A method and system for consistent siting and sizing of a rotary power flow controller

Abstract

The application discloses a kind of rotating tide controller consistency site selection method and system, belong to power system planning and control technical field.The method includes: based on distribution network basic model, construct internal impedance coupling relationship Construct rotating tide controller actual capacity domain reconstruction model, generate linearization capacity domain constraint including physical zero point drift vector and effective radius by convex polyhedron approximation technique;Establish the physical driving adaptive voltage sensitivity model including validity correction coefficient, to characterize the nonlinear feedback mechanism under mixed load environment;Based on the linearization capacity domain constraint and adaptive sensitivity model, construct the double-layer robust model with minimum investment cost as main problem, maximum voltage out-of-limit risk as sub-problem, and obtain site selection and capacity determination scheme by using column and constraint generation algorithm iterative solution.The application realizes the consistency of planning decision and physical operation result, effectively avoids the secondary out-of-limit risk caused by load counterattack effect.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and control technology, and particularly relates to a method and system for consistent addressing and calibrating a rotating power flow controller. Background Technology

[0002] With the large-scale integration of distributed power sources and the continuous growth of load density, distribution networks, especially at the ends of long feeders, face a series of challenges such as voltage exceeding limits and line overload. Rotary Power Flow Controllers (RPFCs), as flexible AC transmission equipment based on the principle of rotary transformers, possess technical advantages such as continuous regulation, strong overload capacity, and relatively low cost, and have been widely used in voltage regulation and power flow optimization of distribution networks. In existing engineering practices, the site selection and capacity determination of RPFCs typically rely on planning methods based on deterministic load models. This involves establishing an optimization model with the goal of minimizing investment costs or achieving optimal voltage improvement, and solving the model in conjunction with typical operating scenarios to determine the installation location and capacity configuration of the equipment. Such methods can, to a certain extent, improve the voltage distribution of distribution networks and enhance the system's adaptability to fluctuating loads, demonstrating good engineering applicability and promotional value.

[0003] However, existing RPFC location and capacity planning methods still have significant limitations in practical applications. First, existing technologies typically simplify the load to a constant power model, ignoring the voltage dependence of loads in actual distribution networks. Especially in low-voltage distribution networks, loads generally exhibit mixed characteristics of constant impedance, constant current, and constant power. The presence of the constant impedance component leads to voltage increases, causing increased load power, which in turn leads to increased current and additional voltage drops, forming a nonlinear backlash effect. This makes it difficult to achieve the voltage improvement predicted during the planning stage in actual operation. Second, existing methods often idealize the RPFC as a controllable voltage source, failing to fully consider the voltage drop caused by its internal impedance when connected in series with the grid. This voltage drop is not only significant in magnitude but also has a complex coupling relationship with the adjustment phase angle, causing the actual output capability domain of the RPFC to shrink and drift, failing to accurately reflect its true regulation capability under heavy load or high constant impedance scenarios. The discrepancy between these physical characteristics and the mathematical model makes planning schemes based on traditional methods prone to insufficient capacity allocation or improper location in actual operation. In severe cases, it can even cause the secondary voltage of the grid to exceed limits, affecting the safe and stable operation of the system. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method and system for consistent addressing and calibrating of a rotating power flow controller, thereby resolving the issues present in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for consistent addressing and calibrating a rotating power flow controller, comprising: A basic distribution network model that takes into account the characteristics of mixed loads is constructed. The basic distribution network model includes a ZP mixed static load model that describes the power dependence of nodes on voltage. Based on the aforementioned distribution network basic model, an actual capability domain reconstruction model of a rotating power flow controller based on internal impedance coupling is constructed. The nonlinear capability domain that is affected by internal impedance and undergoes drift and contraction is reconstructed into a set of linear polygon constraints through convex polyhedron approximation technology, generating a linearized capability domain model containing physical zero drift vectors and effective radii. A physical-driven adaptive voltage sensitivity model is established, which includes an effectiveness correction coefficient for correcting the linear sensitivity matrix. The effectiveness correction coefficient is used to characterize the nonlinear feedback mechanism of voltage-power-voltage drop under mixed load conditions. Based on the linearized capability domain model and the adaptive voltage sensitivity model, a main problem model with the goal of minimizing investment cost and a sub-problem model with the goal of maximizing voltage over-limit risk are constructed. The column and constraint generation algorithm is executed to iteratively solve the problem and obtain the location and capacity scheme.

[0006] Preferably, the ZP hybrid static load model represents the actual active power of node i by the following formula. and reactive power : ; in, The actual voltage amplitude at node i. The reference active and reactive power at rated voltage. These are the proportionality coefficients of the constant impedance components in active and reactive loads, respectively.

[0007] Preferably, the actual capability domain reconstruction model of the rotating power flow controller based on internal impedance coupling specifically includes: Based on the coupling relationship between leakage impedance and excitation impedance of the rotating power flow controller, the equivalent internal impedance function is derived. Calculate the physical zero-point drift vector based on the maximum expected operating current of the installed branch; An inscribed regular polygon approximation technique is used to construct a set of linear inequality constraints that includes the physical zero-point drift vector and the effective radius, which serves as the linearized capability domain model.

[0008] Preferably, the physically driven adaptive voltage sensitivity model is expressed by the following equation: in, The initial linear sensitivity matrix of the voltage injected into the rotating power flow controller to the voltage of all nodes in the network is determined. For the validity correction coefficient vector, For A diagonal matrix composed of elements.

[0009] Preferably, it also includes the step of performing a planning-verification-correction closed-loop solution: The aforementioned location and capacity stabilization scheme was substituted into a nonlinear AC power flow model for physical simulation verification to obtain the actual voltage boost. The effectiveness correction coefficient is updated based on the deviation between the actual voltage increase and the linear model prediction. If the updated validity correction coefficients do not meet the convergence condition, the solution is iterated again based on the updated validity correction coefficients until the convergence condition is met, and the final solution is output.

[0010] Preferably, the update validity correction coefficient is implemented by the following formula: ; in, Let be the validity correction coefficient vector for the t-th iteration. This represents the voltage boost predicted by the linear model. The actual voltage boost is used to verify the nonlinear power flow model.

[0011] Preferably, the main problem model includes addressing logic constraints, the set of linear polygon constraints, and voltage safety constraints for the generated worst-case scenario.

[0012] Preferably, the sub-problem model aims to maximize the voltage over-limit risk function, and generates a new worst-case load fluctuation scenario by solving the dual problem and combining it with the Big M method for linearization.

[0013] Preferably, the distribution network basic model further includes system reference state parameters and upper and lower limits constraints for safe operation of node voltages; the construction of the distribution network basic model is based on distribution network topology parameters and load data containing constant impedance and constant power components.

[0014] In a second aspect, the present invention provides a rotating power flow controller consistent addressing and calibrating system for implementing the method described in the first aspect, comprising: The basic model building module is used to build a basic model of the distribution network that takes into account the characteristics of mixed loads. The basic model of the distribution network includes a ZP mixed static load model that describes the power dependence of nodes on voltage. The capability domain reconstruction module is used to construct an actual capability domain reconstruction model of the rotating power flow controller based on the distribution network basic model. It uses convex polyhedron approximation technology to reconstruct the nonlinear capability domain that is affected by internal impedance and undergoes drift and contraction into a set of linear polygon constraints, generating a linearized capability domain model containing physical zero drift vectors and effective radii. A sensitivity correction module is used to establish a physically driven adaptive voltage sensitivity model. The adaptive voltage sensitivity model includes effectiveness correction coefficients for correcting the linear sensitivity matrix. These effectiveness correction coefficients are used to characterize the nonlinear feedback mechanism of voltage-power-voltage drop under mixed load conditions. The robust planning and solving module is used to construct a main problem model with the goal of minimizing investment cost and a sub-problem model with the goal of maximizing the risk of voltage exceeding limits, based on the linearized capability domain model and the adaptive voltage sensitivity model. It then executes a column and constraint generation algorithm to iteratively solve the problem and obtain the location and capacity grading scheme.

[0015] Compared with the prior art, the present invention has the following advantages and technical effects: This invention constructs a basic distribution network model that considers the characteristics of mixed loads, and based on this basic model, builds a reconstructed actual capacity domain model for a rotating power flow controller (RPFC) that considers internal impedance coupling. The nonlinear capacity domain, which is affected by internal impedance and experiences drift and contraction, is reconstructed into a set of linear polygonal constraints containing physical zero-point drift vectors and effective radii. This solves the problem of traditional planning methods failing in actual operation due to neglecting load voltage dependence and equipment internal impedance voltage drop. By explicitly incorporating the power-voltage coupling relationship of constant impedance loads and the voltage loss and zero-point drift caused by the internal impedance of the RPFC into the planning constraints, this invention enables the generated linearized capacity domain model to accurately characterize the effective output boundary of the equipment under actual operating conditions. This ensures the physical feasibility of the site selection and capacity allocation scheme, avoiding insufficient capacity allocation or improper site selection caused by idealized models.

[0016] This invention solves the problem that traditional linear sensitivity models cannot accurately predict voltage regulation effects under constant impedance loads by establishing a physically driven adaptive voltage sensitivity model that includes effectiveness correction coefficients and using these coefficients to correct the linear sensitivity matrix to characterize the nonlinear feedback mechanism of voltage-power-voltage drop under mixed load environments. The adaptive model in this invention can dynamically capture the power increment backlash effect caused by constant impedance loads during voltage boosting. Through iterative updates of the correction coefficients, the planning algorithm automatically avoids weak nodes with strong backlash effects during decision-making, effectively preventing secondary voltage exceedances caused by power surges in high-proportion constant impedance load scenarios, and significantly improving the adaptability and robustness of the planning scheme under complex load environments.

[0017] This invention constructs a main problem model aiming to minimize investment costs and a sub-problem model aiming to maximize voltage over-limit risk. It then uses a column and constraint generation algorithm for iterative solution to obtain a location and capacity balancing scheme. This solves the problem that traditional open-loop planning processes cannot verify the effectiveness of the scheme in real-world nonlinear physical environments. This invention employs a two-layer robust optimization architecture to solve the scheme under the worst load fluctuation scenarios and combines it with a physics-driven sensitivity correction mechanism. This ensures that the final location and capacity balancing scheme not only meets economic optimization requirements at the mathematical optimization level but also withstands the verification of nonlinear power flow at the physical operation level. This achieves a high degree of consistency between planning decisions and physical operation results, guaranteeing the safety and effectiveness of the scheme throughout its entire lifecycle. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the RPFC topology and access according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the actual physical capability domain reconstruction of RPFC according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the voltage backlash effect under mixed load in an embodiment of the present invention. Figure 4 This is a flowchart illustrating the two-layer architecture solution of the "main problem - sub-problem" in an embodiment of the present invention. Figure 5 This is a schematic diagram of the system functional module structure according to an embodiment of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0021] Example 1 like Figure 4 As shown, this embodiment provides a method for consistent addressing and calibrating a rotating power flow controller, including: Step 1: Construct a basic distribution network model that takes into account the characteristics of mixed loads. The basic distribution network model includes a ZP mixed static load model that describes the power dependence of nodes on voltage. Furthermore, the topology parameters and load data of the distribution network to be planned are obtained; a ZP hybrid static load model considering constant impedance and constant power components is established to quantify the dependence of load power on node voltage; and the system baseline state and safe operation constraints are defined.

[0022] Furthermore, the basic model of the distribution network also includes system baseline state parameters and upper and lower limits constraints for safe operation of node voltages; the basic model of the distribution network is constructed based on the distribution network topology parameters and load data containing constant impedance and constant power components.

[0023] In this embodiment, the RPFC topology is as follows: Figure 1 As shown, the rotor side is connected in parallel to the power supply line as the primary side of the RPST for energy extraction, while the stator side is connected in series to the power supply line as the secondary side of the RPST to provide reasonable compensation for the line voltage. When stationary, the RPST functions similarly to a transformer. Through the physical connection between the servo motor and the stator core of the phase-shifting transformer, the relative rotation of the stator and rotor angles of the phase-shifting transformer is achieved. The specific phase difference is related to the relative angles α1 and α2 between the stator and rotor. Finally, the voltage vectors of the two RPST stators are superimposed to introduce a voltage vector with adjustable amplitude and phase angle into the line.

[0024] Step 1 specifically includes: Step 11: Define the network topology and baseline state.

[0025] Let the set of distribution network nodes be The set of branches is Define the system rated voltage as... Obtain the node voltage vector of the distribution network under baseline operating conditions. , and the baseline load power.

[0026] Step 12: Establish the ZP mixed load model.

[0027] Unlike traditional constant power models, this invention employs a ZP hybrid model that considers voltage dependence to describe the first... The actual active power of each node and reactive power : (1) in, The actual voltage amplitude at node i. The reference active and reactive power at rated voltage. These are the proportionality coefficients of the constant impedance components in active and reactive loads, respectively.

[0028] The model clarifies that when the RPFC boosts the voltage... At this time, the load power will increase accordingly, which will physically cause an increase in line current and additional voltage drop. This is the core physical phenomenon that needs to be corrected in subsequent steps.

[0029] Step 2: Based on the aforementioned distribution network basic model, construct an actual capability domain reconstruction model of the rotating power flow controller based on internal impedance coupling. Using the convex polyhedron approximation technique, reconstruct the nonlinear capability domain that is affected by internal impedance and undergoes drift and contraction into a set of linear polygon constraints, generating a linearized capability domain model containing the physical zero drift vector and the effective radius. Furthermore, the actual capability domain reconstruction model of the rotating power flow controller based on internal impedance coupling specifically includes: deriving the equivalent internal impedance function based on the coupling relationship between the leakage impedance and the excitation impedance of the rotating power flow controller; calculating the physical zero-point drift vector based on the maximum expected operating current of the installed branch; and constructing a set of linear inequality constraints containing the physical zero-point drift vector and the effective radius using the inscribed regular polygon approximation technique, as the linearized capability domain model.

[0030] In this embodiment, based on the physical structure of RPFC, the equivalent internal impedance function considering the coupling effect of leakage impedance and excitation impedance is derived; the physical voltage loss vector under the maximum expected operating current is calculated; and the nonlinear capability domain that is affected by internal impedance and undergoes drift and contraction is reconstructed into a linear polygon constraint set using the convex polyhedron approximation technique, generating a linearized capability domain model containing the physical zero-point drift vector and the effective radius.

[0031] This embodiment illustrates the actual physical capability domain reconstruction diagram of RPFC. Figure 2 It intuitively presents the constraint boundary and reference datum of the voltage vector on the complex plane.

[0032] Step 2 specifically includes: Step 21: Analyze the physical characteristics of the internal impedance of RPFC.

[0033] RPFC consists of two sets of rotating phase-shifting transformers, and the net voltage vector injected into the line is... This is not an ideal value; the voltage drop due to internal impedance must be deducted. (2) In the formula, For ideal induced voltage, derived from rotor half-angle difference and synthesized phase Decide; The line current flowing through the device; This is the equivalent series internal impedance of the RPFC.

[0034] It is a complex function of the control variables and has nonlinear coupling characteristics: (3) In the formula, Leakage impedance, For excitation impedance, is the coupling coefficient.

[0035] Step 22: Construct the physical capability domain under the worst operating conditions.

[0036] To ensure the overall safety of the planning results, the maximum expected operating current of the installed branch circuit is selected. As a design benchmark, the physical feasible region of the RPFC at this current. It exhibits a shape offset from the origin and with nonlinearly contracting boundaries. Define the physical zero-point drift vector. : This vector represents the line voltage base loss that occurs even with zero output command after the RPFC is connected to the grid due to its internal impedance.

[0037] Step 23: Perform linear reconstruction of the convex polyhedron.

[0038] To adapt to mixed-integer linear programming algorithms, this invention employs an inscribed regular polygon approximation technique to convexly reconstruct the nonlinear capability domain.

[0039] Build a containing Regular polygon with striped edges To approximate the actual capability domain. For the first... Taiwan RPFC, introducing planning variables This represents the capacity configuration ratio. The reconstructed linear capacity domain constraint set is represented as follows: (4) In the formula, Re represents the real part. Inject voltage variables into the complex numbers in the planning model; For polygon number 1 The angle of the normal vector of the edge; Let be the effective radius of the inscribed polygon after considering the maximum impedance voltage drop.

[0040] Step 2 encapsulates the complex physical characteristics of the equipment into a set of linear inequality constraints, achieving compatibility between the physical model and mathematical optimization.

[0041] Step 3: Establish a physical-driven adaptive voltage sensitivity model, which includes an effectiveness correction coefficient for correcting the linear sensitivity matrix. The effectiveness correction coefficient is used to characterize the nonlinear feedback mechanism of voltage-power-voltage drop under mixed load conditions. Furthermore, the physically driven adaptive voltage sensitivity model is expressed by the following equation: (5) in, The initial linear sensitivity matrix of the voltage injected into the rotating power flow controller to the voltage of all nodes in the network is determined. For the validity correction coefficient vector, For A diagonal matrix composed of elements.

[0042] Furthermore, the update validity correction coefficient is achieved by the following formula: (6) in, Let be the validity correction coefficient vector for the t-th iteration. This represents the voltage boost predicted by the linear model. The actual voltage boost is used to verify the nonlinear power flow model.

[0043] In this embodiment, the linear sensitivity matrix of the RPFC injection voltage to the voltage of all network nodes is derived; an effectiveness correction coefficient is introduced to construct a physical-driven adaptive sensitivity model; this model dynamically adjusts the sensitivity value through the correction coefficient to characterize the attenuation effect of the nonlinear feedback mechanism of voltage-power-voltage drop on voltage support efficiency under mixed load conditions.

[0044] This embodiment illustrates the principle of voltage backlash effect under mixed load. Figure 3 The left figure in the diagram does not take into account secondary over-limit issues. Before voltage regulation, the voltage along the feeder (solid line) gradually decreases from the RPFC injection point to the end, and the voltage in some sections is lower than the safe voltage line. After voltage regulation, the voltage curve (dashed line) is raised as a whole through RPFC regulation, so that the voltage along the line is higher than the safe voltage line, which meets the operating requirements. Under this condition, the goal is only to restore the voltage to the safe range, without considering other secondary over-limit issues that may be caused after regulation.

[0045] Figure 3 The right figure shows the secondary over-limit caused by Z. Before voltage regulation, similar to the left figure, the voltage curve (solid line) is below the safe voltage line. After voltage regulation, considering the secondary over-limit constraints caused by factors such as system impedance Z, the voltage curve (dashed line) rises only to a level close to the safe voltage line, and cannot completely eliminate the risk of over-limit. This operating condition shows that under actual constraints, the voltage regulation strategy needs to balance voltage recovery and avoidance of secondary over-limit.

[0046] Figure 3 The differences in RPFC voltage regulation performance under ideal and actual constraints were clearly compared, revealing the significant impact of secondary over-limit constraints on voltage regulation strategies and providing an intuitive basis for power system voltage control and RPFC optimization configuration.

[0047] Step 3 specifically includes: Step 31: Define the initial linear sensitivity.

[0048] Based on the distribution network Jacobian matrix, calculate the initial sensitivity matrix of the real and imaginary parts of the RPFC injected voltage to the voltage amplitude of all nodes in the network. At this point, the following conditions are met: (7) in This is the injected voltage vector.

[0049] Step 32: Introduce an effectiveness correction coefficient.

[0050] Considering the mixed load characteristics described in step 1 and the internal impedance voltage drop described in step 2, the actual voltage boost effect will decrease as the voltage increases (i.e., a backlash effect). This invention defines an effectiveness correction coefficient vector. It is used to correct linear models.

[0051] Constructing the effective sensitivity matrix : (8) In the formula, For A diagonal matrix composed of elements. This model equates complex nonlinear backlash effects to a numerical decay of sensitivity.

[0052] Step 4: Based on the linearized capability domain model and the adaptive voltage sensitivity model, construct a main problem model with the goal of minimizing investment cost and a sub-problem model with the goal of maximizing voltage over-limit risk. Execute the column and constraint generation algorithm for iterative solution to obtain the location and capacity setting scheme. Furthermore, the main problem model includes addressing logic constraints, the linear polygon constraint set, and voltage safety constraints for the generated worst-case scenario.

[0053] Furthermore, the sub-problem model aims to maximize the voltage over-limit risk function. By solving the dual problem and combining it with the Big M method for linearization, a new worst-case load fluctuation scenario is generated.

[0054] In this embodiment, the main problem model is constructed with the minimum annualized investment cost of RPFC as the objective function; the sub-problem model is constructed with the maximum risk of system voltage exceeding limits as the objective function; the linearized capability domain constraint described in step 2 is embedded in the main problem, and the column and constraint generation (C&CG) algorithm architecture is used to find the optimal addressing and capacity setting scheme that meets the network voltage safety constraints under the worst load fluctuation scenario.

[0055] This invention employs a two-tiered architecture of "main problem - sub-problem" for solution. For example... Figure 4 The flowchart, step 4 specifically includes: Step 41: Construct the Master Problem (MP).

[0056] The main problem aims to minimize the investment cost of RPFC while satisfying the voltage constraint under the worst known scenario.

[0057] The constraints include: 1. Addressing logic constraints: Whether it is on a side road Install RPFC binary variables, .

[0058] 2. Physical capability domain constraint: The polyhedral linear constraint derived in step 23 is used to ensure the injected voltage. Within the actual physical capabilities of the equipment: (9) in For the form parameters of the polyhedron matrix.

[0059] 3. Safe operation constraints: for the currently generated set of tangent planes Each of the worst-case load disturbance scenarios Satisfying voltage constraints: (10) Step 42: Construct the sub-problem (SP).

[0060] The subproblem aims to find the load fluctuation pattern that maximizes the risk of system voltage exceeding limits, and return it to the main problem as a new tangent plane.

[0061] Define voltage over-limit risk function That is, in a given capacity configuration Next, look for load disturbances. This maximizes the degree to which the voltage deviates from the safe range.

[0062] (11) By utilizing duality theory, the inner minimization problem is transformed into a maximization problem, and linearization is performed using the Big M method to obtain the worst-case load scenario. .

[0063] Step 43: Perform column and constraint generation (C&CG) iteration.

[0064] 1. Solve for MP to obtain the preliminary solution. .

[0065] 2. Input SP and solve for the worst-case scenario. .

[0066] 3. If the maximum risk calculated by SP exceeds the preset threshold, then the scenario will be... The corresponding safety constraints are added to the MP's constraint set. middle.

[0067] 4. Repeat the above process until the main problem contains enough constraints to cover all potential risks and achieve robust convergence.

[0068] Step 5: Execute the "planning-verification-correction" closed-loop solution process.

[0069] Furthermore, the method also includes the steps of performing a planning-verification-correction closed-loop solution: substituting the location and capacity stabilization scheme into a nonlinear AC power flow model for physical simulation verification to obtain the actual voltage increase; updating the effectiveness correction coefficient based on the deviation between the actual voltage increase and the predicted value of the linear model; if the updated effectiveness correction coefficient does not meet the convergence condition, then iterating again based on the updated effectiveness correction coefficient until the convergence condition is met, and outputting the final scheme.

[0070] In this embodiment, the effectiveness correction coefficient is initialized; the two-layer robust planning model is solved to obtain candidate schemes; the candidate schemes are substituted into the nonlinear AC power flow model for physical verification and the actual voltage increase is calculated; the linear model prediction value is compared with the physical actual value, and the effectiveness correction coefficient is updated; if the error exceeds the threshold, the updated sensitivity model is used to return to step 4 for replanning until the planning decision is consistent with the physical operation result.

[0071] While step 4 above ensures mathematical robustness, it relies on... The accuracy of the outer closed loop is ensured. This invention designs an outer closed loop to ensure physical consistency. Step 5 specifically includes: Step 51: Initialization.

[0072] Set initial correction coefficients (All-1 vector), setting the number of outer iterations. .

[0073] Step 52: Perform robust planning.

[0074] Utilize the current and Execute the C&CG algorithm described in step 4 to obtain the optimal configuration scheme. and the corresponding worst working conditions .

[0075] Step 53: Physical simulation verification.

[0076] Build a distribution network model containing the ZP hybrid load described in step 1 in nonlinear AC power flow calculation software (such as OpenDSS or Matpower).

[0077] According to the plan in the model Connect to RPFC and apply the worst-case scenario. .

[0078] Measure the actual voltage rise at critical nodes .

[0079] Step 54: Calculate the prediction bias.

[0080] Calculate the predicted voltage boost using the current linear sensitivity model : (12) Step 55: Adaptive correction and convergence judgment.

[0081] Calculate the updated correction coefficients : (13) Determine the convergence condition: like If the result is positive, then output the final planning solution; otherwise, let... , utilizing new renew Return to step 52 and replan.

[0082] Through the aforementioned outer closed loop The coefficients will gradually converge to a value that accurately reflects the "constant impedance load backlash" and "high impedance line attenuation", thereby forcing the planning algorithm to automatically avoid poor site selection points with excessive electrical distance or strong backlash effect, thus achieving consistency between physics and decision-making.

[0083] Example 2 This embodiment provides a rotating power flow controller consistent addressing and calibrating system for implementing the method described in Embodiment 1, including: The basic model building module is used to build a basic model of the distribution network that takes into account the characteristics of mixed loads. The basic model of the distribution network includes a ZP mixed static load model that describes the power dependence of nodes on voltage. The capability domain reconstruction module is used to construct an actual capability domain reconstruction model of the rotating power flow controller based on the distribution network basic model. It uses convex polyhedron approximation technology to reconstruct the nonlinear capability domain that is affected by internal impedance and undergoes drift and contraction into a set of linear polygon constraints, generating a linearized capability domain model containing physical zero drift vectors and effective radii. A sensitivity correction module is used to establish a physically driven adaptive voltage sensitivity model. The adaptive voltage sensitivity model includes effectiveness correction coefficients for correcting the linear sensitivity matrix. These effectiveness correction coefficients are used to characterize the nonlinear feedback mechanism of voltage-power-voltage drop under mixed load conditions. The robust planning and solving module is used to construct a main problem model with the goal of minimizing investment cost and a sub-problem model with the goal of maximizing the risk of voltage exceeding limits, based on the linearized capability domain model and the adaptive voltage sensitivity model. It then executes a column and constraint generation algorithm to iteratively solve the problem and obtain the location and capacity grading scheme.

[0084] This embodiment shows a schematic diagram of the system functional modules. Figure 5 A robust programming solution and physical simulation verification closed-loop process is demonstrated, and the functions and process relationships of each module are as follows: Data acquisition module: As the starting point of the process, it is responsible for collecting and providing basic data for the system, providing input for subsequent analysis.

[0085] Capability domain reconstruction module and adaptive sensitivity calculation module: receive data from the data acquisition module in parallel, and perform capability domain reconstruction and adaptive sensitivity calculation respectively.

[0086] Robust planning solution module: Combines the outputs of the first two modules to generate a planning scheme.

[0087] Physical simulation verification module: Performs simulation verification on the planning scheme output by the robust planning solution module to determine whether it meets the consistency requirements.

[0088] If the consistency requirement is met, the final solution is output directly.

[0089] If the consistency requirement is not met, a correction coefficient (not converged) is generated and fed back to the adaptive sensitivity calculation module to form a closed-loop iteration until the scheme meets the consistency requirement.

[0090] This embodiment uses a closed-loop mechanism of "planning-simulation verification-iterative correction" to ensure that the final solution meets both the robustness requirements and the physical constraints and operational requirements of the system.

[0091] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for consistent addressing and calibrating a rotating power flow controller, characterized in that, Includes the following steps: A basic distribution network model that takes into account the characteristics of mixed loads is constructed. The basic distribution network model includes a ZP mixed static load model that describes the power dependence of nodes on voltage. Based on the aforementioned distribution network basic model, an actual capability domain reconstruction model of a rotating power flow controller based on internal impedance coupling is constructed. The nonlinear capability domain that is affected by internal impedance and undergoes drift and contraction is reconstructed into a set of linear polygon constraints through convex polyhedron approximation technology, generating a linearized capability domain model containing physical zero drift vectors and effective radii. A physical-driven adaptive voltage sensitivity model is established, which includes an effectiveness correction coefficient for correcting the linear sensitivity matrix. The effectiveness correction coefficient is used to characterize the nonlinear feedback mechanism of voltage-power-voltage drop under mixed load conditions. Based on the linearized capability domain model and the adaptive voltage sensitivity model, a main problem model with the goal of minimizing investment cost and a sub-problem model with the goal of maximizing voltage over-limit risk are constructed. The column and constraint generation algorithm is executed to iteratively solve the problem and obtain the location and capacity scheme.

2. The rotating power flow controller consistent addressing and gradation method according to claim 1, characterized in that, The ZP hybrid static load model expresses the actual active power of node i by the following formula. and reactive power : ； in, The actual voltage amplitude at node i. The reference active and reactive power at rated voltage. These are the proportionality coefficients of the constant impedance components in active and reactive loads, respectively.

3. The rotating power flow controller consistent addressing and sizing method according to claim 1, characterized in that, The actual capability domain reconstruction model of the rotating power flow controller based on internal impedance coupling specifically includes: Based on the coupling relationship between leakage impedance and excitation impedance of the rotating power flow controller, the equivalent internal impedance function is derived. Calculate the physical zero-point drift vector based on the maximum expected operating current of the installed branch; An inscribed regular polygon approximation technique is used to construct a set of linear inequality constraints that includes the physical zero-point drift vector and the effective radius, which serves as the linearized capability domain model.

4. The rotating power flow controller uniform addressing and sizing method according to claim 1, characterized in that, The physical-driven adaptive voltage sensitivity model is expressed by the following equation: in, The initial linear sensitivity matrix of the voltage injected into the rotating power flow controller to the voltage of all nodes in the network is determined. For the validity correction coefficient vector, For A diagonal matrix composed of elements.

5. The method for consistent addressing and calibrating a rotating power flow controller according to claim 1, characterized in that, It also includes the steps of planning, verifying, and correcting the closed-loop solution: The aforementioned location and capacity stabilization scheme was substituted into a nonlinear AC power flow model for physical simulation verification to obtain the actual voltage boost. The effectiveness correction coefficient is updated based on the deviation between the actual voltage increase and the linear model prediction. If the updated validity correction coefficients do not meet the convergence condition, the solution is iterated again based on the updated validity correction coefficients until the convergence condition is met, and the final solution is output.

6. The rotating power flow controller consistent addressing and sizing method according to claim 5, characterized in that, The update validity correction coefficient is achieved by the following formula: ； in, Let be the validity correction coefficient vector for the t-th iteration. This represents the voltage boost predicted by the linear model. The actual voltage boost is used to verify the nonlinear power flow model.

7. The rotating power flow controller consistent addressing and calibrating method according to claim 1, characterized in that, The main problem model includes addressing logic constraints, the set of linear polygon constraints, and voltage safety constraints for the generated worst-case scenario.

8. The rotating power flow controller consistent addressing and calibrating method according to claim 1, characterized in that, The sub-problem model aims to maximize the voltage over-limit risk function. By solving the dual problem and combining it with the Big M method for linearization, a new worst-case load fluctuation scenario is generated.

9. The rotating power flow controller consistent addressing and gradation method according to claim 1, characterized in that, The basic model of the distribution network also includes system baseline state parameters and upper and lower limits constraints for safe operation of node voltages; the construction of the basic model of the distribution network is based on the distribution network topology parameters and load data containing constant impedance and constant power components.

10. A rotating power flow controller consistent addressing and gradation system, characterized in that, To implement the method according to any one of claims 1-9, comprising: The basic model building module is used to build a basic model of the distribution network that takes into account the characteristics of mixed loads. The basic model of the distribution network includes a ZP mixed static load model that describes the power dependence of nodes on voltage. The capability domain reconstruction module is used to construct an actual capability domain reconstruction model of the rotating power flow controller based on the distribution network basic model. It uses convex polyhedron approximation technology to reconstruct the nonlinear capability domain that is affected by internal impedance and undergoes drift and contraction into a set of linear polygon constraints, generating a linearized capability domain model containing physical zero drift vectors and effective radii. A sensitivity correction module is used to establish a physically driven adaptive voltage sensitivity model. The adaptive voltage sensitivity model includes effectiveness correction coefficients for correcting the linear sensitivity matrix. These effectiveness correction coefficients are used to characterize the nonlinear feedback mechanism of voltage-power-voltage drop under mixed load conditions. The robust planning and solving module is used to construct a main problem model with the goal of minimizing investment cost and a sub-problem model with the goal of maximizing the risk of voltage exceeding limits, based on the linearized capability domain model and the adaptive voltage sensitivity model. It then executes a column and constraint generation algorithm to iteratively solve the problem and obtain the location and capacity grading scheme.

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