photovoltaic power generation equipment can be improved. In addition, it is also convenient to timely discover and solve the failure problems of the photovoltaic module, reduce the power loss caused by the component failure, and extend the service life of the photovoltaic system.

[0026] In a third aspect, the present application provides an energy storage system, comprising: a photovoltaic power generation device, an energy storage device, and a processor, wherein the photovoltaic power generation device is connected to the energy storage device; The photovoltaic power generation equipment is used to convert light energy into electrical energy; The energy storage device is used to store the electrical energy; The processor is configured to perform the following operations: Obtain geographic information of the area where the photovoltaic power generation equipment is located, meteorological data of the area where the photovoltaic power generation equipment is located in a first time period, and attribute information of the photovoltaic power generation equipment; determine a predicted photovoltaic power value of the photovoltaic power generation equipment in a second time period based on the geographic information, the meteorological data, and the attribute information. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The features, advantages and technical effects of exemplary embodiments of the present application will be described below with reference to the accompanying drawings.

[0028] Figure 1 A schematic diagram of the system structure of the energy storage system provided in the embodiment of the present application; Figure 2 A schematic diagram of a photovoltaic power prediction method for an energy storage system provided in an embodiment of the present application; Figure 3 A schematic diagram of a photovoltaic power prediction method provided in another embodiment of the present application; Figure 4 A schematic diagram of a photovoltaic power prediction method provided in another embodiment of the present application; Figure 5A schematic flow chart of a discharge control method for an energy storage system provided in an embodiment of the present application; Figure 6 A schematic diagram of a discharge power optimization method provided in an embodiment of the present application; Figure 7 A schematic diagram of a method for charging an energy storage device provided in an embodiment of the present application; Figure 8 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0029] In the drawings, the drawings are not necessarily drawn to scale. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The ‌Photovoltaic Storage and Charging Integrated Site‌ is an integrated energy system that integrates photovoltaic power generation, energy storage and charging functions. This site includes a photovoltaic power generation system, energy storage equipment and a charging station, which can realize self-power supply and energy storage, and provide charging services for new energy vehicles. Among them, the ‌Photovoltaic Power Generation System‌ converts solar energy into electrical energy through solar panels to provide a clean source of electricity. The energy storage equipment can release the stored electrical energy to ensure the continuous operation of the charging station. The ‌Charging Station‌ can provide charging services for electrical equipment, such as charging services for current new energy vehicles. The photovoltaic storage and charging integrated site is mainly used in places that require a large amount of electricity supply, such as new energy vehicle charging stations, industrial parks, and commercial blocks. The energy management system of the traditional photovoltaic storage and charging integrated site mainly calculates the real-time photovoltaic power output based on the performance of the photovoltaic power generation system itself, and then further combines the status of the energy storage device to adjust the power in real time. It has the problem of inaccurate calculation of photovoltaic power, which easily leads to the situation that the user's electricity demand cannot be met when the peak power consumption occurs.

[0032] In response to the above problems, the present application provides a photovoltaic power prediction scheme for an energy storage system, which may refer to an integrated photovoltaic storage and charging site. This scheme achieves photovoltaic power prediction based on multi-source data fusion by acquiring the geographic information of the area where the photovoltaic power generation equipment is located, the meteorological data of the area where the photovoltaic power generation equipment is located in the first time period, and the attribute information of the photovoltaic power generation equipment. It can accurately predict the photovoltaic power of the photovoltaic power generation system in the integrated photovoltaic storage and charging site in the future time period. In this way, the power output of the energy storage equipment can be configured more reasonably to ensure that the user's electricity demand can be met even during peak hours.

[0033] For example, Figure 1 A schematic diagram of the system structure of the energy storage system provided in the embodiment of the present application is shown in FIG. Figure 1 As shown, the photovoltaic power generation device 10 can convert light energy into electrical energy and then store it in the energy storage device 11, and the electrical energy in the energy storage device 11 can be transmitted to other electrical devices 12. However, in order to ensure the normal operation of the energy storage system and prevent excessive consumption of electrical energy in the energy storage device 11, an additional energy management system 13 is required to accurately predict the photovoltaic power output of the photovoltaic power generation device 10, and the energy management system 13 also needs to reasonably control the electrical energy output of the energy storage device 11.

[0034] In this embodiment, by building an energy management system for a photovoltaic storage and charging integrated site, accurate prediction of photovoltaic power and reasonable adjustment of energy storage discharge power can be achieved. By collecting meteorological data in real time, building advanced prediction models, optimizing energy storage discharge power algorithms, and establishing intelligent decision-making systems, the problems of low prediction accuracy, poor real-time performance, and lack of collaborative optimization in traditional systems are solved, thereby improving the economic benefits and resource efficiency of photovoltaic storage and charging integrated sites.

[0035] The technical solution of the present application is described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0036] Figure 2 The schematic diagram of the photovoltaic power prediction method of the energy storage system provided in the embodiment of the present application can be applied to the energy management system. For example, the energy management system can be integrated on an electronic device, such as a computer or a server, etc., taking the electronic device equipped with the energy management system as the execution subject as an example. Figure 2 As shown, the method may include the following steps: Step S210: acquiring geographical information of the region where the photovoltaic power generation equipment is located, meteorological data of the region where the photovoltaic power generation equipment is located in a first time period, and attribute information of the photovoltaic power generation equipment.

[0037] In this embodiment, the photovoltaic power generation equipment is usually fixedly installed in an area with sufficient sunlight, and the geographic information is used to characterize the climate and air pressure of the area where the photovoltaic power generation equipment is located. For example, in some plateau areas, the air pressure is relatively low, while in some plain areas, the air pressure is relatively high.

[0038] Among them, climate can refer to the multi-year average conditions of the atmosphere in a region. The main climate elements include light, temperature and precipitation.

[0039] In this embodiment, the first time period may refer to one or more historical time periods, and correspondingly, the subsequent second time period may be the current time period. The length of the time period may be one day, for example, the meteorological data of the first time period may refer to the meteorological data of the area where the photovoltaic power generation equipment is located yesterday, and the second time period may be today. In addition, the first time period may be a real-time time period, for example, the meteorological data of the first time period may refer to the current time, and correspondingly, the second time period may refer to after the current time, that is, the future time.

[0040] In this embodiment, the meteorological data may refer to the weather, temperature, humidity and other meteorological data of the area where the photovoltaic power generation equipment is located. For example, meteorological sensors (including temperature sensors, humidity sensors, wind speed sensors, light intensity sensors, etc.) may be installed to collect the weather, temperature, humidity and other meteorological data of the area where the photovoltaic power generation equipment is located, and these meteorological data may be analyzed and screened to screen out data that is associated with the photovoltaic power of the photovoltaic power generation equipment, so as to ensure the accuracy of subsequent photovoltaic power prediction.

[0041] Among them, the meteorological data collected by the meteorological sensor can be transmitted to the wireless communication module and then transmitted to the energy management system by wireless communication to ensure the real-time and accuracy of the data.

[0042] In this embodiment, the attribute information of the photovoltaic power generation equipment may refer to the model of the photovoltaic power generation equipment (for example, the type of solar panel; different types of solar panels have different photoelectric conversion efficiencies) and the service life of the photovoltaic power generation equipment (in the long-term application of photovoltaic components, slow power attenuation will occur, that is, the longer the service life, the lower the photoelectric conversion efficiency).

[0043] In this embodiment, photovoltaic power may refer to the output capacity of the photovoltaic power generation equipment, that is, how much electrical energy the photovoltaic power generation equipment can produce for the energy storage device in a certain period of time.

[0044] Step S220: Determine the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period according to the geographic information, meteorological data and attribute information.

[0045] In this embodiment, geographic information, meteorological data and attribute information are correlated with the photovoltaic power of photovoltaic power generation equipment. Exemplarily, a photovoltaic power prediction model can be pre-trained, for example, a photovoltaic power prediction model can be constructed based on a deep learning algorithm, with meteorological data, photovoltaic equipment characteristics and geographic information as model inputs, and the model can dynamically adjust the photovoltaic power prediction value according to real-time meteorological data. In another example, since geographic information, meteorological data and attribute information are correlated with the photovoltaic power of photovoltaic power generation equipment, a function can also be pre-constructed, with meteorological data, photovoltaic equipment characteristics and geographic information as function inputs, and the function output as the photovoltaic power prediction value.

[0046] In this embodiment, the first time period and the second time period may be two adjacent time periods, and the length of the time periods may be adjusted, for example, the first time period may refer to the previous hour, and the second time period may refer to the next hour. The shorter the time period, the closer the photovoltaic power prediction value is to the photovoltaic power actual value, that is, the higher the prediction accuracy.

[0047] In this embodiment, photovoltaic power generation equipment is used to generate solar power, reduce dependence on traditional energy, reduce carbon emissions, and balance power supply and demand through energy storage equipment, reduce grid pressure and electricity bills. At the same time, the site's energy management system can automatically adjust power generation and charging according to demand to improve energy efficiency.

[0048] In the embodiment of the present application, the photovoltaic power of the photovoltaic power generation equipment is predicted by utilizing geographic information, meteorological data and attribute information. Compared with the traditional technology of simply predicting photovoltaic power through experience or based on historical data, this scheme has a higher prediction accuracy for photovoltaic power, and the prediction of photovoltaic power can respond to weather changes in a timely manner, thereby more reasonably controlling the discharge of energy storage equipment, realizing the effective utilization of photovoltaic resources while ensuring the charging needs of users.

[0049] Figure 3 A schematic diagram of a photovoltaic power prediction method provided in another embodiment of the present application is shown in FIG. Figure 3 As shown, it includes the following steps: Step S310: determining a first fluctuation condition of each element in the meteorological data according to the meteorological data of the first time period and the meteorological data of the third time period before the first time period.

[0050] The meteorological data includes at least one element.

[0051] In this embodiment, there may be some elements in the meteorological data that have a weak correlation with the photovoltaic power. For example, if the photovoltaic power does not fluctuate significantly after some non-target elements experience a large fluctuation while ensuring that other elements in the meteorological data remain unchanged, then these non-target elements can actually be regarded as having a weak correlation with the photovoltaic power.

[0052] For example, the elements included in the meteorological data may include: light, temperature, air pressure, wind speed, humidity, precipitation, visibility, cloud cover and haze, etc. Among them, the correlation between light and photovoltaic power is stronger than the correlation between wind speed and photovoltaic power.

[0053] In this embodiment, the length of the time period can be adjusted, for example, it can refer to one day or one hour, etc. Taking the current time as 11:30 and the time period length as one minute as an example, the first time period can refer to the real time, that is, 11:30, and the third time period can be 11:29.

[0054] The meteorological data at 11:30 and the meteorological data at 11:29 can be compared to determine which elements have fluctuated and which have not fluctuated, thereby determining the first fluctuation situation. For example, if the temperature at 11:30 is 30 degrees Celsius and the temperature at 11:29 is 29 degrees Celsius, the fluctuation situation of the temperature is: the temperature has risen by 1 degree Celsius.

[0055] Step S320: determining a second fluctuation of the photovoltaic power of the photovoltaic power generation equipment according to the actual photovoltaic power in the first time period and the actual photovoltaic power in a third time period before the first time period.

[0056] In this embodiment, similar to the above step S310, taking the current time as 11:30 and the time period length as one minute as an example, the first time period may refer to the real time, that is, 11:30, and the third time period may be 11:29.

[0057] For example, assuming that the actual photovoltaic power of the photovoltaic power generation equipment at 11:30 is P1, and the actual photovoltaic power of the photovoltaic power generation equipment at 11:29 is P2, by comparing P1 and P2, it can be found whether the actual photovoltaic power has changed.

[0058] Step S330: extracting target elements associated with photovoltaic power from meteorological data according to the first fluctuation condition and the second fluctuation condition.

[0059] Step S340: Determine the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period according to the target elements and attribute information in the geographic information and meteorological data.

[0060] In this embodiment, if a target element changes from the first time period to the second time period, and the actual photovoltaic power of the photovoltaic device fluctuates dramatically from the first time period to the second time period, then there is a strong correlation between the target element and the photovoltaic power. Exemplarily, a judgment standard can be set, and when the fluctuation change of the actual photovoltaic power from the first time period to the second time period reaches the judgment standard, then there is a correlation between the corresponding target element and the photovoltaic power.

[0061] For example, Table 1 is a schematic diagram of the association provided in the embodiment of the present application: Table 1

[0062] In this embodiment, when the meteorological data contains multiple elements, it is necessary to collect a large amount of sample data (including meteorological data of many time periods and actual photovoltaic power of multiple time periods) and use the control variable method to determine which element is the target element.

[0063] In other embodiments, the collected meteorological data may also be preprocessed, for example, including data cleaning, normalization and other operations to eliminate noise and outliers in the meteorological data, and then features related to photovoltaic power, such as light intensity, temperature, humidity, wind speed, etc., are extracted from the preprocessed meteorological data as target elements.

[0064] In the embodiment of the present application, by screening the meteorological data, extracting the target elements related to the photovoltaic power, and then predicting the photovoltaic power based on these target elements, the prediction accuracy can be further improved.

[0065] Figure 4 A schematic diagram of a photovoltaic power prediction method provided in another embodiment of the present application is shown in FIG. Figure 4 As shown, it includes the following steps: Step S410: Obtain a first power prediction value according to meteorological data and a first power prediction model.

[0066] Step S420: Obtain a second power prediction value according to the geographic information and the second power prediction model.

[0067] Step S430: Obtain a third power prediction value according to the attribute information and the third power prediction model.

[0068] Step S440: determining a photovoltaic power prediction value according to the first power prediction value, the second power prediction value and the third power prediction value.

[0069] In this embodiment, a photovoltaic power prediction model can be constructed based on a deep learning algorithm, using a neural network structure to optimize model parameters by training a large amount of historical data to improve the prediction accuracy of the model.

[0070] Among them, since the degree of influence of meteorological data, attribute information and geographic information on photovoltaic power is different, in order to improve the prediction accuracy, different power prediction models (i.e., the first power prediction model, the second power prediction model and the third power prediction model) can be configured respectively, and the prediction values ​​of meteorological data, attribute information and geographic information on photovoltaic power can be obtained through different power prediction models.

[0071] Among them, the first power prediction model can be trained using the meteorological data at the historical moment and the actual photovoltaic power at the historical moment as training data. The second power prediction model can be trained using the geographical information at the historical moment and the actual photovoltaic power at the historical moment as training data. The third power prediction model can be trained using the attribute information of the photovoltaic power generation equipment and the actual photovoltaic power of the photovoltaic power generation equipment as training data.

[0072] Furthermore, after obtaining the first power prediction value, the second power prediction value and the third power prediction value, corresponding weight coefficients can be configured for the first power prediction value, the second power prediction value and the third power prediction value, and then the photovoltaic power prediction value is calculated by weighted summation.

[0073] In the embodiment of the present application, by using the photovoltaic power prediction model, combined with multi-source meteorological data, attribute information and geographic information, the photovoltaic power is predicted, which can further improve the prediction accuracy and reduce the error caused by a single data source. At the same time, the adaptability of photovoltaic power prediction in different regions is enhanced to better meet the energy management needs of different regions.

[0074] Furthermore, based on the above embodiments, in other embodiments, the network parameters in the power prediction model can also be updated in the following manner. For example, the meteorological data of the second time period and the actual photovoltaic power of the photovoltaic power generation equipment in the second time period are obtained; and the network parameters in the first power prediction model are updated according to the meteorological data and the actual photovoltaic power of the second time period.

[0075] In this embodiment, the first time period may refer to a historical time period, and the second time period may refer to a current real-time time period.

[0076] The first power prediction model can be trained using meteorological data of a historical time period and actual photovoltaic power of the historical time period, and then the photovoltaic power of the current real-time time period is predicted by the trained first power prediction model.

[0077] Among them, since there may be obvious differences in meteorological data in different time periods, in order to further improve the prediction accuracy of the power prediction model, the network parameters in the above-mentioned first power prediction model can be updated according to the real-time meteorological data and the corresponding actual photovoltaic power.

[0078] The updating of the network parameters may include adjusting the weight and bias parameters in the first power prediction model so that the model can better fit the mapping relationship between the meteorological data and the actual photovoltaic power.

[0079] In an embodiment of the present application, after the first power prediction value of the second time period is predicted by the first power prediction model, the network parameters in the first power prediction model are subsequently updated by the actual photovoltaic power in the second time period, which can further improve the prediction accuracy and adaptability of the first power prediction model.

[0080] In order to ensure that the energy storage system can achieve maximum economic benefits and resource efficiency while meeting user needs by reasonably adjusting the energy storage discharge power. Figure 5 A flow chart of a discharge control method for an energy storage system provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, it includes the following steps: Step S510: Obtain the geographic information of the area where the photovoltaic power generation equipment is located, the meteorological data of the area where the photovoltaic power generation equipment is located in the first time period, the attribute information of the photovoltaic power generation equipment and the power demand of the electrical equipment in the area; Step S520: Determine the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period based on the geographic information, meteorological data and attribute information; Step S530: Determine the discharge power of the energy storage device in the second time period based on the power demand and the photovoltaic power prediction value; Step S540: Control the energy storage system to discharge based on the photovoltaic power prediction value and the discharge power.

[0081] In this embodiment, the power demand may refer to the power demanded by the power-consuming devices (such as electric vehicles) in the area. Since the power demanded by the power-consuming devices in different time periods may be different, multiple time periods may be divided to obtain the power demanded in each time period.

[0082] In this embodiment, an energy storage discharge power optimization algorithm can be configured to optimize the discharge power of the energy storage device in combination with the photovoltaic power prediction value and the user's electricity demand. By reasonably adjusting the energy storage discharge power, it is ensured that the system achieves maximum economic benefits and resource efficiency while meeting user needs.

[0083] The power demand may refer to the power demand in the second time period. For example, if the user has made an appointment to charge the electric vehicle in the second time period, the power demand in the second time period can be determined. The discharge power of the energy storage device can be optimized based on the power demand and photovoltaic power forecast value in the second time period.

[0084] For example, if the predicted photovoltaic power value can meet the required electricity, that is, the electricity generated by photovoltaic power generation can be consumed by electrical equipment, the discharge power of the energy storage device can be configured to a standard normal value to meet the electricity demand of the user equipment.

[0085] For example, a corresponding energy storage and discharge strategy can be formulated according to the user's electricity demand and electricity usage habits. For example, if the user is accustomed to charging the electric vehicle at night, when formulating the energy storage and discharge strategy, it is necessary to ensure that the energy storage device has enough power to meet the user's charging needs at night.

[0086] In this embodiment, an intelligent decision-making system can also be configured for the integrated photovoltaic, storage and charging site. The intelligent decision-making system can adjust the operation strategy of the integrated photovoltaic, storage and charging site in real time according to the predicted photovoltaic power value and the discharge power of the energy storage equipment, thereby realizing the automatic operation and intelligent management of the system.

[0087] Specifically, when the user's electricity demand is large, if the energy storage device is insufficient, the discharge power cannot meet the user's demand, and the photovoltaic power forecast value is also low. At this time, in order to meet the user's electricity demand, the external mains power can be used to supplement the energy storage device to control the energy storage device to supply power to the user normally and meet the user's electricity demand.

[0088] In the embodiment of the present application, by optimizing the discharge power of the energy storage device using the demand power and the photovoltaic power prediction value, the service life of the energy storage battery can be extended. At the same time, the energy storage device is controlled to charge the user's electrical equipment according to the photovoltaic power prediction value and the discharge power, which can ensure the user's electricity demand, and achieve efficient use of energy and reduce energy waste by reasonably allocating charging resources. In addition, based on photovoltaic power prediction and energy storage discharge power adjustment, collaborative optimization can be achieved to fully meet user needs. When the user's electricity consumption is at its peak, the energy storage system can provide sufficient power support in a timely manner.

[0089] Further, based on the above embodiments, in some embodiments, the difference between the photovoltaic power prediction value and the required power can be obtained; and then the discharge power of the energy storage device in the second time period is determined according to the difference.

[0090] In this embodiment, when the photovoltaic power prediction value is much greater than the required power, the discharge power of the energy storage device can be increased to improve the charging efficiency. For example, when charging an electric vehicle, it can be divided into fast charging and slow charging. When the photovoltaic power prediction value is much greater than the required power, the electric vehicle can be charged in fast charging mode to improve the charging efficiency.

[0091] In addition, if the predicted photovoltaic power value is small (for example, photovoltaic power generation equipment generates almost no electricity at night), and the user has a large power demand, the discharge power of the energy storage device can be reduced, such as using slow charging mode to charge electric vehicles.

[0092] Specifically, a set threshold value may be configured. When the difference between the photovoltaic power prediction value and the required power is greater than the set threshold value, the discharge power in the second time period may be adjusted to the first discharge power; and when the difference is less than or equal to the set threshold value, the discharge power in the second time period may be adjusted to the second discharge power. The first discharge power is less than the second discharge power.

[0093] In this embodiment, the threshold value is adjustable, and for example, the threshold value can be set to 0. That is, when the photovoltaic power prediction value is greater than the required power, the discharge power is increased to improve the utilization rate of light energy, and when the photovoltaic power prediction value is less than or equal to the required power, the discharge power is reduced to ensure that the electric energy stored in the energy storage device is not consumed quickly, thereby extending the service life of the energy storage battery and ensuring the normal operation of the integrated photovoltaic storage and charging site.

[0094] In other embodiments, intelligent charging strategies can be developed for charging equipment at integrated photovoltaic storage and charging sites. A personalized charging plan is formulated based on the user's charging needs, the predicted photovoltaic power, and the status of the energy storage device (such as the current amount of stored electricity). For example, when it is predicted that the photovoltaic power is sufficient and the energy storage capacity is sufficient, charging services are provided to users first; when the photovoltaic power is insufficient or the energy storage capacity is low, the charging power is adjusted to avoid excessive consumption of the energy storage battery.

[0095] In the embodiment of the present application, by adjusting the discharge power of the energy storage device, on the one hand, effective utilization of light energy can be achieved to avoid waste of resources; on the other hand, excessive consumption of electrical energy stored in the energy storage device can be avoided, thereby extending the service life of the energy storage battery and ensuring the normal operation of the integrated photovoltaic storage and charging site.

[0096] Furthermore, in some embodiments, Figure 6 A schematic diagram of the discharge power optimization method provided in the embodiment of the present application is shown in FIG. Figure 6 As shown, it includes the following steps: Step S610: Obtain the preset discharge power corresponding to each time period, and different time periods correspond to different preset discharge powers; Step S620: Determine the target preset discharge power corresponding to the second time period according to the preset discharge power corresponding to each time period; Step S630: Determine the target preset discharge power as the discharge power of the energy storage device in the second time period.

[0097] In this embodiment, since the photovoltaic power of the photovoltaic power generation equipment may be different in different time periods, for example, the photovoltaic power is high at noon when the light intensity is high, and the photovoltaic power is low in the evening when the light intensity is low. At this time, different discharge powers can be pre-set in different time periods in combination with the photovoltaic power variation characteristics of the photovoltaic power generation equipment.

[0098] For example, the discharge power of the time period [t1, t2] may be p11, the discharge power of the time period [t2, t3] may be p12, and the discharge power of the time period [t3, t4] may be p13. If the second time period is within the time period [t1, t2], the discharge power of the second time period corresponds to p11, that is, the target preset discharge power.

[0099] In addition, in some embodiments, electricity prices may fluctuate in different time periods (for example, electricity prices are lower at night and higher during the day). In order to increase profits, the discharge power of the energy storage device can be controlled to be larger during the day to supply as much power as possible to the power-consuming devices, while the discharge power of the energy storage device can be controlled to be smaller at night to reduce the power supply of the energy storage device to the power-consuming devices.

[0100] Among them, when the discharge power of the energy storage device becomes smaller, in order to meet the power demand of the electrical equipment, it can be connected to an external power grid (such as municipal electricity) to power the electrical equipment.

[0101] Furthermore, in other embodiments, the discharge power of the energy storage device in each time period may be different, and the discharge power in each time period may be configured in combination with the photovoltaic power variation characteristics of the photovoltaic power generation device. For example, taking the first target time period as time period [t1, t2] and the second target time period as time period [t2, t3] as an example, the discharge power p11 of the time period [t1, t2] may be set to be smaller than the discharge power p12 of the time period [t2, t3].

[0102] For example, the first target time period may be daytime, and the second target time period may be nighttime. Thus, since the photovoltaic power generation equipment can generate electricity during the daytime, the second target preset discharge power of the energy storage equipment can be larger during the daytime, while since the photovoltaic power generation equipment hardly generates electricity at night, the first target preset discharge power of the energy storage equipment can be smaller at night.

[0103] Among them, since the electricity price is usually low at night, the energy storage device can reduce discharge at this time and only use the city electricity to charge the vehicle. During the day, due to the high electricity price, the energy storage device can discharge more and use less city electricity, which can maximize the economic benefits of the integrated photovoltaic storage and charging site. In addition, since photovoltaic power generation equipment will generate electricity during the day, if the energy storage device is already full, then in order to avoid wasting light energy, the second target preset discharge power can be set larger at this time to improve the utilization rate of light energy resources.

[0104] Furthermore, in some embodiments, taking the first target time period as daytime and the second target time period as nighttime as an example, in the case of the first target time period, the discharge power of the energy storage device can be adjusted to a preset discharge power lower limit value (e.g., zero or close to zero), and the discharge of the energy storage device can be minimized to retain the amount of electricity stored in the energy storage device, and control the photovoltaic power generation device and / or the external power grid to deliver electric energy to the power-consuming device. In the case of the second target time period, the power generation of the photovoltaic power generation device may be small, and the discharge power of the energy storage device can be adjusted to a preset discharge power upper limit value, so that the electric energy stored in the energy storage device can be used to power the power-consuming device to ensure the power demand of the power-consuming device.

[0105] In the embodiment of the present application, by configuring different discharge powers for different time periods, it is possible to achieve refined control of the discharge power of the energy storage device, ensure the balance between power supply and demand, avoid excessive or insufficient discharge, and improve the energy efficiency of the energy storage device. In addition, reasonable control of the discharge power can help reduce the wear and aging rate of the battery, extend the service life of the energy storage battery, and combine the electricity prices in different time periods to configure a reasonable discharge power, which can improve the economic benefits of the integrated photovoltaic storage and charging site, while also avoiding the waste of light energy and maximizing the utilization of resources.

[0106] Figure 7 A schematic diagram of a method for replenishing power for an energy storage device provided in an embodiment of the present application is shown in FIG. Figure 7 As shown, it includes the following steps: Step S710: When the photovoltaic power prediction value is greater than a preset value and the amount of electricity stored in the energy storage device is greater than an electricity threshold, control the photovoltaic power generation device to supplement the energy storage device with electricity.

[0107] Step S720: When the photovoltaic power prediction value is less than or equal to the preset value, the photovoltaic power generation equipment and the external power grid are controlled to jointly supplement the energy storage equipment with power.

[0108] Step S730: When the amount of electricity stored in the energy storage device is less than or equal to the electricity threshold, control the photovoltaic power generation device and the external power grid to jointly supplement electricity for the energy storage device.

[0109] In this embodiment, it is mentioned above that an intelligent decision-making system can be established to adjust the operation strategy of the integrated photovoltaic storage and charging site in real time according to the photovoltaic power prediction value and the energy storage discharge power optimization algorithm to realize the automatic operation and intelligent management of the system.

[0110] Among them, during the operation of the integrated photovoltaic storage and charging site, when the photovoltaic power prediction value is less than or equal to the preset value (the preset value can be configured according to the user's electricity demand), or when the amount of electricity stored in the energy storage device is less than or equal to the electricity threshold, the energy storage device may be continuously consumed. In order to avoid excessive discharge of the energy storage device, the external power grid can be used to supplement the energy storage device.

[0111] In the embodiment of the present application, when the photovoltaic power prediction value is high, photovoltaic power generation can be used to supplement the energy storage device, thereby improving the utilization rate of light energy resources and reducing dependence on the external power grid. In addition, when the photovoltaic power prediction value is low or the power of the energy storage device is low, supplementing power through the external power grid can avoid excessive discharge of the energy storage device, thereby extending the service life of the energy storage device.

[0112] In some embodiments, a photovoltaic component performance monitoring system may also be provided to monitor operating status parameters of the photovoltaic power generation equipment to determine whether there is any abnormality in the photovoltaic power generation equipment.

[0113] The operating status parameters may include the working status, temperature, current, voltage and other parameters of the photovoltaic module.

[0114] In this embodiment, by performing data analysis and fault diagnosis on the operating status parameters, potential faults or performance degradation problems of the photovoltaic module can be discovered in time, and corresponding maintenance measures can be taken. For example, when the photovoltaic module has abnormal temperature or current fluctuations, the maintenance personnel are notified in time to conduct inspection and repair.

[0115] In this embodiment, the photovoltaic module performance monitoring system and the above-mentioned intelligent decision-making system and energy management system can be integrated on the same platform to achieve coordinated operation and intelligent management among the various systems.

[0116] The intelligent decision-making system can also monitor the operating status of the photovoltaic storage and charging integrated site in real time, including photovoltaic power, energy storage system, power equipment, etc., and promptly detect and handle abnormal situations. In addition, the intelligent decision-making system can also adjust the decision-making plan in time according to the actual operating conditions and user feedback to ensure the stability and reliability of the system operation.

[0117] In the embodiment of the present application, by monitoring the operating status parameters of the photovoltaic power generation equipment, the normal operation of the photovoltaic module can be ensured, and the reliability and stability of the photovoltaic power generation equipment can be improved. In addition, it is also convenient to timely discover and solve the failure problems of the photovoltaic module, reduce the power loss caused by the component failure, and extend the service life of the photovoltaic system.

[0118] Continue to refer to the above Figure 1 In addition to the photovoltaic power generation equipment and the energy storage equipment, the energy storage system may also include a processor. The energy management system may be a processor, which may be used to perform the following operations: obtaining geographic information of the region where the photovoltaic power generation equipment is located, meteorological data of the region where the photovoltaic power generation equipment is located in the first time period, and attribute information of the photovoltaic power generation equipment; and determining the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period based on the geographic information, meteorological data, and attribute information.

[0119] Optionally, the processor can also be used to perform the following operations: determine the discharge power of the energy storage device in the second time period based on the regional power demand and the photovoltaic power prediction value; and then control the energy storage device to discharge based on the photovoltaic power prediction value of the photovoltaic power generation equipment and the discharge power of the energy storage device.

[0120] Optional, above Figure 1 The processing system may also include an external power grid, which may be directly connected to the energy storage device. When the power of the energy storage device is high, it may discharge to the external power grid to provide power to the external power grid. When the power of the energy storage device is insufficient, the external power grid may be used to supplement the power.

[0121] In the embodiments of the present application, by collecting meteorological data in real time and building advanced prediction models, photovoltaic power can be accurately predicted, and the prediction error is significantly reduced. At the same time, it can respond to weather changes in real time, adjust the energy storage discharge power in time, and improve the system's utilization efficiency of photovoltaic resources. In addition, by collaboratively optimizing photovoltaic power prediction and energy storage discharge power adjustment, reasonable resource allocation is achieved, the economic benefits and resource efficiency of the system are improved, and it can ensure that users can obtain stable and reliable power supply during peak power consumption, thereby improving user satisfaction.

[0122] Figure 8 The hardware structure diagram of the electronic device provided in the embodiment of the present application. The electronic device may be integrated with the above processor. Figure 8 As shown, the electronic device 800 may include a processor 801 and a memory 802 storing computer program instructions.

[0123] Specifically, the processor 801 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0124] The memory 802 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 802 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these.

[0125] Where appropriate, the memory 802 may include removable or non-removable (or fixed) media. Where appropriate, the memory 802 may be internal or external to the integrated gateway disaster recovery device. In a specific embodiment, the memory 802 is a non-volatile solid-state memory.

[0126] The memory 802 may include a read-only memory (ROM), a random access memory (RAM), a disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory 802 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any of the methods in the above embodiments. The processor 801 implements any of the methods in the above embodiments by reading and executing the computer program instructions stored in the memory 802.

[0127] In one example, the electronic device 800 may further include a communication interface 803 and a bus 804. Figure 8 As shown, the processor 801, the memory 802, and the communication interface 803 are connected via a bus 804 and communicate with each other.

[0128] The communication interface 803 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0129] Bus 804 includes hardware, software or both, and the components of online data traffic billing equipment are coupled to each other. For example, but not limitation, the bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 804 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0130] In addition, in combination with the method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the methods in the above embodiment is implemented.

[0131] An embodiment of the present application also provides a computer program product, including a computer program, which implements any one of the methods in the above embodiments when the computer program is processed and executed.

[0132] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0133] The functional blocks shown in the above structural block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0134] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0135] Aspects of the present disclosure are described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0136] The above are only specific implementation methods of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

[0137] Although the present application has been described with reference to preferred embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there is no structural conflict. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A photovoltaic power prediction method for an energy storage system, characterized in that: The energy storage system includes a photovoltaic power generation device and an energy storage device, and the method includes: Acquire geographic information of the region where the photovoltaic power generation equipment is located, meteorological data of the region where the photovoltaic power generation equipment is located within a first time period, and attribute information of the photovoltaic power generation equipment; Determining a photovoltaic power forecast value of the photovoltaic power generation equipment in a second time period according to the geographic information, the meteorological data and the attribute information; The step of determining the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period according to the geographic information, the meteorological data and the attribute information includes: Acquire a first power prediction value according to the meteorological data and the first power prediction model; Acquire a second power prediction value according to the geographic information and the second power prediction model; Acquire a third power prediction value according to the attribute information and a third power prediction model; The photovoltaic power prediction value is determined according to the first power prediction value, the second power prediction value and the third power prediction value.

2. The method according to claim 1, characterized in that The step of determining the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period according to the geographic information, the meteorological data and the attribute information includes: determining a first fluctuation condition of each element in the meteorological data according to the meteorological data of the first time period and the meteorological data of a third time period before the first time period; Determining a second fluctuation of the photovoltaic power of the photovoltaic power generation device according to the actual photovoltaic power in the first time period and the actual photovoltaic power in the third time period; determining a target element associated with photovoltaic power from the meteorological data according to the first fluctuation condition and the second fluctuation condition; A photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period is determined according to the geographic information, the target element and the attribute information.

3. The method according to claim 1, characterized in that Also includes: Acquiring meteorological data of the second time period and actual photovoltaic power of the photovoltaic power generation equipment in the second time period; The network parameters in the first power prediction model are updated according to the meteorological data of the second time period and the actual photovoltaic power.

4. A discharge control method for an energy storage system, characterized in that: The energy storage system includes a photovoltaic power generation device and an energy storage device, and the method includes: Acquire geographical information of the region where the photovoltaic power generation equipment is located, meteorological data of the region where the photovoltaic power generation equipment is located in a first time period, attribute information of the photovoltaic power generation equipment, and power demand of electrical equipment in the region; Determining a photovoltaic power prediction value of the photovoltaic power generation equipment in a second time period according to the geographic information, the meteorological data and the attribute information; Determining the discharge power of the energy storage device in the second time period according to the required power and the photovoltaic power prediction value; Controlling the energy storage system to discharge according to the photovoltaic power prediction value and the discharge power; The step of determining the discharge power of the energy storage device in the second time period according to the required power and the photovoltaic power prediction value includes: Obtaining a difference between the photovoltaic power prediction value and the required power; When the difference is greater than a set threshold, adjusting the discharge power in the second time period to the first discharge power; When the difference is smaller than a set threshold, the discharge power in the second time period is adjusted to a second discharge power, and the first discharge power is smaller than the second discharge power.

5. The method according to claim 4, characterized in that The controlling the energy storage system to discharge includes: When the second time period is the first target time period, the discharge power is adjusted to a preset lower limit of the discharge power, and the photovoltaic power generation equipment and / or the external power grid are controlled to discharge to the power consumption equipment; When the second time period is the second target time period, the discharge power is adjusted to a preset discharge power upper limit value, and the energy storage device is controlled to discharge to the power consuming device.

6. The method according to claim 4, characterized in that The determining the discharge power of the energy storage device in the second time period includes: Obtain the preset discharge power corresponding to each time period, where different time periods correspond to different preset discharge powers; According to the preset discharge power corresponding to each time period, the target preset discharge power corresponding to the second time period is determined as the discharge power of the energy storage device in the second time period.

7. The method according to claim 4, characterized in that Also includes: When the photovoltaic power prediction value is greater than a preset value and the amount of electricity stored in the energy storage device is greater than an electricity threshold, controlling the photovoltaic power generation device to supplement the energy storage device with electricity; When the photovoltaic power prediction value is less than or equal to the preset value, controlling the photovoltaic power generation equipment and the external power grid to jointly supplement the energy storage device with electricity; When the amount of electricity stored in the energy storage device is less than or equal to the electricity threshold, the photovoltaic power generation device and the external power grid are controlled to jointly supplement electricity for the energy storage device.

8. The method according to any one of claims 1 to 7, characterized in that Also includes: Obtaining operating status parameters of the photovoltaic power generation equipment; According to the operating status parameters, it is detected whether the photovoltaic power generation equipment has any abnormality.

9. An energy storage system, characterized in that: include: Photovoltaic power generation equipment, energy storage equipment and a processor, wherein the photovoltaic power generation equipment is connected to the energy storage equipment; the photovoltaic power generation equipment is used to convert light energy into electrical energy; and the energy storage equipment is used to store the electrical energy; The processor is configured to perform the following operations: Obtaining geographical information of the region where the photovoltaic power generation equipment is located, meteorological data of the region where the photovoltaic power generation equipment is located in a first time period, and attribute information of the photovoltaic power generation equipment; determining a photovoltaic power prediction value of the photovoltaic power generation equipment in a second time period based on the geographical information, the meteorological data, and the attribute information; Wherein, when determining the photovoltaic power prediction value of the photovoltaic power generation equipment in the second time period, the processor specifically performs the following operations: Acquire a first power prediction value according to the meteorological data and the first power prediction model; Acquire a second power prediction value according to the geographic information and the second power prediction model; Acquire a third power prediction value according to the attribute information and a third power prediction model; The photovoltaic power prediction value is determined according to the first power prediction value, the second power prediction value and the third power prediction value.

10. The energy storage system according to claim 9, characterized in that: The processor is further configured to determine the discharge power of the energy storage device in the second time period according to the required power of the area and the photovoltaic power prediction value; The processor is further used to control the energy storage device to discharge according to the photovoltaic power prediction value of the photovoltaic power generation device and the discharge power of the energy storage device.

11. The energy storage system according to claim 9, characterized in that: It also includes an external power grid, which is connected to the energy storage device and is used to supplement the energy storage device and / or receive the discharge of the energy storage device.

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