Power distribution network soft open point siting and sizing method and system based on mixed second-order cone programming

Abstract

Disclosed in the present invention are a power distribution network soft open point siting and sizing method and system based on mixed second-order cone programming. The method comprises: acquiring interconnected flexible power distribution system data; on the basis of the interconnected flexible power distribution system data and interconnected flexible power distribution system constraints, establishing a flexible power distribution network soft open point siting and sizing programming model by using minimum daily total operation cost as an objective function; combining an improved sparrow algorithm and second-order cone programming to solve and optimize the flexible power distribution network soft open point siting and sizing programming model, to obtain a soft open point siting and sizing solution; and calculating the objective function corresponding to the soft open point siting and sizing solution to obtain an optimal soft open point siting and sizing solution. In the present invention, the economies of different soft open point configuration solutions are more accurately evaluated, so as to select an optimal soft open point optimization configuration solution, thereby achieving flexible power distribution network operation programming that takes both the power supply reliability and economy into consideration, and achieving coordination and complementarity between system devices.

Description

A flexible soft switch site selection and capacity determination method and system for a power distribution network based on a mixed second-order cone programming TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network optimization, and in particular to a flexible soft switch site selection and capacity determination method and system for a power distribution network based on a mixed second-order cone programming. BACKGROUND

[0002] With the rapid advancement of new power systems, power interaction between source, network, load and storage devices becomes increasingly close, bringing unprecedented challenges to traditional power distribution networks. In the face of increasingly complex network structure operation requirements, the traditional power distribution network mode of "closed-loop design and open-loop operation" has shown signs of being unable to cope. This mode mainly relies on one-way energy flow of alternating current networks, but its inherent structural mode lacks sufficient flexibility and limited control means, making it difficult to meet the diversified and complex needs of modern power systems.

[0003] At the same time, the rapid development of new energy generation technology has made it a norm for a higher proportion of distributed generation (DG) to be connected to the power grid. The widespread access of DG does help to reduce system power transmission losses and reduce environmental pollution, but its own volatility and randomness also bring complexity to the power flow distribution of the power grid, and higher requirements for system power quality, network congestion and equipment safe operation state. SUMMARY

[0004] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0005] In view of the above existing problems, the present application is proposed. Therefore, the present application provides a flexible soft switch site selection and capacity determination method for a power distribution network based on a mixed second-order cone programming to solve the problems of insufficient flexibility, limited control means and economic optimization of traditional power distribution networks in response to the widespread access of new energy and complex network structure operation.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a flexible soft switch site selection and capacity determination method for a power distribution network based on a mixed second-order cone programming, comprising:

[0008] Obtaining interconnected flexible power distribution system data;

[0009] Based on the interconnected flexible power distribution system data and the interconnected flexible power distribution system constraints, a flexible power distribution network flexible soft switch site selection and capacity planning model is established with a minimum daily comprehensive operation cost as an objective function;

[0010] The improved sparrow algorithm is combined with the second-order cone programming to solve the flexible power distribution network flexible soft switch site selection and capacity planning model, so that a flexible soft switch site selection and capacity planning scheme is obtained.

[0011] The objective function corresponding to the flexible soft switch site selection and capacity planning scheme is calculated, and an optimal flexible soft switch site selection and capacity planning scheme is obtained.

[0012] As a preferred scheme of the mixed second-order cone programming-based power distribution network flexible soft switch site selection and capacity planning method, the interconnected flexible power distribution system constraints include flexible soft switch operation constraints, flexible soft switch reactive power constraints, flexible soft switch capacity constraints, power flow constraints, system voltage constraints, branch capacity constraints and energy storage device constraints.

[0013] As a preferred scheme of the mixed second-order cone programming-based power distribution network flexible soft switch site selection and capacity planning method, the objective function includes,

[0014] The daily comprehensive operation cost includes main grid output cost, operation loss cost and energy storage charging and discharging loss cost.

[0015] The main grid output cost, operation loss cost and energy storage charging and discharging loss cost are expressed as:

[0016] Wherein, C pg is the main grid output cost, C loss is the operation loss cost, C ESS is the energy storage charging and discharging loss cost, C gt represents the real-time electricity price of the power distribution network, P gt represents the system real-time power purchase power, C e is the system network loss compensation coefficient, C sop is the flexible soft switch operation cost conversion unit price, N bus is the number of system network nodes, c(i) is the initial node set with node i as the starting node, P loss,i,t is the transmission power loss of the flexible soft switch port i at time t, I ij is the current flowing between node i and node j line, C ess represents the energy storage device cost coefficient, ψ ess represents the installation location set of the energy storage device in the system, represents the charging and discharging power of the energy storage device at node i at time t.

[0017] As a preferred scheme of the flexible soft switch site selection and capacity determination method for the power distribution network based on the mixed second-order cone programming, in the scheme, solving the flexible soft switch site selection and capacity determination programming model of the flexible power distribution network by using the improved sparrow algorithm comprises,

[0018] Initializing the sparrow algorithm parameters, determining the initial access position of the flexible soft switch, taking the initial access position of the flexible soft switch as the decision variable of the improved sparrow algorithm and encoding, and randomly generating the flexible soft switch site selection and capacity determination scheme;

[0019] The flexible soft switch connection port is not repeated, and the flexible soft switch port cannot be connected with the power distribution network head node.

[0020] In the process of solving the sparrow algorithm iteration, the Levy flight strategy is introduced, the Levy flight term is added in the flexible soft switch site selection and capacity determination scheme update of the optimal target function value, and the flexible soft switch site selection and capacity determination scheme is disturbed.

[0021] As a preferred scheme of the flexible soft switch site selection and capacity determination method for the power distribution network based on the mixed second-order cone programming, in the scheme, solving the flexible soft switch site selection and capacity determination programming model of the flexible power distribution network by using the improved sparrow algorithm comprises,

[0022] The flexible soft switch operation constraint, the flexible soft switch reactive power constraint, the flexible soft switch capacity constraint and the power flow constraint are converted into a second-order cone optimization model through linearization and second-order cone relaxation, and the SOCP algorithm is used to solve the second-order cone optimization model.

[0023] As a preferred scheme of the flexible soft switch site selection and capacity determination method for the power distribution network based on the mixed second-order cone programming, in the scheme, further comprising,

[0024] Solving the second-order cone optimization model by using the SOCP algorithm is expressed as: Σ i:i→j (P ij -R ij I′ ij )-P j =∑ l:j→l P jl ∑ i:i→j (Q ij -X ij I′ ij )-Q j =∑ l:j→l Q jl

[0025] Wherein, P i link 、 And These represent the active and reactive power outputs at ports i and j, respectively. i → j indicates that node i is the upstream node of node j. U i and U j Let P be the voltage at nodes i and j. ij Q ij P represents the active and reactive power flowing from node i into node j. j and Q j Let be the active and reactive power of the net load at node j. R represents the capacity of the flexible soft-switching converter at different ports. ij and X ij P represents the resistance and reactance of the line between nodes i and j. jl With Q jl I represents the active and reactive power flowing from node j into node l. ij I′ represents the current flowing through the lines between node i and node j. ij u i They represent I respectively ij and U i The squared term.

[0026] As a preferred embodiment of the flexible soft-switching location and capacity determination method for distribution networks based on hybrid second-order cone programming described in this invention, the objective function for calculating the flexible soft-switching location and capacity determination scheme includes:

[0027] The objective function value corresponding to the flexible soft-switching addressing and calibrating scheme is used as the fitness function value of the improved sparrow algorithm;

[0028] If the fitness function value reaches the set threshold or the number of iterations reaches the maximum number of iterations of the improved sparrow algorithm, the optimal flexible soft switch addressing and sizing scheme is output.

[0029] Secondly, this invention provides a system for addressing and sizing flexible soft switches in a distribution network based on hybrid second-order cone programming, comprising:

[0030] The data acquisition module is used to acquire data from the interconnected flexible power distribution system;

[0031] The model building module is used to establish a flexible distribution network flexible switch location and capacity planning model based on the interconnected flexible distribution system data and the interconnected flexible distribution system constraints, with the objective function of minimizing the daily comprehensive operating cost;

[0032] The model solving module is used to solve and optimize the flexible distribution network flexible soft switch location and capacity planning model by combining the improved sparrow algorithm with second-order cone programming, and obtain the flexible soft switch location and capacity planning scheme.

[0033] An optimal scheme acquisition module is configured to calculate a target function corresponding to the flexible soft-switching site selection and capacity determination scheme and acquire an optimal flexible soft-switching site selection and capacity determination scheme.

[0034] In a third aspect, the present application provides a computing device, comprising:

[0035] a memory and a processor;

[0036] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, so as to implement the steps of the method for flexible soft-switching site selection and capacity determination of a power distribution network based on a mixed second-order cone programming.

[0037] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the method for flexible soft-switching site selection and capacity determination of a power distribution network based on a mixed second-order cone programming.

[0038] Compared with the prior art, the present application has the following beneficial effects: the present application takes the normal power supply operation of a flexible power distribution network as a prerequisite, takes economy as an index, considers the flexible soft-switching site selection and capacity determination of the flexible power distribution network, establishes a target function of minimizing the comprehensive cost of the main grid output cost, operation loss cost and energy storage charging and discharging loss cost, more accurately measures the economy of different flexible soft-switching configuration schemes, so as to select an optimal flexible soft-switching optimal configuration scheme, realizes the operation planning of the flexible power distribution network considering the power supply reliability and economy, and realizes the coordination and complementation between the devices of the system. Meanwhile, the method of the present application is simple in calculation process and has high engineering practical value. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative labor. Among them:

[0040] Fig. 1 is a schematic diagram of the overall flow of the method for flexible soft-switching site selection and capacity determination of a power distribution network based on a mixed second-order cone programming according to an embodiment of the present application;

[0041] Fig. 2 is a schematic diagram of the flow of the improved sparrow algorithm and the second-order cone method of the method for flexible soft-switching site selection and capacity determination of a power distribution network based on a mixed second-order cone programming according to an embodiment of the present application;

[0042] Fig. 3 is a schematic diagram of the system load and distributed power output of the method for flexible soft-switching site selection and capacity determination of a power distribution network based on a mixed second-order cone programming according to an embodiment of the present application;

[0043] Fig. 4 is a schematic diagram of different method system power purchase based on the mixed second-order cone programming-based flexible soft switch site selection and capacity determination method according to an embodiment of the present application;

[0044] Fig. 5 is a schematic diagram of active power transmission of the flexible soft switch under the improved sparrow algorithm and second-order cone programming method based on the mixed second-order cone programming-based flexible soft switch site selection and capacity determination method according to an embodiment of the present application;

[0045] Fig. 6 is a schematic diagram of reactive power transmission of the flexible soft switch under the improved sparrow algorithm and second-order cone programming method based on the mixed second-order cone programming-based flexible soft switch site selection and capacity determination method according to an embodiment of the present application;

[0046] Fig. 7 is a schematic diagram of the interconnected IEEE33 example system after optimization based on the mixed second-order cone programming-based flexible soft switch site selection and capacity determination method according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present application.

[0048] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details, which are not described in the present application, and it can be apparent to those skilled in the art that the present application can be implemented in other different ways. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0049] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or selective embodiment that excludes other embodiments.

[0050] The present application is described in detail with reference to the schematic diagram. In the detailed description of the embodiments of the present application, the cross-sectional view of the device structure is locally enlarged without the general proportion for the convenience of description, and the schematic diagram is only an example, which should not limit the scope of protection of the present application. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual manufacture.

[0051] Meanwhile, in the description of the present application, it should be noted that the terms "upper, lower, inner and outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the systems or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0052] Unless otherwise expressly specified and limited, the terms "mounting, connecting, connecting" in the present application should be understood broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0053] Embodiment 1

[0054] Referring to FIGS. 1-2, for an embodiment of the present application, a mixed second-order cone programming-based flexible soft switch site selection and capacity determination method for power distribution network is provided, comprising:

[0055] S100: Obtain interconnected flexible power distribution system data;

[0056] It should be noted that the interconnected flexible power distribution system data includes system network connection topology, simulation of distributed power 24h output data, system node 24h load data, energy storage configuration data, etc.

[0057] Specifically, the original interconnected flexible power distribution system data is obtained by using data acquisition equipment, which covers key information such as system network connection topology, 24-hour output data of distributed power, 24-hour load data of system nodes, and energy storage configuration data, etc. Then, in order to ensure the accuracy and usability of the data, data preprocessing is performed.

[0058] The data preprocessing process includes data cleaning, which is used to process missing values and outliers to ensure the integrity and consistency of the data; data integration, which integrates data from different sources into a unified data set and performs data standardization to eliminate the dimensional differences between different variables; and the original interconnected flexible power distribution system data is obtained after preprocessing the interconnected flexible power distribution system data.

[0059] Taking the time series data and network topology data in the interconnected flexible power distribution system data as an example, the time series data can be preprocessed using sliding window analysis, time series prediction and other techniques; the network topology data can extract key network structure and connection information through network topology analysis.

[0060] S102: Based on the interconnection flexible power distribution system data and the interconnection flexible power distribution system constraints, a flexible power distribution network flexible soft switch site selection and capacity planning model is established with the minimum daily comprehensive operation cost as the objective function;

[0061] Preferably, the interconnection flexible power distribution system constraints include flexible soft switch operation constraints, flexible soft switch reactive power constraints, flexible soft switch capacity constraints, power flow constraints, system voltage constraints, branch capacity constraints and energy storage device constraints;

[0062] Specifically, ① the flexible soft switch operation constraint is expressed as:

[0063] Among them, P and Q respectively represent the port, the output active power and the reactive power, P and Q respectively represent the transmission loss of different ports of the flexible soft switch, P and Q respectively represent the loss coefficient corresponding to the port;

[0064] ② The flexible soft switch reactive power constraint is expressed as:

[0065] Among them, P and Q respectively represent the upper and lower limits of the transmission reactive power of different ports of the flexible soft switch;

[0066] ③ Flexible soft switch capacity constraint

[0067] Among them, P and Q respectively represent the port, the output active power and the reactive power, P and Q respectively represent the converter capacity of different ports of the flexible soft switch;

[0068] ④ The power flow constraint is expressed as:

[0069] The power flow constraint is represented by the DistFlow branch power flow model, ∑ i:i→j (P ij -R ij I ij 2 )-P j =∑ l:j→l P jl Σ i:i→j (Q ij -X ij I ij 2 )-Q j =Σ l:j→l Q jl P j =P load,j-P g,j -P PV,j -P wind,j +P sop,j +P ess Q j =Q load,j -Q g,j -Q svc,j -Q CB,j +Q sop

[0070] Among them, I ij P represents the current flowing through the lines between node i and node j. jl With Q jl P represents the active and reactive power flowing from node j into node l. g,j With Q g,j P represents the active and reactive power outputs of the generator at node j, respectively. SOP,j With Q SOP,j P represents the active and reactive power transmitted at the flexible soft-switching port at node j, respectively. ess Q represents the active power output of the energy storage at node j. svc,j With Q CB,j U represents the continuous and discrete reactive power compensation outputs at node j, respectively, where i→j indicates that node i is an upstream node of node j. i and U j Let P be the voltage at node i and node j. ij With Q ij P represents the active and reactive power flowing from node i into node j. j With Q i R represents the active and reactive power of the net load at node j. ij With X ij P represents the resistance and reactance of the line between nodes i and j. load,j With Q load,j Let P be the active and reactive power of the load at node j. wind,j P is the active power of the wind power unit at node j. PV,j It is the active power of the photovoltaic unit at node j;

[0071] ⑤ System voltage constraint

[0072] Among them, U i,max with U i,min Indicates the upper and lower limits of the node's allowable voltage;

[0073] ⑥ Branch capacity constraints

[0074] in, Indicates the allowable current limit for the branch;

[0075] ⑦Energy storage device constraints

[0076] where μ ch,i,t , μ dis,i,t are 0-1 variables, representing the energy storage device is in charging or discharging state, η ch , η dis represent the charging and discharging efficiency of the energy storage device, represent the charging and discharging power limit of the energy storage device, represent the maximum and minimum storage capacity of the energy storage device, P ch,i,t and P dis,i,t represent the charging and discharging power of the energy storage device at node i at time t, and represent the initial and final storage capacity of the energy storage device, and represent the storage capacity at time t and the next time;

[0077] Preferably, the daily comprehensive operation cost includes the main grid output cost, operation loss cost and energy storage charging and discharging loss cost.

[0078] The main grid output cost, operation loss cost and energy storage charging and discharging loss cost are represented as:

[0079] where C pg is the main grid output cost, C loss is the operation loss cost, C ESS is the energy storage charging and discharging loss cost, C gt represents the real-time electricity price of the distribution network, P gt represents the real-time power purchase of the system, C e is the system network loss compensation coefficient, C sop is the flexible soft switch operation cost conversion unit price, N bus is the number of system network nodes, c(i) is the initial node set with node i, P loss,i,t is the transmission power loss of the flexible soft switch port i at time t, I ij is the current flowing between node i and node j line, C ess represents the energy storage device cost coefficient, ψ ess represents the installation location set of the energy storage device in the system, represents the charging and discharging power of the energy storage device at node i at time t;

[0080] Specifically, the application takes the normal power supply operation of the flexible power distribution network as the premise, takes economy as the index, considers the flexible soft switch site selection and capacity determination of the flexible power distribution network, establishes a main network output cost, operation loss cost and energy storage charging and discharging loss cost comprehensive fee minimum as an objective function, more accurately measures the economy of different flexible soft switch configuration schemes, so as to select the optimal flexible soft switch optimization configuration scheme, realizes the flexible power distribution network operation planning considering the power supply reliability and economy, and realizes the coordination and complementation between the devices in the system. The minimum comprehensive operation cost of the entire flexible soft switch optimization configuration includes three parts: min = min(C pg +C loss +C ESS )

[0081] Among them, the main network output cost, the operation loss cost and the energy storage charging and discharging loss cost;

[0082] In another possible implementation, the objective function can also include environmental cost, maintenance cost and failure cost, etc. The construction of the objective function is not fixed and can be increased or modified according to the actual situation. By reasonably selecting and adjusting the cost items and their weights, the objective function can be ensured to be more in line with the actual situation and business demand of the flexible power distribution network, so as to realize the flexible power distribution network operation planning considering the power supply reliability and economy.

[0083] S104: solving the optimization flexible soft switch site selection and capacity determination planning model by combining the improved sparrow algorithm with the second-order cone programming to obtain the flexible soft switch site selection and capacity determination scheme;

[0084] Preferably, the sparrow algorithm parameters are initialized, the initial access position of the flexible soft switch is determined, the initial access position of the flexible soft switch is taken as the decision variable of the improved sparrow algorithm and is coded, and the flexible soft switch site selection and capacity determination scheme is randomly generated;

[0085] Specifically, the coding method of the initial access position of the flexible soft switch includes binary coding, integer coding, real number coding and hybrid coding, etc.

[0086] Taking binary coding as an example, for site selection coding, assuming that there are N possible access points in the power distribution network, an integer array with a length of N can be used to represent the site selection of the flexible soft switch; each element in the array represents the access state of the corresponding position, for example, 0 represents not access, and 1 represents access; for example, for 5 possible access points, the site selection coding [1, 0, 1, 0, 1] represents that the flexible soft switch is accessed at the 1st, 3rd and 5th access points;

[0087] Taking integer coding as an example, for fixed capacity coding, for each accessed flexible soft switch, its capacity needs to be determined, an integer array can be used to represent the capacity of each access point, the length of the array is the same as the number of 1s in the address coding; for example, if the address coding is [1, 0, 1, 0, 1], and the capacities of the three access points are 50kVA, 100kVA and 75kVA respectively, then the fixed capacity coding can be [50, 75, 100];

[0088] Preferably, the flexible soft switch connection ports are not repeated and the flexible soft switch ports cannot be connected to the power grid head node.

[0089] Preferably, Levy flight strategy is introduced in the iteration solving process of the sparrow algorithm, and a Levy flight term is added in the update of the flexible soft switch location and capacity scheme with a better target function value, so as to increase the interference on the flexible soft switch location and capacity scheme.

[0090] It should be noted that in the sparrow algorithm, the population is divided into three different roles for the location and capacity problem of the flexible soft switch: discoverer, follower and alarm. Among them, the discoverer specifically refers to the flexible soft switch location and capacity scheme with a better target function value. In each iteration of the sparrow algorithm, the discoverer searches the search space to find a flexible soft switch location and capacity scheme with a better target function value. These discoverers usually represent the potential optimal solution in the search process, and their position update is more extensive and random, aiming to cover a larger search range in order to find a better solution.

[0091] Specifically, in the sparrow evolution algorithm, the Levy flight strategy is added in the position update of the discoverer. Levy flight is a random search method subject to Levy distribution, which is a walking mode alternating between short-distance search and occasional long-distance walking. Levy flight makes the change of individual position more flexible and the search range larger, which can prevent the algorithm from stagnating, thereby promoting the discoverer to have good global searchability.

[0092] With the iteration of the sparrow algorithm, the discoverer will constantly move in the search space to find a better flexible soft switch location and capacity scheme. Once a better solution is found, these solutions will be considered as new discoverers and continue to participate in the search process. In this way, the algorithm can gradually approach the optimal solution and finally find a flexible soft switch location and capacity scheme that meets the requirements.

[0093] Specifically, the position update of the discoverer with Levy flight is represented as:

[0094] wherein, represents the position of the ith sparrow individual in the dth dimension at the tth generation, f gPbest represents the current population best fitness, a represents a random number between [0, 1], T represents the set maximum iteration number, L represents a unit vector, Γ represents a random number obeying a standard normal distribution, L F P represents a flight function, R2 represents a warning value, ST represents a safety value, γ represents a flight scale, β represents a flight coefficient taking 1.5, μ and v respectively represent normal distribution random numbers σ v =1,

[0095] Preferably, the flexible soft switch operation constraint, the flexible soft switch reactive power constraint, the flexible soft switch capacity constraint and the power flow constraint are converted into a second-order cone optimization model through linearization and second-order cone relaxation, and the second-order cone optimization model is solved by using an SOCP algorithm;

[0096] Preferably, solving the second-order cone optimization model by using the SOCP algorithm is expressed as: Σ i:i→j (P ij -R ij I′ ij )-P j =Σ l:j→l P jl Σ i:i→j (Q ij -X ij I′ ij )-Q j =Σ l:j→l Q jl

[0097] wherein, P i link , and respectively represent output active power and reactive power of a port i, j, i->j represents that node i is an upstream node of node j, U i and U j are voltages of nodes i and j, P ij and Q ij are active power and reactive power flowing from node i into node j, P j and Q j are active power and reactive power of a net load at node j, R ij and X ij are resistance and reactance values of a line between nodes i and j, P jl and Q jl represent active power and reactive power flowing from node j into node i, I ij represents a line current flowing through nodes i and j, I′ij , u i represent the square terms of I ij and U i ;

[0098] Preferably, the branch current constraint and the node voltage constraint are also changed accordingly:

[0099] wherein U i,max and U i,min represent the upper and lower limits of the node allowed voltage, represent the branch allowed current limit;

[0100] Specifically, in the optimization of the flexible soft switch location and capacity problem, in order to improve the solving efficiency and accuracy, the operation constraints, reactive power constraints, capacity constraints and power flow constraints of the flexible soft switch are preferably converted into a second-order cone optimization model through linearization and second-order cone relaxation;

[0101] This conversion process aims to simplify the originally complex nonlinear constraints into a convex optimization problem that is easier to handle, so that the second-order cone programming (SOCP) algorithm can be used for efficient solving. Specifically, first, the power flow equations of the flexible soft switch and the distribution network are linearized, and the second-order cone relaxation technique is used to convert the nonlinear constraints into the form of a SOCP problem. The square terms of power, node voltage, branch current and other key variables are introduced into the flexible distribution network flexible soft switch location and capacity planning model in an appropriate manner to ensure that the model can accurately reflect the physical characteristics and constraint conditions of the system.

[0102] It should be noted that the converted second-order cone optimization model is solved using the SOCP algorithm, which has the advantage of handling convex optimization problems and can improve the solving speed and efficiency while ensuring global optimal solution. Through the SOCP algorithm, various constraint conditions including flexible soft switch converter capacity limitation, node voltage constraint, branch current constraint, etc. are effectively handled, so as to obtain the optimization result that meets the system requirements.

[0103] S106: Calculate the objective function corresponding to the flexible soft switch location and capacity scheme, and obtain the optimal flexible soft switch location and capacity scheme;

[0104] Preferably, the objective function value corresponding to the flexible soft switch location and capacity scheme is used as the fitness function value of the improved sparrow algorithm;

[0105] Preferably, if the fitness function value reaches a set threshold or the number of iterations reaches the maximum number of iterations of the improved sparrow algorithm, the optimal flexible soft switch location and capacity scheme is output;

[0106] It should be noted that in actual application, when the flexible soft switch location and capacity scheme is calculated, a suitable fitness function threshold and iteration number need to be set, the threshold is set according to the problem nature, calculation resource and accuracy requirement, and the iteration number considers the problem complexity and convergence speed; through continuous experiment and adjustment, the optimal setting is found to efficiently obtain the optimal location and capacity scheme.

[0107] The above is a schematic scheme of the power distribution network flexible soft switch location and capacity method based on mixed second-order cone programming of the embodiment. It should be noted that the technical scheme of the power distribution network flexible soft switch location and capacity system based on mixed second-order cone programming belongs to the same concept as the technical scheme of the power distribution network flexible soft switch location and capacity method based on mixed second-order cone programming described above. The technical scheme of the power distribution network flexible soft switch location and capacity system based on mixed second-order cone programming in the embodiment is not described in detail, and can be referred to the description of the technical scheme of the power distribution network flexible soft switch location and capacity method based on mixed second-order cone programming.

[0108] The power distribution network flexible soft switch location and capacity system based on mixed second-order cone programming in the embodiment comprises:

[0109] The data acquisition module is configured to acquire interconnected flexible power distribution system data.

[0110] The model establishment module is configured to establish a flexible power distribution network flexible soft switch location and capacity planning model based on the interconnected flexible power distribution system data and interconnected flexible power distribution system constraints, with the minimum daily comprehensive operation cost as an objective function.

[0111] The model solution module is configured to solve the optimized flexible power distribution network flexible soft switch location and capacity planning model by using the improved sparrow algorithm combined with the second-order cone programming, and acquire a flexible soft switch location and capacity scheme.

[0112] The optimal scheme acquisition module is configured to calculate the objective function corresponding to the flexible soft switch location and capacity scheme, and acquire an optimal flexible soft switch location and capacity scheme.

[0113] The embodiment also provides a computing device suitable for the power distribution network flexible soft switch location and capacity based on mixed second-order cone programming, comprising:

[0114] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the power distribution network flexible soft switch location and capacity method based on mixed second-order cone programming as proposed in the above embodiment.

[0115] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to implement the power distribution network flexible soft switch location and capacity method based on mixed second-order cone programming as proposed in the above embodiment.

[0116] The storage medium proposed in the embodiment belongs to the same inventive concept as the method for realizing flexible soft switch site selection and capacity determination of a power distribution network based on mixed second-order cone programming proposed in the above embodiment, and technical details not described in detail in the embodiment can be seen from the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the method of each embodiment of the present application.

[0118] Embodiment 2

[0119] Referring to FIGS. 3-7 and Tables 1-3, for an embodiment of the present application, a method for flexible soft switch site selection and capacity determination of a power distribution network based on mixed second-order cone programming is provided, and comparative results of several schemes are provided to verify the beneficial effects.

[0120] For the embodiment of the present application, the voltage level of the IEEE33 system network is set to 12.66 kV, the total active load is 3.715 MW, and the total reactive load is 2.3 Mvar.

[0121] The 12-node and 25-node access photovoltaic power generation systems, the 14-node and 29-node access wind power generation systems, and different capacity energy storage devices are respectively connected to node 18 (the upper limit of the capacity is 1.8 MW, and the lower limit is 0.18 MW) and node 33 (the upper limit of the capacity is 1.0 MW, and the lower limit is 0.1 MW); at the same time, according to the system operation requirements, continuous reactive compensation devices SVC and discrete reactive compensation devices CB are configured at nodes 6, 16 and 32.

[0122] The system time-of-use price is shown in Table 1, wherein the flexible soft switch loss coefficient is 0.02, the operation unit price is set to 400 yuan / (MW·h), the energy storage charging and discharging efficiency is 0.9, and the operation unit price is 400 yuan / (MW·h).

[0123] Table 1 Daily power purchase price table of power distribution network

[0124] As shown in Table 1, Table 1 lists the daily power grid purchase power price in detail, and is distinguished according to time period (peak, flat and valley), which reflects the real-time dynamics and flexibility of the power market; this time-of-use pricing mechanism provides economic incentives for the optimal operation of the energy storage device and the reactive power compensation device in the present application, helps to reduce system cost and improve overall energy efficiency;

[0125] In order to fully verify the effectiveness and practicality of the present application in improving energy utilization efficiency and reducing system cost, relevant data is collected and obtained, as shown in Tables 2 and 3.

[0126] Table 2 Flexible soft switch site selection and system cost optimization results

[0127] Table 3 Flexible distribution network operation loss

[0128] Table 2 compares the flexible soft switch site selection and capacity results and the comprehensive cost under the three strategies of system not interconnected, sparrow algorithm and improved sparrow algorithm. From the table, it can be seen that by introducing flexible soft switch and optimizing its site selection and capacity, the improved sparrow algorithm strategy can significantly reduce the comprehensive cost under the same number of iterations compared with the system not interconnected and the sparrow algorithm strategy, which shows that the improved sparrow algorithm strategy not only improves the economy of the system, but also demonstrates the innovation and effectiveness of the present application in strategy optimization.

[0129] Table 3 further shows the operation loss under the three scenarios of system not interconnected, sparrow algorithm and improved sparrow algorithm. By comparing the system loss, flexible soft switch loss and comprehensive loss, it can be seen that the improved sparrow algorithm strategy performs best in reducing system loss, which proves that the present application can effectively reduce the operation loss of the distribution network and improve the energy utilization efficiency by introducing flexible soft switch and optimizing the strategy.

[0130] In order to visually show the effect and advantage of the present application, the test related results are visualized, as shown in Figures 3-7, which cover multiple aspects such as distributed power output, load change, purchase power under optimized operation strategy and power transmission of flexible soft switch.

[0131] [Corrected according to Rule 91 09.01.2025] Figure 3 shows the load data of each node and the output of distributed power sources (such as photovoltaic and wind power) in IEEE33 node system within 24 hours, which clearly reflects the supply and demand situation of the system at different time periods, providing basic data for the optimized operation of the present application. By reasonably scheduling the output of distributed power sources, the present application can more effectively meet the load demand and reduce the dependence on traditional power grid.

[0132] Fig. 4 shows the typical daily power purchase of the system IEEE33 node system under the operation of different strategies;

[0133] Strategy 1: The system does not operate through flexible soft switch interconnection;

[0134] Strategy 2: The system operates through the original sparrow optimization algorithm for flexible soft switch site selection and capacity optimization;

[0135] Strategy 3: The system operates through the improved sparrow optimization algorithm for flexible soft switch site selection and capacity optimization;

[0136] Fig. 4 shows that the system power purchase is reduced under the operation of the improved sparrow optimization algorithm combined with the second-order cone programming, which shows that the optimization strategy of the present application can significantly improve the economy of the system and reduce the operation cost.

[0137] Fig. 5 shows the flexible soft switch transmission active power of the system, when the DG output is greater than the system load (such as 12:00-14:00), the excess part can only be abandoned or partially charged into the energy storage device, causing waste of power generation. After the flexible soft switch interconnection, the power is transmitted from the side close to the DG (node 8 in strategy 3) to the side far from the DG (node 33 in strategy 3) through the flexible soft switch.

[0138] Fig. 6 shows the data performance of two different nodes (node 8 in strategy 3 and node 33 in strategy 3) at different time intervals (10h, 15h, 20h, 30h), the initial value of node 8 in strategy 3 is 0.02, and the initial value of node 33 in strategy 3 is 0.015. Through the form of bar chart, the data change trend of the two nodes at different time points can be clearly seen.

[0139] The optimized results are shown in Fig. 7, and the above-mentioned embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

[0140] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A hybrid second-order cone programming based method for flexible soft-switching site selection and sizing of distribution networks, characterized in that, The method comprises the following steps: acquiring interconnected flexible power distribution system data; based on the interconnected flexible power distribution system data and the interconnected flexible power distribution system constraints, a flexible power distribution network flexible soft switch site selection and capacity planning model is established with the minimum daily comprehensive operation cost as the objective function; the improved sparrow algorithm is combined with the second-order cone programming to solve and optimize the flexible power distribution network flexible soft switch site selection and capacity planning model, and a flexible soft switch site selection and capacity planning scheme is acquired; the objective function corresponding to the flexible soft switch site selection and capacity planning scheme is calculated to acquire an optimal flexible soft switch site selection and capacity planning scheme.

2. The method of claim 1, wherein the mixed second-order cone programming based flexible soft-switching site and capacity sizing method for distribution networks is characterized by, The interconnected flexible power distribution system constraints comprise flexible soft switch operation constraints, flexible soft switch reactive power constraints, flexible soft switch capacity constraints, power flow constraints, system voltage constraints, branch capacity constraints and energy storage device constraints. 3.The method of claim 1 or 2, wherein, The objective function comprises, The daily comprehensive operation cost comprises main grid output cost, operation loss cost and energy storage charging and discharging loss cost. The main grid output cost, operation loss cost, and energy storage charge and discharge loss cost are expressed as: Wherein, C pg is the main grid output cost, C loss is the operation loss cost, C ESS is the energy storage charging and discharging loss cost, C gt represents the real-time electricity price of the distribution network, P gt represents the system real-time power purchase, C e is the system network loss compensation coefficient, C sop is the flexible soft switch operation cost conversion unit price, N bus is the system network node number, c(i) is the initial node set with node i, P loss,i,t is the transmission power loss of the flexible soft switch port i at time t, I ij is the current flowing between node i and node j line, C ess represents the energy storage device cost coefficient, ψ ess represents the installation position set of the energy storage device in the system, represents the charging and discharging power of the energy storage device at node i at t time.

4. The method of claim 3, wherein the mixed second-order cone programming based flexible soft-switching site and capacity sizing method for distribution networks is characterized by, Solving the flexible power distribution network flexible soft switch site selection and capacity planning model by using the improved sparrow algorithm comprises the following steps: initializing the sparrow algorithm parameters, determining the initial access position of the flexible soft switch, taking the initial access position of the flexible soft switch as the decision variable of the improved sparrow algorithm and encoding, and randomly generating a flexible soft switch site selection and capacity planning scheme; the flexible soft switch connection ports are not repeated and the flexible soft switch ports cannot be connected to the head node of the power distribution network; in the process of iterative solving of the sparrow algorithm, the Levy flight strategy is introduced, the Levy flight term is added in the update of the flexible soft switch site selection and capacity planning scheme with the optimal objective function value, and interference is added to the flexible soft switch site selection and capacity planning scheme.

5. The method of claim 4, wherein the mixed second-order cone programming based flexible soft-switching site and capacity sizing method for distribution networks is characterized by, Optimizing the flexible power distribution network flexible soft switch site selection and capacity planning model by using the second-order cone programming comprises the following steps: the flexible soft switch operation constraints, the flexible soft switch reactive power constraints, the flexible soft switch capacity constraints and the power flow constraints are converted into a second-order cone optimization model through linearization and second-order cone relaxation, and the SOCP algorithm is used to solve the second-order cone optimization model. 6.The method of claim 5, wherein, Further comprising, Solving the second order cone optimization model represented as: Σ i:i→j (P ij -R ij I′ ij )-P j =Σ l:j→l P jl Σ i:i→j (Q ij -X ij I′ ij )-Q j =Σ l:j→l Q jl wherein and represent the active and reactive power output by ports i, j, respectively, i i and U j are the voltages at nodes i and j, P ij , Q ij are the active and reactive power flowing from node i into node j, P j and Q j are the active and reactive power of the net load at node j, R represents the different port converter capacity of the flexible soft switch, R ij and X ij are the resistance value and reactance value of the line between nodes i, j, P jl and Q jl represent the active power and reactive power of the line between nodes i, j, I ij represent the current flowing through the line between nodes i, j, I′ ij , u i respectively represent the square term of I ij and U i .

7. The method of claim 4 or 6, wherein the mixed second-order cone programming based flexible soft-switching siting and sizing method for distribution networks is characterized in that, calculating the objective function corresponding to the flexible soft switch site selection and capacity planning scheme comprises: taking the objective function value corresponding to the flexible soft switch site selection and capacity planning scheme as the fitness function value of the improved sparrow algorithm; if the fitness function value reaches a set threshold or the number of iterations reaches the maximum number of iterations of the improved sparrow algorithm, output the optimal flexible soft switch site selection and capacity planning scheme.

8. A system for flexible soft-switching siting and sizing of distribution networks based on mixed second-order cone programming, characterized in that, comprising: a data acquisition module for acquiring interconnected flexible power distribution system data; a model establishment module for establishing a flexible power distribution network flexible soft switch site selection and capacity planning model based on the interconnected flexible power distribution system data and the interconnected flexible power distribution system constraints, with the minimum daily comprehensive operation cost as the objective function; a model solving module for solving and optimizing the flexible power distribution network flexible soft switch site selection and capacity planning model by using the improved sparrow algorithm combined with the second-order cone programming, and acquiring a flexible soft switch site selection and capacity planning scheme; an optimal scheme acquisition module for calculating the objective function corresponding to the flexible soft switch site selection and capacity planning scheme, and acquiring an optimal flexible soft switch site selection and capacity planning scheme.

9. An electronic device comprising: a memory and a processor; The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the method for flexible soft-switching site selection and capacity determination of a power distribution network based on a mixed second-order cone programming according to any one of claims 1 to 7. 10.A computer readable storage medium storing computer executable instructions, and the computer executable instructions, when executed by a processor, implement the steps of the method for flexible soft-switching site selection and capacity determination of a power distribution network based on a mixed second-order cone programming according to any one of claims 1 to 7.

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