Optical storage and charging system optimization site selection configuration method and system based on power distribution network line bearing capacity model, and medium

Abstract

The invention relates to an optical storage and charging system optimization site selection configuration method and system based on a power distribution network line bearing capacity model and a medium, and the method comprises the following steps: reading an undirected line node number matrix according to a cim file, simplifying and re-sequencing nodes, and converting an undirected external number into a directed internal number; configuring a certain capacity of photovoltaic and energy storage on the basis of the construction of a charging station, and constructing a light storage and charging optimal configuration and site selection model with optimal economy as a target function; constructing a power distribution network line bearing capacity model, and obtaining the maximum configurable charging station capacity and site selection of the power distribution network; second-order cone programming is used for converting alternating-current power distribution network power flow constraints and node voltage constraints, linear constraint conditions are obtained, and convex programming requirements are met. According to the method, the solving precision of the algorithm can be ensured, and the convergence of solving the planning problem can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power station optimization configuration in the electric power system, and in particular to a method, system and medium for optimizing site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model. Background Art

[0002] With the development of the global economy, the number of fuel-powered vehicles has continued to rise, leading to a gradual increase in demand for fossil energy. However, the large-scale extraction and consumption of fossil energy has not only caused an energy crisis but also inflicted significant environmental damage. As a green and clean means of transportation and a superior alternative to fuel-powered vehicles, electric vehicles are becoming a new development direction for the future of the automotive industry. As the scale of electric vehicles continues to expand, the access of large charging loads will pose challenges to the operation of urban distribution networks. With the development of photovoltaic equipment and energy storage facilities, the coordinated use of photovoltaic, storage, and charging equipment can further enhance the two-way interactive capabilities of charging stations, such as flexible adjustment and system support capabilities, becoming a new paradigm for charging station construction.

[0003] Traditional distribution network designs primarily consider fixed load patterns. However, with the widespread integration of distributed power sources and electric vehicle charging infrastructure, distribution network capacity is facing new challenges. In this context, the number of existing electric vehicles continues to increase, and the corresponding electric vehicle charging infrastructure is constantly improving. The large-scale demand for electric vehicle charging places higher demands on the load management and power supply capacity of the distribution network. Furthermore, combined with the low-carbon nature of photovoltaics and the flexible regulation characteristics of energy storage devices, the coordinated operation of photovoltaics and energy storage will become a new approach to the rational utilization of charging resources in the future. However, the optimal configuration and site selection of photovoltaics, storage and charging that considers the carrying capacity of distribution network lines is still underdeveloped. The key research topics are how to determine the site selection and sizing of photovoltaics, storage and charging systems, achieve the coordinated operation of photovoltaics, storage and charging, and enhance the flexibility and stability of distribution networks. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a method, system and medium for optimizing the site selection and configuration of a photovoltaic storage and charging system based on the distribution network line carrying capacity model, and to formulate a reasonable photovoltaic storage and charging optimization configuration and site selection method to achieve the coordinated operation of photovoltaic storage and charging and improve the flexibility and stability of the distribution network.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model, comprising the following steps:

[0007] S01: Read the undirected line node number matrix according to the cim file, simplify and reorder the nodes, and convert the undirected external number into a directed internal number;

[0008] S02: Based on the construction of charging stations, a certain capacity of photovoltaic and energy storage is configured to build a photovoltaic storage and charging optimization configuration and site selection model with economic optimization as the objective function;

[0009] S03: Construct a distribution network line carrying capacity model to obtain the maximum configurable charging station capacity and location of the distribution network;

[0010] S04: Use second-order cone programming to transform the AC distribution network power flow constraints and node voltage constraints to obtain linear constraints and meet convex programming requirements.

[0011] The step S01 is specifically as follows:

[0012] Read the line node number matrix from the cim file and number the nodes according to the order in which they appear in the cim file. Since each node corresponds to a unique ID in the cim file, the node and number also correspond uniquely. Classify the read line type, including lines, load switches, disconnectors, circuit breakers, fuses, and user-specific transformers, and read user access points separately;

[0013] The lines are filtered and simplified. Since the internal topology of the user is irrelevant to this patent research, only the connection lines between user access points are of interest. Therefore, the line matrix is ​​simplified, and the nodes on the line from the user access point to the end of the line are deleted, and the nodes on the line between the user access points are retained;

[0014] Remove discontinuous numbers. Since distflow power flow calculation requires a continuously numbered line node number matrix, the simplified line node number matrix is ​​reordered to remove discontinuous numbers and obtain a continuously numbered line node number matrix.

[0015] Starting from the root node, the network is traversed and the nodes are renumbered sequentially. For the radial distribution network starting from the root node, in order to simplify the calculation and facilitate the search, all the connection lines from the root node to the user access point are traversed, and the nodes are renumbered sequentially from long to short according to the line length. The numbered nodes are stored to avoid duplicate numbering, and finally a directed line node numbering matrix is ​​obtained.

[0016] The step S02 is specifically as follows:

[0017] 1. Objective function:

[0018] When building a charging station, a certain capacity of photovoltaic and energy storage is configured. The objective function is to optimize the economic efficiency after configuration, including:

[0019] minF=min(C pv +C es +C loss +αCbuy ) (1)

[0020] Where: C pv represents the average daily investment cost of photovoltaics; C es represents the average daily investment cost of energy storage; C loss represents line loss cost; C buy represents the sub-distribution network's electricity purchase cost; α is the sub-distribution network's electricity purchase cost adjustment coefficient. Since the sub-distribution network's electricity purchase cost does not need to be considered when configuring PV storage and charging, the configuration plan should minimize line load, that is, reduce the power of the line connecting the distribution network and the main grid, thereby reducing the line current and ensuring that the energy storage charging and discharging meets the peak load demand. Therefore, the sub-distribution network's electricity purchase cost is introduced and multiplied by the adjustment coefficient.

[0021] (1) Average daily investment cost of photovoltaic power generation:

[0022]

[0023] Where: r is the discount rate; is the photovoltaic operation life cycle; u pv is the photovoltaic capital recovery coefficient, which represents the known present value of costs and The equivalence relationship between the equal-year values;

[0024] (2) Average daily investment cost of energy storage:

[0025]

[0026] Where: is the energy storage operation life cycle; u es is the energy storage capital recovery factor, which represents the known present value of cost and The equivalent relationship between the values ​​of the same year; c ess is the construction cost of unit energy storage capacity; S es is the energy storage capacity; c esp The unit maximum power energy storage construction cost; is the maximum power of energy storage; γ es is the energy storage loss coefficient;

[0027] (3) Line loss cost

[0028]

[0029] Where: c loss is the price per unit line loss;

[0030] (4) Sub-distribution network electricity purchase cost

[0031]

[0032] Where: c buyis the time-of-use electricity price; P 1,t Active power of the line connecting the distribution network and the main network;

[0033] 2. Constraints

[0034] (1) Power balance constraints

[0035]

[0036] Where, and Inject active power and reactive power into node i respectively; and is the base load active power and reactive power of node i; and is the active power and reactive power obtained from the main grid; and is the energy storage charging and discharging power; is the load power of the charging station; Producing power for photovoltaics;

[0037] (2) Energy storage constraints

[0038] Energy storage is an electrochemical energy storage system, the main forms of which include lithium-ion batteries, lead-acid batteries, sodium-sulfur batteries, and flow batteries;

[0039] Energy storage capacity constraints:

[0040]

[0041]

[0042] Where, is the energy storage capacity at time t, is the energy storage capacity at time t+1, The energy storage charging power, is the energy storage discharge power, For energy storage charging efficiency, is the energy storage discharge efficiency, is the energy storage configuration capacity; Formula (10) represents the capacity relationship between the next period and the previous period of energy storage; Formula (11) is the energy storage capacity constraint. To extend the service life of energy storage, the energy storage capacity is controlled within the range of 20% to 90% of the maximum capacity; Formula (12) satisfies the equal capacity of energy storage at the beginning and end, thus extending the service life of energy storage;

[0043] Energy storage charging and discharging constraints:

[0044]

[0045] Where, is the minimum power of energy storage, is the maximum power of energy storage; κ ES is an indicator variable. When it is 1, the energy storage is in the charging state, and when it is 0, the energy storage is in the discharging state. β is the ratio of the maximum charge and discharge power of the energy storage to the energy storage capacity. Formulas (13) and (14) indicate the upper and lower limit constraints of the energy storage charge and discharge capacity. Formula (15) shows that the maximum charge and discharge power of the energy storage is proportional to the energy storage capacity.

[0046] (3) Capacity constraints of PV and storage construction

[0047]

[0048] Where, E max is the maximum total capacity of solar-storage construction; Formula (16) shows that the energy storage construction capacity is a multiple of the energy storage unit construction capacity; Formula (17) shows that the photovoltaic construction capacity is a multiple of the photovoltaic unit construction capacity;

[0049] (4) Distflow distribution network AC power flow constraints

[0050]

[0051] Where l(j,:) represents the branch with node j as the root node; l(:,j) represents the branch with node j as the child node; Xi is the reactance of branch l; i and j are the starting point and end point of the branch respectively; B l is the line set; P l,t , Q l,t are the active power and reactive power of branch l at time t; P j,t , Q j,t are the total active load and total reactive load of node j at time t respectively; is the basic active load and charging load active power at node j at time t; is the reactive load at node j at time t; U i,t is the voltage amplitude of the line starting node i in time period t; I l,t 、R l The current in branch 1 and the resistance in branch 1 at time t respectively;

[0052] (5) Safety constraints

[0053]

[0054] Where S l,max is the upper limit of the current carrying capacity of branch l; U max 、U min are the upper and lower limits of the node voltage respectively; I max is the upper limit of the current of branch l;

[0055] (6) Grid power constraints

[0056]

[0057] Where, and and They are the upper and lower limits of the active and reactive power output of the power node respectively.

[0058] The step S03 is specifically as follows:

[0059] Establish an acceptance capacity assessment model that considers static safety constraints to evaluate the distribution network's ability to accept electric vehicles;

[0060] 1. Objective Function

[0061] The objective function is to maximize the carrying capacity of the regional distribution network, that is, the capacity of charging stations that can be configured in the distribution network is the largest. The formula is:

[0062]

[0063] Where, is the capacity of the charging station connected to node i;

[0064] 2. Constraints

[0065] (1) Power balance constraints

[0066]

[0067] (2) Energy storage constraints

[0068]

[0069] (3) Capacity constraints of PV and storage construction

[0070]

[0071] (4) Distflow distribution network AC power flow constraints

[0072]

[0073] (5) Safety constraints

[0074]

[0075] (6) Grid power constraints

[0076]

[0077] The step S04 is specifically as follows:

[0078] Since the above-mentioned photovoltaic storage and charging optimization configuration model and distribution network line carrying capacity model problems are both complex mixed non-convex nonlinear programming problems, traditional numerical solutions cannot guarantee the global optimality of the solutions. Therefore, it is necessary to use second-order cone programming (SOCP) to transform the AC distribution network power flow constraints and node voltage constraints. The resulting constraints are linear constraints and meet the convex programming requirements, which not only ensures the solution accuracy of the algorithm, but also improves the convergence of the solution to the planning problem.

[0079] Use the formula to replace the current and voltage square terms in the power flow constraint, and the corresponding constraint becomes,

[0080]

[0081]

[0082] Perform second-order cone relaxation on the equation, and the corresponding constraints are transformed into:

[0083]

[0084] The model was solved using a commercial solver.

[0085] In a second aspect, an embodiment of the present application provides a system for optimizing site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model, including a memory and a processor, wherein the memory includes a program for an optimized site selection and configuration method of a photovoltaic storage and charging system based on a distribution network line carrying capacity model, and when the program for the optimized site selection and configuration method of a photovoltaic storage and charging system based on a distribution network line carrying capacity model is executed by the processor, the following steps are implemented: reading an undirected line node number matrix according to a cim file, simplifying and reordering the nodes, and converting the undirected external number into a directed internal number; configuring a certain capacity of photovoltaic and energy storage based on the construction of a charging station, and constructing a photovoltaic storage and charging optimization configuration and site selection model with economic optimization as the objective function;

[0086] A distribution network line carrying capacity model is constructed to obtain the maximum configurable charging station capacity and location of the distribution network; second-order cone programming is used to transform the AC distribution network flow constraints and node voltage constraints to obtain linear constraints that meet convex programming requirements.

[0087] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on the distribution network line carrying capacity model as described above are implemented.

[0088] Compared with the existing technology, the beneficial effects of the present invention are: the optimal configuration and site selection method of the photovoltaic storage and charging system based on the distribution network line carrying capacity model proposed in the present invention, by standardizing and simplifying the node numbering, constructs an optimization model with economic optimization as the objective function, evaluates the maximum configurable charging station capacity and site selection of the distribution network, and uses second-order cone programming to convert the AC distribution network flow constraints and node voltage constraints into linear constraints to meet the convex programming requirements, thereby realizing an efficient and reliable optimal configuration and site selection method of the photovoltaic storage and charging system, improving the economy and stability of the distribution network, promoting the efficient use of renewable energy and the development of the electric vehicle industry, and enhancing the flexibility and reliability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0090] Figure 1 It is a design flow chart of the present invention;

[0091] Figure 2 This is a simplified process diagram of the line node numbering matrix of the present invention;

[0092] Figure 3 This is the initial circuit current diagram of the present invention

[0093] Figure 4 It is a structural schematic diagram of the electronic device provided by the invention. DETAILED DESCRIPTION

[0094] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings. It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0095] The terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0096] The terms "first," "second," etc. are only used to distinguish one entity or operation from another entity or operation, and are not to be understood as indicating or implying relative importance, nor are they to be understood as requiring or implying any actual relationship or order between these entities or operations.

[0097] The following combination Figures 1 to 3 Introducing the embodiments of the present invention, Figure 1 This is a design flow chart of a method and device for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model, specifically:

[0098] S01: Read the undirected line node number matrix according to the cim file, simplify and reorder the nodes, and convert the undirected external number into a directed internal number.

[0099] S02: Based on the construction of charging stations, a certain capacity of photovoltaic and energy storage is configured to build a photovoltaic storage and charging optimization configuration and site selection model with economic optimization as the objective function.

[0100] S03: Construct a distribution network line carrying capacity model to obtain the maximum configurable charging station capacity and location of the distribution network.

[0101] S04: Use second-order cone programming to transform the AC distribution network power flow constraints and node voltage constraints to obtain linear constraints and meet convex programming requirements.

[0102] In step S01, the undirected line node number matrix is ​​read according to the cim file, the nodes are simplified and reordered, and the undirected external number is converted into a directed internal number.

[0103] First, read the line node number matrix according to the cim file. Number the nodes according to the order in which they appear in the cim file. Since each node corresponds to a unique ID in the cim file, the node and number also correspond uniquely. Classify the lines according to the read line type, including lines, load switches, disconnectors, circuit breakers, fuses, and user-specific transformers, and read the user access points separately. Taking the 10kV Future City Wei 403 Weishan line as an example, its network topology diagram is obtained according to the cim file, as shown below: Figure 2 (a) In the figure, the red dots are user access points, the green lines are the connections between the user access points, and the yellow dots are the end nodes of the lines, which are usually the user's internal transformers.

[0104] Next, the lines are filtered and simplified. Since the internal topology of the user is irrelevant to this patent research, only the connection lines between user access points are of interest. Therefore, the line matrix is ​​simplified by removing the nodes on the line from the user access point to the end of the line, that is, the blue line nodes, and retaining the nodes on the lines between the user access points, that is, the nodes in the green lines.

[0105] Then, remove the discontinuous numbers and obtain the simplified network topology with continuous numbers, as shown in Figure 2 (b) Since distflow calculations require a continuously numbered line node number matrix, the simplified line node number matrix is ​​reordered to remove discontinuous numbers and obtain a continuously numbered line node number matrix.

[0106] Finally, we traverse the network from the root node and renumber the nodes sequentially. For a radial distribution network originating from the root node, to simplify calculations and facilitate searching, we traverse all connecting lines from the root node to the user access point. In Figure 2(b), the yellow node 146 is the starting node, and the red nodes are the user access points. We renumber the nodes sequentially from longest to shortest line length, and store the numbered nodes to avoid duplicate numbering. This ultimately results in a directed line node number matrix, as shown in Figure 2(c).

[0107] In step S02, a certain capacity of photovoltaic and energy storage is configured on the basis of the construction of the charging station, and a photovoltaic storage and charging optimization configuration and site selection model with economic optimization as the objective function is constructed.

[0108] 1. Objective function:

[0109] When building a charging station, a certain capacity of photovoltaic and energy storage is configured. The objective function is to optimize the economic efficiency after configuration, including:

[0110] minF=min(C pv +C es +C loss +αC buy ) (1)

[0111] Where: C pv represents the average daily investment cost of photovoltaics; C es represents the average daily investment cost of energy storage; C loss represents line loss cost; C buy represents the sub-distribution network's electricity purchase cost; α is the sub-distribution network's electricity purchase cost adjustment coefficient. Since the sub-distribution network's electricity purchase cost does not need to be considered when configuring PV storage and charging, the configuration plan should minimize line load, that is, reduce the power of the line connecting the distribution network and the main grid, thereby reducing the line current and ensuring that the energy storage charging and discharging meets the peak regulation demand. Therefore, the sub-distribution network's electricity purchase cost is introduced and multiplied by the adjustment coefficient.

[0112] (1) Average daily investment cost of photovoltaic power generation:

[0113]

[0114] Where: r is the discount rate; is the photovoltaic operation life cycle; u pvis the photovoltaic capital recovery coefficient, which represents the known present value of costs and The equivalence relationship between the equal-year values.

[0115] (2) Average daily investment cost of energy storage:

[0116]

[0117] Where: is the energy storage operation life cycle; u es is the energy storage capital recovery factor, which represents the known present value of cost and The equivalent relationship between the values ​​of the same year; c ess is the construction cost of unit energy storage capacity; S es is the energy storage capacity; c esp The unit maximum power energy storage construction cost; is the maximum power of energy storage; γ es is the energy storage loss coefficient.

[0118] (3) Line loss cost

[0119]

[0120] Where: c loss is the price per unit line loss.

[0121] (4) Sub-distribution network electricity purchase cost

[0122]

[0123] Where: c buy is the time-of-use electricity price; P 1,t It is the active power of the line connecting the distribution network and the main network.

[0124] 2. Constraints

[0125] (1) Power balance constraints

[0126]

[0127] Where, and Inject active power and reactive power into node i respectively; and is the base load active power and reactive power of node i; and is the active power and reactive power obtained from the main grid; and is the energy storage charging and discharging power; is the load power of the charging station; Producing power for photovoltaics.

[0128] (2) Energy storage constraints

[0129] The energy storage in this invention is an electrochemical energy storage system, primarily in the form of lithium-ion batteries, lead-acid batteries, sodium-sulfur batteries, and flow batteries. Electrochemical energy storage systems have found widespread application in various fields due to their flexibility, efficiency, and scalability. They can improve the stability and reliability of energy supply and effectively address supply-demand imbalances by storing excess electricity and releasing it during peak demand.

[0130] Energy storage capacity constraints:

[0131]

[0132] Where, is the energy storage capacity at time t, is the energy storage capacity at time t+1, The energy storage charging power, is the energy storage discharge power, For energy storage charging efficiency, is the energy storage discharge efficiency, is the energy storage configuration capacity; Equation (10) expresses the capacity relationship between the next period and the previous period of energy storage; Equation (11) is the energy storage capacity constraint. To extend the service life of energy storage, the energy storage capacity is controlled within the range of 20% to 90% of the maximum capacity; Equation (12) satisfies the equal capacity of energy storage at the beginning and end, thus extending the service life of energy storage.

[0133] Energy storage charging and discharging constraints:

[0134]

[0135] Where, is the minimum power of energy storage, is the maximum power of energy storage; κ ES is an indicator variable. When it is 1, the energy storage is in the charging state, and when it is 0, the energy storage is in the discharging state. β is the ratio of the maximum charge and discharge power of the energy storage to the energy storage capacity. Equations (13) and (14) indicate the upper and lower limit constraints of the energy storage charge and discharge capacity. Equation (15) shows that the maximum charge and discharge power of the energy storage is proportional to the energy storage capacity.

[0136] (3) Capacity constraints of PV and storage construction

[0137]

[0138] Where, E max is the maximum total construction capacity of solar energy and energy storage; the formula indicates that the energy storage construction capacity is a multiple of the energy storage unit construction capacity; the formula indicates that the photovoltaic construction capacity is a multiple of the photovoltaic unit construction capacity.

[0139] (4) Distflow distribution network AC power flow constraints

[0140]

[0141] Where l(j,:) represents the branch with node j as the root node; l(:,j) represents the branch with node j as the child node; Xi is the reactance of branch l; i and j are the starting point and end point of the branch respectively; B l is the line set; P l,t , Q l,t are the active power and reactive power of branch l at time t; P j,t , Q j,t are the total active load and total reactive load of node j at time t respectively; is the basic active load and charging load active power at node j at time t; is the reactive load at node j at time t; U i,t is the voltage amplitude of the line starting node i in time period t; I l,t 、R l The current in branch 1 and the resistance in branch 1 at time t respectively.

[0142] (5) Safety constraints

[0143]

[0144] Where S l,max is the upper limit of the current carrying capacity of branch l; U max 、U min are the upper and lower limits of the node voltage respectively; I max is the upper limit of the current in branch 1.

[0145] (6) Grid power constraints

[0146]

[0147] Where, and and They are the upper and lower limits of the active and reactive power output of the power node respectively.

[0148] In step S03, a distribution network line carrying capacity model is constructed to obtain the maximum configurable charging station capacity and location of the distribution network.

[0149] The carrying capacity of a distribution network refers to the maximum load that a power grid can withstand while meeting certain power supply quality standards. Given that the disordered charging of large-scale electric vehicles can easily lead to voltage and current exceeding limits in the distribution network and potentially overload distribution transformers, it is necessary to quantitatively assess the distribution network's ability to accommodate electric vehicles. In this paper, a method for assessing the carrying capacity of regional distribution networks that considers static safety constraints is proposed. First, a capacity assessment model that considers static safety constraints is established to assess the distribution network's ability to accommodate electric vehicles.

[0150] 1. Objective Function

[0151] The objective function is to maximize the carrying capacity of the regional distribution network, that is, the capacity of charging stations that can be configured in the distribution network is the largest. The formula is:

[0152]

[0153] Where, is the capacity of the charging station connected to node i.

[0154] 2. Constraints

[0155] (1) Power balance constraints

[0156]

[0157] (2) Energy storage constraints

[0158]

[0159] (3) Capacity constraints of PV and storage construction

[0160]

[0161] (4) Distflow distribution network AC power flow constraints

[0162]

[0163] (5) Safety constraints

[0164]

[0165] (6) Grid power constraints

[0166]

[0167] In step S04, the AC distribution network power flow constraints and node voltage constraints are transformed using second-order cone programming to obtain linear constraints that meet convex programming requirements.

[0168] Because both the PV-storage-charging optimization model and the distribution network line capacity model described above are complex hybrid non-convex nonlinear programming problems, traditional numerical solutions cannot guarantee global optimality. Therefore, second-order cone programming (SOCP) is used to transform the AC distribution network power flow constraints and node voltage constraints. The resulting constraints are linear and meet convex programming requirements, ensuring not only the algorithm's solution accuracy but also improving the convergence of the planning problem.

[0169] Use the formula to replace the current and voltage square terms in the power flow constraint formula, and the corresponding constraint is transformed into formula ~.

[0170]

[0171] Perform second-order cone relaxation on the equation, and the corresponding constraints are transformed into:

[0172]

[0173] The model was solved using a commercial solver.

[0174] According to step S01, the undirected line node number matrix is ​​read from the cim file of the 10kV Future City Wei 403 Weishan Line, the nodes are simplified and reordered, and the undirected external numbering is converted to a directed internal numbering. Then, in steps S02 to S04, the AC distribution network flow constraints and node voltage constraints are converted using second-order cone programming, and the distflow flow calculation is performed to obtain the initial line current at 96 o'clock in the 24 hours of September 7, 2024. Figure 3 , which is consistent with the actual situation of the initial line current of the 10kV Future City Wei 403 Weishan Line.

[0175] An embodiment of the present application provides a photovoltaic, storage and charging system optimization site selection and configuration system based on a distribution network line carrying capacity model, including a memory and a processor, wherein the memory includes a program of a photovoltaic, storage and charging system optimization site selection and configuration method based on a distribution network line carrying capacity model, and when the program of the photovoltaic, storage and charging system optimization site selection and configuration method based on the distribution network line carrying capacity model is executed by the processor, the following steps are implemented: reading an undirected line node numbering matrix according to a cim file, simplifying and reordering the nodes, and converting the undirected external numbering into a directed internal numbering; configuring a certain capacity of photovoltaic and energy storage based on the construction of charging stations, and constructing a photovoltaic, storage and charging optimization configuration and site selection model with economic optimization as the objective function; constructing a distribution network line carrying capacity model to obtain the maximum configurable charging station capacity and site selection of the distribution network; using second-order cone programming to transform the AC distribution network flow constraints and node voltage constraints to obtain linear constraints and meet convex programming requirements.

[0176] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on the distribution network line carrying capacity model as described above are implemented.

[0177] Figure 4 The figure shows a schematic diagram of the physical structure of an electronic device. Figure 4 As shown, the electronic device may include: a processor, a communications interface, a memory, and a communications bus. The processor, communications interface, and memory communicate with each other via the communications bus. The processor can invoke logic instructions in the memory to execute a method for optimizing the configuration and site selection of a solar-powered storage and charging system based on a distribution network line capacity model.

[0178] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0179] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0180] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0182] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0183] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0184] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0185] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model, characterized in that: The following steps are involved: S01: Read the undirected line node number matrix according to the cim file, simplify and reorder the nodes, and convert the undirected external number into a directed internal number; S02: Based on the construction of charging stations, a certain capacity of photovoltaic and energy storage is configured to build a photovoltaic storage and charging optimization configuration and site selection model with economic optimization as the objective function; S03: Construct a distribution network line carrying capacity model to obtain the maximum configurable charging station capacity and location of the distribution network; S04: Use second-order cone programming to transform the AC distribution network power flow constraints and node voltage constraints to obtain linear constraints and meet convex programming requirements.

2. The method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model according to claim 1 is characterized in that: The step S01 is specifically as follows: Read the line node number matrix from the cim file and number the nodes according to the order in which they appear in the cim file. Since each node corresponds to a unique ID in the cim file, the node and number also correspond uniquely. Classify the read line type, including lines, load switches, disconnectors, circuit breakers, fuses, and user-specific transformers, and read user access points separately; The lines are filtered and simplified. Since the internal topology of the user is irrelevant to this patent research, only the connection lines between user access points are of interest. Therefore, the line matrix is ​​simplified, and the nodes on the line from the user access point to the end of the line are deleted, and the nodes on the line between the user access points are retained; Remove discontinuous numbers. Since distflow power flow calculation requires a continuously numbered line node number matrix, the simplified line node number matrix is ​​reordered to remove discontinuous numbers and obtain a continuously numbered line node number matrix. Starting from the root node, the network is traversed and the nodes are renumbered sequentially. For the radial distribution network starting from the root node, in order to simplify the calculation and facilitate the search, all the connection lines from the root node to the user access point are traversed, and the nodes are renumbered sequentially from long to short according to the line length. The numbered nodes are stored to avoid duplicate numbering, and finally a directed line node numbering matrix is ​​obtained.

3. The method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model according to claim 1 is characterized in that: The step S02 is specifically as follows:

1. Objective function: When building a charging station, a certain capacity of photovoltaic and energy storage is configured. The objective function is to optimize the economic efficiency after configuration, including: min F=min(C pv +C es +C loss +αC buy ) (1) Where: C pv represents the average daily investment cost of photovoltaics; C es represents the average daily investment cost of energy storage; C loss represents line loss cost; C buy represents the sub-distribution network's electricity purchase cost; α is the sub-distribution network's electricity purchase cost adjustment coefficient. Since the sub-distribution network's electricity purchase cost does not need to be considered when configuring PV storage and charging, the configuration plan should minimize line load, that is, reduce the power of the line connecting the distribution network and the main grid, thereby reducing the line current and ensuring that the energy storage charging and discharging meets the peak load demand. Therefore, the sub-distribution network's electricity purchase cost is introduced and multiplied by the adjustment coefficient. (1) Average daily investment cost of photovoltaic power generation: Where: r is the discount rate; T s pv is the photovoltaic operation life cycle; u pv is the photovoltaic capital recovery coefficient, which represents the known present value of cost and T s pv The equivalence relationship between the equal-year values; (2) Average daily investment cost of energy storage: Where: is the energy storage operation life cycle; u es is the energy storage capital recovery coefficient, which represents the known present value of cost and T s pv The equivalent relationship between the values ​​of the same year; c ess is the construction cost of unit energy storage capacity; S es is the energy storage capacity; c esp The unit maximum power energy storage construction cost; is the maximum power of energy storage; γ es is the energy storage loss coefficient; (3) Line loss cost Where: c loss is the price per unit line loss; (4) Sub-distribution network electricity purchase cost Where: c buy is the time-of-use electricity price; P 1,t Active power of the line connecting the distribution network and the main network; 2. Constraints (1) Power balance constraints Where, and Inject active power and reactive power into node i respectively; and is the base load active power and reactive power of node i; and is the active power and reactive power obtained from the main grid; and is the energy storage charging and discharging power; is the load power of the charging station; Producing power for photovoltaics; (2) Energy storage constraints Energy storage is an electrochemical energy storage system, the main forms of which include lithium-ion batteries, lead-acid batteries, sodium-sulfur batteries, and flow batteries; Energy storage capacity constraints: Where, is the energy storage capacity at time t, is the energy storage capacity at time t+1, The energy storage charging power, is the energy storage discharge power, For energy storage charging efficiency, is the energy storage discharge efficiency, is the energy storage configuration capacity; Formula (10) represents the capacity relationship between the next period and the previous period of energy storage; Formula (11) is the energy storage capacity constraint. To extend the service life of energy storage, the energy storage capacity is controlled within the range of 20% to 90% of the maximum capacity; Formula (12) satisfies the equal capacity of energy storage at the beginning and end, thus extending the service life of energy storage; Energy storage charging and discharging constraints: Where, is the minimum power of energy storage, is the maximum power of energy storage; κ ES is an indicator variable. When it is 1, the energy storage is in the charging state, and when it is 0, the energy storage is in the discharging state. β is the ratio of the maximum charge and discharge power of the energy storage to the energy storage capacity. Formulas (13) and (14) indicate the upper and lower limit constraints of the energy storage charge and discharge capacity. Formula (15) shows that the maximum charge and discharge power of the energy storage is proportional to the energy storage capacity. (3) Capacity constraints of PV and storage construction Where, E max is the maximum total capacity of solar-storage construction; Formula (16) shows that the energy storage construction capacity is a multiple of the energy storage unit construction capacity; Formula (17) shows that the photovoltaic construction capacity is a multiple of the photovoltaic unit construction capacity; (4) Distflow distribution network AC power flow constraints Where l(j,:) represents the branch with node j as the root node; l(:,j) represents the branch with node j as the child node; Xi is the reactance of branch l; i and j are the starting point and end point of the branch respectively; B l is the line set; P l,t , Q l,t are the active power and reactive power of branch l at time t; P j,t , Q j,t are the total active load and total reactive load of node j at time t respectively; is the basic active load and charging load active power at node j at time t; is the reactive load at node j at time t; U i,t is the voltage amplitude of the line starting node i in time period t; I l,t 、R l The current in branch 1 and the resistance in branch 1 at time t respectively; (5) Safety constraints Where S l,max is the upper limit of the current carrying capacity of branch l; U max 、U min are the upper and lower limits of the node voltage respectively; I max is the upper limit of the current of branch l; (6) Grid power constraints Where, and and They are the upper and lower limits of the active and reactive power output of the power node respectively.

4. The method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model according to claim 1 is characterized in that: The step S03 is specifically as follows: Establish an acceptance capacity assessment model that considers static safety constraints to evaluate the distribution network's ability to accept electric vehicles; 1. Objective Function The objective function is to maximize the carrying capacity of the regional distribution network, that is, the capacity of charging stations that can be configured in the distribution network is the largest. The formula is: Where, is the capacity of the charging station connected to node i; 2. Constraints (1) Power balance constraints (2) Energy storage constraints (3) Capacity constraints of PV and storage construction (4) Distflow distribution network AC power flow constraints (5) Safety constraints (6) Grid power constraints 5. The method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model according to claim 1 is characterized in that: The step S04 is specifically as follows: Since the above-mentioned photovoltaic storage and charging optimization configuration model and distribution network line carrying capacity model problems are both complex mixed non-convex nonlinear programming problems, traditional numerical solutions cannot guarantee the global optimality of the solutions. Therefore, it is necessary to use second-order cone programming (SOCP) to transform the AC distribution network power flow constraints and node voltage constraints. The resulting constraints are linear constraints and meet the convex programming requirements, which not only ensures the solution accuracy of the algorithm, but also improves the convergence of the solution to the planning problem. Use the formula to replace the current and voltage square terms in the power flow constraint, and the corresponding constraint becomes, Perform second-order cone relaxation on the equation, and the corresponding constraints are transformed into: The model was solved using a commercial solver.

6. A system for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model, characterized in that: The system comprises a memory and a processor, wherein the memory includes a program for a method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model. When the program is executed by the processor, the method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model implements the following steps: reading an undirected line node numbering matrix according to a CIM file, simplifying and reordering the nodes, and converting the undirected external numbering into a directed internal numbering; configuring a certain capacity of photovoltaic and energy storage based on the construction of charging stations, and constructing a photovoltaic storage and charging optimization configuration and site selection model with economic optimization as the objective function; constructing a distribution network line carrying capacity model to obtain the maximum configurable charging station capacity and site selection of the distribution network; Second-order cone programming is used to transform the AC distribution network power flow constraints and node voltage constraints to obtain linear constraints that meet convex programming requirements.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for optimizing the site selection and configuration of a photovoltaic storage and charging system based on a distribution network line carrying capacity model as described in any one of claims 1 to 5.

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