Power output regulation method of distributed power supply

Abstract

The present invention discloses a method for regulating the power output of a distributed power supply, relating to the field of distributed power supply control technology. The method comprises: collecting historical grid characteristic information, predicting future grid characteristics, and analyzing the power demand range and grid fluctuation parameters of the distributed power supply; based on the demand range and fluctuation parameters, optimizing power output to reduce the impact on the grid, obtaining optimal power, and regulating the output of the distributed power supply accordingly. This method solves the technical problem of insufficient power adaptability of existing distributed power supplies due to slow regulation response time under complex grid demand fluctuations, thereby achieving the technical effect of improving the adaptability of the distributed power supply power output to changes in grid demand.

Description

Technical Field

[0001] The present application relates to the field of distributed power supply control technology, and in particular to a power output regulation method of a distributed power supply. Background Art

[0002] With the increasing prevalence of distributed power generation (DGs) in power grids, their power output regulation capabilities are placing higher demands on grid stability. Due to their dynamic response characteristics, DGs, including hydrogen fuel cells, photovoltaic power generation, and wind power generation, may experience operational deviations or even system fluctuations when connected to the grid due to a mismatch between their power output and grid demand. Especially in situations where grid loads fluctuate frequently, power regulation of DGs must account for both load variations and adapt to future grid demand characteristics. However, existing regulation methods, often based on real-time or short-term forecast data, lack systematic analysis and have limited ability to define power output ranges and adapt to fluctuation characteristics, making them incapable of meeting the operational requirements of complex power grids. These technical limitations pose challenges to grid stability and the efficient utilization of DGs. Improved regulation methods are urgently needed to enhance the matching between DGs and the grid and improve operational reliability.

[0003] At present, there is a technical problem in the relevant technologies that distributed power sources have insufficient power adaptability due to slow adjustment response time under complex conditions of grid demand fluctuations. Summary of the Invention

[0004] This application solves the technical problem of insufficient power adaptability of existing distributed power sources due to slow adjustment response time under complex conditions of power grid demand fluctuations by providing a power output adjustment method for distributed power sources.

[0005] This application provides a method for regulating the power output of a distributed power supply, including:

[0006] Collect a historical grid characteristic information sequence from the grid side within a preset time range in the past, predict the grid characteristic information within a preset time range in the future, and obtain a predicted grid characteristic information sequence; perform a power output demand analysis and a grid fluctuation analysis on the distributed power source based on the predicted grid characteristic information sequence to obtain a power output demand range and grid fluctuation parameters; within the power output demand range, perform power output regulation optimization of the distributed power source based on the grid fluctuation parameters, with the purpose of reducing the impact of the distributed power source on the grid and the degree of power output adaptation, and obtain an optimal output power; and use the optimal output power to regulate the output power of the distributed power source.

[0007] The present application also provides a power output regulation system for a distributed power supply, comprising:

[0008] An information sequence acquisition module, the information sequence acquisition module is used to collect the historical grid characteristic information sequence of the grid side within the past preset time range, predict the grid characteristic information within the future preset time range, and obtain the predicted grid characteristic information sequence; a demand interval acquisition module, the demand interval acquisition module is used to perform power output demand analysis and grid fluctuation analysis of the distributed power supply based on the predicted grid characteristic information sequence, and obtain the power output demand interval and grid fluctuation parameters; an adjustment optimization module, the adjustment optimization module is used to perform power output adjustment optimization of the distributed power supply within the power output demand interval and according to the grid fluctuation parameters, with the purpose of reducing the impact of the distributed power supply on the grid and the power output adaptation degree, to obtain the optimal output power; an output power adjustment module, the output power adjustment module is used to use the optimal output power to adjust the output power of the distributed power supply.

[0009] The power output regulation method of distributed power sources proposed in this application first collects historical characteristic information of the power grid, predicts future power grid characteristics, and analyzes the power demand range and power grid fluctuation parameters of the distributed power sources; based on the demand range and fluctuation parameters, optimizes the power output to reduce the impact on the power grid, obtains the optimal power, and adjusts the output of the distributed power sources accordingly, achieving the technical effect of improving the adaptability of the power output of the distributed power sources to changes in power grid demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A schematic diagram of a flow chart of a method for regulating power output of a distributed power supply provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of a flow chart for obtaining a predicted grid characteristic information sequence for a method for regulating power output of a distributed power source provided in an embodiment of the present application;

[0013] Figure 3 A schematic diagram of the structure of a power output regulation system for a distributed power supply provided in an embodiment of the present application.

[0014] Description of the accompanying drawings: information sequence acquisition module 10, demand interval acquisition module 20, adjustment optimization module 30, output power adjustment module 40. DETAILED DESCRIPTION

[0015] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0016] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0018] The present application provides a method for regulating the power output of a distributed power supply. Figure 1 As shown, the method includes:

[0019] Step S100, collect the historical grid characteristic information sequence of the grid side in the past preset time range, predict the grid characteristic information within the future preset time range, and obtain the predicted grid characteristic information sequence. Specifically, for the distributed power supply connected to the grid side (taking hydrogen fuel cells as an example, its output power regulation has a time response characteristic), first start the data acquisition system, use a high-precision power sensor to collect grid demand power data every Δt time in the past time period of T1, and form a historical grid characteristic information sequence as the basic data for prediction. Then, select a suitable prediction model based on the data characteristics and accuracy requirements. For example, when selecting the ARIMA model, first check the stability of the historical data. If it is not stable, differentiate it to make it stable. Then determine the ARIMA model parameters by using the autocorrelation and partial autocorrelation function graphs. Then input the historical data into the training optimization model to make it understand the change law of the grid demand power. The trained model is then used to predict the grid characteristic information sequence of the future duration T2. ​​After obtaining the prediction results, evaluation indicators such as root mean square error and mean absolute error are used for comparison and analysis with some historical verification data. If the error is large, the model parameter settings and data processing process are reviewed or the model is replaced. Through continuous adjustment and optimization, the prediction results are ensured to be accurate and reliable, providing a basis for the power output adjustment of distributed power sources.

[0020] In one possible implementation, Figure 2 As shown, a historical grid characteristic information sequence of the grid side within a preset time range in the past is collected, and grid characteristic information within a preset time range in the future is predicted to obtain a predicted grid characteristic information sequence. Step S100 further includes step S110, which obtains the adjustment response time of the distributed power source for power regulation. Specifically, the adjustment response time of the distributed power source for power regulation is first obtained. For different types of distributed power sources, such as hydrogen fuel cells, wind turbines, or photovoltaic cells, their adjustment response time will vary due to factors such as their physical properties, control mechanisms, and connected power conversion equipment. For example, the power adjustment response time of a hydrogen fuel cell may range from several seconds to tens of seconds due to its chemical reaction process, the response speed of the gas supply system, and the dynamic characteristics of the power electronic converter. Using specialized testing equipment and experimental methods, the response of the distributed power source to different power regulation instructions is monitored and analyzed. The time from the issuance of the power regulation instruction to the actual power output reaching a stable state is recorded. Multiple measurements are taken and the average is used to obtain a more accurate adjustment response time.

[0021] Step S120, calculate the sum of the adjustment response time and the preset time length as the preset time range. Specifically, after obtaining the adjustment response time of the distributed power source, calculate the sum of the time length and the preset time length to determine the preset time range. The setting of the preset time length is usually based on the empirical understanding of the change law of the grid characteristic information and the comprehensive consideration of the prediction accuracy requirements. For example, if it is found based on the analysis of previous grid data that the grid demand power will show a more obvious change trend within a few minutes, and in order to ensure that the prediction result can cover a long enough time span to adapt to the power adjustment plan of the distributed power source, the preset time length can be set to a few minutes. Assuming that the adjustment response time of the distributed power source is measured to be 10 seconds and the preset time length is set to 3 minutes (i.e., 180 seconds), then the sum of the two, 190 seconds, is the preset time range. The preset time range will serve as the time window basis for collecting historical grid characteristic information sequences and predicting future grid characteristic information.

[0022] Step S130 collects a historical grid characteristic information sequence of the grid side within a preset time range in the past, and predicts the grid characteristic information within a preset time range in the future to obtain a predicted grid characteristic information sequence. Specifically, based on the determined preset time range, a comprehensive historical grid characteristic information sequence of the grid side within the preset time range in the past is collected, and high-precision power monitoring equipment, such as smart meters and power sensors, is used to monitor and record parameters such as the voltage, current, and power of the grid in real time. Data is collected at certain time intervals (such as every second or every few milliseconds) to construct a historical grid characteristic information sequence. After accumulating sufficient historical data, a suitable prediction method and model are used to predict the grid characteristic information within the preset time range in the future to obtain a predicted grid characteristic information sequence. For example, an ARIMA model based on time series analysis or a neural network model based on machine learning can be used. Taking the ARIMA model as an example, the stationarity of the historical power grid characteristic information sequence is first tested and processed to determine the model parameters such as the autoregressive order, differential order and moving average order. Then, the model is trained and optimized using historical data. Finally, the trained model is applied to predict the power grid characteristic information within a preset time range in the future to obtain the predicted power grid characteristic information sequence, which provides a key reference basis for the subsequent power output adjustment of distributed power sources, enabling them to adapt to the changing trend of the power grid in advance and achieve more efficient and stable grid-connected operation.

[0023] In one possible implementation, a historical grid characteristic information sequence of the grid side within a preset time range in the past is collected, and grid characteristic information within a preset time range in the future is predicted to obtain a predicted grid characteristic information sequence. Step S130 further includes step S131, where the power demand of the grid side within the preset time range is collected to obtain a set of sample historical grid characteristic information sequences, and the power demand within the preset time range after each sample historical grid characteristic information sequence is collected to obtain a set of sample predicted grid characteristic information sequences. Specifically, based on the determined preset time range, the power demand within the grid side within the preset time range is collected, and for different time intervals, multiple segments of grid power demand data are sequentially extracted with the preset time range as the span. Each such data sequence constitutes a sample historical grid characteristic information sequence. For example, if the preset time range is 10 minutes, a 10-minute segment of power demand data is selected at a certain time interval (e.g., 30 minutes) from the historical grid data of the past several hours or even days, thereby obtaining a series of sample historical grid characteristic information sequences. The set of these sequences is the sample historical grid characteristic information sequence set. After collecting each sample historical grid characteristic information sequence, we then collect demand power data for the same pre-set time range following the corresponding sequence. These subsequent data sequences form the set of sample predicted grid characteristic information sequences. This step is like extracting segments of past and future demand power data within a pre-set time range from a long stream of historical data, providing rich and logically related sample data for subsequent model training.

[0024] Step S132, using the sample historical power grid characteristic information sequence set and the sample predicted power grid characteristic information sequence set as supervised training data, the power grid characteristic prediction channel is trained until the training converges. Specifically, after successfully obtaining the sample historical power grid characteristic information sequence set and the sample predicted power grid characteristic information sequence set, they are used as supervised training data for the training of the power grid characteristic prediction channel. The power grid characteristic prediction channel can be a neural network model built based on a deep learning architecture, such as a long short-term memory network (LSTM) or a convolutional neural network (CNN), etc., or it can be a traditional time series prediction model such as an autoregressive moving average (ARIMA) model after a modified model structure. During the training process, the sample historical power grid characteristic information sequence is used as the input of the model, and the corresponding sample predicted power grid characteristic information sequence is used as the expected output. The model's internal parameters (such as weights and biases in the neural network) are continuously adjusted to minimize the difference between the predicted output and the expected output. Optimization algorithms such as stochastic gradient descent (SGD) or its variants Adagrad and Adadelta are used. Training continues until the model reaches a state of training convergence. Convergence is determined by setting a threshold for a loss function (such as the mean squared error (MSE)). When the value of the loss function is less than the threshold, the model is considered to have converged, that is, the model has fully learned the changing patterns and characteristics of the grid's demand power from the sample data and can more accurately predict future grid characteristics based on the input historical data.

[0025] Step S133, input the historical grid characteristic information sequence into the grid characteristic prediction channel, and predict and output a predicted grid characteristic information sequence. Specifically, after the grid characteristic prediction channel training converges, the previously collected historical grid characteristic information sequence is input into the channel. The model will perform in-depth analysis and processing on the input historical grid characteristic information sequence based on the rules and characteristics learned during the training process, thereby predicting and outputting the grid characteristic information sequence within a preset time range in the future. The predicted output sequence is the predicted grid characteristic information sequence we ultimately need, which will serve as the key basis for subsequent distributed power supply power output adjustment. For example, if it is predicted that the grid demand power will show an upward trend within a period of time in the future, the distributed power supply can adjust its own power output strategy in advance, increase the output power to better match the grid demand, achieve efficient and stable grid-connected operation, and at the same time reduce the impact on the grid and improve the power output adaptability.

[0026] Step S200: Based on the predicted grid characteristic information sequence, the power output demand analysis and grid fluctuation analysis of the distributed power source are performed to obtain a power output demand interval and a grid fluctuation parameter. Specifically, for the obtained predicted grid characteristic information sequence, the power output demand analysis of the distributed power source is first performed, that is, the maximum and minimum required power values ​​are extracted from the sequence to construct a power output demand interval. This interval determines the power output range of the distributed power source to adapt to the grid demand. If it is determined that the future grid demand power fluctuates within a certain interval, the distributed power source needs to dynamically adjust the power output within this interval. To analyze the grid fluctuation, the mean of the predicted grid characteristic information sequence is first calculated, and then multiple values ​​are randomly sampled from the sequence to calculate their mean. Finally, the deviation percentage between the two is used as the grid fluctuation parameter. This parameter intuitively reflects the degree of fluctuation of the grid demand power. If the deviation is large, the distributed power source needs to respond flexibly to reduce the impact. If the deviation is small, it can operate according to a stable strategy to improve efficiency.

[0027] In one possible implementation, based on the predicted grid characteristic information sequence, the power output demand analysis and grid fluctuation analysis of the distributed power source are performed to obtain the power output demand range and grid fluctuation parameters. Step S200 further includes step S210, extracting the maximum demand power and the minimum demand power within the predicted grid characteristic information sequence, and constructing the power output demand range for the distributed power source to output power to the grid side. Specifically, the grid characteristic information sequence is analyzed and predicted, and the maximum and minimum power demands are accurately extracted from it. The extraction process requires careful comparison and screening of each data point in the sequence. For example, when faced with a prediction sequence containing power demand data corresponding to many time nodes, the maximum and minimum values ​​are recorded by checking each data point one by one. Assuming that the predicted grid characteristic information sequence covers the grid power demand data recorded every 1 minute in the next hour, after traversing these 60 data points, the maximum power demand is determined to be 80kW and the minimum power demand is 30kW. These two extreme values ​​are used to construct the power output demand interval of the distributed power supply to the grid side, that is, [30kW, 80kW]. The interval clearly defines the upper and lower limits of the power output of the distributed power supply during this period in the future, so that it can reasonably adjust its own power output based on this interval to better match the needs of the grid, ensure the stability and reliability of the power supply, and avoid adverse effects on the grid operation due to excessive or low power output.

[0028] Step S220: Perform a grid fluctuation analysis based on the predicted grid characteristic information sequence to obtain grid fluctuation parameters. Specifically, after constructing the power output demand range, a grid fluctuation analysis is performed based on the predicted grid characteristic information sequence. First, the mean of the predicted grid characteristic information sequence is calculated. All power demand data in the sequence are summed and then divided by the total number of data points. For example, for the above-mentioned prediction sequence containing 60 data points, all data are summed and divided by 60 to obtain the mean. Multiple pieces of predicted grid characteristic information are randomly extracted from the predicted grid characteristic information sequence. The number of extracted data points can be determined based on actual conditions and analysis accuracy requirements, such as extracting 20 data points. The mean of 20 randomly selected data points is calculated, and the deviation percentage of the two means is calculated and used as the grid fluctuation parameter. Specifically, let the sequence mean be μ and the mean of the randomly selected data points be μ', then the grid fluctuation parameter δ = (μ'-μ) / μ*100%. The grid fluctuation parameter can intuitively reflect the degree of fluctuation of the grid demand power. If the deviation percentage is large, it indicates that the grid demand power fluctuates more violently. The distributed power supply needs to have stronger adaptability and flexibility in the power output adjustment process to cope with such fluctuations and reduce the impact on the grid; conversely, if the deviation percentage is small, it indicates that the grid demand power is relatively stable. The distributed power supply can follow a more stable strategy when outputting power, reducing unnecessary frequent adjustment operations, thereby improving operating efficiency and reducing equipment loss.

[0029] In one possible implementation, a power grid fluctuation analysis is performed based on the predicted power grid characteristic information sequence to obtain power grid fluctuation parameters. Step S220 further includes step S221, calculating the mean of the predicted power grid characteristic information sequence to obtain average predicted power grid characteristic information. Specifically, to perform mean calculation on the predicted power grid characteristic information sequence, it is necessary to accumulate and sum all data points in the sequence and then divide by the total number of data points. For example, if the predicted power grid characteristic information sequence contains power grid demand data recorded every 1 minute in the next 30 minutes, a total of 30 data points, and the 30 data points are added in sequence, assuming the sum is S, then the average predicted power grid characteristic information μ=S / 30. By calculation, an average demand power value representing the overall level of the predicted sequence can be obtained, reflecting the approximate central trend of the power grid demand within the preset time range in the future, and providing a benchmark reference value for subsequent further analysis of power grid fluctuations.

[0030] Step S222 randomly extracts multiple pieces of predicted grid characteristic information from the predicted grid characteristic information sequence, calculates the mean, and obtains random average predicted grid characteristic information. Specifically, after obtaining the average predicted grid characteristic information, a random extraction operation is performed on multiple pieces of predicted grid characteristic information from the predicted grid characteristic information sequence. The number of extractions can be determined based on actual conditions and the requirements for analysis accuracy, such as extracting 10 data points. Using a random number generation algorithm, 10 different index positions are randomly selected within the index range of the sequence, and then the predicted grid characteristic information data at the corresponding positions are obtained. The 10 randomly extracted data points are accumulated and summed, and then divided by the number of extractions, 10, to obtain random average predicted grid characteristic information μ'. For example, if the 10 randomly extracted data points are P1, P2, ..., P10, then μ' = (P1 + P2 + ... + P10) / 10. Random sampling and mean calculation can obtain the local average in the prediction sequence, which is compared with the overall average predicted grid characteristic information, thereby providing a basis for assessing the degree of fluctuation of grid demand power.

[0031] Step S223 calculates the deviation percentage between the random average predicted grid characteristic information and the average predicted grid characteristic information as a grid fluctuation parameter. Specifically, the deviation percentage between the random average predicted grid characteristic information μ' and the average predicted grid characteristic information μ is calculated according to the formula δ = (μ'-μ) / μ*100%. For example, if μ = 50kW and μ' = 55kW, then the deviation percentage δ = (55-50) / 50*100% = 10%. The deviation percentage δ is the grid fluctuation parameter, which intuitively reflects the degree of fluctuation of the grid demand power within the prediction time range. If the deviation percentage value is large, it means that the grid demand power fluctuates more violently. The distributed power generation needs to respond more cautiously when adjusting power output and adopt more flexible adjustment strategies to reduce the impact on the grid and ensure the stable operation of the grid. Conversely, if the deviation percentage value is small, it means that the grid demand power is relatively stable. The distributed power generation can operate in a relatively stable power output mode, reduce unnecessary adjustment operations, improve operating efficiency, and reduce equipment losses and energy consumption.

[0032] Step S300: Within the power output demand interval, based on the grid fluctuation parameters, the power output regulation optimization of the distributed power source is performed to obtain the optimal output power, with the goal of reducing the impact of the distributed power source on the grid and improving the power output adaptability. Specifically, within the power output demand interval, initial optimization parameters are first set, including the initialization iteration count, the search step size, the random number seed, etc., and the upper and lower limits of the interval are clearly defined. A random number generator function is then used to generate an initial output power within the interval, which is used as the starting guess value for the distributed power source. The power regulation adaptability is then calculated according to a specific formula based on this initial output power, the average predicted grid characteristic information, and the grid fluctuation parameters. This adaptability comprehensively reflects the degree of matching between the output power and the grid demand and the impact on the grid. Entering the iterative optimization phase, a new candidate value is generated within the interval based on the current output power and search step size, its fitness is calculated and compared with the current optimal value, and if it is higher, the optimal output power and fitness are updated, and the number of iterations is updated at the same time until the preset number of iterations is reached or the convergence conditions are met. The final output power obtained at the time of convergence is the optimal output power that can minimize the impact on the grid and improve the adaptability of power output according to the grid fluctuation parameters within the interval. The distributed power supply will adjust the power output accordingly to achieve efficient and stable coordinated operation with the grid.

[0033] In one possible implementation, within the power output demand range, based on the power grid fluctuation parameters, the power output regulation optimization of the distributed power source is performed to obtain the optimal output power, with the purpose of reducing the impact of the distributed power source on the power grid and the degree of power output adaptation. Step S300 further includes step S310, randomly generating a first output power within the power output demand range. Specifically, within the determined power output demand range, the first output power is randomly generated by a random number generation algorithm. For example, if the power output demand range is [20kW, 60kW], the random number generator may generate a value such as 35kW as the first output power. The randomly generated power value is the starting point of the subsequent optimization process. It has randomness within the entire power output demand range and can provide a preliminary attempt direction for exploring the optimal output power, avoiding falling into a local optimal solution.

[0034] Step S320, calculate the first power regulation fitness of the first output power according to the first output power, the average predicted grid characteristic information and the grid fluctuation parameter. Specifically, after obtaining the first output power, the first power regulation fitness corresponding to the first output power, the average predicted grid characteristic information and the grid fluctuation parameter is calculated according to a specific formula. Assuming that the average predicted grid characteristic information is 40kW, the grid fluctuation parameter is 0.15, and the first output power is 35kW, according to the formula (where PRA is the first power regulation adaptability of the first output power, w1 and w2 are weights, P s is the first output power, P x is the average demand power in the average predicted grid characteristic information, D is the grid fluctuation parameter), set w1 = 0.6, w2 = 0.4, and substitute the values ​​to obtain the value of the first power regulation fitness. The power regulation fitness is a comprehensive evaluation indicator, which reflects the quality of the current first output power in meeting grid demand and reducing the impact on the grid. The larger the value, the closer the output power is to the ideal output state under the current conditions.

[0035] Step S330, continue to perform power output regulation optimization within the power output demand interval until convergence, and obtain the optimal output power according to the power regulation fitness screening. Specifically, continue to perform power output regulation optimization within the power output demand interval. In each iteration, based on the current output power state, a new output power candidate value is generated within the power output demand interval through a certain optimization strategy (such as random search, gradient descent or other intelligent optimization algorithm). The power regulation fitness of each candidate value is calculated according to the above formula. As the iteration proceeds, the power regulation fitness of the newly generated candidate value is continuously compared with the previous optimal value. If the power regulation fitness of a candidate value is higher, it is updated to the new optimal output power. This iterative process will continue until the convergence condition is met. The convergence condition can reach a preset number of iterations (such as 100 times), or the change in power regulation fitness after multiple consecutive iterations is less than a minimum value (such as 0.001). When the iteration converges, the output power with the highest power regulation fitness finally determined is the optimal output power. The optimal output power can fully consider the grid fluctuation parameters within a given power output demand range, minimize the impact of distributed power sources on the grid, and achieve the best power output adaptation level, thereby ensuring stable and efficient coordinated operation between distributed power sources and the grid.

[0036] In one possible implementation, a first power regulation fitness of the first output power is calculated based on the first output power, the average predicted grid characteristic information, and the grid fluctuation parameter. Step S320 further includes step S321, where the first power regulation fitness of the first output power is calculated based on the first output power, the average predicted grid characteristic information, and the grid fluctuation parameter, as shown in the following formula: Where PRA is the first power regulation adaptability of the first output power, w1 and w2 are weights, P s is the first output power, P xis the average power demand within the average predicted grid characteristic information, and D is the grid fluctuation parameter. Specifically, when calculating the first power regulation adaptability, the required data are first clarified, namely the first output power randomly generated in the power output demand interval, the average power demand calculated by predicting the grid characteristic information sequence, the grid fluctuation parameter that characterizes the degree of fluctuation of the grid power demand, and the weights w1 and w2 pre-set according to the characteristics of distributed power sources, grid operation requirements, and the degree of emphasis on different aspects. Then substitute these data into the formula Assuming P s 40kW, P x The two terms of the formula are calculated separately and added together to obtain a PRA value of 2.98. The PRA value obtained by this calculation is of great significance. The larger the PRA value, the better the first output power performs in terms of comprehensive consideration of the adaptability to the average demand power and the adaptability to grid fluctuations. In the subsequent power output regulation optimization, this value is used as a key indicator to evaluate the pros and cons of different output powers. By comparing the fitness values, a better output power is selected and gradually approached to the optimal output power, so as to achieve the goal of reducing the impact on the grid and improving the adaptability of power output according to the grid fluctuation parameters within the power output demand range of the distributed power source, thereby ensuring stable and efficient coordinated operation with the grid.

[0037] In one possible implementation, the power output regulation optimization is continued within the power output demand range until convergence, and the optimal output power is obtained according to the power regulation fitness screening. Step S330 further includes step S331, and the power output regulation optimization is continued within the power output demand range until the convergence optimization times are reached and the optimization converges. Specifically, an iterative process for power output regulation optimization is initiated within a power output demand interval. An initial iteration count variable is set to 0, and the number of converged optimizations is determined, for example, 100. In each iteration, a new candidate output power value is generated within the power output demand interval based on the current output power state and a specific optimization strategy. The optimization strategy can be based on a random search method, where a small, randomly generated increment or decrement is added to the current output power. The range of this random increment or decrement can be determined based on the size of the power output demand interval and the required optimization accuracy. Alternatively, a gradient-based optimization algorithm can be used to determine the direction and step size of power adjustment by calculating the gradient of the power regulation fitness with respect to the output power. For example, if the current output power is 40 kW, a random search strategy is used to randomly generate an increment within the range [-2 kW, 2 kW], such as 1.5 kW. The new candidate output power is 41.5 kW. Based on the newly generated candidate output power, the average predicted grid characteristic information, and the grid fluctuation parameters, the power regulation fitness is calculated according to a predetermined formula. As the iterations proceed, the iteration count variable gradually increases.

[0038] Step S332, screening the output power with the largest output power adjustment fitness to obtain the optimal output power. Specifically, when the iteration count variable reaches a preset number of convergence optimization times, i.e., 100 times, the optimization converges and enters the optimal output power screening stage. All output power adjustment fitness calculated during the entire iteration process are compared to find the output power corresponding to the fitness with the largest value. For example, during the iteration process, a total of 100 different output power candidate values ​​and their corresponding power adjustment fitness are generated. By comparing these fitness values ​​one by one, it is determined that the output power corresponding to the maximum fitness is 50kW. Then, this 50kW is the optimal output power finally obtained. The optimal output power can fully consider the grid fluctuation parameters within a given power output demand range, minimize the impact of distributed power sources on the grid, and achieve the best power output adaptation degree, thereby ensuring stable and efficient coordinated operation between distributed power sources and the grid.

[0039] Step S400: Regulating the output power of the distributed power source using the optimal output power. Specifically, after determining the optimal output power, the control system of the distributed power source receives a command signal containing the value and activates a power regulation mechanism. Photovoltaic power stations adjust the operating point of the photovoltaic array to change power output, calculate the optimal voltage and current values ​​based on environmental factors such as light intensity and temperature and the optimal output power, and adjust inverter control parameters such as the PWM signal duty cycle to make the output power approach and stabilize at the optimal value. Wind farms rely on adjusting the pitch angle and speed of the wind turbine, calculating the appropriate set values ​​based on wind speed, wind direction and turbine operating status. The pitch angle is adjusted to control the amount of wind energy captured, and the speed is adjusted to optimize power generation efficiency, so that the output power is stabilized at the optimal value. Energy storage power stations, such as lithium-ion battery energy storage systems, achieve this by controlling the battery charge and discharge current. The charge and discharge current values ​​are determined based on the optimal output power command and parameters such as the battery state of charge, voltage, and temperature. During adjustment, the battery status parameters are monitored in real time to ensure its safe and stable operation and prevent abnormal conditions. This allows the energy storage station to continuously and reliably interact with the grid at the optimal output power, achieving efficient coordinated operation of distributed power sources and the grid.

[0040] The embodiment of the present application collects historical characteristic information of the power grid, predicts future power grid characteristics, and analyzes the power demand range and power grid fluctuation parameters of distributed power sources; based on the demand range and fluctuation parameters, optimizes power output to reduce the impact on the power grid, obtains optimal power, and adjusts the output of distributed power sources accordingly, achieving the technical effect of improving the adaptability of distributed power supply power output to changes in power grid demand.

[0041] In the above, refer to Figure 1The power output regulation method of the distributed power supply according to the embodiment of the present invention is described in detail. Figure 3 A power output regulation system of a distributed power source according to an embodiment of the present invention is described.

[0042] A power output regulation system for a distributed power source according to an embodiment of the present invention addresses the technical issue of insufficient power adaptability of existing distributed power sources due to slow regulation response times under complex grid demand fluctuations, thereby achieving the technical effect of improving the adaptability of the distributed power source's power output to changes in grid demand. The power output regulation system for a distributed power source includes an information sequence acquisition module 10, a demand interval acquisition module 20, a regulation optimization module 30, and an output power regulation module 40.

[0043] The information sequence acquisition module 10 is used to acquire the historical grid characteristic information sequence of the grid side within a preset time range in the past, predict the grid characteristic information within a preset time range in the future, and obtain the predicted grid characteristic information sequence.

[0044] The demand interval acquisition module 20 is used to perform power output demand analysis and grid fluctuation analysis of the distributed power source according to the predicted grid characteristic information sequence, and obtain the power output demand interval and grid fluctuation parameters.

[0045] The regulation optimization module 30 is used to optimize the power output regulation of the distributed power source within the power output demand range according to the grid fluctuation parameters, with the purpose of reducing the impact of the distributed power source on the grid and the power output adaptability, so as to obtain the optimal output power.

[0046] The output power regulating module 40 is configured to use the optimal output power to regulate the output power of the distributed power source.

[0047] The specific configuration of the information sequence acquisition module 10 will be described in detail below. As described above, the historical grid characteristic information sequence of the grid side is collected within the past preset time range, and the grid characteristic information within the future preset time range is predicted to obtain the predicted grid characteristic information sequence|. The information sequence acquisition module 10 further includes: a response time acquisition unit, which is used to obtain the adjustment response time of the distributed power supply for power adjustment; a time range calculation unit, which is used to calculate the sum of the adjustment response time and the preset time length as the preset time range; and a grid characteristic information prediction unit, which is used to collect the historical grid characteristic information sequence of the grid side within the past preset time range, and predict the grid characteristic information within the future preset time range to obtain the predicted grid characteristic information sequence.

[0048] Among them, the historical power grid characteristic information sequence of the power grid side within the past preset time range is collected, the power grid characteristic information within the future preset time range is predicted, and the predicted power grid characteristic information sequence is obtained. The power grid characteristic information prediction unit further includes: an information sequence set acquisition subunit, the information sequence set acquisition subunit is used to collect the demand power within the historical time of the power grid side according to the preset time range, obtain a sample historical power grid characteristic information sequence set, and collect the demand power within the preset time range after each sample historical power grid characteristic information sequence to obtain a sample predicted power grid characteristic information sequence set; a prediction channel training subunit, the prediction channel training subunit is used to use the sample historical power grid characteristic information sequence set and the sample predicted power grid characteristic information sequence set as supervised training data to train the power grid characteristic prediction channel until the training converges; a predicted power grid characteristic information sequence acquisition subunit, the predicted power grid characteristic information sequence acquisition subunit is used to input the historical power grid characteristic information sequence into the power grid characteristic prediction channel, and predict the output to obtain a predicted power grid characteristic information sequence.

[0049] The specific configuration of the demand interval acquisition module 20 will be described in detail below. As described above, based on the predicted grid characteristic information sequence, the power output demand analysis and grid fluctuation analysis of the distributed power source are performed to obtain the power output demand interval and grid fluctuation parameters. The demand interval acquisition module 20 further includes: a power output demand interval construction unit, which is used to extract the maximum demand power and the minimum demand power within the predicted grid characteristic information sequence to construct the power output demand interval for the distributed power source to output power to the grid side; and a grid fluctuation analysis unit, which is used to perform grid fluctuation analysis based on the predicted grid characteristic information sequence to obtain grid fluctuation parameters.

[0050] Among them, the power grid fluctuation analysis is performed according to the predicted power grid characteristic information sequence to obtain the power grid fluctuation parameters, and the power grid fluctuation analysis unit further includes: a mean calculation subunit, the mean calculation subunit is used to calculate the mean of the predicted power grid characteristic information sequence to obtain the average predicted power grid characteristic information; an average predicted power grid characteristic information acquisition subunit, the average predicted power grid characteristic information acquisition subunit is used to randomly extract multiple predicted power grid characteristic information in the predicted power grid characteristic information sequence, calculate the mean, and obtain random average predicted power grid characteristic information; a deviation percentage calculation subunit, the deviation percentage calculation subunit is used to calculate the deviation percentage between the random average predicted power grid characteristic information and the average predicted power grid characteristic information as the power grid fluctuation parameter.

[0051] The specific configuration of the regulation optimization module 30 will be described in detail below. As described above, within the power output demand interval, according to the grid fluctuation parameters, with the purpose of reducing the impact of the distributed power source on the grid and the power output adaptability, the power output regulation optimization of the distributed power source is performed to obtain the optimal output power. The regulation optimization module 30 further includes: a first output power generation unit, the first output power generation unit is used to randomly generate a first output power within the power output demand interval; a fitness calculation unit, the fitness calculation unit is used to calculate the first power regulation fitness of the first output power according to the first output power, the average predicted grid characteristic information and the grid fluctuation parameters; a power output regulation optimization unit, the power output regulation optimization unit is used to continue to perform power output regulation optimization within the power output demand interval until convergence, and obtain the optimal output power according to the power regulation fitness screening.

[0052] According to the first output power, the average predicted grid characteristic information, and the grid fluctuation parameter, a first power regulation fitness of the first output power is calculated and obtained. The fitness calculation unit further includes: a first power regulation fitness calculation subunit, and the first power regulation fitness calculation subunit is used to calculate the first power regulation fitness of the first output power according to the first output power, the average predicted grid characteristic information, and the grid fluctuation parameter, as follows: Where PRA is the first power regulation adaptability of the first output power, w1 and w2 are weights, P s is the first output power, P x is the average demand power within the average predicted grid characteristic information, and D is the grid fluctuation parameter.

[0053] Among them, the power output regulation optimization is continued in the power output demand interval until convergence, and the optimal output power is obtained according to the power regulation fitness screening. The power output regulation optimization unit further includes: an optimization convergence subunit, the optimization convergence subunit is used to continue the power output regulation optimization in the power output demand interval until the convergence optimization times are reached and the optimization converges; an optimal output power acquisition subunit, the optimal output power acquisition subunit is used to screen the output power with the largest output power regulation fitness to obtain the optimal output power.

[0054] The power output regulation system of a distributed power supply provided in an embodiment of the present invention can execute the power output regulation method of a distributed power supply provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0055] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0056] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for regulating power output of a distributed power supply, characterized in that: The method is applied to a distributed power source, wherein the distributed power source is connected to the power grid side, and the method includes: Collecting a historical grid characteristic information sequence of the grid side within a preset time range in the past, predicting the grid characteristic information within a preset time range in the future, and obtaining a predicted grid characteristic information sequence; Performing power output demand analysis and power grid fluctuation analysis on the distributed power source according to the predicted power grid characteristic information sequence to obtain a power output demand interval and power grid fluctuation parameters; Within the power output demand range, according to the grid fluctuation parameters, with the purpose of reducing the impact of the distributed power source on the grid and the degree of power output adaptability, the power output regulation optimization of the distributed power source is performed to obtain the optimal output power, including: Randomly generating a first output power within the power output requirement interval; Calculating a first power regulation adaptability of the first output power according to the first output power, average predicted grid characteristic information, and grid fluctuation parameters; Continuing to optimize the power output regulation within the power output demand range until convergence, and obtaining the optimal output power based on the power regulation fitness; According to the first output power, the average predicted grid characteristic information, and the grid fluctuation parameter, a first power regulation adaptability of the first output power is calculated and obtained as follows: Where PRA is the first power regulation adaptability of the first output power, w1 and w2 are weights, P s is the first output power, P x is the average demand power within the average predicted grid characteristic information, and D is the grid fluctuation parameter; The optimal output power is used to adjust the output power of the distributed power supply.

2. The power output regulation method of a distributed power supply according to claim 1, characterized in that: Collecting a historical grid characteristic information sequence of the grid side within a preset time range in the past and predicting the grid characteristic information within a preset time range in the future, including: Obtaining a regulation response time for the distributed power supply to perform power regulation; Calculating the sum of the adjustment response time and the preset time length as the preset time range; The historical grid characteristic information sequence of the grid side within a preset time range in the past is collected, and the grid characteristic information within a preset time range in the future is predicted to obtain a predicted grid characteristic information sequence.

3. The power output regulation method of a distributed power supply according to claim 2, characterized in that: Collecting a historical grid characteristic information sequence of the grid side within a preset time range in the past and predicting the grid characteristic information within a preset time range in the future, including: According to the preset time range, the power demand of the grid side within the historical time is collected to obtain a sample historical grid characteristic information sequence set, and the power demand within the preset time range after each sample historical grid characteristic information sequence is collected to obtain a sample predicted grid characteristic information sequence set; Using the sample historical power grid characteristic information sequence set and the sample predicted power grid characteristic information sequence set as supervised training data, training the power grid characteristic prediction channel until the training converges; The historical power grid characteristic information sequence is input into the power grid characteristic prediction channel, and the prediction output is used to obtain a predicted power grid characteristic information sequence.

4. The power output regulation method of a distributed power supply according to claim 1, characterized in that: According to the predicted grid characteristic information sequence, the power output demand analysis and grid fluctuation analysis of the distributed power supply are performed to obtain the power output demand range and grid fluctuation parameters, including: Extracting the maximum power demand and the minimum power demand in the predicted grid characteristic information sequence, and constructing a power output demand interval for the distributed power source to output power to the grid side; A power grid fluctuation analysis is performed based on the predicted power grid characteristic information sequence to obtain power grid fluctuation parameters.

5. The power output regulation method of a distributed power supply according to claim 4, characterized in that: Performing power grid fluctuation analysis based on the predicted power grid characteristic information sequence to obtain power grid fluctuation parameters includes: Calculating the mean of the predicted power grid characteristic information sequence to obtain average predicted power grid characteristic information; Randomly extracting a plurality of predicted power grid characteristic information from the predicted power grid characteristic information sequence, calculating an average, and obtaining random average predicted power grid characteristic information; The deviation percentage between the random average predicted power grid characteristic information and the average predicted power grid characteristic information is calculated as a power grid fluctuation parameter.

6. The power output regulation method of a distributed power supply according to claim 1, characterized in that: Continuing to optimize the power output regulation within the power output demand range until convergence, and obtaining the optimal output power according to the power regulation fitness screening, including: Continuing to perform power output regulation optimization within the power output demand range until the number of convergence optimization times is reached and the optimization converges; The output power with the greatest output power adjustment adaptability is screened to obtain the optimal output power.

7. The power output regulation system of the distributed power supply is characterized in that: The system is used to implement the power output regulation method of a distributed power supply according to any one of claims 1 to 6, and the system includes: An information sequence acquisition module, the information sequence acquisition module is used to collect a historical grid characteristic information sequence of the grid side within a preset time range in the past, predict the grid characteristic information within a preset time range in the future, and obtain a predicted grid characteristic information sequence; A demand interval acquisition module, configured to perform power output demand analysis and grid fluctuation analysis of the distributed power source based on the predicted grid characteristic information sequence, and obtain a power output demand interval and grid fluctuation parameters; A regulation optimization module, configured to optimize the power output regulation of the distributed power source within the power output demand range and based on the grid fluctuation parameters, with the goal of reducing the impact of the distributed power source on the grid and improving the power output adaptability, to obtain an optimal output power; An output power regulation module is used to adjust the output power of the distributed power supply using the optimal output power.

8. The power output regulation system of a distributed power source according to claim 7, characterized in that: The information sequence acquisition module includes: A response time acquisition unit, configured to acquire a response time for power regulation performed by the distributed power source; a time range calculation unit, configured to calculate a sum of the adjustment response duration and a preset time length as a preset time range; The grid characteristic information prediction unit is used to collect the historical grid characteristic information sequence of the grid side within the past preset time range, predict the grid characteristic information within the future preset time range, and obtain the predicted grid characteristic information sequence.

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