Power output distribution method of non-energy-storage type schedulable photovoltaic power station

Abstract

The invention provides a power output distribution method for a non-energy-storage type schedulable photovoltaic power station. The power output distribution method of the non-energy-storage type schedulable photovoltaic power station comprises the following steps: S1, arranging a plurality of monitoring sensors in the photovoltaic power station to monitor generation power, meteorological data, power grid load and power grid frequency information of photovoltaic modules in real time, by collecting the real-time data, the system can analyze the power generation condition of the photovoltaic power station and the load demand of the power grid in real time, and the meteorological data comprises the illumination intensity, the air temperature and the wind speed; and S2, according to the historical power generation data of the photovoltaic power station and the meteorological prediction model, combining with the load scheduling demand of the power grid. The power output distribution method of the non-energy-storage type schedulable photovoltaic power station has the advantages that the photovoltaic power station can flexibly and efficiently adjust power output without depending on energy storage equipment, and the stability of a power grid and the utilization efficiency of photovoltaic power generation are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation, and particularly relates to a power output distribution method for a non-energy storage type dispatchable photovoltaic power station. Background Art

[0002] With the increasing global demand for renewable energy, photovoltaic power generation, as a clean and renewable energy form, has received extensive attention. The power output of a photovoltaic power station is affected by the sunlight irradiation intensity, and its output power has instability and volatility. Traditional photovoltaic power stations usually adjust the power output through an energy storage system, but this requires high costs and complex system integration. In order to improve the flexibility and stability of photovoltaic power stations and reduce the demand for energy storage systems, the problem of power output scheduling for non-energy storage type photovoltaic power stations has become the focus of research;

[0003] Currently, most photovoltaic power stations adjust the power output through the maximum power point tracking (MPPT) technology. However, MPPT usually takes a single fixed power point as the benchmark and lacks dynamic adaptability to the grid load. Therefore, it cannot effectively match the grid dispatch. To address this problem, a power output distribution method for a non-energy storage type photovoltaic power station is proposed, which can be flexibly dispatched according to the grid load demand and the actual situation of photovoltaic power generation to achieve the purpose of balancing the grid supply and demand.

[0004] Therefore, it is necessary to provide a new power output distribution method for a non-energy storage type dispatchable photovoltaic power station to solve the above technical problems. Summary of the Invention

[0005] The technical problem solved by the present invention is to provide a power output distribution method for a non-energy storage type dispatchable photovoltaic power station that enables the photovoltaic power station to flexibly and efficiently adjust the power output without relying on energy storage devices, improving the stability of the grid and the utilization efficiency of photovoltaic power generation.

[0006] To solve the above technical problems, the power output distribution method for a non-energy storage type dispatchable photovoltaic power station provided by the present invention includes the following steps:

[0007] S1. Arrange a variety of monitoring sensors in the photovoltaic power station to real-time monitor the power generation power of photovoltaic modules, meteorological data, grid load, and grid frequency information. By collecting these real-time data, the system can analyze the power generation situation of the photovoltaic power station and the load demand of the grid in real time. The meteorological data includes light intensity, temperature, and wind speed;

[0008] S2. Based on the historical power generation data of the photovoltaic power station and the meteorological prediction model, combined with the load dispatching requirements of the power grid, an artificial intelligence algorithm is used to predict the photovoltaic power output within a certain period of time in the future. At the same time, combined with the load demand curve of the power grid, analyze the power supply and demand balance of the power grid, and determine the optimal power output dispatching strategy;

[0009] S3. According to the power grid load information and the power generation capacity of the photovoltaic power station collected in real time, through a dynamic power distribution mechanism, adjust the power output of the photovoltaic power station. For example, when the power grid load is low, the power output of the photovoltaic power station can be increased to maximize the utilization of solar energy, while when the power grid load is high, the output of the photovoltaic power station is appropriately reduced to avoid power grid overload. In addition, under the influence of cloud changes and climate change factors, the system can automatically adjust the power distribution strategy to maintain the stability of the power grid;

[0010] S4. Since the power output of the photovoltaic power station is volatile, by setting a reasonable power regulation and limitation mechanism, it can be ensured that the power output of the photovoltaic power station will not exceed the bearing capacity of the power grid load. For example, the power output of the photovoltaic power station can be set within a certain percentage range to avoid power grid instability caused by excessive power fluctuations in a short period of time.

[0011] As a further solution of the present invention, the step S1 helps the system to analyze the power generation situation of the power station and the load demand of the power grid in real time by monitoring the power generation power of the photovoltaic modules, meteorological data, power grid load and power grid frequency information in real time. The method steps for realizing intelligent dispatching and power output distribution include the real-time monitoring of the power generation power of the photovoltaic modules, the real-time monitoring of meteorological data, the real-time monitoring of the power grid load, the real-time monitoring of the power grid frequency, and the construction of a real-time data acquisition and analysis system.

[0012] As a further solution of the present invention, the steps for real-time monitoring of the power generation power of the photovoltaic modules are as follows:

[0013] (1). Arrange power sensors at the output end of each photovoltaic module or the input end of each inverter;

[0014] (2). The power sensors calculate and output the actual power generation power of each photovoltaic module or photovoltaic string in real time by monitoring voltage and current parameters;

[0015] (3). Transmit these power data to the data acquisition system (SCADA system) through the communication network for centralized monitoring and processing;

[0016] By the above steps, the power output data of the photovoltaic modules can be obtained in real time, enabling it to reflect the power generation capacity, performance, and possible faults or performance degradation of the photovoltaic power station, thereby helping to evaluate the overall power generation situation of the power station;

[0017] The real-time monitoring steps of the meteorological data are as follows:

[0018] (1). Install meteorological monitoring instruments in the photovoltaic power station area, such as pyranometers, temperature sensors, anemometers and humidity sensors;

[0019] (2). The meteorological monitoring instruments collect the light intensity, ambient temperature and wind speed data in real time, and transmit the data to the centralized monitoring system wirelessly or by wire;

[0020] (3). Correlate these meteorological data with the photovoltaic power generation power data, conduct comprehensive analysis, and predict the output change trend of photovoltaic power generation;

[0021] Through the meteorological data, the change of the power generation capacity of the photovoltaic power station can be predicted. Especially in the case of cloud changes and sudden weather changes, the power output can be adjusted in advance.

[0022] As a further solution of the present invention, the real-time monitoring steps of the grid load are as follows:

[0023] (1). Install grid load monitoring sensors at the distribution network nodes or power transformers connected to the photovoltaic power station, including current, voltage and frequency sensors;

[0024] (2). The grid load monitoring system regularly collects the real-time load data of the grid and transmits it to the central control system;

[0025] (3). According to the load data, adjust the power output of the photovoltaic power station in real time to ensure the balance between the grid load and the power station output, and avoid overload or insufficient power supply;

[0026] By monitoring the grid load, the system can timely understand the change of the grid demand, ensure that the power output of the photovoltaic power station can match the grid load, and avoid the instability of the grid caused by excessive power fluctuations of the photovoltaic power station;

[0027] The real-time monitoring steps of the grid frequency are as follows:

[0028] (1). Install frequency monitoring sensors at key grid nodes (such as distribution network or power dispatching center);

[0029] (2). The frequency sensors monitor the operating frequency of the grid in real time and transmit the data to the grid dispatching center or the control system of the photovoltaic power station;

[0030] (3). The photovoltaic power station can adjust the power generation power according to the grid frequency fluctuation information. For example, when the grid frequency is lower than the normal value, the power output of the photovoltaic power station can be appropriately increased to help balance the grid;

[0031] By monitoring the grid frequency in real time, the photovoltaic power station can quickly respond to the frequency changes of the grid, thereby effectively assisting the frequency regulation of the grid and ensuring the stable operation of the grid;

[0032] The construction steps of the real-time data acquisition and analysis system are as follows:

[0033] (1) All sensors (photovoltaic power, weather, grid load, grid frequency) are connected to the data acquisition system (such as SCADA system, Internet of Things platform) via wireless or wired means;

[0034] (2) The central data acquisition system processes and stores the received data in real time, analyzes and predicts the data through analytical algorithms, and outputs power station and power grid operation status reports;

[0035] (3) The system generates control instructions based on the analysis results, automatically adjusts the output power of the photovoltaic power station, and sends the instructions to the inverter or other regulating equipment through the control system;

[0036] The data collection and analysis system can integrate the real-time power generation status of photovoltaic modules, meteorological information, grid load and frequency data in multiple dimensions, provide information for power dispatch, and thus optimize the power matching between photovoltaic power stations and the grid.

[0037] As a further solution of the present invention, the step S2 of achieving supply and demand balance by predicting the output power of photovoltaic power generation and analyzing the load demand of the power grid includes data collection and preprocessing, photovoltaic power prediction, power grid load demand prediction and power output scheduling;

[0038] The data collection and preprocessing steps are as follows:

[0039] (1) Historical power generation data of photovoltaic power stations: Collect historical power generation data of photovoltaic power stations, including daily power generation, hourly power generation, and seasonal fluctuations;

[0040] (2) Meteorological data: collect weather data required by the meteorological forecast model, mainly including sunshine intensity, temperature, wind speed, humidity and meteorological parameters that affect photovoltaic power generation;

[0041] (3) Grid load dispatch data: Collect grid load demand data, including real-time load demand curve and load forecast for a period of time in the future;

[0042] (4) Clean the data and remove outliers or missing values;

[0043] (5) Normalize photovoltaic power generation data and meteorological data to ensure consistent data format and unified scale;

[0044] (6)Interpolate or resample the meteorological data to ensure that the time step of the meteorological data is consistent with the power generation data;

[0045] The steps of the photovoltaic power prediction are as follows:

[0046] (1)Use the historical power generation data and corresponding meteorological data of the photovoltaic power station for training

[0047] (2)Evaluate the prediction accuracy of the model through cross-validation and the root mean square error (RMSE) index;

[0048] (3)Improve the prediction accuracy of the model by adjusting hyperparameters and increasing the number of training samples. As a further solution of the present invention, the steps of the grid load demand prediction are as follows:

[0049] (1)Analyze the grid load data to obtain the historical load curve of the grid and its daily cycle and seasonal fluctuation characteristics;

[0050] (2)Use the load prediction model to predict the grid load demand in the future for a period of time;

[0051] The steps of the power output scheduling are as follows:

[0052] (1)Combine the photovoltaic power generation prediction result with the grid load prediction result, analyze the relationship between the power generation capacity of the photovoltaic power station and the grid load demand. If the photovoltaic power generation output can meet the grid load demand, it may not be necessary to dispatch other power sources;

[0053] (2)If the photovoltaic power generation is insufficient or excessive, other power sources (such as thermal power, wind power, energy storage batteries) need to be adjusted to ensure the stable operation of the grid.

[0054] As a further solution of the present invention, the steps of automatically adjusting the power distribution strategy to maintain the stability of the grid in step S3 include real-time grid load monitoring, evaluation of the power generation capacity of the photovoltaic power station, formulation of the power distribution strategy, assistance of the energy storage system, and real-time monitoring and data analysis;

[0055] The steps of the real-time grid load monitoring are as follows:

[0056] (1)Obtain grid load information: Real-time collect the load data of the grid to understand the current supply and demand status of the grid, which can be completed through the monitoring system of the grid to ensure that the load changes at each moment can be accurately obtained;

[0057] (2)Load prediction: Use the load prediction algorithm to predict the grid load changes in the future for a period of time to help formulate the power output strategy of the photovoltaic power station;

[0058] The steps of the evaluation of the power generation capacity of the photovoltaic power station are as follows:

[0059] (1). Real-time photovoltaic power generation capacity monitoring: According to weather conditions, sunlight intensity, and the operating status of photovoltaic modules, the power generation capacity of a photovoltaic power station is evaluated in real time. The power generation power of the photovoltaic power station can be dynamically obtained by installing sensors and weather data interfaces.

[0060] (2). Photovoltaic power generation prediction: Combining meteorological prediction data (such as cloud cover changes, temperature, humidity) and predicting the power generation capacity of a photovoltaic power station in the short term through a model, which helps to foresee the output fluctuations of the photovoltaic power station under different weather conditions.

[0061] The steps for formulating the power distribution strategy are as follows:

[0062] (1). Matching of grid load and photovoltaic power generation: According to the grid load situation and the power generation prediction of the photovoltaic power station, a power distribution strategy is formulated:

[0063] (2). When the grid load is low, increase the power generation output of the photovoltaic power station, make full use of solar energy, and reduce the consumption of other power generation sources.

[0064] (3). When the grid load is high, adjust the output power of the photovoltaic power station (controlled by an inverter device), appropriately reduce the output of the photovoltaic power station, avoid grid overload, and ensure grid stability.

[0065] As a further solution of the present invention, the steps for the assistance of the energy storage system are as follows:

[0066] (1). Energy storage scheduling: Deploy an energy storage system (such as a battery energy storage system) between the photovoltaic power station and the grid. When the grid load is low, the excess power can be stored through the energy storage device, and when the grid load is high, the energy storage system can release electrical energy to further balance supply and demand.

[0067] (2). Intelligent scheduling: According to the grid load, photovoltaic power generation, and the status of the energy storage system, through an intelligent scheduling system, ensure the maximum utilization efficiency of the energy storage system while maintaining grid stability.

[0068] The steps for the real-time monitoring and data analysis are as follows:

[0069] (1). Multi-parameter monitoring: Not only monitor the grid load and photovoltaic power generation, but also monitor meteorological data, the status of energy storage devices, and power flow parameters; through a centralized monitoring platform, integrate the data of each system to ensure the real-time and accuracy of power distribution decisions.

[0070] (2). Data analysis: Through the analysis of historical data and real-time data, optimize the power distribution strategy and provide data support for future scheduling decisions.

[0071] As a further solution of the present invention, in step S4, the steps to ensure that the power output of the photovoltaic power station does not exceed the load-carrying capacity of the power grid and avoid power grid instability include setting upper and lower limits of power output, adopting power prediction and scheduling, power limiting and regulation mechanisms, auxiliary regulation of the energy storage system, power feedback and real-time monitoring, and grid-friendly inverters;

[0072] The steps of setting the upper and lower limits of power output are as follows:

[0073] (1). Power output range setting: The power output of the photovoltaic power station can be dynamically adjusted according to the real-time load demand of the power grid and set as a percentage range. For example, the maximum limit of the output power of the photovoltaic power station is set to 80% of the maximum rated power of the power station, and the minimum limit is set to 30%. This can prevent the power station from outputting too much or too little power in a short period of time;

[0074] (2). Load prediction: Utilize historical load data and weather prediction information to predict the power output of the photovoltaic power station in order to adjust the power control strategy in advance;

[0075] The steps of adopting power prediction and scheduling are as follows:

[0076] (1). Power prediction: Through weather prediction and measurement of solar irradiance, combined with the actual operation data of the photovoltaic power station, the future power generation of the photovoltaic power station can be predicted;

[0077] (2). Real-time scheduling: Combine with the power grid load scheduling system, monitor the power output of the photovoltaic power station in real time, and adjust the power according to the demand of the power grid. If fluctuations are predicted, the scheduling center can limit the power in advance;

[0078] The steps of the power limiting and regulation mechanism are as follows:

[0079] (1). Dynamic power limiting: A dynamic regulation function of power output can be set in the inverter of the photovoltaic power station. For example, when the light suddenly increases and causes the power to be too high, the inverter can automatically reduce the power output to avoid overloading the power grid; conversely, if the light suddenly weakens, the system can automatically increase the power output to the set lower limit;

[0080] (2). Power correction (power smoothing): Adopt power smoothing technology, such as using an energy storage system in combination with the photovoltaic power station. When the power of the photovoltaic power station fluctuates greatly, the energy storage system can provide a regulating effect to smooth the power output and reduce the power volatility.

[0081] As a further solution of the present invention, the steps of the auxiliary regulation of the energy storage system are as follows:

[0082] (1). Regulation function of the energy storage system: The PV power station can be equipped with an energy storage system to balance the load fluctuations of the power grid. When the power output of the PV power station is too high, the energy storage system can absorb the excess electricity. When the power output of the PV power station is too low, the energy storage system can supply electric energy to the power grid to maintain the stability of the power grid;

[0083] (2). Configuration of the energy storage capacity: The capacity of the energy storage system needs to be reasonably configured according to the actual output fluctuations of the PV power station to ensure that the energy storage system has sufficient capacity to cope with short-term power fluctuations;

[0084] The steps of the power feedback and real-time monitoring are as follows:

[0085] (1). Intelligent monitoring system: Utilize smart grid technology to monitor the power output of the PV power station in real time. Through the feedback mechanism, timely understand the power status of the power station. Once there are power fluctuations beyond the set range, the system will automatically adjust to avoid excessive or too small load on the power grid;

[0086] (2). Coordination with the power grid: The PV power station should coordinate with the power grid operator, share the power output information and the power grid load status in real time, ensure that the power output of the PV power station is within a reasonable range, and conduct appropriate dispatching;

[0087] The steps of the grid-friendly inverter are as follows:

[0088] (1). Support power factor regulation: Modern PV inverters support power factor regulation and can adjust the power output of the PV power station in a timely manner according to the needs of the power grid to make it more matched with the load curve of the power grid;

[0089] (2). Virtual power regulation: Advanced inverters can also adjust the voltage and frequency of the power grid by adjusting the reactive power, thereby enhancing the adaptability of the PV power station to power grid fluctuations.

[0090] Compared with the related technologies, the power output distribution method of the non-energy storage type dispatchable PV power station provided by the present invention has the following beneficial effects:

[0091] 1. By combining the power grid load demand, photovoltaic power generation situation and real-time meteorological data, and adopting dynamic dispatching and feedback mechanism, the present invention enables the PV power station to flexibly and efficiently adjust the power output without relying on energy storage equipment, improving the stability of the power grid and the utilization efficiency of photovoltaic power generation;

[0092] 2. Through dynamic power distribution and real-time dispatching, the present invention can effectively avoid the drastic fluctuations in the power output of the PV power station, reduce the pressure brought by the power grid load fluctuations, and improve the stability and reliability of the power grid;

[0093] 3. According to the power grid load demand and the photovoltaic power generation capacity, the present invention flexibly adjusts the output power of the photovoltaic power station, maximizes the utilization of photovoltaic power generation, and avoids the situations of over-output or under-output.

[0094] 4. Through scheduling, the present invention reduces the dependence on the energy storage system, lowers the construction and operation costs of the system, and simplifies the construction and maintenance work of the photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the drawings.

[0096] Figure 1 It is a schematic diagram of the flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0097] Please refer to Figure 1 , wherein Figure 1 It is a schematic diagram of the flow of the present invention. The power output distribution method of the non-energy storage type dispatchable photovoltaic power station includes the following steps:

[0098] S1. Arrange a variety of monitoring sensors in the photovoltaic power station to monitor the power generation power of the photovoltaic modules, meteorological data, power grid load and power grid frequency information in real time. By collecting these real-time data, the system can analyze the power generation situation of the photovoltaic power station and the load demand of the power grid in real time. The meteorological data includes light intensity, temperature and wind speed.

[0099] S2. According to the historical power generation data of the photovoltaic power station and the meteorological prediction model, combined with the load dispatch demand of the power grid, use artificial intelligence algorithms to predict the photovoltaic power output in a certain future time. At the same time, combined with the load demand curve of the power grid, analyze the supply-demand balance situation of the power grid and determine the best power output dispatch strategy.

[0100] S3. According to the real-time collected power grid load information and the power generation capacity of the photovoltaic power station, through the dynamic power distribution mechanism, adjust the power output of the photovoltaic power station. For example, when the power grid load is low, the power output of the photovoltaic power station can be increased to maximize the utilization of solar energy. When the power grid load is high, the output of the photovoltaic power station is appropriately reduced to avoid power grid overload. In addition, under the influence of cloud changes and climate change factors, the system can automatically adjust the power distribution strategy to maintain the stability of the power grid.

[0101] S4. Since the power output of the photovoltaic power station has fluctuations, by setting a reasonable power regulation and limitation mechanism, it can be ensured that the power output of the photovoltaic power station will not exceed the bearing capacity of the power grid load. For example, the power output of the photovoltaic power station can be set within a certain percentage range to avoid power grid instability caused by excessive power fluctuations in a short time.

[0102] Step S1 helps the system to analyze the power generation situation of the power station and the load demand of the power grid in real time by monitoring the power generation power, meteorological data, grid load and grid frequency information of the photovoltaic modules. The method steps for realizing intelligent scheduling and power output distribution include real-time monitoring of the power generation power of the photovoltaic modules, real-time monitoring of meteorological data, real-time monitoring of grid load, real-time monitoring of grid frequency, and construction of a real-time data acquisition and analysis system.

[0103] The steps for real-time monitoring of the power generation power of the photovoltaic modules are as follows:

[0104] (1). Arrange power sensors at the output end of each photovoltaic module or the input end of each inverter;

[0105] (2). The power sensors calculate and output the actual power generation power of each photovoltaic module or photovoltaic string in real time by monitoring voltage and current parameters;

[0106] (3). Transmit these power data to the data acquisition system (SCADA system) through the communication network for centralized monitoring and processing;

[0107] By the above steps, the power output data of the photovoltaic modules can be obtained in real time, which can reflect the power generation capacity, performance and possible faults or performance degradation of the photovoltaic power station, thus helping to evaluate the overall power generation situation of the power station;

[0108] The steps for real-time monitoring of the meteorological data are as follows:

[0109] (1). Install meteorological monitoring instruments in the area of the photovoltaic power station, such as pyranometers, temperature sensors, anemometers and humidity sensors;

[0110] (2). The meteorological monitoring instruments collect the light intensity, ambient temperature and wind speed data in real time, and transmit the data to the centralized monitoring system through wireless or wired means;

[0111] (3). Correlate these meteorological data with the photovoltaic power generation power data and conduct comprehensive analysis to predict the output change trend of photovoltaic power generation;

[0112] Through the meteorological data, the change of the power generation capacity of the photovoltaic power station can be predicted, especially in the case of cloud change and sudden weather change, and the power output can be adjusted in advance.

[0113] The steps for real-time monitoring of the grid load are as follows:

[0114] (1). Install grid load monitoring sensors at the distribution network nodes or power transformers connected to the photovoltaic power station, including current, voltage and frequency sensors;

[0115] (2). The power grid load monitoring system regularly collects the real-time load data of the power grid and transmits it to the central control system;

[0116] (3). According to the load data, the power output of the photovoltaic power station is adjusted in real time to ensure the balance between the power grid load and the power station output, and avoid overload or insufficient power supply;

[0117] By monitoring the power grid load, the system can timely understand the demand changes of the power grid, ensure that the power output of the photovoltaic power station can match the power grid load, and avoid the instability of the power grid caused by excessive power fluctuations of the photovoltaic power station;

[0118] The steps for real-time monitoring of the power grid frequency are as follows:

[0119] (1). Install frequency monitoring sensors at key nodes of the power grid (such as the distribution network or the power dispatching center);

[0120] (2). The frequency sensors continuously monitor the operating frequency of the power grid and transmit the data to the power grid dispatching center or the control system of the photovoltaic power station;

[0121] (3). The photovoltaic power station can adjust the power generation according to the power grid frequency fluctuation information. For example, when the power grid frequency is lower than the normal value, the power output of the photovoltaic power station can be appropriately increased to help balance the power grid;

[0122] By continuously monitoring the power grid frequency, the photovoltaic power station can quickly respond to the frequency changes of the power grid, thus effectively assisting in the frequency regulation of the power grid and ensuring the stable operation of the power grid;

[0123] The construction steps of the real-time data acquisition and analysis system are as follows:

[0124] (1). All sensors (photovoltaic power, meteorology, power grid load, power grid frequency) are connected to the data acquisition system (such as SCADA system, Internet of Things platform) through wireless or wired methods;

[0125] (2). The central data acquisition system processes and stores the received data in real time, analyzes and predicts the data through analysis algorithms, and outputs the operation status reports of the power station and the power grid;

[0126] (3). The system generates control instructions according to the analysis results, automatically adjusts the power output of the photovoltaic power station, and sends the instructions to the inverter or other regulating devices through the control system;

[0127] Through the data acquisition and analysis system, the multi-dimensional data of the real-time power generation situation of the photovoltaic modules, meteorological information, power grid load and frequency can be integrated to provide information for power dispatching, thereby optimizing the power matching between the photovoltaic power station and the power grid.

[0128] The step S2 of achieving supply and demand balance by predicting the output power of photovoltaic power generation and analyzing the load demand of the power grid includes data collection and preprocessing, photovoltaic power prediction, power grid load demand prediction and power output scheduling;

[0129] The data collection and preprocessing steps are as follows:

[0130] (1) Historical power generation data of photovoltaic power stations: Collect historical power generation data of photovoltaic power stations, including daily power generation, hourly power generation, and seasonal fluctuations;

[0131] (2) Meteorological data: collect weather data required by the meteorological forecast model, mainly including sunshine intensity, temperature, wind speed, humidity and meteorological parameters that affect photovoltaic power generation;

[0132] (3) Grid load dispatch data: Collect grid load demand data, including real-time load demand curve and load forecast for a period of time in the future;

[0133] (4) Clean the data and remove outliers or missing values;

[0134] (5) Normalize photovoltaic power generation data and meteorological data to ensure consistent data format and unified scale;

[0135] (6) Interpolate or resample the meteorological data to ensure that the time step of the meteorological data is consistent with the power generation data;

[0136] The steps of photovoltaic power prediction are as follows:

[0137] (1) Use the historical power generation data of photovoltaic power plants and the corresponding meteorological data for training

[0138] (2) Evaluate the prediction accuracy of the model through cross-validation and root mean square error (RMSE) indicators;

[0139] (3) Improve the prediction accuracy of the model by adjusting hyperparameters and increasing training samples.

[0140] The grid load demand prediction steps are as follows:

[0141] (1) Analyze the power grid load data to obtain the historical load curve of the power grid and its daily cycle and seasonal fluctuation characteristics;

[0142] (2) Use the load forecasting model to predict the grid load demand in the future;

[0143] The power output scheduling steps are as follows:

[0144] (1). Combine the photovoltaic power generation prediction results with the power grid load prediction results, analyze the relationship between the power generation capacity of the photovoltaic power station and the power grid load demand. If the photovoltaic power generation output can meet the power grid load demand, it may not be necessary to dispatch other power sources;

[0145] (2). If the photovoltaic power generation is insufficient or excessive, other power sources (such as thermal power, wind power, energy storage batteries) need to be adjusted to ensure the stable operation of the power grid.

[0146] The steps of automatically adjusting the power distribution strategy to maintain the stability of the power grid in step S3 include real-time power grid load monitoring, power generation capacity assessment of the photovoltaic power station, power distribution strategy formulation, assistance of the energy storage system, and real-time monitoring and data analysis;

[0147] The steps of the real-time power grid load monitoring are as follows:

[0148] (1). Obtain power grid load information: Real-time collect the load data of the power grid, understand the current supply and demand status of the power grid, which can be completed through the monitoring system of the power grid to ensure that the load changes at each moment can be accurately obtained;

[0149] (2). Load prediction: Use the load prediction algorithm to predict the power grid load changes in the future period to help formulate the power output strategy of the photovoltaic power station;

[0150] The steps of the power generation capacity assessment of the photovoltaic power station are as follows:

[0151] (1). Real-time photovoltaic power generation capacity monitoring: According to the weather conditions, sunlight intensity and the operating status of the photovoltaic modules, real-time evaluate the power generation capacity of the photovoltaic power station. The power generation power of the photovoltaic power station can be dynamically obtained by installing sensors and weather data interfaces;

[0152] (2). Photovoltaic power generation prediction: Combine meteorological prediction data (such as cloud cover changes, temperature, humidity) and predict the power generation capacity of the photovoltaic power station in the short term through models, which helps to foresee the output fluctuations of the photovoltaic power station under different weather conditions;

[0153] The steps of the power distribution strategy formulation are as follows:

[0154] (1). Matching of power grid load and photovoltaic power generation: According to the power grid load situation and the power generation prediction of the photovoltaic power station, formulate the power distribution strategy:

[0155] (2). When the power grid load is low, increase the power generation output of the photovoltaic power station, make full use of solar energy, and reduce the consumption of other power generation sources;

[0156] (3) When the grid load is high, by adjusting the output power of the PV power station (controlled by the inverter device), appropriately reduce the output of the PV power station to avoid grid overload and ensure grid stability.

[0157] The steps of the auxiliary of the energy storage system are as follows:

[0158] (1) Energy storage scheduling: Deploy an energy storage system (such as a battery energy storage system) between the PV power station and the grid. When the grid load is low, the excess power can be stored through the energy storage device, and when the grid load is high, the energy storage system can release electric energy to further balance supply and demand;

[0159] (2) Intelligent scheduling: According to the grid load, photovoltaic power generation, and the status of the energy storage system, through the intelligent scheduling system, ensure the maximum utilization efficiency of the energy storage system while maintaining grid stability;

[0160] The steps of the real-time monitoring and data analysis are as follows:

[0161] (1) Multi-parameter monitoring: Not only monitor the grid load and photovoltaic power generation, but also monitor meteorological data, the status of energy storage devices, and power flow parameters; Through the centralized monitoring platform, integrate the data of each system to ensure the real-time and accuracy of power distribution decisions;

[0162] (2) Data analysis: Through the analysis of historical data and real-time data, optimize the power distribution strategy and provide data support for future scheduling decisions.

[0163] In step S4, the steps to ensure that the power output of the PV power station does not exceed the carrying capacity of the grid load and avoid grid instability include setting the upper and lower limits of power output, adopting power prediction and scheduling, power limiting and regulation mechanisms, auxiliary regulation of the energy storage system, power feedback and real-time monitoring, and grid-friendly inverters;

[0164] The steps of setting the upper and lower limits of power output are as follows:

[0165] (1) Power output range setting: The power output of the PV power station can be dynamically adjusted according to the real-time load demand of the grid, and set as a percentage range. For example, set the maximum limit of the PV power station output power to 80% of the maximum rated power of the power station, and the minimum limit to 30%. This can avoid the power output of the power station being too large or too small in a short time;

[0166] (2) Load prediction: Use historical load data and weather prediction information to predict the power output of the PV power station in order to adjust the power control strategy in advance;

[0167] The steps of adopting power prediction and scheduling are as follows:

[0168] (1). Power prediction: By weather prediction and measurement of solar irradiance, combined with the actual operation data of the PV power station, the future power generation of the PV power station can be predicted;

[0169] (2). Real-time scheduling: Combined with the grid load scheduling system, the power output of the PV power station is monitored in real time, and power adjustment is carried out according to the grid demand. If fluctuations occur in the prediction, the scheduling center can limit the power in advance;

[0170] The steps of the power limit and adjustment mechanism are as follows:

[0171] (1). Dynamic power limit: The dynamic adjustment function of power output can be set in the inverter of the PV power station. For example, when the light suddenly increases and causes the power to be too high, the inverter can automatically reduce the power output to avoid overloading the grid; conversely, if the light suddenly weakens, the system can automatically increase the power output to the set lower limit;

[0172] (2). Power correction (power smoothing): Power smoothing technology is adopted, such as the energy storage system is used in conjunction with the PV power station. When the power of the PV power station fluctuates greatly, the energy storage system can provide a regulatory effect to smooth the power output and reduce the power volatility.

[0173] The steps of the auxiliary regulation of the energy storage system are as follows:

[0174] (1). Regulation function of the energy storage system: The PV power station can be equipped with an energy storage system to balance the grid load fluctuations. When the power output of the PV power station is too high, the energy storage system can absorb the excess power. When the power output of the PV power station is too low, the energy storage system can supply power to the grid to maintain the stability of the grid;

[0175] (2). Configuration of energy storage capacity: The capacity of the energy storage system needs to be reasonably configured according to the actual output fluctuations of the PV power station to ensure that the energy storage system has sufficient capacity to cope with short-term power fluctuations;

[0176] The steps of the power feedback and real-time monitoring are as follows:

[0177] (1). Intelligent monitoring system: The intelligent grid technology is used to monitor the power output of the PV power station in real time. Through the feedback mechanism, the power status of the power station can be understood in time. Once the power fluctuation exceeds the set range, the system will automatically adjust to avoid the grid load being too large or too small;

[0178] (2). Coordination with the grid: The PV power station should coordinate with the grid operator to share the power output information and grid load status in real time, ensure that the power output of the PV power station is within a reasonable range, and carry out appropriate scheduling;

[0179] The steps of the grid-friendly inverter are as follows:

[0180] (1). Support power factor adjustment: Modern PV inverters support power factor adjustment, which can adjust the power output of the PV power station in a timely manner according to the needs of the power grid, making it more matched with the load curve of the power grid;

[0181] (2). Virtual power adjustment: Advanced inverters can also adjust the voltage and frequency of the power grid by adjusting the reactive power, thereby enhancing the adaptability of the PV power station to power grid fluctuations.

[0182] Through dynamic power distribution and real-time scheduling, it can effectively avoid the drastic fluctuations in the power output of the PV power station, reduce the pressure brought by the power grid load fluctuations, and improve the stability and reliability of the power grid;

[0183] According to the power grid load demand and the PV power generation capacity, flexibly adjust the output power of the PV power station to maximize the utilization of PV power generation and avoid over-output or under-output situations;

[0184] Through intelligent scheduling, the dependence on the energy storage system is reduced, the construction and operation costs of the system are lowered, and at the same time, the construction and maintenance work of the PV power station is simplified;

[0185] By combining the power grid load demand, PV power generation situation and real-time meteorological data, and adopting dynamic scheduling and feedback mechanisms, the PV power station can flexibly and efficiently adjust the power output without relying on energy storage devices, improving the stability of the power grid and the utilization efficiency of PV power generation.

[0186] As mentioned above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A power output distribution method for a non-energy storage type dispatchable photovoltaic power station, characterized in that, It includes the following steps: S1. Arrange a variety of monitoring sensors in the photovoltaic power station to monitor the power generation power, meteorological data, grid load, and grid frequency information of the photovoltaic modules in real time. By collecting these real-time data, the system can analyze the power generation situation of the photovoltaic power station and the load demand of the grid in real time. The meteorological data includes light intensity, temperature, and wind speed; S2. According to the historical power generation data of the photovoltaic power station and the meteorological prediction model, combined with the load dispatching requirements of the grid, use artificial intelligence algorithms to predict the photovoltaic power output within a certain period of time in the future. At the same time, combined with the load demand curve of the grid, analyze the supply-demand balance situation of the grid and determine the optimal power output dispatching strategy; S3. According to the grid load information collected in real time and the power generation capacity of the photovoltaic power station, adjust the power output of the photovoltaic power station through a dynamic power distribution mechanism. For example, when the grid load is low, the power output of the photovoltaic power station can be increased to maximize the utilization of solar energy. When the grid load is high, the output of the photovoltaic power station is appropriately reduced to avoid grid overload. In addition, under the influence of cloud changes and climate change factors, the system can automatically adjust the power distribution strategy to maintain the stability of the grid; S4. Since the power output of the photovoltaic power station is volatile, by setting a reasonable power regulation and limitation mechanism, it can be ensured that the power output of the photovoltaic power station does not exceed the bearing capacity of the grid load. For example, the power output of the photovoltaic power station can be set within a certain percentage range to avoid grid instability caused by excessive power fluctuations in a short period of time.

2. The power output distribution method of the non-energy storage type dispatchable photovoltaic power station according to claim 1, characterized in that: The step S1 helps the system to analyze the power generation situation of the power station and the load demand of the grid in real time by monitoring the power generation power, meteorological data, grid load, and grid frequency information of the photovoltaic modules in real time. The method steps for realizing intelligent dispatching and power output distribution include the real-time monitoring of the power generation power of the photovoltaic modules, the real-time monitoring of meteorological data, the real-time monitoring of grid load, the real-time monitoring of grid frequency, and the construction of a real-time data acquisition and analysis system.

3. The power output distribution method of the non-energy storage type schedulable photovoltaic power station according to claim 2, wherein: The steps for the real-time monitoring of the power generation power of the photovoltaic modules are as follows: (1). Arrange power sensors at the output end of each photovoltaic module or the input end of each inverter; (2). The power sensors calculate and output the actual power generation power of each photovoltaic module or photovoltaic string in real time by monitoring voltage and current parameters; (3). Transmit these power data to the data acquisition system (SCADA system) through the communication network for centralized monitoring and processing; Through the above steps, the power output data of the photovoltaic modules can be obtained in real time, which can reflect the power generation capacity, performance, and possible faults or performance degradation of the photovoltaic power station, thus helping to evaluate the overall power generation situation of the power station; The steps for the real-time monitoring of the meteorological data are as follows: (1). Install meteorological monitoring instruments in the area of the photovoltaic power station, such as pyranometers, temperature sensors, anemometers, and humidity sensors; (2). The meteorological monitoring instruments collect light intensity, ambient temperature, and wind speed data in real time and transmit the data to the centralized monitoring system wirelessly or by wire; (3). Correlate these meteorological data with the photovoltaic power generation data and conduct comprehensive analysis to predict the output change trend of photovoltaic power generation; Through meteorological data, the change in the power generation capacity of a photovoltaic power station can be predicted. Especially in the case of cloud cover changes and sudden weather changes, the power output can be adjusted in advance.

4. The power output distribution method of the non-energy storage type dispatchable photovoltaic power station according to claim 2, characterized in that: The steps for real-time monitoring of the grid load are as follows: (1). Install grid load monitoring sensors at the distribution network nodes or power transformers connected to the photovoltaic power station, including current, voltage, and frequency sensors; (2). The grid load monitoring system regularly collects the real-time load data of the grid and transmits it to the central control system; (3). According to the load data, adjust the power output of the photovoltaic power station in real time to ensure the balance between the grid load and the power station output, and avoid overload or insufficient power supply; (3). By monitoring the grid load, the system can timely understand the change in grid demand, ensure that the power output of the photovoltaic power station can match the grid load, and avoid grid instability caused by excessive power fluctuations of the photovoltaic power station; (3). The steps for real-time monitoring of the grid frequency are as follows: (1). Install frequency monitoring sensors at key grid nodes (such as distribution networks or power dispatch centers); (2). The frequency sensors continuously monitor the operating frequency of the grid and transmit the data to the grid dispatch center or the control system of the photovoltaic power station; (3). The photovoltaic power station can adjust the power generation according to the grid frequency fluctuation information. For example, when the grid frequency is lower than the normal value, the power output of the photovoltaic power station can be appropriately increased to help balance the grid; (3). By continuously monitoring the grid frequency, the photovoltaic power station can quickly respond to the frequency change of the grid, thus effectively assisting the frequency regulation of the grid and ensuring the stable operation of the grid; (3). The steps for building the real-time data collection and analysis system are as follows: (1). All sensors (photovoltaic power, meteorology, grid load, grid frequency) are connected to the data collection system (such as SCADA system, Internet of Things platform) by wireless or wired means; (2). The central data collection system processes and stores the received data in real time, and analyzes and predicts the data through analysis algorithms, and outputs the operation status reports of the power station and the grid; (3). The system generates control instructions according to the analysis results, automatically adjusts the output power of the photovoltaic power station, and sends the instructions to the inverter or other regulating devices through the control system; (3). Through the data collection and analysis system, multi-dimensional data such as the real-time power generation situation of photovoltaic modules, meteorological information, grid load, and frequency can be integrated, providing information for power dispatch, thereby optimizing the power matching between the photovoltaic power station and the grid.

5. The power output allocation method of the non-energy storage type adjustable photovoltaic power station according to claim 1, wherein: (3). The steps for achieving supply-demand balance in step S2 by predicting the output power of photovoltaic power generation and analyzing the load demand of the grid include data collection and preprocessing, photovoltaic power prediction, grid load demand prediction, and power output scheduling; (3). The steps for data collection and preprocessing are as follows: (1). Historical power generation data of the photovoltaic power station: Collect the historical power generation data of the photovoltaic power station, including daily power generation, hourly power generation, and seasonal fluctuations; (2) Meteorological data: collect weather data required by the meteorological forecast model, mainly including sunshine intensity, temperature, wind speed, humidity and meteorological parameters that affect photovoltaic power generation; (3) Grid load dispatch data: Collect grid load demand data, including real-time load demand curve and load forecast for a period of time in the future; (4) Clean the data and remove outliers or missing values; (5) Normalize photovoltaic power generation data and meteorological data to ensure consistent data format and unified scale; (6) Interpolate or resample the meteorological data to ensure that the time step of the meteorological data is consistent with the power generation data; The steps of photovoltaic power prediction are as follows: (1) Use the historical power generation data of photovoltaic power plants and the corresponding meteorological data for training (2) Evaluate the prediction accuracy of the model through cross-validation and root mean square error (RMSE) indicators; (3) Improve the prediction accuracy of the model by adjusting hyperparameters and increasing training samples.

6. The power output distribution method of the non-energy storage type dispatchable photovoltaic power station according to claim 5, characterized in that: The grid load demand prediction steps are as follows: (1) Analyze the power grid load data to obtain the historical load curve of the power grid and its daily cycle and seasonal fluctuation characteristics; (2) Use the load forecasting model to predict the grid load demand in the future; The power output scheduling steps are as follows: (1) Combine the PV power generation forecast results with the grid load forecast results to analyze the relationship between the PV power station's power generation capacity and the grid load demand. If the PV power generation output can meet the grid load demand, there may be no need to dispatch other power sources; (2) If photovoltaic power generation is insufficient or excessive, other power sources (such as thermal power, wind power, and energy storage batteries) need to be adjusted to ensure stable operation of the power grid.

7. The method for power output allocation of a non-energy storage type dispatchable photovoltaic power station according to claim 1, characterized in that: The step of automatically adjusting the power allocation strategy in step S3 to maintain the stability of the power grid includes real-time grid load monitoring, power generation capacity assessment of the photovoltaic power station, power allocation strategy formulation, assistance of the energy storage system and real-time monitoring and data analysis; The steps of real-time grid load monitoring are as follows: (1) Obtaining grid load information: Real-time collection of grid load data to understand the current grid supply and demand status can be accomplished through the grid monitoring system to ensure that the load changes at each moment can be accurately obtained; (2) Load forecasting: Use load forecasting algorithms to predict grid load changes over a period of time in the future to help formulate power output strategies for photovoltaic power plants; The steps of evaluating the power generation capacity of the photovoltaic power station are as follows: (1) Real-time photovoltaic power generation capacity monitoring: Based on weather conditions, sunshine intensity and the operating status of photovoltaic modules, the power generation capacity of the photovoltaic power station can be evaluated in real time. The power generation capacity of the photovoltaic power station can be dynamically obtained by installing sensors and weather data interfaces; (2) Photovoltaic power generation forecast: Combined with meteorological forecast data (such as cloud changes, temperature, humidity) and models to predict the power generation capacity of photovoltaic power stations in the short term, this helps to foresee the output fluctuations of photovoltaic power stations under different weather conditions; The steps of formulating the power allocation strategy are as follows: (1) Matching of grid load and photovoltaic power generation: Formulate power allocation strategy based on grid load and photovoltaic power station power generation forecast: (2). When the grid load is low, increase the power generation output of the photovoltaic power station, make full use of solar energy, and reduce the consumption of other power generation sources; (3). When the grid load is high, by adjusting the output power of the photovoltaic power station (controlled by the inverter device), appropriately reduce the output of the photovoltaic power station to avoid grid overload and ensure grid stability.

8. The power output allocation method of the non-energy storage type dispatchable photovoltaic power station according to claim 7, characterized in that: The steps of the auxiliary energy storage system are as follows: (1). Energy storage scheduling: Deploy an energy storage system (such as a battery energy storage system) between the photovoltaic power station and the grid. When the grid load is low, the redundant power can be stored through the energy storage device, and when the grid load is high, the energy storage system can release electrical energy to further balance the supply and demand; (2). Intelligent scheduling: According to the grid load, photovoltaic power generation, and the status of the energy storage system, through the intelligent scheduling system, ensure the maximum utilization efficiency of the energy storage system while maintaining the stability of the grid; The steps of the real-time monitoring and data analysis are as follows: (1). Multi-parameter monitoring: Not only monitor the grid load and photovoltaic power generation, but also monitor meteorological data, the status of energy storage devices, and power flow parameters; through the centralized monitoring platform, integrate the data of each system to ensure the real-time and accuracy of power distribution decisions; (2). Data analysis: Through the analysis of historical data and real-time data, optimize the power distribution strategy and provide data support for future scheduling decisions.

9. The method for power output distribution of the non-energy storage type schedulable photovoltaic power station according to claim 1, characterized in that: In step S4, the steps to ensure that the power output of the photovoltaic power station does not exceed the carrying capacity of the grid load and avoid grid instability include setting the upper and lower limits of power output, adopting power prediction and scheduling, power limitation and regulation mechanisms, auxiliary regulation of the energy storage system, power feedback and real-time monitoring, and grid-friendly inverters; The steps of setting the upper and lower limits of power output are as follows: (1). Power output range setting: The power output of the photovoltaic power station can be dynamically adjusted according to the real-time load demand of the grid and set as a percentage range. For example, set the maximum limit of the photovoltaic power station's output power to 80% of the maximum rated power of the power station, and the minimum limit to 30%, so as to avoid the power output of the power station being too large or too small in a short period of time; (2). Load prediction: Use historical load data and weather prediction information to predict the power output of the photovoltaic power station in order to adjust the power control strategy in advance; The steps of adopting power prediction and scheduling are as follows: (1). Power prediction: Through weather prediction and the measurement of solar irradiance, combined with the actual operation data of the photovoltaic power station, the future power generation power of the photovoltaic power station can be predicted; (2). Real-time scheduling: Combine with the grid load scheduling system, real-time monitor the power output of the photovoltaic power station, and adjust the power according to the grid demand. If fluctuations are predicted, the scheduling center can limit the power in advance; The steps of the power limitation and regulation mechanism are as follows: (1). Dynamic power limitation: A dynamic adjustment function of power output can be set in the inverter of the photovoltaic power station. For example, when the light suddenly increases and causes the power to be too high, the inverter can automatically reduce the power output to avoid grid overload; conversely, if the light suddenly weakens, the system can automatically increase the power output to the set lower limit; (2). Power correction (power smoothing): The power smoothing technology is adopted. For example, when an energy storage system is used in conjunction with a photovoltaic power station, when the power output of the photovoltaic power station fluctuates greatly, the energy storage system can provide a regulating effect, smooth the power output, and reduce the power volatility.

10. The power output allocation method of the non-energy storage type adjustable photovoltaic power station according to claim 1, characterized in that: The steps of the auxiliary regulation of the energy storage system are as follows: (1). Regulation function of the energy storage system: A photovoltaic power station can be equipped with an energy storage system to balance the grid load fluctuations. When the power output of the photovoltaic power station is too high, the energy storage system can absorb the excess electricity. When the power output of the photovoltaic power station is too low, the energy storage system can supply electric energy to the grid to maintain the stability of the grid. (2). Configuration of the energy storage capacity: The capacity of the energy storage system needs to be reasonably configured according to the actual output fluctuations of the photovoltaic power station to ensure that the energy storage system has sufficient capacity to cope with short-term power fluctuations. The steps of the power feedback and real-time monitoring are as follows: (1). Intelligent monitoring system: The intelligent grid technology is used to monitor the power output of the photovoltaic power station in real time. Through the feedback mechanism, the power status of the power station can be understood in a timely manner. Once the power fluctuation exceeds the set range, the system will automatically adjust to avoid excessive or too small grid load. (2). Coordination with the grid: The photovoltaic power station should coordinate with the grid operator to share the power output information and the grid load status in real time, ensure that the power output of the photovoltaic power station is within a reasonable range, and conduct appropriate scheduling. The steps of the grid-friendly inverter are as follows: (1). Support power factor regulation: Modern photovoltaic inverters support power factor regulation and can adjust the power output of the photovoltaic power station in a timely manner according to the needs of the grid to make it more compatible with the grid load curve. (2). Virtual power regulation: Advanced inverters can also adjust the voltage and frequency of the grid by adjusting the reactive power, thereby enhancing the adaptability of the photovoltaic power station to grid fluctuations.

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