Method and device for site selection and capacity determination of traction power supply system based on double-layer programming

Abstract

The application discloses a site selection and capacity determination method and device for a traction substation of a flexible direct current power supply system based on a double-layer planning, and the method comprises the following steps: a double-layer optimization model of the traction substation of the traction power supply system is established, and decision variables of the double-layer optimization model are determined, objective functions of a planning layer optimization model and a running layer optimization model are established; a planning layer constraint, a planning layer additional constraint and a running layer constraint are established; an updated double-layer optimization model is obtained; an iterative solution algorithm is used to solve the updated double-layer optimization model, optimal configuration parameters are obtained, and the site selection and capacity determination of the traction substation of the traction power supply system are performed based on the optimal configuration parameters. The application considers the investment cost change caused by the system structure parameter change, considers the influence of the system running parameter change on the system running cost, proposes an iterative algorithm of the double-layer optimization model, realizes efficient and accurate solution of the double-layer optimization model, and makes the final optimization scheme more in line with engineering requirements.

Description

Technical Field

[0001] This invention relates to the field of planning and design technology for flexible DC traction power supply systems, and in particular to a method and apparatus for site selection and capacity determination of flexible DC power supply systems based on two-layer planning. Background Technology

[0002] In recent years, urban rail transit has made significant progress. At the same time, this has placed higher demands on traction power supply systems. On the one hand, these systems must provide safe and reliable power to the entire urban rail system; on the other hand, they must offer more advantageous solutions to issues such as improving locomotive regenerative braking energy, achieving efficient and energy-saving system operation, and enhancing the absorption of new energy sources. Flexible traction power supply systems may become the most promising system, as they can achieve efficient energy conversion and flexible management through the extensive application of high-proportion power electronics technology.

[0003] The issue of traction station location and capacity selection in flexible DC traction power supply systems is a current research hotspot. This paper utilizes an exhaustive method for system parameter design involving bidirectional converters, searching for the maximum peak power of the traction station by setting a search step size while ensuring system constraints are met. A capacity-based location optimization model for the inverter feedback device is analyzed and established, and the optimal capacity is obtained by solving the optimization model.

[0004] In solving site selection and capacity optimization problems, nonlinear optimization algorithms and intelligent optimization algorithms are most widely used. An improvement to the traditional genetic algorithm was made in the optimization design of traction substation grounding networks by adding a fitness function and using an adaptive algorithm to dynamically adjust crossover and mutation probabilities based on fitness values, avoiding the algorithm from entering local optima. Considering the energy saving and investment cost of supercapacitor energy storage devices, and optimizing the device's energy management strategy control parameters, multi-objective simultaneous optimization was achieved by combining a city rail power supply simulation platform with a genetic algorithm. A two-layer optimization model including a planning layer and an operation layer was established, considering investment cost, operating cost, and system voltage deviation. The model was transformed into a mixed-integer second-order cone programming problem using a second-order cone relaxation algorithm, enabling efficient and fast solution of the two-layer optimization model. However, the above methods have drawbacks. Exhaustive search methods for system design suffer from unreasonable search methods, making it difficult to obtain optimal parameters. Excessive search step sizes lead to parameter design redundancy, while small search step sizes result in long algorithm solution times and low efficiency. The constraints of the power flow equations inevitably lead to strong nonlinearity and nonconvexity in the optimization model. In particular, when integer or 0-1 variables are introduced into the model, it is no longer possible to solve the problem using direct nonlinear optimization algorithms. Intelligent optimization algorithms can be appropriately modified to adapt to mixed integer programming problems, but when the problem size is large, the algorithm is prone to getting trapped in local optima, the search efficiency of the algorithm will decrease, and the algorithm is very sensitive to the initial search value. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the related art.

[0006] To address this, this invention proposes a bi-level programming-based method for traction location and capacity determination in flexible DC power supply systems. This invention presents a bi-level optimization model for traction location and capacity determination, performing hierarchical optimization on both the system planning and operation problems; and proposes a bi-level programming iterative algorithm. For solving the optimization model, this invention proposes an optimization model processing method that transforms the bi-level programming model into a mixed-integer second-order cone programming model.

[0007] Another objective of this invention is to propose a traction site selection and calibrating device for a flexible DC power supply system based on a two-layer planning approach.

[0008] To achieve the above objectives, this invention proposes a traction site selection and capacity determination method for flexible DC power supply systems based on two-layer planning, comprising: Based on the distribution information of urban rail transit stations and the parameters and load information of the traction power supply system, a two-layer planning model for the traction site selection and capacity determination of the flexible DC power supply system is established. The two-layer planning model includes: an upper-layer optimization model - planning layer optimization model and a lower-layer optimization model - operation layer optimization model. The configuration of traction substations and lines is optimized using an upper-level optimization model. The first optimization objective is the sum of the investment cost of traction substation capacity, the investment cost of lines, and the operating cost. The binary variables of traction substation configuration, the distance between traction substations, the capacity of traction substations, and the binary variables of newly added traction substations are the optimization variables. The system operation is optimized using a lower-level optimization model to minimize the system's electricity cost or minimize system voltage fluctuations, with the traction substation capacity margin as the second optimization objective. The upper-level optimization results obtained from solving the upper-level optimization model are used as the boundary conditions for the constraints of the lower-level optimization model. The lower-level optimization results obtained from solving the lower-level optimization model are then used to correct the upper-level optimization results. Through mutual iteration between the upper-level and lower-level optimization models, the optimal solution that is simultaneously optimal for both the upper-level and lower-level optimization models is finally obtained. The optimal configuration parameters are obtained by solving the bi-level planning model based on the optimization scheme and iterative solution algorithm, and the location and capacity of the traction substation of the traction power supply system are determined based on the optimal configuration parameters.

[0009] The traction site selection and capacity determination method for flexible DC power supply systems based on two-layer planning in this invention may further include the following additional technical features: Furthermore, the traction substation capacity investment cost is the investment cost corresponding to the rated capacity of the converter within the traction substation, the rated capacity of the converter is the integral of the instantaneous peak capacity over a certain period of time and the average value, the line investment cost is the investment cost of a newly built line, and the operating cost is the electricity cost of the traction power supply system in one year.

[0010] Furthermore, the constraints for establishing the planning layer optimization model include: When it is only necessary to determine the layout of traction substations at the candidate traction substation points, the planning layer optimization model aims to determine the location of the traction substations so that they can supply power to all stations; the constraints of the planning layer optimization model include traction substation configuration constraints, traction substation number constraints, and traction substation capacity constraints. When considering the special case of traction substation location selection, when the system is running at N-1, after knowing the coverage capacity of the traction substation and determining the candidate traction substations, a new traction substation is added. The planning layer optimization model is to determine the location of the new traction substation. The constraints of the planning layer optimization model are the traction substation location constraint and the traction substation coverage range constraint.

[0011] Furthermore, constraints are established for the runtime optimization model, and these constraints are linearized and convex relaxed, including the following steps: The optimization results of the planning layer optimization model are used as the boundary conditions for the constraints of the running layer optimization model. The constraints for establishing the operational layer optimization model include: branch current constraints, node injection current constraints, node injection power constraints, branch network loss constraints, branch voltage drop constraints, branch current and branch power and node voltage constraints, and rail potential constraints. By replacing the nonlinear voltage and current variables in branch current constraints, node injected current constraints, node injected power constraints, branch network loss constraints, branch voltage drop constraints, branch current and power constraints, node voltage constraints, and rail potential constraints with linear voltage and current variables, linearized branch current constraints, linearized node injected current constraints, linearized node injected power constraints, linearized branch network loss constraints, linearized branch voltage drop constraints, and linearized rail potential constraints are obtained.

[0012] Furthermore, the iterative solution process of the bi-level programming model includes the following steps: (1) Based on the initialization of the traction station Number of traction stations And the location distribution of traction substations, and the initial capacity limit values ​​of traction substations. Solve the optimization model at the runtime level to obtain the initial values ​​of the decision variables at the planning level. Corresponding runtime decision variables and runtime costs ; (2) Utilizing the operating layer cost Solve the planning-level optimization model to obtain the planning-level decision variables. Total system cost ; (3) Utilizing the decision variables of the planning layer Solve the runtime optimization model to obtain new runtime decision variables. and runtime costs ; (4) Repeat steps (2) and (3) to set the iteration control error. , Less than the error precision The iteration terminates when the solution is obtained. This is the optimal solution of the two-level optimization model, if the error Maximum number of iterations T iter If convergence fails, proceed to step (5); (5) Determine the planning layer scheme Given the known conditions of the system, the constraints of the planning layer optimization model are transformed using preset formulas, and steps (2) and (3) are executed until the required error accuracy is achieved. .

[0013] Furthermore, the branch current constraint is that the branch current is the rated current on the contact network line of the flexible DC traction power supply system, which is obtained by integrating the instantaneous current on the line over a period of time and taking the average value.

[0014] Furthermore, the urban rail transit station distribution information, traction power supply system parameters, and load information include: obtaining station spacing distribution data between each station based on the number of stations and the location of each station on the line; urban rail transit line information, including the above-ground and underground distribution of the line, line gradient, line curves and turning radii, and total line length; traction power supply system related parameters, including the impedance per unit length of the contact wire and rails and the rail-to-ground resistance; and traction power supply system load information, including power lighting load and locomotive load information.

[0015] To achieve the above objectives, another aspect of the present invention proposes a traction site selection and calibrating device for a flexible DC power supply system based on a two-layer planning approach, comprising: The model building module is used to establish a two-layer planning model for the traction selection and capacity determination of the flexible DC power supply system based on the distribution information of urban rail transit stations, as well as the parameters and load information of the traction power supply system. The two-layer planning model includes: an upper-layer optimization model - planning layer optimization model and a lower-layer optimization model - operation layer optimization model. The configuration optimization module is used to optimize the configuration of traction substations and lines using the upper-level optimization model. The first optimization objective is the sum of the investment cost of traction substation capacity, the investment cost of lines, and the operating cost. The binary variables of traction substation configuration, the distance between traction substations, the capacity of traction substations, and the binary variables of newly added traction substations are the optimization variables. The scheme optimization module is used to optimize the system operation using the lower-level optimization model, with the goal of minimizing the system's electricity cost or minimizing system voltage fluctuations and maximizing the traction substation's capacity margin as the second optimization objective. The upper-level optimization results obtained by solving the upper-level optimization model are used as the boundary conditions for the constraints of the lower-level optimization model. The lower-level optimization results obtained by solving the lower-level optimization model are then used to correct the upper-level optimization results. Through mutual iteration between the upper-level and lower-level optimization models, the optimal scheme that is simultaneously optimal for both the upper-level and lower-level optimization models is finally obtained. The model solving module is used to solve the two-level planning model based on the optimization scheme and iterative solving algorithm to obtain the optimal configuration parameters, and to select and determine the location and capacity of the traction substation of the traction power supply system based on the optimal configuration parameters.

[0016] The present invention discloses a method and apparatus for traction site selection and capacity determination in a flexible DC power supply system based on bi-level planning. It establishes a bi-level optimization model that considers both the changes in investment costs caused by variations in system structural parameters and the impact of changes in system operating parameters on system operating costs. Compared to the best existing technology, the proposed scheme establishes a bi-level optimization model that optimizes both total cost and operating cost separately, resulting in optimization results that better meet engineering requirements. Since the bi-level optimization model itself has strong nonlinearity and non-convexity, the present invention linearizes and relaxes the bi-level optimization method, achieving efficient and accurate solution to the bi-level optimization model.

[0017] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a traction site selection and capacity determination method for a flexible DC power supply system based on two-layer planning, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of traction site selection and ductility according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the branch power flow modeling process of the traction power supply system according to an embodiment of the present invention; Figure 4This is a diagram illustrating the iterative solution steps of the two-layer optimization model according to an embodiment of the present invention; Figure 5 This is a schematic diagram of locomotive load data for a traction power supply system according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of the traction site selection and calibrating device of the flexible DC power supply system based on two-layer planning according to an embodiment of the present invention. Detailed Implementation

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

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] The following describes, with reference to the accompanying drawings, a method and apparatus for traction site selection and capacity determination of a flexible DC power supply system based on two-layer planning, according to an embodiment of the present invention.

[0022] Figure 1 This is a flowchart of a traction site selection and capacity determination method for a flexible DC power supply system based on two-layer planning, according to an embodiment of the present invention.

[0023] like Figure 1 As shown, the method includes, but is not limited to, the following steps: S101. Obtain information on the distribution of urban rail transit stations. This mainly includes the number of stations and the location of each station on the line, and further, the distribution of station spacing between each station. Urban rail transit line information mainly includes the above-ground and underground distribution of the line, line gradient, curves and turning radii, and the total length of the line. L s .

[0024] S102. Obtain relevant parameters of the traction power supply system. This includes the impedance per unit length of the contact wire and rail, and the rail-to-ground resistance, denoted as follows: z cat , z rail , z g .

[0025] S103. Obtain traction power supply system load information. This mainly includes power and lighting loads and locomotive load information. The power and lighting loads can be assumed to remain constant throughout the system's operating cycle, and are set as follows: P L Locomotive load information is mainly included in the system operating cycle. T per Internal corresponding to each time section t section The position and power below are denoted as follows: Loc tr as well as P trg Information such as locomotive load can be obtained through relevant calculations, such as traction calculations, etc. Figure 5 As shown.

[0026] S201. Establish a two-layer optimization model for the traction substation of the flexible DC traction power supply system. The model mainly includes a planning layer (PB) and an operation layer (CB). For ease of description, the two-layer planning model can be written in the following compact form: in, F (·), H (·), G (·) represent the objective function, equality constraints, and inequality constraints of the planning layer, respectively. f (·), h (·), g (·) represent the objective function, equality constraint, and inequality constraint expression of the runtime layer, respectively.

[0027] S202. Decision variables at the planning level include traction substation spacing, peak instantaneous capacity of traction substations, and binary variables for newly added line configurations (0-1 variables). Decision variables at the operation level can be selected in two ways: they can be node injection power, node voltage, branch power, and branch current; or they can be node injection current, node voltage, branch power, and branch current. Considering the need for constraint linearization and convex relaxation processes, the above decision variables may also include related substitute variables.

[0028] S301. Determine the objective functions for the planning and operation layers. The objective function for the planning layer fully considers factors from both the system planning and operation phases, primarily including factors reflecting the investment cost of the system planning and design. C inv and operating costs that reflect the system's operating characteristics. C ope Two aspects, as shown in the following formula: Investment costs mainly include line investment costs and traction substation investment costs: in, G TSS These are the traction depots. k line , k TSS These are the equivalent coefficients of annual investment in the railway line and traction substation; , These are the investment cost per unit length of the line and the investment cost per unit capacity of the traction substation; The binary variables related to the traction configuration are 0-1 variables, where This indicates that a traction station will not be configured. Indicates the configuration of a traction station; This is the rated capacity of the converter in the traction substation, the value of which is given below; , These are the discount rates for the railway line and the traction unit, respectively. d line , d TSS These refer to the design service life of the power line and the design service life of the traction substation, respectively. The operating cost is the annual electricity cost of the traction power supply system, as shown in the following formula: in, N per It is the number of cycles in a day. It is the public grid electricity price during system operation. This refers to the electricity consumption of the traction substation converted to the AC side, and its value is specifically calculated in the operating layer constraints.

[0029] S302. Determine the objective function of the operation layer. The operation layer needs to simulate the normal operation mode of the traction power supply system, as well as the N-1 operation mode when any traction station in the system is withdrawn. Therefore, the objective function of the operation layer needs to be selected according to the operation mode. Under normal mode, the objective function of the operation layer is: The objective function for the operating layer under the N-1 mode of the traction station is: Where var(·) is the function for calculating variance. U tss It is the voltage vector of the traction substation node. In the N-1 mode, the objective function of the operating layer aims to minimize the voltage fluctuation of the system.

[0030] S303. Establish planning-level constraints, such as Figure 2As shown, considering a more general case, when it is only necessary to determine the layout of traction substations at the candidate substation locations, the planning layer model at this time aims to determine the location of the traction substations so that they can supply power to all stations. The constraints mainly include traction substation configuration constraints, traction substation number constraints, and traction substation capacity constraints, as shown in the following equations: in, S The traction units to be configured are assembled. N TSS The number of traction stations, , These are the upper and lower limits of the rated capacity of the traction substation converter. This represents the set of capabilities covered by the traction system, and its meaning is as follows: S304. Establish additional constraints at the planning level, considering special cases of traction substation location selection. When the system is running at N-1, after knowing the coverage capacity of the traction substations and determining the candidate locations, the traction power supply system cannot operate safely due to station distances and other reasons. In this case, additional traction substations should be added. Let the set of newly added traction substations be . N ETSS The planning constraints are transformed into a site selection problem for the new traction substation, with the following constraints: in, D min It is the minimum allowable distance between two adjacent traction substations. This indicates the location of the newly added traction station. d j Indicates the distance between traction stations. N DTSS Represents the set of traction station spacings. C j This indicates the upper limit of the allowable coverage area for the traction unit.

[0031] S305. Calculate the actual set of nodes, the set of branches, and the simplified set of nodes in the traction power supply system, which are abbreviated as follows: N rn , N br , N sn Their ranges are respectively N rn ={1,2,…, 2 N t +2 N s +1}; N br ={1,2,…, Nt +2 N s}; N sn ={1,2,…, N t + N s The location of the traction substations is calculated based on the traction substation spacing obtained from the planning layer optimization, and the network topology and the length of each branch are further generated.

[0032] S306. Establishing operational layer constraints: This invention constructs system constraints based on a branch power flow model. Figure 3 Figure (1) is the overall electrical model diagram of the traction power supply system. Figure 3 (2) is a simplified electrical model between nodes i and j. Figure 3 (3) is a branch power flow model, such as Figure 3 As shown in (3), for branch l ( i ~ j )∈ N br ,in l ( i ~ j ) indicates a branch l Connecting nodes respectively i , j ,node i , j ∈ N sn Then, for this branch, the following constraints apply: in, I ij The current flowing through branch l, I i For nodes i The injected current, p i For nodes i Injection power, y ij = 1 / z ij , is a branch road l The conductivity, equation (9) is derived from equations (6) to (8). Establish the branch circuit. l The network loss constraints, voltage drop constraints, and branch current constraints are described as follows: in P ij branch road l upper node i Flow to Nodej power, P ji branch road l upper node j Flow to Node i The power. Define the column vector of node injected currents. and node voltage column vectors Establish rail potential constraints: in U w for( N t + N s () dimensional column vector; defines the system operating range, mainly including the upper and lower limits of node voltages. V nlb , V nub Node injection power upper and lower limits P lb , P ub Branch current upper and lower limits , Branch instantaneous power upper and lower limits , Rail potential upper and lower limits For any node k ∈ N sn branch road l ∈ N br (corresponding node is) i , j If the system's operating range constraints are given by the following formulas: S307. Noting that the constraints of the operating layer have nonlinearity and nonconvexity, the relevant constraints are linearized and convex relaxed. Specifically, for equation (9), the node injection power can be described by the power of all branches connected to the node, thereby eliminating the quadratic terms related to the node voltage, as shown in the following equation: in, For the AC power consumption of the traction substation, Efficiency conversion This refers to the rated capacity of the traction converter.

[0033] For the network loss constraint shown in equation (10), new variables can be introduced. Thus, the constraint becomes linear, as shown in the following equation: For the branch voltage drop constraint shown in equation (18), the following transformations (29) to (31) can be made: Further introduce variables After linearization, equation (31) is restated as follows: For the branch current constraint shown in equation (19), we first perform convex relaxation on it, and then restate it as follows: Then, equation (33) is rewritten in the form of a second-order cone constraint, as shown in the following equation: in This indicates the calculation of the L2 norm of a vector.

[0034] S308. In order to simulate the N-1 operation mode of the traction substation, the objective function and constraints of the operation layer need to be adjusted. Specifically, the objective function is changed from Equation (5) to Equation (6), and the upper and lower limits of the power constraint for exiting the traction substation in Equation (22) are further modified, and both the upper and lower limits are set to 0.

[0035] S401. Establish an iterative solution algorithm for the two-level optimal configuration model, such as... Figure 4 As shown, the specific solution steps are as follows: S4011, Pre-solution of the operational layer optimization model. For the upper-level decision variables... Initialize to obtain Specifically, initialize the traction station Further obtain the number of traction stations And the location distribution of traction substations, and the initial capacity limit values ​​of traction substations. Solve the runtime planning model to obtain the initial values ​​of the planning layer decision variables. Corresponding runtime decision variables and operating layer costs .

[0036] S4012, Solving the planning layer model. Using the results obtained from S4011... By further solving the planning-level model, the decision variables at the planning level can be obtained. and total system cost .

[0037] S4013, Solving the operational layer model. Using the planning layer decision variables obtained in S4012. By further solving the operational layer model, new operational layer decision variables can be obtained. and operating layer costs .

[0038] S4014, Repeat steps S4012 and S4013. Set the iteration control error. , Less than the given error precision The iteration terminates when the solution is obtained. This is the optimal solution of the two-layer optimization configuration model. If the error... Maximum number of iterations T iter If convergence still fails, proceed to step S4015.

[0039] S4015. Determine the planning layer scheme. Given the known conditions of the system, the planning layer constraints are transformed into equations (11) to (12), and steps S4012 and S4013 are executed until the given error accuracy is achieved.

[0040] To achieve the above embodiments, such as Figure 6 As shown, this embodiment also provides a traction site selection and capacity grading device 10 for a flexible DC power supply system based on two-layer planning. The device 10 includes: a model building module 100, a configuration optimization module 200, a scheme optimization module 300, and a model solving module 400.

[0041] The model building module 100 is used to establish a two-layer planning model for the traction site selection and capacity determination of the flexible DC power supply system based on the distribution information of urban rail transit stations, as well as the parameters and load information of the traction power supply system. The two-layer planning model includes: an upper-layer optimization model - planning layer optimization model and a lower-layer optimization model - operation layer optimization model. The configuration optimization module 200 is used to optimize the configuration of traction substations and line configurations using the upper-level optimization model. The first optimization objective is the sum of the investment cost of traction substation capacity, the investment cost of line, and the operating cost. The binary variables of traction substation configuration, the distance between traction substations, the capacity of traction substations, and the binary variables of newly added traction substations are the optimization variables. The scheme optimization module 300 is used to optimize the system operation using the lower-level optimization model, with the second optimization objective being to minimize the system power cost or minimize the system voltage fluctuation and maximize the traction substation capacity margin. The upper-level optimization result obtained by solving the upper-level optimization model is used as the boundary condition of the lower-level optimization model. The lower-level optimization result obtained by solving the lower-level optimization model is then used to correct the upper-level optimization result. Through mutual iteration between the upper-level optimization model and the lower-level optimization model, the optimal scheme that is simultaneously optimal for both the upper-level optimization model and the lower-level optimization model is finally obtained. The model solving module 400 is used to solve the bi-level planning model based on the optimization scheme and iterative solving algorithm to obtain the optimal configuration parameters, and to select and determine the location and capacity of the traction substation of the traction power supply system based on the optimal configuration parameters.

[0042] According to an embodiment of the present invention, a two-layer optimization model is established for the traction site selection and capacity stabilization device of a flexible DC power supply system based on two-layer planning. This model considers both the changes in investment costs caused by variations in system structural parameters and the impact of changes in system operating parameters on system operating costs. The proposed scheme establishes a two-layer optimization model that optimizes both total cost and operating cost separately, and the results obtained from the optimization scheme are more in line with engineering requirements. Since the two-layer optimization model itself has strong nonlinearity and nonconvexity, the present invention linearizes and relaxes the two-layer optimization method, achieving efficient and accurate solution of the two-layer optimization model.

[0043] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0044] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0045] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for traction site selection and capacity determination in a flexible DC power supply system based on two-layer planning, characterized in that, Includes the following steps: Based on the distribution information of urban rail transit stations and the parameters and load information of the traction power supply system, a two-layer planning model for the traction site selection and capacity determination of the flexible DC power supply system is established. The two-layer planning model includes: an upper-layer optimization model - planning layer optimization model and a lower-layer optimization model - operation layer optimization model. The configuration of traction substations and lines is optimized using an upper-level optimization model. The first optimization objective is the sum of the investment cost of traction substation capacity, the investment cost of lines, and the operating cost. The binary variables of traction substation configuration, the distance between traction substations, the capacity of traction substations, and the binary variables of newly added traction substations are the optimization variables. The system operation is optimized using a lower-level optimization model to minimize the system's electricity cost or minimize system voltage fluctuations, with the traction substation capacity margin as the second optimization objective. The upper-level optimization results obtained from solving the upper-level optimization model are used as the boundary conditions for the constraints of the lower-level optimization model. The lower-level optimization results obtained from solving the lower-level optimization model are then used to correct the upper-level optimization results. Through mutual iteration between the upper-level and lower-level optimization models, the optimal solution that is simultaneously optimal for both the upper-level and lower-level optimization models is finally obtained. The two-level planning model is solved based on the optimization scheme and iterative solution algorithm to obtain the optimal configuration parameters, and the location and capacity of the traction substation of the traction power supply system are determined based on the optimal configuration parameters. The iterative solution process of the bilevel programming model includes the following steps: (1) Based on the initialization of the traction station Number of traction stations And the location distribution of traction substations, and the initial capacity limit values ​​of traction substations. Solve the optimization model at the runtime level to obtain the initial values ​​of the decision variables at the planning level. Corresponding runtime decision variables and runtime costs ; (2) Utilizing the operating layer cost Solve the planning-level optimization model to obtain the planning-level decision variables. Total system cost ; (3) Utilizing the decision variables of the planning layer Solve the runtime optimization model to obtain new runtime decision variables. and runtime costs ; (4) Repeat steps (2) and (3) to set the iteration control error. , Less than the error precision The iteration terminates when the solution is obtained. This is the optimal solution of the two-level optimization model, if the error Maximum number of iterations T iter If convergence fails, proceed to step (5); (5) Determine the planning layer scheme Given the known conditions of the system, the constraints of the planning layer optimization model are transformed using preset formulas, and steps (2) and (3) are executed until the required error accuracy is achieved. .

2. The method according to claim 1, characterized in that, The traction substation capacity investment cost is the investment cost corresponding to the rated capacity of the converter in the traction substation. The rated capacity of the converter is the integral of the instantaneous peak capacity over a certain period of time and the average value. The line investment cost is the investment cost of a newly built line. The operating cost is the electricity cost of the traction power supply system in one year.

3. The method according to claim 1, characterized in that, The constraints for establishing the optimization model at the planning level include: When it is only necessary to determine the layout of traction substations at the candidate traction substation points, the planning layer optimization model aims to determine the location of the traction substations so that they can supply power to all stations; the constraints of the planning layer optimization model include traction substation configuration constraints, traction substation number constraints, and traction substation capacity constraints. When considering the special case of traction substation location selection, when the system is running at N-1, after knowing the coverage capacity of the traction substation and determining the candidate traction substations, a new traction substation is added. The planning layer optimization model is to determine the location of the new traction substation. The constraints of the planning layer optimization model are the traction substation location constraint and the traction substation coverage range constraint.

4. The method according to claim 1, characterized in that, Establishing constraints for the runtime optimization model and performing linearization and convex relaxation on these constraints includes the following steps: The optimization results of the planning layer optimization model are used as the boundary conditions for the constraints of the running layer optimization model. The constraints for establishing the operational layer optimization model include: branch current constraints, node injection current constraints, node injection power constraints, branch network loss constraints, branch voltage drop constraints, branch current and branch power and node voltage constraints, and rail potential constraints. By replacing the nonlinear voltage and current variables in branch current constraints, node injected current constraints, node injected power constraints, branch network loss constraints, branch voltage drop constraints, branch current and power constraints, node voltage constraints, and rail potential constraints with linear voltage and current variables, linearized branch current constraints, linearized node injected current constraints, linearized node injected power constraints, linearized branch network loss constraints, linearized branch voltage drop constraints, and linearized rail potential constraints are obtained.

5. The method according to claim 4, characterized in that, The branch current constraint is the rated current on the contact network line of the flexible DC traction power supply system, which is obtained by integrating the instantaneous current on the line over a period of time and taking the average value.

6. The method according to claim 1, characterized in that, The information on the distribution of urban rail transit stations, as well as the parameters and load information of the traction power supply system, includes: station spacing distribution data between each station based on the number of stations and the location of each station on the line; urban rail transit line information, including the above-ground and underground distribution of the line, line gradient, line curves and turning radii, and total line length; relevant parameters of the traction power supply system, including the impedance per unit length of the contact wire and rails and the rail-to-ground resistance; and traction power supply system load information, including power and lighting loads and locomotive load information.

7. A traction site selection and calibrating device for a flexible DC power supply system based on dual-layer planning, characterized in that, include: The model building module is used to establish a two-layer planning model for the traction selection and capacity determination of the flexible DC power supply system based on the distribution information of urban rail transit stations, as well as the parameters and load information of the traction power supply system. The two-layer planning model includes: an upper-layer optimization model - planning layer optimization model and a lower-layer optimization model - operation layer optimization model. The configuration optimization module is used to optimize the configuration of traction substations and lines using the upper-level optimization model. The first optimization objective is the sum of the investment cost of traction substation capacity, the investment cost of lines, and the operating cost. The binary variables of traction substation configuration, the distance between traction substations, the capacity of traction substations, and the binary variables of newly added traction substations are the optimization variables. The scheme optimization module is used to optimize the system operation using the lower-level optimization model, with the goal of minimizing the system's electricity cost or minimizing system voltage fluctuations and maximizing the traction substation's capacity margin as the second optimization objective. The upper-level optimization results obtained by solving the upper-level optimization model are used as the boundary conditions for the constraints of the lower-level optimization model. The lower-level optimization results obtained by solving the lower-level optimization model are then used to correct the upper-level optimization results. Through mutual iteration between the upper-level and lower-level optimization models, the optimal scheme that is simultaneously optimal for both the upper-level and lower-level optimization models is finally obtained. The model solving module is used to solve the bi-level planning model based on the optimization scheme and iterative solving algorithm to obtain the optimal configuration parameters, and to select and determine the location and capacity of the traction substation of the traction power supply system based on the optimal configuration parameters. The model solving module performs an iterative solution process for the bilevel programming model, specifically including the following steps: (1) Based on the initialization of the traction station Number of traction stations And the location distribution of traction substations, and the initial capacity limit values ​​of traction substations. Solve the optimization model at the runtime level to obtain the initial values ​​of the decision variables at the planning level. Corresponding runtime decision variables and runtime costs ; (2) Utilizing the operating layer cost Solve the planning-level optimization model to obtain the planning-level decision variables. Total system cost ; (3) Utilizing the decision variables of the planning layer Solve the runtime optimization model to obtain new runtime decision variables. and runtime costs ; (4) Repeat steps (2) and (3) to set the iteration control error. , Less than the error precision The iteration terminates when the solution is obtained. This is the optimal solution of the two-level optimization model, if the error Maximum number of iterations T iter If convergence fails, proceed to step (5); (5) Determine the planning layer scheme Given the known conditions of the system, the constraints of the planning layer optimization model are transformed using preset formulas, and steps (2) and (3) are executed until the required error accuracy is achieved. .

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