A method and apparatus for determining optimized dispatching schemes for flexible interconnected distribution networks

Abstract

This specification provides a method and apparatus for determining an optimized scheduling scheme for a flexible interconnected distribution network. Based on the distribution network structure information of the target area to be scheduled, the operating constraint set of the intelligent soft switch is determined. Based on the operating constraint set, the predicted load power of each phase, and the predicted output of each phase's distributed power source, an initial optimized scheduling model is determined. Based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimized scheduling model, semi-definite matrix variables are constructed. By utilizing the semi-definite matrix variables, the non-convex constraints in the initial optimized scheduling model are transformed into linear constraints about the semi-definite matrix variables, and semi-definite constraints are applied to the semi-definite matrix variables, transforming the initial optimized scheduling model into a target optimized scheduling model. Based on the target optimized scheduling model, the optimized scheduling scheme for the target area to be scheduled within the scheduling cycle is determined. This results in a precise and suitable optimized scheduling scheme.

Description

Technical Field

[0001] This manual belongs to the field of power system operation and dispatching technology, and in particular relates to a method and apparatus for determining an optimized dispatching scheme for a flexible interconnected distribution network. Background Technology

[0002] With the large-scale integration of distributed power sources, existing distribution network dispatching schemes are finding it difficult to achieve precise phase-by-phase control and approach the global optimal decision-making when dealing with the challenges of distributed power source grid connection.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This specification provides a method and apparatus for determining the optimal scheduling scheme of a flexible interconnected distribution network, which solves the technical problem that existing distribution network scheduling schemes are limited by inter-phase control coupling and model non-convexity and nonlinearity, making it difficult to achieve precise phase-by-phase control and approach the global optimal decision.

[0005] This specification provides a method for determining an optimized dispatching scheme for a flexible interconnected distribution network, including: Based on the distribution network structure information of the target area to be dispatched, the set of operating constraints for the intelligent soft switch is determined; wherein, the intelligent soft switch includes a three-phase four-wire converter and an energy storage unit; Based on the historical power consumption data, historical distributed power output data and meteorological data of the target area to be dispatched, the predicted load power and the predicted output of each phase of the target area to be dispatched during the dispatching period are determined. Based on the set of operating constraints, the predicted load power of each phase, and the predicted output of each phase distributed power source, an initial optimized scheduling model is determined. Based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimization scheduling model, construct a positive semidefinite matrix variable; By utilizing the positive semidefinite matrix variables, the non-convex constraints in the initial optimal scheduling model are transformed into linear constraints with respect to the positive semidefinite matrix variables, and positive semidefinite constraints with respect to the positive semidefinite matrix variables are applied to transform the initial optimal scheduling model into the target optimal scheduling model. Based on the target optimization scheduling model, an optimized scheduling scheme for the target area to be scheduled within the scheduling period is determined.

[0006] In one embodiment, the three-phase four-wire converter includes three phase arms and one neutral arm; The three phase bridge arms are respectively connected to each phase of the AC power grid of the target area to be dispatched, and are used to independently adjust the active power and reactive power of the corresponding phase according to the phase power control command. The neutral line arm is connected to the neutral line of the target area to be dispatched, and is used to provide a path for the unbalanced current of each phase, so as to realize independent decoupling control of the three-phase power.

[0007] In one embodiment, determining the initial optimized scheduling model based on the operating constraint set, the predicted load power values ​​for each phase, and the predicted output values ​​of the distributed power sources for each phase includes: A multi-objective optimization function is constructed with the optimization objectives of minimizing total network loss, distributed generation abandoned power, and system voltage imbalance; Based on the operating constraint set of the intelligent soft switch, the predicted load power of each phase, and the predicted output of each phase distributed power source, a target constraint set is determined; wherein, the target constraint set includes power balance constraints, power flow constraints, and voltage and current safety boundary constraints. The initial optimization scheduling model is determined based on the multi-objective optimization function and the objective constraint set; wherein the initial optimization scheduling model uses node voltage variables and branch power variables as decision variables.

[0008] In one embodiment, the step of transforming the non-convex constraints in the initial optimal scheduling model into linear constraints with respect to the positive semi-definite matrix variables by utilizing the positive semi-definite matrix variables, and applying positive semi-definite constraints with respect to the positive semi-definite matrix variables to transform the initial optimal scheduling model into a target optimal scheduling model includes: Based on the node voltage variables in the initial optimized scheduling model, determine the mapping relationship between the semi-positive definite matrix variables and the second-order product terms of the node voltage variables; Based on the mapping relationship, the variable product terms in the initial optimization scheduling model are linearly replaced by the elements of the semi-positive definite matrix variables to obtain linear running constraints; Based on the semi-positive definite matrix variables and the preset network admittance parameters, a trace function is constructed, and the trace function is used to linearly transform the power flow constraints in the initial optimized scheduling model to obtain linear power constraints. The positive semi-definite matrix variable is constructed by setting it to be a symmetric matrix and all its eigenvalues ​​to be greater than or equal to a preset value; wherein the positive semi-definite constraint is used to limit the physical compatibility of the variable space. Based on the linear operation constraints, the linear power constraints, and the semi-positive definite constraints, the initial optimization scheduling model is transformed into the target optimization scheduling model.

[0009] In one embodiment, determining the set of operating constraints for the intelligent soft switch based on the distribution network structure information of the target area to be dispatched includes: Based on the AC-side switching power, DC-side interactive power, and internal losses of each phase arm of the three-phase four-wire converter, the phase-by-phase active power balance constraints and phase-by-phase reactive power balance constraints of the three-phase four-wire converter are determined. Based on the state of charge, charging and discharging power, and charging and discharging efficiency of the energy storage unit in adjacent scheduling periods, the energy evolution constraints of the energy storage unit within the scheduling cycle are determined. The set of operating constraints for the intelligent soft switch is determined based on the phase active power balance constraint, the phase reactive power balance constraint, and the energy evolution constraint.

[0010] In one embodiment, determining the predicted load power and predicted output of each phase of the target area during the scheduling period based on historical power consumption data, historical distributed power generation output data, and meteorological data within the scheduling period includes: Based on the historical power consumption data, the historical distributed power output data, and the meteorological data, determine the total load power prediction sequence and the total distributed power output prediction sequence of the target area to be dispatched within the dispatching cycle. Based on the total load power prediction sequence, the total output power prediction sequence of the distributed power source, and the preset phase ratio coefficient, the predicted load power value of each phase and the predicted output power value of each phase of the distributed power source are obtained.

[0011] In one embodiment, determining the optimized scheduling scheme for the target area to be scheduled within the scheduling period based on the target optimized scheduling model includes: The target optimization scheduling model is optimized to determine the optimal solution set corresponding to each decision variable in the target optimization scheduling model. Based on the active power variable value, reactive power variable value and energy storage state variable value in the optimal solution set, the phase power control command of each phase converter in the intelligent soft switch and the charging and discharging power command of the energy storage unit are determined respectively. Based on the phase power control command and the charge / discharge power command, an optimal scheduling scheme for the target area to be scheduled within the scheduling period is determined.

[0012] This specification provides a device for determining an optimized dispatching scheme for a flexible interconnected distribution network, comprising: The constraint set determination module is used to determine the operating constraint set of the intelligent soft switch based on the distribution network structure information of the target area to be dispatched; wherein, the intelligent soft switch includes a three-phase four-wire converter and an energy storage unit; The prediction value determination module is used to determine the predicted load power of each phase and the predicted output of each phase of the target area to be dispatched within the dispatching period based on the historical power consumption data, historical distributed power output data and meteorological data within the dispatching period. The initial model determination module is used to determine the initial optimized scheduling model based on the set of operating constraints, the predicted load power of each phase, and the predicted output of each phase distributed power source. The variable determination module is used to construct a positive semidefinite matrix variable based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimization scheduling model. The target model determination module is used to transform the non-convex constraints in the initial optimal scheduling model into linear constraints with respect to the semi-positive definite matrix variables by utilizing the semi-positive definite matrix variables, and to apply semi-positive definite constraints with respect to the semi-positive definite matrix variables to transform the initial optimal scheduling model into a target optimal scheduling model. The scheduling scheme determination module is used to determine the optimized scheduling scheme for the target area to be scheduled within the scheduling period based on the target optimized scheduling model.

[0013] This specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements a method for determining an optimized scheduling scheme for a flexible interconnected distribution network.

[0014] This specification also provides a computer-readable storage medium storing computer instructions that, when executed, implement a method for determining an optimized scheduling scheme for a flexible interconnected distribution network.

[0015] Based on the method for determining an optimized dispatch scheme for a flexible interconnected distribution network provided in this specification, the following steps are taken: First, an operational constraint set for a smart soft switch is determined based on the distribution network structure information of the target area to be dispatched. The smart soft switch includes a three-phase four-wire converter and an energy storage unit. Second, based on historical power consumption data, historical distributed power output data, and meteorological data within the dispatch period of the target area to be dispatched, the predicted load power and distributed power output of each phase within the dispatch period are determined. Third, based on the operational constraint set, the predicted load power and distributed power output of each phase, an initial optimized dispatch model is determined. Fourth, based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimized dispatch model, a semi-definite matrix variable is constructed. Fifth, by utilizing the semi-definite matrix variable, the non-convex constraints in the initial optimized dispatch model are transformed into linear constraints with respect to the semi-definite matrix variable, and semi-definite constraints are applied to the semi-definite matrix variable, transforming the initial optimized dispatch model into a target optimized dispatch model. Finally, based on the target optimized dispatch model, an optimized dispatch scheme for the target area to be dispatched within the dispatch period is determined. In this way, by determining the set of operating constraints including the three-phase four-wire converter and energy storage unit, an accurate physical characterization of the independent decoupling control characteristics of inter-phase power was achieved, solving the problem that traditional distribution network topologies are difficult to achieve phase-independent control. By introducing semi-positive definite matrix variables to transform the non-convex constraints in the initial optimal scheduling model into linear constraints and applying semi-positive definite constraints, the equivalent linearization of the non-convex nonlinear constraints in the scheduling model was achieved, overcoming the shortcomings of conventional algorithms that are difficult to solve and difficult to obtain solutions that tend to the global optimum, thus determining an accurate and appropriate optimal scheduling scheme. Attached Figure Description

[0016] To more clearly illustrate the embodiments of this specification, the accompanying drawings used in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for determining an optimized scheduling scheme for a flexible interconnected distribution network, provided in one embodiment of this specification. Figure 2 This is a schematic diagram of the electronic device structure provided in one embodiment of this specification; Figure 3 This is a schematic diagram of the structural composition of a device for determining an optimized dispatching scheme for a flexible interconnected distribution network, provided in one embodiment of this specification. Figure 4 This is a schematic diagram illustrating a scenario example result provided by one embodiment of this specification. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0019] See Figure 1 As shown in the embodiments of this specification, a method for determining an optimized scheduling scheme for a flexible interconnected distribution network is provided, wherein the method is specifically applied to the server side. In specific implementation, the method may include the following: S101: Determine the set of operating constraints for the intelligent soft switch based on the distribution network structure information of the target area to be dispatched; wherein, the intelligent soft switch includes a three-phase four-wire converter and an energy storage unit; S102: Based on the historical power consumption data, historical distributed power output data and meteorological data of the target area to be dispatched, determine the predicted load power of each phase and the predicted output of each phase of the target area to be dispatched during the dispatching period. S103: Determine the initial optimized scheduling model based on the set of operating constraints, the predicted load power of each phase, and the predicted output of each phase distributed power source. S104: Construct a positive semidefinite matrix variable based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimized scheduling model; S105: By utilizing the positive semidefinite matrix variables, the non-convex constraints in the initial optimal scheduling model are transformed into linear constraints with respect to the positive semidefinite matrix variables, and positive semidefinite constraints with respect to the positive semidefinite matrix variables are applied to transform the initial optimal scheduling model into the target optimal scheduling model. S106: Based on the target optimization scheduling model, determine the optimal scheduling scheme for the target area to be scheduled within the scheduling period.

[0020] The aforementioned power distribution network structure information can refer to the physical topology connection relationship and electrical parameters of the network structure within the area to be dispatched, specifically covering the node connection method of the three-phase four-wire system, the neutral grounding status, the admittance matrix parameters of the line, and the access location information of the intelligent soft switch.

[0021] The aforementioned set of operational constraints can be a set of mathematical restrictions constructed based on the hardware characteristics and safety boundaries of a three-phase four-wire SOP, including apparent power limit constraints for each phase arm of the converter, power balance constraints for energy conversion between the AC and DC sides, and dynamic evolution of the state of charge (SOC) and charging / discharging power limits of the energy storage unit during continuous scheduling cycles.

[0022] The aforementioned predicted load power values ​​for each phase refer to the active and reactive power demand sequences of phases A, B, and C respectively, estimated using historical electricity consumption data and external influencing factors for the future dispatch cycle. Unlike traditional total forecasts, the predicted values ​​for each phase accurately depict the asymmetry of load distribution in a three-phase four-wire system. These values ​​are key input data for subsequent quantitative assessment of three-phase imbalance and precise phase-by-phase control, directly determining the effectiveness of the dispatch scheme in addressing local overload and imbalance conditions.

[0023] The above-mentioned distributed power output prediction values ​​for each phase can refer to the quantitative estimation of the active power that distributed resources (such as photovoltaic and wind power) connected to the grid in each phase can provide during the scheduling cycle, taking into account meteorological environmental characteristics (such as sunlight and temperature) and equipment access capacity. Since distributed resources have significant randomness and volatility, the output prediction values ​​for each phase can reflect the differences in injected power on the power source side in terms of physical phase.

[0024] The aforementioned initial optimization scheduling model can be a mathematical optimization framework composed of nonlinear power flow equations, voltage safety constraints, and SOP operation constraints, with the goal of minimizing network loss and optimizing imbalance management.

[0025] The aforementioned positive semidefinite matrix variables can be high-dimensional matrices constructed using spatial lifting techniques, with each element corresponding to a second-order product term of the node voltage variables in the initial model (i.e., the product of voltage and voltage conjugate) through a pre-defined mapping relationship.

[0026] The aforementioned positive semi-definite constraint can refer to the requirement, through mathematical means, that the positive semi-definite matrix variable be a symmetric matrix, and that all its eigenvalues ​​are greater than or equal to zero (or not less than a preset minimum positive value).

[0027] The aforementioned objective optimization scheduling model can be a standardized optimization model generated by convexifying the initial model using positive semi-definite matrix variables. Its objective function and constraints are both expressed as trace functions or linear equality / inequality expressions with respect to positive semi-definite matrix variables. This model eliminates the non-convexity barrier in the original model, transforms the complex optimization process into a global search within a convex set, and ensures that the solution algorithm can stably converge to the global optimum in polynomial time.

[0028] The aforementioned optimized scheduling scheme can refer to a set of control instructions derived from the target optimized scheduling model, used to guide the actual operation of the target area to be scheduled. Specifically, it includes the active / reactive power output settings of each phase converter in each time period of the scheduling cycle and the charging and discharging plan of the energy storage unit. This scheme has global optimality and physical feasibility, and can collaboratively achieve the lowest grid loss, the optimal imbalance, and the maximum absorption of distributed power sources.

[0029] In some embodiments, constructing a positive semidefinite matrix variable based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimized scheduling model may specifically include: S1: Extract the voltage variables of each node in the initial optimized scheduling model and construct a complex voltage vector composed of the voltage variables of each node; S2: Perform a conjugate transpose operation on the complex voltage vector to obtain a conjugate transpose vector, and calculate the product of the complex voltage vector and the conjugate transpose vector to obtain a second-order term matrix; S3: Based on the second-order nonlinear mapping relationship, establish an equivalent correspondence between each element in the second-order term matrix and the corresponding second-order product term of the node voltage; S4: Based on the equivalent correspondence, the second-order term matrix is ​​defined as a linear variable to be solved, and the positive semi-definite matrix variable is constructed.

[0030] By transforming the second-order nonlinear mapping relationship between node voltage variables into the construction process of semi-positive definite matrix variables, the equivalent transformation from non-convex nonlinear space to linear matrix space was successfully achieved.

[0031] In some embodiments, the method may further include the following: S1: Based on the operating status data of the target area to be scheduled and the optimized scheduling scheme, determine the power prediction deviation of the target area to be scheduled in the current scheduling period; S2: Based on the power prediction deviation and the adjustment margin of the intelligent soft switch, the optimized scheduling scheme is corrected and calculated to determine the compensation control command of the intelligent soft switch. S3: Adjust the operating state of the intelligent soft switch according to the compensation control command.

[0032] Based on the above embodiments, deep synergy between optimized scheduling and real-time operating status is achieved, significantly improving the robustness of the scheduling scheme in the face of distributed power output fluctuations and load forecasting deviations.

[0033] In some embodiments, the method may further include the following: the three-phase four-wire converter includes three phase bridge arms and one neutral bridge arm; The three phase bridge arms are respectively connected to each phase of the AC power grid of the target area to be dispatched, and are used to independently adjust the active power and reactive power of the corresponding phase according to the phase power control command. The neutral line arm is connected to the neutral line of the target area to be dispatched, and is used to provide a path for the unbalanced current of each phase, so as to realize independent decoupling control of the three-phase power.

[0034] Specifically, the AC output terminals of the three phase bridge arms are respectively connected to the AC power grids of phases A, B, and C of the target area to be dispatched. When executing the day-ahead dispatch plan, the control system issues a phase-by-phase power control command to the converter; each phase bridge arm independently adjusts the active and reactive power output of its corresponding phase according to the command. For example, when phase A has excess distributed power output while phase B is under heavy load, the phase A bridge arm can controllably absorb the excess active power and transfer it to the DC bus, while the phase B bridge arm extracts power from the DC bus and compensates for the phase B load. The entire process does not require three-phase linkage, thus achieving flexible power transfer between phases.

[0035] Furthermore, the AC output terminal of the neutral line arm is directly connected to the neutral line (N line) of the target area to be dispatched. Under asymmetrical load conditions, the sum of the currents in each phase is not zero, and the resulting zero-sequence current or unbalanced current flows into the DC bus or out through the neutral line arm. By providing a low-impedance path for the unbalanced current in each phase through the neutral line arm, the neutral point voltage offset is offset, making the current loop control of the three phase arms independent of each other, thus eliminating the coupling relationship of inter-phase power regulation at the physical topology level. This structure ensures that the converter can accurately suppress the voltage imbalance in the target area under complex distributed power source fluctuation environments, improving the dynamic regulation performance of the distribution network under asymmetrical conditions.

[0036] In some embodiments, the method for determining the initial optimized scheduling model based on the set of operating constraints, the predicted load power of each phase, and the predicted output of each phase distributed power source may further include the following: S1: Construct a multi-objective optimization function with the optimization objectives of minimizing total network loss, distributed generation abandoned power, and system voltage imbalance; S2: Determine the target constraint set based on the operating constraint set of the intelligent soft switch, the predicted load power of each phase, and the predicted output power of each phase distributed power source; wherein, the target constraint set includes power balance constraints, power flow constraints, and voltage and current safety boundary constraints. S3: Determine the initial optimization scheduling model based on the multi-objective optimization function and the objective constraint set; wherein, the initial optimization scheduling model uses node voltage variables and branch power variables as decision variables.

[0037] Specifically, by introducing a weight allocation mechanism, the three core indicators of total network loss, distributed power curtailment, and system voltage imbalance are integrated to construct a multi-objective optimization function.

[0038] By adjusting the weighting coefficients of each indicator, the scheduling model can be adapted to different operational needs. For example, during peak output periods of distributed power sources, by increasing the weighting ratio of abandoned power, the model is guided to prioritize the phase-to-phase power transfer capability of intelligent soft switches to achieve full absorption of green energy; while during periods of heavy load, by focusing on the voltage imbalance indicator, the phase-by-phase compensation capability of the converter is used to improve the power supply quality of the target area.

[0039] By combining forecast data with hardware limitations, a set of target constraints for ensuring the safe operation of the power grid was established, specifically including the following three dimensions of constraints: Power balance constraint: Using the pre-determined predicted load power values ​​of each phase and the predicted output values ​​of each phase distributed power source as known inputs, combined with the real-time power injection of intelligent soft switching in phases A, B, C and the neutral line, it is required that the sum of active and reactive power flowing into each node at each moment is equal to the sum of power flowing out, so as to maintain the energy supply and demand balance of the system.

[0040] Power flow constraints: Based on the grid admittance parameters of the target area to be dispatched, correlation equations describing the physical relationship between node voltage variables and branch power variables are established. Due to the significant interphase mutual inductance and asymmetry in a three-phase four-wire system, this constraint ensures that the dispatching scheme strictly follows Kirchhoff's law and Ohm's law.

[0041] Voltage and current safety boundary constraints: To prevent equipment overload or power supply abnormalities, the safe operating range of voltage for each node and the rated current carrying capacity of each branch conductor are pre-set. During the optimization process, the model must ensure that the voltage scalars of all nodes and the branch currents are within the set safety range.

[0042] The aforementioned multi-objective optimization function is defined as the target direction for optimization, and the set of objective constraints is defined as the search boundary for optimization, thus integrating them to obtain the initial optimization scheduling model. In this model, node voltage variables and branch power variables are set as core decision variables, i.e., the unknown parameters that the algorithm needs to solve for. Since power flow constraints involve second-order nonlinear coupling relationships between voltage variables, this initial model exhibits non-convex characteristics mathematically. This modeling approach fully preserves the original physical characteristics of the flexible interconnected distribution network under phase-specific control, laying a logical foundation for the subsequent rapid acquisition of the global optimal solution through semi-definite matrix transformation.

[0043] In some embodiments, the method involves transforming the non-convex constraints in the initial optimal scheduling model into linear constraints with respect to the positive semi-definite matrix variables, and applying positive semi-definite constraints with respect to the positive semi-definite matrix variables to transform the initial optimal scheduling model into a target optimal scheduling model. In specific implementations, this method may further include the following: S1: Based on the node voltage variables in the initial optimized scheduling model, determine the mapping relationship between the semi-positive definite matrix variables and the second-order product terms of the node voltage variables; S2: Based on the mapping relationship, the variable product terms in the initial optimization scheduling model are linearly replaced by the elements of the semi-positive definite matrix variables to obtain linear running constraints; S3: Based on the semi-positive definite matrix variables and the preset network admittance parameters, construct the trace function, and use the trace function to linearly transform the power flow constraints in the initial optimized scheduling model to obtain linear power constraints; S4: The positive semi-definite matrix variable is constructed by setting it to be a symmetric matrix and all its eigenvalues ​​to be greater than or equal to a preset value; wherein the positive semi-definite constraint is used to limit the physical compatibility of the variable space; S5: Based on the linear operation constraints, the linear power constraints, and the semi-positive definite constraints, the initial optimization scheduling model is converted into the target optimization scheduling model.

[0044] The aforementioned linear operating constraints refer to transforming the originally nonlinear safety boundary conditions in the distribution network into a set of linear inequalities about the elements of a semi-positive definite matrix through variable mapping.

[0045] The aforementioned linear power constraint refers to using trace function operations to transform the nonlinear power flow equations containing voltage product terms and trigonometric functions in the initial model into a set of linear equations with respect to semi-positive definite matrix variables.

[0046] The aforementioned positive semi-definite constraint requires that the constructed matrix variable must be a Hermitian symmetric matrix and that all its eigenvalues ​​are not less than a preset non-negative threshold, thereby limiting the optimization of the variable in the convex positive semi-definite cone space.

[0047] The trace function mentioned above can refer to an operator that uses the linear properties of addition and scalar multiplication in matrix trace operations to multiply a high-dimensional matrix variable with its corresponding weight operator matrix and extract the sum of the main diagonal elements.

[0048] Specifically, the first step in model transformation is to map variables from a low-dimensional nonlinear space to a high-dimensional linear space. This involves extracting the nodal voltage variables from the initial model and establishing a one-to-one correspondence between their second-order product terms (i.e., the product of each phase voltage with its own or other phase voltages) and the elements in the positive semi-definite matrix variables.

[0049] Based on the aforementioned mapping logic, a linear substitution is performed on the variable product terms in the initial model. Specifically, the voltage square terms and the power coupling terms between nodes, which originally existed in a nonlinear form, are directly replaced with the corresponding linear elements in the positive semi-definite matrix variables. Through this process, the nonlinear constraints, which were originally extremely difficult to solve due to their non-convex characteristics, are equivalently transformed into linear constraints on the elements of the positive semi-definite matrix variables. This step eliminates the surface search obstacle caused by the second-order terms in the model.

[0050] In addressing the core power flow constraints, this embodiment introduces preset grid admittance parameters (covering the mutual impedance and mutual inductance characteristics of a three-phase four-wire line). This parameter matrix is ​​combined with semi-definite matrix variables to construct a trace function with linear operator characteristics. Utilizing the properties of trace operations, the extremely complex power flow equations in the initial model, containing numerous trigonometric functions and quadratic couplings, are linearized into linear power constraints.

[0051] To ensure that the transformed mathematical model does not deviate from the actual physical operating state, this embodiment imposes strict structural constraints on the positive semi-definite matrix variables: First, the matrix is ​​required to be a symmetric matrix; second, a positive semi-definite constraint is constructed by constraining all its eigenvalues ​​to be no less than a preset minimum threshold. The technical essence of this constraint lies in using a positive semi-definite cone to limit the feasible domain space of the variables.

[0052] Combining the generated linear operating constraints, linear power constraints, and semidefinite constraints for preserving physical characteristics, the system completely transforms the initial non-convex optimization problem into an objective optimization scheduling model. Since all constraints and objective functions in this model have been linearized or convexized with respect to the semidefinite matrix variables, the model possesses the favorable mathematical properties of convex optimization problems. The scheduling scheme determined by this model ensures that the optimal solution is locked globally, completely solving the problem of traditional nonlinear programming algorithms easily getting trapped in local optima, and significantly improving the decision-making efficiency of the distribution network under extreme imbalance conditions.

[0053] In some embodiments, the step of converting the initial optimization scheduling model into a target optimization scheduling model based on the linear operating constraints, the linear power constraints, and the semi-positive definite constraints may specifically include: The linear operation constraints and the linear power constraints are used to replace the corresponding non-convex operation constraints and non-convex power flow constraints in the initial optimization scheduling model, respectively. Based on the mapping relationship between the semi-positive definite matrix variables and the node voltage variables, the optimization objective in the initial optimization scheduling model is mapped to the trace function of the semi-positive definite matrix variables, which serves as the objective function of the objective optimization scheduling model. By logically integrating the trace function, the linear operation constraint, the linear power constraint, and the semidefinite constraint, the target optimization scheduling model conforming to the semidefinite programming form is determined.

[0054] In some embodiments, the target optimization scheduling model can also be determined according to the following formula:

[0055] in, For scheduling periods, For the set of scheduling periods, For matrix trace operator, This is the weight matrix. For positive semi-definite matrix variables, Let cost function be This represents the voltage deviation.

[0056] In some embodiments, the method for determining the operating constraint set of the smart soft switch based on the distribution network structure information of the target area to be scheduled may further include the following: S1: Based on the AC-side switching power, DC-side interactive power, and internal losses of each phase arm of the three-phase four-wire converter, determine the phase-specific active power balance constraint and phase-specific reactive power balance constraint of the three-phase four-wire converter. S2: Determine the energy evolution constraints of the energy storage unit within the scheduling cycle based on the state of charge, charging and discharging power, and charging and discharging efficiency of the energy storage unit in adjacent scheduling periods; S3: Determine the set of operating constraints for the intelligent soft switch based on the phase active power balance constraint, the phase reactive power balance constraint, and the energy evolution constraint.

[0057] In some embodiments, energy conservation modeling is performed on the physical topology of a three-phase four-wire converter. Unlike traditional three-phase linkage control, independent balance equations are constructed for phases A, B, and C by collecting the AC-side switching power, DC-side interactive power, and internal losses of each phase arm.

[0058] Specifically, each phase arm is considered an independent energy conversion unit. The system is set so that the power absorbed by that phase arm from the AC grid, after deducting the switching and conduction losses inside the converter (i.e., the internal losses of each phase), must be strictly equal to the DC-side interactive power injected into the DC bus.

[0059] A dynamic model spanning multiple time periods is constructed for energy storage units integrated on the DC side of a smart soft-switching system. Based on charging and discharging power and efficiency, this model characterizes the evolution logic of the state of charge (SOC) of the energy storage unit in adjacent scheduling periods.

[0060] Energy evolution constraints ensure the continuity of energy storage capacity within a scheduling cycle (e.g., 24 hours). This process not only limits the charge / discharge rate at each moment but also ensures the energy storage system has the ability to continuously regulate itself by setting energy balance requirements at the beginning and end of the cycle.

[0061] The aforementioned phase-specific active power balance constraints, phase-specific reactive power balance constraints, and energy evolution constraints are logically integrated to form a complete set of intelligent soft-switching operation constraints.

[0062] In this constraint set, the stability of the DC bus voltage is implicitly considered and is achieved by dynamically balancing the sum of the DC interactive power of each phase with the sum of the energy storage charging and discharging power. This integrated modeling approach transforms the electronic power conversion characteristics within the SOP and the chemical energy storage characteristics of the energy storage into a unified mathematical boundary.

[0063] In some embodiments, the method for determining the predicted load power and predicted output of each phase of the target area to be dispatched during the dispatching period based on historical power consumption data, historical distributed power generation output data, and meteorological data within the dispatching period may further include the following: S1: Based on the historical power consumption data, the historical distributed power output data, and the meteorological data, determine the total load power prediction sequence and the total distributed power output prediction sequence of the target area to be dispatched within the dispatching cycle; S2: Based on the total load power prediction sequence, the total output power prediction sequence of the distributed power source, and the preset phase ratio coefficient, obtain the predicted load power value of each phase and the predicted output power value of each phase of the distributed power source.

[0064] Specifically, the historical power consumption data and historical distributed power output data of the target area to be dispatched are obtained through a Supervisory Control and Data Acquisition (SCADA) system over a continuous period (such as the past 90 days). At the same time, meteorological data for the dispatching period are obtained by connecting to an external meteorological service interface, including but not limited to hourly predicted irradiance, ambient temperature, wind speed, and humidity.

[0065] The above multidimensional data is input into a preset deep learning model (such as a Long Short-Term Memory Network (LSTM) or a Gated Recurrent Unit (GRU) to first determine the total load power prediction sequence and the total output power prediction sequence of the target area to be scheduled within the scheduling period.

[0066] After obtaining the total power prediction sequence, the system calls upon preset phase-specific proportional coefficients based on the physical connection status and historical operation records of the loads in each phase within the target area. These proportional coefficients are not simply equal distributions (e.g., 1 / 3 for each phase), but rather a coefficient matrix dynamically determined based on the typical three-phase imbalance characteristics of the area. Phase-specific load power determination: Considering that phase A primarily connects to single-phase residential loads, while phases B and C have heavier commercial loads, the proportional coefficients decouple and map the total load to each phase at different proportions, obtaining the predicted load power values ​​for each phase. Phase-specific distributed power output determination: Based on the actual phase sequence connection capacity of the photovoltaic array in the physical grid, the total output prediction value is allocated by phase, obtaining the predicted distributed power output value for each phase.

[0067] The prediction results obtained through the above steps fully cover the power distribution of phases A, B, and C and their relationship with the neutral line. This provides crucial boundary conditions for establishing power balance constraints in the subsequent initial optimization scheduling model. Compared to traditional balance prediction schemes, this embodiment can identify potential periods of severe imbalance in advance, thereby triggering the phase-to-phase power transfer function of intelligent soft switching and achieving pre-optimization of regional power quality.

[0068] In some embodiments, the method for determining the optimal scheduling scheme for the target area to be scheduled within the scheduling period based on the target optimization scheduling model may further include the following: S1: Perform optimization calculations on the target optimization scheduling model to determine the optimal solution set corresponding to each decision variable in the target optimization scheduling model; S2: Based on the active power variable value, reactive power variable value and energy storage state variable value in the optimal solution set, determine the phase power control command of each phase converter in the intelligent soft switch and the charging and discharging power command of the energy storage unit respectively. S3: Determine the optimal scheduling scheme for the target area to be scheduled within the scheduling period based on the phase power control command and the charging and discharging power command.

[0069] Specifically, a preset interior-point solver is invoked to perform optimization calculations on the target optimization scheduling model. Since the model has been transformed into a convex optimization form through semidefinite relaxation (SDP), the solver can converge stably in polynomial time, thereby determining the optimal solution set corresponding to each decision variable in the target optimization scheduling model.

[0070] After obtaining the optimal solution set, a mapping from the mathematical matrix space to the physical control space is performed. For each element in the positive semi-definite matrix variable, the system obtains the corresponding node voltage magnitude and phase angle using singular value decomposition or rank-one extraction algorithms. Subsequently, based on the active power variable values, reactive power variable values, and energy storage state variable values ​​in the optimal solution set, the following instruction transformation actions are executed: Phase-by-phase power control command determination: Extract the optimal value of the injected power of the intelligent soft switch at each phase node of A, B, and C, and calculate the corresponding phase-by-phase power control command for each phase arm by combining the efficiency characteristics of the three-phase four-wire converter. Energy storage charge and discharge command determination: Based on the optimal state of charge (SOC) of the DC-side energy storage unit in each time period and the power flow direction, the charge and discharge power command of the energy storage unit is determined.

[0071] All phase power control commands, charge / discharge power commands, and voltage reference trajectories of each node are integrated in a timing manner to determine the optimal scheduling scheme for the target area to be scheduled within the scheduling cycle. Before finalizing the scheme, an offline safety verification is performed using a preset power flow model to ensure that the commands do not cause instantaneous voltage jumps or frequency overruns after issuance.

[0072] As can be seen from the above, the method for determining an optimized scheduling scheme for a flexible interconnected distribution network provided in this specification involves: determining the operating constraint set of a smart soft switch based on the distribution network structure information of the target area to be scheduled; wherein the smart soft switch includes a three-phase four-wire converter and an energy storage unit; determining the predicted load power and the predicted output of each phase of the target area to be scheduled within the scheduling period based on historical power consumption data, historical distributed power generation output data, and meteorological data within the scheduling period; determining an initial optimized scheduling model based on the operating constraint set, the predicted load power and the predicted output of each phase of the distributed power generation within the scheduling period; constructing a semi-positive definite matrix variable based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimized scheduling model; transforming the non-convex constraints in the initial optimized scheduling model into linear constraints about the semi-positive definite matrix variable by using the semi-positive definite matrix variable, and applying semi-positive definite constraints about the semi-positive definite matrix variable to transform the initial optimized scheduling model into a target optimized scheduling model; and determining the optimized scheduling scheme for the target area to be scheduled within the scheduling period based on the target optimized scheduling model. In this way, by determining the set of operating constraints including the three-phase four-wire converter and energy storage unit, an accurate physical characterization of the independent decoupling control characteristics of inter-phase power was achieved, solving the problem that traditional distribution network topologies are difficult to achieve phase-independent control. By introducing semi-positive definite matrix variables to transform the non-convex constraints in the initial optimal scheduling model into linear constraints and applying semi-positive definite constraints, the equivalent linearization of the non-convex nonlinear constraints in the scheduling model was achieved, overcoming the shortcomings of conventional algorithms that are difficult to solve and difficult to obtain solutions that tend to the global optimum, thus determining an accurate and appropriate optimal scheduling scheme.

[0073] See Figure 2 As shown in the embodiments of this specification, a specific electronic device is also provided, wherein the electronic device includes a network communication port 201, a processor 202 and a memory 203, and the above structures are connected by internal cables so that the various structures can perform specific data interaction.

[0074] Specifically, the network communication port 201 can be used to determine the operating constraint set of the intelligent soft switch based on the distribution network structure information of the target area to be dispatched; wherein the intelligent soft switch includes a three-phase four-wire converter and an energy storage unit.

[0075] The processor 202 can be specifically used to determine the predicted load power and the predicted output of each phase of the target area to be scheduled within the scheduling period based on historical power consumption data, historical distributed power generation output data, and meteorological data within the scheduling period; determine an initial optimized scheduling model based on the operating constraint set, the predicted load power and the predicted output of each phase of the distributed power generation; construct a positive semi-definite matrix variable based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimized scheduling model; transform the non-convex constraints in the initial optimized scheduling model into linear constraints about the positive semi-definite matrix variable by using the positive semi-definite matrix variable, and apply positive semi-definite constraints about the positive semi-definite matrix variable to transform the initial optimized scheduling model into a target optimized scheduling model; and determine the optimized scheduling scheme for the target area to be scheduled within the scheduling period based on the target optimized scheduling model.

[0076] The memory 203 can be used to store the corresponding instruction program.

[0077] Based on the above method, the relevant structural performance of electronic equipment can be effectively utilized to improve the data processing speed of electronic equipment and efficiently realize a method for determining the optimal scheduling scheme of flexible interconnected distribution networks.

[0078] In this embodiment, the network communication port 201 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0079] In this embodiment, the processor 202 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0080] In this embodiment, the memory 203 may include a hierarchy. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0081] This specification also provides a computer-readable storage medium based on the above-described method for determining an optimized dispatch scheme for a flexible interconnected distribution network. The method determines the operating constraint set of a smart soft switch based on the distribution network structure information of the target area to be dispatched. The smart soft switch includes a three-phase four-wire converter and an energy storage unit. Based on historical power consumption data, historical distributed power output data, and meteorological data within the dispatch period of the target area to be dispatched, the method determines the predicted load power and distributed power output of each phase within the dispatch period. Based on the operating constraint set, the predicted load power and distributed power output of each phase, an initial optimized dispatch model is determined. Based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimized dispatch model, a semi-definite matrix variable is constructed. By utilizing the semi-definite matrix variable, the non-convex constraints in the initial optimized dispatch model are transformed into linear constraints with respect to the semi-definite matrix variable, and semi-definite constraints are applied to the semi-definite matrix variable, transforming the initial optimized dispatch model into a target optimized dispatch model. Based on the target optimized dispatch model, an optimized dispatch scheme for the target area to be dispatched within the dispatch period is determined.

[0082] In this embodiment, the storage medium includes, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured according to standards specified in the communication protocol for network connection communication.

[0083] In this embodiment, the specific functions and effects implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other embodiments, and will not be repeated here.

[0084] See Figure 3 At the software level, embodiments of this specification also provide a device for determining an optimized scheduling scheme for a flexible interconnected distribution network, which may specifically include the following structural modules: The constraint set determination module 301 is used to determine the operating constraint set of the intelligent soft switch based on the distribution network structure information of the target area to be dispatched; wherein, the intelligent soft switch includes a three-phase four-wire converter and an energy storage unit; The prediction value determination module 302 is used to determine the predicted load power of each phase and the predicted output of each phase of the target area to be dispatched within the dispatching period based on the historical power consumption data, historical distributed power output data and meteorological data within the dispatching period. The initial model determination module 303 is used to determine the initial optimized scheduling model based on the set of operating constraints, the predicted load power of each phase, and the predicted output of each phase distributed power source. The variable determination module 304 is used to construct a positive semidefinite matrix variable based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimization scheduling model. The target model determination module 305 is used to transform the non-convex constraints in the initial optimal scheduling model into linear constraints with respect to the semi-positive definite matrix variables by utilizing the semi-positive definite matrix variables, and to apply semi-positive definite constraints with respect to the semi-positive definite matrix variables to transform the initial optimal scheduling model into a target optimal scheduling model. The scheduling scheme determination module 306 is used to determine the optimized scheduling scheme of the target area to be scheduled within the scheduling period based on the target optimized scheduling model.

[0085] In some embodiments, the above-described device further includes: the three-phase four-wire converter includes three phase bridge arms and one neutral bridge arm; wherein, the three phase bridge arms are respectively connected to each phase of the AC power grid of the target area to be dispatched, and are used to independently adjust the active power and reactive power of the corresponding phase according to the phase power control command; the neutral bridge arm is connected to the neutral line of the target area to be dispatched, and is used to provide a path for the unbalanced current of each phase, so as to realize the independent decoupling control of the three-phase power.

[0086] In some embodiments, the initial model determination module 303, in its specific implementation, constructs a multi-objective optimization function with the optimization objectives of minimizing total network loss, distributed power curtailment, and system voltage imbalance; determines a target constraint set based on the operating constraint set of the intelligent soft switch, the predicted load power of each phase, and the predicted output power of each phase distributed power source; wherein the target constraint set includes power balance constraints, power flow constraints, and voltage and current safety boundary constraints; and determines the initial optimization scheduling model based on the multi-objective optimization function and the target constraint set; wherein the initial optimization scheduling model uses node voltage variables and branch power variables as decision variables.

[0087] In some embodiments, the target model determination module 305, in specific implementation, determines the mapping relationship between the positive semi-definite matrix variables and the second-order product terms of the node voltage variables based on the node voltage variables in the initial optimized scheduling model; based on the mapping relationship, linearly replaces the variable product terms in the initial optimized scheduling model using the elements of the positive semi-definite matrix variables to obtain linear operating constraints; constructs a trace function based on the positive semi-definite matrix variables and preset grid admittance parameters, and uses the trace function to linearly transform the power flow constraints in the initial optimized scheduling model to obtain linear power constraints; constructs the positive semi-definite constraints by setting the positive semi-definite matrix variables to be symmetric matrices and all their eigenvalues ​​to be greater than or equal to preset values; wherein, the positive semi-definite constraints are used to limit the physical compatibility of the variable space; and converts the initial optimized scheduling model into a target optimized scheduling model based on the linear operating constraints, the linear power constraints, and the positive semi-definite constraints.

[0088] In some embodiments, the constraint set determination module 301, in specific implementation, determines the phase-specific active power balance constraints and phase-specific reactive power balance constraints of the three-phase four-wire converter based on the AC-side switching power, DC-side interactive power, and internal losses of each phase arm of the three-phase four-wire converter; determines the energy evolution constraints of the energy storage unit within the scheduling cycle based on the state of charge, charging and discharging power, and charging and discharging efficiency of the energy storage unit in adjacent scheduling periods; and determines the operating constraint set of the intelligent soft switch based on the phase-specific active power balance constraints, the phase-specific reactive power balance constraints, and the energy evolution constraints.

[0089] In some embodiments, the prediction value determination module 302, in specific implementation, determines the total load power prediction sequence and the total distributed power output prediction sequence of the target area to be dispatched within the dispatching cycle based on the historical power consumption data, the historical distributed power output data, and the meteorological data; and obtains the load power prediction value and the distributed power output prediction value of each phase based on the total load power prediction sequence, the total distributed power output prediction sequence, and the preset phase ratio coefficient.

[0090] In some embodiments, the scheduling scheme determination module 306, in specific implementation, performs optimization calculations on the target optimization scheduling model to determine the optimal solution set corresponding to each decision variable in the target optimization scheduling model; based on the active power variable value, reactive power variable value, and energy storage state variable value in the optimal solution set, it determines the phase power control command of each phase converter in the smart soft switch and the charging and discharging power command of the energy storage unit; based on the phase power control command and the charging and discharging power command, it determines the optimal scheduling scheme of the target area to be scheduled within the scheduling period.

[0091] It should be noted that the units, devices, or modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described by dividing them into various modules according to their functions. Of course, in implementing this specification, the functions of each module can be implemented in the same software and / or hardware, or modules that implement the same function can be implemented by a combination of sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection between the devices or units shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0092] As can be seen from the above, the flexible interconnected distribution network optimization scheduling scheme determination device provided in the embodiments of this specification determines the operating constraint set of the intelligent soft switch according to the distribution network structure information of the target area to be scheduled; wherein, the intelligent soft switch includes a three-phase four-wire converter and an energy storage unit; based on the historical power consumption data, historical distributed power output data, and meteorological data within the scheduling period of the target area to be scheduled, the predicted load power and the predicted output of each phase of the target area to be scheduled within the scheduling period are determined; based on the operating constraint set, the predicted load power and the predicted output of each phase of the distributed power, an initial optimization scheduling model is determined; based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimization scheduling model, a semi-positive definite matrix variable is constructed; by using the semi-positive definite matrix variable, the non-convex constraints in the initial optimization scheduling model are transformed into linear constraints about the semi-positive definite matrix variable, and a semi-positive definite constraint about the semi-positive definite matrix variable is applied to transform the initial optimization scheduling model into a target optimization scheduling model; based on the target optimization scheduling model, the optimization scheduling scheme of the target area to be scheduled within the scheduling period is determined.

[0093] In a specific scenario example, the method and apparatus for determining the optimal dispatching scheme of a flexible interconnected distribution network provided in this specification can be applied to solve the technical problem that existing distribution network dispatching schemes are limited by inter-phase control coupling and the non-convex and nonlinear obstacles of the model, making it difficult to achieve precise phase-by-phase control and approach the global optimal decision. The specific implementation process may include the following:

[0094] In some embodiments, two adjacent distribution substations are flexibly interconnected. These two substations are physically connected through a commutated E-SOP (i.e., a smart switch), thereby constructing a flexible closed-loop network configuration.

[0095] In practice, each converter within the commutation E-SOP is configured in independent control mode, capable of receiving dispatch commands in real time and continuously and decoupledly regulating the active and reactive power outputs of phases A, B, and C. To maintain energy conservation, strict power balance constraints are set during modeling: the sum of the input power and the sum of the output power of all phases at the soft-open point of the phase change must remain zero. This model not only considers inter-phase power transfer but also fully characterizes the energy interaction characteristics under a three-phase four-wire system.

[0096] After defining the physical model, a multi-objective optimization function is constructed to comprehensively address the challenges posed by the high proportion of renewable energy integration. This optimization function specifically includes the following three sub-objectives: Economic objectives: Minimize total grid losses and the amount of abandoned distributed power sources to ensure the efficient use of green energy; Power quality objective: Minimize voltage imbalance and suppress neutral line current through the phase-to-phase compensation capability of SOP.

[0097] Simultaneously, it integrates a complete set of constraints, including the commutation soft-switching operating domain, the state-of-charge evolution of energy storage, the output curves of distributed generation sources, and the power flow equations of three-phase distribution networks. This set of constraints defines the safety boundaries of operation, ensuring that the dispatching scheme does not violate physical laws while satisfying voltage quality requirements.

[0098] Since the power flow constraints in the initial model are non-convex, this embodiment uses the symmetric semidefinite programming (SDP) algorithm for model transformation.

[0099] First, a high-dimensional positive semi-definite matrix variable is introduced, and the non-convex voltage product terms in the original problem are transformed into linear terms in this matrix variable through a mapping relationship. Subsequently, the operational constraints of E-SOP are also rewritten in a trace function form compatible with SDP. To constrain the physical meaning of the solution space, positive semi-definite constraints are added to ensure that all eigenvalues ​​of the matrix variable are non-negative.

[0100] Finally, the dispatch center used a high-efficiency mathematical solver to quickly solve the transformed convex optimization problem. The resulting dispatch scheme was then sent to the commutation E-SOP, where load balancing and voltage stability between transformer substations were achieved by adjusting the trigger phase and amplitude of each phase converter.

[0101] In some embodiments, two adjacent low-voltage distribution substations (Substation A and Substation B) in a certain area are selected as the analysis objects. The two substations are flexibly interconnected through a commutated E-SOP (i.e., intelligent soft switch) with a rated capacity of 200kVA. The distributed photovoltaic penetration rate in this area is high, and most of them are single-phase connected, resulting in prominent problems of uneven load between substations and between phases. The total installed photovoltaic capacity is 150kW, the peak load reaches 125kW, and an energy storage system (ESS) with a capacity of 100kWh and a rated power of 50kW is configured on the DC side of the E-SOP. The simulation analysis uses a 24-hour scheduling cycle with a 1-hour time step to evaluate the performance of the proposed optimized scheduling method.

[0102] Comparison scenarios are set up: Scenario 1: E-SOP is not in operation, and the two distribution zones operate as independent distribution networks. Scenario 2: The day-ahead optimization scheduling method described in this invention is used, as shown in Table 1 for the results of the two scenarios.

[0103] Table 1

[0104] It can be seen that by flexibly transferring power flow through E-SOP, the power distribution between distribution stations is optimized, and overloaded lines are avoided, thereby significantly reducing the total grid loss. By controlling E-SOP to transfer the surplus photovoltaic power of distribution station A to distribution station B for consumption, and by rationally arranging energy storage charging, the phenomenon of curtailment of solar power is greatly reduced.

[0105] Furthermore, in some embodiments... Figure 4 This is a voltage imbalance coefficient diagram for area A under two scenarios. It can be seen that during periods of high photovoltaic power generation and peak load (such as noon and evening), the voltage imbalance is stably controlled at a low level.

[0106] Case studies demonstrate that the day-ahead optimization dispatch method for flexible interconnected distribution networks based on phase-switched E-SOP proposed in this specification can effectively solve the problems of voltage overruns, three-phase imbalances, and power curtailment caused by high-proportion photovoltaic grid integration. At the same time, it achieves the goals of reducing network losses and improving operational economy, verifying the method's advanced nature, effectiveness, and engineering application value.

[0107] While this specification provides the steps of operation for the methods described in the embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. The terms "first," "second," etc., are used to denote names and do not indicate any particular order.

[0108] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0109] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of this specification.

[0110] Although this specification has been described by way of examples, those skilled in the art will recognize that many variations and modifications are possible without departing from the spirit of this specification, and it is intended that the appended claims cover such variations and modifications without departing from the spirit of this specification.

Claims

1. A method for determining an optimized dispatch scheme for a flexible interconnected distribution network, characterized in that, include: Based on the distribution network structure information of the target area to be dispatched, the set of operating constraints for the intelligent soft switch is determined; wherein, the intelligent soft switch includes a three-phase four-wire converter and an energy storage unit; Based on the historical power consumption data, historical distributed power output data and meteorological data of the target area to be dispatched, the predicted load power and the predicted output of each phase of the target area to be dispatched during the dispatching period are determined. Based on the set of operating constraints, the predicted load power of each phase, and the predicted output of each phase distributed power source, an initial optimized scheduling model is determined. Based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimization scheduling model, construct a positive semidefinite matrix variable; By utilizing the positive semidefinite matrix variables, the non-convex constraints in the initial optimal scheduling model are transformed into linear constraints with respect to the positive semidefinite matrix variables, and positive semidefinite constraints with respect to the positive semidefinite matrix variables are applied to transform the initial optimal scheduling model into the target optimal scheduling model. Based on the target optimization scheduling model, an optimized scheduling scheme for the target area to be scheduled within the scheduling period is determined.

2. The method according to claim 1, characterized in that, The three-phase four-wire converter includes three phase bridge arms and one neutral bridge arm. The three phase bridge arms are respectively connected to each phase of the AC power grid of the target area to be dispatched, and are used to independently adjust the active power and reactive power of the corresponding phase according to the phase power control command. The neutral line arm is connected to the neutral line of the target area to be dispatched, and is used to provide a path for the unbalanced current of each phase, so as to realize independent decoupling control of the three-phase power.

3. The method according to claim 2, characterized in that, The step of determining the initial optimized scheduling model based on the operating constraint set, the predicted load power values ​​of each phase, and the predicted output values ​​of each phase distributed power source includes: A multi-objective optimization function is constructed with the optimization objectives of minimizing total network loss, distributed generation abandoned power, and system voltage imbalance; Based on the operating constraint set of the intelligent soft switch, the predicted load power of each phase, and the predicted output of each phase distributed power source, a target constraint set is determined; wherein, the target constraint set includes power balance constraints, power flow constraints, and voltage and current safety boundary constraints. The initial optimization scheduling model is determined based on the multi-objective optimization function and the objective constraint set; wherein the initial optimization scheduling model uses node voltage variables and branch power variables as decision variables.

4. The method according to claim 3, characterized in that, The step of transforming the non-convex constraints in the initial optimal scheduling model into linear constraints with respect to the positive semi-definite matrix variables by utilizing the positive semi-definite matrix variables, and applying positive semi-definite constraints with respect to the positive semi-definite matrix variables to transform the initial optimal scheduling model into the target optimal scheduling model includes: Based on the node voltage variables in the initial optimized scheduling model, determine the mapping relationship between the semi-positive definite matrix variables and the second-order product terms of the node voltage variables; Based on the mapping relationship, the variable product terms in the initial optimization scheduling model are linearly replaced by the elements of the semi-positive definite matrix variables to obtain linear running constraints; Based on the semi-positive definite matrix variables and the preset network admittance parameters, a trace function is constructed, and the trace function is used to linearly transform the power flow constraints in the initial optimized scheduling model to obtain linear power constraints. The positive semi-definite matrix variable is constructed by setting it to be a symmetric matrix and all its eigenvalues ​​to be greater than or equal to a preset value; wherein the positive semi-definite constraint is used to limit the physical compatibility of the variable space. Based on the linear operation constraints, the linear power constraints, and the semi-positive definite constraints, the initial optimization scheduling model is transformed into the target optimization scheduling model.

5. The method according to claim 4, characterized in that, The step of determining the set of operating constraints for the intelligent soft switch based on the distribution network structure information of the target area to be dispatched includes: Based on the AC-side switching power, DC-side interactive power, and internal losses of each phase arm of the three-phase four-wire converter, the phase-by-phase active power balance constraints and phase-by-phase reactive power balance constraints of the three-phase four-wire converter are determined. Based on the state of charge, charging and discharging power, and charging and discharging efficiency of the energy storage unit in adjacent scheduling periods, the energy evolution constraints of the energy storage unit within the scheduling cycle are determined. The set of operating constraints for the intelligent soft switch is determined based on the phase active power balance constraint, the phase reactive power balance constraint, and the energy evolution constraint.

6. The method according to claim 5, characterized in that, The step of determining the predicted load power and predicted output of each phase of the target area during the scheduling period based on historical power consumption data, historical distributed power generation output data, and meteorological data within the scheduling period includes: Based on the historical power consumption data, the historical distributed power output data, and the meteorological data, determine the total load power prediction sequence and the total distributed power output prediction sequence of the target area to be dispatched within the dispatching cycle. Based on the total load power prediction sequence, the total output power prediction sequence of the distributed power source, and the preset phase ratio coefficient, the predicted load power value of each phase and the predicted output power value of each phase of the distributed power source are obtained.

7. The method according to claim 6, characterized in that, The step of determining the optimal scheduling scheme for the target area to be scheduled within the scheduling period based on the target optimization scheduling model includes: The target optimization scheduling model is optimized to determine the optimal solution set corresponding to each decision variable in the target optimization scheduling model. Based on the active power variable value, reactive power variable value and energy storage state variable value in the optimal solution set, the phase power control command of each phase converter in the intelligent soft switch and the charging and discharging power command of the energy storage unit are determined respectively. Based on the phase power control command and the charge / discharge power command, an optimal scheduling scheme for the target area to be scheduled within the scheduling period is determined.

8. A device for determining an optimized dispatching scheme for a flexible interconnected distribution network, characterized in that, include: The constraint set determination module is used to determine the operating constraint set of the intelligent soft switch based on the distribution network structure information of the target area to be dispatched; wherein, the intelligent soft switch includes a three-phase four-wire converter and an energy storage unit; The prediction value determination module is used to determine the predicted load power of each phase and the predicted output of each phase of the target area to be dispatched within the dispatching period based on the historical power consumption data, historical distributed power output data and meteorological data within the dispatching period. The initial model determination module is used to determine the initial optimized scheduling model based on the set of operating constraints, the predicted load power of each phase, and the predicted output of each phase distributed power source. The variable determination module is used to construct a positive semidefinite matrix variable based on the second-order nonlinear mapping relationship between node voltage variables in the initial optimization scheduling model. The target model determination module is used to transform the non-convex constraints in the initial optimal scheduling model into linear constraints with respect to the semi-positive definite matrix variables by utilizing the semi-positive definite matrix variables, and to apply semi-positive definite constraints with respect to the semi-positive definite matrix variables to transform the initial optimal scheduling model into a target optimal scheduling model. The scheduling scheme determination module is used to determine the optimized scheduling scheme for the target area to be scheduled within the scheduling period based on the target optimized scheduling model.

9. An electronic device, characterized in that, It includes a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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