Optimized scheduling and operation and maintenance management method and system for intelligent distributed photovoltaic power station

Abstract

The invention discloses an intelligent distributed photovoltaic power station optimal scheduling and operation and maintenance management method and system, and relates to the technical field of optimal scheduling. Historical power generation data, real-time meteorological data and future weather forecast of a photovoltaic power station are acquired; according to the historical power generation data, the real-time meteorological data and the future weather forecast, inputting the historical power generation data, the real-time meteorological data and the future weather forecast into a pre-trained power generation prediction model to obtain a photovoltaic power generation prediction value in a future time period; and obtaining the power grid demand power of the photovoltaic power station, and planning the charging and discharging strategy of the energy storage system according to the absolute value of the difference between the power grid demand power and the photovoltaic power generation prediction value. The difference between the power grid demand power and the photovoltaic generating capacity prediction value is analyzed, the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system are adjusted according to the difference, the completion degree of the charging and discharging strategy is judged through the charging and discharging time of the energy storage system, the adjusting function of the energy storage system is fully played, and the energy storage efficiency is improved. And the reliability and continuity of power supply are improved.

Description

Technical Field

[0001] The present application relates to the field of optimization and scheduling technology, and in particular to an optimization and scheduling and operation and maintenance management method and system for intelligent distributed photovoltaic power stations. Background Art

[0002] The optimal dispatching and operation and maintenance management of intelligent distributed photovoltaic power stations is an important topic in modern energy systems. With the development of renewable energy technology and the increasing demand for energy conservation and emission reduction, distributed photovoltaic power generation systems have been widely used due to their environmental friendliness and economic benefits. In order to improve the efficiency and reliability of these systems, advanced technical means are needed to optimize dispatching and operation and maintenance management.

[0003] Energy storage systems are part of distributed photovoltaic power stations. Distributed photovoltaic power stations are usually combined with energy storage systems to form an overall solution that can flexibly respond to changes in power supply and demand. Energy storage systems can not only smooth the output fluctuations of photovoltaic power stations, but also provide power support when photovoltaic power stations cannot generate electricity (such as at night or on cloudy days), thereby enhancing the stability of the power grid. In addition, energy storage systems can also improve the economic benefits and operating efficiency of the overall system by shaving peaks and filling valleys.

[0004] The charging and discharging strategies of energy storage systems often rely on relatively simple rules, such as time-based strategies (such as peak and valley time division), fixed threshold strategies (such as charging after the power reaches a certain percentage), etc. Although such rules are easy to implement, they have many shortcomings in practical applications. First, they fail to make full use of real-time electricity price information, which means that energy storage devices may not be fully utilized during periods of low electricity prices, and fail to discharge effectively when electricity prices are high, resulting in a waste of energy storage resources. Secondly, these rules also fail to take into account the load forecast results, that is, the actual electricity demand of the power grid in the future. Therefore, when the electricity demand is low, the energy storage system may discharge prematurely, and there may be no electricity available during peak demand periods, which results in excessive use or idleness of energy storage resources and the inability to give full play to the regulating role of energy storage facilities. Since the power generation of distributed photovoltaic power stations is greatly affected by weather conditions, efficient management and optimized scheduling of energy storage systems are crucial to ensure the continuity and reliability of power supply. If the energy storage system cannot be intelligently scheduled according to real-time electricity prices and load forecasts, then in the case of unstable power generation of photovoltaic power stations, the stability of the power grid and the economy of the overall system will be affected. Summary of the invention

[0005] The purpose of this application is to provide an intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method and system, based on the proposed energy storage system charging and discharging strategy, to improve the reliability and continuity of power supply.

[0006] On the one hand, according to the embodiment of the present application, a method for optimizing scheduling and operation and maintenance management of an intelligent distributed photovoltaic power station is proposed, comprising the following steps:

[0007] Step 1, obtaining historical power generation data, real-time meteorological data and future weather forecasts of the photovoltaic power station;

[0008] Step 2, inputting the historical power generation data, real-time meteorological data and future weather forecast into a pre-trained power generation prediction model to obtain a predicted value of photovoltaic power generation in a future time period;

[0009] Step 3, obtaining the grid demand power of the photovoltaic power station, and planning the charging and discharging strategy of the energy storage system according to the absolute value of the difference between the grid demand power and the photovoltaic power generation forecast value;

[0010] Step 31, obtaining actual load data of the current power grid and factors affecting the load, and determining an adjustment factor according to a change in the influencing factors;

[0011] Step 32, calculating the power demand of the power grid in the future time period according to the load data and the adjustment factor;

[0012] Step 33, converting the photovoltaic power generation prediction value into photovoltaic power generation; obtaining the expected charge and discharge amount; obtaining the maximum charge and discharge capacity of the energy storage system; calculating the charge and discharge rate according to the absolute value of the difference between the power demand of the power grid and the photovoltaic power generation power, the expected charge and discharge amount, and the maximum charge and discharge capacity;

[0013] Step 34, adjusting the charge and discharge strategy according to the charge and discharge rate and the absolute value of the difference between the grid demand power and the photovoltaic power generation power.

[0014] According to one aspect of the embodiment of the present application, the calculation formula of the power grid demand power is:

[0015] P g =P b +(1+∑ΔP i );

[0016] Where P g is the power demanded by the grid, P b is the actual load data of the current power grid, ΔP i Represents the adjustment factor of the i-th influencing factor.

[0017] According to one aspect of an embodiment of the present application, adjusting the charge and discharge strategy includes: adjusting the output power of the photovoltaic power station and the charge and discharge mode of the energy storage system according to the absolute value of the difference between the power demanded by the grid and the power generated by the photovoltaic power; at the same time, determining whether the charge and discharge rate reaches the maximum charge and discharge capacity, if not, calculating the charge and discharge time, and if so, there is no need to calculate the charge and discharge time.

[0018] According to one aspect of an embodiment of the present application, the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system are adjusted according to the absolute value of the difference between the power demand of the power grid and the predicted value of the photovoltaic power generation, including:

[0019] Step 341, when the absolute value of the difference between the power demanded by the grid and the power of the photovoltaic power generation is greater than zero, specifically, when the power of the photovoltaic power generation is greater than the power demanded by the grid, the output power of the photovoltaic power station is reduced, and the charging mode of the energy storage system is started; when the power of the photovoltaic power generation is less than the power demanded by the grid, the output power of the photovoltaic power station is increased, and the discharging mode of the energy storage system is started;

[0020] Step 342: When the photovoltaic power generation is equal to the power demanded by the power grid, the output power of the photovoltaic power station does not need to be adjusted and the energy storage system does not need to be started.

[0021] According to one aspect of an embodiment of the present application, obtaining the expected charge and discharge amount includes:

[0022] Step 331, obtaining a future time interval according to the photovoltaic power generation prediction value;

[0023] Step 332, obtaining electricity price information, the electricity price information including electricity price time and electricity value;

[0024] Step 333, according to the electricity price information, determine the electricity price moment of the current electricity value, and obtain the electricity price moment corresponding to the next changed electricity value, and determine the electricity price change time interval according to the electricity price moment of the current electricity value and the electricity price moment corresponding to the next changed electricity value;

[0025] Step 333, calculate the expected charge and discharge amount according to the difference between the power demanded by the grid and the photovoltaic power generation, the time interval of the electricity price change, and the future time interval.

[0026] According to one aspect of the embodiment of the present application, the calculation formula of the charge and discharge rate is:

[0027]

[0028] In the formula, r is the charge and discharge rate, F is the expected charge and discharge capacity, P v -P g is the difference between the grid demand power and the photovoltaic power generation power, P v is the power demanded by the grid, P g is the photovoltaic power generation power, C m The maximum charge and discharge capacity.

[0029] According to one aspect of the embodiments of the present application, the calculation formula for the expected charge and discharge capacity is:

[0030]

[0031] F is the expected charge and discharge capacity, P v -P g is the difference between the grid demand power and the photovoltaic power generation power, ΔT represents the future time interval, t d is the time interval for electricity price changes.

[0032] According to one aspect of the embodiment of the present application, the charging and discharging time is calculated by the difference between the power demanded by the grid and the photovoltaic power generation power, the future time interval, and the charging and discharging rate;

[0033] The calculated charge and discharge time is compared with the time interval of electricity price change. If the calculated charge and discharge time is less than or equal to the time interval of electricity price change, the adjustment of charge and discharge strategy is terminated. If the calculated charge and discharge time is greater than the time interval of electricity price change, the calculated charge and discharge time is recalculated until the recalculated charge and discharge time is less than or equal to the time interval of electricity price change, then the adjustment of charge and discharge strategy is terminated.

[0034] According to one aspect of the embodiment of the present application, the calculation formula of the charge and discharge time is:

[0035]

[0036] t n is the charge and discharge time, P v -P g is the difference between the grid demand power and the photovoltaic power generation power, ΔT represents the future time interval, and r is the charge and discharge rate.

[0037] On the other hand, the embodiment of the present application proposes an intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management system, including the following modules:

[0038] Photovoltaic power station acquisition module: used to obtain historical power generation data, real-time meteorological data and future weather forecasts of photovoltaic power stations;

[0039] Prediction module: connected to the photovoltaic power station acquisition module, used to input the historical power generation data, real-time meteorological data and future weather forecast into the pre-trained power generation prediction model to obtain the photovoltaic power generation prediction value in the future time period;

[0040] Strategy planning module: connected to the prediction module, used to obtain the grid demand power of the photovoltaic power station, and plan the charging and discharging strategy of the energy storage system according to the difference between the grid demand power and the predicted value of photovoltaic power generation.

[0041] In summary, the beneficial technical effects of this application are:

[0042] The present invention analyzes the difference between the power demand of the power grid and the predicted value of photovoltaic power generation, and adjusts the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system accordingly. At the same time, the completion of the charging and discharging strategy is determined by the charging and discharging time of the energy storage system, so as to give full play to the regulating function of the energy storage system. Based on the proposed charging and discharging strategy of the energy storage system, the reliability and continuity of the power supply are improved, the stability of the power grid and the economy of the overall system are improved.

[0043] By monitoring the power demand of the power grid in real time and comparing it with the predicted value of photovoltaic power generation, the output power of the photovoltaic power station can be adjusted more accurately. This precise matching of supply and demand can reduce unnecessary power waste and ensure that sufficient power supply can be provided at all times.

[0044] The present invention takes electricity price factors into consideration when planning strategies. During peak demand periods, the energy storage system can respond quickly and release stored electricity to make up for the shortfall; during low demand periods, the energy storage system can absorb excess electricity for storage. This dynamic balancing mechanism helps maintain the stable operation of the power grid.

[0045] By determining the charging and discharging time of the energy storage system, the allocation of power resources can be optimized. For example, charging during periods of low electricity prices and discharging during periods of high electricity prices can maximize economic benefits.

[0046] By precisely controlling the charging and discharging time of the energy storage system, it is possible to ensure that the charging and discharging operations are completed within the time interval of electricity price changes, thereby achieving optimal cost-effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flow chart of this application;

[0048] Figure 2 It is a schematic diagram of the structure of this application. DETAILED DESCRIPTION

[0049] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0050] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0051] In the embodiments of the present application, the same reference numerals represent the same components, and for the sake of brevity, detailed descriptions of the same components are omitted in different embodiments. It should be understood that the thickness, length, width and other dimensions of various components in the embodiments of the present application shown in the drawings, as well as the overall thickness, length, width and other dimensions of the integrated device are only exemplary descriptions and should not constitute any limitation to the present application.

[0052] The term "plurality" used in the present application refers to two or more (including two).

[0053] On the one hand, the following combination Figure 1 to Figure 2 The intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method and system according to the embodiment of the present application are described in detail.

[0054] See attached Figure 1 and attached Figure 2 , an intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method and system, including the following steps:

[0055] Step 1: Obtain historical power generation data, real-time meteorological data and future weather forecasts of the photovoltaic power station.

[0056] Obtain historical power generation data from the monitoring system of the photovoltaic power station, including daily power generation, peak power generation time, average power generation and other information. Obtain real-time meteorological data from the weather station or third-party meteorological service provider, including current temperature, humidity, wind speed, wind direction, cloud cover and other information. Also obtain weather forecast data for the next few days from the weather station or third-party meteorological service provider. Including temperature, humidity, wind speed, wind direction, cloud cover and other information for the next few days.

[0057] Step 2: input the historical power generation data, real-time meteorological data and future weather forecast into a pre-trained power generation prediction model to obtain a predicted value of photovoltaic power generation in a future time period.

[0058] Combine historical power generation data, real-time meteorological data, and future weather forecast data into a complete data set. Extract useful features based on data characteristics, such as the impact of temperature, humidity, wind speed, etc. on power generation. Input real-time meteorological data and future weather forecast data into the trained model, and the model outputs the predicted value of photovoltaic power generation in the future time period.

[0059] Step 3: Obtain the grid demand power of the photovoltaic power station, and plan the charging and discharging strategy of the energy storage system according to the absolute value of the difference between the grid demand power and the photovoltaic power generation forecast value.

[0060] The photovoltaic power station in this embodiment is used to transmit electricity to each user through the power grid.

[0061] Step 31, obtaining the actual load data of the current power grid and the factors affecting the load, and determining the adjustment factor according to the change amount of the influencing factors.

[0062] The actual load data at the current moment is obtained from the power grid management system. The factors affecting the load include: time characteristics, weather characteristics, and holiday characteristics. The adjustment factor is determined according to the change in the influencing factors. For example, for every 5 degrees rise in temperature, the load data increases by 5%, and the adjustment factor corresponding to the temperature is defined as 5%.

[0063] Step 32, calculating the power demand of the power grid in the future time period according to the load data and the adjustment factor;

[0064] P g =P b +(1+∑ΔP i );

[0065] Where P g is the power demanded by the grid, P b is the actual load data of the current power grid, ΔP i Represents the adjustment factor of the i-th influencing factor.

[0066] Step 33, converting the predicted value of photovoltaic power generation into photovoltaic power generation; obtaining the expected charge and discharge amount; obtaining the maximum charge and discharge capacity of the energy storage system; and calculating the charge and discharge rate according to the absolute value of the difference between the power demand of the grid and the photovoltaic power generation, the expected charge and discharge amount, and the maximum charge and discharge capacity.

[0067] Converting the predicted photovoltaic power generation value into photovoltaic power generation usually involves converting the energy unit from kilowatt-hour (kWh) to kilowatt (kW). This is because power refers to the transmission or conversion of energy per unit time, while energy is the energy accumulated over a certain period of time. In this embodiment, the future time period corresponding to the predicted photovoltaic power generation value obtained by prediction is defined as a time interval. Within this time interval ΔT, the predicted photovoltaic power generation value E pConverted into photovoltaic power P p The formula is:

[0068]

[0069] Get the expected charge and discharge capacity, including:

[0070] Step 331, obtaining a future time interval according to the photovoltaic power generation prediction value.

[0071] Step 332, obtaining electricity price information, wherein the electricity price information includes electricity price time and electricity value.

[0072] Obtain electricity price information from power grid companies or third-party service providers, including electricity price moments (i.e., the time point when electricity prices change) and electricity value (i.e., electricity prices).

[0073] Step 333, based on the electricity price information, determine the electricity price moment of the current electricity value, and obtain the electricity price moment corresponding to the next changed electricity value, and determine the electricity price change time interval based on the electricity price moment of the current electricity value and the electricity price moment corresponding to the next changed electricity value.

[0074] A core of this embodiment is that by charging when the electricity price is low and discharging when the electricity price is high, the cost can be effectively reduced. Therefore, considering the change in electricity price can ensure that charging is performed when the electricity price is low and discharging is performed when the electricity price is high, thereby maximizing economic benefits. In this regard, this embodiment searches for the first time point when the electricity price drops after the current electricity price moment as the electricity price moment corresponding to the next change in electricity value, and calculates the time interval between the current electricity price moment and the next electricity price drop moment as the electricity price change time interval to maximize economic benefits.

[0075] Step 333, calculate the expected charge and discharge amount according to the difference between the power demanded by the grid and the photovoltaic power generation, the time interval of the electricity price change, and the future time interval.

[0076]

[0077] F is the expected charge and discharge capacity, P v -P g is the difference between the grid demand power and the photovoltaic power generation power, ΔT represents the future time interval, t d is the time interval for electricity price changes.

[0078] The calculation formula of the charge and discharge rate is:

[0079]

[0080] In the formula, r is the charge and discharge rate, F is the expected charge and discharge capacity, P v -P gis the difference between the grid demand power and the photovoltaic power generation power, P v is the power demanded by the grid, P g is the photovoltaic power generation power, C m The maximum charge and discharge capacity.

[0081] Step 34, adjusting the charge and discharge strategy according to the charge and discharge rate and the absolute value of the difference between the grid demand power and the photovoltaic power generation power.

[0082] Adjusting the charging and discharging strategy includes: adjusting the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system according to the absolute value of the difference between the power demanded by the grid and the power generated by the photovoltaic power; at the same time, judging whether the charging and discharging rate reaches the maximum charging and discharging capacity, if not, calculating the charging and discharging time, if yes, there is no need to calculate the charging and discharging time.

[0083] Another core of this embodiment is to match the power generation of the photovoltaic power station with the grid demand, so that the energy storage system can provide additional power during peak demand periods; during low demand periods, the photovoltaic power station can store excess power. In this way, the supply and demand relationship can be better matched to ensure the stable operation of the power system. Specifically:

[0084] According to the absolute value of the difference between the power demand of the grid and the predicted value of the photovoltaic power generation, the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system are adjusted, including:

[0085] Step 341, when the absolute value of the difference between the power demanded by the grid and the power of photovoltaic power generation is greater than zero, specifically, when the power of photovoltaic power generation is greater than the power demanded by the grid, the output power of the photovoltaic power station is reduced, and the charging mode of the energy storage system is started; when the power of photovoltaic power generation is less than the power demanded by the grid, the output power of the photovoltaic power station is increased, and the discharging mode of the energy storage system is started.

[0086] The power of photovoltaic power generation is greater than the power demand of the power grid, which means that the current power generation of the photovoltaic power station exceeds the demand of the power grid and is in a period of underestimated demand. The output power of the photovoltaic power station is reduced, the inverter output of the photovoltaic power station is adjusted, and the amount of electricity transmitted to the power grid is reduced to maintain the stable operation of the power grid. The charging mode of the energy storage system is started to store excess electricity in the energy storage system for subsequent use. The power storage process should choose to charge during the period of low electricity prices, thereby reducing the charging cost of the energy storage system.

[0087] The photovoltaic power generation is less than the power demand of the grid, which means that the photovoltaic power generation is lower than the grid demand. During the peak demand period, the output power of the photovoltaic power station is increased, and the discharge mode of the energy storage system is started to supply the previously charged electricity to the photovoltaic power station to ensure the power demand. The energy storage system can complete the discharge during the period of high electricity price, release the previously stored low-priced electricity, and sell it at a higher electricity price, thereby saving costs and increasing profits.

[0088] Step 342: When the photovoltaic power generation is equal to the power demanded by the power grid, the output power of the photovoltaic power station does not need to be adjusted and the energy storage system does not need to be started.

[0089] The energy storage system has certain requirements for the charging and discharging time in the charging and discharging mode. By charging during the period of low electricity prices and discharging during the period of high electricity prices, the economic benefits brought by the price difference can be better utilized. At the same time, shorter charging and discharging times can prevent the energy storage system from being in a high load state for a long time, thereby reducing equipment wear and maintenance costs. Completing the charging and discharging operations within the time interval of electricity price changes can better match electricity supply and demand and ensure the stable operation of the power grid. However, the charging and discharging time should be controlled within the time interval of electricity price changes to ensure that the charging and discharging operations are optimal.

[0090] The charging and discharging time is calculated by the difference between the power demanded by the power grid and the photovoltaic power generation power, the future time interval, and the charging and discharging rate.

[0091] The calculated charge and discharge time is compared with the time interval of electricity price change. If the calculated charge and discharge time is less than or equal to the time interval of electricity price change, the adjustment of charge and discharge strategy is terminated. If the calculated charge and discharge time is greater than the time interval of electricity price change, the calculated charge and discharge time is recalculated until the recalculated charge and discharge time is less than or equal to the time interval of electricity price change, then the adjustment of charge and discharge strategy is terminated.

[0092] The calculation formula of the charge and discharge time is:

[0093]

[0094] t n is the charge and discharge time, P v -P g is the difference between the grid demand power and the photovoltaic power generation power, ΔT represents the future time interval, and r is the charge and discharge rate.

[0095] This embodiment also discloses an intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management system, including the following modules:

[0096] Photovoltaic power station acquisition module: used to obtain historical power generation data, real-time meteorological data and future weather forecasts of photovoltaic power stations;

[0097] Prediction module: connected to the photovoltaic power station acquisition module, used to input the historical power generation data, real-time meteorological data and future weather forecast into the pre-trained power generation prediction model to obtain the photovoltaic power generation prediction value in the future time period;

[0098] Strategy planning module: connected to the prediction module, used to obtain the grid demand power of the photovoltaic power station, and plan the charging and discharging strategy of the energy storage system according to the difference between the grid demand power and the predicted value of photovoltaic power generation.

[0099] The above description is only a preferred specific implementation manner of the present application, and is not intended to limit the protection scope of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method, characterized by: The following steps are involved: Step 1, obtaining historical power generation data, real-time meteorological data and future weather forecasts of the photovoltaic power station; Step 2, inputting the historical power generation data, real-time meteorological data and future weather forecast into a pre-trained power generation prediction model to obtain a predicted value of photovoltaic power generation in a future time period; Step 3, obtaining the grid demand power of the photovoltaic power station, and planning the charging and discharging strategy of the energy storage system according to the absolute value of the difference between the grid demand power and the photovoltaic power generation forecast value; Step 31, obtaining the actual load data of the current power grid and the factors affecting the load, and determining the adjustment factor according to the change amount of the influencing factors; Step 32, calculating the power demand of the power grid in the future time period according to the load data and the adjustment factor; Step 33, converting the photovoltaic power generation prediction value into photovoltaic power generation; obtaining the expected charge and discharge amount; obtaining the maximum charge and discharge capacity of the energy storage system; calculating the charge and discharge rate according to the absolute value of the difference between the power demand of the power grid and the photovoltaic power generation power, the expected charge and discharge amount, and the maximum charge and discharge capacity; Step 34, adjusting the charge and discharge strategy according to the charge and discharge rate and the absolute value of the difference between the grid demand power and the photovoltaic power generation power.

2. The intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method according to claim 1 is characterized by: The calculation formula of the power grid demand power is: P g =P b +(1+∑ΔP i ); Where P g is the power demanded by the grid, P b is the actual load data of the current power grid, ΔP i Represents the adjustment factor of the i-th influencing factor.

3. The intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method according to claim 1 is characterized by: Adjusting the charging and discharging strategy includes: adjusting the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system according to the absolute value of the difference between the power demanded by the grid and the power generated by the photovoltaic power; at the same time, judging whether the charging and discharging rate reaches the maximum charging and discharging capacity, if not, calculating the charging and discharging time, if yes, there is no need to calculate the charging and discharging time.

4. The method for optimizing scheduling and operation and maintenance of an intelligent distributed photovoltaic power station according to claim 3 is characterized in that: According to the absolute value of the difference between the power demand of the grid and the predicted value of the photovoltaic power generation, the output power of the photovoltaic power station and the charging and discharging mode of the energy storage system are adjusted, including: Step 341, when the absolute value of the difference between the power demanded by the grid and the power of the photovoltaic power generation is greater than zero, specifically, when the power of the photovoltaic power generation is greater than the power demanded by the grid, the output power of the photovoltaic power station is reduced, and the charging mode of the energy storage system is started; when the power of the photovoltaic power generation is less than the power demanded by the grid, the output power of the photovoltaic power station is increased, and the discharging mode of the energy storage system is started; Step 342: When the photovoltaic power generation is equal to the power demanded by the power grid, the output power of the photovoltaic power station does not need to be adjusted and the energy storage system does not need to be started.

5. The method for optimizing scheduling and operation and maintenance of an intelligent distributed photovoltaic power station according to claim 2 is characterized in that: Get the expected charge and discharge capacity, including: Step 331, obtaining a future time interval according to the photovoltaic power generation prediction value; Step 332, obtaining electricity price information, wherein the electricity price information includes electricity price time and electricity value; Step 333, according to the electricity price information, determine the electricity price moment of the current electricity value, and obtain the electricity price moment corresponding to the next changed electricity value, and determine the electricity price change time interval according to the electricity price moment of the current electricity value and the electricity price moment corresponding to the next changed electricity value; Step 333, calculate the expected charge and discharge amount according to the difference between the grid demand power and the photovoltaic power generation power, the time interval of the electricity price change, and the future time interval.

6. The intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method according to claim 3 is characterized by: The calculation formula of the charge and discharge rate is: In the formula, r is the charge and discharge rate, F is the expected charge and discharge capacity, P v -P g is the difference between the grid demand power and the photovoltaic power generation power, P v is the power demanded by the grid, P g is the photovoltaic power generation power, C m The maximum charge and discharge capacity.

7. The method for optimizing scheduling and operation and maintenance of an intelligent distributed photovoltaic power station according to claim 5, characterized in that: The expected charge and discharge capacity is calculated as: F is the expected charge and discharge capacity, P v -P g is the difference between the grid demand power and the photovoltaic power generation power, ΔT represents the future time interval, t d is the time interval for electricity price changes.

8. The intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method according to claim 1 is characterized by: The charging and discharging time is calculated by the difference between the power demanded by the grid and the photovoltaic power generation, the future time interval, and the charging and discharging rate; The calculated charge and discharge time is compared with the time interval of electricity price change. If the calculated charge and discharge time is less than or equal to the time interval of electricity price change, the adjustment of charge and discharge strategy is terminated. If the calculated charge and discharge time is greater than the time interval of electricity price change, the calculated charge and discharge time is recalculated until the recalculated charge and discharge time is less than or equal to the time interval of electricity price change, then the adjustment of charge and discharge strategy is terminated.

9. The intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method according to claim 1, characterized in that: The calculation formula of the charge and discharge time is: t n is the charge and discharge time, P v -P g is the difference between the grid demand power and the photovoltaic power generation power, ΔT represents the future time interval, and r is the charge and discharge rate.

10. An intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management system, used to execute the intelligent distributed photovoltaic power station optimization scheduling and operation and maintenance management method according to any one of claims 1 to 9, characterized in that: Includes the following modules: Photovoltaic power station acquisition module: used to obtain historical power generation data, real-time meteorological data and future weather forecasts of photovoltaic power stations; Prediction module: connected to the photovoltaic power station acquisition module, used to input the historical power generation data, real-time meteorological data and future weather forecast into the pre-trained power generation prediction model to obtain the photovoltaic power generation prediction value in the future time period; Strategy planning module: connected to the prediction module, used to obtain the grid demand power of the photovoltaic power station, and plan the charging and discharging strategy of the energy storage system according to the difference between the grid demand power and the predicted value of photovoltaic power generation.

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