Medium and low voltage distribution network cooperative voltage regulation method and device considering power distribution network voltage safety margin, and medium and low voltage distribution network

Abstract

The invention discloses a middle and low voltage distribution network coordinated voltage regulation method and device considering the voltage safety margin of a distribution network and the low voltage distribution network. The method comprises the steps that distribution network parameter data and distribution network historical operation data are acquired, the historical operation data are preprocessed, and abnormal data are replaced; wherein the power distribution network comprises new energy; quantizing an energy margin index of the power distribution network based on the distribution network parameter data and single machine infinite bus system equivalence; wherein an energy function method is adopted to quantify a voltage safety margin index; the nonlinear voltage safety margin constraint is converted into a linear form by adopting trajectory sensitivity analysis; and according to the preprocessed historical operation data of the power distribution network and the preprocessed real-time operation data of the power distribution network, constructing and solving a double-layer robust optimization model, cooperatively optimizing a preventive scheduling and emergency control strategy, and outputting a final voltage regulation scheme. According to the invention, an effective cooperative voltage regulation strategy can be provided, so that uncertain disturbance is dealt with, and the power supply quality and reliability are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method, apparatus, and low-voltage distribution network for coordinated voltage regulation of medium and low voltage distribution networks that takes into account the voltage safety margin of the distribution network. Background Technology

[0002] With rapid economic development, the demand for electricity from all sectors continues to increase, and renewable energy sources, represented by distributed new energy sources, are widely used in medium and low voltage distribution networks. However, distribution networks with a high proportion of distributed power sources operate under complex and variable conditions. In particular, the inherent randomness and uncertainty of new energy output can frequently alter the system power balance and even trigger large power fluctuations, posing a severe challenge to the voltage stability of medium and low voltage distribution networks and seriously threatening the safe and stable operation of the power system. Summary of the Invention

[0003] This invention provides a method, apparatus, and low-voltage distribution network for coordinated voltage regulation of medium and low voltage distribution networks that considers the voltage safety margin of the distribution network. It aims to assess the voltage safety margin of the distribution network and provide an effective coordinated voltage regulation strategy to cope with uncertain disturbances and ensure power supply quality and reliability.

[0004] According to one aspect of the present invention, a method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network is provided, comprising:

[0005] The system acquires distribution network parameter data and historical distribution network operation data, preprocesses the historical operation data, and replaces abnormal data; wherein the distribution network includes new energy sources.

[0006] Based on the distribution network parameter data and the equivalent of a single-machine infinite bus system, the energy margin index of the distribution network is quantified; among which, the voltage safety margin index is quantified using the energy function method.

[0007] Trajectory sensitivity analysis is used to transform the nonlinear voltage safety margin constraint into a linear form;

[0008] Based on the preprocessed historical and real-time operating data of the distribution network, a two-layer robust optimization model is constructed and solved to collaboratively optimize preventive scheduling and emergency control strategies, and output the final voltage regulation scheme.

[0009] Optionally, the preprocessing of the historical operational data and the replacement of abnormal data specifically includes:

[0010] Based on time series methods, a sliding window is moved point by point to calculate the statistical indicators within the window;

[0011] If the deviation between the current data point and the statistical indicator exceeds a preset threshold, the current data point is determined to be abnormal data.

[0012] The abnormal data is replaced; and trajectory sensitivity analysis is triggered.

[0013] Optionally, the time-series-based method calculates statistical indicators within a sliding window by moving the window point by point, specifically including:

[0014] Select several sampling points, determine the set interval and the preset threshold; traverse the historical operation data of the distribution network using a sliding window; extract the window from the historical data sequence;

[0015] Calculate statistics on the data within the window, including the mean, standard deviation, and bias.

[0016] The statistical index is obtained based on the mean, the standard deviation, and the deviation.

[0017] Optionally, the step of quantifying the energy margin index of the distribution network based on the distribution network parameter data and the equivalent of a single-machine infinite bus system specifically includes:

[0018] Based on the generator inertia center theory, a group of key generators in the distribution network that have similar dynamic behaviors under disturbances are equivalent to a single generator with a weighted average inertia constant, and its equivalent motion state is described by the inertia center.

[0019] Using network simplification techniques, the generators, transmission lines, and loads in the power distribution network are equivalent to an infinite power bus with constant voltage and frequency; wherein, the network simplification techniques include at least one of Kron reduction and Thevenin equivalence.

[0020] Construct a transient energy function in the form of a combination of generator kinetic and potential energy, and simplify it;

[0021] Based on the simplified transient energy function, the energy function of the single-machine infinite bus model is obtained;

[0022] Based on the energy function, the energy margin index is quantified by calculating the maximum energy that the distribution network can absorb from its current state to the instability critical point.

[0023] Optionally, the use of trajectory sensitivity analysis to transform the nonlinear voltage safety margin constraint into a linear form specifically includes:

[0024] A Taylor series expansion of the parameters of the power distribution network is constructed, and by assuming that the disturbance is small enough and ignoring higher-order terms, an approximate expression for the trajectory change is obtained.

[0025] By applying a small perturbation and comparing the state changes of the distribution network before and after the perturbation, the trajectory sensitivity with respect to voltage can be determined.

[0026] Combining the energy margin index of the distribution network and the voltage trajectory sensitivity, an expression for the linearized relationship of the energy margin index with respect to load disturbance is obtained;

[0027] Based on the expression of the linearized relationship, a complete linear expression is obtained that includes the initial stability margin, the change in stability margin caused by the uncertainty fluctuation of new energy output, the increase in stability margin after emergency load shedding, and the transient stability constraint that the distribution network needs to satisfy to maintain stable operation.

[0028] Optionally, the complete linear expression for the transient stability constraint is: ;

[0029] in, It is the initial stability margin of the power distribution network before any disturbance occurs but no control measures are taken; It is the change in stability margin caused by the uncertainty fluctuations in new energy output; It is the increase in stability margin resulting from the execution of emergency load shedding; It is the minimum stability margin threshold that the system needs to meet to maintain stable operation;

[0030] The calculation formula is: ;in, This refers to the load shedding amount; This refers to trajectory sensitivity.

[0031] Optionally, the step of constructing and solving a two-layer robust optimization model based on preprocessed historical and real-time distribution network operation data, coordinating preventive scheduling and emergency control strategies, and outputting the final voltage regulation scheme specifically includes:

[0032] A two-level robust optimization model with a core decision-making framework of minimization-maximization-minimization is constructed. The inputs to the two-level robust optimization model are the topology parameters of the distribution network, the uncertainty range of renewable energy output, and load forecast data. The first minimization is achieved by optimizing the first-level decision variables. To minimize generation costs; the maximum value is to minimize the load shedding action objective, within the uncertainty set. Find the uncertainty variable inside The worst-case scenario; the second minimum is achieved by optimizing the load shedding action decision variables. To minimize the cost of emergency load reduction;

[0033] The column and constraint generation solution strategy is applied to the two-layer robust optimization model to obtain the final voltage regulation scheme.

[0034] Optionally, the two-layer robust optimization model with the minimum-maximum-minimum as the core decision-making framework is as follows:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] Among them, C G It is the cost of electricity generation; C LS It is the cost of emergency load reduction; P G It is the power generation capacity; P L It is the load power; , and These represent the coefficients of the quadratic, linear, and constant terms of the power generation cost, respectively. It is the minimum stability margin threshold that the system needs to meet to maintain stable operation;

[0040] The step of applying a column and constraint generation solution strategy to the two-layer robust optimization model to obtain the final voltage regulation scheme specifically includes:

[0041] The maxima-minima problem of the subproblems is transformed into a maxima problem through strong duality, expressed as:

[0042] ;

[0043] ;

[0044] in, and It is a dual variable; These are non-concave bilinear terms;

[0045] An external approximation algorithm is used to linearize the non-concave bilinear terms around intermediate solution points in the subproblem; and a linear form is added to the main problem and solved iteratively.

[0046] After solving the subproblem, the worst-case scenario is searched for as the obtained value of the uncertainty variable u, and the uncertainty variable u is added to the solution set S1 as a fixed parameter used in the main problem.

[0047] Generate new cost variables and their corresponding constraints for minimizing emergency load cuts, and add them to the main problem;

[0048] Solve the main problem using the worst-case scenario and its corresponding variables and constraints;

[0049] The main problem and the subproblems are solved iteratively until the lower bound given by the main problem and the upper bound given by the subproblems converge.

[0050] According to another aspect of the present invention, a medium- and low-voltage distribution network coordinated voltage regulation device considering the voltage safety margin of the distribution network is provided, comprising:

[0051] A data preprocessing module is used to acquire distribution network parameter data and historical distribution network operation data, preprocess the historical operation data, and replace abnormal data; wherein, the distribution network includes new energy sources;

[0052] The safety margin index quantification module is used to quantify the energy margin index of the distribution network based on the distribution network parameter data and the equivalent of a single-machine infinite bus system; wherein, the voltage safety margin index is quantified using the energy function method.

[0053] The linear transformation module is used to convert nonlinear voltage safety margin constraints into a linear form using trajectory sensitivity analysis.

[0054] The solution output module is used to construct and solve a two-layer robust optimization model based on the preprocessed historical operation data and real-time operation data of the distribution network, to coordinate the optimization of preventive scheduling and emergency control strategies, and to output the final voltage regulation solution.

[0055] According to another aspect of the present invention, a medium- and low-voltage distribution network is provided, comprising: generators, new energy sources, power lines, loads, and a dispatch controller, wherein the dispatch controller performs a medium- and low-voltage distribution network coordinated voltage regulation method considering the voltage safety margin of the distribution network as described in any embodiment of the present invention.

[0056] This invention achieves preventative dispatch by considering voltage margin indicators. Furthermore, the single-machine infinite bus system equivalent refers to equating a complex distribution network to a single generator connected to a large power grid with a perpetually constant voltage via an impedance, thereby quantifying the distribution network's energy margin indicator. For the safety margin indicator, trajectory sensitivity analysis is employed to transform the nonlinear voltage safety margin constraint into a linear form, reducing computational difficulty and simplifying the complex problem, thus simplifying program design and improving response speed. The two-layer robust optimization model includes a main problem and sub-problems. The main problem minimizes generation costs, focusing on preventative dispatch; the sub-problems minimize the costs incurred by load shedding after disturbances, focusing on emergency control strategies. This invention can accurately quantify the transient stability level of the distribution network under high-proportion renewable energy access and synergistically optimize preventative dispatch and emergency control strategies, effectively addressing the voltage stability challenges caused by the uncertainty of renewable energy output and improving the operational reliability and robustness of the distribution network.

[0057] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 A flowchart illustrating a method for coordinated voltage regulation of medium and low voltage distribution networks that considers the voltage safety margin of the distribution network, provided as an embodiment of the present invention;

[0060] Figure 2 A flowchart illustrating another method for coordinated voltage regulation of medium and low voltage distribution networks that considers the voltage safety margin of the distribution network, provided as an embodiment of the present invention;

[0061] Figure 3 A flowchart illustrating another method for coordinated voltage regulation of medium and low voltage distribution networks that considers the voltage safety margin of the distribution network, provided as an embodiment of the present invention;

[0062] Figure 4 A flowchart illustrating another method for coordinated voltage regulation of medium and low voltage distribution networks that considers the voltage safety margin of the distribution network, provided as an embodiment of the present invention;

[0063] Figure 5 A schematic diagram of a medium- and low-voltage distribution network coordinated voltage regulation device considering the voltage safety margin of the distribution network provided in an embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of a scheduling controller provided in an embodiment of the present invention. Detailed Implementation

[0065] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0066] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0067] This invention provides a method for coordinated voltage regulation of medium and low voltage distribution networks that considers the voltage safety margin of the distribution network. This method can be executed by a coordinated voltage regulation device for medium and low voltage distribution networks that considers the voltage safety margin of the distribution network. This device can be implemented by software and / or hardware, and it can be configured in the dispatch controller of the medium and low voltage distribution network.

[0068] Figure 1 This is a flowchart illustrating a method for coordinated voltage regulation of medium- and low-voltage distribution networks that considers the voltage safety margin of the distribution network, provided as an embodiment of the present invention. (See also...) Figure 1 The method includes:

[0069] S110. Obtain distribution network parameter data and historical distribution network operation data, preprocess the historical operation data, and replace abnormal data; the distribution network includes new energy sources.

[0070] S120. Based on distribution network parameter data and the equivalent of a single-machine infinite bus system, the energy margin index of the distribution network is quantified; among which, the voltage safety margin index is quantified using the energy function method.

[0071] S130. Trajectory sensitivity analysis is used to transform the nonlinear voltage safety margin constraint into a linear form.

[0072] S140. Based on the preprocessed historical operation data and real-time operation data of the distribution network, construct and solve a two-layer robust optimization model, coordinate the optimization of preventive scheduling and emergency control strategies, and output the final voltage regulation scheme.

[0073] In this embodiment of the invention, the distribution network can also be referred to as the power system, or simply the system. A larger voltage safety margin indicates a greater capacity to accommodate power fluctuations. This embodiment of the invention achieves preventative dispatch by considering the voltage margin indicator. Furthermore, the single-machine infinite bus system equivalence refers to equating a complex distribution network to a single generator connected to a large power grid with a perpetually constant voltage via an impedance, thereby quantifying the energy margin indicator of the distribution network. For the safety margin indicator, trajectory sensitivity analysis is employed to transform the nonlinear voltage safety margin constraint into a linear form, which helps reduce computational difficulty, simplifies complex problems, and thus simplifies program design and improves response speed. The two-layer robust optimization model includes a main problem and sub-problems. The main problem minimizes generation costs, focusing on preventative dispatch; the sub-problems minimize the cost of load shedding after a disturbance, focusing on emergency control strategies. This embodiment of the invention can accurately quantify the transient stability level of the distribution network under high-proportion renewable energy access and synergistically optimize preventative dispatch and emergency control strategies, thereby effectively addressing the voltage stability challenges brought about by the uncertainty of renewable energy output and improving the operational reliability and robustness of the distribution network.

[0074] Figure 2 A flowchart illustrating another method for coordinated voltage regulation of medium- and low-voltage distribution networks, considering the voltage safety margin of the distribution network, provided as an embodiment of the present invention. See also... Figure 2 Based on the above embodiments, optionally, S110 involves acquiring distribution network parameter data and historical distribution network operation data, preprocessing the historical operation data, and replacing abnormal data, specifically including:

[0075] S111. Obtain distribution network parameter data and historical distribution network operation data;

[0076] S112. Based on historical operational data and using the time series method, the statistical indicators within the window are calculated by moving the sliding window point by point.

[0077] Optionally, select several sampling points, determine the set interval and preset threshold; traverse the historical operation data of the distribution network through a sliding window; extract the window from the historical data sequence; calculate statistics for the data within the window, including the mean, standard deviation and deviation; obtain statistical indicators based on the mean, standard deviation and deviation.

[0078] S113. If the deviation between the current data point and the statistical indicator exceeds the preset threshold, the current data point is determined to be abnormal data.

[0079] S114. Replace abnormal data; and perform trigger trajectory sensitivity analysis.

[0080] In scenarios with a high proportion of renewable energy integration, voltage anomalies may originate from sudden increases in renewable energy output leading to voltage rises, sudden decreases in renewable energy output leading to voltage drops, or external disturbances amplifying voltage fluctuations due to uncertainties in renewable energy output. A time-series-based approach is used, moving a sliding window point by point to calculate statistical indicators (e.g., mean, standard deviation, median, or variance) within the window, and then comparing the current data point with these indicators. If the deviation exceeds a preset threshold, it is considered an anomaly. In anomaly cases, trajectory sensitivity analysis is triggered, thereby implementing emergency control strategies. If the statistical indicators within the current calculation window are abnormal, it indicates the presence of abnormal data within the current window. The abnormal data is replaced to ensure that historical operating data better represents the steady-state operating data of the distribution network system.

[0081] The following explanation focuses on voltage time series in scenarios with a high proportion of renewable energy sources, assuming a voltage data sequence... ,in Indicates the first Voltage sample value at any given time (unit: pu or volt). The sequence length is given.

[0082] Select 10 sampling points, with a 1-minute interval and a threshold of 3, and iterate through the voltage sequence using a sliding window. Extract window Then calculate the statistics, including the mean. Standard deviation and deviation as follows:

[0083] ;

[0084] ;

[0085] ;

[0086] .

[0087] in, For specific deviations, if ,but This is abnormal. , marked as an anomaly.

[0088] In high-proportion renewable energy distribution networks, voltage dynamic changes, such as voltage dips caused by cloud cover, can trigger trajectory sensitivity analysis to further optimize scheduling.

[0089] This setup allows for the preprocessing of historical operational data, which helps improve the accuracy of data processing and the accuracy of triggering emergency control strategies.

[0090] Figure 3 A flowchart illustrating another method for coordinated voltage regulation of medium- and low-voltage distribution networks, considering the voltage safety margin of the distribution network, provided as an embodiment of the present invention. See also... Figure 3 Based on the above embodiments, optionally, in step S120, based on distribution network parameter data and the equivalent of a single-machine infinite bus system, the energy margin index of the distribution network is quantified, specifically including:

[0091] S121. Based on the generator inertia center theory, a group of key generators in the distribution network that have similar dynamic behaviors under disturbances are equivalent to a single generator with a weighted average inertia constant. The equivalent motion state is described by the inertia center.

[0092] Specifically, a quantitative model for voltage safety margin is established using the energy function method. The basic framework for this quantitative model is built, focusing on the transient stability of a multi-machine system. First, the complex, high-dimensional real-world power system is simplified into a mathematical model that is easy to analyze and calculate using theoretical equivalence, laying the foundation for subsequent energy function construction and voltage safety margin quantification. Since directly establishing and solving the energy function for a multi-machine system is very difficult, it is equivalent to a single-machine infinite bus system (SMIB). This simplification method significantly reduces the complexity of the analysis while preserving the key electromechanical dynamic characteristics of the system.

[0093] Specifically, the first step is to identify and equivalence the key generator groups. Based on the generator center of inertia theory, a group of key generator groups with similar dynamic behaviors under disturbances is equivalent to a single generator with a weighted average inertia constant. Its equivalent motion state is described by the center of inertia.

[0094] ;

[0095] .

[0096] In the formula, It is the total number of generators in the system. It is the first The moment of inertia of the generator and They are the first The power angle and angular velocity of the generator.

[0097] Once the key machine group with similar dynamic behavior under disturbance is identified, the equivalent moment of inertia of the machine group is determined. Typically, it is the sum of the inertia of all generators within the group: .

[0098] S122. Using network simplification techniques, the generators, transmission lines and loads in the distribution network are equivalent to an infinite power bus with constant voltage and frequency; wherein, the network simplification techniques include at least one of Kron reduction and Thevenin equivalence.

[0099] In network simplification, the node admittance matrix of the system is typically used as a starting point. Departure. Divide the nodes into a set of nodes that need to be retained (i.e., generator nodes, denoted by index). (represented by) and the set of nodes that need to be eliminated (i.e., load nodes, indicated by subscripts) (representation), the matrix can be divided into blocks as follows: Among them, the nodes that need to be eliminated refer to the emergency load reduction strategies implemented during emergency control.

[0100] Assume the injected current at the eliminated node is zero. =0), and through matrix operations, the reduced admittance matrix can be obtained. for ;in, It only includes the nodes that need to be retained, thus simplifying the network topology.

[0101] Ultimately, for critical generator cluster connection points, the external power grid can be equivalently represented as a constant voltage source connected in series with an equivalent impedance. The mathematical formula is as follows: .in, It is the bus voltage at the critical unit connection point. It is an equivalent Thevenin voltage source, which is considered as an infinite bus voltage. Equivalent Thevenin equivalent impedance viewed from the connection point , It is the current flowing from the critical machine group into the external power grid.

[0102] Through this step, the complex grid topology and multi-machine interaction problem is transformed into a relative motion problem between an equivalent generator and an ideal grid, thus establishing a simplified model for subsequent energy analysis.

[0103] S123. Construct the transient energy function in the form of a combination of generator kinetic and potential energy, and simplify it.

[0104] Among them, the transient energy function, which is a classic combination of generator kinetic and potential energy, is simplified to the following form: In the formula, Indicates electromagnetic power. Indicates mechanical power. The internal potential of the generator is the power angle, and Let be the moment of inertia.

[0105] S124. Based on the simplified transient energy function, the energy function of the single-machine infinite bus model is obtained.

[0106] In this system, stability after a disturbance depends on the generator's ability to quickly absorb the disturbance energy and re-establish synchronization. The energy function expression for the single-machine infinite bus model is: In the formula, 0 represents the initial value of the generator's internal electromotive force angle; This represents the maximum value of the mechanical power.

[0107] In this model, node voltage is a key factor affecting system stability, indirectly influencing the energy margin index through electromagnetic power. When the system experiences a voltage drop during a disturbance, it leads to a decrease in electromagnetic power, which in turn causes the system's power to rise rapidly. This increases the risk of the system crossing the critical equilibrium point and becoming unstable.

[0108] S125. Based on the energy function, the maximum energy that the distribution network can absorb from its current state to the unstable critical point is calculated, and the energy margin index is quantified.

[0109] The integral form of the energy margin index is: In the formula, The critical power angle corresponds to the power angle value at the unstable equilibrium point after a system fault. When the generator power angle exceeds this value... When this happens, the system will lose its synchronization ability, leading to instability. The larger this margin value, the stronger the system's ability to buffer against disturbances and the better its transient stability.

[0110] This configuration enables the quantification of the energy margin index of the distribution network based on the equivalent single-machine infinite bus system.

[0111] Figure 4 A flowchart illustrating another method for coordinated voltage regulation of medium- and low-voltage distribution networks, considering the voltage safety margin of the distribution network, provided as an embodiment of the present invention. See also... Figure 4 Based on the above embodiments, optionally, in step S130, trajectory sensitivity analysis is used to transform the nonlinear voltage safety margin constraint into a linear form, specifically including:

[0112] S131. Construct a Taylor series expansion of the parameters of the distribution network, and assume that the disturbance is small enough and ignore higher-order terms to obtain an approximate expression for the trajectory change.

[0113] The introduction of trajectory sensitivity theory transforms the nonlinear, dynamic transient stability constraints (i.e., voltage safety margin constraints) defined in the above steps into linear constraints that can be efficiently processed, establishing a direct quantitative relationship between power generation output, load adjustment, and system stability. In distribution systems with a high proportion of renewable energy, the system operating state is extremely sensitive to small disturbances. Based on the trajectory sensitivity analysis method, the impact of system power generation output or load disturbances on voltage changes can be used as an intermediary, thereby representing the voltage stability margin index as a linear combination of scheduling variables, and thus achieving embedded modeling of transient stability constraints.

[0114] Trajectory sensitivity analysis can quantitatively assess the impact of small perturbations in parameters on the dynamic characteristics of nonlinear systems. Its core calculation method is to construct a Taylor series expansion: In the formula, It is a tiny perturbation. This is the initial value of the power. It is a higher-order infinitesimal. When the perturbation is sufficiently small, ignoring the higher-order terms, the trajectory change can be approximated as... In the formula, It is defined as trajectory sensitivity.

[0115] S132. By applying a small disturbance, compare the changes in the state of the distribution network before and after the disturbance to determine the trajectory sensitivity with respect to voltage.

[0116] In this embodiment of the invention, a numerical method is used to calculate the trajectory sensitivity. Specifically, by applying a small perturbation and comparing the changes in the system state before and after the perturbation, the trajectory sensitivity with respect to voltage is determined. In the formula, This represents the node voltage vector under the initial steady-state condition of the system. To apply load disturbance, The result is after calculating the power flow.

[0117] S133. Combining the energy margin index of the distribution network and the trajectory sensitivity with respect to voltage, we obtain an expression for the linearized relationship between the energy margin index and load disturbance.

[0118] Among them, the stability margin calculated based on the energy function in the above steps is combined. (Right now ) and the above trajectory sensitivity The linearized relationship of the system stability margin with respect to load disturbance can be expressed as: .

[0119] S134. Based on the linearized relationship, a complete linear expression is obtained, which includes the initial stability margin, the change in stability margin caused by the uncertainty fluctuation of new energy output, the increase in stability margin after emergency load shedding, and the transient stability constraint that the distribution network needs to satisfy to maintain stable operation.

[0120] Among them, the initial stability margin calculated based on the energy function in the above steps is combined. Compared with the above trajectory sensitivity The final stability margin of the system after implementing emergency load shedding and other control measures. This can be expressed linearly. Alternatively, the complete linear expression for the transient stability constraint is: ;in, It is the initial stability margin of the distribution network before a disturbance occurs but no control measures are taken; It is the change in stability margin caused by the uncertainty fluctuations in new energy output; It is the increase in stability margin resulting from the execution of emergency load shedding; It is the minimum stability margin threshold that the system needs to meet to maintain stable operation.

[0121] The calculation formula is: ;in, This refers to the load shedding amount; This refers to trajectory sensitivity.

[0122] This expression indicates that the total margin increase is per load node. load shearing The sensitivity here is the sum of the products of its corresponding "margin-load" sensitivity. Integrating the impact of load changes on voltage The impact of voltage changes on stability margin The impact, It is the minimum stability margin threshold that the system must satisfy to maintain stable operation, and it is usually a very small positive number. .

[0123] In this way, the transient stability requirement, which originally required complex time-domain simulations for verification, was successfully transformed into a requirement related to the load shedding amount. Linear inequality constraints on the variables to be optimized. These linear constraints can be directly embedded into the main problem and subproblems of the two-layer robust optimization model, enabling the entire problem to be solved efficiently using mature convex optimization or linear programming solvers, greatly improving the model's usability in real-world scheduling scenarios.

[0124] Based on the above embodiments, optionally, in step S140, a two-layer robust optimization model is constructed and solved according to the preprocessed historical operation data and real-time operation data of the distribution network, and the preventive scheduling and emergency control strategies are coordinated to output the final voltage regulation scheme, specifically including:

[0125] First, a two-level robust optimization model with a core decision-making framework of minimization-maximization-minimization is constructed. The inputs to the two-level robust optimization model are the topology parameters of the distribution network, the uncertainty range of renewable energy output, and load forecast data. The first minimization is achieved by optimizing the first-level decision variables. To minimize generation costs; the maximum value is to minimize the load shedding action objective, within the uncertainty set. Find the uncertainty variable inside The worst-case scenario; the second minimum is achieved by optimizing the load shedding action decision variables. To minimize the cost of emergency load reduction.

[0126] Specifically, to address the uncertainty of new energy output and to coordinate prevention and emergency control strategies, a two-layer robust optimization model with a minimum-maximum-minimum core decision-making framework is proposed:

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] Among them, C G It is the cost of electricity generation; C LS It is the cost of emergency load reduction; P G It is the power generation capacity; P L It is the load power; , and These represent the coefficients of the quadratic, linear, and constant terms of the power generation cost, respectively. It is the minimum stability margin threshold that the system needs to meet to maintain stable operation.

[0132] The steady-state condition is: and ;in, and These represent the power flow equation (i.e., power flow balance) under steady-state conditions and the operating limits (i.e., the upper and lower limits of the power at which nodes do not exceed the power limits), respectively.

[0133] After the contingency (i.e., the disturbance occurs), the aforementioned steady-state constraints are also included, specifically: ;in, Indicates instantaneous power flow balance. This represents the limit of decision variable operations in emergency control strategies. It is a stability constraint. This is the required stability threshold. The method provided in this embodiment includes a first stage of minimizing generation costs and improving transient margins, and a second stage of continuing to prevent and optimize scheduling in the event of renewable energy fluctuations after load shedding. The solution to this model is the first-stage generation scheduling decision and the second-stage load shedding action in the worst-case scenario (i.e., the scenario with the largest renewable energy fluctuations) within each scheduling interval. Generation scheduling decisions are executed at the beginning of each scheduling interval. Shedding measures are stored in a decision table, containing data such as the power of the load shedding and the power of the generators scheduled. Once an unexpected event is detected within this time interval (i.e., the deviation of statistical indicators exceeds a preset threshold), it will be implemented immediately. Due to the optimization of renewable energy generation power and the consideration of the worst-case load shedding amount, the system is expected to be robustly stable against any uncertainties in renewable energy generation changes during the operating interval.

[0134] In the model proposed in this embodiment of the invention, x is a continuous decision variable, the objective function is quadratic, and the constraints include a nonlinear AC power flow model. Fixed uncertainty variables obtained from subproblems are utilized. The main problem can be solved using the interior-point method. It's important to note that the optimal solution obtained (x, C)... LS ( ) is when the uncertainty variable is fixed The current solution. For example, the subproblems are as follows:

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] .

[0140] Then, the column and constraint generation solution strategy is applied to the two-layer robust optimization model to obtain the final voltage regulation scheme.

[0141] Optionally, the maxima-minima problem of the subproblem can be transformed into a maxima problem through strong duality, expressed as:

[0142] ;

[0143] ;

[0144] in, and It is a dual variable; It is a non-concave bilinear term.

[0145] An external approximation algorithm is employed to linearize the non-concave bilinear terms around intermediate solution points in the subproblems; and a linear form is added to the main problem, which is then solved iteratively. After solving the subproblems, the worst-case scenario is searched for as the obtained value of the uncertainty variable u, and u is added to the solution set S1 as a fixed parameter used in the main problem. New cost variables for minimizing emergency load reductions and their corresponding constraints are generated and added to the main problem. The main problem is solved using the worst-case scenario and its corresponding variables and constraints. The main problem and subproblems are solved iteratively until the lower bound given by the main problem and the upper bound given by the subproblems converge.

[0146] In summary, the embodiments of the present invention can accurately quantify the transient stability level of the distribution network under a high proportion of new energy access, and coordinate and optimize preventive scheduling and emergency control strategies, thereby effectively addressing the voltage stability challenges caused by the uncertainty of new energy output and improving the operational reliability and robustness of the distribution network.

[0147] This invention also provides a medium- and low-voltage distribution network coordinated voltage regulation device that takes into account the voltage safety margin of the distribution network. The device can be implemented by software and / or hardware, and the device can be configured in the dispatch controller of the medium- and low-voltage distribution network. Figure 5 This is a schematic diagram of a medium- and low-voltage distribution network coordinated voltage regulation device that considers the voltage safety margin of the distribution network, provided as an embodiment of the present invention. See also... Figure 5 The device includes:

[0148] The data preprocessing module 310 is used to acquire distribution network parameter data and historical distribution network operation data, preprocess the historical operation data, and replace abnormal data; wherein, the distribution network includes new energy sources;

[0149] The safety margin index quantification module 320 is used to quantify the energy margin index of the distribution network based on distribution network parameter data and the equivalent of a single-machine infinite bus system; among which, the voltage safety margin index is quantified by the energy function method.

[0150] The linear transformation module 330 is used to convert nonlinear voltage safety margin constraints into linear form using trajectory sensitivity analysis;

[0151] The solution output module 340 is used to construct and solve a two-layer robust optimization model based on the preprocessed historical operation data and real-time operation data of the distribution network, to coordinate the optimization of preventive scheduling and emergency control strategies, and to output the final voltage regulation scheme.

[0152] The medium- and low-voltage distribution network coordinated voltage regulation device considering the voltage safety margin of the distribution network provided in the embodiments of the present invention can execute the medium- and low-voltage distribution network coordinated voltage regulation method considering the voltage safety margin of the distribution network provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0153] This invention also provides a medium- and low-voltage distribution network, which includes generators, new energy sources, power lines, loads, and a dispatch controller. The dispatch controller executes a medium- and low-voltage distribution network coordinated voltage regulation method that considers the voltage safety margin of the distribution network, as provided in any embodiment of this invention.

[0154] Figure 6 This is a schematic diagram of a scheduling controller provided for an embodiment of the present invention. The scheduling controller is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0155] like Figure 6 As shown, the scheduling controller 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the scheduling controller 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0156] Multiple components in the dispatch controller 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the dispatch controller 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0157] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the medium- and low-voltage distribution network coordinated voltage regulation method that takes into account the voltage safety margin of the distribution network.

[0158] In some embodiments, the medium- and low-voltage distribution network coordinated voltage regulation method considering the distribution network voltage safety margin can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the dispatch controller 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the medium- and low-voltage distribution network coordinated voltage regulation method considering the distribution network voltage safety margin described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the medium- and low-voltage distribution network coordinated voltage regulation method considering the distribution network voltage safety margin by any other suitable means (e.g., by means of firmware).

[0159] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0160] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0161] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0162] To provide user interaction, the systems and techniques described herein can be implemented on a dispatch controller having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the dispatch controller. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0163] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0164] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0165] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0166] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network, characterized in that, include: The system acquires distribution network parameter data and historical distribution network operation data, preprocesses the historical operation data, and replaces abnormal data; wherein the distribution network includes new energy sources. Based on the distribution network parameter data and the equivalent of a single-machine infinite bus system, the energy margin index of the distribution network is quantified; among which, the voltage safety margin index is quantified using the energy function method. Trajectory sensitivity analysis is used to transform the nonlinear voltage safety margin constraint into a linear form; Based on the preprocessed historical and real-time operating data of the distribution network, a two-layer robust optimization model is constructed and solved to collaboratively optimize preventive scheduling and emergency control strategies, and output the final voltage regulation scheme.

2. The method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network according to claim 1, characterized in that, The preprocessing of the historical operational data and the replacement of abnormal data specifically include: Based on time series methods, a sliding window is moved point by point to calculate the statistical indicators within the window; If the deviation between the current data point and the statistical indicator exceeds a preset threshold, the current data point is determined to be abnormal data. The abnormal data is replaced; and trajectory sensitivity analysis is triggered.

3. The method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network according to claim 2, characterized in that, The time-series-based method calculates statistical indicators within a sliding window by moving the window point by point, specifically including: Select several sampling points, determine the set interval and the preset threshold; traverse the historical operation data of the distribution network using a sliding window; extract the window from the historical data sequence; Calculate statistics on the data within the window, including the mean, standard deviation, and bias. The statistical index is obtained based on the mean, the standard deviation, and the deviation.

4. The method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network according to claim 1, characterized in that, Based on the distribution network parameter data and the equivalent of a single-machine infinite bus system, the energy margin index of the distribution network is quantified, specifically including: Based on the generator inertia center theory, a group of key generators in the distribution network that have similar dynamic behaviors under disturbances are equivalent to a single generator with a weighted average inertia constant, and its equivalent motion state is described by the inertia center. Using network simplification techniques, the generators, transmission lines, and loads in the power distribution network are equivalent to an infinite power bus with constant voltage and frequency; wherein, the network simplification techniques include at least one of Kron reduction and Thevenin equivalence. Construct a transient energy function in the form of a combination of generator kinetic and potential energy, and simplify it; Based on the simplified transient energy function, the energy function of the single-machine infinite bus model is obtained; Based on the energy function, the energy margin index is quantified by calculating the maximum energy that the distribution network can absorb from its current state to the instability critical point.

5. The method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network according to claim 1, characterized in that, The aforementioned trajectory sensitivity analysis transforms the nonlinear voltage safety margin constraint into a linear form, specifically including: A Taylor series expansion of the parameters of the power distribution network is constructed, and by assuming that the disturbance is small enough and ignoring higher-order terms, an approximate expression for the trajectory change is obtained. By applying a small perturbation and comparing the state changes of the distribution network before and after the perturbation, the trajectory sensitivity with respect to voltage can be determined. Combining the energy margin index of the distribution network and the voltage trajectory sensitivity, an expression for the linearized relationship of the energy margin index with respect to load disturbance is obtained; Based on the expression of the linearized relationship, a complete linear expression is obtained that includes the initial stability margin, the change in stability margin caused by the uncertainty fluctuation of new energy output, the increase in stability margin after emergency load shedding, and the transient stability constraint that the distribution network needs to satisfy to maintain stable operation.

6. The method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network according to claim 5, characterized in that, The complete linear expression for the transient stability constraint is: ; in, It is the initial stability margin of the power distribution network before any disturbance occurs but no control measures are taken; It is the change in stability margin caused by the uncertainty fluctuations in new energy output; It is the increase in stability margin resulting from the execution of emergency load shedding; It is the minimum stability margin threshold that the system needs to meet to maintain stable operation; The calculation formula is: ;in, This refers to the load shedding amount; This refers to trajectory sensitivity.

7. The method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network according to claim 1, characterized in that, The process involves constructing and solving a two-layer robust optimization model based on preprocessed historical and real-time distribution network operation data, collaboratively optimizing preventive dispatching and emergency control strategies, and outputting the final voltage regulation scheme. Specifically, this includes: A two-level robust optimization model with a core decision-making framework of minimization-maximization-minimization is constructed. The inputs to the two-level robust optimization model are the topology parameters of the distribution network, the uncertainty range of renewable energy output, and load forecast data. The first minimization is achieved by optimizing the first-level decision variables. To minimize generation costs; the maximum value is to minimize the load shedding action objective, within the uncertainty set. Find the uncertainty variable inside The worst-case scenario; the second minimum is achieved by optimizing the load shedding action decision variables. To minimize the cost of emergency load reduction; The column and constraint generation solution strategy is applied to the two-layer robust optimization model to obtain the final voltage regulation scheme.

8. The method for coordinated voltage regulation of medium and low voltage distribution networks considering the voltage safety margin of the distribution network according to claim 7, characterized in that, The two-layer robust optimization model with the minimum-maximum-minimum as its core decision-making framework is as follows: ； ； ； ； Among them, C G It is the cost of electricity generation; C LS It is the cost of emergency load reduction; P G It is the power generation capacity; P L It is the load power; , and These represent the coefficients of the quadratic, linear, and constant terms of the power generation cost, respectively. It is the minimum stability margin threshold that the system needs to meet to maintain stable operation; The step of applying a column and constraint generation solution strategy to the two-layer robust optimization model to obtain the final voltage regulation scheme specifically includes: The maxima-minima problem of the subproblems is transformed into a maxima problem through strong duality, expressed as: ； ； in, and It is a dual variable; These are non-concave bilinear terms; An external approximation algorithm is used to linearize the non-concave bilinear terms around intermediate solution points in the subproblem; and a linear form is added to the main problem and solved iteratively. After solving the subproblem, the worst-case scenario is searched for as the obtained value of the uncertainty variable u, and the uncertainty variable u is added to the solution set S1 as a fixed parameter used in the main problem. Generate new cost variables and their corresponding constraints for minimizing emergency load cuts, and add them to the main problem; Solve the main problem using the worst-case scenario and its corresponding variables and constraints; The main problem and the subproblems are solved iteratively until the lower bound given by the main problem and the upper bound given by the subproblems converge.

9. A medium- and low-voltage distribution network coordinated voltage regulation device considering the voltage safety margin of the distribution network, characterized in that, include: A data preprocessing module is used to acquire distribution network parameter data and historical distribution network operation data, preprocess the historical operation data, and replace abnormal data; wherein, the distribution network includes new energy sources; The safety margin index quantification module is used to quantify the energy margin index of the distribution network based on the distribution network parameter data and the equivalent of a single-machine infinite bus system; wherein, the voltage safety margin index is quantified using the energy function method. The linear transformation module is used to convert nonlinear voltage safety margin constraints into a linear form using trajectory sensitivity analysis. The solution output module is used to construct and solve a two-layer robust optimization model based on the preprocessed historical operation data and real-time operation data of the distribution network, to coordinate the optimization of preventive scheduling and emergency control strategies, and to output the final voltage regulation solution.

10. A medium- and low-voltage distribution network, characterized in that, include: Generators, new energy sources, power lines, loads, and dispatch controllers, wherein the dispatch controllers execute the medium- and low-voltage distribution network coordinated voltage regulation method as described in any one of claims 1-8, which considers the voltage safety margin of the distribution network.

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