Photovoltaic power generation scheduling method, system, device and storage medium

Abstract

The present application discloses a photovoltaic power generation scheduling method, system, device and storage medium, and the present application relates to the technical field of general control systems. The photovoltaic power generation scheduling method includes: collecting power generation, weather conditions and equipment status in real time based on intelligent monitoring equipment in each individual household photovoltaic system; determining the expected power of the corresponding individual household photovoltaic system according to the power generation, weather conditions and equipment status; determining the scheduling target power of the management area corresponding to the individual household photovoltaic system according to the power consumption corresponding to each individual household photovoltaic system; determining the scheduling plan corresponding to each individual household photovoltaic system based on the fluctuation range of the power difference between the expected power and the scheduling target power; and controlling each individual household photovoltaic system to be connected to the power grid according to the scheduling plan. The technical problem of low scheduling efficiency in the region in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the general field of control system technology, and in particular to a photovoltaic power generation scheduling method, system, device and storage medium. Background Art

[0002] As the global demand for renewable energy continues to grow, solar photovoltaic systems are rapidly developing as an efficient and environmentally friendly way of generating energy. However, photovoltaic power generation systems have instabilities such as day-night changes and seasonal fluctuations, which affect the power dispatching of the power grid.

[0003] In the related art, in order to cope with the above-mentioned instability, the technical solution adopted is that the inverter determines the current power generation of the photovoltaic panel, and then obtains the power consumption of each user's load circuit, and then determines the power dispatching strategy based on the relationship between the sum of the power consumption and the current power generation.

[0004] However, the above scheme is a control strategy for a single photovoltaic panel, and its scheduling efficiency is low in a large area. Summary of the invention

[0005] The main purpose of this application is to provide a photovoltaic power generation scheduling method, system, device and storage medium, aiming to solve the technical problem of low efficiency of photovoltaic power generation scheduling in a region.

[0006] To achieve the above objectives, the present application provides a photovoltaic power generation scheduling method, the photovoltaic power generation scheduling method comprising:

[0007] Intelligent monitoring devices in each household’s photovoltaic system collect power generation, weather conditions and equipment status in real time;

[0008] Determine the expected power of the corresponding individual household photovoltaic system according to the power generation, the weather conditions and the equipment status;

[0009] Determine the dispatch target power of the management area corresponding to the photovoltaic system of the individual household according to the power consumption corresponding to the photovoltaic system of each individual household;

[0010] Determine a scheduling scheme corresponding to each of the individual household photovoltaic systems based on the fluctuation range of the power difference between the expected power and the scheduling target power;

[0011] The photovoltaic systems of each individual household are controlled to be connected to the power grid according to the dispatching plan.

[0012] In one embodiment, the device status includes at least one of the number of devices, the power of the device, the area of ​​the photovoltaic panel, and the type of wire, and the step of determining the expected power of the corresponding individual household photovoltaic system according to the power generation, the weather conditions, and the device status includes:

[0013] Obtaining networked weather data at the location of the individual household photovoltaic system, and obtaining on-site environmental data through environmental sensors;

[0014] Determine the weather condition based on the networked weather data and the field environment data;

[0015] Determining expected power generation according to the device status and the power generation;

[0016] According to the weather conditions, determine the weather impact coefficient by looking up a table in the historical power generation data;

[0017] The expected power is determined according to the weather influence coefficient and the expected power generation.

[0018] In one embodiment, the step of determining the dispatch target power of the management area corresponding to the photovoltaic system of each individual household according to the power consumption corresponding to the photovoltaic system of each individual household includes:

[0019] Determine the target individual household photovoltaic systems included in each of the management areas;

[0020] Determine the internal power consumption corresponding to the management area according to the power consumption of the target individual household photovoltaic system and the distance between the target individual household photovoltaic system and the power distribution station corresponding to the management area;

[0021] The scheduling target power is determined according to the scheduling power of the management area and the internal power consumption.

[0022] In one embodiment, the step of determining the scheduling scheme corresponding to each of the individual household photovoltaic systems based on the fluctuation amplitude of the power difference between the expected power and the scheduling target power includes:

[0023] Based on mapping the power difference between the expected power and the scheduling target power in a time series, determining the first power and the second power according to the mapped fluctuation amplitude;

[0024] The scheduling plan is determined by performing multi-objective optimization based on the first power, the second power, the location information between the photovoltaic systems of each of the individual households, and the historical loss information of each of the management areas.

[0025] In one embodiment, the step of mapping the power difference between the expected power and the scheduling target power in a time series and determining the first power and the second power according to the mapped fluctuation amplitude includes:

[0026] Acquire the power difference between the expected power and the scheduling target power at each time point of the time series;

[0027] Determine a first time interval during which the power difference is greater than a preset threshold, and a second time interval during which the power difference is less than the preset threshold;

[0028] The power difference value in the first time interval is determined to be the first power, and the power difference value in the second time interval is determined to be the second power.

[0029] In one embodiment, an energy storage module is provided between the self-employed photovoltaic system and the power grid, the historical loss information is the difference between the output power of all the self-employed photovoltaic systems in the management area and the input power of the power grid, and the step of performing multi-objective optimization according to the first power, the second power, the location information between the self-employed photovoltaic systems, and the historical loss information of each management area to determine the scheduling scheme includes:

[0030] Determining the output power of the energy storage module based on the state of charge of the energy storage module, the first power, and the second power;

[0031] Determine the first path loss between the individual household photovoltaic system and the energy storage module according to the location information;

[0032] Determining a second path loss from the energy storage module to an input point of the power grid;

[0033] The scheduling scheme is determined by performing multi-objective optimization based on the output power, the first path loss, the second path loss, and the historical loss information.

[0034] In one embodiment, the step of determining the output power of the energy storage module based on the state of charge of the energy storage module, the first power, and the second power includes:

[0035] Determine an output power reference value of the energy storage module based on sliding filtering processing of the first power and the second power;

[0036] The output power is determined according to the output power reference value and the state of charge.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a photovoltaic power generation scheduling system, which includes a scheduling module, a power grid, at least one individual household photovoltaic system and at least one energy storage module.

[0038] In addition, to achieve the above-mentioned purpose, the present application also provides a photovoltaic power generation scheduling device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the photovoltaic power generation scheduling method as described above.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, which is a computer-readable storage medium, and a program for implementing the photovoltaic power generation scheduling method is stored on the computer-readable storage medium. The program for implementing the photovoltaic power generation scheduling method is executed by a processor to implement the steps of the photovoltaic power generation scheduling method as described above.

[0040] The present application provides a photovoltaic power generation scheduling method, which first collects power generation, weather conditions and equipment status in real time based on the intelligent monitoring equipment in each individual household photovoltaic system; determines the expected power of the corresponding individual household photovoltaic system according to the power generation, weather conditions and equipment status; determines the scheduling target power of the management area corresponding to the individual household photovoltaic system according to the power consumption corresponding to each individual household photovoltaic system; determines the scheduling plan corresponding to each individual household photovoltaic system based on the fluctuation amplitude of the power difference between the expected power and the scheduling target power; and controls each individual household photovoltaic system to be connected to the power grid according to the scheduling plan. The present application focuses on individual household photovoltaic systems and manages multiple individual household photovoltaic systems at the same time, so that based on the detailed data of each household, combined with weather location, etc., the power generation of each household is predicted, and then the fluctuation data of the power difference is determined in combination with the scheduling power of the management area, and then the fluctuation data is used as one of the parameters of multi-objective optimization to determine the scheduling plan for each individual household photovoltaic system to be connected to the power grid, which solves the technical problem of low scheduling efficiency in the region in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A schematic diagram of a process flow provided for Embodiment 1 of the photovoltaic power generation scheduling method of the present application;

[0044] Figure 2 A schematic diagram of a process flow provided for Embodiment 2 of the photovoltaic power generation scheduling method of the present application;

[0045] Figure 3 A flow chart of steps S411-S413 in the fourth embodiment of the photovoltaic power generation scheduling method of the present application;

[0046] Figure 4 A flow chart of steps S421-S424 in Embodiment 4 of the photovoltaic power generation scheduling method of the present application;

[0047] Figure 5 This is a schematic diagram of the module structure of the photovoltaic power generation scheduling system according to an embodiment of the present application;

[0048] Figure 6 This is a schematic diagram of the hardware structure involved in the photovoltaic power generation scheduling equipment embodiment of this application.

[0049] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0051] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0052] At present, in order to deal with the instability of photovoltaic power generation systems caused by weather, the technical solution adopted is to determine the current power generation of photovoltaic panels by inverters, and then obtain the power consumption of each user's load circuit, and then determine the power dispatch strategy based on the relationship between the sum of the power consumption and the current power generation. However, the above solution is a control strategy for a single photovoltaic panel, and the dispatch efficiency is low in a large area.

[0053] The present application provides a photovoltaic power generation scheduling method, which first collects power generation, weather conditions and equipment status in real time based on the intelligent monitoring equipment in each individual household photovoltaic system; determines the expected power of the corresponding individual household photovoltaic system according to the power generation, weather conditions and equipment status; determines the scheduling target power of the management area corresponding to the individual household photovoltaic system according to the power consumption corresponding to each individual household photovoltaic system; determines the scheduling plan corresponding to each individual household photovoltaic system based on the fluctuation amplitude of the power difference between the expected power and the scheduling target power; and controls each individual household photovoltaic system to be connected to the power grid according to the scheduling plan. The present application focuses on individual household photovoltaic systems and manages multiple individual household photovoltaic systems at the same time, so that based on the detailed data of each household, combined with weather location, etc., the power generation of each household is predicted, and then the fluctuation data of the power difference is determined in combination with the scheduling power of the management area, and then the fluctuation data is used as one of the parameters of multi-objective optimization to determine the scheduling plan for each individual household photovoltaic system to be connected to the power grid, which solves the technical problem of low scheduling efficiency in the region in the related technology.

[0054] It should be noted that the execution subject of this embodiment can be a photovoltaic power generation dispatching system, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a photovoltaic power generation dispatching device capable of realizing the above functions, etc., and this embodiment does not specifically limit this. The following takes the photovoltaic power generation dispatching system as an example of the execution subject to illustrate this embodiment and the following embodiments.

[0055] Based on this, the first embodiment of the present application proposes a photovoltaic power generation scheduling method, please refer to Figure 1 , the photovoltaic power generation scheduling method comprises:

[0056] Step S110, based on the intelligent monitoring equipment in each individual household photovoltaic system, the power generation, weather conditions and equipment status are collected in real time.

[0057] It should be noted that the intelligent monitoring equipment is installed in the photovoltaic system of the individual household, which can monitor and record key data such as power generation, weather conditions and equipment status in real time. Power generation: refers to the amount of electrical energy generated by the photovoltaic system within a specific period of time, usually measured in kilowatt-hours. Weather conditions: including sunshine intensity, temperature, wind speed, etc. These factors directly affect the power generation efficiency of photovoltaic panels. Equipment status: involves the operating status of the photovoltaic system, such as the type, quantity, and orientation of photovoltaic panels, and the working status of key equipment such as inverters. This embodiment focuses on the photovoltaic system of individual households, and each household with photovoltaic power generation panels installed is regarded as an individual household photovoltaic system.

[0058] As an optional implementation, the intelligent monitoring equipment in each individual household's photovoltaic system collects power generation, weather conditions and equipment status in real time, and sends the above data to the scheduling module of the photovoltaic power generation scheduling system for processing.

[0059] Step S120, determining the expected power of the corresponding individual household photovoltaic system according to the power generation, the weather conditions and the equipment status.

[0060] It should be noted that expected power: the average power generated by the photovoltaic system in the future. Networked weather data: real-time or predicted weather information obtained from the weather station, used to assist in calculating the expected power generation of the photovoltaic system. Weather impact coefficient: a coefficient determined based on the relationship between historical power generation data and weather conditions, used to adjust the expected power generation to make it more consistent with the expected power generation performance under actual weather conditions.

[0061] As an optional implementation, the system uses historical data and machine learning algorithms, combined with current real-time data, to predict the expected power of individual household photovoltaic systems in the future. The prediction model takes into account the impact of weather changes on power generation and the impact of equipment status on power generation efficiency.

[0062] Since the present embodiment is implemented in each individual household photovoltaic system, based on the characteristics of sufficient data sample quantity, small number of individual household equipment, simple equipment structure, etc., the prediction accuracy of the expected power of the individual household photovoltaic system is improved.

[0063] Step S130, determining the dispatch target power of the management area corresponding to the photovoltaic system of each individual household according to the power consumption corresponding to the photovoltaic system of each individual household.

[0064] It should be noted that power consumption refers to the total power required by all power-consuming devices in the management area at a certain moment. Dispatch target power refers to the power target that each photovoltaic system should contribute according to the power demand and photovoltaic power generation capacity, so as to ensure the balance of power supply and demand in the power grid.

[0065] As an optional implementation, the system first counts the power demand in the management area, and then calculates the dispatch target power of each photovoltaic system through optimization algorithms such as genetic algorithm and particle swarm optimization, based on the expected power of each photovoltaic system.

[0066] Step S140: determining a scheduling scheme corresponding to each of the individual household photovoltaic systems based on the fluctuation range of the power difference between the expected power and the scheduling target power.

[0067] It should be noted that the power difference is the difference between the dispatch target power of the management area at each time point and the expected power of each individual PV system in the area. The dispatch plan is the specific time point when each individual PV system needs to be connected to the grid and the power that needs to be output. The fluctuation amplitude refers to the difference between the highest power value and the lowest power value in a cycle mapped to the time scale.

[0068] The system calculates the difference between the expected power of each photovoltaic system and the dispatch target power, and formulates a corresponding dispatch plan based on the size and positive and negative of the difference. For example, if the expected power of a photovoltaic system is higher than the dispatch target power, its grid-connected power may be reduced or its excess power may be stored; if it is lower than the expected power, it may need to dispatch power from other photovoltaic systems or the power grid to make up for it.

[0069] Step S150, controlling each of the individual households' photovoltaic systems to be connected to the power grid according to the scheduling plan.

[0070] Finally, according to the formulated dispatch plan, the system adjusts the grid-connected status of each photovoltaic system through remote control means such as SCADA (Supervisory Control And Data Acquisition) system and PLC (Programmable Logic Controller) to ensure the balance of power supply and demand and stable operation of the power grid in the entire management area.

[0071] For example, there are 10 individual PV systems in a management area, and each system is equipped with intelligent monitoring equipment. At noon one day, the system collected the following data: PV system A: power generation 500kWh, light intensity 800W / m², equipment status normal. PV system B: power generation 450kWh, light intensity 750W / m², inverter temperature slightly high. And so on, other system data are omitted. The current power demand in the management area is 4500kWh. The system calculates the expected power of each PV system through the prediction model, and determines the scheduling target power in combination with the power demand. For example, the scheduling target power of PV system A may be set to 550kWh to make up for the lack of power in other systems or power grids. Subsequently, the system found that the expected power of PV system A (500kWh) was lower than the scheduling target power (550kWh), and the expected power of PV system B may be lower than normal due to the inverter temperature problem. Therefore, the system formulates a dispatching plan to dispatch additional electricity from the grid to the management area, monitors the equipment status of photovoltaic system B, and takes cooling measures to improve its power generation efficiency when necessary. Finally, the system controls each photovoltaic system to be connected to the grid according to the dispatching plan to ensure the balance of power supply and demand.

[0072] In this embodiment, the photovoltaic power generation dispatching system is first combined with map resources to calibrate and manage the photovoltaic power system in a set area. This regionalized management method enables the system to perform customized dispatching for the photovoltaic power generation characteristics in a specific geographical area. The set area is divided into multiple blocks, each block corresponds to a photovoltaic power generation system, and this block management method helps the system to independently monitor and dispatch each photovoltaic system. At the same time, since the basic unit considered by the system is "individual households", that is, a single photovoltaic power generation system or a small photovoltaic power station, this means that the system can go deep into the power generation status of each individual and obtain more detailed data. The system integrates the route transmission loss in the historical transmission data and optimizes the power transmission path using advanced technical means such as path search algorithms or neural network algorithms. This data-driven method can significantly improve the accuracy and efficiency of power dispatching. During the dispatching process, the system not only considers the current actual weather conditions, but also combines historical loss data and the real-time power generation of each photovoltaic power generation system to construct a multi-dimensional dispatching decision model. Different from the traditional dispatching method that only considers transmission between two systems, this embodiment establishes a dispatching scheme between multiple power systems. This means that the system can optimize resource allocation in a wider range to ensure that the overall power transmission has the lowest loss and the highest efficiency. By comprehensively considering the power generation capacity and transmission conditions of multiple photovoltaic power generation systems, the system can formulate a more scientific and reasonable scheduling plan to maximize the overall benefits. For each individual photovoltaic power generation system, the system uses its simple structure and predictable power generation status to make detailed power generation forecasts. This personalized prediction method can more accurately reflect the power generation capacity of each photovoltaic system. On this basis, the system combines the overall scheduling plan to generate an overall photovoltaic power generation scheduling plan. This plan not only takes into account the power generation of individual households, but also takes into account the overall power demand and transmission conditions of the region, ensuring the comprehensiveness and effectiveness of the scheduling plan.

[0073] In summary, by integrating multiple data sources and algorithmic means, the refined management and optimized scheduling of multiple photovoltaic power generation systems in a set area are realized, which not only improves the efficiency and accuracy of power transmission, but also promotes the full utilization and sustainable development of clean energy.

[0074] The present application provides a photovoltaic power generation scheduling method, which first collects power generation, weather conditions and equipment status in real time based on the intelligent monitoring equipment in each individual household photovoltaic system; determines the expected power of the corresponding individual household photovoltaic system according to the power generation, weather conditions and equipment status; determines the scheduling target power of the management area corresponding to the individual household photovoltaic system according to the power consumption corresponding to each individual household photovoltaic system; determines the scheduling plan corresponding to each individual household photovoltaic system based on the fluctuation amplitude of the power difference between the expected power and the scheduling target power; and controls each individual household photovoltaic system to be connected to the power grid according to the scheduling plan. The present application focuses on individual household photovoltaic systems and manages multiple individual household photovoltaic systems at the same time, so that based on the detailed data of each household, combined with weather location, etc., the power generation of each household is predicted, and then the fluctuation data of the power difference is determined in combination with the scheduling power of the management area, and then the fluctuation data is used as one of the parameters of multi-objective optimization to determine the scheduling plan for each individual household photovoltaic system to be connected to the power grid, which solves the technical problem of low scheduling efficiency in the region in the related technology.

[0075] Based on the first embodiment, the second embodiment of the present application proposes a photovoltaic power generation scheduling method, referring to Figure 2 , step S120, comprising:

[0076] Step S210, obtaining networked weather data of the location of the individual household photovoltaic system, and obtaining field environmental data through environmental sensors.

[0077] It should be noted that the equipment status includes at least one of the number of equipment, equipment power, photovoltaic panel area, and wire type. Networked weather data: refers to real-time or predicted data on weather conditions at the location of the photovoltaic system, obtained through the Internet and provided by a meteorological station or a professional meteorological service agency, including but not limited to light intensity, temperature, humidity, wind speed, etc. Environmental sensor: equipment installed at the photovoltaic system site for real-time monitoring of environmental parameters, such as light sensors, temperature sensors, etc., whose data directly reflects the actual environmental conditions of the photovoltaic system operation.

[0078] Step S220: determining the weather conditions according to the networked weather data and the field environment data.

[0079] The system integrates the online weather data with the on-site environmental data to more accurately reflect the weather conditions of the photovoltaic system at present and in the future. For example, when the online data shows that there will be cloud cover soon, and the on-site environmental sensor detects that the current light intensity has begun to decrease, the system can comprehensively judge the weather conditions as "about to turn cloudy".

[0080] Step S230: determining the expected power generation according to the device state and the power generation.

[0081] Equipment status: In this implementation, it specifically refers to equipment parameters that affect power generation, including equipment quantity, equipment power, photovoltaic panel area, wire type, etc. The number of equipment and equipment power directly affect the total power generation; the photovoltaic panel area determines the amount of solar radiation that the photovoltaic system can receive; the wire type may affect the efficiency of power transmission. Power generation: Here refers to the actual amount of electricity generated by the photovoltaic system at the current moment or the previous moment, which serves as the basic data for predicting future power generation.

[0082] The system first calculates the theoretical power generation based on the equipment status, such as the area of ​​​​the photovoltaic panel and the power of the equipment, and then corrects it based on the current power generation to obtain the expected power generation.

[0083] Step S240, looking up a table in historical power generation data to determine the weather impact coefficient according to the weather conditions.

[0084] The system maintains a historical power generation database that records the power generation of the photovoltaic system under different weather conditions. In this step, the system searches for power generation data under similar weather conditions in the historical data based on the current weather conditions (such as light intensity, temperature, etc.) and calculates a weather impact coefficient. This coefficient reflects the change in the power generation capacity of the photovoltaic system under the current weather conditions relative to the standard (or optimal) weather conditions.

[0085] Step S250: determining the expected power according to the weather impact coefficient and the expected power generation.

[0086] Finally, the system multiplies the expected power generation by the weather impact coefficient to obtain the expected power after considering weather factors. This expected power more accurately reflects the power generation capacity of the photovoltaic system in the future under current weather conditions and equipment status.

[0087] For example, suppose a household photovoltaic system contains 100 photovoltaic panels with a total area of ​​250 square meters, an equipment power of 50kW, and uses high-efficiency transmission wires. At noon one day, the system learned from the online weather data that the local light intensity will remain at around 1000W / m² in the next two hours. At the same time, the environmental sensor shows that the current temperature is 25℃ and the wind speed is low. The system first calculates the theoretical power generation based on the equipment status, photovoltaic panel area, equipment power, etc., and fine-tunes it in combination with the current power generation to obtain an expected power generation of approximately 100kWh. Next, the system searches for power generation under similar weather conditions in the historical power generation data based on the online weather data: light intensity 1000W / m², temperature 25℃, and determines a weather impact coefficient of 1.05, indicating that the current weather conditions are slightly better than the standard weather conditions. Finally, the system multiplies the expected power generation (100kWh) by the weather impact coefficient (1.05) to obtain an expected power of 105kW, that is, the photovoltaic system is expected to generate 105kWh of electricity in the next two hours. The system formulates a dispatch plan based on this to ensure the balance of supply and demand in the power grid.

[0088] The application of intelligent monitoring equipment in this application realizes real-time monitoring of key parameters of the photovoltaic system, providing accurate and timely data support for subsequent scheduling decisions. By collecting multi-dimensional information such as power generation, weather conditions and equipment status, the system can have a more comprehensive understanding of the operating status of the photovoltaic system, laying a solid foundation for accurate prediction and scheduling. Then, by comprehensively considering various factors, the expected power of the photovoltaic system is predicted using historical data and machine learning algorithms, which improves the accuracy and reliability of the prediction. Accurate prediction of expected power helps the system to formulate a more reasonable scheduling plan, avoids the imbalance of power supply and demand caused by inaccurate predictions, and improves the stability and reliability of the power grid.

[0089] Based on any of the above embodiments, the third embodiment of the present application proposes a photovoltaic power generation scheduling method, step S130, including:

[0090] Step S310, determining the target individual household photovoltaic systems included in each of the management areas;

[0091] Step S320, determining the internal power consumption corresponding to the management area according to the power consumption of the target individual household photovoltaic system and the distance between the target individual household photovoltaic system and the power distribution station corresponding to the management area;

[0092] Step S330: determining the scheduling target power according to the scheduling power of the management area and the internal power consumption.

[0093] In this embodiment, the target individual household photovoltaic systems included in each of the management areas are determined. Management area: refers to a specific geographical area that contains multiple individual household photovoltaic systems, which may be operated, maintained and dispatched by the same power company or management agency. Target individual household photovoltaic system: within the management area, the individual household photovoltaic system selected to participate in the current dispatch process. These systems are usually determined based on certain selection criteria (such as power generation capacity, stability, geographical location, etc.).

[0094] In this step, the system first identifies which individual PV systems belong to the current management area and selects them as dispatch targets.

[0095] Then, according to the power consumption of the target self-employed photovoltaic system and the distance between the target self-employed photovoltaic system and the corresponding distribution station in the management area, the internal power consumption corresponding to the management area is determined. Power consumption: refers to the total amount of electric energy that the target self-employed photovoltaic system is expected to consume in the current or future period. Distribution station: a facility responsible for the distribution and dispatch of electricity in the management area, usually connected to the power grid, responsible for delivering electricity to various users. Internal power consumption: refers to the total amount of electric energy consumed by all target self-employed photovoltaic systems in the management area. Considering their distance from the distribution station, factors such as transmission loss may need to be considered.

[0096] In this step, the system first collects the power consumption data of the photovoltaic systems of each target individual household, and combines their distances from the distribution station to calculate the internal power consumption of the management area through weighted average method or loss model.

[0097] Then, the dispatch target power is determined according to the dispatch power of the management area and the internal power consumption. Dispatching power: refers to the total amount of electric energy received or sent by the management area from the external power grid or other energy system to meet internal power demand or export excess power to the outside. Dispatching target power: the power target set by the system for the management area in a specific time period to achieve power supply and demand balance and optimize energy utilization.

[0098] In this step, the dispatch power is allocated by the upper-level power grid dispatch center according to the overall supply and demand situation. The system combines the dispatch power and internal power consumption of the management area and determines the dispatch target power through linear programming or genetic algorithm. This target power must not only meet the power demand in the management area, but also optimize energy utilization and reduce costs as much as possible.

[0099] For example, suppose a certain management area contains 5 target individual household photovoltaic systems, which are located in different locations within the management area and at different distances from the power distribution station. The system collects the following data:

[0100] Individual household photovoltaic system A: power consumption is 20kW, and the distance from the distribution station is 1km.

[0101] Individual household photovoltaic system B: power consumption is 15kW, and the distance from the distribution station is 2km.

[0102] Individual household photovoltaic system C: power consumption 30kW, 0.5km away from the distribution station.

[0103] Individual household photovoltaic system D: power consumption 10kW, 3km away from the distribution station.

[0104] Individual household photovoltaic system E: power consumption 25kW, 1.5km away from the distribution station.

[0105] At the same time, it is assumed that the dispatching power obtained by the management area from the external power grid is 80kW. Taking into account the transmission loss, assuming that 1% of the power is lost per kilometer, the system first calculates the internal power consumption of each system:

[0106] System A: 20kW * (1 - 0.01 * 1) = 19.8kW.

[0107] System B: 15kW * (1 - 0.01 * 2) = 14.85kW.

[0108] System C: 30kW * (1 - 0.01 * 0.5) = 29.85kW.

[0109] System D: 10kW * (1 - 0.01 * 3) = 9.7kW.

[0110] System E: 25kW * (1 - 0.01 * 1.5) = 24.625kW.

[0111] The total internal power consumption in the management area is 99.825kW. Since the dispatch power is 80kW, which is not enough to meet the internal power demand, the system needs to adjust the dispatch target power through the optimization algorithm. Assume that after optimization, it is decided to dispatch an additional 15kW of power from the external power grid and adjust the power generation mode of some photovoltaic systems to reduce internal power demand or improve power generation efficiency. Finally, the system determines the dispatch target power to be 95kW to ensure the balance of power supply and demand and the optimization of energy utilization in the management area.

[0112] In this embodiment, by considering the actual power consumption of each photovoltaic system, the system can more accurately grasp the power demand in the management area. This helps to reduce the waste or shortage caused by the mismatch between power supply and demand. At the same time, taking the distance between the photovoltaic system and the distribution station into consideration can more accurately estimate the loss during power transmission, thereby further improving the accuracy of power dispatching.

[0113] Based on any of the above embodiments, the fourth embodiment of the present application proposes a photovoltaic power generation scheduling method, step S140, including:

[0114] Step S410, based on mapping the power difference between the expected power and the scheduling target power in a time series, determining a first power and a second power according to the mapped fluctuation amplitude.

[0115] It should be noted that expected power: the power generation capacity of the photovoltaic system in a certain period of time in the future predicted based on factors such as power generation, weather conditions and equipment status. Dispatch target power: the power generation power that the photovoltaic system is expected to achieve in order to meet the power demand in the management area. Power difference: the difference between expected power and dispatch target power, reflecting the power generation that needs to be adjusted by the photovoltaic system. Time series: a sequence that arranges time in a certain order, used to analyze the change pattern of data over time.

[0116] In this step, the system first calculates the difference between the expected power and the scheduling target power at each time point, and maps these differences to the time series. This helps to understand the dynamic changes of the power gap and provide a basis for the formulation of subsequent scheduling plans.

[0117] First power: In the time series, the power value at one or some time points with a large power difference represents the time period that needs to be adjusted. Second power: Corresponding to the first power, it refers to the power value at the time point with a small power difference or that needs to be fine-tuned. In this embodiment, the power difference on the time series is further analyzed to identify the time period that needs to be processed first (first power) and the relatively less important time period (second power). This helps to prioritize key issues when resources are limited.

[0118] Step S420, performing multi-objective optimization according to the first power, the second power, the location information between the photovoltaic systems of each of the individual households, and the historical loss information of each of the management areas to determine the scheduling plan.

[0119] It should be noted that location information: the geographical distribution of each household photovoltaic system may affect the efficiency and cost of power transmission. Historical loss information: the loss data of power transmission and distribution in the management area over the past period of time, which is used to evaluate the efficiency of different scheduling schemes. Multi-objective optimization: the process of seeking the best compromise solution through optimization algorithms when there are multiple conflicting objectives.

[0120] In this embodiment, the system comprehensively considers multiple factors such as the first power, the second power, location information, and historical loss information, and uses a multi-objective optimization algorithm, such as NSGA-II and MOEA / D, to formulate a scheduling plan. The plan aims to minimize power differences, reduce power transmission losses, optimize resource allocation, and other multiple goals.

[0121] For example, suppose a certain management area contains three individual photovoltaic systems A, B, and C, which are located in different geographical locations. Based on historical data and real-time predictions, the system obtains the following information: Expected power: In the next 24 hours, system A is expected to generate 100kWh, system B 80kWh, and system C 120kWh. Dispatch target power: The total demand in the management area is 280kWh, but due to grid restrictions or other reasons, the actual dispatch target is 290kWh. Time series analysis: It is found that 10 am to 2 pm is the period with the largest power gap, that is, the first power period; the rest of the period is the second power period. The system uses a multi-objective optimization algorithm, taking into account the location differences of systems A, B, and C, such as system A is closer to the distribution station and system B is farther away, as well as historical loss data such as the aging of power lines in the area where system B is located, and the loss is large, and formulates the following dispatch plan: In the first power period, system A and system C are prioritized for power generation because they have strong power generation capacity and superior location; at the same time, the power generation of system B is reduced to reduce transmission losses. In the second power period, the power generation of each system is adjusted according to actual demand to maintain the overall supply and demand balance.

[0122] Through this implementation, the system can formulate an efficient and economical scheduling plan based on the power difference between the expected power and the scheduling target power, combined with time series analysis, location information and historical loss data. This plan can not only effectively make up for the power gap and meet the power demand, but also reduce power transmission losses and optimize resource allocation. At the same time, through the application of multi-objective optimization algorithms, the system can find the best balance point between multiple conflicting goals and improve the overall scheduling efficiency and effect.

[0123] Optionally, refer to Figure 3 , step S410, comprising:

[0124] Step S411, obtaining the power difference between the expected power and the scheduling target power at each time point of the time series.

[0125] It should be noted that the system first calculates the expected power and scheduling target power at each time point in the time series based on the prediction model and real-time data, and then calculates the difference between them to obtain the power difference sequence.

[0126] Step S412, determining a first time interval during which the power difference is greater than a preset threshold, and a second time interval during which the power difference is less than the preset threshold.

[0127] It should be noted that the preset threshold is a standard value set in advance to determine whether the power difference is significant. This value is determined based on factors such as power grid operation experience and system regulation capabilities. The first time interval is a continuous time period in the time series when the power difference is greater than the preset threshold. These time periods usually indicate that the system requires a larger adjustment in power generation. The second time interval is a continuous time period in the time series when the power difference is less than the preset threshold. These time periods may indicate that the system power generation is close to the target, or there is little room for adjustment.

[0128] The system traverses the power difference sequence, compares the difference at each time point with a preset threshold, and divides the time series into a first time interval and a second time interval.

[0129] Step S413: determine the power difference value in the first time interval as the first power, and determine the power difference value in the second time interval as the second power.

[0130] It should be noted that the first power: in the first time interval, it represents the power difference that the system needs to focus on adjusting the power generation. Since the power difference in these time periods is large, the impact on system scheduling is more significant. The second power: in the second time interval, it represents the power difference when the system power generation is close to the target or has less room for adjustment. In these time periods, it may not be necessary or only requires fine-tuning of the system power generation.

[0131] It should be noted that the "first power" and "second power" here do not refer to specific power values, but to the set or characteristics of power differences in different intervals in the time series. In practical applications, the maximum value, average value or other statistics in these intervals can be selected as representative values ​​for analysis as needed.

[0132] For example, assuming that the expected power and dispatch target power of a photovoltaic system in the next 24 hours are known, the system predicts and dispatches on an hourly basis. The preset threshold is set to 10kWh. By analyzing the power difference in the time series, the following results are obtained: During the period from 10 am to 2 pm (the first time interval), the power difference is greater than 10kWh, indicating that the system needs to increase power generation during this period to meet the dispatch target. Therefore, the power difference in this interval is regarded as the first power. In the remaining time periods (the second time interval), the power difference is less than 10kWh, indicating that the system power generation is close to the dispatch target or the adjustment space is small. Therefore, the power difference in these intervals is regarded as the second power.

[0133] Through this implementation, the system can clearly identify the power generation adjustment period (first time interval) and the relatively minor period (second time interval) that need to be focused on in the time series. This division helps the system to formulate a more accurate and efficient scheduling plan, giving priority to solving the problem of large power generation gaps, while avoiding excessive adjustments in unnecessary periods. In addition, by setting preset thresholds, the system can also flexibly adapt to different grid operating conditions and system regulation capacity requirements, improving the flexibility and robustness of scheduling.

[0134] Optionally, refer to Figure 4 , step S420, comprising:

[0135] Step S421, determining the output power of the energy storage module based on the state of charge of the energy storage module, the first power and the second power;

[0136] Step S422: determining the first path damage between the individual household photovoltaic system and the energy storage module according to the location information.

[0137] In this embodiment, an energy storage module is provided between the self-employed photovoltaic system and the power grid, and the historical loss information is the difference between the output power of all the self-employed photovoltaic systems in the management area and the input power of the power grid. This implementation describes in detail how to combine the charge state of the energy storage module, the first power and the second power, the location information and the historical loss information to determine the scheduling plan of the self-employed photovoltaic system through multi-objective optimization.

[0138] It should be noted that the state of charge (SOC) of the energy storage module: indicates the current state of the energy storage module's stored electricity, usually expressed as a percentage, reflecting the amount of remaining electricity in the energy storage module. Output power: The amount of electricity that the energy storage module can output under specific conditions. This value is affected by factors such as the capacity of the energy storage module, the state of charge, and the current scheduling needs. Location information: includes the geographical distribution of individual photovoltaic systems and energy storage modules. This information is crucial for evaluating losses during power transmission. First path loss: refers to the power loss caused by factors such as line resistance and cable length during the transmission of electricity from individual photovoltaic systems to energy storage modules.

[0139] In this embodiment, the system first monitors the state of charge of the energy storage module, and combines the first power, which indicates the time period that needs to be adjusted, and the second power, which indicates the demand for the time period with less adjustment space, to calculate the optimal output power of the energy storage module in different time periods. For example, in the first power period, the energy storage module may need to release more electricity to make up for the power generation gap; while in the second power period, it may reduce output or charge to maintain the state of charge. The system uses GIS (geographic information system) technology or related algorithms to calculate the power transmission path and corresponding losses between the photovoltaic system and the energy storage module based on the specific location information of the photovoltaic system and the energy storage module. This helps to more accurately evaluate the amount of electricity that the energy storage module can actually receive.

[0140] Step S423, determining whether a second path from the energy storage module to an input point of the power grid is damaged.

[0141] It should be noted that the second path loss refers to the power loss caused by the same factors during the process of power transmission from the energy storage module to the grid input point (such as a substation or distribution station).

[0142] In this embodiment, the system determines the power transmission path between the energy storage module and the grid input point and calculates the corresponding losses, which helps to evaluate the actual contribution of the energy storage module to the grid.

[0143] Step S424: performing multi-objective optimization based on the output power, the first path loss, the second path loss, and the historical loss information to determine the scheduling solution.

[0144] It should be noted that multi-objective optimization is the process of seeking the best compromise solution through mathematical methods or intelligent algorithms when there are multiple conflicting or interrelated optimization objectives.

[0145] In this embodiment, the system comprehensively considers multiple factors such as the output power of the energy storage module, the first path loss, the second path loss, and historical loss information, and uses multi-objective optimization algorithms such as NSGA-II and MOEA / D to formulate a scheduling plan. The plan aims to maximize the utilization rate of the photovoltaic system's power generation, minimize power transmission losses, and optimize the charging and discharging strategies of the energy storage module.

[0146] For example, suppose there are three individual photovoltaic systems A, B, and C in a certain management area. They are connected to the energy storage module through cables, and the energy storage module is connected to the grid input point. Based on historical data and real-time prediction, the system obtains the following information: the current state of charge of the energy storage module is 60%. The first power period is from 10 am to 2 pm, during which the power generation of the photovoltaic system is lower than the scheduling target, and the energy storage module needs to release power. The second power period is the remaining time period, and the power generation of the photovoltaic system is close to or slightly higher than the scheduling target. According to the location information, the first path loss from system A to the energy storage module is 2%, system B is 3%, and system C is 1%. The second path loss from the energy storage module to the grid input point is 1%. Historical loss information shows that the average daily power transmission loss in the management area is 5%. Based on the above information, the system formulates the following scheduling plan through a multi-objective optimization algorithm: in the first power period, the power generation of system A and system C is prioritized (because their first path loss is small), and the energy storage module is controlled to release an appropriate amount of power to make up for the power generation gap. In the second power period, the charging and discharging strategy of the energy storage module is adjusted according to the actual power generation of the photovoltaic system to maintain its state of charge within a reasonable range. Taking into account the first path loss, the second path loss and historical loss information, the power transmission path and scheduling strategy are optimized to reduce overall losses and improve grid efficiency.

[0147] Through this implementation, the system can make full use of the regulation ability of the energy storage module, combined with the power generation characteristics and location information of the photovoltaic system, to develop a more accurate and efficient scheduling plan. This plan can not only effectively make up for the power generation gap and improve the power generation utilization rate of the photovoltaic system, but also significantly reduce the loss during power transmission. At the same time, through the application of multi-objective optimization algorithms, the system can find the best balance point between multiple conflicting objectives and improve the flexibility and robustness of the overall scheduling.

[0148] Optionally, step S421 includes:

[0149] Step S4211: Processing the first power and the second power based on sliding filtering to determine an output power reference value of the energy storage module.

[0150] It should be noted that sliding filtering is a signal processing technology that smoothes the signal by taking the average or weighted average value within a window of the input signal to reduce the impact of noise and random fluctuations. Output power reference value: The output power target value that the energy storage module should achieve based on the first power and the second power after sliding filtering, which is used to guide the adjustment of the actual output power.

[0151] In this embodiment, the system first performs sliding filtering on the first power and the second power to reduce inaccuracies caused by instantaneous fluctuations or measurement errors. The window size of the sliding filter can be adjusted according to actual conditions to balance the smoothing effect and response speed. The processed data can better reflect the long-term trend and main characteristics of power changes. Then, based on the filtered first power and the second power, combined with the system scheduling strategy and objectives, the output power reference value of the energy storage module is calculated.

[0152] Step S4212: determining the output power according to the output power reference value and the state of charge.

[0153] It should be noted that the state of charge (SOC): as mentioned above, indicates the state of the current storage capacity of the energy storage module. Output power: the actual amount of power output by the energy storage module under specific conditions.

[0154] After determining the output power reference value, the system needs to further consider the state of charge of the energy storage module. If the state of charge is high, it means that the energy storage module has enough power to release; if the state of charge is low, the output power needs to be carefully controlled to avoid excessive discharge. According to the output power reference value and the state of charge, the system adjusts the output power of the energy storage module through control algorithms or strategies (such as PID control, fuzzy control, etc.), so that it can not only meet the scheduling requirements, but also ensure the long-term stable operation of the energy storage module.

[0155] For example, suppose a photovoltaic system is equipped with an energy storage module to supplement the grid demand when the photovoltaic power generation is insufficient. The system obtains the first power and second power data through real-time monitoring and prediction, and plans to use sliding filtering for processing. The specific parameters are as follows: The sliding filter window size is 3 hours (i.e., considering the data at the current time and the previous two times). In the first power period (10 am to 2 pm), the average power difference after sliding filtering is +50kW (indicating that the energy storage module needs to supplement 50kW of electricity). In the second power period, the power difference is small and is close to 0kW after sliding filtering. The current state of charge of the energy storage module is 70%. Based on the above information, the system performs the following operations: Sliding filtering is performed on the first power and the second power to obtain a smoothed power difference curve. It is determined that in the first power period, the output power reference value of the energy storage module is +50kW (consistent with the average power difference after filtering). Considering that the state of charge of the energy storage module is 70%, the system determines that there is enough electricity to be released, so the actual output power is set to 50kW (or fine-tuned according to the control strategy). In the second power period, since the power difference is small, the system may adjust the energy storage module to enter the charging mode or maintain a low power output state to keep the charge state stable.

[0156] Through this implementation, the system can use sliding filtering technology to effectively reduce noise and fluctuations in power data, and improve the accuracy and reliability of data. At the same time, the output power is adjusted in combination with the charge state of the energy storage module, which not only meets the needs of power grid dispatching, but also ensures the safe and stable operation of the energy storage module. This method improves the intelligence level and response speed of system dispatching, reduces operation and maintenance costs, and improves overall energy efficiency.

[0157] The present application embodiment provides a photovoltaic power generation scheduling system, please refer to Figure 5 The photovoltaic power generation scheduling system includes a scheduling module, a power grid, at least one individual household photovoltaic system and at least one energy storage module. The scheduling module is used to execute the steps of the photovoltaic power generation scheduling method in the above embodiment.

[0158] The photovoltaic power generation scheduling system provided in the embodiment of the present application focuses on individual household photovoltaic systems and manages multiple individual household photovoltaic systems at the same time, so that based on the detailed data of each household, combined with weather location, etc., the power generation of each household is predicted, and then the fluctuation data of the power difference is determined in combination with the scheduling power of the management area, and then the fluctuation data is used as one of the parameters of multi-objective optimization to determine the scheduling plan for each individual household photovoltaic system to be connected to the power grid, thereby solving the technical problem of low scheduling efficiency within the region in related technologies.

[0159] The present application provides a photovoltaic power generation scheduling device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the photovoltaic power generation scheduling method in any of the above embodiments.

[0160] Reference below Figure 6 , which shows a schematic diagram of the structure of a photovoltaic power generation scheduling device suitable for implementing the embodiment of the present application. The photovoltaic power generation scheduling device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The photovoltaic power generation scheduling device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0161] like Figure 6As shown, the photovoltaic power generation scheduling device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the photovoltaic power generation scheduling device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the photovoltaic power generation scheduling device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a photovoltaic power generation scheduling device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0162] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0163] The photovoltaic power generation dispatching device provided by the present application adopts the photovoltaic power generation dispatching method in the above embodiment, which can solve the technical problem of low efficiency of photovoltaic power generation dispatching in the region. Compared with the relevant technology, the beneficial effects of the photovoltaic power generation dispatching device provided by the present application are the same as the beneficial effects of the photovoltaic power generation dispatching device provided by the above embodiment, and the other technical features in the photovoltaic power generation dispatching device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0164] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0165] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0166] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the photovoltaic power generation scheduling method in the above-mentioned embodiment.

[0167] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0168] The computer-readable storage medium may be included in the photovoltaic power generation scheduling device; or may exist independently without being assembled into the photovoltaic power generation scheduling device.

[0169] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the photovoltaic power generation dispatching device, the photovoltaic power generation dispatching device: collects power generation, weather conditions and equipment status in real time based on the intelligent monitoring devices in each individual photovoltaic system;

[0170] Determine the expected power of the corresponding individual household photovoltaic system according to the power generation, the weather conditions and the equipment status;

[0171] Determine the dispatch target power of the management area corresponding to the photovoltaic system of the individual household according to the power consumption corresponding to the photovoltaic system of each individual household;

[0172] Determine a scheduling scheme corresponding to each of the individual household photovoltaic systems based on the fluctuation range of the power difference between the expected power and the scheduling target power;

[0173] The photovoltaic systems of each individual household are controlled to be connected to the power grid according to the dispatching plan.

[0174] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0175] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0176] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0177] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned photovoltaic power generation scheduling method, and can solve the technical problem of low efficiency of photovoltaic power generation scheduling in the region. Compared with the related art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the photovoltaic power generation scheduling method provided in the above-mentioned embodiment, and will not be repeated here.

[0178] An embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the photovoltaic power generation scheduling method as described above when the computer program is executed by a processor.

[0179] The computer program product provided in this application can solve the technical problem of low efficiency of photovoltaic power generation scheduling in the region. Compared with the related art, the beneficial effects of the computer program product provided in the embodiment of this application are the same as the beneficial effects of the photovoltaic power generation scheduling method provided in the above embodiment, which will not be repeated here.

[0180] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A photovoltaic power generation scheduling method, characterized in that: The photovoltaic power generation scheduling method comprises: Based on the real-time collection of power generation, weather conditions and equipment status by intelligent monitoring devices in each individual household photovoltaic system, the photovoltaic power generation dispatching system includes a dispatching module, a power grid, at least one individual household photovoltaic system and at least one energy storage module; Determine the expected power of the corresponding individual household photovoltaic system according to the power generation, the weather conditions and the equipment status, wherein the equipment status includes at least one of the number of equipment, equipment power, photovoltaic panel area and wire type; Determining the expected power of the corresponding individual household photovoltaic system according to the power generation, the weather conditions and the equipment status includes: Obtaining networked weather data at the location of the individual household photovoltaic system, and obtaining on-site environmental data through environmental sensors; Determine the weather condition based on the networked weather data and the field environment data; Determining expected power generation according to the device status and the power generation; According to the weather conditions, determine the weather impact coefficient by looking up a table in the historical power generation data; Determine the expected power according to the weather influence coefficient and the expected power generation; Determine the dispatch target power of the management area corresponding to the photovoltaic system of the individual household according to the power consumption corresponding to the photovoltaic system of each individual household; Based on the power difference between the expected power and the scheduling target power being mapped in a time series, a first power and a second power are determined according to the fluctuation amplitude after mapping, wherein the first power is a power whose power difference is greater than a preset threshold, and the second power is a power whose power difference is less than a preset threshold; Perform multi-objective optimization according to the first power, the second power, the location information between the photovoltaic systems of the individual households, and the historical loss information of each management area, and determine the scheduling scheme corresponding to each photovoltaic system of the individual households, wherein the energy storage module is provided between the photovoltaic system of the individual households and the power grid, and the historical loss information is the difference between the output power of all the photovoltaic systems of the individual households in the management area and the input power of the power grid; Performing multi-objective optimization according to the first power, the second power, the location information between the photovoltaic systems of the individual households, and the historical loss information of the management areas, and determining the scheduling scheme corresponding to the photovoltaic systems of the individual households, including: Determine an output power reference value of the energy storage module based on sliding filtering processing of the first power and the second power; Determining the output power of the energy storage module according to the output power reference value and the charge state of the energy storage module; Determine the first path loss between the individual household photovoltaic system and the energy storage module according to the location information; Determining a second path loss from the energy storage module to an input point of the power grid; Perform multi-objective optimization based on the output power, the first path loss, the second path loss, and the historical loss information to determine the scheduling solution; The photovoltaic systems of each individual household are controlled to be connected to the power grid according to the dispatching plan.

2. The photovoltaic power generation scheduling method according to claim 1, characterized in that: The step of determining the dispatch target power of the management area corresponding to the photovoltaic system of each individual household according to the power consumption corresponding to the photovoltaic system of each individual household comprises: Determine the target individual household photovoltaic systems included in each of the management areas; Determine the internal power consumption corresponding to the management area according to the power consumption of the target individual household photovoltaic system and the distance between the target individual household photovoltaic system and the power distribution station corresponding to the management area; The scheduling target power is determined according to the scheduling power of the management area and the internal power consumption.

3. The photovoltaic power generation scheduling method according to claim 1, characterized in that: The step of mapping the power difference between the expected power and the scheduling target power in a time series and determining the first power and the second power according to the mapped fluctuation amplitude comprises: Acquire the power difference between the expected power and the scheduling target power at each time point of the time series; Determine a first time interval during which the power difference is greater than a preset threshold, and a second time interval during which the power difference is less than the preset threshold; The power difference value in the first time interval is determined to be the first power, and the power difference value in the second time interval is determined to be the second power.

4. A photovoltaic power generation dispatching device, characterized in that: The photovoltaic power generation scheduling device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the photovoltaic power generation scheduling method according to any one of claims 1 to 3.

5. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the photovoltaic power generation scheduling method according to any one of claims 1 to 3 are implemented.

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