A Hierarchical Distributed Voltage Optimization Method for AC / DC Distribution Networks Based on Objective Cascade Analysis

Abstract

This invention discloses a hierarchical distributed voltage optimization method for AC / DC distribution networks based on the objective cascade analysis method. First, a steady-state model of the AC / DC distribution network is established, and the relevant power flow equation constraints are subjected to second-order cone relaxation and linearization to establish an easily solvable hierarchical optimization model. Second, based on the characteristics of the AC / DC distribution network, a three-layer distributed optimization architecture based on the objective cascade analysis method is proposed. Based on this, the Lagrange multiplier method is introduced to establish voltage optimization models for each level. Finally, based on power-voltage sensitivity, an alternating iterative and rolling optimization method is used to solve the model. This invention can effectively solve the voltage overshoot and fluctuation problems caused by backflow of power flow by coordinating flexible interconnection devices with wind, solar, and energy storage under hierarchical control.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a hierarchical distributed voltage optimization method for AC / DC distribution networks based on the objective cascade analysis method. Background Technology

[0002] With the accelerating trend of high-proportion renewable energy and high-proportion power electronics in distribution networks, distributed generation, flexible interconnection devices, energy storage, and new loads are widely being integrated into distribution networks. Traditional AC-based, unidirectional power transmission medium- and low-voltage distribution networks are evolving towards a flexible, multi-regional AC / DC interconnection model. AC / DC distribution networks possess strong distributed generation capacity, improving transmission efficiency and power quality, thus becoming the future development trend. However, the large-scale integration of distributed generation into distribution networks, with its intermittency and randomness, may cause power flow reversal, threatening grid voltage security.

[0003] Traditional centralized voltage optimization methods currently face challenges such as heavy computational burden, low efficiency, high communication difficulty, and demanding control system requirements, and they do not consider the interests of different stakeholders. Typical AC / DC distribution networks, however, possess natural zoning characteristics, and the AC / DC parties belong to different stakeholders, exhibiting potential for decentralized autonomy and mutual support. Therefore, hierarchical distributed optimization has become the mainstream research approach. To adapt to the new trend of coordinated interaction between power generation, grid, load, and energy storage, and to realize the transformation of distribution networks from "closed-loop design, open-loop operation" to "closed-loop operation, flexible interconnection," it is urgent to study how to coordinate flexible interconnection devices to alleviate the spatiotemporal mismatch between power generation and load. Therefore, researching hierarchical distributed voltage optimization methods for AC / DC distribution networks is of great significance for improving grid security, flexibility, and the absorption capacity of new energy sources. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a hierarchical distributed voltage optimization method for AC / DC distribution networks based on the objective cascade analysis method. This method first establishes a steady-state model of the AC / DC distribution network and performs second-order cone relaxation and linearization on the relevant power flow equation constraints to establish an easily solvable hierarchical optimization model. Second, based on the characteristics of the AC / DC distribution network, a three-layer distributed optimization architecture based on the objective cascade analysis method is proposed. Based on this, voltage optimization models for each level are established, and the Lagrange multiplier method is introduced, employing a rolling optimization and alternating iteration method for solution. The aim is to effectively solve the voltage overshoot and fluctuation problems caused by backflow power flow under a hierarchical distributed control structure by coordinating flexible interconnection devices with wind, solar, and energy storage.

[0005] According to one aspect of the present invention, a hierarchical distributed voltage optimization method for AC / DC distribution networks based on target cascade analysis is provided, comprising: Step S1: Establish a steady-state model of AC / DC distribution network including AC distribution network, DC distribution network and flexible interconnection device, and perform convex optimization on the non-convex power flow equation constraints in the model to build the mathematical basis of the optimization model. Step S2: Based on the characteristics of AC / DC distribution networks being multi-entity and regional, a three-layer distributed optimization architecture based on the target cascade analysis method is constructed. The three-layer distributed optimization architecture includes: AC distribution network layer, flexible interconnection device coordination layer, and DC distribution network layer. Step S3: Based on the three-layer distributed optimization architecture, the global voltage optimization problem is decomposed into local optimization sub-problems at each level, and a distributed collaborative solution mechanism between levels is established. Step S4: Through the distributed collaborative solution mechanism, coordinate the optimization processes at each level to obtain the solution for global system optimization; Step S5: Based on the globally optimized solution, generate and issue real-time voltage control commands.

[0006] As a further technical solution, the convex optimization processing of the non-convex power flow equation constraints specifically includes: The Distflow branch power flow model is used to describe the power flow in AC and DC distribution networks; By introducing auxiliary variables to replace the square terms of node voltage and branch current, the non-convex voltage drop equation is reconstructed into a second-order form. The reconstructed second-order constraints are relaxed by rotating the second-order cone form, thereby transforming the non-convex power flow model into a convex optimization model.

[0007] As a further technical solution, the three-layer distributed optimization architecture is constructed as follows: The AC distribution network layer and the DC distribution network layer are each treated as a decentralized autonomous subsystem; The flexible interconnection device coordination layer is used as an independent intermediate coordination layer to connect and coordinate the AC distribution network layer and the DC distribution network layer. Information and power exchange is only allowed between adjacent layers, and subsystems within the same layer do not communicate directly, in order to achieve decoupling between the physical and information layers.

[0008] As a further technical solution, the establishment of a distributed collaborative solution mechanism between levels includes: Define the physical coupling variables that connect adjacent layers as shared variables; The shared variables are decomposed into upper-level preset target variables and lower-level decision response variables; The Lagrange multiplier method is adopted, and an augmented Lagrange function containing a first-order penalty function term and a second-order penalty function term is constructed to relax the consistency constraint that the objective variable and the response variable must be strictly equal, so that the optimization subproblems at each level can be solved independently and in parallel.

[0009] As a further technical solution, the coordination of optimization processes at each level employs an iterative solution using the alternating direction multiplier method, including: In the k-th iteration, each level solves the optimization problem in parallel based on the average value of the shared variables updated by its neighboring levels after the (k-1)-th iteration; After each level of solution is completed, the Lagrange multipliers are updated according to the shared variable errors generated in this round, following these rules: , Among them, superscript k Represents the number of iterations. For the first k Shared variable error in the next iteration v , w These are the multiplier vectors of the first and second penalty functions, respectively. β These are the weighting coefficients; The iteration continues until the preset convergence condition is met: , in, This represents the shared variable error threshold.

[0010] According to one aspect of the present invention, a hierarchical distributed voltage optimization system for AC / DC distribution networks based on the target cascade analysis method is provided for implementing the method, the system comprising: The model building and processing unit is configured to execute S1, establish a steady-state model and perform convex optimization processing; The architecture management unit is optimized and configured to execute S2 to build and maintain the three-layer distributed optimized architecture. The collaborative solution mechanism unit is configured to execute S3 to realize the decomposition of the problem and the establishment of a collaborative mechanism; A distributed coordination and optimization unit is configured to execute S4 to coordinate the optimization process at each level. The control command generation and issuance unit is configured to execute S5 to generate and issue voltage control commands; The system further includes at least one AC area controller deployed on the AC distribution network layer, at least one DC area controller deployed on the DC distribution network layer, at least one flexible interconnection coordinator deployed on the flexible interconnection device coordination layer, and a distributed collaborative solution engine for inter-layer coordination.

[0011] As a further technical solution, the flexible interconnection coordinator includes: The sensitivity analysis module is used to calculate or obtain the power-voltage sensitivity matrix, which characterizes the linearization effect of power adjustment of flexible interconnect devices on the voltage of key nodes in the system. The quadratic programming solution module is used to construct and solve a quadratic programming model with the goal of minimizing voltage deviation and control cost based on the sensitivity matrix, voltage reference value and boundary information. The power command output module is used to generate active and reactive power setting commands for the flexible interconnection device based on the solution results.

[0012] As a further technical solution, the system also includes a rolling optimization scheduling unit, configured for: Set the length of the rolling optimization time window and the step size; At the beginning of each rolling cycle, each unit of the system is triggered to start a new round of collaborative optimization based on the updated source-load prediction data, generating a sequence of control instructions for multiple time sections within a future window; Control the execution of the instruction for the first section in the instruction sequence, and start the next cycle optimization after the window scrolls.

[0013] According to one aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the hierarchical distributed voltage optimization method for AC / DC distribution networks based on the target cascade analysis method.

[0014] According to one aspect of the present invention, a voltage optimization control device is provided, applied to a flexible interconnection device in an AC / DC distribution network. The device includes a processor, a memory, and a communication interface. The memory stores a computer program, which, when executed by the processor, controls the device to perform the following operations: The boundary information from the AC distribution network and the DC distribution network is received through the communication interface; Based on the power-voltage sensitivity relationship, a local optimization model is constructed and solved with the objectives of minimizing voltage deviation and minimizing operating cost; According to the preset distributed coordination protocol, the shared variables and the coordination multiplier are interacted and updated. The optimized solution is converted into control commands and sent to the power execution unit of the flexible interconnect device through the communication interface.

[0015] Compared with existing technologies, this invention presents a hierarchical distributed voltage optimization method for AC / DC distribution networks based on the objective cascade analysis method. By constructing a three-layer distributed optimization architecture—AC distribution network layer, flexible interconnection device coordination layer, and DC distribution network layer—and introducing the Lagrange multiplier method and the alternating direction multiplier method to achieve collaborative solution, significant beneficial effects are achieved. First, this invention transforms the non-convex power flow model into an efficient and solvable convex optimization problem, laying the foundation for online optimization. Second, its three-layer architecture effectively coordinates the decentralized autonomy and global mutual assistance of multiple stakeholders. Through rapid correction and rolling optimization based on power-voltage sensitivity, it can accurately suppress voltage over-limit and fluctuations, significantly reduce voltage deviation rate and system network loss, and improve the level of renewable energy consumption.

[0016] This invention, through its constructed three-layer distributed optimization architecture and collaborative mechanism, can effectively coordinate and regulate multiple flexible interconnected devices (such as VSCs and SOPs), thereby alleviating the power imbalance caused by uneven spatiotemporal distribution of sources and loads between AC and DC regions. In this process, through the coordinated optimization of multiple VSCs and SOPs, the system voltage can be quickly and accurately corrected, effectively solving the voltage limit exceedance problem in AC / DC distribution networks and significantly reducing the voltage deviation rate. Simultaneously, by optimizing the output of distributed power sources and the transmission power of flexible interconnected devices, this method improves the absorption level of high-proportion renewable energy, reduces system network losses, and effectively suppresses voltage limit exceedance caused by backflow and voltage dips under heavy load conditions, comprehensively enhancing the voltage security and operational economy of AC / DC distribution networks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the hierarchical distributed voltage optimization method for AC / DC distribution networks based on target cascade analysis provided in this embodiment of the invention; Figure 2 This is a schematic diagram of a three-level hierarchical structure of an AC / DC distribution network in an embodiment of the present invention; Figure 3 This is a schematic diagram of hierarchical distributed optimization of AC / DC distribution networks in an embodiment of the present invention; Figure 4 This is a schematic diagram of the rolling optimization of AC / DC distribution network in an embodiment of the present invention. Detailed Implementation

[0019] This invention first establishes a steady-state model of an AC / DC distribution network including AC distribution network, DC distribution network, and flexible interconnection devices. The non-convex power flow equations in the model are then subjected to convex optimization, serving as constraints for each level of the optimization model. Based on the multi-entity, regional characteristics of the AC / DC distribution network, a three-layer distributed optimization architecture based on the objective cascade analysis method is constructed. The Lagrange multiplier method is introduced to establish voltage optimization models at each level. An alternating iteration method is used to ensure the consistency of boundary variables, and rolling correction is performed based on power-voltage sensitivity to achieve model solution. This invention effectively alleviates the spatiotemporal mismatch between source and load in different regions through the coordinated control of multiple flexible interconnection devices. Through the coordinated optimization of multiple voltage source converters and intelligent soft switches, it effectively solves voltage exceedance issues in the AC / DC distribution network, significantly reduces voltage deviation rate, improves the absorption level of high-proportion renewable energy access, reduces network losses, and suppresses voltage exceedances caused by backflow and voltage dips under heavy loads.

[0020] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention. Example 1

[0022] Figure 2 This is a schematic diagram of a three-level hierarchical structure of an AC / DC distribution network in an embodiment of the present invention. Figure 3 This is a schematic diagram of hierarchical distributed optimization of AC / DC distribution networks in an embodiment of the present invention. Figure 4 This is a schematic diagram of the rolling optimization of the AC / DC distribution network in an embodiment of the present invention. Specifically, it will... Figure 3 The hierarchical distributed optimization method is applied to the Figure 2In the three-tiered structure of AC / DC distribution networks, Figure 4 The rolling optimization timing method described above is applied to Figure 3 In the aforementioned AC / DC distribution network rolling optimization method, such as... Figure 1 As shown, the overall method includes: Step S1: Establish a steady-state model of AC / DC distribution network including AC distribution network, DC distribution network and flexible interconnection device, and perform convex optimization on the non-convex power flow equation constraints in the model to build the mathematical basis of the optimization model. Step S2: Based on the characteristics of AC / DC distribution networks being multi-entity and regional, a three-layer distributed optimization architecture based on the target cascade analysis method is constructed. The three-layer distributed optimization architecture includes: AC distribution network layer, flexible interconnection device coordination layer, and DC distribution network layer. Step S3: Based on the three-layer distributed optimization architecture, the global voltage optimization problem is decomposed into local optimization sub-problems at each level, and a distributed collaborative solution mechanism between levels is established. Step S4: Through the distributed collaborative solution mechanism, coordinate the optimization processes at each level to obtain the solution for global system optimization; Step S5: Based on the globally optimized solution, generate and issue real-time voltage control commands.

[0023] In step S1, the AC / DC distribution network model adopts the Distflow power flow model. To characterize the uncertainty of renewable energy output, the prediction error probability model for wind power output is fitted by a Weibull distribution, and the prediction error probability model for photovoltaic power output is represented by a Beta distribution. Since the Distflow power flow equations contain square terms of node voltage and branch current, their constraints are non-convex, making it difficult to solve the global optimization problem directly and efficiently. Therefore, this invention performs convex optimization processing on the constraints of the non-convex power flow equations. Specifically, this includes: first, introducing auxiliary variables to replace the square terms of node voltage and branch current, thereby reconstructing the non-convex voltage drop equations into a second-order form; then, relaxing the reconstructed second-order form constraints through a rotated second-order cone form, thereby transforming the original non-convex mixed-integer nonlinear programming problem into an efficiently solvable mixed-integer second-order cone programming model, thus realizing the convex optimization model.

[0024] The establishment of the steady-state model for AC / DC distribution networks is specifically as follows: the Distflow branch power flow model is used to describe the power flow of AC and DC distribution networks; for the AC distribution network, the following power flow constraint equations are established: , DC power flow does not have reactive power and reactance compared to AC power flow, as shown below: , In the formula: δ (j ), π j ) represent nodes respectively j For the set of child and parent nodes; P ij , Q ij and I ij Representing branch roads ij Active power, reactive power, and current; R ij and X ij Indicates a branch ij Resistance and reactance; P j , Q j and U j For nodes j The injected active and reactive power and voltage amplitude; N A and L A For communication nodes and branch sets; P jk , Q jk Representing branch roads jk The merits and demerits; U i Represents a node i The voltage amplitude.

[0025] The square terms of current and voltage in AC / DC power flow constraints (2), (3), (5), and (6) are replaced with auxiliary variables, specifically:

[0026] Then, the node capacity constraint equations (3) and (6) are relaxed using a second-order cone method. The relaxed constraints are shown below:

[0027]

[0028] Specifically, step S2 treats the flexible interconnection device as a separate layer for optimization decisions. Considering that the DC distribution network and each AC distribution network belong to different stakeholders and are relatively independent, a hierarchical model can be constructed using their decentralized and autonomous characteristics. The AC and DC distribution networks are divided into three layers: AC distribution network (AC), flexible interconnection device (FID), and DC distribution network (DC). The AC and DC layers are their respective decentralized and autonomous subsystems, and the FID layer is the intermediate coordination layer connecting the AC and DC layers. Furthermore: 1) Information and power flows only exist between adjacent layers; 2) Each layer belongs to different stakeholders, and the stakeholders of each subsystem within a layer are also different; 3) There is no information exchange between subsystems within a layer, and power flows need to be redistributed through the upper-level layer. This allows for parallel optimization within the same layer, with only coupling variables passed between layers, alleviating the pressure of computing power allocation and communication, and fully leveraging the autonomous capabilities. The specific hierarchical structure diagram is shown below. Figure 2 As shown.

[0029] In one implementation, the objective cascade analysis method decomposes the overall objective function of a centralized problem into multiple subsystems and unifies boundary conditions through consistency constraints on interaction variables between subsystems, thereby ensuring that the solution result of the entire system is in an optimal state. The objective cascade analysis method allows for parallel solution of optimization problems in subsystems within the same layer; secondly, there is no variable interaction between odd-numbered layers, so all subproblems within odd-numbered layers can be solved in parallel. The updated interaction variable values ​​are then passed to even-numbered layers, and after parallel computation, the even-numbered layers pass the updated interaction variables back to the odd-numbered layers, forming an iteration. Parallel computation between layers can significantly improve the running speed. Under this hierarchical structure, each layer is responsible for solving the optimization model within its layer, while sharing variable information between layers. The hierarchical distributed optimization framework is as follows: Figure 3 As shown.

[0030] In one implementation, the optimization variables for each region are divided into two categories: 1) Local variables: These are variables that are only related to the optimization of their own region and do not participate in the optimization decisions of other regions. Examples include the active and reactive power output of controllable resources within the region. Figure 3 The variables are X, Y, and Z. 2) Shared Variables: These are variables shared with optimization in other regions and participate in the optimization decisions of multiple regions. These variables are iteratively updated through continuous optimization within each layer. For example, the active and reactive power transmitted by the VSC to the AC network, and the active power transmitted to the DC network, such as... Figure 3 middle variable.

[0031] To address the issue that shared variables prevent the optimization problems at each level from being solved independently, the shared variables are further processed by decomposing them into target variables. and response variables In this decomposition, the response variable is the optimization variable for the AC and DC network layers, while the objective variable is the optimization variable for the FID layer. By introducing the Lagrange multiplier method using the above decomposition method, the optimization problem for each region can be solved independently.

[0032] While each region is being optimized independently, the core is to ensure that... and To maintain consistency, distributed optimization methods relax the consistency constraint on shared variables by introducing a Lagrange penalty function. The penalty function is defined as follows: It includes linear and quadratic terms, representing the difference between the objective and response variables between layers after independent optimization. Furthermore, an update mechanism for the penalty function multiplier is defined to ensure that the penalty function at each layer reaches its minimum during alternating iterations, thereby achieving overall optimization.

[0033] Specifically, consistency constraints need to be introduced in distributed optimization. c i ( t ):

[0034] In the Analytical Target Cascading (ATC) distributed optimization method, the consistency constraint on shared variables is relaxed by introducing a Lagrange penalty function at each level. The penalty function is defined as follows:

[0035] In the formula, v , w These are the multiplier vectors of the first and second penalty functions, respectively. c i,t for t Time of the first i Layer coupling variable consistency error vector For the corresponding consistency penalty, the symbol This represents the Hadamard product, which is calculated by multiplying each term.

[0036] In one implementation, an alternating iteration method is used. During intra-layer optimization calculations, the average value of the shared variables from the previous iteration of the adjacent layer is used as a reference value for the current iteration. If the penalty function meets the accuracy requirements and the change between two iterations is minimal, the alternating iteration can be considered converged.

[0037] In one implementation, the established hierarchical optimization model uses distributed rolling optimization at the AC and DC levels to minimize boundary variable errors, network losses, voltage deviations, and wind / solar curtailment in each rolling cycle. Constraints include AC power flow constraints, DC power flow constraints, wind and solar power output constraints, energy storage battery charging / discharging constraints, and AC / DC line safety constraints.

[0038] In one implementation, the established hierarchical optimization model uses the port power transmitted from the AC and DC layers as a reference in the FID layer, taking control costs into account, and establishes a quadratic programming model based on voltage sensitivity correction. An alternating iterative and rolling optimization two-layer optimization algorithm is employed. In the inner layer, the distributed algorithm continuously corrects the optimization path through multiple rounds of variable interaction; in the outer layer, the rolling optimization uses a periodic update mechanism to prevent error accumulation across time sections. Constraints include VSC output characteristic constraints and SOP output characteristic constraints.

[0039] Specifically, in step S3, the voltage optimization models for each level are established. For the AC / DC level, the objectives are to minimize the boundary variable error, network loss, voltage deviation, and wind / solar curtailment in each rolling cycle. The objective function is: , In the formula, I l,t for t Timetable l The current, R l For the line l The resistance, L A , L D , N A , N D They are AC / DC branches and node sets, respectively. U i,t for t Time Node i voltage, U N Rated voltage, P PV,i,av ( t )for t Time of the first i The active power that a photovoltaic cell can output P PV,i ( t ) represents the actual active power output. These are the weighting coefficients. N is a dimensional correction factor to ensure that each term is of the same order of magnitude. PV N WTThe number of solar power and wind power plants, respectively. for t Time of the first i The active power that a wind turbine can output This refers to the actual active power output.

[0040] The FID layer is a quadratic programming problem based on power-voltage sensitivity, with the objective function being: , In the formula, The correction matrix includes the active and reactive power of VSC and SOP. This is the voltage reference value. The voltage change matrix after correction control can be calculated using the following formula: , In the formula, For the voltage vectors of the AC and DC networks, These are the sensitivity matrices for the active and reactive power correction quantities of AC / DC networks to flexible interconnected devices.

[0041] Specifically, in S3, the voltage optimization models at each level include relaxed second-order cone power flow constraints. Energy storage battery charge and discharge constraints: , In the formula, The charge / discharge flag is a 0-1 variable. For energy storage t State of charge at time t, This refers to the charge / discharge efficiency. The first k Energy storage t The discharge and charge power at any time This refers to the upper limit of the charging and discharging power of energy storage. For time cross-section intervals, This represents the maximum value of the energy storage state of charge.

[0042] SOP execution constraints: , In the formula: for t Time Node i , j Active power injected at SOP, They are respectively t Time Node i , j Reactive power injected at SOP For SOP power loss, The installation capacity of the SOP. This is the loss coefficient.

[0043] Specifically, in S4, the alternating iteration method is used. When performing intra-layer optimization calculations, the average value of the shared variables from the previous iteration of the adjacent layer is used as the reference value for the current iteration. If the penalty function meets the accuracy requirements and the change between two iterations is minimal, then the alternating iteration can be considered converged.

[0044] After each optimization, the Lagrange penalty function's first and second multipliers and β The update expression is as follows: , In the formula, superscript k Represents the number of iterations. for t The first time section k Shared variable error in each iteration.

[0045] When performing intra-layer optimization calculations, the average value of the shared variables from the previous iteration of adjacent layers is used as the reference value for calculation in this iteration. The shared variable update process is as follows: , If the penalty function meets the accuracy requirement or the change between two iterations is extremely small, then it can be determined that the alternating iteration has converged. The overall convergence condition is as follows: , in, This represents the shared variable error threshold, which is usually taken as 1e-3.

[0046] In one implementation, step S5 employs a rolling optimization method, setting the rolling scheduling window duration to 1 hour and the window advancement step size to 15 minutes. In each round of distributed optimization, based on the latest source-load prediction data, the control strategy sequence within the next rolling scheduling window is calculated, resulting in scheduling instructions for four 15-minute time segments. Only the control strategy for the first 15 minutes is issued and executed; subsequently, the time window is advanced by 15 minutes, the source-load data is updated, and the next round of optimization is initiated. In the FID layer model, the voltage change is calculated using the power-voltage sensitivity matrix. , This linearized relationship is used to replace the nonlinear power flow constraints for rapid correction.

[0047] Example 2 Corresponding to the method steps of Embodiment 1, this embodiment provides a hierarchical distributed voltage optimization system for AC / DC distribution networks based on the target cascade analysis method, used to implement the method described in Embodiment 1. The system includes: The model building and processing unit is configured to: establish a steady-state model of AC / DC distribution network including AC distribution network, DC distribution network and flexible interconnection device, and perform convex optimization processing on the non-convex power flow equation constraints in the model to build the mathematical basis of the optimization model; The optimized architecture management unit is configured as follows: based on the characteristics of multiple entities and regional divisions in AC / DC distribution networks, a three-layer distributed optimization architecture based on the target cascading analysis method is constructed. The three-layer distributed optimization architecture includes: AC distribution network layer, flexible interconnection device coordination layer, and DC distribution network layer. The collaborative solution mechanism unit is configured as follows: based on the three-layer distributed optimization architecture, the global voltage optimization problem is decomposed into local optimization sub-problems at each level, and a distributed collaborative solution mechanism between levels is established; A distributed coordination optimization unit is configured to coordinate the optimization process at each level through the distributed collaborative solution mechanism to obtain a solution for global system optimization. The control command generation and issuance unit is configured to generate and issue real-time voltage control commands based on the globally optimized solution. The system further includes at least one AC area controller deployed on the AC distribution network layer, at least one DC area controller deployed on the DC distribution network layer, at least one flexible interconnection coordinator deployed on the flexible interconnection device coordination layer, and a distributed collaborative solution engine for inter-layer coordination.

[0048] In one implementation, the flexible interconnect coordinator includes: The sensitivity analysis module is used to calculate or obtain the power-voltage sensitivity matrix, which characterizes the linearization effect of power adjustment of flexible interconnect devices on the voltage of key nodes in the system. The quadratic programming solution module is used to construct and solve a quadratic programming model with the goal of minimizing voltage deviation and control cost based on the sensitivity matrix, voltage reference value and boundary information. The power command output module is used to generate active and reactive power setting commands for the flexible interconnection device based on the solution results.

[0049] In one embodiment, the system further includes a rolling optimization scheduling unit configured to: Set the length of the rolling optimization time window and the step size; At the beginning of each rolling cycle, each unit of the system is triggered to start a new round of collaborative optimization based on the updated source-load prediction data, generating a sequence of control instructions for multiple time sections within a future window; Control the execution of the instruction for the first section in the instruction sequence, and start the next cycle optimization after the window scrolls.

[0050] Example 3 Based on the same inventive concept as any of the foregoing embodiments, this embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the hierarchical distributed voltage optimization method for AC / DC distribution networks based on the target cascade analysis method, including the following steps: Step S1: Establish a steady-state model of AC / DC distribution network including AC distribution network, DC distribution network and flexible interconnection device, and perform convex optimization on the non-convex power flow equation constraints in the model to build the mathematical basis of the optimization model. Step S2: Based on the characteristics of AC / DC distribution networks being multi-entity and regional, a three-layer distributed optimization architecture based on the target cascade analysis method is constructed. The three-layer distributed optimization architecture includes: AC distribution network layer, flexible interconnection device coordination layer, and DC distribution network layer. Step S3: Based on the three-layer distributed optimization architecture, the global voltage optimization problem is decomposed into local optimization sub-problems at each level, and a distributed collaborative solution mechanism between levels is established. Step S4: Through the distributed collaborative solution mechanism, coordinate the optimization processes at each level to obtain the solution for global system optimization; Step S5: Based on the globally optimized solution, generate and issue real-time voltage control commands.

[0051] Example 4 Based on the same inventive concept as any of the foregoing embodiments, this embodiment also provides a voltage optimization control device, applied to a flexible interconnection device in an AC / DC distribution network. The device includes a processor, a memory, and a communication interface. The memory stores a computer program, and when the program is executed by the processor, it controls the device to perform the following operations: The boundary information from the AC distribution network and the DC distribution network is received through the communication interface; Based on the power-voltage sensitivity relationship, a local optimization model is constructed and solved with the objectives of minimizing voltage deviation and minimizing operating cost; According to the preset distributed coordination protocol, the shared variables and the coordination multiplier are interacted and updated. The optimized solution is converted into control commands and sent to the power execution unit of the flexible interconnect device through the communication interface.

[0052] In summary, this invention proposes a hierarchical distributed voltage optimization method for AC / DC distribution networks based on the objective cascade analysis method. First, a steady-state model of the AC / DC distribution network is established, and the relevant power flow equation constraints are subjected to second-order cone relaxation and linearization to establish an easily solvable hierarchical optimization model. Second, based on the characteristics of the AC / DC distribution network, a three-layer distributed optimization architecture based on the objective cascade analysis method is proposed. Based on this, the Lagrange multiplier method is introduced to establish voltage optimization models for each level. Finally, based on power-voltage sensitivity, an alternating iterative and rolling optimization method is used to solve the model. This invention can effectively solve the voltage overshoot and fluctuation problems caused by backflow of power flow by coordinating flexible interconnection devices with wind, solar, and energy storage under hierarchical control.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A layered distributed voltage optimization method for AC / DC distribution network based on target cascade analysis method, characterized in that, The method comprises the following steps: S1, a steady-state model of an AC-DC power distribution network including an alternating current power distribution network, a direct current power distribution network and a flexible interconnection device is established, and a non-convex power flow equation constraint in the model is processed by convex optimization to construct a mathematical basis of an optimization model; S2, according to the characteristics of multi-agent and sub-regional of the AC-DC power distribution network, a three-layer distributed optimization architecture based on a target cascade analysis method is constructed, the three-layer distributed optimization architecture comprises an alternating current power distribution network layer, a flexible interconnection device coordination layer and a direct current power distribution network layer; S3, based on the three-layer distributed optimization architecture, a global voltage optimization problem is decomposed into local optimization sub-problems of each layer, and a distributed collaborative solving mechanism between layers is established; S4, through the distributed collaborative solving mechanism, the optimization processes of each layer are coordinated to obtain a solution of global optimization of the system; S5, based on the solution of global optimization, real-time voltage control instructions are generated and issued.

2. The layered distributed voltage optimization method for AC / DC hybrid distribution network based on target cascade analysis method according to claim 1, characterized in that, The processing of the non-convex power flow equation constraint by convex optimization specifically comprises: A Distflow branch power flow model is used to describe the power flow of the alternating current power distribution network and the direct current power distribution network; An auxiliary variable is introduced to replace the square term of the node voltage and the branch current, so that the non-convex voltage drop equation is reconstructed into a second-order form; The second-order form constraint after reconstruction is relaxed by rotating a second-order cone form, so that the non-convex power flow model is converted into a convex optimization model.

3. The hierarchical distributed voltage optimization method for AC / DC hybrid distribution network based on target cascade analysis method according to claim 1, characterized in that, The construction method of the three-layer distributed optimization architecture is as follows: The alternating current power distribution network layer and the direct current power distribution network layer are taken as respective decentralized autonomous subsystems; The flexible interconnection device coordination layer is taken as an independent intermediate coordination layer, which connects and coordinates the alternating current power distribution network layer and the direct current power distribution network layer; Only adjacent layers are allowed to exchange information and power between layers, and there is no direct communication between subsystems in the same layer, so as to realize decoupling in the physical and information levels.

4. The layered distributed voltage optimization method for AC / DC hybrid distribution network based on target cascade analysis method according to claim 3, characterized in that, The establishment of the distributed collaborative solving mechanism between layers comprises: A physical coupling variable connecting adjacent layers is defined as a shared variable; The shared variable is decomposed into a target variable preset by an upper layer and a response variable decided by a lower layer; By constructing an augmented Lagrangian function including a first penalty function term and a second penalty function term, the consistency constraint that the target variable and the response variable must be strictly equal is relaxed by using the Lagrange multiplier method, so that the optimization sub-problems of each layer can be independently and parallelly solved.

5. The hierarchical distributed voltage optimization method for AC / DC hybrid distribution network based on target cascade analysis method according to claim 4, characterized in that, The coordination of the optimization processes of each layer is iteratively solved by using an alternating direction multiplier method, which comprises: In the kth iteration, each layer solves the optimization problem in the layer based on the updated shared variable average of the adjacent layer in the (k-1) th iteration, and parallelly solves the optimization problem in the layer; After each layer is solved, the Lagrange multipliers are updated according to the shared variable error generated in this round according to the following rules: ， where the superscript k represents the iteration number, is the shared variable error for the k th iteration, v , w are the first and second penalty function multiplier vectors, respectively, β is the weight coefficient; The iteration is performed until a preset convergence condition is met: ， wherein, represents a shared variable error threshold.

6. The hierarchical distributed voltage optimization system for AC / DC distribution network based on target cascade analysis method, characterized in that, The system for implementing the method in any one of claims 1-5 comprises: a model construction and processing unit configured to perform S1 to establish a steady-state model and perform convex optimization processing; an optimization architecture management unit configured to perform S2 to construct and maintain the three-layer distributed optimization architecture; a collaborative solving mechanism unit configured to perform S3 to realize problem decomposition and collaborative mechanism establishment. a distributed coordination and optimization unit configured to perform the S4, coordinating the optimization processes of each level; a control instruction generation and issuing unit configured to perform the S5, generating and issuing voltage control instructions; wherein the system further comprises at least one alternating current regional controller deployed at the alternating current power distribution network level, at least one direct current regional controller deployed at the direct current power distribution network level, at least one flexible interconnection coordinator deployed at the flexible interconnection device coordination level, and a distributed collaborative solving engine for realizing inter-level coordination.

7. The layered distributed voltage optimization system for AC / DC hybrid distribution network based on target cascade analysis method according to claim 6, characterized in that, The flexible interconnection coordinator comprises: a sensitivity analysis module for calculating or obtaining a power-voltage sensitivity matrix representing the linearized influence of power adjustment of the flexible interconnection device on the voltage of the key nodes of the system; a quadratic programming solving module for constructing and solving a quadratic programming model aiming to minimize voltage deviation and control cost based on the sensitivity matrix, voltage reference value and boundary information; a power instruction output module for generating active and reactive power setting instructions of the flexible interconnection device according to the solving result.

8. The layered distributed voltage optimization system for AC / DC distribution network based on target cascade analysis method of claim 6, wherein, The system further comprises a rolling optimization scheduling unit configured to: set the length of the rolling optimization time window and the advancing step; at the beginning of each rolling period, trigger each unit of the system to start a new round of collaborative optimization based on updated source-load prediction data, and generate a control instruction sequence of multiple time sections within a future window; control the execution of the instructions of the first section in the sequence, and start the optimization of the next period after the window rolls.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the layered distributed voltage optimization method for alternating and direct current power distribution networks based on the target cascade analysis method according to any one of claims 1 to 5.

10. A voltage optimization control device, characterized by, The flexible interconnection device applied to alternating and direct current power distribution networks, the device comprising a processor, a memory and a communication interface, the memory storing a computer program, when the program is executed by the processor, controlling the device to perform the following operations: receiving boundary information from alternating and direct current power distribution networks through the communication interface; based on the power-voltage sensitivity relationship, constructing and solving a local optimization model aiming to minimize voltage deviation and operating cost; according to a preset distributed collaborative protocol, interacting and updating shared variables and coordination multipliers; converting the optimization solution into control instructions and issuing them to the power execution unit of the flexible interconnection device through the communication interface.

Images