An optimized scheduling method and system for a photovoltaic power source, grid, load, and energy storage

Abstract

An optimization scheduling method and system for a photovoltaic power source-network-load-storage, which obtains the distribution network topology structure, photovoltaic output data, and feeder sectionalizing switch status under each photovoltaic grid-connected scenario; calculates the equivalent impedance of the photovoltaic grid connection point according to the distribution network topology structure and the feeder sectionalizing switch status to obtain the impedance change characteristics; extracts the output fluctuation characteristics according to the photovoltaic output data; calculates the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics in each photovoltaic grid-connected scenario to adjust the feeder sectionalizing switch status to obtain an initial topology adjustment plan; calculates the transient characteristics of the power grid nodes under the initial topology adjustment plan, performs iterative simulation on the initial topology adjustment plan to obtain an optimized topology adjustment plan; and uses the optimized topology adjustment plan for the optimization scheduling of the photovoltaic power source-network-load-storage. By adopting the embodiment of the present invention, the ability of the distribution network to absorb photovoltaic power can be effectively improved, and the safe and stable operation of the power grid can be ensured at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and particularly relates to an optimized dispatching method and system for a photovoltaic power source-network-load-storage system. Background Art

[0002] Under different time periods, different load levels, and different photovoltaic power output conditions, there are multiple operating scenarios in the distribution network, and each scenario has its specific network topology structure, feeder sectionalizer switch state, and photovoltaic grid connection characteristics.

[0003] During the distribution network reconstruction process, there is a complex coupling relationship between the dynamic change of the network topology structure and the fluctuation of the photovoltaic power output, and the operation behavior of the feeder sectionalizer switch will also affect the stability of the system. At present, the interaction mechanism and coupling relationship among the three have not been clearly and explicitly explained. The traditional method of only planning the photovoltaic power source-network-load-storage based on experience is difficult to fully exert the power supply quality benefits of the distribution network. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide an optimized dispatching method and system for a photovoltaic power source-network-load-storage system, which can effectively improve the absorption capacity of the distribution network for photovoltaic power generation while ensuring the safe and stable operation of the power grid.

[0005] The embodiments of the present invention provide an optimized dispatching method for a photovoltaic power source-network-load-storage system, including:

[0006] Obtaining the distribution network topology structure, photovoltaic power output data, and feeder sectionalizer switch state under each photovoltaic grid connection scenario;

[0007] According to the distribution network topology structure and the feeder sectionalizer switch state, calculating the equivalent impedance of the photovoltaic grid connection points in each photovoltaic grid connection scenario to obtain the impedance change characteristics of each photovoltaic grid connection scenario;

[0008] Extracting the output fluctuation characteristics under each photovoltaic grid connection scenario according to the photovoltaic power output data;

[0009] Calculating the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics in each photovoltaic grid connection scenario, and adjusting the feeder sectionalizer switch state according to the correlation coefficient to obtain an initial topology adjustment plan;

[0010] Calculating the transient characteristics of the power grid nodes under the initial topology adjustment plan, and performing iterative simulation on the initial topology adjustment plan according to the transient characteristics to obtain an optimized topology adjustment plan;

[0011] Using the optimized topology adjustment plan to perform optimized dispatching of the photovoltaic power source-network-load-storage for the photovoltaic grid connection scenario.

[0012] As an improvement to the above solution, calculating the equivalent impedance of the PV grid connection points in each of the PV grid connection scenarios based on the distribution network topology and the status of the feeder sectionalizing switches, and obtaining the impedance change characteristics of each of the PV grid connection scenarios, includes:

[0013] Obtain the three-phase voltage and current values of each feeder sectionalizing switch and the feeder impedance parameters of the distribution line;

[0014] Calculate the sequence impedance value of the distribution line according to the three-phase voltage and current values and the distribution network topology;

[0015] Calculate the equivalent impedance value of the PV grid connection point according to the sequence impedance value and the feeder impedance parameters;

[0016] Calculate the change data of the equivalent impedance of the PV grid connection point in each PV grid connection scenario according to the status of the feeder sectionalizing switch;

[0017] Adopt the exponential smoothing method to conduct trend analysis on the change data of the equivalent impedance, and obtain the impedance change characteristics of each of the PV grid connection scenarios.

[0018] As an improvement to the above solution, extracting the output power fluctuation characteristics in each of the PV grid connection scenarios according to the PV output power data, includes:

[0019] Adopt the sliding window method to perform noise reduction and outlier processing on the PV output power data to obtain the PV output power time series data;

[0020] Adopt wavelet transform to perform multi-scale decomposition on the PV output power time series data, extract the fluctuation amplitude, frequency and duration, construct a PV output power fluctuation characteristic matrix, and obtain the output power fluctuation characteristics in each of the PV grid connection scenarios.

[0021] As an improvement to the above solution, calculating the correlation coefficient between the impedance change characteristics and the output power fluctuation characteristics in each of the PV grid connection scenarios, and adjusting the status of the feeder sectionalizing switch according to the correlation coefficient to obtain an initial topology adjustment plan, includes:

[0022] Adopt the Pearson correlation coefficient method to calculate the correlation coefficient between the impedance change characteristics and the output power fluctuation characteristics in each of the PV grid connection scenarios;

[0023] Divide the PV grid connection scenarios into impedance optimization scenarios and multi-objective optimization scenarios according to the correlation coefficient and a preset correlation coefficient threshold;

[0024] For the impedance optimization scenarios, adopt the genetic algorithm to optimize and calculate the impedance optimization target value, and adjust the status of the feeder sectionalizing switch according to the impedance optimization target to generate an initial topology adjustment plan;

[0025] For the multi-objective optimization scenario, according to the preset optimization metrics, a heuristic search algorithm is used to adjust the states of the feeder sectionalizing switches to generate an initial topology adjustment plan.

[0026] As an improvement to the above solution, for the impedance optimization scenario, a genetic algorithm is used to optimize and calculate the impedance optimization target value, and according to the impedance optimization target, the states of the feeder sectionalizing switches are adjusted to generate an initial topology adjustment plan, including:

[0027] Construct an optimization objective function for the impedance optimization scenario; the optimization objective function includes a node impedance term and a power loss term;

[0028] According to the optimization objective function, a genetic algorithm is used to optimize and calculate the impedance optimization target value;

[0029] According to the impedance optimization target value, an algorithm based on graph theory is used to adjust the states of the feeder sectionalizing switches to generate an initial topology adjustment plan.

[0030] As an improvement to the above solution, the optimization metrics include a voltage stability margin metric and a power balance metric; then for the multi-objective optimization scenario, according to the preset optimization metrics, a heuristic search algorithm is used to adjust the states of the feeder sectionalizing switches to generate an initial topology adjustment plan, including:

[0031] Obtain the voltage curve and power curve of the multi-objective optimization scenario;

[0032] According to the voltage curve, calculate the voltage stability margin metric;

[0033] According to the power curve, calculate the power balance metric;

[0034] Use a fuzzy comprehensive evaluation algorithm to automatically calculate the weight values of the voltage stability margin metric and the power balance metric to obtain a multi-objective optimization evaluation matrix;

[0035] According to the correlation coefficient, obtain the optimization objective of the multi-objective optimization evaluation matrix;

[0036] According to the optimization objective of the multi-objective optimization evaluation matrix, a heuristic search algorithm is used to adjust the states of the feeder sectionalizing switches to generate an initial topology adjustment plan.

[0037] As an improvement to the above solution, calculate the transient characteristics of the power grid nodes under the initial topology adjustment plan, and according to the transient characteristics, perform iterative simulation on the initial topology adjustment plan to obtain an optimized topology adjustment plan, including:

[0038] Obtain the voltage data and current data of the power grid nodes under the initial topology adjustment plan;

[0039] Calculate the transient power transfer characteristics between grid nodes based on the voltage data to obtain the transient stability risk;

[0040] Calculate the line power distribution data of the initial topology adjustment scheme based on the voltage data and the current data to obtain the static security margin;

[0041] If the transient stability risk is greater than the preset stability risk threshold, use a recurrent neural network to predict the law of transient disturbance propagation, and calculate the transient response process caused by short-circuit faults and switching operations through time-domain simulation to obtain transient response optimization data;

[0042] If the static security margin is less than the preset security margin threshold, calculate the power flow distribution of the distribution network based on the Newton iteration method, and check through power transfer constraints and voltage violation constraints to obtain static constraint optimization data;

[0043] Iteratively simulate the initial topology adjustment scheme according to the transient response optimization data and the static constraint optimization data to obtain an optimized topology adjustment scheme.

[0044] As an improvement of the above scheme, the calculating the transient power transfer characteristics between grid nodes based on the voltage data to obtain the transient stability risk includes:

[0045] Process the voltage data through wavelet transform to obtain voltage fluctuation characteristics;

[0046] Based on the voltage data, obtain the voltage phase angle change trend before and after impedance mutation under the initial topology adjustment scheme;

[0047] Obtain the nodal admittance matrix according to the state of the feeder section switch and the distribution network topology structure;

[0048] Calculate the transient power transfer characteristics between grid nodes according to the voltage phase angle change trend and the nodal admittance matrix to obtain a transient stability margin curve;

[0049] According to the transient stability margin curve, use a recurrent neural network to predict the transient response of key network nodes under impedance mutation, and calculate the transient disturbance propagation path through the phase angle stability criterion to obtain the transient stability risk.

[0050] As an improvement of the above scheme, the calculating the line power distribution data of the initial topology adjustment scheme based on the voltage data and the current data to obtain the static security margin includes:

[0051] Calculate the line power distribution data of the initial topology adjustment scheme based on the voltage data and the current data;

[0052] The line overload level and the node voltage violation degree are calculated through power flow sensitivity to obtain the static security margin.

[0053] An embodiment of the present invention also provides an optimized scheduling system for a photovoltaic power source-network-load-storage, including:

[0054] A multi-scenario data acquisition module, configured to acquire the distribution network topology structure, photovoltaic output data, and feeder sectional switch status under each photovoltaic grid connection scenario;

[0055] An impedance change characteristic calculation module, configured to calculate the equivalent impedance of the photovoltaic grid connection points in each of the photovoltaic grid connection scenarios according to the distribution network topology structure and the feeder sectional switch status, so as to obtain the impedance change characteristics of each of the photovoltaic grid connection scenarios;

[0056] An output fluctuation characteristic calculation module, configured to extract the output fluctuation characteristics in each of the photovoltaic grid connection scenarios according to the photovoltaic output data;

[0057] An initial scheme generation module, configured to calculate the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics in each of the photovoltaic grid connection scenarios, and adjust the feeder sectional switch status according to the correlation coefficient to obtain an initial topology adjustment scheme;

[0058] A scheme optimization module, configured to calculate the transient characteristics of the power grid nodes under the initial topology adjustment scheme, and perform iterative simulation on the initial topology adjustment scheme according to the transient characteristics to obtain an optimized topology adjustment scheme;

[0059] An optimized scheduling module, configured to perform optimized scheduling of the photovoltaic power source-network-load-storage on the photovoltaic grid connection scenarios by using the optimized topology adjustment scheme.

[0060] Compared with the prior art, an optimized scheduling method and system for a photovoltaic source-network-load-storage according to the present invention obtains the distribution network topology structure, photovoltaic output data, and feeder sectional switch states under various photovoltaic grid connection scenarios; calculates the equivalent impedance of the photovoltaic grid connection points in each of the photovoltaic grid connection scenarios according to the distribution network topology structure and the feeder sectional switch states to obtain the impedance change characteristics of each of the photovoltaic grid connection scenarios; extracts the output fluctuation characteristics in each of the photovoltaic grid connection scenarios according to the photovoltaic output data; calculates the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics in each of the photovoltaic grid connection scenarios, and adjusts the feeder sectional switch states according to the correlation coefficient to obtain an initial topology adjustment plan; calculates the transient characteristics of the power grid nodes under the initial topology adjustment plan, and performs iterative simulation on the initial topology adjustment plan according to the transient characteristics to obtain an optimized topology adjustment plan; and uses the optimized topology adjustment plan to perform optimized scheduling of the photovoltaic source-network-load-storage for the photovoltaic grid connection scenarios. By using the embodiments of the present invention, the power consumption capacity of the distribution network for photovoltaic power generation can be effectively improved, and the safe and stable operation of the power grid can be ensured at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a schematic flowchart of the steps of the optimized scheduling method for a photovoltaic source-network-load-storage provided by an embodiment of the present invention;

[0062] Figure 2 is a schematic structural diagram of the optimized scheduling system for a photovoltaic source-network-load-storage provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] In the description of the specification and the claims, it should be understood that the terms first, second, etc. in the specification and the claims are only used for the purpose of distinguishing the description of the same technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features, nor necessarily describing the order or time sequence. The terms may be interchanged under appropriate circumstances. Thus, the features defined with "first", "second" may explicitly or implicitly include at least one of the features.

[0065] An embodiment of the present invention provides an optimized scheduling method for a photovoltaic source-network-load-storage. Please refer to Figure 1 , in this embodiment, the optimized scheduling method for the photovoltaic source-network-load-storage is specifically executed through steps S1 to S6:

[0066] S1. Obtain the distribution network topology structure, photovoltaic output data, and feeder sectional switch status under each photovoltaic grid-connected scenario.

[0067] It should be noted that according to different time periods, load levels, and photovoltaic output conditions, several photovoltaic grid-connected scenarios can be obtained, and at least one of the network topology structure, feeder sectional switch status, and photovoltaic grid-connected characteristics in each photovoltaic grid-connected scenario is different.

[0068] In some preferred embodiments, the distribution network topology structure includes the position status of the feeder sectional switch, the distribution information of the photovoltaic grid connection points, and the distribution information of the distribution network nodes. The feeder sectional switch status includes two types of status information: closed / open status and normal / fault.

[0069] It can be understood that in the embodiments of the present invention, there is partial identity between the content included in the distribution network topology structure and the feeder sectional switch status, because the operation of the feeder switch will necessarily cause changes in the distribution network topology structure.

[0070] S2. According to the distribution network topology structure and the feeder sectional switch status, calculate the equivalent impedance of the photovoltaic grid connection points under each photovoltaic grid-connected scenario, and obtain the impedance change characteristics of each photovoltaic grid-connected scenario.

[0071] It should be noted that by operating the opening and closing status of the feeder sectional switch, it will cause changes in the distribution network topology structure, and also cause changes in the load of the photovoltaic grid connection points, thereby affecting the stability and controllability of the photovoltaic output. In the embodiments of the present invention, by calculating the equivalent impedance of the photovoltaic grid connection points under each photovoltaic grid-connected scenario, the interaction mechanism and coupling relationship between the operation of the feeder sectional switch and the network topology change and the photovoltaic output fluctuation are measured from the perspective of the feeder sectional switch operation.

[0072] S3. According to the photovoltaic output data, extract the output fluctuation characteristics under each photovoltaic grid-connected scenario.

[0073] It should be noted that there is a complex coupling relationship between the dynamic change of the distribution network topology structure and the fluctuation of the photovoltaic output during the distribution network reconstruction process, and the two affect and restrict each other. The change of the distribution network topology structure will cause the redistribution of the power flow, resulting in fluctuations in the local node voltage and line power, and further affecting the maximum power point tracking and reactive power regulation capabilities of the photovoltaic inverter, that is, causing fluctuations in the photovoltaic output. The fluctuation of the photovoltaic output will also affect the network power flow and voltage level, exacerbating the system instability factors caused by the network topology change.

[0074] S4. Calculate the correlation coefficient between the impedance change characteristics and the output power fluctuation characteristics in each of the photovoltaic grid-connected scenarios, and adjust the states of the feeder sectionalizing switches according to the correlation coefficient to obtain an initial topology adjustment plan.

[0075] In the embodiments of the present invention, by calculating the correlation coefficient between the impedance change characteristics and the output power fluctuation characteristics, the differential influence degree of the distribution network topology adjustment on the photovoltaic accommodation capacity can be represented. Then, based on the differential influence degree, different operations of the feeder sectionalizing switch states are performed for different photovoltaic grid-connected scenarios, and the obtained initial topology adjustment plan takes into account the complex coupling between the power grid topology structure and the photovoltaic output power fluctuation.

[0076] S5. Calculate the transient characteristics of the power grid nodes under the initial topology adjustment plan, and perform iterative simulation on the initial topology adjustment plan according to the transient characteristics to obtain an optimized topology adjustment plan.

[0077] It should be noted that in different photovoltaic grid-connected scenarios, the evaluation indexes for the power grid may be different. Therefore, further analysis of the transient characteristics of the power grid nodes is carried out to ensure that the finally obtained optimized topology adjustment plan can take into account the safety and stability constraints and ensure the reliable operation of the power grid.

[0078] S6. Use the optimized topology adjustment plan to perform optimized scheduling of the photovoltaic power source, grid, load, and storage in the photovoltaic grid-connected scenario.

[0079] In the above solution, by calculating the correlation coefficient between the impedance change characteristics and the output power fluctuation characteristics and then adjusting the states of the feeder sectionalizing switches, the complex coupling between the power grid topology structure and the photovoltaic output power fluctuation can be fully considered. And further analyzing the transient characteristics of the power grid nodes to optimize the topology adjustment plan can verify the feasibility and optimization effect of the plan. The embodiments of the present invention can effectively improve the photovoltaic accommodation capacity of the distribution network and ensure the safe and stable operation of the power grid at the same time.

[0080] In some preferred embodiments, the photovoltaic grid-connected scenario described in step S1 is obtained in advance based on different combinations of time periods, load levels, and photovoltaic output conditions, and can be directly obtained. Exemplarily, the method for obtaining the photovoltaic grid-connected scenario includes: calculating a distribution network topology structure data set according to the feeder sectional switch status; performing normalization processing on the distribution network topology structure data set, and establishing an output time series matrix in combination with the photovoltaic active power data; using the kernel density estimation method to extract features from the output time series matrix to obtain output features; performing hierarchical clustering according to the output features to obtain a set of typical photovoltaic output scenarios; according to the set of typical photovoltaic output scenarios, establishing a mapping relationship between the node injection power and the node voltage in combination with the distribution line impedance parameters to obtain a multi-scenario network power flow calculation model; inputting the real-time feeder sectional switch status and photovoltaic output data into the multi-scenario network power flow calculation model to obtain the photovoltaic grid-connected scenario.

[0081] Exemplarily, in a distribution network in a certain area, there are 12 substation outgoing circuit breakers, 8 tie switches, 15 sectional switches, and 14 photovoltaic grid connection points, and the sampling period is 100 milliseconds. The system records two types of switch status quantities, namely position status and fault status. Each switch contains two types of information, namely closed / open status and normal / fault. The switch status association matrix is a 35×35 matrix composed of 0 and 1, where 1 indicates an electrical connection relationship between two circuit breakers. The current topology structure of the distribution network is obtained according to the photovoltaic grid connection capacity, the geographical coordinates of the grid connection points, and the switch association matrix. During the standardization process, the original data in three dimensions, namely voltage level, load level, and voltage margin, are normalized. Taking the voltage level as an example, the rated voltage of the substation bus is 10 kV, and the measured voltage range is 9.5 - 10.5 kV. The normalized value range is 0 - 1. The load level is standardized based on the rated capacity of the transformer, and abnormal data exceeding 3 times the standard deviation are removed. The historical operation data of the photovoltaic power station includes operation parameters such as active power, reactive power, and DC side voltage with a 15-minute sampling interval. For the power data of 96 time points at 14 photovoltaic grid connection points, a 14×96 time series feature matrix is constructed. The kernel density estimation method is used to extract the output feature curves of each grid connection point for 24 hours. The hierarchical clustering algorithm obtains 4 types of typical scenarios by calculating the Euclidean distance, corresponding to overcast, cloudy, sunny, and rainy conditions respectively. In the distribution network power flow calculation model, the substation is used as the balance node, the photovoltaic grid connection points are used as the injection power nodes, and the load points are used as the power extraction nodes. The voltage margin constraint range is ±7% of the rated voltage, and the line impedance parameters are calculated according to the standard parameters of overhead lines and cables. The influence coefficient of each grid connection point on the bus voltage is obtained through power flow sensitivity analysis, and a 14×35 voltage influence coefficient matrix is constructed to divide the photovoltaic grid-connected scenario.

[0082] As a preferred embodiment, in step S2, according to the distribution network topology structure and the feeder section switch states, calculate the equivalent impedance of the PV grid connection points in each of the PV grid connection scenarios, and obtain the impedance change characteristics of each of the PV grid connection scenarios, including:

[0083] Obtain the three-phase voltage and current values of each feeder section switch and the feeder impedance parameters of the distribution line;

[0084] According to the three-phase voltage and current values and the distribution network topology structure, calculate the sequence impedance values of the distribution line;

[0085] According to the sequence impedance values and the feeder impedance parameters, calculate the equivalent impedance values of the PV grid connection points;

[0086] According to the feeder section switch states, calculate the change data of the equivalent impedance of the PV grid connection points in each of the PV grid connection scenarios;

[0087] Adopt the exponential smoothing method to conduct trend analysis on the change data of the equivalent impedance, and obtain the impedance change characteristics of each of the PV grid connection scenarios.

[0088] It should be noted that the feeder impedance parameters include the line length and the line cross-section data; the sequence impedance values include the positive sequence impedance value and the zero sequence impedance value. When calculating the sequence impedance values, the distribution network lines are segmented based on the positions of the feeder section switches to obtain the sequence impedance values of several line segments, and then the equivalent impedance values of the PV grid connection points are calculated.

[0089] In some preferred embodiments, obtain the electrical distances and electrical signal transmission information between each node from the distribution network topology structure, and obtain the dynamic change characteristics of the distribution network topology structure based on the feeder section switch states. Then, according to the node voltage amplitude data, obtain the voltage stability margin curve, calculate the change rate of the equivalent impedance of the PV grid connection points in different scenarios, and obtain the dynamic impedance characteristic curve. According to the dynamic impedance characteristic curve, classify the impedance change trends of the PV grid connection points in different scenarios through the support vector machine algorithm, and obtain the impedance change characteristics of each of the PV grid connection scenarios.

[0090] Exemplarily, taking a certain 10 kV distribution line as an example, this line includes 12 section switches, and the switch state matrix is a 12×12 symmetric matrix, and the matrix elements take values of 0 or 1, where 1 indicates that there is an electrical connection relationship between two switches, and 0 indicates that there is no electrical connection relationship. The switch on-line monitoring device collects the three-phase voltage and current data every 100 milliseconds, and the sampling data includes the phase voltage amplitude, the phase current amplitude, and the phase angle information.

[0091] During the measurement of line impedance parameters, the positive sequence impedance value is calculated from three-phase voltage and current data. For an overhead line with a cross-sectional area of 35 square millimeters, the positive sequence resistance value per kilometer of the line is approximately 0.927 ohms, the positive sequence reactance value is approximately 0.372 ohms, and the zero sequence impedance value is 2.5 to 3 times the positive sequence impedance value. The online feeder parameter measurement device measures the line length by the time domain reflectometry method. For a distribution line with many branches, the multi-point injection method is adopted to improve the measurement accuracy. The operating data of the PV connection point includes parameters such as active power, reactive power, and the voltage at the connection point, and the sampling interval is 1 minute. For the mutation points and outliers in the original data, the 5-point median filtering method is used for processing, and the filtered data is used to construct a 75×75 electrical connection relationship matrix. Each element in the matrix represents the comprehensive impedance value between two nodes, considering the influence of the impedance of the line itself and the equivalent impedance of the load.

[0092] The improved Euclidean distance method is used to calculate the electrical distance between nodes, and a voltage sensitivity weight factor is introduced on the basis of the traditional Euclidean distance. For a PV power station with an installed capacity of 500 kW, the allowable voltage fluctuation range at its connection point is ±7% of the rated value. The voltage stability margin curve is drawn through 24-hour continuous voltage monitoring data, and the moment corresponding to the lowest point of the curve often appears during the period of rapid change in PV output. The dynamic impedance characteristic curve reflects the change law of the impedance at the PV connection point under different operating scenarios, and the curve shape is closely related to the PV output, load level, and network topology. The support vector machine algorithm is used to classify the scenarios by extracting the curve feature quantities, and the impedance change characteristics of each PV grid connection scenario are obtained. Among them, the feature quantities include information such as the curve slope, fluctuation amplitude, and periodicity.

[0093] As a preferred implementation manner, step S3: Extract the output fluctuation characteristics under each of the PV grid connection scenarios according to the PV output data, including:

[0094] The sliding window method is used to denoise and process the outliers of the PV output data to obtain the PV output time series data;

[0095] Wavelet transform is used to perform multi-scale decomposition on the PV output time series data, extract the fluctuation amplitude, frequency, and duration, construct a PV output fluctuation feature matrix, and obtain the output fluctuation characteristics under each of the PV grid connection scenarios.

[0096] Exemplarily, the data acquisition period of the photovoltaic output data is 1 s, and a 60 s sliding window is used to preprocess the photovoltaic output data. For a 500-kilowatt photovoltaic power station, the amplitude of the output fluctuation is within 5% during normal operation, and can reach 20% when the cloud cover changes rapidly, with a duration of 3 to 5 minutes. High-frequency fluctuation components caused by equipment jitter are removed through noise reduction processing, and the fluctuation characteristics caused by natural factors such as cloud occlusion are retained. Wavelet transform is used to perform multi-scale decomposition on the photovoltaic output fluctuation curve. The db4 wavelet basis function is selected, and the original signal is decomposed into 5 layers. After decomposition, the fluctuation characteristics in different frequency bands are obtained. Among them, the high-frequency component reflects the instantaneous fluctuation characteristics, and the low-frequency component reflects the long-term change trend. The fluctuation amplitude characteristic is characterized by the standard deviation, the frequency characteristic is calculated by the zero-crossing rate, and the duration is obtained based on the peak detection method.

[0097] As a preferred implementation manner, in step S4, calculate the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics in each of the photovoltaic grid connection scenarios, and adjust the state of the feeder sectionalizing switch according to the correlation coefficient to obtain an initial topology adjustment scheme, which is specifically executed through steps S41 - S44:

[0098] S41. Calculate the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics in each of the photovoltaic grid connection scenarios by using the Pearson correlation coefficient method.

[0099] S42. Divide the photovoltaic grid connection scenarios into impedance optimization scenarios and multi-objective optimization scenarios according to the correlation coefficient and a preset correlation coefficient threshold.

[0100] It should be noted that by calculating the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics by using the Pearson correlation coefficient method, the coupling relationship between the grid impedance and the output fluctuation in each of the photovoltaic grid connection scenarios can be obtained.

[0101] S43. For the impedance optimization scenario, use a genetic algorithm to optimize and calculate the impedance optimization target value, and adjust the state of the feeder sectionalizing switch according to the impedance optimization target to generate an initial topology adjustment scheme.

[0102] S44. For the multi-objective optimization scenario, adjust the state of the feeder sectionalizing switch by using a heuristic search algorithm according to a preset optimization index to generate an initial topology adjustment scheme.

[0103] In some preferred embodiments, step S42, according to the correlation coefficient and the preset correlation coefficient threshold, the photovoltaic grid-connected scenario is divided into an impedance optimization scenario and a multi-objective optimization scenario, by grouping the correlation coefficients based on the coupling relationship between the grid impedance and the output fluctuation, using a hierarchical clustering method, and calculating the similarity between each group by Euclidean distance to obtain a scenario classification result. Further, according to the scenario classification result, the photovoltaic grid-connected scenarios are arranged in descending order based on the values ​​of the correlation coefficients to achieve the division of the photovoltaic grid-connected scenarios.

[0104] For example, taking a photovoltaic power station as an example, in the normal operation scenario on a sunny day, the correlation coefficient between output fluctuation and impedance change is 0.15, showing a weak correlation. In the scenario of frequent fluctuations in rainy weather, the correlation coefficient rises to 0.68, showing a significant positive correlation. The hierarchical clustering process uses Euclidean distance to measure the similarity of scenarios, and the clustering results are displayed through a dendrogram. The clustering threshold is set to 0.5, and all operation scenarios are divided into 4 categories. The first category corresponds to the stable operation scenario, characterized by small output fluctuations and slow impedance changes. The second category corresponds to the violent fluctuation scenario, where rapid output fluctuations cause significant impedance changes. The third category corresponds to the topology reconstruction scenario, where impedance changes suddenly and the output is relatively stable. The fourth category corresponds to the fault recovery scenario, where both output and impedance show violent fluctuations. According to the correlation coefficient sorting results, the coupling degree of the violent fluctuation scenario is the highest, with a mean correlation coefficient of 0.72. The second is the fault recovery scenario, with a mean correlation coefficient of 0.65. The coupling degree of the stable operation scenario and the topology reconstruction scenario is relatively low, with mean correlation coefficients of 0.18 and 0.25, respectively.

[0105] Further, based on the above analysis, in an embodiment of the present invention, a photovoltaic grid-connected scenario with a correlation coefficient greater than 0.7 is classified as an impedance optimization scenario, and a photovoltaic grid-connected scenario with a correlation coefficient not greater than 0.7 is classified as a multi-objective optimization scenario.

[0106] It should be noted that the impedance optimization scenario mainly considers improving the equivalent impedance characteristics of the grid connection point and reducing the impedance value by switching the tie switch. The multi-objective optimization scenario focuses on optimizing the overall operation status of the network and balancing the load rate of each feeder under the premise of meeting the voltage stability constraint.

[0107] Further, preferably, step S43, for the impedance optimization scenario, adopting a genetic algorithm to find and calculate an impedance optimization target value, adjusting the feeder segment switch state according to the impedance optimization target, and generating an initial topology adjustment scheme, includes:

[0108] Constructing an optimization objective function of the impedance optimization scenario; the optimization objective function includes a node impedance term and a power loss term;

[0109] According to the optimization objective function, a genetic algorithm is used to find and calculate the impedance optimization target value;

[0110] According to the impedance optimization target value, an algorithm based on graph theory is used to adjust the states of the feeder sectionalizing switches, and an initial topology adjustment scheme is generated.

[0111] In some preferred embodiments, based on the distribution network topology, the states of the feeder sectionalizing switches are marked by a graph coloring algorithm, the voltage amplitude data of each node is obtained from the voltage measurement terminals, the voltage margin and transmission loss between nodes are calculated, and a basic topology evaluation data set is obtained. According to the basic topology evaluation data set, a depth-first search algorithm is used to calculate the path combinations from each grid connection point to the substation, and the comprehensive impedance values of each path are calculated by impedance accumulation to obtain a path impedance optimization data set. If the optimization object focuses on impedance characteristics, an ant colony algorithm is used for optimization to calculate the optimal impedance path, and the optimization objectives include two dimensions of minimizing the comprehensive impedance of the path and minimizing the transmission loss.

[0112] Preferably, the optimization indexes include a voltage stability margin index and a power balance index. Then, in step S44, for the multi-objective optimization scenario, according to the preset optimization indexes, a heuristic search algorithm is used to adjust the states of the feeder sectionalizing switches, and an initial topology adjustment scheme is generated, including:

[0113] Obtain the voltage curve and power curve of the multi-objective optimization scenario;

[0114] Calculate the voltage stability margin index according to the voltage curve;

[0115] Calculate the power balance index according to the power curve;

[0116] Adopt a fuzzy comprehensive evaluation algorithm to automatically calculate the weight values of the voltage stability margin index and the power balance index to obtain a multi-objective optimization evaluation matrix;

[0117] Obtain the optimization objective of the multi-objective optimization evaluation matrix according to the correlation coefficient;

[0118] According to the optimization objective of the multi-objective optimization evaluation matrix, a heuristic search algorithm is used to adjust the states of the feeder sectionalizing switches, and an initial topology adjustment scheme is generated.

[0119] In some preferred embodiments, the fuzzy comprehensive evaluation adopts a triangular membership function, and according to the preset evaluation matrix, the weights of voltage stability and power balance are obtained.

[0120] It should be noted that for a distribution network, even when its distribution network topology and the states of feeder sectionalizing switches are exactly the same, it may still be classified into different photovoltaic grid-connected scenarios due to different weather conditions. In the embodiments of the present invention, an initial topology adjustment scheme and an optimized adjustment scheme for each photovoltaic grid-connected scenario are recorded in the scenario-based topology adjustment instruction library to achieve scenario adaptive adjustment operations.

[0121] In a preferred embodiment, taking a certain 10 kV distribution line as an example, this line includes 15 sectionalizing switches and 12 tie switches, forming a switch state correlation matrix. Each element in the matrix takes a value of 0 or 1, where 1 indicates that the two switches are in a connected state. A network topology graph is constructed based on the minimum spanning tree algorithm, and the weight value of each edge is jointly determined by the line impedance and transmission loss. For an overhead line with a cross-sectional area of 300 square millimeters, the impedance value per kilometer is approximately 0.12 ohms. The graph coloring algorithm uses 4 colors to mark the switch states, including normally open, normally closed, switchable, and faulty states. The node voltage measurement data shows that when operating normally, the voltages of each node fluctuate between 9.7 and 10.3 kV, and the voltage margin is maintained within the range of plus or minus 3%. The transmission losses of each path are calculated based on the voltage and impedance data, and the average loss rate is 2.5%. The depth-first search calculates all feasible paths from the grid connection point to the substation. There are a total of 8 feasible paths from a certain 500 kW photovoltaic grid connection point to the substation, and the comprehensive impedance values of each path are calculated by accumulation. The comprehensive impedance of the shortest path is 0.85 ohms, and the comprehensive impedance of the longest path reaches 1.42 ohms. Considering the influence of transmission loss, the path with a smaller comprehensive impedance is preferentially selected during actual operation. During the optimization process of the ant colony algorithm, the initial pheromone concentration is set to 1, and the pheromone evaporation coefficient is 0.3. After 200 iterations, the optimal path is obtained, and the comprehensive impedance of this path is 0.78 ohms, and the transmission loss is reduced to 1.8%. The particle swarm algorithm simultaneously considers voltage stability and power balance, the population size is set to 100, and the inertia weight linearly decreases from 0.9 to 0.4.

[0122] The multi-objective optimization results show that the voltage deviation is controlled within plus or minus 2%, and the load rates of each feeder tend to be balanced. The topology reconstruction sequence includes a combination of switch operations, and its feasibility is verified through power flow calculation. The verification indicators include that the allowable deviation of the node voltage does not exceed plus or minus 7%, the line current-carrying capacity does not exceed 85% of the rated capacity, and the power factor is not less than 0.95. The neural network prediction model adopts a three-layer structure. The input layer includes characteristic quantities such as voltage, power, and impedance. The number of hidden layer nodes is 20, and the output layer predicts the dynamic characteristic indicators of each scheme.

[0123] In practical applications, in scenarios with drastic fluctuations in photovoltaic power output (i.e., in photovoltaic grid-connected scenarios where the correlation coefficient is greater than 0.7), the scheme with the optimal impedance characteristics is preferentially selected. In scenarios with uneven load distribution (i.e., in photovoltaic grid-connected scenarios where the correlation coefficient is not greater than 0.7), the scheme with the optimal comprehensive performance is selected. Measured data shows that through scenario adaptive adjustment, the operation indicators of the distribution network have been significantly improved.

[0124] As a preferred implementation manner, in step S5, calculate the transient characteristics of the grid nodes under the initial topology adjustment scheme, and based on the transient characteristics, perform iterative simulation on the initial topology adjustment scheme to obtain an optimized topology adjustment scheme, which is specifically executed through steps S51 - S56:

[0125] S51. Obtain the voltage data and current data of the grid nodes under the initial topology adjustment scheme.

[0126] S52. According to the voltage data, calculate the transient power transmission characteristics between grid nodes to obtain the transient stability risk.

[0127] S53. According to the voltage data and the current data, calculate the line power distribution data of the initial topology adjustment scheme to obtain the static security margin.

[0128] It should be noted that the transient stability risk reflects the voltage phase angle change trend before and after impedance mutation, while the static security margin reflects the line overload level and the degree of node voltage over-limit. In some preferred embodiments, for the impedance optimization scenario, the transient stability risk is used to evaluate whether the transient characteristics of the initial topology adjustment scheme meet the requirements; for the multi-objective optimization scenario, the static security margin is used to evaluate whether the transient characteristics of the initial topology adjustment scheme meet the requirements.

[0129] S54. If the transient stability risk is greater than the preset stability risk threshold, then use a recurrent neural network to predict the transient disturbance propagation law, and calculate the transient response process caused by short-circuit faults and switching operations through time-domain simulation to obtain transient response optimization data.

[0130] S55. If the static security margin is less than the preset security margin threshold, then calculate the power flow distribution of the distribution network based on the Newton iteration method, and perform verification through power transmission constraints and voltage over-limit constraints to obtain static constraint optimization data.

[0131] It should be noted that in the embodiments of the present invention, if the transient stability risk is prominent, then carry out transient simulation analysis; if the static security margin is insufficient, then carry out static security verification; to further perform iterative optimization on the initial topology adjustment scheme until the requirements for the safe and stable operation of the multi-scenario power grid are met.

[0132] S56. Iteratively simulate the initial topology adjustment scheme based on the transient response optimization data and the static constraint optimization data to obtain an optimized topology adjustment scheme.

[0133] Further, preferably, in step S52, according to the voltage data, calculate the transient power transmission characteristics between each power grid node to obtain the transient stability risk, including:

[0134] Process the voltage data through wavelet transform to obtain voltage fluctuation characteristics;

[0135] According to the voltage data, obtain the voltage phase angle change trend before and after impedance mutation under the initial topology adjustment scheme;

[0136] According to the states of the feeder sectionalizing switches and the distribution network topology structure, obtain the nodal admittance matrix;

[0137] According to the voltage phase angle change trend and the nodal admittance matrix, calculate the transient power transmission characteristics between each power grid node to obtain a transient stability margin curve;

[0138] According to the transient stability margin curve, use a recursive neural network to predict the transient response of key network nodes under impedance mutation, and calculate the transient disturbance propagation path through a phase angle stability criterion to obtain the transient stability risk.

[0139] Exemplarily, taking a certain 10 kV distribution line as an example, this line contains 10 sectionalizing switches and 6 tie switches, and uses a high-speed synchronous phasor measurement device to collect data with a sampling frequency of 100 Hz. The wavelet transform uses the db4 wavelet basis function to decompose the voltage fluctuation characteristics and extract the fluctuation components in different frequency bands.

[0140] During normal operation, the voltage fluctuation amplitude is controlled within 0.02 per unit value, and the phase angle deviation does not exceed 2 degrees. The nodal admittance matrix reflects the electrical connection between each node in the network. For an overhead line with a cross-sectional area of 300 square millimeters, the positive sequence admittance value per kilometer is approximately 8.33 Siemens. Through the analysis of transient power transmission characteristics, it is found that the power fluctuation caused by impedance mutation can spread to the adjacent 3 nodes within 0.1 second, and the fluctuation amplitude decays exponentially with the propagation distance.

[0141] The transient stability margin curve shows that under the impedance mutation condition, the transient power margin of the key node drops to 65% of the nominal value. The recursive neural network prediction model contains 10 input nodes, which respectively correspond to characteristic quantities such as voltage amplitude, phase angle, and active power. The accuracy of the model obtained through training with historical data reaches 92%, and the prediction results show that the duration of the transient process caused by impedance mutation is about 0.5 second. The phase angle stability criterion is calculated based on the power angle difference between adjacent nodes, and when the angle difference exceeds 30 degrees, it is determined that there is a risk of instability.

[0142] The dynamic impedance scan is carried out in the frequency range of 0.1 to 10 Hz, and the scan step is 0.1 Hz. The scan results show that in the scenario where the photovoltaic output fluctuates frequently, the equivalent impedance at the grid connection point has a resonance peak in the frequency band of 1 to 2 Hz, and the peak impedance reaches 3 times the steady-state value.

[0143] Preferably, in step S53, according to the voltage data and the current data, calculate the line power distribution data of the initial topology adjustment scheme to obtain the static security margin, including:

[0144] According to the voltage data and the current data, calculate the line power distribution data of the initial topology adjustment scheme;

[0145] Calculate the line overload level and the degree of node voltage violation through power flow sensitivity to obtain the static security margin.

[0146] The static security margin considers two dimensions: the depth and duration of voltage violation, and obtains a comprehensive evaluation value through weighted summation. Power flow sensitivity analysis shows that there is a positive correlation between the line overload level and the degree of node voltage violation.

[0147] Exemplarily, when the load rate of a certain line reaches 90%, the node voltage at the end of the line drops to 9.4 kV. The support vector machine uses a Gaussian kernel function to classify the operating state, and the feature vector includes parameters such as line load rate, node voltage, and power factor. All operating scenarios are divided into three categories: safe, warning, and dangerous. The warning state mainly appears during the period of rapid daily load growth. The sub-scenario safety assessment result library records the transient characteristics and static characteristics under various operating scenarios. In the scenario of sudden load increase, the transient stability risk is relatively high but the static constraint satisfaction is relatively good. In the scenario of network topology reconstruction, the opposite characteristics are presented. The comprehensive evaluation data shows that the overall operating level of the distribution network is within the controllable range.

[0148] Furthermore, in some preferred embodiments, in step S56, according to the transient response optimization data and the static constraint optimization data, perform iterative simulation on the initial topology adjustment scheme to obtain an optimized topology adjustment scheme, that is, according to the transient response optimization data and the static constraint optimization data, use a genetic algorithm to perform parameter optimization on the topology adjustment scheme, and calculate the comprehensive evaluation value of each scheme through a fitness function. For the comprehensive evaluation value, classify the optimized scheme based on a support vector machine, extract the optimization experience of similar scenarios from the historical topology scheme library, and obtain a scheme optimization iteration data set. According to the scheme optimization iteration data set, perform repeated verification on each iteration scheme through power flow calculation until the voltage stability and power transmission capacity constraints are met.

[0149] Exemplarily, taking a certain 10 kV distribution line as an example, this line is connected to 3 photovoltaic power stations with a total installed capacity of 2000 kW. The sampling period of the photovoltaic output fluctuation data is 1 second, and the sampling period of the load change data is 1 minute. The digital twin network contains 35 nodes and 42 lines, and the π-type equivalent circuit is used to simulate the line parameters. For the overhead line with a cross-sectional area of 300 square millimeters, the positive sequence impedance is 0.12 + j0.095 ohms / km. The nodal admittance matrix reflects the electrical connection between the nodes in the network, and the transient response characteristics are obtained by calculating the sensitivity of the nodal voltage to the injected power.

[0150] The transient stability risk includes two dimensions: phase angle stability and voltage stability. The phase angle stability threshold is set at 30 degrees, and the voltage stability threshold is 0.1 per unit value. When the photovoltaic output suddenly drops by 50%, the grid-connected voltage drops to 0.92 per unit value, and the phase angle deviation reaches 25 degrees. The recursive neural network prediction model adopts a three-layer structure. The input layer includes characteristic quantities such as voltage amplitude, phase angle, and frequency, and the number of hidden layer nodes is 20. The model trained with 1000 groups of historical data has a transient disturbance prediction accuracy of 93%. Time-domain simulation analysis shows that the voltage drop recovery time caused by a three-phase short-circuit fault is about 0.3 seconds, and the transient process caused by switch switching lasts about 0.2 seconds.

[0151] The static security check uses the Newton iteration method for power flow calculation, and the iteration convergence accuracy is set at 0.0001. The power transmission constraint requires that the line load rate does not exceed 85%, and the voltage violation constraint requires that the nodal voltage deviation does not exceed ±7%. The calculation results show that under heavy load conditions, the load rates of 3 feeders reach 82%, 78%, and 75% respectively, and the voltage of the end node drops to 9.5 kV. During the genetic algorithm optimization process, the population size is set at 100, and the number of evolution generations is 200. The fitness function comprehensively considers the transient stability margin and the static security margin, and the weight coefficients are taken as 0.6 and 0.4 respectively.

[0152] The support vector machine uses the Gaussian kernel function to classify the optimization schemes, and all the schemes are divided into three categories: safe, warning, and dangerous. A total of 15 groups of optimization experiences with a similarity exceeding 85% are screened out from the historical scheme library. During the iterative optimization process, power flow verification is carried out in each round of iteration. The voltage stability constraint requires that the nodal voltage deviation is less than 5%, and the power transmission constraint requires that the line overload duration does not exceed 5 minutes. After an average of 6 rounds of iterative optimization, all indicators meet the constraint requirements. The finally determined topology adjustment scheme realizes the optimal configuration of the network static parameters while ensuring the transient characteristics.

[0153] Adopting an optimal scheduling method for photovoltaic source-network-load-storage provided by an embodiment of the present invention, by calculating the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics, and then adjusting the states of the feeder sectionalizing switches, it can fully consider the complex coupling between the power grid topological structure and the photovoltaic output fluctuation, and further analyze the transient characteristics of the power grid nodes to optimize the topological adjustment scheme, and can verify the feasibility and optimization effect of the scheme. The embodiment of the present invention can effectively improve the accommodation capacity of the distribution network for photovoltaic power generation while ensuring the safe and stable operation of the power grid.

[0154] An embodiment of the present invention provides an optimal scheduling system for photovoltaic source-network-load-storage. Please refer to Figure 2 , the optimal scheduling system for photovoltaic source-network-load-storage includes a multi-scenario data acquisition module 11, an impedance change characteristic calculation module 12, an output fluctuation characteristic calculation module 13, an initial scheme generation module 14, a scheme optimization module 15, and an optimal scheduling module 16, where:

[0155] The multi-scenario data acquisition module 11 is configured to acquire the distribution network topological structure, photovoltaic output data, and feeder sectionalizing switch states under each photovoltaic grid-connection scenario;

[0156] The impedance change characteristic calculation module 12 is configured to calculate the equivalent impedance of the photovoltaic grid-connection points in each photovoltaic grid-connection scenario according to the distribution network topological structure and the feeder sectionalizing switch states, and obtain the impedance change characteristics of each photovoltaic grid-connection scenario;

[0157] The output fluctuation characteristic calculation module 13 is configured to extract the output fluctuation characteristics in each photovoltaic grid-connection scenario according to the photovoltaic output data;

[0158] The initial scheme generation module 14 is configured to calculate the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics in each photovoltaic grid-connection scenario, and adjust the feeder sectionalizing switch states according to the correlation coefficient to obtain an initial topological adjustment scheme;

[0159] The scheme optimization module 15 is configured to calculate the transient characteristics of the power grid nodes under the initial topological adjustment scheme, and perform iterative simulation on the initial topological adjustment scheme according to the transient characteristics to obtain an optimized topological adjustment scheme;

[0160] The optimal scheduling module 16 is configured to perform optimal scheduling of photovoltaic source-network-load-storage for the photovoltaic grid-connection scenario by adopting the optimized topological adjustment scheme.

[0161] As a preferred implementation manner, the impedance change characteristic calculation module 12 is configured to:

[0162] Acquire the three-phase voltage and current values of each feeder sectionalizing switch and the feeder impedance parameters of the distribution line;

[0163] Calculate the sequence impedance value of the distribution line according to the three-phase voltage and current values and the distribution network topology structure;

[0164] Calculate the equivalent impedance value of the PV grid connection point according to the sequence impedance value and the feeder impedance parameters;

[0165] Calculate the change data of the equivalent impedance of the PV grid connection point under each PV grid connection scenario according to the feeder sectionalizing switch state;

[0166] Adopt the exponential smoothing method to conduct trend analysis on the change data of the equivalent impedance, and obtain the impedance change characteristics of each PV grid connection scenario.

[0167] As a preferred implementation manner, the output power fluctuation characteristic calculation module 13 is used for:

[0168] Adopt the sliding window method to perform noise reduction and outlier processing on the PV output power data to obtain the PV output power time series data;

[0169] Adopt wavelet transform to perform multi-scale decomposition on the PV output power time series data, extract the fluctuation amplitude, frequency and duration, construct a PV output power fluctuation characteristic matrix, and obtain the output power fluctuation characteristics of each PV grid connection scenario.

[0170] As a preferred implementation manner, the initial scheme generation module 14 includes:

[0171] The correlation coefficient calculation unit is used to calculate the correlation coefficient between the impedance change characteristics and the output power fluctuation characteristics under each PV grid connection scenario by using the Pearson correlation coefficient method;

[0172] The scenario division unit is used to divide the PV grid connection scenarios into impedance optimization scenarios and multi-objective optimization scenarios according to the correlation coefficient and a preset correlation coefficient threshold;

[0173] The first initial scheme generation unit is used for the impedance optimization scenario, adopt the genetic algorithm to optimize and calculate the impedance optimization target value, and adjust the feeder sectionalizing switch state according to the impedance optimization target to generate an initial topology adjustment scheme;

[0174] The second initial scheme generation unit is used for the multi-objective optimization scenario, and adjust the feeder sectionalizing switch state by using a heuristic search algorithm according to the preset optimization indexes to generate an initial topology adjustment scheme.

[0175] Furthermore, preferably, the first initial scheme generation unit is specifically used for:

[0176] Construct the optimization objective function of the impedance optimization scenario; the optimization objective function includes a node impedance term and a power loss term;

[0177] According to the optimized objective function, a genetic algorithm is used to optimize and calculate the impedance optimization target value;

[0178] According to the impedance optimization target value, an algorithm based on graph theory is used to adjust the states of the feeder sectionalizing switches, and an initial topology adjustment scheme is generated.

[0179] Preferably, the second initial scheme generating unit is specifically configured to:

[0180] Obtain the voltage curve and power curve of the multi-objective optimization scenario;

[0181] According to the voltage curve, calculate the voltage stability margin index;

[0182] According to the power curve, calculate the power balance index;

[0183] Adopt a fuzzy comprehensive evaluation algorithm to automatically calculate the weight values of the voltage stability margin index and the power balance index, and obtain a multi-objective optimization evaluation matrix;

[0184] According to the correlation coefficient, obtain the optimization objective of the multi-objective optimization evaluation matrix;

[0185] According to the optimization objective of the multi-objective optimization evaluation matrix, a heuristic search algorithm is used to adjust the states of the feeder sectionalizing switches, and an initial topology adjustment scheme is generated.

[0186] As a preferred implementation manner, the scheme optimization module 15 includes:

[0187] Obtain the voltage data and current data of the power grid nodes under the initial topology adjustment scheme;

[0188] The transient stability risk calculation unit is configured to calculate the transient power transmission characteristics between power grid nodes according to the voltage data, and obtain the transient stability risk;

[0189] The static security margin calculation unit is configured to calculate the line power distribution data of the initial topology adjustment scheme according to the voltage data and the current data, and obtain the static security margin;

[0190] The transient response optimization data calculation unit is configured to, if the transient stability risk is greater than a preset stability risk threshold, use a recurrent neural network to predict the transient disturbance propagation law, and calculate the transient response process caused by short-circuit faults and switching operations through time-domain simulation, and obtain the transient response optimization data;

[0191] A static constraint optimization data calculation unit is used to calculate the power flow distribution of the distribution network based on the Newton iteration method if the static safety margin is less than a preset safety margin threshold, and check it through power transmission constraints and voltage violation constraints to obtain static constraint optimization data;

[0192] A scheme optimization unit is used to perform iterative simulation on the initial topology adjustment scheme according to the transient response optimization data and the static constraint optimization data to obtain an optimized topology adjustment scheme.

[0193] Further, preferably, the transient stability risk calculation unit is specifically used for:

[0194] Process the voltage data through wavelet transform to obtain voltage fluctuation characteristics;

[0195] Obtain the voltage phase angle change trend before and after impedance mutation under the initial topology adjustment scheme according to the voltage data;

[0196] Obtain the nodal admittance matrix according to the status of the feeder sectionalizing switch and the distribution network topology structure;

[0197] Calculate the transient power transmission characteristics between grid nodes according to the voltage phase angle change trend and the nodal admittance matrix to obtain a transient stability margin curve;

[0198] According to the transient stability margin curve, use a recurrent neural network to predict the transient response of key network nodes under impedance mutation, and calculate the transient disturbance propagation path through the phase angle stability criterion to obtain the transient stability risk.

[0199] Preferably, the static safety margin calculation unit is specifically used for:

[0200] Calculate the line power distribution data of the initial topology adjustment scheme according to the voltage data and the current data;

[0201] Calculate the line overload level and the degree of node voltage violation through power flow sensitivity to obtain the static safety margin.

[0202] By using the optimized dispatching system of a photovoltaic source-network-load-storage provided by the embodiments of the present invention, by calculating the correlation coefficient between the impedance change characteristics and the output fluctuation characteristics, and then adjusting the status of the feeder sectionalizing switch, the complex coupling between the power grid topology structure and the photovoltaic output fluctuation can be fully considered, and the transient characteristics of the power grid nodes are further analyzed to optimize the topology adjustment scheme, and the feasibility and optimization effect of the scheme can be verified. The embodiments of the present invention can effectively improve the consumption capacity of the distribution network for photovoltaic power generation while ensuring the safe and stable operation of the power grid.

[0203] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0204] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A photovoltaic source grid load storage optimization scheduling method, characterized in that: include: Obtain the distribution network topology, photovoltaic output data and feeder section switch status under each photovoltaic grid-connected scenario; According to the distribution network topology and the feeder segment switch state, the equivalent impedance of the photovoltaic grid-connected point in each photovoltaic grid-connected scenario is calculated to obtain the impedance change characteristics of each photovoltaic grid-connected scenario; Extracting output fluctuation characteristics under each photovoltaic grid-connected scenario according to the photovoltaic output data; Calculating the correlation coefficient between the impedance change characteristic and the output fluctuation characteristic in each photovoltaic grid-connected scenario, adjusting the feeder segment switch state according to the correlation coefficient, and obtaining an initial topology adjustment scheme; Calculating transient characteristics of power grid nodes under the initial topology adjustment scheme, and iteratively simulating the initial topology adjustment scheme according to the transient characteristics to obtain an optimized topology adjustment scheme; Adopting the optimized topology adjustment scheme to optimize the scheduling of photovoltaic source, grid, load and storage in the photovoltaic grid-connected scenario; The calculating the correlation coefficient between the impedance variation characteristic and the output fluctuation characteristic in each photovoltaic grid-connected scenario, and adjusting the feeder segment switch state according to the correlation coefficient to obtain an initial topology adjustment scheme includes: The Pearson correlation coefficient method is used to calculate the correlation coefficient between the impedance change characteristic and the output fluctuation characteristic in each photovoltaic grid-connected scenario; According to the correlation coefficient and a preset correlation coefficient threshold, the photovoltaic grid-connected scenario is divided into an impedance optimization scenario and a multi-objective optimization scenario; For the impedance optimization scenario, a genetic algorithm is used to find and calculate an impedance optimization target value, and the feeder segment switch state is adjusted according to the impedance optimization target to generate an initial topology adjustment plan; For the multi-objective optimization scenario, according to preset optimization indicators, a heuristic search algorithm is used to adjust the feeder segment switch state to generate an initial topology adjustment plan; the optimization indicators include a voltage stability margin indicator and a power balance indicator.

2. The photovoltaic source grid load storage optimization scheduling method according to claim 1, characterized in that: The calculating the equivalent impedance of the photovoltaic grid-connected point in each photovoltaic grid-connected scenario according to the distribution network topology and the feeder segment switch state to obtain the impedance change characteristics of each photovoltaic grid-connected scenario includes: Obtain the three-phase voltage and current values ​​of each feeder section switch and the feeder impedance parameters of the distribution line; Calculating the sequence impedance value of the distribution line according to the three-phase voltage and current values ​​and the distribution network topology; Calculating an equivalent impedance value of a photovoltaic grid-connected point according to the sequence impedance value and the feeder impedance parameter; According to the feeder segment switch state, calculating the change data of the equivalent impedance of the photovoltaic grid-connected point in each photovoltaic grid-connected scenario; An exponential smoothing method is used to perform trend analysis on the change data of the equivalent impedance to obtain impedance change characteristics of each photovoltaic grid-connected scenario.

3. The photovoltaic source grid load storage optimization scheduling method according to claim 1, characterized in that: The step of extracting output fluctuation characteristics in each photovoltaic grid-connected scenario according to the photovoltaic output data includes: A sliding window method is used to reduce noise and process outliers on the photovoltaic output data to obtain photovoltaic output time series data; Wavelet transform is used to perform multi-scale decomposition on the photovoltaic output time series data, extract the fluctuation amplitude, frequency and duration, construct a photovoltaic output fluctuation feature matrix, and obtain the output fluctuation characteristics under each photovoltaic grid-connected scenario.

4. The photovoltaic source grid load storage optimization scheduling method according to claim 1, characterized in that: For the impedance optimization scenario, a genetic algorithm is used to find and calculate an impedance optimization target value, and the feeder segment switch state is adjusted according to the impedance optimization target to generate an initial topology adjustment plan, including: Constructing an optimization objective function of the impedance optimization scenario; the optimization objective function includes a node impedance term and a power loss term; According to the optimization objective function, a genetic algorithm is used to find and calculate the impedance optimization target value; According to the impedance optimization target value, an algorithm based on graph theory is used to adjust the feeder segment switch state to generate an initial topology adjustment plan.

5. The photovoltaic source grid load storage optimization scheduling method according to claim 1, characterized in that: The optimization index includes a voltage stability margin index and a power balance index; then for the multi-objective optimization scenario, according to the preset optimization index, a heuristic search algorithm is used to adjust the feeder segment switch state to generate an initial topology adjustment scheme, including: Obtaining a voltage curve and a power curve of the multi-objective optimization scenario; Calculating a voltage stability margin index according to the voltage curve; Calculating a power balance index according to the power curve; A fuzzy comprehensive evaluation algorithm is used to automatically calculate the weight values ​​of the voltage stability margin index and the power balance index to obtain a multi-objective optimization evaluation matrix; According to the correlation coefficient, the optimization target of the multi-objective optimization evaluation matrix is ​​obtained; According to the optimization target of the multi-objective optimization evaluation matrix, a heuristic search algorithm is used to adjust the feeder segment switch state to generate an initial topology adjustment plan.

6. The photovoltaic source grid load storage optimization scheduling method according to claim 1, characterized in that: The calculating the transient characteristics of the power grid nodes under the initial topology adjustment scheme, and iteratively simulating the initial topology adjustment scheme according to the transient characteristics to obtain an optimized topology adjustment scheme, comprises: Acquiring voltage data and current data of power grid nodes under the initial topology adjustment scheme; According to the voltage data, calculating the transient power transmission characteristics between each grid node to obtain the transient stability risk; Calculating the line power distribution data of the initial topology adjustment scheme according to the voltage data and the current data to obtain a static safety margin; If the transient stability risk is greater than a preset stability risk threshold, a recursive neural network is used to predict the transient disturbance propagation law, and the transient response process caused by the short-circuit fault and the switching operation is calculated through time domain simulation to obtain transient response optimization data; If the static safety margin is less than the preset safety margin threshold, the power flow distribution of the distribution network is calculated based on the Newton iteration method, and the power transmission constraint and the voltage over-limit constraint are checked to obtain static constraint optimization data; According to the transient response optimization data and the static constraint optimization data, the initial topology adjustment scheme is iteratively simulated to obtain an optimized topology adjustment scheme.

7. The photovoltaic source grid load storage optimization scheduling method according to claim 6, characterized in that: The step of calculating the transient power transmission characteristics between the power grid nodes according to the voltage data to obtain the transient stability risk includes: Processing the voltage data by wavelet transform to obtain voltage fluctuation characteristics; According to the voltage data, a voltage phase angle variation trend before and after the impedance mutation under the initial topology adjustment scheme is obtained; Obtaining a node admittance matrix according to the state of the feeder section switch and the distribution network topology; According to the voltage phase angle variation trend and the node admittance matrix, the transient power transmission characteristics between the power grid nodes are calculated to obtain a transient stability margin curve; According to the transient stability margin curve, a recursive neural network is used to predict the transient response of key network nodes under impedance mutation, and the transient disturbance propagation path is calculated through the phase angle stability criterion to obtain the transient stability risk.

8. The photovoltaic source grid load storage optimization scheduling method according to claim 6, characterized in that: The calculating, according to the voltage data and the current data, the line power distribution data of the initial topology adjustment scheme to obtain a static safety margin comprises: Calculating line power distribution data of the initial topology adjustment solution according to the voltage data and the current data; The line overload level and node voltage over-limit degree are calculated through power flow sensitivity to obtain the static safety margin.

9. An optimized dispatching system for photovoltaic source, grid, load and storage, characterized in that: include: Multi-scenario data acquisition module, used to obtain the distribution network topology, photovoltaic output data and feeder section switch status under various photovoltaic grid-connected scenarios; An impedance change characteristic calculation module is used to calculate the equivalent impedance of the photovoltaic grid-connected point in each photovoltaic grid-connected scenario according to the distribution network topology and the feeder segment switch state, and obtain the impedance change characteristics of each photovoltaic grid-connected scenario; An output fluctuation characteristic calculation module, used to extract the output fluctuation characteristics under each photovoltaic grid-connected scenario according to the photovoltaic output data; An initial scheme generating module, used for calculating the correlation coefficient between the impedance variation characteristic and the output fluctuation characteristic in each photovoltaic grid-connected scenario, adjusting the feeder segment switch state according to the correlation coefficient, and obtaining an initial topology adjustment scheme; A scheme optimization module, used to calculate the transient characteristics of the power grid nodes under the initial topology adjustment scheme, and iteratively simulate the initial topology adjustment scheme according to the transient characteristics to obtain an optimized topology adjustment scheme; An optimization scheduling module, used to optimize the scheduling of photovoltaic source, grid, load and storage for the photovoltaic grid-connected scenario by adopting the optimization topology adjustment scheme; The initial solution generation module comprises: A correlation coefficient calculation unit, used to calculate the correlation coefficient between the impedance change characteristic and the output fluctuation characteristic in each photovoltaic grid-connected scenario by using a Pearson correlation coefficient method; A scenario division unit, configured to divide the photovoltaic grid-connected scenario into an impedance optimization scenario and a multi-objective optimization scenario according to the correlation coefficient and a preset correlation coefficient threshold; A first initial solution generating unit is used to use a genetic algorithm to find and calculate an impedance optimization target value for the impedance optimization scenario, adjust the feeder segment switch state according to the impedance optimization target, and generate an initial topology adjustment solution; The second initial solution generating unit is used to adjust the feeder segment switch state according to preset optimization indicators for the multi-objective optimization scenario by using a heuristic search algorithm to generate an initial topology adjustment solution; the optimization indicators include a voltage stability margin indicator and a power balance indicator.

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