SOP configuration optimization method and system based on comprehensive revenue

Abstract

The invention discloses an SOP configuration optimization method and system based on comprehensive revenue, and relates to the technical field of power distribution network optimization, and the method comprises the steps: collecting data information, carrying out the preprocessing, and constructing the constraint of a distributed power supply; based on the electricity price and the operation cost of the distributed power supply, optimization is carried out by taking the minimum operation cost of the system as a target; an SOP optimization model is constructed, and SOP constraints are determined; the SOP constraint and the constraint of the distributed power supply are converted into a second-order cone programming model by adopting a cone relaxation technology; and calling a commercial solver Cplex to solve the second-order cone programming model, outputting the annual minimum comprehensive cost, and completing SOP configuration optimization. According to the method, the annual comprehensive cost of the power distribution network is established as a target function, the position of the SOP is configured, and a second-order cone model is established for solving. The economical efficiency and the flexibility of the power distribution network are improved by giving full play to the SOP regulation capability, and the network loss is reduced. And the operation potential of the power distribution network containing the distributed power supply is explored.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network optimization, and in particular to a SOP configuration optimization method and system based on comprehensive benefits. Background Art

[0002] In recent years, with the "Thousands of Households Bathed in Light Action", "Photovoltaic Model" and "Blue Book on the Development of New Power Systems" to promote flexible access to distributed photovoltaics and accelerate the construction of new power systems to promote low-carbon energy transformation, distributed photovoltaic power generation technology has developed rapidly, installed capacity has continued to grow, and photovoltaic development has shown a development pattern from centralized to centralized and distributed.

[0003] For the new power system with high penetration rate of photovoltaic access, on the one hand, the photovoltaic output is strongly correlated with complex random meteorological factors, and on the other hand, due to the characteristics of massive distributed grid connection of photovoltaic and new loads, the grid dispatching operation mode and power flow distribution are randomized and multi-sourced. At the same time, the time-space mismatch between source and load is not only not conducive to the local consumption of photovoltaic resources, but also leads to a large number of high / low voltage coexistence problems in the high penetration rate photovoltaic distribution network, and even causes voltage limit exceeding, which seriously affects the safe and stable operation of the distribution network.

[0004] In summary, there is an urgent need for a SOP site selection and sizing planning method for distribution networks with high penetration rate of photovoltaic access that takes into account comprehensive benefits, so as to rationally plan and design a SOP collaborative planning method for distribution networks at the design level. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: how to optimize the site selection and sizing of SOP in a distribution network with high penetration of photovoltaic access through comprehensive benefits, reduce system operating costs, and improve power supply reliability and voltage stability.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a SOP configuration optimization method based on comprehensive benefits, which comprises the following steps:

[0008] In a distribution network with distributed power sources, data information is collected for preprocessing and constraints on distributed power sources are constructed;

[0009] Based on the electricity price and the operating cost of distributed power sources, optimization is performed with the goal of minimizing the system operating cost;

[0010] Build SOP optimization model and determine SOP constraints;

[0011] The cone relaxation technique is used to transform the SOP constraints and the distributed generation constraints into a second-order cone programming model;

[0012] The commercial solver Cplex is called to solve the second-order cone programming model, output the annual minimum comprehensive cost, and complete the configuration optimization of the SOP.

[0013] As a preferred solution of the SOP configuration optimization method based on comprehensive benefits described in the present invention, the data information includes the voltage, current, active power, reactive power, output of photovoltaic loads and impedance of the line at each node in the distribution network;

[0014] The preprocessing is to use filtering technology to remove noise in the data, normalize all the data to a uniform range, detect and process outliers through the interquartile range, and exclude abnormal data points caused by measurement errors and equipment failures.

[0015] As a preferred solution of the SOP configuration optimization method based on comprehensive benefits described in the present invention, the constraint of the distributed power source is to constrain the output of the distributed power source configured in the system, and the expression is:

[0016]

[0017] Among them, Q i,t is the injected reactive power of node i during period t; V i,t and θ ij,t are the voltage amplitude of node j in period t and the phase angle difference between nodes i and j, G ii , B ii and G ij , B ij are the self-conductance, self-susceptance, mutual conductance and mutual susceptance in the node admittance matrix, is the predicted value of active power output by DG V i,max ,V i,min are the upper and lower limits of the voltage amplitude of node i, respectively, ij,t , I ij,max are the current amplitude and upper limit of branch ij in period t, β DG,i,t ,S DG,i are the power factor angle and access capacity of DG at node i in period t respectively.

[0018] As a preferred solution of the SOP configuration optimization method based on comprehensive benefits described in the present invention, the optimization with the minimum system operating cost as the goal includes constructing the annual minimum comprehensive cost as the objective function, and the expression is:

[0019]

[0020] in, C lossThey are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power supply loss cost of the distribution network.

[0021] As a preferred solution of the SOP configuration optimization method based on comprehensive benefits described in the present invention, the annual minimum comprehensive includes the SOP annual investment cost, the SOP annual operation and maintenance cost, and the distribution network annual power supply loss cost, and the expression is:

[0022]

[0023] in, C loss They are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of distribution network. is the set of adjacent nodes of node i, Csop is the unit capacity investment cost of SOP, S SOP,ij is the capacity of the SOP installed between nodes i and j, y is the service life of the SOP, β is the annual operation and maintenance cost coefficient of the SOP, μ is the annual power loss cost coefficient of the distribution network, and n t is the total number of time periods, Δt is the duration of each time period, P i,t is the injected active power of node i in period t, is the active power loss of SOP at node i during period t.

[0024] As a preferred solution of the SOP configuration optimization method based on comprehensive benefits described in the present invention, the construction of the SOP optimization model is to model the position and quantity of the SOPs configured in the system, and the expression is:

[0025]

[0026]

[0027]

[0028] Among them, P SOP,i,t , P SOP,j,t are the active power injected by SOP at node i and node j in period t, are the active losses of the converters in the SOP at nodes i and j during period t, A sop,i , A sop,j are the converter loss coefficients in the SOP at nodes i and j, respectively, Q SOP,i,t , Q SOP,j,t are the reactive power injected by SOP at node i and node j during period t, Q SOP,i,min , Q SOP,i,max and Q SOP,j,min , QSOP,j,max are the upper and lower limits of reactive power injected by SOP at node i and node j respectively.

[0029] As a preferred solution of the SOP configuration optimization method based on comprehensive benefits described in the present invention, the second-order cone programming model uses cone relaxation technology to transform the SOP constraints and the constraints of distributed power sources, and the expression is:

[0030]

[0031] in, C loss They are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of distribution network;

[0032] The commercial solver Cplex is called to solve the second-order cone programming model, solve the annual comprehensive cost of all nodes, determine the optimal SOP access location based on the annual minimum comprehensive cost, and complete the SOP configuration optimization based on comprehensive benefits.

[0033] Another object of the present invention is to provide a SOP configuration optimization system based on comprehensive benefits, which can collect and preprocess data such as voltage, current, active power, reactive power, etc. at each node of the distribution network, build and optimize the SOP model, and ultimately determine the optimal SOP access position and capacity, thereby solving the problems of voltage over-limit and unstable power supply caused by high-penetration photovoltaic access in existing methods.

[0034] To solve the above technical problems, the present invention provides the following technical solutions: a SOP configuration optimization system based on comprehensive benefits, comprising: a data acquisition and preprocessing module, a system modeling module, an optimization target construction module and a SOP optimization solution module.

[0035] The data acquisition and preprocessing module collects the voltage, current, active power, reactive power, output of photovoltaic loads and line impedance of each node from the distribution network, removes noise in the data through filtering technology, normalizes and standardizes the data, detects and processes outliers using the interquartile range, and eliminates abnormal data points caused by measurement errors and equipment failures.

[0036] The system modeling module is used to model the output of distributed power sources, taking into account the constraints of distributed power sources. The modeling includes the reactive power injected into the node, the node voltage amplitude, the phase angle difference, etc. The model generated by this module will be used for subsequent optimization calculations.

[0037] The optimization target building module is to build an optimization model with the goal of minimizing the system operation cost. The objective function comprehensively considers the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of the distribution network. By determining the unit capacity investment cost of SOP, the operation and maintenance cost coefficient, and the power loss cost coefficient, the optimal cost allocation is calculated.

[0038] The SOP optimization solution module converts the SOP optimization model into a second-order cone programming model and calls a commercial solver for solution. This module obtains the optimal SOP access position and the corresponding planning scheme indicators through solution, completes the SOP configuration optimization based on comprehensive benefits, makes the solution result of the model meet the various constraints in practical applications, and optimizes the system operation cost.

[0039] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the SOP configuration optimization method based on comprehensive benefits as described above are implemented.

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the SOP configuration optimization method based on comprehensive benefits as described above.

[0041] Beneficial effects of the present invention: The SOP configuration optimization method based on comprehensive benefits provided by the present invention proposes a SOP configuration optimization method based on comprehensive benefits by solving the abnormal problem of distribution network loss caused by the strong volatility of the output of the distributed power source and the load power of the distribution network. By establishing the annual comprehensive cost of the distribution network as the objective function, the position of the SOP is configured and a second-order cone model is established to solve it. Give full play to the SOP adjustment ability to improve the economy and flexibility of the distribution network and reduce network losses. Explore the operating potential of the distribution network containing distributed power sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0043] Figure 1 An overall flow chart of a SOP configuration optimization method based on comprehensive benefits provided by the first embodiment of the present invention;

[0044] Figure 2 A structural diagram of a SOP configuration optimization system based on comprehensive benefits provided by a second embodiment of the present invention;

[0045] Figure 3 A power grid structure topology diagram of a SOP configuration optimization method based on comprehensive benefits provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0047] Example 1

[0048] Reference Figure 1 According to an embodiment of the present invention, a SOP configuration optimization method based on comprehensive benefits is provided, comprising:

[0049] In a distribution network containing distributed power sources, data information is collected for preprocessing and constraints on distributed power sources are constructed.

[0050] Based on electricity prices and the operating costs of distributed power sources, optimization is performed with the goal of minimizing system operating costs.

[0051] Build SOP optimization model and determine SOP constraints.

[0052] Cone relaxation technology is used to transform the SOP constraints and distributed generation constraints into a second-order cone programming model.

[0053] The commercial solver Cplex is called to solve the second-order cone programming model, output the annual minimum comprehensive cost, and complete the configuration optimization of the SOP.

[0054] The data information includes the voltage, current, active power, reactive power, output of photovoltaic loads and line impedance of each node in the distribution network.

[0055] Preprocessing is to use filtering technology to remove noise from the data, normalize all data to a uniform range, detect and process outliers through the interquartile range, and eliminate abnormal data points caused by measurement errors and equipment failures.

[0056] The output of the distributed power sources configured in the system is modeled, and the constraints of the distributed power sources are expressed as follows:

[0057]

[0058] V i,min ≤V i,t ≤V i,max

[0059]

[0060] Among them, Q i,t is the injected reactive power of node i during period t; V i,t and θ ij,t are the voltage amplitude of node j in period t and the phase angle difference between nodes i and j, G ii , B ii and G ij , B ij are the self-conductance, self-susceptance, mutual conductance and mutual susceptance in the node admittance matrix, is the predicted value of active power output by DG V i,max ,V i,min are the upper and lower limits of the voltage amplitude of node i, respectively, ij,t , I ij,max are the current amplitude and upper limit of branch ij in period t, β DG,i,t ,S DG,i are the power factor angle and access capacity of DG at node i in period t respectively.

[0061] Optimization with the goal of minimizing system operating costs includes constructing the annual minimum comprehensive cost as the objective function, which is expressed as:

[0062]

[0063] in, C loss They are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power supply loss cost of the distribution network.

[0064] The annual minimum comprehensive cost includes the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of the distribution network, and the expression is:

[0065]

[0066]

[0067] in, C loss They are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of distribution network. is the set of adjacent nodes of node i, Csop is the unit capacity investment cost of SOP, S SOP,ij is the capacity of the SOP installed between nodes i and j, y is the service life of the SOP, β is the annual operation and maintenance cost coefficient of the SOP, μ is the annual power loss cost coefficient of the distribution network, and n tis the total number of time periods, Δt is the duration of each time period, P i,t is the injected active power of node i in period t, is the active power loss of SOP at node i during period t.

[0068] The construction of SOP optimization model is to model the position and quantity of SOP configured in the system, and the expression is:

[0069] The SOP constraint expression is:

[0070]

[0071]

[0072] The SOP constraint expression is:

[0073]

[0074] Among them, P SOP,i,t , P SOP,j,t are the active power injected by SOP at node i and node j in period t, are the active losses of the converters in the SOP at nodes i and j during period t, A sop,i , A sop,j are the converter loss coefficients in the SOP at nodes i and j, respectively, Q SOP,i,t , Q SOP,j,t are the reactive power injected by SOP at node i and node j during period t, Q SOP,i,min , Q SOP,i,max and Q SOP,j,min , Q SOP,j,max are the upper and lower limits of reactive power injected by SOP at node i and node j respectively.

[0075] It is further explained that:

[0076] The comprehensive benefit optimal problem is the most common type of optimization problem in the power system. It means that under the premise of satisfying the physical constraints of the power network such as system stable operation and safety constraints, the relevant parameters of the generator or load are adjusted by controlling the relevant power devices to optimize the objective functions such as the total power generation cost and total network loss of the system. The objective function or constraint equation of the comprehensive benefit problem is usually nonlinear, and the optimization variables may be continuous or discrete. The standard form of the comprehensive benefit optimal problem is as follows

[0077]

[0078] Where x is the optimization variable: f(u, x) represents the optimization objective function; g(ux) = 0 represents the equality constraint; h(ux) < 0) represents the inequality constraint. The variables in the optimal comprehensive benefit are used to characterize the operating state of the power system, generally including: node voltage amplitude and phase angle, node injected active power and reactive power variables.

[0079] However, due to the non-convexity caused by the quadratic power flow constraint, the optimal comprehensive benefit problem is a non-convex optimization problem that is difficult to solve accurately and is prone to fall into a local solution during the solution process. Therefore, the efficient solution of the optimal comprehensive benefit problem depends on the progress of convex optimization theory.

[0080] The main idea of ​​the convex relaxation method is to relax some non-convex constraints into convex optimization problems about new variables through variable substitution, and then solve the global optimal solution of the convex optimization problem. The optimization solution using the convex relaxation method is highly efficient, and in the case of precise relaxation, it can be guaranteed to obtain the global optimal solution of the original problem within a polynomial solution time. Among them, convex relaxation technology represented by second-order cone programming relaxation has been widely used in solving the problem of optimal comprehensive benefits.

[0081] As a special convex optimization problem, the second-order cone programming has the following mathematical expression:

[0082]

[0083] Where: x is the optimization variable; A i is the second-order cone constraint coefficient. Second-order cone programming is between linear programming and semidefinite programming and is a convex optimization problem. i = 0, the second-order cone programming problem becomes a linear optimization problem; when c i =0, the second-order cone programming problem becomes a quadratic constrained subprogramming problem.

[0084] The nonlinear equality constraints in the optimal mathematical optimization model of comprehensive benefits make the optimal comprehensive benefits problem a non-convex nonlinear optimization problem. In order to solve the global optimal solution of the optimization problem within the polynomial solution time, the second-order cone convex relaxation method is adopted to process the non-convex constraints and convert the non-convex problem into a convex optimization problem, so as to obtain the optimal solution by solving the relaxed optimization problem.

[0085] The second-order cone convex relaxation process is as follows: first, the variables and constraints are transformed accordingly, with the goal of transforming the non-convex power flow equation into a convex form and performing the corresponding optimization solution. During the transformation process, the variables or phasors can be relaxed toward the constraints, and the optimization model can be converted into a convex optimization problem to solve the global optimal solution. Although this relaxation method expands the feasible domain of the constraints, in the case of precise relaxation, the optimal solution of the original problem can be obtained on the boundary of the original feasible domain.

[0086] For the model with the best comprehensive benefit, the second-order cone convex relaxation method eliminates the phase angle information of voltage and current by introducing the square of the node voltage modulus and the square of the line current modulus, and relaxes the power flow constraint to the relationship between the node injection power and the square of the node voltage and the square of the node injection power and the square of the line current. The relaxed variables are the square of the node voltage modulus and the square of the line current modulus, which need to be restored to the optimal solution of the problem with the best comprehensive benefit. The specific variables that need to be restored are the node voltage modulus, phase angle information and line circuit modulus information, among which the node voltage modulus and the line current modulus can be obtained by taking the square root of the node voltage modulus square and the line current modulus square, respectively, to obtain the node voltage modulus and the line current modulus, and the node voltage phase angle information, that is, to restore the phase angle information of the voltage difference at both ends of the line, and the recovery process is as follows:

[0087]

[0088] Among them, β ij is the phase angle difference of the voltage at both ends of the line. By defining the voltage phase angle information of the root node as zero, the voltage phase angle information of the remaining nodes can be restored accordingly.

[0089] It should be further explained that the present invention first briefly introduces the optimal problem of comprehensive benefits and the second-order cone convex relaxation method, and then takes a 40-node distribution network in Liaoning as an example to introduce the SOP configuration optimization model based on comprehensive benefits, and derives the optimization mathematical model based on second-order cone convex relaxation.

[0090] The above method is used to convert the original model into a second-order cone programming model, and the expression is:

[0091]

[0092]

[0093] in, C loss They are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power supply loss cost of the distribution network.

[0094] It should be further explained that the constraints of distributed power generation and SOP constraints are converted through second-order cones, a second-order cone programming model is constructed, and the commercial solver Cplex is called to solve the second-order cone programming model to solve the annual comprehensive cost of all nodes. The optimal SOP access position is determined according to the annual minimum comprehensive cost, and the SOP configuration optimization based on comprehensive benefits is completed.

[0095] Example 2

[0096] Reference Figure 2, which is an embodiment of the present invention, provides a system for a SOP configuration optimization method based on comprehensive benefits, including: a data acquisition and preprocessing module, a system modeling module, an optimization target building module and a SOP optimization solving module.

[0097] The data acquisition and preprocessing module collects the voltage, current, active power, reactive power, output of photovoltaic loads and line impedance of each node in the distribution network, removes noise in the data through filtering technology, normalizes and standardizes the data, detects and processes outliers using the interquartile range, and eliminates abnormal data points caused by measurement errors and equipment failures.

[0098] The system modeling module models the output of distributed power sources, taking into account the constraints of distributed power sources. The modeling includes the reactive power injected into the node, the node voltage amplitude, the phase angle difference, etc. The model generated by this module will be used for subsequent optimization calculations.

[0099] The optimization target construction module is to build an optimization model with the goal of minimizing the system operation cost. The objective function comprehensively considers the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of the distribution network. By determining the unit capacity investment cost of SOP, the operation and maintenance cost coefficient, and the power loss cost coefficient, the optimal cost allocation is calculated.

[0100] The SOP optimization solution module converts the SOP optimization model into a second-order cone programming model and calls a commercial solver to solve it. This module obtains the optimal SOP access location and the corresponding planning scheme indicators through solving, completes the SOP configuration optimization based on comprehensive benefits, makes the model solution meet the various constraints in practical applications, and optimizes the system operation cost.

[0101] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0103] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0104] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] Example 3

[0106] Reference Figure 3 This is another embodiment of the present invention. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment experiments the existing traditional method and the method of this embodiment respectively.

[0107] Based on the above model, this paper establishes the SOP operation planning model, and uses the mixed integer second-order cone programming method to call the commercial solver Cplex for solution. The specific planning process is as follows Figure 3The planning results obtained include the optimal SOP access location and various indicators corresponding to the planning scheme.

[0108] In order to fully verify the effectiveness and embodiment of the planning method proposed in this paper and its significant role in the distribution network, the following two schemes are given for comparative analysis based on the above example.

[0109] Scheme I: Do not plan SOP and obtain the initial operating state of the distribution network.

[0110] Plan II: Consider the coordinated planning and construction of SOPs and configure SOPs with the minimum annual comprehensive cost of the distribution network.

[0111] The simulation experiment was conducted on a fifth-generation Intel processor computer with a CPU main frequency of 3.2GHx and a storage memory of 8GB. Matlab 2019 software was used and the YALMIP toolbox was embedded to construct a second-order cone convex relaxation optimal power flow model. When solving, the CPLEX solver was used to solve the SOP configuration optimization problem based on comprehensive benefits.

[0112] Reference Figure 3 Taking a distribution network in Liaoning as an example, the distribution network topology is shown in the figure. The voltage level of the distribution network is 10kV. The area is in a place with sufficient sunlight and large photovoltaic installed capacity. There are large line branches in the distribution network, and low voltage occurs frequently at the terminal nodes. The voltage of the distribution network can be optimized by installing intelligent soft switches. The original contact switch of the actual distribution network is set between two geographically adjacent nodes. In the simulation example, a SOP with a capacity of 0.5MW is used to replace the contact switch. The location of the photovoltaic power station is shown in the figure. Through simulation analysis, the comprehensive benefit cost of different SOP locations is obtained.

[0113] Table 1 SOP solution access cost

[0114]

[0115] Through this example, it can be seen that in the actual distribution network example with a large number of branches, the optimization algorithm proposed in this paper, compared with the existing methods, effectively reduces the operating loss by reasonably installing SOPs at key locations on the basis of fully considering the comprehensive costs of all aspects of the distribution network, and also achieves the goal of reducing voltage over-limit and reducing power flow backflow. Through the coordinated planning and coordination of SOPs, flexible interconnection and energy complementarity between feeders and stations are achieved.

[0116] The above schemes are solved using the proposed planning method and the commercial solver Cplex to obtain the corresponding objective function values ​​and the optimal SOP configuration scheme for each scheme.

[0117] In Scheme I, the line loss is large, and when the power load reaches its peak or the photovoltaic output reaches its peak, the economic efficiency of the distribution network operation is poor.

[0118] Compared with Scheme I, Scheme II effectively reduces operating losses by reasonably installing SOPs at key locations based on full consideration of the comprehensive costs of all aspects of the distribution network. The table specifically shows the SOP installation results corresponding to Scheme II. Through the coordinated planning and cooperation of multi-voltage level SOPs, the lowest comprehensive benefit is achieved.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A SOP configuration optimization method based on comprehensive benefits, characterized in that: include: In a distribution network with distributed power sources, data information is collected for preprocessing and constraints on distributed power sources are constructed; Based on the electricity price and the operating cost of distributed power sources, optimization is performed with the goal of minimizing the system operating cost; Build SOP optimization model and determine SOP constraints; The cone relaxation technique is used to transform the SOP constraints and the distributed generation constraints into a second-order cone programming model; The commercial solver Cplex is called to solve the second-order cone programming model, output the annual minimum comprehensive cost, and complete the configuration optimization of the SOP.

2. A SOP configuration optimization method based on comprehensive benefits as claimed in claim 1, characterized in that: The data information includes the voltage, current, active power, reactive power, output of photovoltaic loads and impedance of the line at each node in the distribution network; The preprocessing is to use filtering technology to remove noise in the data, normalize all the data to a uniform range, detect and process outliers through the interquartile range, and exclude abnormal data points caused by measurement errors and equipment failures.

3. A SOP configuration optimization method based on comprehensive benefits as claimed in claim 2, characterized in that: The constraint of the distributed power source is to constrain the output of the distributed power source configured in the system, and the expression is: In i,min ≤V i,t ≤V i,max Among them, Q i,t is the injected reactive power of node i during period t; V i,t and θ ij,t are the voltage amplitude of node j in period t and the phase angle difference between nodes i and j, G ii , B ii and G ij , B ij are the self-conductance, self-susceptance, mutual conductance and mutual susceptance in the node admittance matrix, is the predicted value of active power output by DG V i,max ,V i,min are the upper and lower limits of the voltage amplitude of node i, respectively, ij,t , I ij,max are the current amplitude and upper limit of branch ij in period t, β DG,i,t ,S DG,i are the power factor angle and access capacity of DG at node i in period t respectively.

4. A SOP configuration optimization method based on comprehensive benefits as claimed in claim 3, characterized in that: The optimization with the minimum system operation cost as the goal includes constructing the annual minimum comprehensive cost as the objective function, which is expressed as: in, C loss They are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power supply loss cost of the distribution network.

5. A SOP configuration optimization method based on comprehensive benefits as claimed in claim 4, characterized in that: The annual minimum comprehensive cost includes the annual investment cost of SOP, the annual operation and maintenance cost of SOP and the annual power supply loss cost of distribution network, and the expression is: in, C loss They are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of distribution network. is the set of adjacent nodes of node i, Csop is the unit capacity investment cost of SOP, S SOP,ij is the capacity of the SOP installed between nodes i and j, y is the service life of the SOP, β is the annual operation and maintenance cost coefficient of the SOP, μ is the annual power loss cost coefficient of the distribution network, and n t is the total number of time periods, Δt is the duration of each time period, P i,t is the injected active power of node i in period t, is the active power loss of SOP at node i during period t.

6. A SOP configuration optimization method based on comprehensive benefits as claimed in claim 5, characterized in that: The construction of the SOP optimization model is to model the SOP position and quantity configured in the system, and the expression is: Among them, P SOP,i,t , P SOP,j,t are the active power injected by SOP at node i and node j in period t, are the active losses of the converters in the SOP at nodes i and j during period t, A sop,i , A sop,j are the converter loss coefficients in the SOP at nodes i and j, respectively, Q SOP,i,t , Q SOP,j,t are the reactive power injected by SOP at node i and node j during period t, Q SOP,i,min , Q SOP,i,max and Q SOP,j,min , Q SOP,j,max are the upper and lower limits of reactive power injected by SOP at node i and node j respectively.

7. A SOP configuration optimization method based on comprehensive benefits as claimed in claim 6, characterized in that: The second-order cone programming model uses cone relaxation technology to transform the SOP constraints and the distributed power constraints, and the expression is: in, C loss They are the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of distribution network; The commercial solver Cplex is called to solve the second-order cone programming model, solve the annual comprehensive cost of all nodes, determine the optimal SOP access location based on the annual minimum comprehensive cost, and complete the SOP configuration optimization based on comprehensive benefits.

8. A system using a SOP configuration optimization method based on comprehensive benefits as described in any one of claims 1 to 7, characterized in that: It includes data acquisition and preprocessing module, system modeling module, optimization target building module and SOP optimization solution module; The data acquisition and preprocessing module collects the voltage, current, active power, reactive power, output of photovoltaic loads and impedance of the line of each node from the power distribution network, removes the noise in the data through filtering technology, normalizes and standardizes the data, detects and processes outliers using the interquartile range, and eliminates abnormal data points caused by measurement errors and equipment failures; The system modeling module is used to model the output of distributed power sources, taking into account the constraints of distributed power sources. The modeling includes the reactive power injected into the node, the node voltage amplitude, the phase angle difference, etc. The model generated by this module will be used for subsequent optimization calculations; The optimization target construction module is to construct an optimization model with the goal of minimizing the system operation cost. The objective function comprehensively considers the annual investment cost of SOP, the annual operation and maintenance cost of SOP, and the annual power loss cost of the distribution network. By determining the unit capacity investment cost of SOP, the operation and maintenance cost coefficient, and the power loss cost coefficient, the optimal cost allocation is calculated; The SOP optimization solution module converts the SOP optimization model into a second-order cone programming model and calls a commercial solver for solution. This module obtains the optimal SOP access position and the corresponding planning scheme indicators through solution, completes the SOP configuration optimization based on comprehensive benefits, makes the solution result of the model meet the various constraints in practical applications, and optimizes the system operation cost.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a SOP configuration optimization method based on comprehensive benefits described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a SOP configuration optimization method based on comprehensive benefits as described in any one of claims 1 to 7 are implemented.

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