Distributed energy optimization management method and system

Abstract

The invention relates to the technical field of energy management, in particular to a distributed energy optimization management method and system, and the method comprises the steps: determining an optimal switching control strategy of a switching device through employing a Q-Learning particle swarm optimization algorithm; performing intermittent conduction and turn-off control on the switching device according to the optimal switching control strategy, obtaining a time domain action signal of the switching device, and extracting harmonic spectrum data in the conduction and turn-off processes of the switching device by using a short-time Fourier transform algorithm; obtaining total harmonic power according to the harmonic frequency spectrum data, and if the total harmonic power exceeds a harmonic power dynamic threshold value, extracting an overproof harmonic signal; and the standard-exceeding harmonic signals are converted into energy storage voltage and current matched with the energy storage device, and the energy storage voltage and current are stored in the energy storage device. According to the invention, through management analysis of the harmonic waves, precise identification and efficient energy storage of the harmonic waves generated by the nonlinear load are realized, so that optimal management of the distributed energy system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a distributed energy optimization management method and system. Background Art

[0002] In distributed energy systems, nonlinear loads are increasingly widely used, especially various power loads containing switching devices are increasing, and their impact on the power grid is becoming increasingly significant. Especially in distributed energy systems, due to the wide distribution and diverse types of energy, harmonic problems are more complex and changeable. However, traditional harmonic detection and management methods have limitations in dealing with such nonlinear loads. The main problem is that it is difficult to accurately identify the harmonic components and their dynamic changes generated during the switching process of switching devices, resulting in increased harmonic pollution, which will not only increase the power loss of the power grid, but also cause resonance, seriously threatening the stability and safe operation of the power grid. Therefore, it is critical to accurately distinguish the harmonics generated by the switching process of switching devices. This requires that the harmonic feature extraction and matching algorithm must have high accuracy and real-time performance, so as to extract the specific harmonic components generated by the switching of switching devices in a complex power grid environment. However, current technology is often difficult to meet this requirement in a complex and changeable power grid environment, resulting in inaccurate harmonic identification, which in turn affects the effectiveness of subsequent treatment measures.

[0003] Secondly, the extraction, conversion and storage of harmonic power in distributed energy systems involve multiple links, including harmonic extraction circuits, power conversion circuits and energy storage devices. The efficiency and reliability of each link directly affect the performance of the entire harmonic management system. The harmonic extraction circuit needs to accurately capture and separate the harmonic components of a specific frequency, and the complexity of the distributed energy system makes this process more difficult. The power conversion circuit needs to efficiently convert the harmonic power into a form of electrical energy suitable for energy storage. The working efficiency of these circuits directly affects the effect of harmonic energy storage. However, the efficiency of existing technologies in these links is often limited, and it is difficult to cope with the dynamic changes of harmonic power, resulting in low energy storage efficiency or energy storage failure. In summary, existing technologies still face many challenges in dealing with complex harmonic components generated by nonlinear loads in distributed energy systems. Solving these problems is of great significance to improving the power quality of the power grid and ensuring the stable operation of equipment. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a distributed energy optimization management method and system.

[0005] In a first aspect, the present invention provides a distributed energy optimization management method, the method comprising the following steps: Collect the grid load status data in the distributed energy system in real time, and determine the optimal switching control strategy of the switch device using the Q-Learning particle swarm optimization algorithm based on the grid load status data; the optimal switching control strategy includes the optimal on-time, optimal off-time and optimal switching frequency of the switch device; intermittently turning on and off the switch device according to the optimal switching control strategy, and obtaining a time domain action signal of the switch device generated during the intermittent turning on and off process; Performing frequency domain analysis on the time domain action signal of the switching device using a short-time Fourier transform algorithm to extract harmonic spectrum data during the on and off process of the switching device; Obtaining the total harmonic power according to the harmonic spectrum data, and obtaining the harmonic power dynamic threshold using an adaptive threshold strategy according to the current power grid operation state; Comparing the total harmonic power with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, extracting an excessive harmonic signal; The excessive harmonic signal is converted into an energy storage voltage and current that is compatible with an energy storage device, and the energy storage voltage and current are stored in the energy storage device.

[0006] In a further embodiment, the step of determining the optimal switching control strategy of the switch device using the Q-Learning particle swarm optimization algorithm according to the grid load state data includes: Real-time collection of grid load status data; the grid load status data includes power load power, power factor, voltage fluctuation range and current harmonic content; Using an autoregressive moving average model to perform time series analysis on the power grid load state data within each sliding time window, and extracting key feature vectors of the power grid load; The current grid load state is defined as the state space of the Q-Learning algorithm, and the switching control instructions of the switching devices are defined as the action space; Initialize the position and speed of the particle swarm according to the key characteristic vector of the power grid load, and randomly distribute them in the action space, each particle represents a set of switching control strategies of the switching device; the switching control strategies of the switching device include on-time, off-time and switching frequency; Execute the switching control strategy of each particle's switch device under the current grid state, and calculate the reward value according to the change of grid state; Update the Q value table according to the reward value and record the cumulative rewards obtained after performing different actions under the current power grid state; The position and velocity of each particle are updated through the particle swarm algorithm. After multiple iterative optimizations, the switching control strategy of the switch device corresponding to the particle with the largest cumulative reward is selected as the optimal switching control strategy.

[0007] In a further embodiment, the step of using a short-time Fourier transform algorithm to perform frequency domain analysis on the time domain action signal of the switching device to extract harmonic spectrum data during the switching device on and off process includes: Preprocessing the time domain action signal of the switch device by using a median filter to obtain a time domain filtered action signal; Performing frequency domain analysis on the time domain filtering action signal using a short-time Fourier transform algorithm to obtain a short-time Fourier transform result; According to the short-time Fourier transform result, a signal-to-noise ratio-based adaptive threshold method is used to identify the main harmonic component, and if the signal-to-noise ratio of the main harmonic component is greater than the signal-to-noise ratio adaptive threshold, the main harmonic component is identified as a valid harmonic component; The harmonic components and their corresponding frequency, amplitude and phase information during the on and off process of the switching device are calculated according to the effective harmonic components to form harmonic spectrum data.

[0008] In a further embodiment, the step of obtaining the total harmonic power according to the harmonic spectrum data comprises: Calculate the harmonic power data of each harmonic according to the harmonic spectrum data, wherein the harmonic power data includes harmonic active power and harmonic reactive power; Perform complex number superposition operation on the harmonic power data of each harmonic to obtain the total harmonic active power and total harmonic reactive power; Taking total harmonic active power and total harmonic reactive power as the output characteristics of harmonic sources, and considering the influence of line impedance and load impedance on harmonic propagation, a harmonic equivalent circuit model is constructed. The harmonic equivalent circuit model is solved by the Newton-Raphson iteration method, and before the iteration starts, the complex superposition result of the total harmonic active power and the total harmonic reactive power is set as the initial value, and the total harmonic power after considering the interaction between harmonics is obtained through iterative solution.

[0009] In a further embodiment, the step of calculating the harmonic power data of each harmonic according to the harmonic spectrum data comprises: Performing windowing processing on the harmonic spectrum data using a Hanning window function to obtain a windowed harmonic data set; Extract the frequency components, amplitudes and phase information of each harmonic of the switch device during the on and off process according to the windowed harmonic data set, and convert the phase information from the angle system to the radian system to obtain the phase radian value; According to the frequency component, amplitude and phase radian value of each harmonic, the harmonic active power and harmonic reactive power of each harmonic are calculated.

[0010] In a further embodiment, the step of obtaining the harmonic power dynamic threshold value by using an adaptive threshold strategy according to the current grid operation state includes: Obtain the current grid operation status and use statistical analysis methods to quantitatively evaluate the fluctuation degree of the current grid operation status; Obtain historical harmonic power data, and use the fluctuation degree of the current power grid operation status to assign a weight coefficient to each historical harmonic power data; According to the weight coefficient, the historical harmonic power data is weighted and summed using an exponentially weighted moving average model to obtain a harmonic power exponentially weighted moving average value; A dynamic threshold prediction model is constructed using support vector machine. The current grid operation status and the weighted moving average of the harmonic power index are taken as input variables, and the dynamic threshold prediction model is used to predict the dynamic threshold of harmonic power.

[0011] In a further embodiment, the step of comparing the total harmonic power with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, extracting the excessive harmonic signal comprises: The total harmonic power is compared with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, a multi-channel bandpass filter array is used to perform segmentation filtering on the harmonic spectrum data to extract harmonic signals of multiple frequency bands; It is determined whether the harmonic signal strength of each frequency band exceeds the preset harmonic limit of each frequency band. If the harmonic signal strength of the frequency band exceeds the limit, the harmonic signal of the frequency band is identified as an excessive harmonic signal.

[0012] In a further embodiment, if the total harmonic power does not exceed the harmonic power dynamic threshold, the total harmonic power is fed to the grid or the load side.

[0013] In a further embodiment, the step of converting the excessive harmonic signal into an energy storage voltage and current adapted to the energy storage device, and storing the energy storage voltage and current in the energy storage device comprises: A high-speed analog-to-digital converter is used to sample the excessive harmonic signal to obtain sampling data; the sampling frequency of the high-speed analog-to-digital converter is a preset multiple of the frequency of the excessive harmonic signal; The sampling data is fast Fourier transformed using the Hanning window function to obtain the harmonic amplitude and phase information of the excessive harmonic signal; Generate a harmonic polar coordinate diagram using a vector diagram representation method according to the harmonic amplitude and phase information of the harmonic signal; According to the harmonic power in the harmonic polar coordinate diagram, setting the output parameters of the H-bridge inverter; The H-bridge inverter converts the harmonic power into the energy storage voltage and current required by the energy storage device and stores them in the energy storage device.

[0014] In a second aspect, the present invention provides a distributed energy optimization management system, the system comprising: A particle swarm optimization module is used to collect the grid load status data in the distributed energy system in real time, and determine the optimal switching control strategy of the switch device using the Q-Learning particle swarm optimization algorithm according to the grid load status data; the optimal switching control strategy includes the optimal on-time, optimal off-time and optimal switching frequency of the switch device; A time domain signal acquisition module, used to perform intermittent on and off control on the switch device according to the optimal switching control strategy, and acquire a time domain action signal of the switch device generated during the intermittent on and off process; A harmonic frequency domain analysis module, used to perform frequency domain analysis on the time domain action signal of the switching device using a short-time Fourier transform algorithm, and extract harmonic spectrum data during the switching device on and off process; A dynamic threshold analysis module, used to obtain the total harmonic power according to the harmonic spectrum data, and obtain the harmonic power dynamic threshold using an adaptive threshold strategy according to the current grid operation state; An excessive harmonic extraction module is used to compare the total harmonic power with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, extract an excessive harmonic signal; The harmonic energy storage conversion module is used to convert the excessive harmonic signal into an energy storage voltage and current that is compatible with the energy storage device, and store the energy storage voltage and current in the energy storage device.

[0015] The present invention provides a distributed energy optimization management method and system. The method collects grid load status data in a distributed energy system in real time, and determines an optimal switching control strategy of a switch device based on the grid load status data using a Q-Learning particle swarm optimization algorithm; intermittently turns on and off the switch device according to the optimal switching control strategy, and obtains a time domain action signal of the switch device generated during the intermittent turning on and off process; performs frequency domain analysis on the time domain action signal of the switch device using a short-time Fourier transform algorithm, and extracts harmonic spectrum data during the turning on and off process of the switch device; obtains total harmonic power based on the harmonic spectrum data, and obtains a harmonic power dynamic threshold using an adaptive threshold strategy based on the current grid operation state; compares the total harmonic power with the harmonic power dynamic threshold, and extracts an excessive harmonic signal if the total harmonic power exceeds the harmonic power dynamic threshold; converts the excessive harmonic signal into an energy storage voltage and current adapted to an energy storage device, and stores the energy storage voltage and current in the energy storage device. Compared with the existing technology, this method combines the short-time Fourier transform algorithm for frequency domain analysis and the adaptive threshold judgment algorithm to analyze harmonics, thereby achieving accurate identification and efficient energy storage of harmonics generated by nonlinear loads, thereby reducing the generation of harmonics in distributed energy systems and providing a strong guarantee for the stable operation of distributed energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the process of the distributed energy optimization management method provided by an embodiment of the present invention; Figure 2 It is a block diagram of a distributed energy optimization management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0018] refer to Figure 1 , the embodiment of the present invention provides a distributed energy optimization management method, such as Figure 1 As shown, the method comprises the following steps: S1. Collect the grid load status data in the distributed energy system in real time, and use the Q-Learning particle swarm optimization algorithm to determine the optimal switching control strategy of the switching device based on the grid load status data; the optimal switching control strategy includes the optimal on-time, optimal off-time and optimal switching frequency of the switching device.

[0019] In this embodiment, the step of determining the optimal switching control strategy of the switch device using the Q-Learning particle swarm optimization algorithm according to the power grid load state data includes: Real-time collection of grid load status data; the grid load status data includes power load power, power factor, voltage fluctuation range and current harmonic content; Using an autoregressive moving average model to perform time series analysis on the power grid load state data within each sliding time window, and extracting key feature vectors of the power grid load; The current grid load state is defined as the state space of the Q-Learning algorithm, and the switching control instructions of the switching devices are defined as the action space; Initialize the position and speed of the particle swarm according to the key characteristic vector of the power grid load, and randomly distribute them in the action space, each particle represents a set of switching control strategies of the switching device; the switching control strategies of the switching device include on-time, off-time and switching frequency; Execute the switching control strategy of each particle's switch device under the current grid state, and calculate the reward value according to the change of grid state; Update the Q value table according to the reward value and record the cumulative rewards obtained after performing different actions under the current power grid state; The position and velocity of each particle are updated through the particle swarm algorithm. After multiple iterative optimizations, the switching control strategy of the switch device corresponding to the particle with the largest cumulative reward is selected as the optimal switching control strategy.

[0020] Specifically, this embodiment collects grid load status data in real time, and the grid load status data includes but is not limited to key parameters such as power load power, power factor, voltage fluctuation range, and current harmonic content, so as to fully reflect the real-time operation status of the grid. Then, this embodiment uses the autoregressive moving average (ARIMA) model to perform time series analysis on the grid load status data in each preset sliding time window, and extracts key feature vectors reflecting the dynamic characteristics of the grid load, and obtains the grid load key feature vectors. Then, the Q-Learning environment is constructed. This embodiment defines the current grid load state as the state space of the Q-Learning algorithm, and sets the switching control instructions of the switching device (including key parameters such as on-time, off-time and switching frequency) as the action space. The position and speed of the particle swarm are initialized based on the grid load key feature vector to ensure that the particle swarm can be evenly and randomly distributed in the action space, and each particle represents a possible switching control strategy of the switching device. In the current power grid state, the switch device switching control strategy of each particle is executed in turn, and the changes in the power grid state are monitored in real time. The reward value obtained after executing each strategy is calculated to quantify the degree of improvement of the switch device switching control strategy on the power grid performance. Based on the principle of Q-Learning, the Q value table is dynamically updated using the reward value obtained, and the cumulative rewards obtained after executing different actions under the current power grid state are recorded. Finally, according to the current fitness of the particle (calculated based on the cumulative reward) and the position information of the global optimal particle, the particle swarm optimization algorithm is used to update the position and speed of each particle. The strategy evaluation, reward calculation, Q value table update and particle swarm optimization steps are repeated for multiple iterations until the preset convergence condition is reached. Through multiple iterative optimizations, the optimal switch device switching control strategy is gradually approached. After multiple iterative optimizations, the particle with the largest cumulative reward is selected from the particle swarm, and the switch device switching control strategy corresponding to it is the optimal switching control strategy sought, so that the strategy can be applied to the actual control of the power grid.

[0021] S2. Perform intermittent on and off control on the switching device according to the optimal switching control strategy, and obtain a time domain action signal of the switching device generated during the intermittent on and off process.

[0022] S3. Use a short-time Fourier transform algorithm to perform frequency domain analysis on the time domain action signal of the switching device to extract harmonic spectrum data during the switching device's on and off process.

[0023] In this embodiment, the step of performing frequency domain analysis on the time domain action signal of the switching device using a short-time Fourier transform algorithm to extract harmonic spectrum data during the switching device on and off process includes: Preprocessing the time-domain action signal of the switching device by using a median filter to obtain a time-domain filtered action signal; the time-domain action signal of the switching device includes a time-domain action voltage and current signal of the switching device; Performing frequency domain analysis on the time domain filtering action signal using a short-time Fourier transform algorithm to obtain a short-time Fourier transform result; According to the short-time Fourier transform result, a signal-to-noise ratio-based adaptive threshold method is used to identify the main harmonic component, and if the signal-to-noise ratio of the main harmonic component is greater than the signal-to-noise ratio adaptive threshold, the main harmonic component is identified as a valid harmonic component; The harmonic components and their corresponding frequency, amplitude and phase information during the on and off process of the switching device are calculated according to the effective harmonic components to form harmonic spectrum data.

[0024] Specifically, nonlinear loads will generate harmonic components in the current, which can be captured by measuring the harmonic content of the current. At the same time, the harmonics of the nonlinear load are mainly generated by the switching of the switching device. Therefore, this embodiment can accurately capture the harmonic components introduced by the switching action, including its frequency and amplitude, by analyzing these time domain signals. This embodiment uses a median filter to perform pre-processing such as denoising on the time domain action signal of the switching device to remove noise interference in the signal to obtain a time domain filtered action signal, and then uses a short-time Fourier transform algorithm to perform frequency domain analysis on the time domain filtered action signal to obtain the spectral characteristics of the signal changing with time, and obtain a short-time Fourier transform containing time-frequency characteristics. The short-time Fourier transform result is obtained, and the adaptive threshold method based on the signal-to-noise ratio is used to identify the main harmonic components according to the short-time Fourier transform result. Specifically, the signal power of each frequency component in the short-time Fourier transform result is calculated according to the short-time Fourier transform result. For example, the signal power can be obtained by calculating the average value of the square of the amplitude of each frequency component, and the noise power is estimated by analyzing the non-main part or the known noise area of ​​each frequency component. The signal-to-noise ratio can be calculated by dividing the signal power by the noise power. If the signal-to-noise ratio of a main harmonic component is greater than the signal-to-noise ratio adaptive threshold, the main harmonic component is determined to be a valid harmonic component; otherwise, it is regarded as a noise or interference component.

[0025] It should be noted that the adaptive threshold of the signal-to-noise ratio is dynamically adjusted according to the characteristics of the time-domain filtering action signal. In this embodiment, an initial signal-to-noise ratio threshold can be set based on empirical data, and the initial signal-to-noise ratio threshold can be dynamically adjusted according to the specific characteristics of the signal and the noise level. For example, if the signal-to-noise ratio of the overall signal is low, the initial signal-to-noise ratio threshold may need to be lowered to include more harmonic components. In this embodiment, the main harmonic components are identified by the adaptive threshold method based on the signal-to-noise ratio, which ensures that the harmonic extraction circuit can accurately capture and separate the harmonic components of a specific frequency, and provide a high-quality input signal for the power conversion circuit. Then, This embodiment further analyzes the identified effective harmonic components, calculates the total harmonic distortion (THD) and the relative amplitude of each harmonic, obtains the information of each harmonic component and its specific frequency, amplitude and phase during the on and off process of the switching device, forms complete harmonic spectrum data, and provides accurate data support for the efficient conversion and energy storage of harmonic power. This embodiment solves the problem that traditional harmonic detection methods are difficult to accurately identify harmonic components in complex power grid environments by combining short-time Fourier transform and an adaptive threshold method based on signal-to-noise ratio, thereby improving the accuracy and real-time performance of detecting harmonic components generated by nonlinear loads.

[0026] S4. Obtain the total harmonic power according to the harmonic spectrum data, and obtain the harmonic power dynamic threshold using an adaptive threshold strategy according to the current grid operation status.

[0027] In this embodiment, the step of obtaining the total harmonic power according to the harmonic spectrum data includes: Calculate the harmonic power data of each harmonic according to the harmonic spectrum data, wherein the harmonic power data includes harmonic active power and harmonic reactive power; Perform complex number superposition operation on the harmonic power data of each harmonic to obtain the total harmonic active power and total harmonic reactive power; Taking total harmonic active power and total harmonic reactive power as the output characteristics of harmonic sources, and considering the influence of line impedance and load impedance on harmonic propagation, a harmonic equivalent circuit model is constructed. The harmonic equivalent circuit model is solved by the Newton-Raphson iteration method, and before the iteration starts, the complex superposition result of the total harmonic active power and the total harmonic reactive power is set as the initial value, and the total harmonic power after considering the interaction between harmonics is obtained through iterative solution.

[0028] Specifically, this embodiment uses a complex power calculation method or a harmonic power calculation formula to calculate the harmonic active power and harmonic reactive power of each harmonic based on the harmonic spectrum data, which respectively reflect the energy conversion and energy exchange of each harmonic in the power system. The harmonic spectrum data is a data set containing information such as the frequency, amplitude and phase angle of each harmonic. It should be noted that the complex power calculation method is based on the voltage and current waveforms of each harmonic, and the conjugate product of the complex numbers of the voltage and current is calculated and the real and imaginary parts are taken to obtain the harmonic active power and harmonic reactive power, respectively. Then, this embodiment performs complex number superposition operation on the harmonic active power and harmonic reactive power of each harmonic, that is, the harmonic active power and harmonic reactive power of each harmonic are accumulated to obtain the total harmonic active power and the total harmonic reactive power. Then, according to the known line parameters (such as resistance, inductance, capacitance, etc.), load characteristics (such as impedance, power factor, etc.) and total harmonic active power and total harmonic reactive power in the power system, the equivalent circuit method in circuit theory is used to determine the harmonic equivalent circuit model. The components and their parameters of the type are determined. These components include harmonic sources, line impedances, load impedances, etc., which jointly determine the propagation and interaction characteristics of harmonics in the circuit. According to the determined components and their parameters, the total harmonic active power and the total harmonic reactive power are connected to the harmonic equivalent circuit model as the output characteristics of the harmonic source. At the same time, the influence of line impedance and load impedance on harmonic propagation is considered, and the corresponding components and parameters are added to the harmonic equivalent circuit model to construct a harmonic equivalent circuit model. The harmonic equivalent circuit model includes components such as harmonic sources, line impedances, load impedances, etc., which can simulate the propagation, reflection and interaction process of harmonics in the power system. In the constructed harmonic equivalent circuit model, the total harmonic active power and the total harmonic reactive power are the output characteristics of the harmonic source, which determine the energy conversion and energy exchange of the harmonic source in the circuit. They affect the propagation and interaction of harmonics in the circuit through the action of line impedance and load impedance. By solving the harmonic equivalent circuit model, the parameters such as harmonic voltage, harmonic current and harmonic power on each component can be obtained, so as to analyze the influence of harmonics on the power system.

[0029] On the basis of establishing the harmonic equivalent circuit model, this embodiment derives a nonlinear equation group describing the behavior of the harmonic equivalent circuit according to basic principles such as Kirchhoff's voltage law (KVL) and current law (KCL). The nonlinear equation group includes unknown quantities about harmonic voltage, harmonic current and harmonic power, as well as known parameters such as harmonic source, line impedance and load impedance. By solving this equation group, the influence of the interaction between harmonics on the circuit behavior can be reflected. In order to solve the above nonlinear equation group, this embodiment adopts the Newton-Raphson iteration method for numerical calculation. Before the iteration starts, the initial value is set according to the complex superposition result of the total harmonic active power and the total harmonic reactive power. An initial value is set as an approximate solution of the nonlinear equation group, and then the solution or unknown quantity of the nonlinear equation group is adjusted iteratively until the iterative convergence condition (such as the error is less than a preset threshold) is met. After iterative solution, the total harmonic power after considering the interaction between harmonics is obtained. The result is a complex number. The total harmonic power is expressed in the form of a complex number, and its real part and imaginary part correspond to the total harmonic active power and the total harmonic reactive power, respectively, indicating the comprehensive effect of the total harmonic active power and the total harmonic reactive power. Compared with the total harmonic power result obtained by simple superposition, the total harmonic power considering the interaction between harmonics in this embodiment can more accurately reflect the harmonic power in the actual system.

[0030] In this embodiment, the step of calculating the harmonic power data of each harmonic according to the harmonic spectrum data includes: Performing windowing processing on the harmonic spectrum data using a Hanning window function to obtain a windowed harmonic data set; Extract the frequency components, amplitudes and phase information of each harmonic of the switch device during the on and off process according to the windowed harmonic data set, and convert the phase information from the angle system to the radian system to obtain the phase radian value; According to the frequency component, amplitude and phase radian value of each harmonic, the harmonic active power and harmonic reactive power of each harmonic are calculated.

[0031] Specifically, this embodiment uses a Hanning window function to preprocess the harmonic spectrum data to suppress spectrum leakage and improve the accuracy of spectrum analysis, and obtain a windowed harmonic data set. Those skilled in the art can set the window function length according to the specific implementation situation. This embodiment further analyzes and extracts the frequency components, amplitudes and phase information (phase angle) of each harmonic of the switching device during the turn-on and turn-off processes based on the windowed harmonic data set, converts the phase information from the angle system to the radian system, obtains the phase radian value, and then according to the frequency components, amplitudes and converted phase radian values ​​of each harmonic, The harmonic active power and harmonic reactive power of each harmonic are calculated. The specific implementation process is: determine the amplitude of voltage and current according to the frequency component, amplitude and phase angle difference radian value of each harmonic, and then use the phase radian value to calculate the active power and reactive power. For the harmonic active power, this embodiment multiplies the amplitude of each harmonic with the cosine value of the corresponding phase radian value, and then multiplies it by the frequency component to obtain the harmonic active power; similarly, for the harmonic reactive power, this embodiment multiplies the amplitude of each harmonic with the sine value of the corresponding phase radian value, and then multiplies it by the frequency component to obtain the harmonic reactive power.

[0032] In this embodiment, the step of obtaining the harmonic power dynamic threshold value by using the adaptive threshold strategy according to the current grid operation state includes: Obtain the current grid operation status and use statistical analysis methods to quantitatively evaluate the fluctuation degree of the current grid operation status; Obtain historical harmonic power data, and use the fluctuation degree of the current power grid operation status to assign a weight coefficient to each historical harmonic power data; According to the weight coefficient, the historical harmonic power data is weighted and summed using an exponentially weighted moving average model to obtain a harmonic power exponentially weighted moving average value; A dynamic threshold prediction model is constructed using support vector machine. The current grid operation status and the weighted moving average of the harmonic power index are taken as input variables, and the dynamic threshold prediction model is used to predict the dynamic threshold of harmonic power.

[0033] Specifically, the present embodiment can capture the operating status of the current power grid in real time through monitoring equipment. The data includes but is not limited to key parameters such as voltage, current, and power factor. Then, a statistical analysis method is used to quantitatively evaluate the degree of fluctuation of the current power grid operating status. For example, the present embodiment can use a time series analysis method to quantitatively evaluate the degree of fluctuation of the current power grid operating status, so as to provide a basis for subsequent weight coefficient allocation. The present embodiment obtains historical harmonic power data of a sufficient time range from a historical database. These data may include harmonic power values ​​at different time points and their corresponding timestamps. Then, according to the degree of fluctuation of the power grid operating status, a corresponding weight coefficient is allocated to each historical harmonic power data to ensure that when the power grid status fluctuates greatly, the corresponding data point is assigned a higher weight. After obtaining the historical harmonic power data and its weight coefficient , the exponentially weighted moving average (EWMA) model is used to perform weighted summation on the historical data. The EWMA model can take into account the timeliness of the data and give a higher weight to the recent data, thereby more accurately reflecting the current trend of the power grid state, thereby obtaining the exponentially weighted moving average value of the harmonic power. Finally, the support vector machine (SVM) algorithm is used to construct a dynamic threshold prediction model. The dynamic threshold prediction model takes the characteristic parameters of the current power grid operation state (such as voltage, current, etc.) and the exponentially weighted moving average value of the harmonic power as input variables. By training the SVM model, a mapping relationship between the input variables and the dynamic threshold value of the harmonic power is established, thereby predicting the dynamic threshold value of the harmonic power of the current power grid. This embodiment realizes the accurate calculation and prediction of the dynamic threshold value of the harmonic power through the dynamic threshold prediction model, which provides strong support for the safe and stable operation of the power grid.

[0034] S5. Compare the total harmonic power with the harmonic power dynamic threshold value, and if the total harmonic power exceeds the harmonic power dynamic threshold value, extract the excessive harmonic signal.

[0035] In this embodiment, the step of comparing the total harmonic power with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, extracting the excessive harmonic signal includes: The total harmonic power is compared with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, a multi-channel bandpass filter array is used to perform segmentation filtering on the harmonic spectrum data to extract harmonic signals of multiple frequency bands; It is determined whether the harmonic signal strength of each frequency band exceeds the preset harmonic limit of each frequency band. If the harmonic signal strength of the frequency band exceeds the limit, the harmonic signal of the frequency band is identified as an excessive harmonic signal.

[0036] Specifically, the present embodiment compares the total harmonic power with the harmonic power dynamic threshold to evaluate whether the harmonic pollution status of the current power grid has reached or exceeded a predetermined safety range. If the total harmonic power exceeds the harmonic power dynamic threshold, it indicates that the harmonic pollution exceeds the standard. At this time, the present embodiment uses a multi-channel bandpass filter array to segment and filter the harmonic spectrum data. The multi-channel bandpass filter array includes multiple channels, each channel corresponds to a specific frequency band (frequency range). When the harmonic spectrum data is input into the multi-channel bandpass filter array, the multi-channel bandpass The filter array can extract the harmonic signal of each frequency band through the corresponding channel, and perform filtering processing respectively to obtain the harmonic signal of each frequency band. Next, the harmonic signal strength of each frequency band is judged. Specifically, the harmonic signal strength of each frequency band in this embodiment is compared with the preset harmonic limit. If the harmonic signal strength of a certain frequency band exceeds its corresponding preset harmonic limit, it indicates that the harmonic signal strength of the frequency band exceeds the standard. For the harmonic signal of the frequency band with excessive strength, it is identified as an excessive harmonic signal so that the extracted excessive harmonic signal can be suppressed, eliminated or converted.

[0037] S6. Convert the excessive harmonic signal into energy storage voltage and current that are compatible with the energy storage device, and store the energy storage voltage and current in the energy storage device.

[0038] In this embodiment, the step of converting the excessive harmonic signal into an energy storage voltage and current adapted to the energy storage device, and storing the energy storage voltage and current in the energy storage device includes: A high-speed analog-to-digital converter is used to sample the excessive harmonic signal to obtain sampling data; the sampling frequency of the high-speed analog-to-digital converter is a preset multiple of the frequency of the excessive harmonic signal; The sampling data is fast Fourier transformed using the Hanning window function to obtain the harmonic amplitude and phase information of the excessive harmonic signal; Generate a harmonic polar coordinate diagram using a vector diagram representation method according to the harmonic amplitude and phase information of the harmonic signal; According to the harmonic power in the harmonic polar coordinate diagram, setting the output parameters of the H-bridge inverter; The H-bridge inverter converts the harmonic power into the energy storage voltage and current required by the energy storage device and stores them in the energy storage device.

[0039] Since directly eliminating the excessive harmonic components in the power grid will not only have a significant impact on the power grid or equipment, but also lead to energy waste, the energy storage technology can temporarily store this energy and release it for use when needed, thereby improving the efficiency of energy utilization. Therefore, this embodiment can convert these originally harmful harmonic energies into useful energy and store it by accurately extracting the excessive harmonic components within a specific frequency range and converting them into voltage and current forms suitable for energy storage devices, thereby reducing the negative impact on the power grid. At the same time, the converted energy storage system can be used for load regulation of the power grid, that is, storing energy when the power demand is low and releasing energy when the power demand is high, thereby reducing the load fluctuation of the power grid.

[0040] Specifically, this embodiment can select a high-speed analog-to-digital converter according to the frequency range of the excessive harmonic signal and the required sampling accuracy, and set the sampling frequency of the high-speed analog-to-digital converter to a preset multiple of the frequency of the excessive harmonic signal to ensure that the details of the harmonic signal can be accurately captured. For example, according to the Nyquist sampling theorem, the sampling frequency can be at least twice the highest frequency of the signal, and then the excessive harmonic signal source is connected to the input end of the high-speed analog-to-digital converter to ensure the impedance matching and signal integrity of the signal transmission path, and send a start signal to the high-speed analog-to-digital converter to start collecting sample data of the excessive harmonic signal to obtain sampling data, and then use the Hanning window function to perform a fast Fourier transform (FFT) on the sampling data to obtain the harmonic amplitude and phase information of the excessive harmonic signal. This embodiment uses the Hanning window function to reduce spectrum leakage in the FFT result, and uses the FFT algorithm to process the windowed sampling data to obtain spectrum information, and extracts the amplitude of each harmonic from the spectrum information output by the FFT algorithm. and phase information, converting the extracted harmonic amplitude and phase information into the format required by the vector diagram, drawing a harmonic polar coordinate diagram, in which each harmonic component is represented by a vector, whose length represents the amplitude and direction represents the phase, calculating the power of each harmonic according to the amplitude and phase information in the harmonic polar coordinate diagram, adding the power of each harmonic to obtain the total power demand, setting the modulation ratio, switching frequency and other output parameters of the H-bridge inverter according to the total power demand and the characteristics of the energy storage device (such as voltage range, current capacity, etc.), connecting the output end of the H-bridge inverter to the energy storage device (such as a battery pack, a supercapacitor, etc.) to ensure electrical compatibility and safety, sending a start signal to the H-bridge inverter, and starting to work according to the previously set output parameters, when the energy storage device reaches a predetermined energy storage level, stopping the H-bridge inverter and disconnecting from the energy storage device, through the above steps, this embodiment converts the excessive harmonic signal into an energy storage voltage and current adapted to the energy storage device, and stores it in the energy storage device.

[0041] An embodiment of the present invention provides a distributed energy optimization management method, which collects grid load status data in a distributed energy system in real time, and determines an optimal switching control strategy of a switch device based on the grid load status data using a Q-Learning particle swarm optimization algorithm; intermittently turns on and off the switch device according to the optimal switching control strategy, and obtains a time domain action signal of the switch device generated during the intermittent conduction and shutdown process; performs frequency domain analysis on the time domain action signal of the switch device using a short-time Fourier transform algorithm, and extracts harmonic spectrum data during the conduction and shutdown process of the switch device; obtains a total harmonic power based on the harmonic spectrum data, and obtains a harmonic power dynamic threshold using an adaptive threshold strategy based on the current grid operation state; compares the total harmonic power with the harmonic power dynamic threshold, and extracts an excessive harmonic signal if the total harmonic power exceeds the harmonic power dynamic threshold; converts the excessive harmonic signal into an energy storage voltage and current that is compatible with an energy storage device, and stores the energy storage voltage and current in the energy storage device. Compared with the existing technology, this method monitors and analyzes the characteristic parameters of the power grid load in real time, dynamically adjusts the on and off strategies of the switching devices, and combines the short-time Fourier transform algorithm for frequency domain analysis and the adaptive threshold judgment algorithm to optimize the analysis of harmonics. It effectively solves the shortcomings of traditional switch control strategies in harmonic suppression, and realizes accurate identification and efficient energy storage of harmonics generated by nonlinear loads, thereby reducing the generation of harmonics in distributed energy systems, providing a strong guarantee for the stable operation of distributed energy systems, and improving the power quality and operation stability of the power grid.

[0042] It should be noted that the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0043] In one embodiment, Figure 2 As shown, an embodiment of the present invention provides a distributed energy optimization management system, the system comprising: The particle swarm optimization module 101 is used to collect the grid load status data in the distributed energy system in real time, and determine the optimal switching control strategy of the switch device by using the Q-Learning particle swarm optimization algorithm according to the grid load status data; the optimal switching control strategy includes the optimal on-time, the optimal off-time and the optimal switching frequency of the switch device; A time domain signal acquisition module 102, configured to perform intermittent on and off control on the switch device according to the optimal switching control strategy, and acquire a time domain action signal of the switch device generated during the intermittent on and off process; The harmonic frequency domain analysis module 103 is used to perform frequency domain analysis on the time domain action signal of the switching device using a short-time Fourier transform algorithm to extract harmonic spectrum data during the switching device on and off process; A dynamic threshold analysis module 104 is used to obtain the total harmonic power according to the harmonic spectrum data, and obtain the harmonic power dynamic threshold using an adaptive threshold strategy according to the current grid operation state; The excessive harmonic extraction module 105 is used to compare the total harmonic power with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, extract the excessive harmonic signal; The harmonic energy storage conversion module 106 is used to convert the excessive harmonic signal into an energy storage voltage and current that is compatible with the energy storage device, and store the energy storage voltage and current in the energy storage device.

[0044] For the specific definition of a distributed energy optimization management system, please refer to the above-mentioned definition of a distributed energy optimization management method, which will not be repeated here. A person of ordinary skill in the art can appreciate that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0045] The embodiment of the present invention provides a distributed energy optimization management system, wherein the system determines an optimal switching control strategy of a switching device by using a Q-Learning particle swarm optimization algorithm through a particle swarm optimization module; a time domain signal acquisition module performs intermittent on and off control on the switching device according to the optimal switching control strategy, and obtains a time domain action signal of the switching device generated during the intermittent on and off process; a harmonic frequency domain analysis module performs frequency domain analysis on the time domain action signal of the switching device by using a short-time Fourier transform algorithm, and extracts harmonic spectrum data during the on and off process of the switching device; a dynamic threshold analysis module obtains a total harmonic power according to the harmonic spectrum data, and obtains a harmonic power dynamic threshold by using an adaptive threshold strategy according to the current power grid operation state; an over-standard harmonic extraction module compares the total harmonic power with the harmonic power dynamic threshold, and extracts an over-standard harmonic signal if the total harmonic power exceeds the harmonic power dynamic threshold; a harmonic energy storage conversion module converts the over-standard harmonic signal into an energy storage voltage and current adapted to an energy storage device, and stores the energy storage voltage and current in the energy storage device. Compared with the existing technology, the system monitors and analyzes the characteristic parameters of the power grid load in real time, dynamically adjusts the on and off strategies of the switching devices, and combines the short-time Fourier transform algorithm for frequency domain analysis and the adaptive threshold judgment algorithm to optimize the analysis of harmonics. It effectively solves the shortcomings of traditional switch control strategies in harmonic suppression, and realizes accurate identification and efficient energy storage of harmonics generated by nonlinear loads, thereby reducing the generation of harmonics in distributed energy systems, providing a strong guarantee for the stable operation of distributed energy systems, and improving the power quality and operation stability of the power grid.

[0046] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A distributed energy optimization management method, characterized in that: The following steps are involved: Collect the grid load status data in the distributed energy system in real time, and determine the optimal switching control strategy of the switch device using the Q-Learning particle swarm optimization algorithm based on the grid load status data; the optimal switching control strategy includes the optimal on-time, optimal off-time and optimal switching frequency of the switch device; intermittently turning on and off the switch device according to the optimal switching control strategy, and obtaining a time domain action signal of the switch device generated during the intermittent turning on and off process; Performing frequency domain analysis on the time domain action signal of the switching device using a short-time Fourier transform algorithm to extract harmonic spectrum data during the on and off process of the switching device; Obtaining the total harmonic power according to the harmonic spectrum data, and obtaining the harmonic power dynamic threshold using an adaptive threshold strategy according to the current power grid operation state; Comparing the total harmonic power with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, extracting an excessive harmonic signal; The excessive harmonic signal is converted into an energy storage voltage and current that is compatible with an energy storage device, and the energy storage voltage and current are stored in the energy storage device.

2. A distributed energy optimization management method according to claim 1, characterized in that: The step of determining the optimal switching control strategy of the switch device using the Q-Learning particle swarm optimization algorithm according to the power grid load state data includes: Real-time collection of grid load status data; the grid load status data includes power load power, power factor, voltage fluctuation range and current harmonic content; Using an autoregressive moving average model to perform time series analysis on the power grid load state data within each sliding time window, and extracting key feature vectors of the power grid load; The current grid load state is defined as the state space of the Q-Learning algorithm, and the switching control instructions of the switching devices are defined as the action space; Initialize the position and speed of the particle swarm according to the key characteristic vector of the power grid load, and randomly distribute them in the action space, each particle represents a set of switching control strategies of the switching device; the switching control strategies of the switching device include on-time, off-time and switching frequency; Execute the switching control strategy of each particle's switch device under the current grid state, and calculate the reward value according to the change of grid state; Update the Q value table according to the reward value and record the cumulative rewards obtained after performing different actions under the current power grid state; The position and velocity of each particle are updated through the particle swarm algorithm. After multiple iterative optimizations, the switching control strategy of the switch device corresponding to the particle with the largest cumulative reward is selected as the optimal switching control strategy.

3. A distributed energy optimization management method according to claim 1, characterized in that: The step of using a short-time Fourier transform algorithm to perform frequency domain analysis on the time domain action signal of the switching device to extract harmonic spectrum data during the switching device on and off process comprises: Preprocessing the time domain action signal of the switch device by using a median filter to obtain a time domain filtered action signal; Performing frequency domain analysis on the time domain filtering action signal using a short-time Fourier transform algorithm to obtain a short-time Fourier transform result; According to the short-time Fourier transform result, a signal-to-noise ratio-based adaptive threshold method is used to identify the main harmonic component, and if the signal-to-noise ratio of the main harmonic component is greater than the signal-to-noise ratio adaptive threshold, the main harmonic component is identified as a valid harmonic component; The harmonic components and their corresponding frequency, amplitude and phase information during the on and off process of the switching device are calculated according to the effective harmonic components to form harmonic spectrum data.

4. A distributed energy optimization management method according to claim 1, characterized in that: The step of obtaining the total harmonic power according to the harmonic spectrum data comprises: Calculate the harmonic power data of each harmonic according to the harmonic spectrum data, wherein the harmonic power data includes harmonic active power and harmonic reactive power; Perform complex number superposition operation on the harmonic power data of each harmonic to obtain the total harmonic active power and total harmonic reactive power; Taking total harmonic active power and total harmonic reactive power as the output characteristics of harmonic sources, and considering the influence of line impedance and load impedance on harmonic propagation, a harmonic equivalent circuit model is constructed. The harmonic equivalent circuit model is solved by the Newton-Raphson iteration method, and before the iteration starts, the complex superposition result of the total harmonic active power and the total harmonic reactive power is set as the initial value, and the total harmonic power after considering the interaction between harmonics is obtained through iterative solution.

5. A distributed energy optimization management method as claimed in claim 4, characterized in that: The step of calculating the harmonic power data of each harmonic according to the harmonic spectrum data comprises: Performing windowing processing on the harmonic spectrum data using a Hanning window function to obtain a windowed harmonic data set; Extract the frequency components, amplitudes and phase information of each harmonic of the switch device during the on and off process according to the windowed harmonic data set, and convert the phase information from the angle system to the radian system to obtain the phase radian value; According to the frequency component, amplitude and phase radian value of each harmonic, the harmonic active power and harmonic reactive power of each harmonic are calculated.

6. A distributed energy optimization management method according to claim 1, characterized in that: The step of obtaining the harmonic power dynamic threshold value by using the adaptive threshold strategy according to the current grid operation state comprises: Obtain the current grid operation status and use statistical analysis methods to quantitatively evaluate the fluctuation degree of the current grid operation status; Obtain historical harmonic power data, and use the fluctuation degree of the current power grid operation status to assign a weight coefficient to each historical harmonic power data; According to the weight coefficient, the historical harmonic power data is weighted and summed using an exponentially weighted moving average model to obtain a harmonic power exponentially weighted moving average value; A dynamic threshold prediction model is constructed using support vector machine. The current grid operation status and the weighted moving average of the harmonic power index are taken as input variables, and the dynamic threshold prediction model is used to predict the dynamic threshold of harmonic power.

7. A distributed energy optimization management method according to claim 1, characterized in that: The step of comparing the total harmonic power with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, extracting the excessive harmonic signal comprises: The total harmonic power is compared with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, a multi-channel bandpass filter array is used to perform segmentation filtering on the harmonic spectrum data to extract harmonic signals of multiple frequency bands; It is determined whether the harmonic signal strength of each frequency band exceeds the preset harmonic limit of each frequency band. If the harmonic signal strength of the frequency band exceeds the limit, the harmonic signal of the frequency band is identified as an excessive harmonic signal.

8. A distributed energy optimization management method as claimed in claim 7, characterized in that: The method further includes: if the total harmonic power does not exceed the harmonic power dynamic threshold, feeding the total harmonic power to a power grid or a load side.

9. A distributed energy optimization management method as claimed in claim 1, characterized in that: The step of converting the excessive harmonic signal into an energy storage voltage and current adapted to the energy storage device, and storing the energy storage voltage and current in the energy storage device comprises: A high-speed analog-to-digital converter is used to sample the excessive harmonic signal to obtain sampling data; the sampling frequency of the high-speed analog-to-digital converter is a preset multiple of the frequency of the excessive harmonic signal; The sampling data is fast Fourier transformed using the Hanning window function to obtain the harmonic amplitude and phase information of the excessive harmonic signal; Generate a harmonic polar coordinate diagram using a vector diagram representation method according to the harmonic amplitude and phase information of the harmonic signal; According to the harmonic power in the harmonic polar coordinate diagram, setting the output parameters of the H-bridge inverter; The H-bridge inverter converts the harmonic power into the energy storage voltage and current required by the energy storage device and stores them in the energy storage device.

10. A distributed energy optimization management system, characterized in that: The system comprises: A particle swarm optimization module is used to collect the grid load status data in the distributed energy system in real time, and determine the optimal switching control strategy of the switch device using the Q-Learning particle swarm optimization algorithm according to the grid load status data; the optimal switching control strategy includes the optimal on-time, optimal off-time and optimal switching frequency of the switch device; A time domain signal acquisition module, used to perform intermittent on and off control on the switch device according to the optimal switching control strategy, and acquire a time domain action signal of the switch device generated during the intermittent on and off process; A harmonic frequency domain analysis module, used to perform frequency domain analysis on the time domain action signal of the switching device using a short-time Fourier transform algorithm, and extract harmonic spectrum data during the switching device on and off process; A dynamic threshold analysis module, used to obtain the total harmonic power according to the harmonic spectrum data, and obtain the harmonic power dynamic threshold using an adaptive threshold strategy according to the current grid operation state; An excessive harmonic extraction module is used to compare the total harmonic power with the harmonic power dynamic threshold, and if the total harmonic power exceeds the harmonic power dynamic threshold, extract an excessive harmonic signal; The harmonic energy storage conversion module is used to convert the excessive harmonic signal into an energy storage voltage and current that is compatible with the energy storage device, and store the energy storage voltage and current in the energy storage device.

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