Optimized scheduling method for source-network-load-storage integrated project

Abstract

The invention belongs to the technical field of power system operation scheduling, and particularly relates to an optimal scheduling method for a source-grid-load-storage integrated project, which comprises the following steps of: preprocessing historical data of renewable energy output power and load in a given data sequence period in combination with a source-grid-load-storage area; a seasonal autoregressive integral moving average model and a grey prediction model GM (1, 1) for prediction are constructed and trained according to the model, and then the two kinds of prediction data are subjected to weighted average to obtain the final prediction result of the output power and the load of the renewable energy sources in the future; constructing a source network load storage collaborative scheduling model considering the inverter to provide the auxiliary service, and inputting the prediction data into the collaborative scheduling model to obtain an initial scheduling scheme; and finally, scheduling distribution network and energy optimization in source network load storage by adopting an improved tree species optimization algorithm. According to the invention, distribution network security scheduling is optimized to minimize the total cost of market transaction, so that the economical efficiency of a source-network-load-storage integrated project is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and dispatching, and in particular relates to an optimization dispatching method for a source-grid-load-storage integrated project. Background Art

[0002] At present, the power system field is facing many challenges and also ushering in new development opportunities. The global demand for renewable energy is rising rapidly, especially relying on a high proportion of renewable energy, such as wind and solar energy, which has become a key strategy to reduce greenhouse gas emissions and respond to climate change. This trend requires the power system to adapt to a more complex energy structure and unstable energy supply, and many parts of the world have taken this model as the main development direction. The volatility of these energy sources significantly increases the uncertainty of the system, complicates the operating environment of the power grid, increases the operating risk, and may trigger large-scale power outages. At the same time, the rapid development of power electronics technology provides great potential for the reliability and efficiency of the power system. The high degree of electronicization of the system means that more power transmission, distribution and conversion processes will be completed by electronic equipment and control systems, greatly enhancing the flexibility and rapid response capabilities of the power system. However, this transformation of energy structure and power electronics also brings new challenges, the most notable of which is the problem of maintaining power balance. Due to the volatility of renewable energy, the power system must effectively manage the balance of power supply and demand to ensure the stability and reliability of power supply.

[0003] In this context, the integration of power generation, grid, load and storage is particularly important. This comprehensive strategy effectively coordinates the interaction between power generation, grid, load and energy storage systems by optimizing the production, transmission, distribution and storage of energy, thereby improving the efficiency and flexibility of the entire system. The integration of power generation, grid, load and storage can balance the supply and demand of electricity under unstable power supply conditions through efficient energy storage solutions and intelligent load management, thereby significantly improving the stability of the power grid and preventing power outages. Traditional power system dispatching strategies are not suitable for this new model, and there is an urgent need to further develop the application of power generation, grid, load and storage integration through innovative theories and technologies to meet the complex challenges facing future power systems. Summary of the invention

[0004] In view of the technical problem that the above-mentioned traditional power system dispatching strategy is not sufficiently adapted to this new mode, the present invention provides an optimization dispatching method for source-grid-load-storage integrated projects.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] An optimization scheduling method for a source-grid-load-storage integration project includes the following steps:

[0007] S1. Collect historical data at a fixed data sequence period and preprocess the historical data set, wherein the historical data set includes light intensity data, photovoltaic power data, wind power data, electric vehicle load data, ambient temperature data, ambient humidity data, wind speed, wind direction, atmospheric pressure, irradiance, cloud cover and electric vehicle traffic flow recorded by the wind and solar power station in the historical period;

[0008] S2. Input the preprocessed historical data into the SARIMA model and the grey prediction model GM(1,1), where SARIMA calculates and analyzes the mean, variance, residual confidence and other parameters of the real data and the predicted data, and uses this to train the model to obtain its initial optimal parameters, and then predict the renewable energy output power and load data for the future time period. When the subsequent prediction data error does not meet the requirements, dynamically update the m historical data and re-optimize the SARIMA model parameters to improve the prediction accuracy of data affected by seasons, while GM(1,1) realizes the data prediction of renewable energy output power and load with large short-term fluctuations. The GM(1,1) model parameters can be re-optimized by analyzing the changes in the mean square error ratio and the probability of small errors. In order to improve the prediction accuracy affected by seasons and short-term climate, the outputs of the SARIMA model and the grey prediction model GM(1,1) are combined to obtain the final future output power and load of renewable energy through weighted averaging;

[0009] S3. Install power electronic controllers on distributed power generation equipment, build a source-grid-load-storage coordinated scheduling model that takes into account the auxiliary services provided by inverters, input the renewable energy output power and load data into the source-grid-load-storage coordinated scheduling model, and obtain an initial scheduling plan;

[0010] S4. Build a day-ahead clearing model and input the source-grid-load-storage coordinated dispatch results that consider the inverter providing auxiliary services into the model to verify the economic feasibility of the dispatch scheme;

[0011] S5. Combining the coordinated scheduling model and the day-ahead clearing model, an improved tree species optimization algorithm is used to optimize scheduling to minimize the total market transaction cost and ensure that the distribution network meets safety verification.

[0012] The method for preprocessing historical data in S2 is:

[0013] S2A, performing noise reduction processing on the light intensity data, wind speed data, wind direction data and traffic flow of electric vehicles of the historical data by using a wavelet transform algorithm;

[0014] S2B, improve the accuracy of the decomposition results of the denoised light intensity data, wind speed data, wind direction data and electric vehicle traffic flow through the improved overall average empirical mode decomposition algorithm;

[0015] S2C, normalize the algorithm result data to obtain preprocessed data;

[0016] S2D. Normalize the ambient temperature data, ambient humidity data, atmospheric pressure, irradiance, and cloud cover of the historical data to obtain pre-processed data.

[0017] The method for performing noise reduction processing on the light intensity data, wind speed data, wind direction data and traffic flow of electric vehicles in the historical data by using the wavelet transform algorithm in S21 is:

[0018] S2A1. Perform wavelet transform on the light intensity data, wind speed data, wind direction data and the traffic flow of electric vehicles, where the wavelet transform formula is:

[0019]

[0020] Where f is the data to be denoised; a and b represent the scaling factor and translation factor respectively; ψ(t) is the basic wavelet;

[0021] S2A2, using the soft threshold filtering method, select the optimal threshold through the general threshold and use the threshold as the standard to retain or modify the wavelet coefficients; the soft threshold filtering processing formula is:

[0022]

[0023] Among them, f n is the wavelet coefficient; ε is the optimal threshold;

[0024] The formula for calculating the optimal threshold is:

[0025]

[0026] Where σ is the standard deviation of noise, N is the sum of wavelet coefficients;

[0027] S2A3, the noise-reduced light intensity data, wind speed data, wind direction data and electric vehicle traffic flow are obtained by inverse wavelet transform, where the inverse wavelet transform calculation formula is:

[0028] f′=CWT ―1 [F[CWT(f)]]

[0029] Among them, f′(t) is the data after noise reduction.

[0030] The MEEMD algorithm is used in S22 to improve the accuracy of the de-noised light intensity data, wind speed data, wind direction data and the traffic flow decomposition result of electric vehicles, including the following steps:

[0031] S2B1. Add white noise signals with a mean of 0 to the denoised light intensity data, wind speed data, wind direction data and electric vehicle traffic flow, respectively. The addition formula is:

[0032]

[0033] Among them, n i (t) is the added white noise signal; a i is the amplitude of the added noise signal, i=1,2,...,M, M is the logarithm of the added white noise;

[0034] S2B2, yes and Perform empirical mode decomposition to obtain the first-order intrinsic modal component sequence, and integrate it to obtain the component s(t);

[0035] S2B3, check the entropy value of s(t). If it is less than or equal to η, it is not an abnormal signal; if it is greater than η, it is an abnormal signal, and return to S221 until the IMF component s p (t) is not an abnormal signal; where η is taken as 0.55 to 0.6;

[0036] S2B4. Eliminate the decomposed p-1 components from the light intensity data, wind speed data, wind direction data and the traffic flow of electric vehicles, where the elimination formula is:

[0037]

[0038] S2B5. Perform EMD decomposition on the residual signal h(t), arrange all the obtained IMF components from high frequency to low frequency, and synthesize the processed signal H(t).

[0039] The SARIMA model and the grey prediction model GM (1,1) in S2 are constructed and the prediction method for parameter optimization is as follows:

[0040] S2a1, according to the processed signal H(t), with a fixed data sequence period T, where T can be a number of months or weeks, obtain pre-processed historical data;

[0041] S2a2. Calculate and analyze the trend and periodic changes of the output power and load data of renewable energy using autocorrelation function and partial autocorrelation function;

[0042] S2a3, make the time series data stable through difference operation, determine the non-seasonal difference order d and seasonal difference order D of the SARIMA model according to the data characteristics, use ACF and PACF to calculate the order of autoregression and moving average, determine the maximum lag order p of the non-seasonal part and the maximum lag order q of the moving average operator, as well as the maximum lag order P of the seasonal part and the maximum lag order Q of the periodic moving average operator;

[0043] S2a4, based on the analysis in the previous step, determine the initial parameter combination (p, d, q) and (P, D, Q, T), where T is the cycle length;

[0044] S2a5, use the Bayesian information criterion to select the best combination of model parameters, then diagnose the fitted model and verify that the model fits well based on the residual confidence level;

[0045] S2a6, use the fitted SARIMA model to make predictions, and return to S2a3 to train and optimize the model parameters when the mean absolute error, root mean square error and residual between the predicted value and the true value exceed the threshold;

[0046] S2a7, since some historical data are very easily affected by climate, if there is a large prediction error in one of the data, the most recent m historical data are introduced, and the earlier m historical data are deleted, and S2a1 is returned again to train and optimize the model parameters;

[0047] S2b1: Obtain a fixed number of historical data, generate a cumulative sequence, and then generate an equal-weighted neighbor value generation sequence by weighted neighbor values;

[0048] S2b2, construct a first-order one-variable differential equation based on the whitening form of time series, and use the least squares method to fit and solve the development gray number and control gray number, thereby obtaining the gray prediction model GM (1,1);

[0049] S2b3, using historical data to make predictions, by calculating the mean square error ratio, if the mean square error ratio does not meet the requirements, return to step S2b1, increase the number of data sequences, and recalculate the development gray number and control gray number of the model;

[0050] S2b4. When new historical data is generated, the historical data of the output power and load of renewable energy are dynamically updated respectively;

[0051] S2c, combining the outputs of the SARIMA model and the grey prediction model GM(1,1), calculates the final future output power and load of renewable energy by weighted average.

[0052] When the inverter provides auxiliary services in S3, the operating state of distributed generation is regulated by the droop control model. The specific formula is as follows:

[0053]

[0054] Among them, the active and reactive power output by the inverter in the distributed generator are set as P G,i and Q G,i , the output voltage phase angle of the inverter during stable operation is set to δ; E is the amplitude of the inverter output voltage, V is the node voltage amplitude of the distributed generation grid-connected node; Z is the self-impedance of the distributed generation;

[0055] The active power output of the inverter is proportional to the phase of the voltage, and the reactive power output is proportional to the voltage amplitude. The output voltage can be regulated by calculating the active and reactive power output of the inverter in each distributed power generation.

[0056] The method in which the inverter provides support for the safe and stable operation of the power grid and provides paid auxiliary services is:

[0057] S31, obtaining the three-phase voltage Vabc on the inverter side, the three-phase voltage Uabc on the grid side, and the three-phase current Iabc;

[0058] S32, coordinate conversion is performed on the three-phase voltage Vabc on the inverter side, the three-phase voltage Uabc on the grid side, and the three-phase current Iabc;

[0059] S33, inputting the three-phase voltage on the inverter side and the three-phase voltage on the grid side after coordinate conversion into a phase-locked loop module, obtaining the phase angle on the inverter side and the phase angle on the grid side, and calculating the power angle difference according to the phase angle on the inverter side and the phase angle on the grid side;

[0060] S34, using a PID controller to calculate the phase compensation angle of the power angle according to the power angle difference, and compensate the power angle according to the phase compensation angle; wherein the formula for calculating the phase compensation angle of the power angle using the PID controller is,

[0061] δ=K θp (θ ref ―θ v +θ u )+K θi ∫(θ ref ―θ v +θ u )dt

[0062] Among them, K θp is the phase compensation angle proportional gain, θ ref is the reference phase angle, θ v is the phase angle on the inverter side, θ u is the phase angle on the grid side, K θi is the phase compensation angle integral gain;

[0063] S35. Output an inverter control signal according to the compensated signal, and control the inverter to restore the power system fault.

[0064] The phase-locked loop module is composed of a PI controller and an integral operation module. The PI controller is used to adjust the phase of the input voltage, and the adjusted voltage is input into the integral operation module to obtain the phase angle of the inverter side and the phase angle of the grid side. The control equation of the PI controller is:

[0065]

[0066] Among them, PI v is the output of the inverter side PI controller, K pv is the proportional gain of the voltage loop PI controller, K iv is the integral gain, K P is the scale parameter, K i is the integral parameter, PI μ is the output of the grid-side PI controller.

[0067] The objective function established for the day-ahead clearing model in S4 is:

[0068]

[0069] Among them, C is the total market transaction cost, Power is the output power of the source-grid-load-storage model, Load is the load of the source-grid-load-storage model, and C sell is the revenue from electricity sales in the day-ahead market, C dh is the day-ahead market electricity purchase cost, C rm It is the reserve capacity fee;

[0070] The day-ahead market electricity purchase cost is the electricity purchase cost from the external grid when the net output power of the source-grid-load-storage model is negative, including the electricity purchase cost of conventional power generators and the electricity purchase cost of new energy units. In the model, conventional power generators are thermal power units, and new energy units include wind power generators and photovoltaic power generators. Therefore, the day-ahead market electricity purchase cost can be expressed as:

[0071]

[0072] Among them, N G and N C are the number of conventional power generators and the number of quotations made by conventional power generators in each period, N W is the number of wind turbines, N PV is the number of photovoltaic units, ρ c,g,t is the market price of conventional power producer g in period c during period t, P c,g,t is the power cleared by conventional power generator g in the cth segment of period t, and are the market price and clearing power of wind power supplier w in period t, and are the market quotation and clearing power of photovoltaic electricity supplier pv in period t respectively;

[0073] The revenue from electricity sales in the day-ahead market is the revenue from selling electricity to the external grid when the net output power of the source-grid-load-storage model is positive. The day-ahead market revenue can be expressed as:

[0074]

[0075] Among them, ρ out,t is the market quotation of the external network to the source network load storage model in period t; out,t is the clearing power from the external grid to the source grid-load-storage model in period t;

[0076] The reserve capacity fee is the cost incurred by the market to purchase reserve capacity from conventional units in order to reduce the adverse impact of uncertainty in renewable energy output on the system. The reserve capacity fee consists of two parts: the upper reserve capacity fee and the lower reserve capacity fee. The mathematical expression is:

[0077]

[0078] Among them, N g is the number of conventional units, are the upper reserve price and lower reserve price declared by the conventional unit g in period t, They are the upper and lower reserve capacities bid by the conventional unit g in the reserve capacity market during period t respectively.

[0079] The specific steps of optimizing the dispatching of distribution network and energy storage by using the improved tree species optimization algorithm based on the coordinated dispatching model and the day-ahead clearing model in S5 are as follows:

[0080] S51. The adaptive search strategy for constructing the improved tree species optimization algorithm is:

[0081]

[0082] Among them, ST represents the adaptive search strategy, k represents the current number of iterations, and k max is the maximum number of iterations;

[0083] S52. The variation factor α of the improved tree species optimization algorithm is constructed as follows:

[0084] α=N(μ,σ 2 )

[0085] Among them, μ is the midpoint between the individual optimal position and the global optimal position, σ 2 is the deviation between the individual optimal position and the global optimal position;

[0086] S53. The improved tree species optimization algorithm expression is:

[0087]

[0088] r=―1+2*r 1

[0089] Among them, X is the current position of the individual, r 1 is a random number in the range [0, 1], X best represents the current individual optimal value, X rand represents the position of a random individual selected from the population, and N represents the introduced variation factor;

[0090] S54, initializing the parameters of the improved tree species optimization algorithm; namely, taking the active output and reactive output of the renewable energy generators and the node voltage in the scheduling model as the population;

[0091] S55. Obtain the optimal tree species of the current population, use this tree species to carry out optimized scheduling and perform safety verification on the scheduling results. If the verification passes, the optimal distribution network scheduling plan is obtained, otherwise return to S54.

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] The present invention obtains accurate prediction data by performing double noise reduction on the collected historical data and dynamically updating the historical data of some renewable energy power generation and load data, and then optimizes the tree species algorithm using adaptive search strategy, mutation factor, and balance mechanism to construct a source-grid-load-storage collaborative scheduling model. Finally, the inverter is considered to provide auxiliary services to construct the source-grid-load-storage collaborative scheduling model, generate a scheduling plan based on the scheduling model, and use an improved tree species optimization algorithm to optimize the scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0095] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0096] Figure 1 This is the overall network structure diagram of the present invention.

[0097] Figure 2 This is a double denoising flow chart of historical data of the present invention.

[0098] Figure 3 This is a flow chart for predicting the output power and load of renewable energy in the present invention. DETAILED DESCRIPTION

[0099] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. These descriptions are only to further illustrate the features and advantages of the present invention, rather than to limit the claims of the present invention. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0100] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0101] An optimization scheduling method for source-grid-load-storage integrated projects, such as Figure 1 As shown, the method includes:

[0102] Step 1: Collect historical data at a fixed data sequence period and preprocess the historical data set, wherein the historical data set includes light intensity data, photovoltaic power data, wind power data, electric vehicle load data, ambient temperature data, ambient humidity data, wind speed, wind direction, atmospheric pressure, irradiance, cloud cover and electric vehicle traffic flow recorded by the wind and solar power station in the historical period.

[0103] In this embodiment, the specific steps of step 1 are as follows:

[0104] Step 11: performing noise reduction processing on the light intensity data, wind speed data, wind direction data and traffic flow of electric vehicles of the historical data by using a wavelet transform algorithm;

[0105] Step 111: Perform wavelet transform on the light intensity data, wind speed data, wind direction data and the traffic flow of electric vehicles, wherein the wavelet transform formula is:

[0106]

[0107] Among them, f is the data to be denoised; a and b represent the scaling factor and translation factor respectively; ψ(t) is the basic wavelet.

[0108] Step 112: Use soft threshold filtering method to select the optimal threshold through the general threshold and retain or modify the wavelet coefficients based on the threshold. The soft threshold filtering formula is:

[0109]

[0110] Among them, f n is the wavelet coefficient; ε is the optimal threshold.

[0111] The formula for calculating the optimal threshold is:

[0112]

[0113] Where σ is the standard deviation of noise and N is the sum of wavelet coefficients.

[0114] Step 113: Obtain the de-noised light intensity data, wind speed data, wind direction data and electric vehicle traffic flow through inverse wavelet transform. The inverse wavelet transform calculation formula is:

[0115] f′=CWT ―1 [F[CWT(f)]]

[0116] Among them, f′(t) is the data after noise reduction.

[0117] Step 12: Improve the accuracy of the decomposition results of the denoised light intensity data, wind speed data, wind direction data and electric vehicle traffic flow through the improved ensemble average empirical mode decomposition (MEEMD) algorithm;

[0118] Step 121: Add white noise signals with a mean value of 0 to the denoised light intensity data, wind speed data, wind direction data, and electric vehicle traffic flow, respectively, where the addition formula is:

[0119]

[0120] Among them, n i (t) is the added white noise signal; a iis the amplitude of the added noise signal, i=1,2,...,M, M is the logarithm of the added white noise.

[0121] Step 122: Right and Perform EMD decomposition to obtain the first-order IMF component sequence, and integrate it to obtain the component s(t).

[0122] Step 123: Check the entropy value of s(t). If it is less than or equal to η, it is not an abnormal signal; if it is greater than η, it is an abnormal signal, and return to step 121 until the IMF component s p (t) is not an abnormal signal. η is usually taken as 0.55~0.6.

[0123] Step 124: remove the decomposed p-1 components from the light intensity data, wind speed data, wind direction data and the traffic flow of electric vehicles, wherein the removal formula is:

[0124]

[0125] Step 125: Perform EMD decomposition on the residual signal h(t), arrange all the obtained IMF components from high frequency to low frequency, and synthesize the processed signal H(t).

[0126] The double noise reduction process for light intensity data, wind speed data, wind direction data and electric vehicle traffic flow is as follows Figure 2 shown.

[0127] Step 13: Normalize the algorithm result data to obtain preprocessed data;

[0128] Step 14: normalize the ambient temperature data, ambient humidity data, atmospheric pressure, irradiance, and cloud cover of the historical data to obtain pre-processed data.

[0129] Step 2: Input the preprocessed historical data into the SARIMA (Seasonal Autoregressive Integrated Moving Average) model and the grey prediction model GM(1,1), where SARIMA calculates and analyzes the mean, variance, and residual confidence of the real data and the predicted data, and uses this to train the model to obtain its initial optimal parameters, and then predict the output power and load data of renewable energy in the future time period. When the subsequent prediction data error does not meet the requirements, dynamically update the m historical data and re-optimize the SARIMA model parameters to improve the prediction accuracy of data affected by seasons, while GM(1,1) realizes the data prediction of renewable energy output power and load with large short-term fluctuations. The GM(1,1) model parameters can be re-optimized by analyzing the changes in the mean square error ratio and the probability of small errors. In order to improve the prediction accuracy affected by seasons and short-term climate, the outputs of the SARIMA model and the grey prediction model GM(1,1) are combined to obtain the final future output power and load of renewable energy through weighted averaging;

[0130] In this embodiment, the specific steps of step 2 are as follows:

[0131] Step 211: Obtain historical data based on the processed signal H(t) with a fixed data sequence period T (T can be a number of months or weeks);

[0132] Step 212: Calculate and analyze the trend and periodic changes of renewable energy output power and load data using autocorrelation function (ACF) and partial autocorrelation function (PACF);

[0133] Step 213: Make the time series data stable through difference operation, and determine the non-seasonal difference order d and seasonal difference order D of the SARIMA model according to the data characteristics;

[0134] Step 214: using ACF and PACF to calculate the order of autoregression (AR) and moving average (MA), determine the maximum lag order p of the non-seasonal part and the maximum lag order q of the moving average operator, as well as the maximum lag order P of the seasonal part and the maximum lag order Q of the periodic moving average operator;

[0135] Step 215: Based on the analysis in the previous step, determine the possible parameter combinations (p, d, q) and (P, D, Q, T), where T is the cycle length. The SARIMA model can be expressed as:

[0136]

[0137] Among them, B is the extension operator, Δ d =(1-B) d is the difference operation, is the seasonal difference operator, ε t is a white noise sequence; Φ p (B)=1―φ 1 B 1 ―…―φ p B p is the p-order autoregressive coefficient polynomial, Φ P (B T )=1―a 1 B T ―…―a P B PT is the seasonal autoregressive parameter of order P, Θ q (B)=1―θ 1 B 1 ―…―θ q B q is the q-order moving average coefficient polynomial, θ is the q-order moving average operator, Θ Q (B T )=1―b 1 B T ―…―b q B QT is the Q-order seasonal moving average parameter.

[0138] Step 216: Use the Bayesian Information Criterion (BIC) to select the best model parameter combination, then diagnose the fitted model, and verify that the model fits well based on the residual confidence level;

[0139] BIC=K ln(n)―2ln(L)

[0140] Among them, K is the number of model parameters, n is the number of samples, and L is the maximum likelihood estimate of the model.

[0141] Step 217: Use the fitted SARIMA model to predict the output power and load of renewable energy, and evaluate the prediction accuracy of the model through the mean absolute error (MAE), root mean square error (RMSE) and residual. When the evaluation index exceeds the threshold, return to step 212 to train and optimize the model parameters.

[0142]

[0143] Where n is the output power of renewable energy and the number of distributed energy storage data, xv (i) is the true value, is the predicted value, and v is 1 and 2, representing renewable energy and load respectively.

[0144] Step 218: Since some historical data are easily affected by climate, if one of the data has a large prediction error, the most recent m (0<m≤n) historical data are introduced, and the earlier m historical data are deleted, and step 211 is returned again to train and optimize the model parameters.

[0145] Step 221: Obtain a fixed number of historical data sequences of renewable energy output power and load v=1 represents the renewable energy type, v=2 represents the load type, and a cumulative series is generated. Then generate a sequence of equal-weighted neighbor values ​​by weighted neighbor values

[0146] in,

[0147] Step 222: Construct a first-order one-variable differential equation based on the whitening form of the time series, and use the least squares method to fit and solve to obtain the development gray number α and the control gray number β, so as to obtain the gray prediction model GM(1,1) as follows:

[0148]

[0149] Step 223: Use historical data to make predictions, and calculate the mean square error ratio. If the mean square error ratio does not meet the requirements, return to step 221, increase the number of data sequences, and recalculate the development gray number and control gray number of the model;

[0150] Step 224: When new historical data is generated, the historical data of renewable energy output power and load are dynamically updated respectively.

[0151] Step 23: In order to improve the prediction accuracy affected by seasons and short-term climate, the outputs of the SARIMA model and the grey prediction model GM(1,1) are combined to obtain the final future output power and load of renewable energy through weighted average. The fused prediction data can be expressed as:

[0152]

[0153] Among them, x 1 represents renewable energy, x 2 represents load, and k represents data of the same time series. Therefore, when the SARIMA model and the grey prediction model GM(1,1) are used, it is necessary to ensure that the number of predicted data is the same.

[0154] The forecasting process of the output power and load of renewable energy based on the ensemble SARIMA model and the grey forecasting model GM(1,1) is as follows: Figure 3 described.

[0155] Step 3: Construct a source-grid-load-storage coordinated scheduling model that takes into account the auxiliary services provided by the inverter, input the predicted renewable energy output power and load data into the source-grid-load-storage coordinated scheduling model, and obtain the initial scheduling plan.

[0156] In this embodiment, the specific steps of step 3 are as follows:

[0157] Step 31: Determine the output power according to the prediction result;

[0158] Step 32: When considering the inverter providing auxiliary services, the operating state of distributed generation is regulated by the droop control model. The specific formula is as follows:

[0159]

[0160] Among them, the active and reactive power output by the inverter in the distributed generator are set as P G,i and Q G,i , the output voltage phase angle of the inverter during stable operation is set to δ; E is the amplitude of the inverter output voltage, V is the node voltage amplitude of the distributed generation grid-connected node; Z is the self-impedance of the distributed generation;

[0161] The active power output of the inverter is proportional to the phase of the voltage, and the reactive power output is proportional to the voltage amplitude. The output voltage can be regulated by calculating the active and reactive power output of the inverter in each distributed power generation.

[0162] Step 321: Obtain the three-phase voltage Vabc on the inverter side, the three-phase voltage Uabc on the grid side, and the three-phase current Iabc.

[0163] Step 322: Perform coordinate transformation on the inverter-side three-phase voltage Vabc, the grid-side three-phase voltage Uabc, and the three-phase current Iabc.

[0164] Step 323: Input the three-phase voltage on the inverter side and the three-phase voltage on the grid side after coordinate conversion into the phase-locked loop module, obtain the phase angle on the inverter side and the phase angle on the grid side, and calculate the power angle difference based on the phase angle on the inverter side and the phase angle on the grid side. The phase-locked loop module is composed of a PI controller and an integral operation module. The input voltage is phase-regulated by the PI controller, and the regulated voltage is input into the integral operation module to obtain the phase angle on the inverter side and the phase angle on the grid side. The control equation of the PI controller is:

[0165]

[0166] Among them, PI v is the output of the inverter side PI controller, K pv is the proportional gain of the voltage loop PI controller, K iv is the integral gain, K P is the scale parameter, K i is the integral parameter, PI μ is the output of the grid-side PI controller.

[0167] Step 324: Calculate the phase compensation angle of the power angle using a PID controller according to the power angle difference, and compensate the power angle according to the phase compensation angle. The formula for calculating the phase compensation angle of the power angle using the PID controller is:

[0168] δ=K θp (θ ref ―θ v +θ u )+K θi ∫(θ ref ―θ v +θ u )dt

[0169] Among them, K θp is the phase compensation angle proportional gain, θ ref is the reference phase angle, θ v is the phase angle on the inverter side, θ u is the phase angle on the grid side, K θi is the phase compensation angle integral gain.

[0170] Step 325: Output an inverter control signal according to the compensated signal, and control the inverter to restore the power system fault.

[0171] Step 33: Obtain energy consumption cost, demand side response cost, energy storage loss cost, pollutant emission penalty cost, and inverter service cost;

[0172] The function for calculating the energy consumption cost of the renewable energy is expressed as:

[0173]

[0174] Among them, C PV , C M are the cost electricity prices per unit output power of photovoltaic and wind turbines, N PV 、N M are the number of photovoltaic and wind turbines, respectively, PV,i,t , P M,i,t are the output power of photovoltaic and wind turbines respectively;

[0175] The function for calculating the overall cost of the thermal power unit is expressed as:

[0176]

[0177] Among them, N F is the number of thermal power generating units, P it is the output of thermal power unit i at time t, a it is the decision value of the unit output, 0 means no processing, 1 means output, C s,it , C P,t They are the energy consumption costs when the unit is started and when it is output, respectively.

[0178] The function for calculating the demand side response cost is expressed as:

[0179]

[0180] Among them, N DR is the number of users who can respond, The critical power in response to the compensation price, P DRk,t , P DRl,t are higher than and below The compensation amount, ε a With ε b P DR Above, Below The compensation factor when .

[0181] The function for calculating the energy storage loss cost is expressed as:

[0182]

[0183] Among them, N ES is the number of energy storage devices, C 0,it is the loss cost of energy storage device i at time t, C ES,i is the inherent investment of energy storage device i, P ES,i is the storage capacity of the device, is the discharge amount of energy storage device i at time t, m i and n i is the charge and discharge coefficient.

[0184] The function for calculating the pollutant emission penalty cost is expressed as:

[0185]

[0186] Among them, N F is the number of thermal power generating units, λ ki , β ki ,υ ki are the relationship coefficients between the unit pollutant emissions and output, ρ kis the coefficient between the pollutant penalty cost and the emission, s it is the influencing factor of pollutant emissions during unit operation, generally between 0.8 and 1.3, P it is the output of thermal power generating unit i at time t.

[0187] The inverter service cost function is calculated as:

[0188] F 6 =C P P G,t +C Q Q G,t

[0189] Among them, C P and C Q are the compensation unit prices of active power and reactive power of the virtual power plant, P G,i and Q G,i The active power and reactive power outputs provide auxiliary services for the inverter respectively.

[0190] Step 34: construct an objective function based on constraints using renewable energy generation cost, energy consumption cost of thermal power units, demand-side response cost, energy storage loss cost, pollutant emission penalty cost, and inverter service cost;

[0191] The constraints include power balance constraints, capacity constraints of each component in the network, and operation constraints of each component in the network.

[0192] The power balance constraint is expressed as:

[0193] P load =P w +P PV +P f +P ES +P grid

[0194] Among them, P load is the user load, P w is wind power, P PV is the photovoltaic power, P f is the power of the thermal power generator set, P ES is the power of the energy storage device (positive value indicates discharge, negative value indicates charge), P grid is the power transmitted from the grid.

[0195] The capacity constraints of the components in the grid, including wind turbine capacity constraints, photovoltaic capacity constraints, thermal power generation unit capacity constraints and energy storage device capacity constraints, are expressed as:

[0196] The fan capacity constraint is:

[0197] EWW,maxW,min

[0198] Among them, E W,min 、E W,max are the upper and lower limits of the fan capacity respectively.

[0199] The photovoltaic capacity constraint is:

[0200] EPV PV,maxPV,min

[0201] Among them, E PV,min 、E PV,max are the upper and lower limits of photovoltaic capacity respectively.

[0202] The capacity constraint of thermal power generating units is:

[0203] E f,maxf,min

[0204] Among them, E f,min 、E f,max They are the upper and lower limits of the capacity of thermal power generating units respectively.

[0205] The capacity constraint of the energy storage device is:

[0206] EES ES,maxES,min

[0207] Among them, E ES,min 、E ES,max are the upper and lower limits of the energy storage device capacity respectively.

[0208] The operation constraints of each component in the grid include wind power operation constraints, photovoltaic operation constraints, thermal power generation unit operation constraints and energy storage device operation constraints, which are expressed as:

[0209] Wind power operation constraints are:

[0210] PW W,maxW,min

[0211] Among them, P W,min and P W,max They are the upper and lower output limits of wind turbines respectively.

[0212] The photovoltaic operation constraints are:

[0213] PPV PV,maxPV,min

[0214] Among them, P PV,min and P PV,max It is the upper and lower output limits of the photovoltaic unit.

[0215] The operating constraints of thermal power generating units are:

[0216] Pf f,maxf,min

[0217] Among them, P f,min and P f,max They are the upper and lower output limits of thermal power generating units respectively.

[0218] The operating constraints of the energy storage device are:

[0219]

[0220] Among them, S OC,min , S OC,max are the minimum and maximum charge states of the energy storage device, respectively; are the maximum charging and discharging power of the energy storage device respectively.

[0221] Step 4: Construct a day-ahead clearing model and input the source-grid-load-storage coordinated dispatch results of distributed energy storage into the model to verify the economic feasibility of the dispatch scheme.

[0222] In this embodiment, the specific steps of step 4 are as follows:

[0223] Step 41: Based on the results of coordinated dispatch of sources, grids, loads and storage in the new electricity market environment, a day-ahead market clearing model including internal clearing and external clearing is constructed to verify the economic feasibility of coordinated dispatch.

[0224] Step 42: Consider the joint optimization clearing of multiple energy sources, with the minimum total market transaction cost as the optimization goal. The objective function consists of three parts: the day-ahead market electricity sales revenue, the day-ahead market electricity purchase cost, and the standby capacity cost.

[0225] The mathematical expression of the objective function of the day-ahead market clearing model is:

[0226]

[0227] Among them, C is the total market transaction cost, Power is the output power of the source-grid-load-storage model, Load is the load of the source-grid-load-storage model, and C sell is the revenue from electricity sales in the day-ahead market, C dh is the day-ahead electricity purchase cost,

[0228] C rm It is the cost of spare capacity.

[0229] The day-ahead market electricity purchase cost is the electricity purchase cost from the external grid when the net output power of the source-grid-load-storage model is negative, including the electricity purchase cost of conventional power generators and the electricity purchase cost of new energy generators. In the model, conventional power generators are thermal power generators, and new energy generators include wind power generators and photovoltaic power generators. Therefore, the day-ahead market electricity purchase cost can be expressed as:

[0230]

[0231] Among them, NG and N C are the number of conventional power generators and the number of quotations made by conventional power generators in each period, N W is the number of wind turbines, N PV is the number of photovoltaic units, ρ c,g,t is the market price of conventional power producer g in period c during period t, P c,g,t is the power cleared by conventional power generator g in the cth segment of period t, and are the market price and clearing power of wind power supplier w in period t, and They are the market quotation and clearing power of photovoltaic e-commerce pv in period t respectively.

[0232] The revenue from electricity sales in the day-ahead market is the revenue from selling electricity to the external grid when the net output power of the source-grid-load-storage model is positive. The day-ahead market revenue can be expressed as:

[0233]

[0234] Among them, ρ out,t is the market quotation of the external network to the source network load storage model in period t; out,t is the clearing power from the external grid to the source grid-load-storage model in period t.

[0235] The reserve capacity fee is the cost incurred by the market to purchase reserve capacity from conventional units in order to reduce the adverse impact of uncertainty in renewable energy output on the system. The reserve capacity fee consists of two parts: the upper reserve capacity fee and the lower reserve capacity fee. The mathematical expression is:

[0236]

[0237] Among them, N g is the number of conventional units, are the upper reserve price and lower reserve price declared by the conventional unit g in period t, They are the upper and lower reserve capacities bid by the conventional unit g in the reserve capacity market during period t respectively.

[0238] Step 43: Set constraints; specifically including:

[0239] 1) System load balancing constraints

[0240]

[0241] Among them, P t,g , P t,w , P t,pv are the outputs of conventional unit g, wind turbine unit w and photovoltaic power station pv in time period t respectively; is the discharge amount of energy storage device i at time t; P c,g,t The power cleared by conventional power generator g in segment c during period t; are the clearing power of wind power supplier w in period t; are the clearing power of photovoltaic electricity supplier pv in period t; D t is the total system load during period t.

[0242] 2) Unit operation constraints

[0243]

[0244]

[0245] in, and They are the minimum output power and maximum output power limits of conventional power generator g respectively; The maximum power reported by wind power supplier w in the market during period t; The maximum power reported by the photovoltaic electricity supplier pv in the market during the t period; and They are the upper and lower reserve capacities of the conventional unit g that won the bid in the day-ahead market period t respectively; and They are the maximum upper and lower spare capacities declared by the conventional unit g in time period t respectively.

[0246] 3) In-network constraints

[0247]

[0248] P ij,t =(θ i,t ―θ j,t ) / x ij

[0249]

[0250] Where B is the imaginary part of the node admittance matrix; θ t is the node voltage phase angle column vector during period t; P is the active power column vector injected into the node during period t; ij,t and are the branch active power and power limit of the first node i and the last node j in time period t respectively; θ i,t is the voltage phase angle of node i during period t; x ij is the reactance of branch ij.

[0251] 4) System spare capacity constraints

[0252]

[0253] Where Ng is the number of conventional units, and are the maximum upper and lower reserve capacities that can be declared by conventional unit g in period t, respectively. and are the upper and lower system reserve capacities in period t, respectively, and u t,g is the reserve capacity factor.

[0254] Step 5: Combining the coordinated dispatch model with the day-ahead clearing model, the Improved Tree Seed Optimizer Algorithm (ITSA) is used to optimize the dispatch to minimize the total market transaction cost and ensure that the distribution network meets the safety verification, thus obtaining the optimal distribution network dispatch plan.

[0255] In this embodiment, the specific steps of step 5 are as follows:

[0256] Step 51: Constructing the adaptive search strategy of the improved tree species optimization algorithm (ITSA) as follows:

[0257]

[0258] Among them, ST represents the adaptive search strategy, k represents the current number of iterations, and k max is the maximum number of iterations.

[0259] Step 52: Construct the variation factor α of the improved tree species optimization algorithm as follows:

[0260] α=N(μ,σ 2 )

[0261] Among them, μ is the midpoint between the individual optimal position and the global optimal position, σ 2 is the deviation between the individual optimal position and the global optimal position.

[0262] Step 53: The improved tree species optimization algorithm expression is:

[0263]

[0264] r=―1+2*r 1

[0265] Among them, X is the current position of the individual; r 1 is a random number in the range [0, 1], X best represents the current individual optimal value, X rand represents the position of a random individual selected from the population, and N represents the introduced variation factor.

[0266] Step 54: Initialize the parameters of the improved tree species optimization algorithm, that is, use the active output and reactive output of the renewable energy generators and the node voltage in the dispatch model as the population;

[0267] Step 55: Obtain the optimal tree species of the current population, use this tree species to carry out optimized scheduling and perform a safety check on the scheduling results. If the check passes, the optimal distribution network scheduling plan is obtained, otherwise return to step 54.

[0268] Only the preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention, and various changes should be included in the protection scope of the present invention.

Claims

1. An optimization scheduling method for a source-grid-load-storage integrated project, characterized in that: The following steps are involved: S1. Collect historical data at a fixed data sequence period and preprocess the historical data set, wherein the historical data set includes light intensity data, photovoltaic power data, wind power data, electric vehicle load data, ambient temperature data, ambient humidity data, wind speed, wind direction, atmospheric pressure, irradiance, cloud cover and electric vehicle traffic flow recorded by the wind and solar power station in the historical period; S2. Input the preprocessed historical data into the SARIMA model and the grey prediction model GM(1,1), where SARIMA calculates and analyzes the mean, variance, residual confidence and other parameters of the real data and the predicted data, and uses this to train the model to obtain its initial optimal parameters, and then predict the output power and load data of renewable energy in the future time period. When the error of the subsequent predicted data does not meet the requirements, dynamically update the m historical data and re-optimize the SARIMA model parameters to improve the prediction accuracy of data affected by seasons, while GM(1,1) realizes the data prediction of renewable energy output power and load with large short-term fluctuations. The GM(1,1) model parameters can be re-optimized by analyzing the changes in the mean square error ratio and the probability of small errors. In order to improve the prediction accuracy affected by seasons and short-term climate, the outputs of the SARIMA model and the grey prediction model GM(1,1) are combined to obtain the final future output power and load of renewable energy through weighted averaging; S3. Install power electronic controllers on distributed power generation equipment, build a source-grid-load-storage coordinated scheduling model that takes into account the auxiliary services provided by inverters, input the renewable energy output power and load data into the source-grid-load-storage coordinated scheduling model, and obtain an initial scheduling plan; S4. Build a day-ahead clearing model and input the source-grid-load-storage coordinated dispatch results that consider the inverter providing auxiliary services into the model to verify the economic feasibility of the dispatch scheme; S5. Combining the coordinated scheduling model and the day-ahead clearing model, an improved tree species optimization algorithm is used to optimize scheduling to minimize the total market transaction cost and ensure that the distribution network meets safety verification.

2. The optimization scheduling method for source-grid-load-storage integrated projects according to claim 1 is characterized in that: The method for preprocessing historical data in S2 is: S2A, performing noise reduction processing on the light intensity data, wind speed data, wind direction data and traffic flow of electric vehicles of the historical data by using a wavelet transform algorithm; S2B, improve the accuracy of the decomposition results of the denoised light intensity data, wind speed data, wind direction data and electric vehicle traffic flow through the improved overall average empirical mode decomposition algorithm; S2C, normalize the algorithm result data to obtain preprocessed data; S2D. Normalize the ambient temperature data, ambient humidity data, atmospheric pressure, irradiance, and cloud cover of the historical data to obtain pre-processed data.

3. The optimization scheduling method for source-grid-load-storage integrated projects according to claim 2 is characterized in that: The method for performing noise reduction processing on the light intensity data, wind speed data, wind direction data and traffic flow of electric vehicles in the historical data by using the wavelet transform algorithm in S21 is: S2A1. Perform wavelet transform on the light intensity data, wind speed data, wind direction data and the traffic flow of electric vehicles, where the wavelet transform formula is: Where f is the data to be denoised; a and b represent the scaling factor and translation factor respectively; ψ(t) is the basic wavelet; S2A2, using the soft threshold filtering method, select the optimal threshold through the general threshold and use the threshold as the standard to retain or modify the wavelet coefficients; the soft threshold filtering processing formula is: Among them, f n is the wavelet coefficient; ε is the optimal threshold; The formula for calculating the optimal threshold is: Where σ is the standard deviation of noise, N is the sum of wavelet coefficients; S2A3, the noise-reduced light intensity data, wind speed data, wind direction data and electric vehicle traffic flow are obtained by inverse wavelet transform, where the inverse wavelet transform calculation formula is: f′=CWT -1 [F[CWT(f)]] Among them, f′(t) is the data after noise reduction.

4. The optimization scheduling method for source-grid-load-storage integrated projects according to claim 2 is characterized in that: The MEEMD algorithm is used in S22 to improve the accuracy of the de-noised light intensity data, wind speed data, wind direction data and the traffic flow decomposition result of electric vehicles, including the following steps: S2B1. Add white noise signals with a mean of 0 to the denoised light intensity data, wind speed data, wind direction data and electric vehicle traffic flow, respectively. The addition formula is: Among them, n i (t) is the added white noise signal; a i is the amplitude of the added noise signal, i=1, 2, ..., M, M is the logarithm of the added white noise; S2B2, yes and Perform empirical mode decomposition to obtain the first-order intrinsic modal component sequence, and integrate it to obtain the component s(t); S2B3, check the entropy value of s(t). If it is less than or equal to η, it is not an abnormal signal; if it is greater than η, it is an abnormal signal, and return to S221 until the IMF component s p (t) is not an abnormal signal; where η is taken as 0.55 to 0.6; S2B4. Eliminate the decomposed p-1 components from the light intensity data, wind speed data, wind direction data and the traffic flow of electric vehicles, where the elimination formula is: S2B5. Perform EMD decomposition on the residual signal h(t), arrange all the obtained IMF components from high frequency to low frequency, and synthesize the processed signal H(t).

5. The optimization scheduling method for source-grid-load-storage integrated projects according to claim 1 is characterized in that: The SARIMA model and the grey prediction model GM (1,1) in S2 are constructed and the prediction method for parameter optimization is as follows: S2a1, according to the processed signal H(t), with a fixed data sequence period T, where T can be a number of months or weeks, obtain pre-processed historical data; S2a2. Calculate and analyze the trend and periodic changes of the output power and load data of renewable energy using autocorrelation function and partial autocorrelation function; S2a3, make the time series data stable through difference operation, determine the non-seasonal difference order d and seasonal difference order D of the SARIMA model according to the data characteristics, use ACF and PACF to calculate the order of autoregression and moving average, determine the maximum lag order p of the non-seasonal part and the maximum lag order q of the moving average operator, as well as the maximum lag order P of the seasonal part and the maximum lag order Q of the periodic moving average operator; S2a4, based on the analysis in the previous step, determine the initial parameter combination (p, d, q) and (P, D, Q, T), where T is the cycle length; S2a5, use the Bayesian information criterion to select the best combination of model parameters, then diagnose the fitted model and verify that the model fits well based on the residual confidence level; S2a6, use the fitted SARIMA model to make predictions, and return to S2a3 to train and optimize the model parameters when the mean absolute error, root mean square error and residual between the predicted value and the true value exceed the threshold; S2a7, since some historical data are very easily affected by climate, if there is a large prediction error in one of the data, the most recent m historical data are introduced, and the earlier m historical data are deleted, and S2a1 is returned again to train and optimize the model parameters; S2b1: Obtain a fixed number of historical data, generate a cumulative sequence, and then generate an equal-weighted neighbor value generation sequence by weighted neighbor values; S2b2, construct a first-order one-variable differential equation based on the whitening form of time series, and use the least squares method to fit and solve the development gray number and control gray number, thereby obtaining the gray prediction model GM (1,1); S2b3, using historical data to make predictions, by calculating the mean square error ratio, if the mean square error ratio does not meet the requirements, return to step S2b1, increase the number of data sequences, and recalculate the development gray number and control gray number of the model; S2b4. When new historical data is generated, the historical data of the output power and load of renewable energy are dynamically updated respectively; S2c, combining the outputs of the SARIMA model and the grey prediction model GM(1,1), calculates the final future output power and load of renewable energy by weighted average.

6. The optimization scheduling method for source-grid-load-storage integrated projects according to claim 1 is characterized in that: When the inverter provides auxiliary services in S3, the operating state of distributed generation is regulated by the droop control model. The specific formula is as follows: Among them, the active and reactive power output by the inverter in the distributed generator are set as P G,i and Q G,i , the output voltage phase angle of the inverter during stable operation is set to δ; E is the amplitude of the inverter output voltage, V is the node voltage amplitude of the distributed generation grid-connected node; Z is the self-impedance of the distributed generation; The active power output of the inverter is proportional to the phase of the voltage, and the reactive power output is proportional to the voltage amplitude. The output voltage can be regulated by calculating the active and reactive power output of the inverter in each distributed power generation.

7. The optimization scheduling method for source-grid-load-storage integrated projects according to claim 6 is characterized in that: The method in which the inverter provides support for the safe and stable operation of the power grid and provides paid auxiliary services is: S31, obtaining the three-phase voltage Vabc on the inverter side, the three-phase voltage Uabc on the grid side, and the three-phase current Iabc; S32, coordinate conversion is performed on the three-phase voltage Vabc on the inverter side, the three-phase voltage Uabc on the grid side, and the three-phase current Iabc; S33, inputting the three-phase voltage on the inverter side and the three-phase voltage on the grid side after coordinate conversion into a phase-locked loop module, obtaining the phase angle on the inverter side and the phase angle on the grid side, and calculating the power angle difference according to the phase angle on the inverter side and the phase angle on the grid side; S34, using a PID controller to calculate the phase compensation angle of the power angle according to the power angle difference, and compensate the power angle according to the phase compensation angle; wherein the formula for calculating the phase compensation angle of the power angle using the PID controller is, δ=K θp (i ref -θ v +θ u )+K θi ∫(θ ref -θ v +θ u )dt Among them, K θp is the phase compensation angle proportional gain, θ ref is the reference phase angle, θ v is the phase angle on the inverter side, θ u is the phase angle on the grid side, K θi is the phase compensation angle integral gain; S35. Output an inverter control signal according to the compensated signal, and control the inverter to restore the power system fault.

8. The optimization scheduling method for source-grid-load-storage integrated projects according to claim 7 is characterized in that: The phase-locked loop module is composed of a PI controller and an integral operation module. The PI controller is used to adjust the phase of the input voltage, and the adjusted voltage is input into the integral operation module to obtain the phase angle of the inverter side and the phase angle of the grid side. The control equation of the PI controller is: Among them, PI v is the output of the inverter side PI controller, K pv is the proportional gain of the voltage loop PI controller, K iv is the integral gain, K P is the scale parameter, K i is the integral parameter, PI μ is the output of the grid-side PI controller.

9. The optimization scheduling method for source-grid-load-storage integrated project according to claim 1 is characterized in that: The objective function established for the day-ahead clearing model in S4 is: Among them, C is the total market transaction cost, Power is the output power of the source-grid-load-storage model, Load is the load of the source-grid-load-storage model, and C sell is the revenue from electricity sales in the day-ahead market, C dh is the day-ahead market electricity purchase cost, C rm It is the reserve capacity fee; The day-ahead market electricity purchase cost is the electricity purchase cost from the external grid when the net output power of the source-grid-load-storage model is negative, including the electricity purchase cost of conventional power generators and the electricity purchase cost of new energy units. In the model, conventional power generators are thermal power units, and new energy units include wind power generators and photovoltaic power generators. Therefore, the day-ahead market electricity purchase cost can be expressed as: Among them, N G and N C are the number of conventional power generators and the number of quotations made by conventional power generators in each period, N W is the number of wind turbines, N PV is the number of photovoltaic units, ρ c，g，t is the market price of conventional power producer g in period c during period t, P c，g，t is the power cleared by conventional power generator g in the cth segment of period t, and are the market price and clearing power of wind power supplier w in period t, and are the market quotation and clearing power of photovoltaic electricity supplier pv in period t respectively; The revenue from electricity sales in the day-ahead market is the revenue from selling electricity to the external grid when the net output power of the source-grid-load-storage model is positive. The day-ahead market revenue can be expressed as: Among them, ρ out，t is the market quotation of the external network to the source network load storage model in period t; out，t is the clearing power from the external grid to the source grid-load-storage model in period t; The reserve capacity fee is the cost incurred by the market to purchase reserve capacity from conventional units in order to reduce the adverse impact of uncertainty in renewable energy output on the system. The reserve capacity fee includes upper reserve capacity fee and lower reserve capacity fee. The mathematical expression is: Among them, N g is the number of conventional units, are the upper reserve price and lower reserve price declared by the conventional unit g in period t, They are the upper and lower reserve capacities bid by the conventional unit g in the reserve capacity market during period t respectively.

10. The optimization scheduling method for source-grid-load-storage integrated project according to claim 1, characterized in that: The specific steps of optimizing the dispatching of distribution network and energy storage by using the improved tree species optimization algorithm based on the coordinated dispatching model and the day-ahead clearing model in S5 are as follows: S51. The adaptive search strategy for constructing the improved tree species optimization algorithm is: Among them, ST represents the adaptive search strategy, k represents the current number of iterations, and k max is the maximum number of iterations; S52. The variation factor α of the improved tree species optimization algorithm is constructed as follows: α=N(μ,σ 2 ) Among them, μ is the midpoint between the individual optimal position and the global optimal position, σ 2 is the deviation between the individual optimal position and the global optimal position; S53. The improved tree species optimization algorithm expression is: r＝-1+2*r1 Among them, X is the position of the current individual, r1 is a random number in the range of [0, 1], and X best represents the current individual optimal value, X rand represents the position of a random individual selected from the population, and N represents the introduced variation factor; S54, initializing the parameters of the improved tree species optimization algorithm; namely, taking the active output and reactive output of the renewable energy generators and the node voltage in the scheduling model as the population; S55. Obtain the optimal tree species of the current population, use this tree species to carry out optimized scheduling and perform safety verification on the scheduling results. If the verification passes, the optimal distribution network scheduling plan is obtained, otherwise return to S54.

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