Power distribution network station area voltage regulation and control method, system and equipment considering multi-point relevance and medium

Abstract

The invention discloses a power distribution network station area voltage regulation and control method, system and device considering multipoint relevance and a medium, and the method comprises the following steps: collecting factors influencing node voltage relevance, quantifying node voltage relevance, and constructing a station area node voltage relevance matrix; acquiring operation data of each node through a monitoring terminal; when it is monitored that the fluctuation quantity of the node operation data exceeds a threshold value, emergency updating of the node voltage correlation degree is triggered; through the updated transformer area node voltage incidence matrix, a multi-objective optimization function is constructed, constraint conditions are set, and an optimal regulation and control strategy is obtained through solving; and the master station issues a regulation and control instruction to the voltage regulation equipment according to the optimal regulation and control strategy. According to the method, the node voltage correlation degree model considering the electrical correlation factor and the load fluctuation factor is constructed, the voltage linkage effect between the nodes in the transformer area is described, and the defect that electrical coupling between the nodes is neglected in a traditional single-point independent regulation and control mode is overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power distribution network operation and control, and in particular to a power distribution network area voltage regulation method, system, device and medium considering multi-point correlation. BACKGROUND

[0002] The power distribution network area is a key link between the power system and the user, and its voltage quality directly affects the user's power experience and equipment safety. In recent years, with the large-scale access of distributed photovoltaic, energy storage and other devices, and the surge of electric vehicle charging, smart home and other volatile loads, the voltage of each node in the area presents the characteristics of multi-source disturbance and mutual correlation. For example, when the distributed photovoltaic output of a node in the area suddenly increases, it will not only cause the voltage of the node to rise, but also affect the voltage of other connected nodes through the connection relationship of the line, and when the charging load of a node suddenly increases, it will also lower the voltage of other nodes, forming a one-node fluctuation and multi-node influence linkage effect.

[0003] The existing area voltage regulation method ignores the multi-point correlation, and the regulation is fragmented. Traditional methods mostly use single-point independent regulation mode, without considering the electrical coupling between nodes, which is easy to cause regulation chain reaction, for example, to suppress the overvoltage of a node by removing the capacitor, which may cause the voltage of the associated node to drop below the qualified lower limit, requiring secondary regulation, reducing efficiency and increasing device loss. Then, the correlation model is static, with poor adaptability. Some methods try to consider node correlation, but only build a static correlation model based on the fixed topology of the area, without considering the influence of the time sequence fluctuation of the load and the distributed power output on the correlation relationship, resulting in a disconnection between the model and the actual operation scene, and regulation lag. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a power distribution network area voltage regulation method, system, device and medium considering multi-point correlation to solve the problems of ignoring the multi-node voltage correlation of the power distribution network area, poor regulation effect, low resource utilization, and inability to adapt to the area voltage regulation demand under high proportion of new energy and diversified load access in the prior art.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a power distribution network substation voltage regulation method considering multi-point correlation, comprising the following steps: collecting factors affecting node voltage correlation, quantifying node voltage correlation degree, and constructing a substation node voltage correlation matrix; collecting node operation data through a monitoring terminal, updating node voltage correlation degree and the substation node voltage correlation matrix based on the collected data; triggering an emergency update of the node voltage correlation degree when the node operation data fluctuation exceeds a threshold value; constructing a multi-objective optimization function and setting a constraint condition through the updated substation node voltage correlation matrix, and solving to obtain an optimal regulation strategy; the main station issues a regulation instruction to a voltage regulation device according to the optimal regulation strategy, and the substation adjusts the regulation amount in combination with the operation data of the correlated nodes after receiving the regulation instruction.

[0007] As a preferred scheme of the power distribution network substation voltage regulation method considering multi-point correlation, the step of constructing the substation node voltage correlation matrix comprises: determining factors affecting node voltage correlation, including electrical correlation factors and load fluctuation factors; calculating the node voltage correlation degree through the electrical correlation factors and the load fluctuation factors; and constructing the substation node voltage correlation matrix according to the node voltage correlation degree, with the matrix dimension being N×N, and N being the total number of monitored nodes in the substation.

[0008] As a preferred scheme of the power distribution network substation voltage regulation method considering multi-point correlation, the step of updating the node voltage correlation degree and the substation node voltage correlation matrix based on the collected data comprises: collecting node operation data through a monitoring terminal in the substation; recalculating the node voltage correlation degree of each node and updating the substation node voltage correlation matrix based on the collected node operation data every X minutes; triggering an emergency update of the node voltage correlation degree when the node operation data fluctuation exceeds a threshold value; the node operation data fluctuation exceeding the threshold value comprises: the distributed power output fluctuation being greater than 10% of the rated capacity, or the node load fluctuation being greater than 15% of the rated load; and the update interval of the node voltage correlation degree is shortened to Y minutes during the emergency update.

[0009] The beneficial effect of the preferred technical scheme is that by setting the threshold value judgment conditions of the distributed power output fluctuation and the node load fluctuation, the update interval is shortened from X minutes to Y minutes when the fluctuation exceeds the threshold value, so that the correlation degree is updated in an emergency.

[0010] As a preferred scheme of the power distribution network area voltage regulation method considering multi-point correlation provided in the application, in the obtaining step of the optimal regulation strategy, a multi-objective optimization function is constructed, and a constraint condition is set; a non-dominated sorting genetic algorithm is used to solve the multi-objective optimization function, the multi-point correlation constraint is integrated into a fitness function, and a Pareto optimal solution set is obtained; and an optimal regulation strategy is selected from the Pareto optimal solution set according to an area operation requirement.

[0011] The preferred technical scheme has the beneficial effects that: the multi-point correlation constraint is integrated into the fitness function, the non-dominated sorting genetic algorithm is used to solve the multi-objective optimization function, the Pareto optimal solution set is obtained, and then the optimal regulation strategy is selected according to the area operation requirement. The method can realize multi-objective balance of voltage qualification rate, regulation cost and equipment loss, and avoid chain reaction caused by single-point regulation.

[0012] As a preferred scheme of the power distribution network area voltage regulation method considering multi-point correlation provided in the application, in the step of fine-tuning the regulation amount combined with the operation data of the associated nodes, the main station issues a regulation instruction to the voltage regulation device according to the optimal regulation strategy; after receiving the regulation instruction, each sub-station synchronously monitors real-time voltages of the nodes and the associated nodes, the associated nodes are nodes with a node voltage correlation degree greater than or equal to 0.6, the regulation amount is fine-tuned combined with the real-time voltages of the associated nodes, and the fine-tuning amplitude is not more than 5% of the regulation instruction value; the voltage regulation device executes the fine-tuned regulation instruction, and feeds back an execution state to the main station.

[0013] As a preferred scheme of the power distribution network area voltage regulation method considering multi-point correlation provided in the application, after the voltage regulation device executes the fine-tuned regulation instruction, the method further includes: one minute after the regulation execution is completed, the main station collects node voltage data in the node operation data, and verifies whether the voltage qualification rate meets the standard; if there is an associated node voltage out of limit, a multi-objective optimization function is reconstructed and a constraint condition is set according to an updated area node voltage correlation matrix, a new optimal regulation strategy is obtained by using a non-dominated sorting genetic algorithm, a new regulation instruction is issued to the voltage regulation device according to the new optimal regulation strategy, and the regulation amount is fine-tuned combined with the operation data of the associated nodes after the new regulation instruction is received by the sub-station, until all node voltages in the area meet the voltage constraint.

[0014] The preferred technical scheme has the beneficial effects that: after the regulation execution is completed, the voltage qualification rate is verified, if there is an associated node voltage out of limit, the regulation is re-solved and executed, and a closed-loop feedback mechanism is formed.

[0015] As a preferred scheme of the power distribution network area voltage regulation method considering multi-point correlation provided in the application, the calculation formula of the node voltage correlation degree is: ​ ; wherein, , are the coefficients of the electrical correlation factor, the load fluctuation factor, respectively, , are the active power and the reactive power flowing from node i to node j, , are the resistance and the reactance of the line between node i and node j, is the rated voltage of node j, is the distributed power output fluctuation of node i, is the load fluctuation of node i, is the total apparent capacity of the transformer area, is the maximum value of the electrical term, is the maximum value of the dynamic term.

[0016] In a second aspect, the present application provides a transformer area voltage regulation system of a power distribution network considering multi-point correlation, comprising: a monitoring terminal, deployed at each node in the transformer area, for real-time collection of operation data of each node; a master station, for receiving the operation data of each node collected by the monitoring terminal, calculating node voltage correlation degree based on the operation data of each node and constructing a transformer area node voltage correlation matrix, constructing a multi-objective optimization function through the transformer area node voltage correlation matrix and setting constraint conditions to obtain an optimal regulation strategy, issuing a regulation instruction to a voltage regulation device according to the optimal regulation strategy, and verifying the voltage qualification rate after regulation execution; a sub-station, provided at each node, for receiving the regulation instruction issued by the master station, monitoring the real-time voltage of the associated node, and fine-tuning the regulation amount in combination with the real-time voltage of the associated node; a voltage regulation device, comprising a load voltage regulation transformer, a static var generator, and an energy storage system, for executing the fine-tuned regulation instruction of the sub-station and feeding back the execution status to the master station.

[0017] In a third aspect, the present application provides an electronic device, comprising: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which, when executed by the processor, implement the steps of the transformer area voltage regulation method of the power distribution network considering multi-point correlation.

[0018] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the transformer area voltage regulation method of the power distribution network considering multi-point correlation.

[0019] Compared with the prior art, the beneficial effects of the present application: by constructing the node voltage correlation degree model considering the electrical correlation factor and the load fluctuation factor, and establishing the distribution area node voltage correlation matrix, the voltage linkage effect between nodes in the distribution area is described, and the defect that the traditional single-point independent regulation mode ignores the electrical coupling between nodes is overcome. By setting an emergency update mechanism, when the distributed power output or the node load fluctuation exceeds the threshold, the correlation degree and the correlation matrix are quickly updated to ensure that the correlation model is synchronized with the actual operation state, and the problems of poor adaptability and regulation lag of the static correlation model are solved.

[0020] The present application integrates the multi-point correlation constraint into the fitness function of the multi-objective optimization function, and obtains the Pareto optimal solution set considering the voltage qualification rate, regulation cost and equipment loss by solving with the non-dominated sorting genetic algorithm, realizes the global optimization configuration of the regulation resource, and avoids excessive regulation and regulation blind area. The hierarchical collaborative regulation mechanism of the master station issuing instructions, the sub-station combining the real-time voltage fine adjustment of the correlation nodes, the voltage regulation equipment execution and feedback, and the closed-loop feedback mechanism of verifying the voltage qualification rate after regulation and re-solving when the correlation nodes exceed the limit, ensure the accurate control of the voltage of all nodes in the distribution area, improve the voltage regulation precision of the distribution network distribution area, reduce the regulation cost and equipment loss, and enhance the adaptability to the high proportion of new energy and diversified load access scene. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 The overall flowchart of the distribution network distribution area voltage regulation method considering the multi-point correlation of an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0024] Embodiment 1, refer to Figure 1 For an embodiment of the present application, a distribution network distribution area voltage regulation method considering multi-point correlation is provided, including the following steps S100-S400: S100, collect factors affecting node voltage correlation, quantify node voltage correlation degree, and construct a substation node voltage correlation matrix.

[0025] S200, collect node operation data by monitoring terminals, update node voltage correlation degree and substation node voltage correlation matrix based on collected data; when the node operation data fluctuation exceeds the threshold, trigger emergency update of node voltage correlation degree.

[0026] S300, construct a multi-objective optimization function and set constraints based on the updated substation node voltage correlation matrix, and solve to obtain the optimal control strategy.

[0027] S400, the main station issues control instructions to the voltage regulating device according to the optimal control strategy, and the substation adjusts the control amount in combination with the correlation node operation data after receiving the control instructions.

[0028] It should be noted that the distribution network substation is a key link directly connected between the power system and the user, and the voltage quality directly affects the user's power experience and equipment safety. In recent years, with the large-scale access of distributed photovoltaic, energy storage and other flexible devices, and the surge of electric vehicle charging, smart home and other volatile loads, the voltage of each node in the substation presents the characteristics of "multi-source disturbance and mutual correlation". For example, when the distributed photovoltaic output of a node in the substation increases suddenly, not only will it cause the voltage of the node to rise, but also it will affect the voltage of other connected nodes through the connection relationship of the line; while the charging load of a node increases suddenly, it will also pull down the voltage of the associated nodes in the opposite direction, forming a "one-node fluctuation, multiple-node influence" linkage effect. Traditional substation voltage control methods mostly use the "single-point independent control" mode, which separately switches capacitors or adjusts SVG for a certain voltage out-of-limit node, without considering the electrical coupling and dynamic correlation between nodes, which is easy to cause control chain reaction; at the same time, although some methods try to consider node correlation, they only construct a static correlation model based on the fixed topology of the substation, without considering the dynamic influence of the time sequence fluctuation of load and distributed power output on the correlation relationship, which leads to the disconnection between the model and the actual operation scene, and the lag of the control strategy; in addition, most of the existing strategies take voltage qualification rate as the only target, without optimizing the allocation of control resources in combination with the multi-point correlation characteristics, which is easy to cause excessive control or control blind area, resulting in waste of energy storage, SVG and other device resources; therefore, it is also very important to consider the scientific control and dynamic optimization of substation voltage considering multi-point correlation.

[0029] Therefore, in order to solve the problems of insufficient regulation accuracy, ignoring node relevance and low resource utilization, through the steps of S100-S400, a dynamic relevance model is constructed based on electrical relevance factors and load fluctuation factors, the voltage relevance degree between each node in the transformer area is accurately quantified, and a node voltage relevance matrix is obtained, which provides a reliable mathematical basis for multi-point collaborative regulation; through real-time collection of operation data of each node by the monitoring terminal, the relevance matrix is dynamically updated, and an emergency update mechanism is triggered when the output of the distributed power or the load fluctuates significantly, so as to ensure that the relevance model is synchronized with the actual operation state and adapts to the dynamic scene of high proportion of new energy and diversified load access; a non-dominated sorting genetic algorithm is used for multi-objective path optimization, the maximum voltage qualification rate, the minimum regulation cost and the minimum equipment loss are taken as optimization objectives, the multi-point relevance constraint is integrated into the fitness function, and the optimal regulation strategy with the best comprehensive safety and economy is quickly searched out, so as to avoid voltage fluctuation of adjacent nodes caused by single-point regulation; a hierarchical execution mechanism is adopted, the substation combines the real-time voltage fine-tuning regulation amount of the relevant nodes, and verifies the regulation effect through closed-loop feedback, so as to realize dynamic correction and early warning for the voltage out-of-limit situation; at the same time, based on the dynamic update of the relevance matrix and the multi-objective optimization strategy, all-around accurate control and efficient resource utilization are realized in the process of transformer area voltage regulation.

[0030] Embodiment 2, refer to Figure 1 For an embodiment of the present application, based on the above embodiment, a power distribution network transformer area voltage regulation method considering multi-point relevance is provided.

[0031] In the embodiment of the present application, the step of constructing the transformer area node voltage relevance matrix in step S100 includes A1-A3: A1, determine the factors affecting node voltage relevance, including electrical relevance factors and load fluctuation factors.

[0032] Specifically, the electrical relevance factors include physical parameters affecting the electrical coupling strength between nodes, such as line impedance (R+jX) between nodes, power flow direction (P / Q), distributed power (DG) access location and capacity, voltage regulation device (such as SVG, on-load voltage regulation transformer OLTC, energy storage ESS) configuration parameters, and time-varying parameters reflecting the dynamic operation state of the transformer area, such as time sequence fluctuation characteristics of node load, intermittent change law of distributed power (photovoltaic / wind power) output, influence coefficient of meteorological factors (light intensity / wind speed) on DG output. Through collection of real-time operation data of the transformer area, electrical parameters of each node such as active power, reactive power, line resistance, line reactance and rated voltage, as well as distributed power output fluctuation and load fluctuation dynamic parameters are obtained, which provide data basis for quantitative calculation of node voltage relevance degree.

[0033] A2, calculate the node voltage relevance degree through the electrical relevance factors and the load fluctuation factors.

[0034] the node voltage correlation degree The calculation formula is: ; wherein, 、 are the coefficients of the electrical correlation factor and the load fluctuation factor, 、 are the active power and the reactive power flowing from node i to node j, 、 are the resistance and the reactance of the line between node i and node j, is the rated voltage of node j, is the distributed power output fluctuation of node i, is the load fluctuation of node i, is the total apparent capacity of the transformer area, is the maximum value of the electrical term, is the maximum value of the dynamic term.

[0035] A3, constructing a transformer area node voltage correlation matrix according to the node voltage correlation degree, the matrix dimension is N*N, and N is the total number of monitored nodes in the transformer area.

[0036] Based on the correlation degree between each node pair calculated in step A2, a transformer area node voltage correlation matrix M is constructed, the matrix dimension is N*N, wherein N is the total number of monitored nodes in the transformer area, and the matrix element represents the voltage influence intensity of node i on node j. The correlation matrix intuitively depicts the correlation relationship network between all nodes in the transformer area: the larger the θij value in the matrix, the more significant the influence of the voltage or load change of node i on the voltage of node j; the diagonal elements are 0 (the node itself does not produce correlation), and the asymmetric matrix structure reflects the directionality characteristics of the voltage influence (for example, the influence of node i on j may be stronger than the influence of j on i). The correlation matrix provides an accurate mathematical basis for subsequent multi-objective optimization regulation and hierarchical collaborative control, ensuring that the regulation strategy can fully consider the correlation effect between nodes.

[0037] In an optional embodiment, the calculation of the node voltage correlation degree in step S100 can also be based on a correlation degree prediction method of machine learning, such as using a deep learning model such as a long short-term memory network (LSTM) or a graph neural network (GNN). By training the historical operation data of the transformer area (including time series data such as node voltage, power, DG output, and load), the model can automatically learn the complex nonlinear correlation relationship between nodes, and is particularly suitable for complex transformer area scenarios with high penetration of distributed power and severe load fluctuations, and has stronger correlation degree prediction accuracy and generalization ability.

[0038] In another alternative embodiment, the step S100 of constructing the substation node voltage correlation matrix can also adopt a technical solution of combining sensitivity analysis with measured data. The sensitivity matrix of voltage to power is obtained through power flow calculation of the distribution network to provide the theoretical correlation between nodes; at the same time, the measured voltage-power response data of the substation are used for correction to construct a node voltage correlation matrix with both theoretical accuracy and practical applicability. This method is particularly suitable for fine regulation scenarios that require high-precision modeling of substation correlation, and can effectively compensate for the deviation between the theoretical model and the actual operation.

[0039] In the embodiments of the present application, the step S200 of updating the node voltage correlation degree and the substation node voltage correlation matrix based on the collected data includes B1-B4: B1, collecting the operation data of each node through the monitoring terminal in the substation.

[0040] Specifically, the edge monitoring terminal (including smart meters, distributed power controllers, voltage sensors, current transformers, etc.) is deployed in the substation to collect the operation parameters of each node in real time. The collected data includes: node voltage , node current , node active power , node reactive power , real-time output of distributed power , node load and other key operation data. The data sampling frequency is set to 1 minute / time to ensure that the rapid changes in the operation state of the substation can be captured, providing high-frequency and high-precision data support for dynamic updating of the correlation degree. All collected data are preliminarily processed and time-stamped through the edge computing gateway, and uploaded to the database of the substation control master station for storage and analysis.

[0041] B2, based on the collected operation data of each node, the node voltage correlation degree of each node is recalculated every X minutes, and the substation node voltage correlation matrix is updated.

[0042] In this embodiment, a periodic dynamic updating mechanism is adopted, and based on the real-time operation data collected in step B1, the correlation degree between each pair of nodes in the substation is recalculated every X minutes (preferably X=5 minutes) .

[0043] Specifically, the power flow direction , , between nodes at the current time , the fluctuation of distributed power output , and the load fluctuation are substituted into the correlation degree calculation formula: ; Get the correlation degree value at the current time, and update the cell area node voltage correlation matrix , where the matrix element .

[0044] This periodic update mechanism ensures that the correlation matrix can reflect the medium-term change trend of the cell area operation state, such as the change of daily load curve and the periodic fluctuation of photovoltaic output, so that the regulation strategy is synchronized with the actual scene.

[0045] B3, when the node operation data fluctuation is monitored to exceed the threshold value, the emergency update of the node voltage correlation degree is triggered.

[0046] In order to deal with the sudden disturbance in the cell area (such as sudden drop of distributed power output and sudden input of heavy load), on the basis of the periodic update of step B2, an emergency update triggering mechanism is set. When the real-time monitoring finds that the operation data fluctuation of any node exceeds the preset threshold value, the emergency update of the correlation degree is triggered immediately without waiting for the regular X-minute update period. The emergency update uses the same calculation method as step B2, but the update interval is greatly shortened to Y minutes, preferably Y = 1 minute, to quickly capture the instantaneous change of the correlation relationship in the cell area, ensure that the regulation strategy can respond to the sudden disturbance in time, and avoid the regulation failure or voltage out-of-limit caused by the lag of the correlation model.

[0047] B4, the node operation data fluctuation exceeding the threshold value includes: the fluctuation of distributed power output is greater than 10% of the rated capacity, or the fluctuation of node load is greater than 15% of the rated load; when the emergency update, the update interval of the node voltage correlation degree is shortened to Y minutes.

[0048] Specifically, the trigger condition of emergency update is set as: for any node , when the fluctuation of its distributed power output meets , where is the rated capacity of the distributed power source at the node , or when the fluctuation of its load meets , where is the rated load of the node , it is determined that the operation data fluctuation exceeds the threshold value.

[0049] At this time, the cell area regulation master station immediately starts the emergency update program, shortens the correlation degree update interval from the regular X minutes to Y minutes, preferably Y = 1 minute. During the emergency update, the system continuously monitors the operation state of the fluctuation node and its associated nodes until the fluctuation is restored to below the threshold value, and then restores the regular X-minute periodic update mode. This double-rate update mechanism takes into account the calculation efficiency and response speed, avoiding the waste of calculation resources caused by frequent calculation, and ensuring the rapid response ability to sudden disturbance.

[0050] In the embodiments of the present application, the obtaining step of the optimal regulation strategy in step S300 comprises C1-C3: C1, a multi-objective optimization function is constructed, and a constraint condition is set.

[0051] Specifically, for the multiple demands of the voltage regulation of the transformer area, a multi-objective optimization function containing three sub-targets is constructed. Target 1 is the maximization of the voltage qualification rate, which is expressed as: ; Wherein, is the total length of the regulation period, is the cumulative length of time when the voltage of each node exceeds the upper limit or is lower than the lower limit in the regulation period.

[0052] Target 2: Minimization of regulation cost, expressed as: ; Wherein, is the total number of regulation actions in the regulation period, is the single regulation cost of the on-load voltage regulating transformer, is the number of tap position adjustments of the OLTC in the th regulation, is the unit power regulation cost of the static var generator, is the reactive power compensation amount of the SVG in the th regulation, is the unit power regulation cost of the energy storage system, is the charge and discharge power of the ESS in the th regulation.

[0053] Target 3 is the minimization of device loss, expressed as: ; Wherein, , , are the regulation loss powers of the OLTC, the SVG and the ESS in the th regulation, respectively. At the same time, three types of constraint conditions are set to ensure the feasibility and safety of the regulation scheme.

[0054] The voltage constraint is: ; Wherein, is the lower limit of the voltage, is the upper limit of the voltage, is the rated voltage.

[0055] For the node pair with the correlation degree , the following conditions must be met: ; wherein, is the voltage variation of node , is the upper limit of the allowed voltage difference, which ensures the coordinated voltage variation between the associated nodes and avoids the dramatic voltage fluctuation of the adjacent nodes caused by single-point regulation.

[0056] The device capacity constraint is: ; ; ; wherein, , is the reactive power output range of the SVG, , is the allowed range of the state of charge of the energy storage, , is the adjustment range of the tap of the OLTC.

[0057] C2, the multi-objective optimization function is solved by using a non-dominated sorting genetic algorithm, the multi-point correlation constraint is integrated into the fitness function, and a Pareto optimal solution set is obtained.

[0058] In this embodiment, the multi-objective optimization problem constructed in step C1 is solved by using an improved non-dominated sorting genetic algorithm.

[0059] In order to fully consider the correlation effect between the nodes in the transformer area, a correlation constraint factor is introduced, which is defined as: ; The multi-point correlation constraint is integrated into the fitness function. Specifically, for each candidate regulation scheme , the comprehensive fitness is calculated as: ; wherein, , , is the weight coefficient of the three objective functions, is the correlation constraint violation penalty term, which is defined as: ; wherein, is the penalty coefficient. The fitness function preferentially selects the regulation scheme that satisfies the correlation constraint, and avoids the single-point regulation leading to the voltage out-of-limit of the associated nodes.

[0060] Through non-dominated sorting, crowding degree calculation and elite reservation strategy, after multiple iterations, the algorithm converges to a set of Pareto optimal solution , where each solution represents a regulation strategy that achieves a different balance between voltage compliance, regulation cost, and equipment wear.

[0061] C3. Selecting an optimal regulation strategy from the Pareto optimal solution set according to the operating requirements of the transformer area.

[0062] From the Pareto optimal solution set obtained in step C2, select the final regulation strategy according to the current operating requirements and operating scenarios of the transformer area. During the peak electricity consumption period, the transformer area load is large and the voltage fluctuates frequently. At this time, priority is given to ensuring voltage compliance, and the solution that maximizes is selected. During the off-peak electricity consumption period, the transformer area load is light and the voltage is relatively stable. At this time, priority is given to reducing regulation cost, and the solution that minimizes is selected. During the equipment maintenance period, in order to prolong the service life of the equipment, the solution that minimizes is selected. In addition, fuzzy comprehensive evaluation method or analytic hierarchy process can be used to select the scheme with the highest comprehensive score from the Pareto solution set as the optimal regulation strategy according to the preference weights of the three objectives of the transformer area management personnel. This strategy includes specific regulation instructions, such as OLTC adjustment to which gear, SVG input of how much reactive power compensation, ESS charging and discharging power, etc., providing accurate control parameters for subsequent hierarchical execution.

[0063] In the embodiments of the present application, the step of fine-tuning the regulation amount in step S400 in combination with the operating data of the associated nodes includes D1-D3: D1. The main station issues regulation instructions to the voltage regulation equipment according to the optimal regulation strategy.

[0064] Specifically, the transformer area regulation main station issues accurate regulation instructions to each voltage regulation equipment in the transformer area according to the optimal regulation strategy solved in step S300. The regulation instructions are packaged using standardized communication protocols (such as IEC 61850 or Modbus) and contain the following key information: device identification code, regulation type, regulation amount value, execution timestamp, priority identifier. The main station issues instructions to each node substation through the edge computing gateway or the power distribution automation system, and starts the instruction execution monitoring module to track the status and progress of the regulation instructions in real time, ensuring the reliability and timeliness of the instruction transmission.

[0065] D2. After receiving the regulation instructions, each substation synchronously monitors the real-time voltage of the associated nodes of the node, which are nodes with a node voltage correlation degree greater than or equal to 0.6, and fine-tunes the regulation amount in combination with the real-time voltage of the associated nodes. The fine-tuning amplitude does not exceed 5% of the regulation instruction value.

[0066] In this embodiment, after receiving the control commands from the master station, each node substation does not directly execute the original commands, but first performs localized intelligent fine-tuning. Specifically, the substation first retrieves the voltage correlation matrix of the transformer substations stored locally. Querying the current node The degree of correlation is satisfied All associated nodes This constitutes a set of related nodes. Then, the substation synchronously collects the real-time voltage of these associated nodes through the edge monitoring terminal. And calculate the average voltage deviation of the associated nodes: ; in, Rated voltage, This represents the number of elements in the associated node set. Based on this average voltage deviation, the substation fine-tunes the control quantities issued by the master station. Taking SVG reactive power compensation as an example, if the master station command is to activate... The reactive power is calculated by the substation based on the voltage conditions of the associated nodes, and the fine-tuning coefficient is determined accordingly. ; in, This represents the upper limit of the allowable voltage deviation. The actual control amount after fine-tuning is: ; in, For a sign function, when When the voltage at the associated node is too high, a positive value is taken, and the reactive power compensation is increased to reduce the voltage; when When the voltage of the associated node is low, a negative value is used to reduce reactive power compensation and improve the voltage. The fine-tuning range is strictly limited to within 5% of the control command value, which ensures both response to the voltage of the associated node and avoids global control imbalance caused by excessive deviation from the master station's optimization strategy. For OLTC level adjustment and ESS charging and discharging, the substation adopts a similar fine-tuning logic to ensure that the control action fully considers the multi-point correlation effect.

[0067] D3. The voltage regulating device executes the fine-tuned control command and feeds back the execution status to the main station.

[0068] After each node substation completes fine-tuning of the control quantity, the fine-tuned instruction is immediately issued to the on-site voltage regulating equipment (OLTC, SVG, ESS, etc.). After receiving the instruction, the voltage regulating equipment performs the corresponding control action, the OLTC adjusts to the target gear through the motor-driven gear shifting mechanism, the SVG adjusts the output reactive power through the IGBT inverter to achieve the specified compensation amount, and the ESS controls the charge and discharge power through the battery management system (BMS) to perform energy throughput according to the instruction. After the control action is completed, the device controller collects the execution result and feeds back the execution status information to the node substation. The substation verifies the execution result: if the deviation between the actual control quantity and the fine-tuning instruction is within ±2%, it is determined that the execution is successful; if the deviation exceeds ±2% or the device reports a fault, it is marked as an execution exception. Whether the execution is successful or abnormal, the substation feeds back the device execution status, actual control quantity, execution timestamp, device health status, etc. to the transformer area control master station in real time through the communication network. After receiving the feedback, the master station updates the control execution log and triggers the subsequent closed-loop feedback verification process in step S400, providing accurate execution basis for the next round of control decision, forming a complete closed-loop control link of issuance-execution-feedback-verification.

[0069] In an optional implementation, the node substation fine-tunes the control quantity in step S400, which can also be based on the fine-tuning method of model predictive control. Specifically, the node substation establishes a voltage-power dynamic response model of the node and its associated nodes, and predicts the voltage variation trend in the future time steps through rolling optimization. After receiving the control instruction from the master station, the node substation calculates the optimal fine-tuning quantity under the premise of meeting the associated constraints using the MPC algorithm, so as to minimize the voltage deviation in the prediction time domain. This method is particularly suitable for scenarios where distributed power output fluctuates rapidly or load changes suddenly, and can predict the influence of control action on associated nodes in advance, realize more accurate forward-looking fine-tuning, and effectively avoid voltage oscillation and over-regulation.

[0070] In another optional implementation, the voltage regulating equipment executes the control instruction in step S400, which can also adopt a distributed control scheme of multiple device collaborative action. When there are multiple voltage regulating devices (such as multiple SVGs and multiple ESS groups) in the transformer area, the substations of each device communicate with each other through a local communication network (such as CAN bus or wireless Mesh network) to exchange the voltage information of the local associated nodes and the control quantity to be executed. Based on the consensus algorithm, each substation negotiates to form a collaborative fine-tuning scheme, ensuring that the control actions of multiple devices are synchronized in time and coordinated in amplitude, avoiding the problems of single device overload or mutual cancellation of multiple devices. This distributed collaborative approach is particularly suitable for large-scale transformer areas or multi-branch line scenarios, and can significantly improve the robustness and device utilization efficiency of control, reducing the computational burden of the master station.

[0071] In the embodiments of the present application, the voltage regulating device further includes E1-E2 after executing the fine-tuned control instruction: E1, 1 minute after the control execution is completed, the master station collects node voltage data in the node operation data and verifies whether the voltage qualified rate meets the standard.

[0072] Specifically, after the voltage regulating device executes the fine-tuned control instruction in step D3 and feeds back the execution state, the substation control master station starts a closed-loop verification mechanism. The master station waits for 1 minute of stable time (this time window is used to ensure that the influence of the control action on the voltage is fully reflected, and at the same time, the interference of the transient transition process is filtered out), and then re-collects the real-time voltage data of all N monitoring nodes in the substation through the edge monitoring terminal , wherein, minute.

[0073] The master station determines the eligibility of the voltage of each node and checks whether the voltage constraint is met: ; , wherein, is the lower limit of the voltage, is the upper limit of the voltage.

[0074] The number of nodes with qualified voltage is counted , and the voltage qualified rate after control is calculated: ; When (wherein, is the target voltage qualified rate threshold, usually 99% or higher), it is determined that the current control is successful, the master station records the control result and enters the normal monitoring mode; when , it indicates that there are still nodes with voltage out of limit, and the master station further analyzes the distribution characteristics of the nodes with voltage out of limit, especially checks whether the nodes with high correlation degree (j ) associated with the nodes of the current control appear voltage out of limit to determine whether the associated effect caused by the control leads to it.

[0075] E2, if there are associated nodes with voltage out of limit, a multi-objective optimization function is reconstructed according to the updated substation node voltage correlation matrix and the constraint condition is set, a non-dominated sorting genetic algorithm is used to obtain a new optimal control strategy, a new control instruction is issued to the voltage regulating device according to the new optimal control strategy, and the substation receives the new control instruction and fine-tunes the control amount combined with the operation data of the associated nodes until all node voltages in the substation meet the voltage constraint.

[0076] In the present embodiment, when the verification result of step E1 shows that there are associated nodes with voltage out of limit (i.e., there are nodes j that satisfy or , and ), the master station immediately starts the iterative regulation mechanism.

[0077] Firstly, the master station calls the dynamic updating module of step S200, and recalculates the node voltage correlation degree based on the latest collected node operation data (including voltage , power , distributed power output , load , etc.), and updates the substation node voltage correlation matrix . The updated correlation matrix can accurately reflect the influence of the previous round of regulation on the correlation between nodes, providing a mathematical basis for the new round of optimization.

[0078] Then, the master station re-executes the multi-objective optimization process of step S300 based on the updated correlation matrix . Specifically, the master station reconstructs the multi-objective optimization function (including maximizing the voltage qualification rate , minimizing the regulation cost , and minimizing the device loss ), and updates the constraint conditions: the voltage constraint remains unchanged. The multi-point correlation constraint is re-set based on the new correlation degree $\theta_{ij}(t+\Delta t)$. The device capacity constraint considers the current state of the device. The master station uses the non-dominated sorting genetic algorithm to solve the updated multi-objective optimization problem, and in the iteration process, the correlation constraint factor is integrated into the fitness function to preferentially select regulation schemes that can improve the voltage out-of-limit problem of correlated nodes, and through rapid convergence, a new Pareto optimal solution set is obtained, and a new optimal regulation strategy is selected according to the current operation demand of the substation .

[0079] In an optional embodiment, the verification of the voltage qualification rate in step E1 can also be based on a comprehensive evaluation method of the weighted voltage deviation index. Specifically, after collecting the node voltage data, the master station not only judges whether the voltage is out of limit, but also calculates the weighted voltage deviation index: ; wherein is the weight coefficient of node i, which is determined according to the importance of the node. By setting the threshold of WVDI (such as ), when , it is determined that the regulation effect is good; when , even if all node voltages are not out of limit, the fine tuning process is started. This method is particularly suitable for high-end user substations or areas with a high concentration of precision equipment with extremely high voltage quality requirements, and can achieve preventive regulation before the voltage is out of limit, significantly improve the voltage stability margin, and avoid the use quality problems caused by the "gray zone" where the voltage is close to the limit but not out of limit.

[0080] In another alternative embodiment, the iterative regulation optimization in step E2 can also adopt an adaptive regulation strategy of reinforcement learning. The master station establishes an intelligent regulation agent based on deep Q network (DQN) or proximal policy optimization (PPO), takes the voltage state of the transformer area, the correlation matrix and the device state as the environmental state input, takes the regulation action (OLTC position, SVG power, ESS charging and discharging) as the action space, and takes the voltage qualification rate improvement and regulation cost reduction as the reward function. In each iteration regulation, the agent autonomously selects the optimal action according to the current state, and performs online learning and policy update through the execution result feedback. After multiple iteration training, the reinforcement learning agent can learn the complex multi-point correlation rule and the optimal regulation mode, and is particularly suitable for new transformer area scenes with high penetration of distributed power and complex load fluctuation. This method does not need to manually set complex constraint conditions and weight coefficients, has stronger adaptive ability and generalization performance, can automatically adjust the regulation strategy under different operating conditions, and realizes intelligent and autonomous precise control of the transformer area voltage.

[0081] In summary, by constructing a node voltage correlation degree model considering electrical correlation factors and load fluctuation factors, and establishing a transformer area node voltage correlation matrix, the voltage linkage effect between nodes in the transformer area is accurately described, and the defect of ignoring the electrical coupling between nodes in the traditional single-point independent regulation mode is overcome. By setting an emergency update mechanism, the correlation degree and correlation matrix are quickly updated when the distributed power output or node load fluctuation exceeds the threshold, ensuring that the correlation model is synchronized with the actual operating state, and solving the problems of poor adaptability and regulation lag of the static correlation model.

[0082] The present application integrates multi-point correlation constraints into the fitness function of the multi-objective optimization function, and obtains a Pareto optimal solution set considering voltage qualification rate, regulation cost and device loss by solving with a non-dominated sorting genetic algorithm, realizes global optimization configuration of the regulation resources, and avoids excessive regulation and regulation blind area. A hierarchical collaborative regulation mechanism of master station issuing instructions, sub-station combining real-time voltage fine-tuning of associated nodes, voltage regulating device execution and feedback, and a closed-loop feedback mechanism of verifying the voltage qualification rate after regulation and re-solving when the associated nodes exceed the limit, ensure accurate control of the voltage of all nodes in the transformer area, improve the voltage regulation precision of the distribution network transformer area, reduce the regulation cost and device loss, and enhance the adaptability to high proportion of new energy and diversified load access scenarios.

[0083] Embodiment 3, the above is a schematic scheme of a power distribution network substation voltage regulation method considering multi-point correlation. It should be noted that the technical scheme of the power distribution network substation voltage regulation system considering multi-point correlation and the technical scheme of the power distribution network substation voltage regulation method considering multi-point correlation described above belong to the same concept. The technical details of the power distribution network substation voltage regulation system considering multi-point correlation in this embodiment are not described in detail, and can be referred to the description of the technical scheme of the power distribution network substation voltage regulation method considering multi-point correlation described above.

[0084] The embodiment also provides a power distribution network substation voltage regulation system considering multi-point correlation, comprising: A monitoring terminal is arranged at each node in the substation and is used to collect real-time operation data of each node; A master station is used to receive the operation data of each node collected by the monitoring terminal, calculate the node voltage correlation degree based on the operation data of each node, construct a substation node voltage correlation matrix, construct a multi-objective optimization function through the substation node voltage correlation matrix, set a constraint condition to obtain an optimal regulation strategy, issue a regulation instruction to a voltage regulation device according to the optimal regulation strategy, and verify the voltage qualification rate after regulation execution; A substation is arranged at each node and is used to receive the regulation instruction issued by the master station, monitor the real-time voltage of the associated node, and fine-tune the regulation amount in combination with the real-time voltage of the associated node; A voltage regulation device, including an on-load voltage regulation transformer, a static var generator, and an energy storage system, is used to execute the fine-tuned regulation instruction of the substation and feed back the execution status to the master station.

[0085] The embodiment also provides an electronic device suitable for the power distribution network substation voltage regulation considering multi-point correlation, comprising a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the power distribution network substation voltage regulation method considering multi-point correlation proposed in the above embodiment.

[0086] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the power distribution network substation voltage regulation method considering multi-point correlation proposed in the above embodiment.

[0087] The storage medium proposed in the embodiment and the power distribution network substation voltage regulation method considering multi-point correlation proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0088] Those skilled in the art can clearly understand the present application by the above description of the embodiments, and the present application can be realized by software and necessary general hardware, and of course, can also be realized by hardware. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A power distribution network substation voltage regulation method considering multipoint correlation, characterized in that, The method comprises the following steps: Collecting factors affecting node voltage correlation, quantifying node voltage correlation degree, and constructing a substation node voltage correlation matrix; Collecting node operation data through monitoring terminals, updating node voltage correlation degree and the substation node voltage correlation matrix based on the collected data, and triggering emergency update of the node voltage correlation degree when the fluctuation of the node operation data is monitored to exceed a threshold value; Constructing a multi-objective optimization function and setting a constraint condition based on the updated substation node voltage correlation matrix, and obtaining an optimal control strategy through solving; The main station issues a control instruction to a voltage regulating device according to the optimal control strategy, and the substation adjusts the control amount in combination with the operation data of the associated nodes after receiving the control instruction.

2. The power distribution network substation voltage regulation method considering multipoint correlation of claim 1, wherein, The step of constructing the substation node voltage correlation matrix comprises: Determining factors affecting node voltage correlation, including electrical correlation factors and load fluctuation factors; Calculating node voltage correlation degree based on the electrical correlation factors and the load fluctuation factors; Constructing a substation node voltage correlation matrix based on the node voltage correlation degree, and the matrix dimension is N x N, where N is the total number of monitored nodes in the substation.

3. The power distribution network substation voltage regulation method considering multipoint correlation of claim 2, wherein, The step of updating node voltage correlation degree and the substation node voltage correlation matrix based on the collected data comprises: Collecting node operation data through monitoring terminals in the substation; Recalculating the node voltage correlation degree of each node and updating the substation node voltage correlation matrix based on the collected node operation data every X minutes; Triggering emergency update of the node voltage correlation degree when the fluctuation of the node operation data is monitored to exceed a threshold value; The fluctuation of the node operation data exceeding the threshold value includes that the fluctuation of the output of the distributed power source is greater than 10% of the rated capacity, or the fluctuation of the node load is greater than 15% of the rated load; and the update interval of the node voltage correlation degree is shortened to Y minutes during the emergency update.

4. The power distribution network substation voltage regulation method considering multipoint correlation of claim 3, wherein, The step of obtaining the optimal control strategy comprises: Constructing a multi-objective optimization function and setting a constraint condition; Solving the multi-objective optimization function by using a non-dominated sorting genetic algorithm, integrating the multi-point correlation constraint into a fitness function, and obtaining a Pareto optimal solution set; Selecting an optimal control strategy from the Pareto optimal solution set according to the operation requirements of the substation.

5. The power distribution network substation voltage regulation method considering multipoint correlation of claim 4, wherein, The step of adjusting the control amount in combination with the operation data of the associated nodes comprises: The main station issues a control instruction to a voltage regulating device according to the optimal control strategy; Each substation synchronously monitors the real-time voltage of the associated nodes of the node after receiving the control instruction, the associated nodes are nodes with a node voltage correlation degree greater than or equal to 0.6, adjusts the control amount in combination with the real-time voltage of the associated nodes, and the adjustment amplitude does not exceed 5% of the control instruction value; The voltage regulating device executes the adjusted control instruction and feeds back the execution status to the main station.

6. The power distribution network substation voltage regulation method considering multipoint correlation of claim 5, wherein, After the voltage regulating device executes the adjusted control instruction, the step further comprises: The main station collects node voltage data in the node operation data one minute after the control execution is completed, and verifies whether the voltage qualification rate meets the requirements. If there is an associated node voltage out-of-limit, a multi-objective optimization function is reconstructed according to an updated substation node voltage association matrix and constraint conditions are set, a new optimal control strategy is obtained by using a non-dominated sorting genetic algorithm, a new control instruction is issued to a voltage regulating device according to the new optimal control strategy, and the new control instruction is received by a substation and combined with associated node operation data to fine-tune the control amount until all node voltages in the substation meet the voltage constraint.

7. The power distribution network substation voltage regulation method considering multipoint correlation of claim 6, wherein, The node voltage correlation degree The calculation formula is: ； wherein, , are coefficients of electrical correlation factor, load fluctuation factor respectively, , are active power and reactive power from node i to node j, , are resistance and reactance of line between node i and node j, is rated voltage of node j, is distributed power output fluctuation of node i, is load fluctuation of node i, is total apparent capacity of transformer area, is maximum value of electrical term, is maximum value of dynamic term.

8. A power distribution network substation voltage regulation system considering multipoint correlation, applying the method of any one of claims 1-7, characterized in that, Comprise: a monitoring terminal deployed at each node in a substation for real-time collection of operation data of each node; a main station for receiving operation data of each node collected by the monitoring terminal, calculating node voltage correlation based on the operation data of each node and constructing a substation node voltage association matrix, constructing a multi-objective optimization function through the substation node voltage association matrix and setting constraint conditions to obtain an optimal control strategy, issuing a control instruction to a voltage regulating device according to the optimal control strategy, and verifying the voltage qualification rate after control execution; a substation set at each node for receiving the control instruction issued by the main station, monitoring the real-time voltage of the associated node, and fine-tuning the control amount in combination with the real-time voltage of the associated node; a voltage regulating device comprising an on-load voltage regulating transformer, a static var generator, and an energy storage system for executing the fine-tuned control instruction of the substation and feeding back the execution status to the main station. 9.An electronic device comprising: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which, when executed by the processor, implement the steps of the power distribution network substation voltage control method considering multi-point correlation according to any one of claims 1 to 7. 10.A computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the power distribution network substation voltage control method considering multi-point correlation according to any one of claims 1 to 7.

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