A distributed energy optimization management method and system

Abstract

The present invention relates to the technical field of energy management, and particularly to a distributed energy optimization management method and system, which includes determining an optimal switching control strategy for switching devices by using a Q-Learning particle swarm optimization algorithm; performing intermittent conduction and turn-off control on the switching devices according to the optimal switching control strategy, obtaining the time-domain action signals of the switching devices, and extracting the harmonic spectrum data during the conduction and turn-off processes of the switching devices by using a short-time Fourier transform algorithm; obtaining the total harmonic power according to the harmonic spectrum data, and if the total harmonic power exceeds the harmonic power dynamic threshold, extracting the exceeding-standard harmonic signals; converting the exceeding-standard harmonic signals into energy storage voltage and current adapted to the energy storage device, and storing the energy storage voltage and current in the energy storage device. Through the management and analysis of harmonics, the present invention realizes the accurate identification and efficient energy storage of harmonics generated by non-linear loads, thereby realizing the optimization management of the distributed energy system.

Description

Technical Field

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

[0002] In distributed energy systems, the application of nonlinear loads is becoming increasingly widespread. In particular, the power loads containing various switching devices are continuously 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, the harmonic problem is 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 generated during the switching process of switching devices and their dynamic changes, resulting in increased harmonic pollution. This will not only increase the power loss of the power grid but also trigger resonance phenomena, seriously threatening the stable and safe operation of the power grid. Therefore, accurately distinguishing the harmonics generated during the switching process of switching devices is the key, which requires that the harmonic feature extraction and matching algorithms must have a high degree of accuracy and real-time performance in order to extract specific harmonic components generated by the switching of switching devices in a complex power grid environment. However, current technologies often fail to meet this requirement in a complex and changeable power grid environment, resulting in inaccurate harmonic identification and thus affecting the effectiveness of subsequent treatment measures.

[0003] Secondly, in the process of extracting, converting, and storing harmonic power in a distributed energy system, multiple links are involved, including a harmonic extraction circuit, a power conversion circuit, and an energy storage device. 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 harmonic components of specific frequencies, and the complexity of the distributed energy system makes this process more difficult. The power conversion circuit needs to efficiently convert harmonic power into an electrical energy form 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 the complex harmonic components generated by nonlinear loads in distributed energy systems. Solving these problems is of great significance for improving the power quality of the power grid and ensuring the stable operation of equipment. Summary of the Invention

[0004] 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, and the method includes the following steps:

[0006] Real-time collect the grid load status data in the distributed energy system, and based on the grid load status data, use the Q-Learning particle swarm optimization algorithm to determine the optimal switching control strategy of the switching device; the optimal switching control strategy includes the optimal conduction time, optimal turn-off time and optimal switching frequency of the switching device;

[0007] According to the optimal switching control strategy, perform intermittent conduction and turn-off control on the switching device to obtain the time-domain action signal of the switching device generated during the intermittent conduction and turn-off process;

[0008] Use the short-time Fourier transform algorithm to perform frequency-domain analysis on the time-domain action signal of the switching device, and extract the harmonic spectrum data during the conduction and turn-off processes of the switching device;

[0009] Obtain the total harmonic power according to the harmonic spectrum data, and use the adaptive threshold strategy to obtain the dynamic threshold of the harmonic power according to the current grid operation status;

[0010] Compare the total harmonic power with the dynamic threshold of the harmonic power. If the total harmonic power exceeds the dynamic threshold of the harmonic power, extract the excessive harmonic signal;

[0011] Convert the excessive harmonic signal into the energy storage voltage and current adapted to the energy storage device, and store the energy storage voltage and current in the energy storage device.

[0012] In a further embodiment, the step of using the Q-Learning particle swarm optimization algorithm to determine the optimal switching control strategy of the switching device according to the grid load status data includes:

[0013] Real-time collect the grid load status data; the grid load status data includes the power consumption load power, power factor, voltage fluctuation range and current harmonic content;

[0014] Use the autoregressive moving average model to perform time series analysis on the grid load status data within each sliding time window, and extract the key feature vectors of the grid load;

[0015] Define the current grid load status as the state space of the Q-Learning algorithm, and define the switching control instruction of the switching device as the action space;

[0016] Initialize the position and velocity of the particle swarm according to the key feature vectors of the grid load, and randomly allocate them within the action space. Each particle represents a set of switching device switching control strategies; the switching device switching control strategy includes the conduction time, turn-off time and switching frequency;

[0017] Execute the switching device switching control strategy of each particle in the current grid state, and calculate the reward value according to the change of the grid state;

[0018] 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;

[0019] Update the position and velocity of each particle through the particle swarm optimization algorithm. After multiple iterations of optimization, select the switching device switching control strategy corresponding to the particle with the maximum cumulative reward as the optimal switching control strategy.

[0020] In a further implementation, the steps of performing frequency-domain analysis on the time-domain action signal of the switching device by using the short-time Fourier transform algorithm and extracting the harmonic spectrum data during the on and off processes of the switching device include:

[0021] Preprocess the time-domain action signal of the switching device by using a median filter to obtain a time-domain filtered action signal;

[0022] Perform frequency-domain analysis on the time-domain filtered action signal by using the short-time Fourier transform algorithm to obtain the short-time Fourier transform result;

[0023] According to the short-time Fourier transform result, use an adaptive threshold method based on signal-to-noise ratio to identify the main harmonic components. If the signal-to-noise ratio of the main harmonic components is greater than the signal-to-noise ratio adaptive threshold, then identify the main harmonic components as effective harmonic components;

[0024] Calculate the harmonic components and their corresponding frequencies, amplitudes, and phase information during the on and off processes of the switching device according to the effective harmonic components to form harmonic spectrum data.

[0025] In a further implementation, the steps of obtaining the total harmonic power according to the harmonic spectrum data include:

[0026] According to the harmonic spectrum data, calculate the harmonic power data of each harmonic. The harmonic power data includes harmonic active power and harmonic reactive power;

[0027] Perform complex superposition operation on the harmonic power data of each harmonic to obtain the total harmonic active power and the total harmonic reactive power;

[0028] Take the total harmonic active power and the total harmonic reactive power as the output characteristics of the harmonic source, and consider the influence of line impedance and load impedance on harmonic propagation to construct a harmonic equivalent circuit model;

[0029] Solve the harmonic equivalent circuit model by using the Newton-Raphson iteration method. Before the iteration starts, set the complex superposition result of the total harmonic active power and the total harmonic reactive power as the initial value, and obtain the total harmonic power considering the interaction between harmonics through iterative solution.

[0030] In a further embodiment, the step of calculating the harmonic power data of each harmonic according to the harmonic spectrum data includes:

[0031] Perform windowing processing on the harmonic spectrum data by using a Hanning window function to obtain a windowed harmonic data set;

[0032] According to the windowed harmonic data set, extract the frequency components, amplitudes and phase information of each harmonic during the on and off processes of the switching device, and convert the phase information from degree system to radian system to obtain a phase radian value;

[0033] Calculate the harmonic active power and harmonic reactive power of each harmonic according to the frequency components, amplitudes and phase radian values of each harmonic.

[0034] In a further embodiment, the step of obtaining the dynamic threshold of harmonic power by using an adaptive threshold strategy according to the current power grid operation state includes:

[0035] Obtain the current power grid operation state, and quantitatively evaluate the fluctuation degree of the current power grid operation state by using a statistical analysis method;

[0036] Obtain historical harmonic power data, and assign a weight coefficient to each historical harmonic power data by using the fluctuation degree of the current power grid operation state;

[0037] According to the weight coefficient, perform weighted summation on the historical harmonic power data by using an exponentially weighted moving average model to obtain an exponentially weighted moving average value of harmonic power;

[0038] Use a support vector machine to construct a dynamic threshold prediction model, take the current power grid operation state and the exponentially weighted moving average value of harmonic power as input variables, and predict the dynamic threshold of harmonic power by using the dynamic threshold prediction model.

[0039] In a further embodiment, the step of comparing the total harmonic power with the dynamic threshold of harmonic power, and if the total harmonic power exceeds the dynamic threshold of harmonic power, extracting the excessive harmonic signal includes:

[0040] Compare the total harmonic power with the dynamic threshold of harmonic power. If the total harmonic power exceeds the dynamic threshold of harmonic power, use a multi-channel bandpass filter array to perform segmentation filtering on the harmonic spectrum data, and extract harmonic signals in multiple frequency bands;

[0041] Judge whether the intensity of the harmonic signal in each frequency band exceeds the preset harmonic limit value of each frequency band. If the intensity of the harmonic signal in this frequency band exceeds the standard, identify the harmonic signal in this frequency band as an excessive harmonic signal.

[0042] 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 power grid or the load side.

[0043] In a further embodiment, the step of converting the out-of-standard harmonic signal into a stored energy voltage and current adapted to the energy storage device and storing the stored energy voltage and current in the energy storage device includes:

[0044] Sampling the out-of-standard harmonic signal by using a high-speed analog-to-digital converter to obtain sampling data; the sampling frequency of the high-speed analog-to-digital converter is a preset multiple of the frequency of the out-of-standard harmonic signal;

[0045] Performing a fast Fourier transform on the sampling data by using a Hanning window function to obtain the harmonic amplitude and phase information of the out-of-standard harmonic signal;

[0046] Generating a harmonic polar coordinate diagram by using a vector diagram representation method according to the harmonic amplitude and phase information of the harmonic signal;

[0047] Setting the output parameters of the H-bridge inverter according to the harmonic power in the harmonic polar coordinate diagram;

[0048] Converting the harmonic power into the stored energy voltage and current required by the energy storage device through the H-bridge inverter and storing it in the energy storage device.

[0049] In a second aspect, the present invention provides a distributed energy optimization management system, and the system includes:

[0050] A particle swarm optimization module, configured to collect the power grid load status data in the distributed energy system in real time, and determine the optimal switching control strategy of the switching device by using the Q-Learning particle swarm optimization algorithm according to the power grid load status data; the optimal switching control strategy includes the optimal conduction time, the optimal turn-off time and the optimal switching frequency of the switching device;

[0051] A time-domain signal acquisition module, configured to perform intermittent conduction and turn-off control on the switching device according to the optimal switching control strategy, and acquire the switching device time-domain action signal generated during the intermittent conduction and turn-off process;

[0052] A harmonic frequency-domain analysis module, configured to perform frequency-domain analysis on the switching device time-domain action signal by using a short-time Fourier transform algorithm, and extract the harmonic spectrum data during the conduction and turn-off processes of the switching device;

[0053] A dynamic threshold analysis module, configured to obtain the total harmonic power according to the harmonic spectrum data, and obtain the harmonic power dynamic threshold by using an adaptive threshold strategy according to the current power grid operation state;

[0054] An excessive harmonic extraction module is configured to compare the total harmonic power with the dynamic threshold of harmonic power. If the total harmonic power exceeds the dynamic threshold of harmonic power, an excessive harmonic signal is extracted.

[0055] A harmonic energy storage conversion module is configured to convert the excessive harmonic signal into a stored energy voltage and current adapted to an energy storage device, and store the stored energy voltage and current in the energy storage device.

[0056] The present invention provides a distributed energy optimization management method and system. The method includes: collecting in real time grid load status data in a distributed energy system, and determining an optimal switching control strategy of a switching device by using a Q-Learning particle swarm optimization algorithm according to the grid load status data; performing intermittent conduction and turn-off control on the switching device according to the optimal switching control strategy, and obtaining a time-domain action signal of the switching device generated during the intermittent conduction and turn-off; performing frequency-domain analysis on the time-domain action signal of the switching device by using a short-time Fourier transform algorithm, and extracting harmonic spectrum data during the conduction and turn-off of the switching device; obtaining the total harmonic power according to the harmonic spectrum data, and obtaining a dynamic threshold of harmonic power by using an adaptive threshold strategy according to the current grid operation status; comparing the total harmonic power with the dynamic threshold of harmonic power, and if the total harmonic power exceeds the dynamic threshold of harmonic power, extracting an excessive harmonic signal; converting the excessive harmonic signal into a stored energy voltage and current adapted to an energy storage device, and storing the stored energy voltage and current in the energy storage device. Compared with the prior art, by combining frequency-domain analysis using a short-time Fourier transform algorithm and an adaptive threshold judgment algorithm to analyze harmonics, the method realizes accurate identification and efficient energy storage of harmonics generated by non-linear loads, thereby reducing the generation of harmonics in the distributed energy system and providing a strong guarantee for the stable operation of the distributed energy system. Description of the Drawings

[0057] Figure 1 is a schematic flowchart of a distributed energy optimization management method provided by an embodiment of the present invention;

[0058] Figure 2 is a block diagram of a distributed energy optimization management system provided by an embodiment of the present invention. Detailed Embodiments

[0059] The following specifically illustrates the embodiments of the present invention in conjunction with the drawings. The given embodiments are only for illustrative purposes and should not be construed as limiting the present invention. The drawings are only for reference and illustration, and do not constitute a limitation on the protection scope of the present invention patent. Because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0060] Refer to Figure 1 , an embodiment of the present invention provides a distributed energy optimization management method, asFigure 1 As shown in the figure, the method includes the following steps:

[0061] S1. Real-time collect the grid load status data in the distributed energy system, and based on the grid load status data, use the Q-Learning particle swarm optimization algorithm to determine the optimal switching control strategy of the switching device; the optimal switching control strategy includes the optimal conduction time, optimal turn-off time, and optimal switching frequency of the switching device.

[0062] In this embodiment, the step of using the Q-Learning particle swarm optimization algorithm to determine the optimal switching control strategy of the switching device according to the grid load status data includes:

[0063] Real-time collect the grid load status data; the grid load status data includes the power consumption load power, power factor, voltage fluctuation range, and current harmonic content;

[0064] Use the autoregressive moving average model to perform time series analysis on the grid load status data in each sliding time window, and extract the key feature vectors of the grid load;

[0065] Define the current grid load status as the state space of the Q-Learning algorithm, and define the switching control instruction of the switching device as the action space;

[0066] Initialize the position and velocity of the particle swarm according to the key feature vectors of the grid load, and randomly allocate them within the action space. Each particle represents a set of switching device switching control strategies; the switching device switching control strategy includes the conduction time, turn-off time, and switching frequency;

[0067] Execute the switching device switching control strategy of each particle in the current grid state, and calculate the reward value according to the change of the grid state;

[0068] Update the Q-value table according to the reward value, and record the cumulative reward obtained after performing different actions in the current grid state;

[0069] Update the position and velocity of each particle through the particle swarm algorithm. After multiple iterations of optimization, select the switching device switching control strategy corresponding to the particle with the largest cumulative reward as the optimal switching control strategy.

[0070] Specifically, in this embodiment, the power grid load status data is collected in real time. The power grid load status data includes, but is not limited to, key parameters such as power consumption load power, power factor, voltage fluctuation range, and current harmonic content, so as to comprehensively reflect the real-time operation status of the power grid. Then, in this embodiment, the autoregressive integrated moving average (ARIMA) model is used to perform time series analysis on the power grid load status data within each preset sliding time window, and key feature vectors reflecting the dynamic characteristics of the power grid load are extracted therefrom to obtain the key feature vectors of the power grid load. Then, a Q-Learning environment is constructed. In this embodiment, the current power grid load status is defined as the state space of the Q-Learning algorithm, and at the same time, the switching control instructions of the switching device (including key parameters such as conduction time, turn-off time, and switching frequency) are set as the action space. Based on the key feature vectors of the power grid load, the positions and velocities of the particle swarm are initialized to ensure that the particle swarm can be evenly and randomly distributed within the action space. Each particle represents a possible switching control strategy for the switching device. In the current power grid state, the switching control strategies of each particle are sequentially executed, and the changes in the power grid state are monitored in real time, and the reward values obtained after executing each strategy are calculated to quantify the improvement degree of the switching control strategy of the switching device on the power grid performance. Based on the principle of Q-Learning, the Q-value table is dynamically updated using the obtained reward values, and the cumulative rewards obtained after executing different actions in 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 positions and velocities of each particle. The steps of policy evaluation, reward calculation, Q-value table update, and particle swarm optimization are repeatedly executed for multiple iterations until the preset convergence condition is reached. Through multiple iterations of optimization, the optimal switching control strategy of the switching device is gradually approximated. After multiple iterations of optimization, the particle with the largest cumulative reward is selected from the particle swarm, and the corresponding switching control strategy of the switching device is the required optimal switching control strategy, so as to apply this strategy to the actual control of the power grid.

[0071] S2. Perform intermittent conduction and turn-off control on the switching device according to the optimal switching control strategy, and obtain the time-domain action signal of the switching device generated during the intermittent conduction and turn-off process.

[0072] S3. Use the short-time Fourier transform algorithm to perform frequency-domain analysis on the time-domain action signal of the switching device, and extract the harmonic spectrum data during the conduction and turn-off processes of the switching device.

[0073] In this embodiment, the step of using the short-time Fourier transform algorithm to perform frequency-domain analysis on the time-domain action signal of the switching device and extracting the harmonic spectrum data during the conduction and turn-off processes of the switching device includes:

[0074] The median filter is used to preprocess the time-domain action signal of the switching device to obtain a time-domain filtered action signal; the time-domain action signal of the switching device includes the time-domain action voltage and current signals of the switching device;

[0075] The short-time Fourier transform algorithm is used to perform frequency-domain analysis on the time-domain filtered action signal to obtain a short-time Fourier transform result;

[0076] According to the short-time Fourier transform result, an adaptive threshold method based on the signal-to-noise ratio is used to identify the main harmonic components. If the signal-to-noise ratio of the main harmonic components is greater than the signal-to-noise ratio adaptive threshold, the main harmonic components are identified as effective harmonic components;

[0077] According to the effective harmonic components, the harmonic components, their corresponding frequencies, amplitudes and phase information during the on and off processes of the switching device are calculated to form harmonic spectrum data.

[0078] Specifically, nonlinear loads will generate harmonic components in the current. These harmonic components can be captured by measuring the current harmonic content. At the same time, the harmonics of nonlinear loads are mainly generated by the switching of switching devices. Therefore, in this embodiment, by analyzing these time-domain signals, the harmonic components introduced by the switching action can be accurately captured, including their frequencies and amplitudes. In this embodiment, the median filter is used to perform preprocessing such as denoising on the time-domain action signal of the switching device to remove the noise interference in the signal and obtain a time-domain filtered action signal. Then, the short-time Fourier transform algorithm is used to perform frequency-domain analysis on the time-domain filtered action signal to obtain the spectral characteristics of the signal changing with time, and a short-time Fourier transform result including time-frequency characteristics is obtained. According to the short-time Fourier transform result, an adaptive threshold method based on the signal-to-noise ratio is used to identify the main harmonic components. 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 squared amplitudes of each frequency component, and the noise power is estimated by analyzing the non-main part or the known noise region 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 certain main harmonic component is greater than the signal-to-noise ratio adaptive threshold, it is determined that the main harmonic component is an effective harmonic component; otherwise, it is regarded as a noise or interference component.

[0079] It should be noted that the signal-to-noise ratio adaptive threshold is dynamically adjusted according to the characteristics of the time-domain filtered 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, it may be necessary to lower the initial signal-to-noise ratio threshold 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, ensuring that the harmonic extraction circuit can accurately capture and separate the harmonic components of specific frequencies, providing 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 amplitudes of each harmonic, obtains information such as the harmonic components, their specific frequencies, amplitudes, and phases during the on and off processes of the switching device, forms complete harmonic spectrum data, and provides accurate data support for the efficient conversion and energy storage of harmonic power. By combining the short-time Fourier transform and the adaptive threshold method based on the signal-to-noise ratio, this embodiment solves the problem that traditional harmonic detection methods are difficult to accurately identify harmonic components in complex power grid environments, and improves the accuracy and real-time performance of detecting harmonic components generated by nonlinear loads.

[0080] S4. Obtain the total harmonic power according to the harmonic spectrum data, and use the adaptive threshold strategy to obtain the harmonic power dynamic threshold according to the current power grid operation state.

[0081] In this embodiment, the step of obtaining the total harmonic power according to the harmonic spectrum data includes:

[0082] According to the harmonic spectrum data, calculate the harmonic power data of each harmonic, and the harmonic power data includes harmonic active power and harmonic reactive power;

[0083] Perform a complex superposition operation on the harmonic power data of each harmonic to obtain the total harmonic active power and the total harmonic reactive power;

[0084] Take the total harmonic active power and the total harmonic reactive power as the output characteristics of the harmonic source, and consider the influence of line impedance and load impedance on harmonic propagation to construct a harmonic equivalent circuit model;

[0085] Use the Newton-Raphson iteration method to solve the harmonic equivalent circuit model, and before the iteration starts, set the complex superposition result of the total harmonic active power and the total harmonic reactive power as the initial value, and obtain the total harmonic power considering the interaction between harmonics through iterative solution.

[0086] Specifically, in this embodiment, based on the harmonic spectrum data, the complex power calculation method or the harmonic power calculation formula is used to calculate the harmonic active power and harmonic reactive power of each harmonic. They 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. By calculating the conjugate product of the complex numbers of voltage and current and taking the real part and the imaginary part, the harmonic active power and harmonic reactive power are obtained respectively. Then, in this embodiment, the harmonic active power and harmonic reactive power of each harmonic obtained by calculation are respectively subjected to complex superposition operation, that is, the harmonic active power and harmonic reactive power of each harmonic are respectively 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.) in the power system, as well as the total harmonic active power and the total harmonic reactive power, the equivalent circuit method in circuit theory is used to determine the components and their parameters of the harmonic equivalent circuit model. These components include harmonic sources, line impedance, load impedance, etc. They 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, considering the influence of line impedance and load impedance on harmonic propagation, the corresponding components and parameters are added to the harmonic equivalent circuit model to construct a harmonic equivalent circuit model. This harmonic equivalent circuit model includes components such as harmonic sources, line impedance, and load impedance, and 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, as the output characteristics of the harmonic source, 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, 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.

[0087] Based on the establishment of the harmonic equivalent circuit model, in this embodiment, according to the basic principles such as Kirchhoff's voltage law (KVL) and current law (KCL), a set of non - linear equations describing the behavior of the harmonic equivalent circuit is derived. This set of non - linear equations contains unknowns such as harmonic voltages, harmonic currents, and harmonic powers, as well as known parameters such as harmonic sources, line impedances, and load impedances. By solving this set of equations, the influence of the interaction between harmonics on the circuit behavior can be reflected. In order to solve the above - mentioned non - linear equations, this embodiment uses the Newton - Raphson iteration method for numerical calculation. Before the iteration starts, an initial value is set according to the complex superposition result of the total harmonic active power and the total harmonic reactive power. The initial value is set as an approximate solution of the non - linear equations. Then, by continuously iterating to adjust the solution or unknowns of the non - linear equations until the iteration convergence condition is met (such as the error is less than a preset threshold). After iterative solution, the total harmonic power considering the interaction between harmonics is obtained. This result is a complex number. The total harmonic power is represented in the form of a complex number, and its real part and imaginary part respectively correspond to the total harmonic active power and the total harmonic reactive power, representing the comprehensive effect of the total harmonic active power and the total harmonic reactive power. Compared with the result of the total harmonic power 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.

[0088] In this embodiment, the step of calculating the harmonic power data of each harmonic according to the harmonic spectrum data includes:

[0089] Perform windowing processing on the harmonic spectrum data using a Hanning window function to obtain a windowed harmonic data set;

[0090] According to the windowed harmonic data set, extract the frequency components, amplitudes, and phase information of each harmonic during the on - and off - processes of the switching device, and convert the phase information from degree system to radian system to obtain phase radian values;

[0091] According to the frequency components, amplitudes, and phase radian values of each harmonic, calculate the harmonic active power and harmonic reactive power of each harmonic.

[0092] Specifically, in this embodiment, a Hanning window function is used to preprocess the harmonic spectrum data to suppress spectrum leakage and improve the accuracy of spectrum analysis, obtaining a windowed harmonic data set. Those skilled in the art can set the window function length according to specific implementation circumstances. Based on the windowed harmonic data set, this embodiment further analyzes and extracts the frequency components, amplitudes, and phase information (phase angles) of each harmonic during the on and off processes of the switching device, converts the phase information from degree system to radian system to obtain phase radian values, and then calculates the harmonic active power and harmonic reactive power of each harmonic according to the frequency components, amplitudes, and the converted phase radian values. The specific implementation process is as follows: Determine the amplitudes of voltage and current according to the frequency components, amplitudes, and phase angle difference radian values of each harmonic, and then calculate the active power and reactive power using the phase radian values. For the harmonic active power, in this embodiment, multiply the amplitude of each harmonic by the cosine value of the corresponding phase radian value, and then multiply by the frequency component to obtain the harmonic active power; similarly, for the harmonic reactive power, multiply the amplitude of each harmonic by the sine value of the corresponding phase radian value, and then multiply by the frequency component to obtain the harmonic reactive power.

[0093] In this embodiment, the step of obtaining the dynamic threshold of harmonic power using the adaptive threshold strategy according to the current power grid operation state includes:

[0094] Obtain the current power grid operation state, and use statistical analysis methods to quantitatively evaluate the fluctuation degree of the current power grid operation state;

[0095] Obtain historical harmonic power data, and assign weight coefficients to each historical harmonic power data using the fluctuation degree of the current power grid operation state;

[0096] According to the weight coefficients, use the exponentially weighted moving average model to perform weighted summation on the historical harmonic power data to obtain the exponentially weighted moving average value of harmonic power;

[0097] Use a support vector machine to construct a dynamic threshold prediction model, use the current power grid operation state and the exponentially weighted moving average value of harmonic power as input variables, and use the dynamic threshold prediction model to predict the dynamic threshold of harmonic power.

[0098] Specifically, in this embodiment, the operation state of the current power grid can be captured in real time by a monitoring device. The data includes, but is not limited to, key parameters such as voltage, current, power factor, etc. Then, statistical analysis methods are used to quantitatively evaluate the fluctuation degree of the current power grid operation state. For example, in this embodiment, time series analysis methods can be used to quantitatively evaluate the fluctuation degree of the current power grid operation state to provide a basis for subsequent weight coefficient allocation. In this embodiment, historical harmonic power data within a sufficient time range is obtained from the historical database. These data can include harmonic power values at different time points and their corresponding timestamps. Then, according to the fluctuation degree of the power grid operation state, corresponding weight coefficients are assigned to each historical harmonic power data to ensure that when the power grid state fluctuates greatly, the corresponding data points are given higher weights. After obtaining the historical harmonic power data and their weight coefficients, the exponential weighted moving average (EWMA) model is used to perform weighted summation on the historical data. The EWMA model can consider the timeliness of the data and give higher weights to recent data, thus more accurately reflecting the current trend of the power grid state. Thereby, the exponentially weighted moving average value of the harmonic power is obtained. 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 of the harmonic power is established, so as to predict the dynamic threshold of the harmonic power of the current power grid. In this embodiment, the accurate calculation and prediction of the dynamic threshold of the harmonic power are realized through the dynamic threshold prediction model, providing strong support for the safe and stable operation of the power grid.

[0099] S5. Compare the total harmonic power with the dynamic threshold of the harmonic power. If the total harmonic power exceeds the dynamic threshold of the harmonic power, extract the excessive harmonic signal.

[0100] In this embodiment, the step of comparing the total harmonic power with the dynamic threshold of the harmonic power and extracting the excessive harmonic signal if the total harmonic power exceeds the dynamic threshold of the harmonic power includes:

[0101] Compare the total harmonic power with the dynamic threshold of the harmonic power. If the total harmonic power exceeds the dynamic threshold of the harmonic power, use a multi-channel bandpass filter array to segment and filter the harmonic spectrum data, and extract harmonic signals in multiple frequency bands;

[0102] Judge whether the intensity of the harmonic signal in each frequency band exceeds the preset harmonic limit value of each frequency band. If the intensity of the harmonic signal in this frequency band exceeds the standard, identify the harmonic signal in this frequency band as an excessive harmonic signal.

[0103] Specifically, in this embodiment, the total harmonic power is compared with the harmonic power dynamic threshold to evaluate whether the harmonic pollution condition of the current power grid has reached or exceeded the predetermined safety range. If the total harmonic power exceeds the harmonic power dynamic threshold, it indicates that there is a situation where the harmonic pollution exceeds the standard. At this time, this embodiment uses a multi-channel bandpass filter array to segment and filter the harmonic spectrum data. This multi-channel bandpass filter array includes multiple channels, and each channel corresponds to a specific frequency band (frequency range). When the harmonic spectrum data is input into this multi-channel bandpass filter array, this multi-channel bandpass filter array can extract the harmonic signals of each frequency band through the corresponding channels and perform filtering processing separately to obtain the harmonic signals of each frequency band. Next, the intensity of the harmonic signals in each frequency band is judged. Specifically, in this embodiment, the intensity of the harmonic signals in each frequency band is compared with the preset harmonic limit value. If the intensity of the harmonic signals in a certain frequency band exceeds its corresponding preset harmonic limit value, it indicates that the intensity of the harmonic signals in this frequency band exceeds the standard. For the harmonic signals in the frequency band with excessive intensity, they are identified as excessive harmonic signals so as to perform suppression, elimination or conversion processing on the extracted excessive harmonic signals.

[0104] S6. Convert the excessive harmonic signals into the energy storage voltage and current adapted to the energy storage device, and store the energy storage voltage and current in the energy storage device.

[0105] In this embodiment, the step of converting the excessive harmonic signals into the 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:

[0106] Use a high-speed analog-to-digital converter to sample the excessive harmonic signals 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 signals;

[0107] Use the Hann window function to perform a fast Fourier transform on the sampling data to obtain the harmonic amplitude and phase information of the excessive harmonic signals;

[0108] According to the harmonic amplitude and phase information of the harmonic signals, use the vector diagram representation method to generate a harmonic polar coordinate diagram;

[0109] According to the harmonic power in the harmonic polar coordinate diagram, set the output parameters of the H-bridge inverter;

[0110] Convert the harmonic power into the energy storage voltage and current required by the energy storage device through the H-bridge inverter and store it in the energy storage device.

[0111] 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, while energy storage technology can temporarily store this energy and release it when needed, thereby improving the energy utilization efficiency. Therefore, in this embodiment, by precisely extracting the excessive harmonic components within a specific frequency range and converting them into voltage and current forms suitable for energy storage devices, these originally harmful harmonic energies can be converted into useful energy for storage, 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, reducing the load fluctuation of the power grid.

[0112] Specifically, in this embodiment, a high-speed analog-to-digital converter can be selected according to the frequency range of the excessive harmonic signal and the required sampling accuracy, and the sampling frequency of the high-speed analog-to-digital converter can be set 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 should be at least twice the highest frequency of the signal. Then, connect the excessive harmonic signal source to the input end of the high-speed analog-to-digital converter to ensure impedance matching and signal integrity of the signal transmission path, send a start signal to the high-speed analog-to-digital converter, start collecting the sample data of the excessive harmonic signal to obtain the sampling data. Next, use the Hann 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. In this embodiment, using the Hann window function can reduce the spectral leakage in the FFT result. Use the FFT algorithm to process the windowed sampling data to obtain the spectral information, extract the amplitude and phase information of each harmonic from the spectral information output by the FFT algorithm, convert the extracted harmonic amplitude and phase information into the format required for the vector diagram, and draw the harmonic polar diagram. In the harmonic polar diagram, each harmonic component is represented by a vector, the length of which represents the amplitude and the direction represents the phase. According to the amplitude and phase information in the harmonic polar diagram, calculate the power of each harmonic, add up the powers of each harmonic to obtain the total power demand. According to the total power demand and the characteristics of the energy storage device (such as voltage range, current capacity, etc.), set the output parameters such as the modulation ratio and switching frequency of the H-bridge inverter, connect the output end of the H-bridge inverter to the energy storage device (such as a battery pack, supercapacitor, etc.) to ensure electrical compatibility and safety, send a start signal to the H-bridge inverter, and start working according to the previously set output parameters. When the energy storage device reaches the predetermined energy storage level, stop the operation of the H-bridge inverter and disconnect it from the energy storage device. Through the above steps, this embodiment converts the excessive harmonic signal into the energy storage voltage and current adapted to the energy storage device and stores it in the energy storage device.

[0113] An embodiment of the present invention provides a distributed energy optimization management method. The method collects the grid load status data in the distributed energy system in real time, and determines the optimal switching control strategy of the switching device according to the grid load status data by using the Q-Learning particle swarm optimization algorithm; controls the intermittent conduction and turn-off of the switching device according to the optimal switching control strategy, and obtains the time-domain action signal of the switching device generated during the intermittent conduction and turn-off; performs frequency-domain analysis on the time-domain action signal of the switching device by using the short-time Fourier transform algorithm, and extracts the harmonic spectrum data during the conduction and turn-off of the switching device; obtains the total harmonic power according to the harmonic spectrum data, and obtains the dynamic threshold of the harmonic power by using the adaptive threshold strategy according to the current grid operation status; compares the total harmonic power with the dynamic threshold of the harmonic power, and if the total harmonic power exceeds the dynamic threshold of the harmonic power, extracts the excessive harmonic signal; converts the excessive harmonic signal into the energy storage voltage and current adapted to the energy storage device, and stores the energy storage voltage and current in the energy storage device. Compared with the prior art, by real-time monitoring and analyzing the grid load characteristic parameters, dynamically adjusting the conduction and turn-off strategies of the switching device, and combining the short-time Fourier transform algorithm for frequency-domain analysis and the adaptive threshold judgment algorithm for optimizing the analysis of harmonics, the method effectively solves the deficiency of the traditional switching control strategy in harmonic suppression, realizes the accurate identification and efficient energy storage of harmonics generated by nonlinear loads, thereby reducing the generation of harmonics in the distributed energy system, providing a strong guarantee for the stable operation of the distributed energy system, and improving the power quality and operation stability of the power grid.

[0114] It should be noted that the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0115] In one embodiment, as Figure 2 shown, an embodiment of the present invention provides a distributed energy optimization management system, and the system includes:

[0116] A particle swarm optimization module 101, configured to collect the grid load status data in the distributed energy system in real time, and determine the optimal switching control strategy of the switching device according to the grid load status data by using the Q-Learning particle swarm optimization algorithm; the optimal switching control strategy includes the optimal conduction time, the optimal turn-off time and the optimal switching frequency of the switching device;

[0117] A time-domain signal acquisition module 102, configured to control the intermittent conduction and turn-off of the switching device according to the optimal switching control strategy, and obtain the time-domain action signal of the switching device generated during the intermittent conduction and turn-off;

[0118] The harmonic frequency-domain analysis module 103 is used to perform frequency-domain analysis on the time-domain action signal of the switching device by using the short-time Fourier transform algorithm, and extract the harmonic spectrum data during the on and off processes of the switching device;

[0119] The dynamic threshold analysis module 104 is used to obtain the total harmonic power based on the harmonic spectrum data, and obtain the dynamic threshold of the harmonic power by using the adaptive threshold strategy according to the current power grid operation state;

[0120] The excessive harmonic extraction module 105 is used to compare the total harmonic power with the dynamic threshold of the harmonic power. If the total harmonic power exceeds the dynamic threshold of the harmonic power, the excessive harmonic signal is extracted;

[0121] The harmonic energy storage conversion module 106 is used to convert the excessive harmonic signal into the energy storage voltage and current adapted to the energy storage device, and store the energy storage voltage and current in the energy storage device.

[0122] For the specific limitations of a distributed energy optimization management system, reference can be made to the above limitations of a distributed energy optimization management method, which will not be elaborated here. Those of ordinary skill in the art can realize that, in combination with the various modules and steps described in the embodiments disclosed in the present application, they can be implemented by hardware, software, or a combination of both. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0123] An embodiment of the present invention provides a distributed energy optimization management system. The system uses the Q-Learning particle swarm optimization algorithm through a particle swarm optimization module to determine the optimal switching control strategy of the switching device; a time-domain signal acquisition module controls the intermittent conduction and turn-off of the switching device according to the optimal switching control strategy, and acquires the time-domain action signal of the switching device generated during the intermittent conduction and turn-off; a harmonic frequency-domain analysis module uses the short-time Fourier transform algorithm to perform frequency-domain analysis on the time-domain action signal of the switching device, and extracts the harmonic spectrum data during the conduction and turn-off of the switching device; a dynamic threshold analysis module obtains the total harmonic power according to the harmonic spectrum data, and obtains a dynamic threshold of the harmonic power by using an adaptive threshold strategy according to the current grid operation state; an excessive harmonic extraction module compares the total harmonic power with the dynamic threshold of the harmonic power, and if the total harmonic power exceeds the dynamic threshold of the harmonic power, extracts the excessive harmonic signal; a harmonic energy storage conversion module converts the excessive harmonic signal into a storage voltage and current adapted to the energy storage device, and stores the storage voltage and current in the energy storage device. Compared with the prior art, the system effectively solves the deficiency of the traditional switching control strategy in harmonic suppression by real-time monitoring and analyzing the grid load characteristic parameters, dynamically adjusting the conduction and turn-off strategies of the switching device, and combining the short-time Fourier transform algorithm for frequency-domain analysis and the adaptive threshold judgment algorithm for optimizing the analysis of harmonics, realizes the accurate identification and efficient energy storage of harmonics generated by nonlinear loads, thereby reducing the generation of harmonics in the distributed energy system, provides a strong guarantee for the stable operation of the distributed energy system, and improves the power quality and operation stability of the power grid.

[0124] The above embodiments only represent several preferred embodiments of the present application, and the description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and substitutions can be made, 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 subject to the protection scope of the claims.

Claims

1. A distributed energy optimization management method, characterized in that It includes the following steps: Collect the grid load status data in the distributed energy system in real time, and based on the grid load status data, use the Q-Learning particle swarm optimization algorithm to determine the optimal switching control strategy of the switching device; the optimal switching control strategy includes the optimal conduction time, optimal turn-off time and optimal switching frequency of the switching device; Perform intermittent conduction and turn-off control on the switching device according to the optimal switching control strategy, and obtain the time-domain action signal of the switching device generated during the intermittent conduction and turn-off process; Use the short-time Fourier transform algorithm to perform frequency-domain analysis on the time-domain action signal of the switching device, and extract the harmonic spectrum data during the conduction and turn-off processes of the switching device; Obtain the total harmonic power according to the harmonic spectrum data, and obtain the dynamic threshold of the harmonic power using the adaptive threshold strategy according to the current grid operation status; Compare the total harmonic power with the dynamic threshold of the harmonic power. If the total harmonic power exceeds the dynamic threshold of the harmonic power, extract the excessive harmonic signal; Convert the excessive harmonic signal into the energy storage voltage and current adapted to the energy storage device, and store the energy storage voltage and current in the energy storage device; Among them, the step of using the Q-Learning particle swarm optimization algorithm to determine the optimal switching control strategy of the switching device according to the grid load status data includes: Collect the grid load status data in real time; the grid load status data includes the power consumption load power, power factor, voltage fluctuation range and current harmonic content; Use the autoregressive moving average model to perform time series analysis on the grid load status data in each sliding time window, and extract the key feature vectors of the grid load; Define the current grid load status as the state space of the Q-Learning algorithm, and define the switching control instruction of the switching device as the action space; Initialize the position and velocity of the particle swarm according to the key feature vectors of the grid load, and randomly distribute them in the action space. Each particle represents a set of switching device switching control strategies; the switching device switching control strategy includes the conduction time, turn-off time and switching frequency; Execute the switching device switching control strategy of each particle under the current grid state, and calculate the reward value according to the change of the grid state; Update the Q-value table according to the reward value, and record the cumulative rewards obtained after executing different actions under the current grid state; Update the position and velocity of each particle through the particle swarm algorithm. After multiple iterations of optimization, select the switching device switching control strategy corresponding to the particle with the largest cumulative reward as the optimal switching control strategy.

2. The distributed energy optimization management method according to claim 1, characterized in that The step of using the short-time Fourier transform algorithm to perform frequency-domain analysis on the time-domain action signal of the switching device and extract the harmonic spectrum data during the conduction and turn-off processes of the switching device includes: Preprocess the time-domain action signal of the switching device using a median filter to obtain the time-domain filtered action signal; Use the short-time Fourier transform algorithm to perform frequency-domain analysis on the time-domain filtered action signal to obtain the short-time Fourier transform result; According to the short-time Fourier transform result, an adaptive threshold method based on signal-to-noise ratio is used to identify the main harmonic components. If the signal-to-noise ratio of the main harmonic components is greater than the signal-to-noise ratio adaptive threshold, the main harmonic components are identified as effective harmonic components; According to the effective harmonic components, calculate the harmonic components and their corresponding frequencies, amplitudes and phase information during the on and off processes of the switching device to form harmonic spectrum data.

3. The 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 includes: According to the harmonic spectrum data, calculate the harmonic power data of each harmonic. The harmonic power data includes harmonic active power and harmonic reactive power; Perform complex superposition operation on the harmonic power data of each harmonic to obtain the total harmonic active power and the total harmonic reactive power; Take the total harmonic active power and the total harmonic reactive power as the output characteristics of the harmonic source, and consider the influence of line impedance and load impedance on harmonic propagation to construct a harmonic equivalent circuit model; Use the Newton-Raphson iteration method to solve the harmonic equivalent circuit model. Before the iteration starts, set the complex superposition result of the total harmonic active power and the total harmonic reactive power as the initial value, and obtain the total harmonic power considering the interaction between harmonics through iterative solution.

4. The distributed energy optimization management method according to claim 3, characterized in that The step of calculating the harmonic power data of each harmonic according to the harmonic spectrum data includes: Use the Hanning window function to window the harmonic spectrum data to obtain a windowed harmonic data set; According to the windowed harmonic data set, extract the frequency components, amplitudes and phase information of each harmonic during the on and off processes of the switching device, and convert the phase information from degree system to radian system to obtain the phase radian value; According to the frequency components, amplitudes and phase radian values of each harmonic, calculate the harmonic active power and harmonic reactive power of each harmonic.

5. A distributed energy optimization management method according to claim 1, characterized in that, The step of obtaining the dynamic threshold of harmonic power using an adaptive threshold strategy according to the current power grid operation state includes: Obtain the current power grid operation state, and use statistical analysis methods to quantitatively evaluate the fluctuation degree of the current power grid operation state; Obtain historical harmonic power data, and assign weight coefficients to each historical harmonic power data using the fluctuation degree of the current power grid operation state; According to the weight coefficients, use the exponentially weighted moving average model to perform weighted summation on the historical harmonic power data to obtain the exponentially weighted moving average value of harmonic power; Use support vector machines to construct a dynamic threshold prediction model, take the current power grid operation state and the exponentially weighted moving average value of harmonic power as input variables, and use the dynamic threshold prediction model to predict the dynamic threshold of harmonic power.

6. A distributed energy optimization management method according to claim 1, characterized in that The step of comparing the total harmonic power with the dynamic threshold of harmonic power. If the total harmonic power exceeds the dynamic threshold of harmonic power, the step of extracting the over-standard harmonic signal includes: Compare the total harmonic power with the dynamic threshold of harmonic power. If the total harmonic power exceeds the dynamic threshold of harmonic power, use a multi-channel band-pass filter array to segment and filter the harmonic spectrum data to extract harmonic signals in multiple frequency bands; Determine whether the harmonic signal intensity of each frequency band exceeds the preset harmonic limit value of each frequency band. If the harmonic signal intensity of this frequency band exceeds the standard, identify the harmonic signal of this frequency band as an over-standard harmonic signal.

7. The distributed energy optimization management method according to claim 6, wherein, The method further includes: if the total harmonic power does not exceed the harmonic power dynamic threshold, feed the total harmonic power to the power grid or the load side.

8. The distributed energy optimization management method according to claim 1, wherein The step of converting the over-standard harmonic signal into a storage voltage and current adapted to the energy storage device and storing the storage voltage and current in the energy storage device includes: Sampling the over-standard harmonic signal by using a high-speed analog-to-digital converter to obtain sampling data; the sampling frequency of the high-speed analog-to-digital converter is a preset multiple of the frequency of the over-standard harmonic signal; Performing a fast Fourier transform on the sampling data by using a Hanning window function to obtain the harmonic amplitude and phase information of the over-standard harmonic signal; Generating a harmonic polar coordinate diagram by using a vector diagram representation method according to the harmonic amplitude and phase information of the harmonic signal; Setting the output parameters of the H-bridge inverter according to the harmonic power in the harmonic polar coordinate diagram; Converting the harmonic power into a storage voltage and current required by the energy storage device through the H-bridge inverter and storing it in the energy storage device.

9. A distributed energy optimization management system, characterized in that, The system includes: A particle swarm optimization module, configured to collect the power grid load status data in the distributed energy system in real time, and determine the optimal switching control strategy of the switching device by using the Q-Learning particle swarm optimization algorithm according to the power grid load status data; the optimal switching control strategy includes the optimal conduction time, the optimal turn-off time, and the optimal switching frequency of the switching device; A time-domain signal acquisition module, configured to perform intermittent conduction and turn-off control on the switching device according to the optimal switching control strategy, and acquire the switching device time-domain action signal generated during the intermittent conduction and turn-off process; A harmonic frequency-domain analysis module, configured to perform frequency-domain analysis on the switching device time-domain action signal by using the short-time Fourier transform algorithm, and extract the harmonic spectrum data during the conduction and turn-off processes of the switching device; A dynamic threshold analysis module, configured to obtain the total harmonic power according to the harmonic spectrum data, and obtain the harmonic power dynamic threshold by using an adaptive threshold strategy according to the current power grid operation state; An over-standard harmonic extraction module, configured to compare the total harmonic power with the harmonic power dynamic threshold. If the total harmonic power exceeds the harmonic power dynamic threshold, extract the over-standard harmonic signal; A harmonic energy storage conversion module, configured to convert the over-standard harmonic signal into a storage voltage and current adapted to the energy storage device, and store the storage voltage and current in the energy storage device; Among them, determining the optimal switching control strategy of the switching device by using the Q-Learning particle swarm optimization algorithm according to the power grid load status data specifically includes: Collecting the power grid load status data in real time; the power grid load status data includes the power consumption load power, the power factor, the voltage fluctuation range, and the current harmonic content; Performing time series analysis on the power grid load status data in each sliding time window by using an autoregressive moving average model, and extracting the key feature vectors of the power grid load; Define the current power grid load status as the state space of the Q-Learning algorithm, and define the switching control instruction of the switching device as the action space; Initialize the position and velocity of the particle swarm according to the key feature vector of the power grid load, and randomly distribute them within the action space. Each particle represents a set of switching control strategies for the switching device; the switching control strategy of the switching device includes the conduction time, the turn-off time, and the switching frequency; Execute the switching control strategy of the switching device for each particle under the current power grid state, and calculate the reward value according to the change of the power grid state; Update the Q-value table according to the reward value, and record the cumulative reward obtained after performing different actions under the current power grid state; Update the position and velocity of each particle through the particle swarm algorithm. After multiple iterations of optimization, select the switching control strategy corresponding to the particle with the largest cumulative reward as the optimal switching control strategy.

Images