Intra-day reactive power optimization method based on flexible direct traction converter and second-order cone programming

Abstract

The invention discloses an intraday reactive power optimization method based on a flexible direct traction converter and second-order cone programming, and belongs to the technical field of rail transit operation. The method comprises the following steps: determining the number of traction converters participating in intraday reactive power optimization according to topological structures of a power distribution network and a traction power supply system, and further determining the number of decision variables; then acquiring daily actual data of the load and the photovoltaic output; inputting the acquired daily actual data of the load and the photovoltaic output into a pre-established reactive power optimization model, performing second-order cone relaxation on a nonlinear part in the reactive power optimization model, and solving the reactive power optimization model based on a CPLEX solver to obtain an intraday reactive power optimization scheme; wherein constraint conditions of the reactive power optimization model comprise a power flow equation constraint of the power distribution network, an operation safety constraint and a capacity limitation constraint of reactive power output of the traction converter; the objective function of the reactive power optimization model is established by taking reduction of system operation cost and reduction of voltage deviation as objectives.

Description

Technical Field

[0001] The invention belongs to the technical field of rail transit operation, and relates to an intra-day reactive power optimization method based on a flexible DC traction converter and second-order cone programming. Background Technique

[0002] With the continuous progress of power technologies in the rail transit system, especially the wide application of renewable energy in the rail transit power supply network, the challenges of voltage stability and efficiency faced by the rail grid are becoming increasingly severe. In the rail transit traction power supply system with intensive power electronic devices and variable loads, how to achieve efficient and real-time reactive power optimization management has become a key issue for improving the operation efficiency and reliability of rail transit. The optimization of reactive power is not only directly related to the voltage quality of the rail grid, but also deeply affects the operation safety and economic benefits of the entire system. Facing the complex voltage fluctuation problems introduced by flexible train loads and renewable energy, it is particularly important to explore reactive power optimization strategies with strong adaptability and high efficiency.

[0003] As an important application of modern power electronic technology, the flexible DC traction converter has four-quadrant reactive power regulation ability and can accurately control reactive power compensation under different working conditions. Different from traditional AC power grids, the traction power supply system uses a traction converter to connect to the railway traction load, which can provide more flexible and efficient reactive power regulation. Since the traction power supply system usually has large load fluctuations and significant dynamic changes in power demand, the four-quadrant characteristics of the traction converter enable it to adjust the voltage in a timely manner when the load changes, ensuring the voltage stability of the power grid. However, the existing reactive power optimization methods often face challenges of high computational complexity and poor real-time performance in practical applications. Especially in the intra-day optimization process, traditional methods usually rely on intelligent algorithms or traditional optimization models, but these methods will encounter the problem of "curse of dimensionality" when facing large-scale systems and are difficult to meet the real-time requirements. Therefore, how to efficiently utilize the characteristics of the traction converter for real-time and fine reactive power optimization regulation has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the invention is to provide an intra-day reactive power optimization method based on a flexible DC traction converter and second-order cone programming to solve the technical problems of high complexity and poor real-time performance of the existing reactive power optimization methods.

[0005] To achieve the above purpose, the invention adopts the following technical solutions:

[0006] In a first aspect, the invention provides an intra-day reactive power optimization method based on a flexible DC traction converter and second-order cone programming, including the following steps:

[0007] According to the topological structures of the distribution network and the traction power supply system, determine the number of traction converters participating in the intra-day reactive power optimization, and then clarify the number of decision variables;

[0008] Collect the actual intra-day data of the load and the PV output;

[0009] Input the actual intra-day data of the load and the PV output collected into the reactive power optimization model established in advance based on the number of decision variables, perform second-order cone relaxation on the non-linear part in the reactive power optimization model, and then solve the reactive power optimization model based on the CPLEX solver to obtain the intra-day reactive power optimization scheme;

[0010] Among them, the constraint conditions of the reactive power optimization model include the power flow equation constraint of the distribution network, the operation safety constraint, and the capacity limit constraint of the reactive power output of the traction converter; the objective function of the reactive power optimization model is an objective function established with the goal of reducing the system operation cost and reducing the voltage deviation.

[0011] Furthermore, the step of determining the number of traction converters participating in the intra-day reactive power optimization according to the topological structures of the distribution network and the traction power supply system, and then clarifying the number of decision variables specifically includes:

[0012] According to the topological structures of the distribution network and the traction power supply system, count the positions and numbers of the traction converters in the system, and determine the number of traction converters participating in the intra-day reactive power optimization;

[0013] Based on the determined number of traction converters, combined with the actual topological structure, confirm the specific number of variables to be regulated in the reactive power optimization model; the specific variables to be regulated only include the reactive power output of the traction converter.

[0014] Furthermore, the step of collecting the actual intra-day data of the load and the PV output based on the number of decision variables specifically includes:

[0015] Collect the intra-day load data from the real-time monitoring system to ensure that the data covers all load nodes of the power grid;

[0016] Collect the intra-day output data of the distributed PV power generation system, and preprocess the intra-day output data to remove abnormal data.

[0017] Furthermore, the expression of the power flow equation constraint of the distribution network is:

[0018]

[0019] In the formula, P ij represents the active power flow of branch ij; Q ij represents the reactive power flow of branch ij; represents the active power injected by the generator into node j; Denotes the reactive power injected by the generator into node j; Denotes the active power injected by the photovoltaic into node j; Denotes the reactive power injected by the traction converter into node j; z ij = r ij + x ij Denotes the impedance of branch ij, composed of resistance r ij and reactance x ij ; E L Denotes the set of all branches in the distribution network; Denotes the active power of the load at node j; Denotes the reactive power of the load at node j;

[0020] The expression of the operating security constraint is:

[0021]

[0022] In the formula, V j Denotes the voltage of node j; Denotes the lower limit of the voltage of node j; Denotes the upper limit of the voltage of node j; I ij Denotes the current of branch ij; Denotes the upper limit value of the current of branch ij; E L Denotes the set of all branches in the distribution network; E N Denotes the set of all nodes in the distribution network.

[0023] Furthermore, the establishment process of the capacity limit constraint of the reactive power output of the traction converter includes:

[0024] According to the connection circuit between the AC power grid and the traction converter converter station, perform Y-Δ transformation to obtain the admittance circuit model, and obtain the capacity calculation formula of the traction converter from the admittance circuit model as:

[0025]

[0026] In the formula, S vsc Denotes the complex power of the AC network flowing into the converter; U s Denotes the amplitude of the AC network voltage; Y f Denotes the grounding admittance in the Y-type circuit; Y sf Denotes the AC grid side admittance in the Y-type circuit; Denotes the complex voltage on the AC network side; Denotes the complex current on the converter side; Denotes the complex voltage on the converter side; Y se Denotes the grounding admittance of the AC grid side in the Δ-type circuit; Y sc Denotes the admittance between the AC grid and the converter in the Δ-type circuit;

[0027] According to the capacity calculation formula of the traction converter, the current limit range of the traction converter is centered on with a radius of U s I cmax ; the voltage limit range of the traction converter is composed of two circles centered on with radii of U s U cmin and U s U cmax respectively, forming an annulus; the active power of the traction converter is calculated through the DC side traction, and thus the reactive power capacity of the traction converter is obtained.

[0028] Furthermore, the construction process of the objective function of the reactive power optimization model includes:

[0029] Define the voltage deviation index and construct the first objective function, and the specific formula is:

[0030]

[0031] In the formula, f1 represents the first objective function; N b is the total number of nodes, V i is the voltage of node i at time t, and V i.set is the rated voltage of node i;

[0032] Linearize the absolute value term in the first objective function f1, introduce auxiliary variables a i and b i , and add additional constraints; the additional constraints are:

[0033]

[0034] Finally, the first objective function f1 is transformed into:

[0035]

[0036] In the formula, a i and b i are both auxiliary variables;

[0037] Define the network loss index and construct the second objective function, and the specific formula is:

[0038]

[0039] In the formula, f2 represents the second objective function; P i,t represents the active power of branch i at time t; Q i,t represents the reactive power of branch i at time t; R i is the resistance of branch i at time t; Vi,t is the voltage at the head of branch i at time t.

[0040] Furthermore, the step of performing second-order cone relaxation on the non-linear part in the reactive power optimization model specifically includes:

[0041] Perform second-order cone relaxation conversion on the non-linear part in the reactive power optimization model. The expression of the converted model is:

[0042]

[0043] In the formula, the objective function f represents the minimum active power loss and minimum voltage deviation of the distribution network; ζ is the target weight of the active power loss of the distribution network; ψ is the target weight of the voltage deviation; P i,t represents the active power of branch i at time t; Q i,t represents the reactive power of branch i at time t; R i is the resistance of branch i; V i,t is the voltage at the head of branch i at time t; P ij represents the active power flow of branch ij; Q ij represents the reactive power flow of branch ij; represents the active power injected by the generator into node j; represents the reactive power injected by the generator into node j; represents the active power injected by the photovoltaic into node j; q j VSC represents the reactive power injected by the traction converter into node j; E L represents the set of all branches of the distribution network; E N represents the set of all nodes of the distribution network; V j represents the voltage of node j.

[0044] In a second aspect, the present invention provides an intraday reactive power optimization system based on a flexible DC traction converter and second-order cone programming, including:

[0045] A decision variable determination module, configured to determine the number of traction converters participating in intraday reactive power optimization according to the topological structures of the distribution network and the traction power supply system, and further clarify the number of decision variables;

[0046] A data acquisition module, which acquires the actual intraday data of the load and photovoltaic output;

[0047] A reactive power optimization module, configured to input the actual intraday data of the load and photovoltaic output acquired into a reactive power optimization model established in advance based on the number of decision variables, perform second-order cone relaxation on the non-linear part in the reactive power optimization model, and then solve the reactive power optimization model based on a CPLEX solver to obtain an intraday reactive power optimization plan;

[0048] Among them, the constraint conditions of the reactive power optimization model include the power flow equation constraint of the distribution network, the operation safety constraint, and the capacity limit constraint of the reactive power output of the traction converter; the objective function of the reactive power optimization model is an objective function established with the goal of reducing the system operation cost and reducing the voltage deviation.

[0049] In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention combines a flexible DC traction converter with second-order cone programming, and proposes an intra-day reactive power optimization method for a rail transit traction power supply system, aiming to solve the real-time and computational efficiency problems of reactive power optimization in the rail transit system. Compared with traditional methods, the present invention makes full use of the four-quadrant reactive power regulation ability of the traction converter, and can achieve real-time reactive power regulation in response to the dynamic changes of traction load fluctuations and voltage deviations during the train operation process, ensuring the voltage stability and operation reliability of the power supply system. By using SOCP for solution, this method greatly improves the optimization solution speed, effectively overcomes the computational bottleneck existing in traditional methods for high-dimensional problems, and meets the strict requirements of intra-day optimization for real-time response. In practical applications, the present invention can accurately adapt to the network topology characteristics in the rail transit power supply system, improve the economy and stability of the system through efficient reactive power regulation, and reduce the local optimum problem of traditional methods. This technology provides a more flexible and efficient reactive power optimization scheme for the flexible DC traction power supply system of rail transit, and is of great significance for improving the reliability, adaptability and sustainable development of the rail transit power supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1 is a flowchart of the method of the present invention;

[0055] Figure 2 is a schematic diagram of the system of the present invention;

[0056] Figure 3 This is the overall flowchart of an intra-day reactive power optimization method based on a flexible DC traction converter and second-order cone programming according to an embodiment of the present invention;

[0057] Figure 4 This is the network topology diagram of the reactive power optimization model according to an embodiment of the present invention;

[0058] Figure 5 This is the topology diagram of the admittance circuit model according to an embodiment of the present invention;

[0059] Figure 6 This is the power circle diagram of the traction converter according to an embodiment of the present invention;

[0060] Figure 7 This is the schematic diagram of the computer device structure according to the present invention. Detailed implementation manners

[0061] The present invention will be described in detail below with reference to the drawings and in conjunction with embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0062] The following detailed descriptions are all exemplary descriptions, aiming to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms adopted by the present invention have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs. The terms used in the present invention are only for the purpose of describing specific implementation manners, and are not intended to limit the exemplary implementation manners according to the present invention.

[0063] See Figure 1 and Figure 3 , an embodiment of the present invention discloses an intra-day reactive power optimization method based on a flexible DC traction converter and second-order cone programming, including the following steps:

[0064] S1. According to the topological structures of the distribution network and the traction power supply system, determine the number of traction converters participating in the intra-day reactive power optimization, and further clarify the number of decision variables.

[0065] S101. According to the topological structures of the distribution network and the traction power supply system, count the positions and numbers of the traction converters in the system, and determine the number of traction converters participating in the intra-day reactive power optimization.

[0066] S102. Combining the actual topology, confirm the specific number of variables to be regulated in the reactive power optimization model, which only includes the reactive power output of the traction converter and does not involve other day-ahead reactive power compensation devices such as on-load tap changers (OLTCs) or capacitor banks (CBs).

[0067] S2. Collect the actual intra-day data of the load and PV output to accurately reflect the intra-day fluctuations and use them as the input parameters for the reactive power optimization model.

[0068] S201. Collect the intra-day load data from the real-time monitoring system to ensure that the data covers all load nodes of the power grid and provide accurate load fluctuations.

[0069] S202. Collect the intra-day output data of the distributed PV power generation system and perform data preprocessing to remove abnormal data and ensure the accuracy and reliability of the PV output data.

[0070] In a feasible embodiment of the present invention, the network topology diagram of the reactive power optimization model in this embodiment is as Figure 4 shown, where VSC represents the traction converter; the establishment process of the reactive power optimization model includes the following steps:

[0071] Step 1. Establish various constraint conditions according to the power flow equation of the distribution network, operation safety constraints, and capacity limit of the reactive power output of the traction converter.

[0072] Step 101. According to the power flow equation of the distribution network, set the active and reactive power balance constraints of the nodes to ensure that each node of the system satisfies the power balance during the optimization process. The formula is as follows:

[0073]

[0074] In the formula, P ij , Q ij represent the active power flow and reactive power flow of branch ij; represent the active power and reactive power injected by the generator into node j; represent the active power injected by the PV into node j; represent the reactive power injected by the traction converter into node j; z ij =r ij +x ij represent the impedance of branch ij, which is composed of resistance r ij and reactance x ij ; E L represents the set of all branches of the distribution network.

[0075] Step 102. Define the operation safety constraints of the distribution network to ensure system stability. The formula is as follows:

[0076]

[0077] V j represents the voltage of node j; represent the lower and upper limits of the voltage of node j; I ij , Denote the current of branch ij and its upper limit value; E L Denote the set of all branches of the distribution network; E N Denote the set of all nodes of the distribution network

[0078] Step 103, calculate the reactive power compensation capacity of the traction converter. According to the connection circuit between the AC power grid and the converter station of the traction converter, Y-Δ transformation can be performed to obtain Figure 5 The admittance circuit model shown;

[0079] Based on the admittance circuit model, the capacity calculation formula of the traction converter can be derived as follows:

[0080]

[0081] As can be seen from the above formula, S vsc Is limited by both I cmax , and also affected by the range of U c , that is, U c ∈[U cmin , U cmax . It is not difficult to see that the limitation range of the traction converter current is a circle with As the center and U s I cmax As the radius. The voltage limitation range is a circular ring composed of two circles with Taking U s U cmin , U s U cmax As the radius respectively. If the current limitation circle is inside the circular ring, the operating range of S vsc Is completely limited by U s I cmax . If the current limitation circle has no intersection with the circular ring, the corresponding traction converter cannot operate. If the voltage limitation circular ring passes through the current limitation circle, the operating range shown in Figure 6 Will be formed. Through the active power calculated by the traction substation on the DC side, the reactive power capacity of the corresponding traction converter can be obtained.

[0082] Step 2, aiming at reducing the system operation cost and reducing the voltage deviation, construct the objective function of the distribution network.

[0083] Step 201, to measure the stability of the distribution network system, define the voltage deviation index, and the formula is as follows:

[0084]

[0085] In the formula, N b Is the total number of nodes, V t Is the voltage of node i at time t, and V i.set Is the rated voltage of node i.

[0086] Since the objective function f1 contains absolute value terms It is necessary to linearize it. First, introduce two sets of auxiliary variables a i and b i , and add the following additional constraints:

[0087]

[0088] Finally, the objective function f1 is transformed into:

[0089]

[0090] Step 202, to measure the economy of the distribution network system, define a network loss index, and the formula is as follows:

[0091]

[0092] In the formula, N l is the number of branches, P i,t and Q i,t are the active power and reactive power of the i-th branch at time t respectively, R i and V i,t are the resistance and the head voltage of the i-th branch at time t respectively.

[0093] S3. Input the actual intra-day data of the collected load and photovoltaic output into the pre-established reactive power optimization model, perform second-order cone relaxation on the non-linear part in the reactive power optimization model, transform the complex non-linear optimization problem into an SOCP problem, and then solve the reactive power optimization model based on the CPLEX solver to obtain the intra-day reactive power optimization scheme.

[0094] Since the reactive power optimization model contains a non-linear part and the solver cannot directly solve it, perform a second-order cone relaxation (SOCR) transformation on it, and the following model can be obtained:

[0095]

[0096] In the formula, the objective function f represents the minimum active power network loss and the minimum voltage deviation of the distribution network, and ζ and ψ are the objective weight values.

[0097] The above model constitutes the basic form of the relaxed static optimal power flow. When the objective function is a convex function and a strictly increasing function, SOCR is strictly accurate for most distribution network structures. Using existing commercial solvers such as Cplex, Gurobi, etc., when solving the original SOCP problem, the optimal solution of the variable can be obtained, and thus the reactive power output curve of the traction converter can be obtained.

[0098] An intra-day reactive power optimization method that combines the four-quadrant reactive power regulation ability of a flexible DC traction converter with the solution of second-order cone programming (SOCP). This method makes full use of the advantages of the traction converter in the traction power supply system, can accurately regulate reactive power, solve the problems of voltage deviation and fluctuation, and at the same time uses second-order cone programming for optimization and solution, which can significantly improve the solution speed and avoid the curse of dimensionality problem that may occur in intelligent algorithms. Second-order cone programming not only ensures the solution accuracy but also meets the real-time requirements of intra-day optimization, and can quickly adjust the reactive power compensation strategy under the conditions of power grid load fluctuation and dynamic change. Through this method, not only can the reactive power regulation performance of the traction converter in the traction power supply system be improved, but also the stability and economy of the distribution network under the condition of high proportion of renewable energy access can be enhanced, providing effective technical support for the construction of smart grid and the modernization of power system.

[0099] See Figure 2 , an intra-day reactive power optimization system based on a flexible DC traction converter and second-order cone programming is disclosed in an embodiment of the present invention, including a decision variable determination module, a data acquisition module, and a reactive power optimization module.

[0100] The decision variable determination module is used to determine the number of traction converters participating in intra-day reactive power optimization according to the topological structures of the distribution network and the traction power supply system, and then clarify the number of decision variables;

[0101] This module counts the positions and numbers of traction converters in the system according to the topological structures of the distribution network and the traction power supply system, determines the number of traction converters participating in intra-day reactive power optimization; then based on the determined number of traction converters, combined with the actual topological structure, confirms the specific number of variables to be regulated in the reactive power optimization model; the specific variables to be regulated only include the reactive power output of the traction converter.

[0102] The data acquisition module is used to collect the actual intra-day data of load and photovoltaic output;

[0103] This module collects the intra-day load data from the real-time monitoring system to ensure that the data covers all load nodes of the power grid, collects the intra-day output data of the distributed photovoltaic power generation system, and preprocesses the intra-day output data to remove abnormal data.

[0104] The reactive power optimization module is used to input the actual intra-day data of load and photovoltaic output collected into the reactive power optimization model established in advance based on the number of decision variables, perform second-order cone relaxation on the non-linear part in the reactive power optimization model, and then solve the reactive power optimization model based on the CPLEX solver to obtain the intra-day reactive power optimization scheme.

[0105] It should be noted that the constraint conditions of the reactive power optimization model include:

[0106] 1) According to the power flow equation of the distribution network, the active and reactive power balance constraints of the nodes are set to ensure that the power balance of each node in the system is satisfied during the optimization process. The formula is as follows:

[0107]

[0108] In the formula, P ij , Q ij represent the active power flow and reactive power flow of branch ij; represent the active power and reactive power injected by the generator into node j; represent the active power injected by the photovoltaic into node j; represent the reactive power injected by the traction converter into node j; z ij = r ij + x ij represents the impedance of branch ij, which is composed of resistance r ij and reactance x ij ; E L represents the set of all branches in the distribution network.

[0109] 2) Define the security constraints for the operation of the distribution network to ensure system stability. The formula is as follows:

[0110]

[0111] V j represents the voltage of node j; represent the lower and upper limits of the voltage of node j; I ij , represent the current of branch ij and its upper limit value; E L represents the set of all branches in the distribution network; E N represents the set of all nodes in the distribution network.

[0112] 3) Calculation of the reactive power compensation capacity of the traction converter. According to the connection circuit between the AC power grid and the traction converter substation, the Y-Δ transformation can be performed to obtain the admittance circuit model shown in Figure 5 ;

[0113] Based on the admittance circuit model, the capacity calculation formula of the traction converter can be deduced as follows:

[0114]

[0115] It can be seen from the above formula that S vsc is limited by both I cmax and the range of U c , that is, U c ∈ [U cmin , U cmax. It is not difficult to see that the current limit range of the traction converter is centered on with a radius of U s I cmax is a circle. The voltage limit range is an annular ring composed of two circles with with U s U cmin and U s U cmax as the radii respectively. If the current limit circle is inside the annular ring, the operating range of S vsc is completely limited by U s I cmax . If the current limit circle has no intersection with the annular ring, the corresponding traction converter cannot operate. If the voltage limit annular ring passes through the current limit circle, the operating range shown in Figure 6 will be formed. The reactive power capacity of the corresponding traction converter can be obtained from the active power calculated by the traction on the DC side.

[0116] The objective function of the reactive power optimization model described above is:

[0117] 1) To measure the stability of the distribution network system, a voltage deviation index is defined, and the formula is as follows:

[0118]

[0119] In the formula, N b is the total number of nodes, V t is the voltage of node i at time t, and V i.set is the rated voltage of node i.

[0120] Since the objective function f1 contains an absolute value term it is necessary to linearize it. First, introduce two sets of auxiliary variables a i and b i , and add the following additional constraints:

[0121]

[0122] Finally, the objective function f1 is transformed into:

[0123]

[0124] 2) To measure the economy of the distribution network system, a network loss index is defined, and the formula is as follows:

[0125]

[0126] In the formula, N l is the number of branches, P i,t and Q i,t are the active power and reactive power of branch i at time t respectively, and Ri and V i,t are respectively the resistance and the head-end voltage of branch i at time t.

[0127] This embodiment gives full play to the reactive power regulation advantage of the flexible DC traction converter and combines a dynamic adjustment strategy based on the actual load and photovoltaic output, enabling accurate response to load fluctuations and voltage fluctuations. Compared with traditional methods, the present invention significantly improves the optimization solution speed by adopting the SOCP solution strategy, avoids the "curse of dimensionality" problem in intelligent algorithms, and ensures the efficiency and real-time nature of the optimization process. While improving the reactive power optimization efficiency of the rail transit traction power supply system, this method guarantees the reliability of equipment operation and the stability of the system, provides technical support for the intelligent operation of the rail transit system and the high-proportion access of renewable energy, and helps to promote the development of rail transit electrification and intelligence.

[0128] In one embodiment of the present invention, referring to Figure 7 , a computer device is provided. The computer device includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function. The processor described in the embodiment of the present invention can be used for the operation of the intra-day reactive power optimization method based on the flexible DC traction converter and second-order cone programming.

[0129] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the operating system of the terminal. Moreover, in this storage space, one or more instructions suitable for being loaded and executed by the processor are also stored. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the intra-day reactive power optimization method based on the flexible DC traction converter and the second-order cone programming in the above embodiments.

[0130] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0131] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0132] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions in Figure 1 one flow or multiple flows and / or blocksFigure 1 The functions specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 One process or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for optimizing reactive power within a day based on flexible DC traction converter and second-order cone programming, characterized in that: The following steps are involved: According to the topological structure of the distribution network and traction power supply system, the number of traction converters involved in the reactive power optimization within the day is determined, and then the number of decision variables is clarified; Collect actual daily data of load and photovoltaic output; The collected actual daily data of load and photovoltaic output are input into the reactive power optimization model pre-established based on the number of decision variables, and the nonlinear part of the reactive power optimization model is relaxed by second order cone. Then, the reactive power optimization model is solved based on the CPLEX solver to obtain the reactive power optimization solution within the day. Among them, the constraints of the reactive power optimization model include the power flow equation constraints of the distribution network, the operation safety constraints and the capacity limit constraints of the reactive power output of the traction converter; the objective function of the reactive power optimization model is an objective function established with the goal of reducing the system operation cost and reducing the voltage deviation.

2. The method for optimizing reactive power within a day based on flexible DC traction converter and second-order cone programming according to claim 1 is characterized in that: The step of determining the number of traction converters participating in the intra-day reactive power optimization according to the topological structure of the distribution network and the traction power supply system, and then clarifying the number of decision variables, specifically includes: According to the topology of the distribution network and traction power supply system, the location and number of traction converters in the system are counted to determine the number of traction converters involved in reactive power optimization within the day; Based on the determined number of traction converters and in combination with the actual topology, the number of specific variables that need to be regulated in the reactive power optimization model is confirmed; the specific variables that need to be regulated only include the reactive power output of the traction converter.

3. The method for optimizing reactive power within a day based on flexible DC traction converter and second-order cone programming according to claim 1 is characterized in that: The step of collecting actual daily data of load and photovoltaic output based on the number of decision variables specifically includes: Collect daily load data from the real-time monitoring system to ensure that the data covers all load nodes of the power grid; Collect the daily output data of distributed photovoltaic power generation systems, pre-process the daily output data and remove abnormal data.

4. The method for optimizing reactive power within a day based on flexible DC traction converter and second-order cone programming according to claim 1 is characterized in that: The expression of the power flow equation constraint of the distribution network is: Where P ij Indicates the active power flow of branch ij; Q ij Represents the reactive power flow of branch ij; represents the active power injected into node j by the generator; represents the reactive power injected into node j by the generator; represents the active power injected by PV into node j; represents the reactive power injected into node j by the traction converter; z ij =r ij +x ij Represents the impedance of branch ij, which is composed of resistor r ij and reactance x ij Composition: E L Represents the set of all branches of the distribution network; represents the load active power of node j; represents the load reactive power of node j; The expression of the operational safety constraint is: Where V j represents the voltage at node j; V j represents the lower limit of the node j voltage; represents the upper limit of the node j voltage; I ij represents the current in branch ij; Indicates the upper limit of the current in branch ij; E L Represents the set of all branches of the distribution network; E N Represents the set of all nodes in the distribution network.

5. The method for optimizing reactive power within a day based on flexible DC traction converter and second-order cone programming according to claim 1 is characterized in that: The process of establishing the capacity limit constraint of the reactive output of the traction converter includes: According to the connection circuit between the AC power grid and the traction converter station, a Y-△ transformation is performed to obtain the admittance circuit model. The capacity calculation formula of the traction converter is obtained from the admittance circuit model: In the formula, S vsc represents the AC network complex power flowing into the converter; U s Indicates the voltage amplitude of the AC network; Y f Indicates the grounding admittance in the Y-type circuit; Y sf It represents the admittance on the AC grid side in the Y-type circuit; U s Indicates the complex voltage on the AC network side; Indicates the complex current on the converter side; U c Represents the complex voltage on the converter side; Y se Indicates the grounding admittance of the AC power grid side in the △ type circuit; Y sc It represents the admittance between the AC grid and the converter in a delta circuit; According to the capacity calculation formula of the traction converter, the current limit range of the traction converter is As the center of the circle, with U s I cmax The limit range of the traction converter voltage is As the center of the circle, with U s U cmin and U s U cmax A ring consisting of two circles with radii of respectively; the active power of the traction converter is calculated by the DC side traction, thereby obtaining the reactive capacity of the traction converter.

6. The method for optimizing reactive power within a day based on flexible DC traction converter and second-order cone programming according to claim 1 is characterized in that: The process of constructing the objective function of the reactive power optimization model includes: Define the voltage deviation index and construct the first objective function. The specific formula is: Where f1 represents the first objective function; N b is the total number of nodes, V i is the voltage of node i at time t, V i.set is the rated voltage of node i; Linearize the absolute value term in the first objective function f1 and introduce the auxiliary variable a i and b i , and add additional constraints; the additional constraints are: Finally, the first objective function f1 is transformed into: In the formula, a i and b i All are auxiliary variables; Define the network loss index and construct the second objective function. The specific formula is: Where f2 represents the second objective function; P i,t represents the active power of branch i at time t; Q i,t represents the reactive power of branch i at time t; R i is the resistance of branch i at time t; V i,t is the voltage at the beginning of branch i at time t.

7. The method for optimizing reactive power within a day based on flexible DC traction converter and second-order cone programming according to claim 1 is characterized in that: The step of performing second-order cone relaxation on the nonlinear part in the reactive power optimization model specifically includes: The nonlinear part of the reactive power optimization model is transformed into a second-order cone relaxation, and the model expression after the transformation is: st Where, the objective function f represents the minimum active network loss and voltage deviation of the distribution network; ζ is the target weight of the active network loss of the distribution network; ψ is the target weight of the voltage deviation; P i,t represents the active power of branch i at time t; Q i,t represents the reactive power of branch i at time t; R i is the resistance of branch i; V i,t is the voltage at the first end of branch i at time t; P ij Indicates the active power flow of branch ij; Q ij Represents the reactive power flow of branch ij; represents the active power injected into node j by the generator; represents the reactive power injected into node j by the generator; represents the active power injected by PV into node j; represents the reactive power injected into node j by the traction converter; E L Represents the set of all branches of the distribution network; E N Represents the set of all nodes in the distribution network; V j represents the voltage at node j.

8. A daytime reactive power optimization system based on flexible DC traction converter and second-order cone programming, characterized in that: include: A decision variable determination module is used to determine the number of traction converters participating in the intra-day reactive power optimization according to the topological structure of the distribution network and the traction power supply system, thereby clarifying the number of decision variables; Data collection module, which collects actual daily data of load and photovoltaic output; The reactive power optimization module is used to input the collected actual daily data of load and photovoltaic output into the reactive power optimization model pre-established based on the number of decision variables, perform second-order cone relaxation on the nonlinear part of the reactive power optimization model, and then solve the reactive power optimization model based on the CPLEX solver to obtain the reactive power optimization solution within the day; Among them, the constraints of the reactive power optimization model include the power flow equation constraints of the distribution network, the operation safety constraints and the capacity limit constraints of the reactive power output of the traction converter; the objective function of the reactive power optimization model is an objective function established with the goal of reducing the system operation cost and reducing the voltage deviation.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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