Method for constructing safe domain of photovoltaic-electric vehicle coupling in power distribution network and related product

Abstract

This invention discloses a method for constructing a photovoltaic-electric vehicle coupled safety domain in a distribution network and related products, belonging to the field of distribution network coordination and optimization technology. This method establishes a multi-objective optimization model of carrying capacity with both distributed photovoltaic carrying capacity and electric vehicle carrying capacity as dual objectives, incorporating second-order cone relaxation power flow constraints of the distribution network. Based on the Epsilon-constraint method, the multi-objective model is transformed into multiple single-objective sub-problems for solution, obtaining the Pareto optimal solution set. Mapped to a two-dimensional plane with the dual carrying capacity as coordinate axes, a continuous photovoltaic-electric vehicle coupled safety domain is constructed based on its boundary points. This invention handles nonlinear power flow constraints through second-order cone relaxation and overcomes the shortcomings of traditional weighted methods in handling non-convex Pareto fronts using the Epsilon-constraint method, accurately constructing a continuous coupled safety domain, thereby effectively improving the distribution network's comprehensive capacity to accommodate distributed energy resources.

Description

Technical Field

[0001] This invention relates to the field of coordination and optimization technology for power distribution networks, specifically to a method for constructing a photovoltaic-electric vehicle coupled security domain in a power distribution network and related products. Background Technology

[0002] Currently, distributed power sources, represented by photovoltaics, and new loads, represented by electric vehicle charging stations, are increasingly penetrating the distribution network. The carrying capacity of the distribution network is a key indicator for assessing its ability to accommodate distributed energy, defined as the maximum capacity of distributed power sources or new loads that the grid can accommodate without violating safety constraints such as voltage deviation, branch thermal stability, transformer capacity, and power quality.

[0003] In power distribution networks, large-scale integration of distributed photovoltaic (PV) systems primarily injects active power into the grid, which can easily lead to voltage rise at feeder end nodes, causing overvoltage risks and reverse power flow. Conversely, electric vehicle (EV) charging stations, as high-power loads, mainly absorb active power from the grid, which can easily cause node voltage drops, leading to undervoltage problems and line overloads. Accurately assessing the integration capacity of PV and EV systems relies on precise power flow calculations to determine node voltage and branch power distribution.

[0004] In existing technologies, the methods for assessing the carrying capacity of distribution networks for distributed photovoltaic (PV) and electric vehicles (EVs) are mainly divided into two categories. The first is the independent decoupling assessment method, which treats PV and EVs as independent variables and calculates their single-sided carrying limits under the safety constraints of the distribution network. This method completely ignores the physical synergistic cancellation effect between the voltage boost from PV active power injection and the voltage drop from high-power charging of EVs. This results in an overly conservative safe operating domain for the distribution network, limiting the overall scale of the distribution network's acceptance of PV and EVs and causing low utilization of grid resources. The second is the multi-objective optimization method based on linear weighted sums. This method attempts to comprehensively assess the joint access capability of PV and EVs. However, it is limited by the high non-convexity of the AC power flow of the distribution network, making it difficult to accurately handle the non-convex Pareto front. In the solution process, problems such as uneven distribution of solution sets and omission of key feasible operating points easily occur, making it impossible to accurately and completely construct a continuous PV-EV coupled safe operating boundary.

[0005] Therefore, how to fully utilize the synergistic and complementary characteristics of photovoltaics and electric vehicles to efficiently and accurately construct a continuous photovoltaic-electric vehicle coupling safety domain in the distribution network, so as to improve the comprehensive acceptance capacity of the distribution network for distributed energy, has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method for constructing a photovoltaic-electric vehicle coupling safety domain in a distribution network and related products, so as to overcome the problem that the existing technology cannot accurately and completely construct a reasonable photovoltaic-electric vehicle coupling safety operation boundary, which leads to a limited assessment of the distribution network's comprehensive acceptance capacity for distributed energy.

[0007] The present invention solves the above-mentioned technical problems through the following technical solution: This invention provides a method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network, comprising the following steps: A multi-objective optimization model for the photovoltaic-electric vehicle carrying capacity of the distribution network is established, with the distributed photovoltaic carrying capacity and electric vehicle carrying capacity as dual objectives, and including the second-order cone relaxation power flow constraint of the distribution network. Based on the Epsilon-constraint method, the multi-objective optimization model of photovoltaic-electric vehicle carrying capacity of distribution network is transformed into multiple single-objective optimization sub-problems for solution, and a uniformly distributed Pareto optimal solution set is obtained; The Pareto optimal solution set is mapped to a two-dimensional plane with photovoltaic load factor and electric vehicle load factor as coordinate axes. Based on the boundary points of the Pareto optimal solution set, a continuous photovoltaic-electric vehicle coupled security domain is constructed.

[0008] A further improvement of this invention is that the second-order cone relaxation power flow constraint of the distribution network is obtained through the following method: Based on the DistFlow branch power flow model, a second-order cone relaxation method is introduced to convexify the nonlinear terms in the DistFlow branch power flow model.

[0009] A further improvement of this invention lies in that, based on the Epsilon-constraint method, the multi-objective optimization model of the photovoltaic-electric vehicle carrying capacity of the distribution network is transformed into multiple single-objective optimization sub-problems for solution, thereby obtaining a uniformly distributed Pareto optimal solution set. Specifically, this includes the following steps: The extreme value ranges of photovoltaic and electric vehicle carrying capacity are determined by solving the problem with the single objective of maximizing photovoltaic carrying capacity and maximizing electric vehicle carrying capacity respectively, under the premise of satisfying the preset set of distribution network operation constraints. The extreme value ranges of photovoltaic load factor and electric vehicle load factor are discretized according to a preset step size to generate continuous constraint boundary values; By taking either the photovoltaic load factor or the electric vehicle load factor as the objective function and the other as a constraint condition with bounded boundary values, a single-objective optimization subproblem is constructed and solved sequentially. Summarize the solutions to all single-objective optimization subproblems, and select non-dominated solutions to obtain a uniformly distributed Pareto optimal solution set.

[0010] A further improvement of the present invention is that the set of operating constraints for the distribution network includes node power balance constraints, second-order cone relaxation power flow constraints of the distribution network, operating constraints of the energy storage system, and operating constraints of network security.

[0011] A further improvement of this invention is that, based on the boundary points of the Pareto optimal solution set, a continuous photovoltaic-electric vehicle coupling safety domain is constructed. Specifically, the discrete boundary points in the Pareto optimal solution set are smoothly connected by data fitting interpolation to form a continuous maximum bearing capacity Pareto front boundary curve. The closed region enclosed by the maximum bearing capacity Pareto front boundary curve and the coordinate axis is the continuous photovoltaic-electric vehicle coupling safety domain.

[0012] A further improvement of this invention lies in the fact that the multi-objective optimization model for the load-bearing capacity of the photovoltaic-electric vehicle distribution network is specifically as follows:

[0013] in, To maximize the load-bearing capacity of photovoltaic-electric vehicles; The carrying capacity of distributed photovoltaic power generation; For the load capacity of electric vehicles; This refers to the actual active power connected to the distributed photovoltaic system. This refers to the actual active power connected to the electric vehicle. This represents the maximum adjustable capacity of distributed photovoltaic systems. This represents the maximum deployability of electric vehicles. For electric vehicle access nodes; This refers to the set of access nodes for distributed photovoltaic power generation. This refers to the set of access nodes for electric vehicles. This refers to the actual access node for distributed photovoltaic power generation. For time; It is a periodicity.

[0014] This invention also provides a system for constructing a photovoltaic-electric vehicle coupling safety domain in a power distribution network, comprising: The first module is used to establish a multi-objective optimization model for the photovoltaic-electric vehicle carrying capacity of the distribution network with the dual objectives of distributed photovoltaic carrying capacity and electric vehicle carrying capacity, and including the second-order cone relaxation power flow constraint of the distribution network. The second module is used to transform the multi-objective optimization model of the photovoltaic-electric vehicle carrying capacity of the distribution network into multiple single-objective optimization sub-problems based on the Epsilon-constraint method, and to obtain a uniformly distributed Pareto optimal solution set; The third module is used to map the Pareto optimal solution set to a two-dimensional plane with photovoltaic load factor and electric vehicle load factor as coordinate axes. Based on the boundary points of the Pareto optimal solution set, a continuous photovoltaic-electric vehicle coupled security domain is constructed.

[0015] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for constructing a photovoltaic-electric vehicle coupled security domain in a power distribution network.

[0016] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for constructing a photovoltaic-electric vehicle coupled security domain in a power distribution network.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for constructing a photovoltaic-electric vehicle coupled security domain in a power distribution network.

[0018] Compared with the prior art, the positive and progressive effects of the present invention are as follows: The present invention provides a method for constructing a photovoltaic-electric vehicle coupled security domain in a distribution network. By constructing a multi-objective optimization model covering both photovoltaic and electric vehicles, it fully explores the physical hedging synergy effect between photovoltaic voltage support and electric vehicle load absorption, overcoming the conservative limitations of traditional independent decoupling assessments and expanding the comprehensive acceptance boundary and asset utilization of the distribution network. Simultaneously, by employing the Epsilon-constraint method instead of the traditional linear weighted algorithm, it effectively overcomes the problem of difficulty in accurately solving the non-convex Pareto front under highly nonlinear constraints of the distribution network. This method can obtain a uniformly distributed optimal solution set and accurately reconstruct a continuous and complete two-dimensional coupled security domain, thereby fully exploring the synergistic and complementary effects of photovoltaic and electric vehicles in voltage regulation and effectively improving the distribution network's comprehensive acceptance capacity for distributed energy.

[0019] Furthermore, by employing the second-order cone relaxation method to convexify the complex improved DistFlow power flow equations, the inherent weakness of traditional nonlinear programming in easily getting trapped in local optima is fundamentally eliminated. This improves the solution efficiency while ensuring global optimality, providing efficient and reliable decision support for real-time assessment and over-limit early warning of distribution network operation status. Attached Figure Description

[0020] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1This is a flowchart illustrating a method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network according to the present invention. Figure 2 A schematic diagram of the IEEE 33-node power distribution system; Figure 3 This is a boundary diagram of the EV's load-bearing capacity. Figure 4 This is a boundary diagram of PV carrying capacity. Figure 5 This is a schematic diagram of the PV-EV coupled security domain. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0024] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0026] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0027] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This is an explanation of the present invention and not a limitation thereof.

[0028] This invention provides a method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network, comprising the following steps: A multi-objective optimization model for the photovoltaic-electric vehicle carrying capacity of the distribution network is established, with the distributed photovoltaic carrying capacity and electric vehicle carrying capacity as dual objectives, and including the second-order cone relaxation power flow constraint of the distribution network. Based on the Epsilon-constraint method, the multi-objective optimization model of photovoltaic-electric vehicle carrying capacity of distribution network is transformed into multiple single-objective optimization sub-problems for solution, and a uniformly distributed Pareto optimal solution set is obtained; The Pareto optimal solution set is mapped to a two-dimensional plane with photovoltaic load factor and electric vehicle load factor as coordinate axes. Based on the boundary points of the Pareto optimal solution set, a continuous photovoltaic-electric vehicle coupled security domain is constructed.

[0029] The method for constructing a coupled safety domain of photovoltaic and electric vehicle in a power distribution network provided by this invention solves the problems of neglecting synergistic effects and handling non-convex fronts in traditional methods by integrating the carrying capacity assessment of photovoltaic and electric vehicles and using precise optimization and mapping techniques, thus achieving efficient construction of a continuous safety domain. Specifically, in establishing the multi-objective optimization model, the distributed photovoltaic (PV) carrying capacity and electric vehicle (EV) carrying capacity are considered as dual objectives, and the second-order cone relaxation power flow constraint of the distribution network is included. By considering the physical synergistic effect of PV active power injection and EV active power absorption, the conservatism caused by independent evaluation is avoided. At the same time, by introducing second-order cone relaxation, the non-convex AC power flow problem is transformed into a convex optimization form, ensuring the accuracy and feasibility of power flow calculation and laying the foundation for subsequent optimization. The multi-objective optimization model is transformed into multiple single-objective optimization sub-problems. By discretizing the constraint boundary values, a uniformly distributed Pareto optimal solution set is generated, avoiding the uneven distribution of the solution set that may be caused by the linear weighted sum method. This ensures that all key feasible operating points are fully captured, thereby improving the comprehensiveness of the solution set. The Pareto optimal solution set is mapped to a two-dimensional plane with PV carrying capacity and EV carrying capacity as coordinate axes, and a continuous PV-EV coupled safety domain is constructed based on the boundary points. This achieves a smooth transformation from discrete solution set to continuous boundary, intuitively demonstrating the comprehensive carrying capacity of the distribution network and solving the problem that traditional methods cannot accurately construct continuous boundaries.

[0030] Preferably, the second-order cone relaxation power flow constraint of the distribution network is obtained through the following method: Based on the DistFlow branch power flow model, a second-order cone relaxation method is introduced to convexify the nonlinear terms in the DistFlow branch power flow model.

[0031] By specifying the method for obtaining the second-order cone relaxation power flow constraints of the distribution network, the accuracy and efficiency of the convexity transformation process are ensured. Specifically, the DistFlow branch power flow model is used as the foundation. This model can accurately describe the power flow distribution of the distribution network, providing a reliable starting point for the convexity transformation process. The second-order cone relaxation method is introduced to transform the nonlinear terms into a convex form, making the optimization problem solvable and avoiding the solution difficulties caused by non-convexity. The nonlinear terms in the DistFlow branch power flow model are convexized. Convexification is performed on the nonlinear parts of the model to ensure that the optimization model is easy to solve while maintaining accuracy, thereby improving the solution efficiency and accuracy of the optimization model.

[0032] Preferably, based on the Epsilon-constraint method, the multi-objective optimization model of the photovoltaic-electric vehicle carrying capacity of the distribution network is transformed into multiple single-objective optimization sub-problems for solution, to obtain a uniformly distributed Pareto optimal solution set, specifically including the following steps: The extreme value ranges of photovoltaic and electric vehicle carrying capacity are determined by solving the problem with the single objective of maximizing photovoltaic carrying capacity and maximizing electric vehicle carrying capacity respectively, under the premise of satisfying the preset set of distribution network operation constraints. The extreme value ranges of photovoltaic load factor and electric vehicle load factor are discretized according to a preset step size to generate continuous constraint boundary values; By taking either the photovoltaic load factor or the electric vehicle load factor as the objective function and the other as a constraint condition with bounded boundary values, a single-objective optimization subproblem is constructed and solved sequentially. Summarize the solutions to all single-objective optimization subproblems, and select non-dominated solutions to obtain a uniformly distributed Pareto optimal solution set.

[0033] By concretizing the transformation process using the Epsilon-constraint method, a uniformly distributed Pareto optimal solution set is obtained when solving multi-objective optimization models, thus accurately constructing the safety region. Specifically, by solving for maximizing photovoltaic load factor and maximizing electric vehicle load factor as single objectives, the extreme value intervals are determined. This provides a clear boundary range for subsequent discretization, avoiding omissions in the solution set due to unclear ranges. Next, the extreme value intervals are discretized with a preset step size to generate continuous constraint boundary values, ensuring systematic changes in constraint values ​​and promoting a uniform distribution of the solution set within the feasible region. Then, a single-objective subproblem is constructed using one load factor as the objective function and the other as the constraint condition. By gradually adjusting the constraint boundary values, it is ensured that the solution covers the entire feasible region, preventing the loss of key points. Finally, the solution results are summarized and non-dominated solutions are filtered to directly obtain a uniformly distributed Pareto optimal solution set, solving the problem of uneven distribution and achieving accurate construction of the safety region.

[0034] Preferably, the set of power distribution network operation constraints includes node power balance constraints, second-order cone relaxation power flow constraints of the power distribution network, energy storage system operation constraints, and network security operation constraints.

[0035] By clearly defining the specific components of the distribution network operation constraint set, the key elements of safe distribution network operation are comprehensively covered, thereby avoiding constraint omissions during the optimization solution process and improving the accuracy and completeness of the safety domain construction. Specifically, the node power balance constraint ensures the conservation of power input and output at each node in the distribution network, preventing voltage fluctuations or power flow anomalies caused by power imbalance; the second-order cone relaxation power flow constraint of the distribution network uses the convexity method to handle nonlinear power flow problems, making the optimization model solvable and maintaining physical accuracy, avoiding solution set deviations caused by nonconvexity; the energy storage system operation constraint incorporates the charging and discharging behavior of energy storage devices, optimizing their scheduling to balance the power fluctuations of photovoltaics and electric vehicles, enhancing the system's flexibility; the network security operation constraint covers safety indicators such as voltage deviation and branch thermal stability, ensuring that the distribution network does not exceed safety thresholds during operation, preventing overvoltage, undervoltage, or equipment overload risks, solving the problem of incomplete constraint sets, and enabling the optimization results to accurately reflect the actual carrying capacity of the distribution network.

[0036] Preferably, a continuous photovoltaic-electric vehicle coupled safety domain is constructed based on the boundary points of the Pareto optimal solution set. Specifically, the discrete boundary points in the Pareto optimal solution set are smoothly connected by data fitting interpolation to form a continuous maximum bearing capacity Pareto front boundary curve. The closed region enclosed by the maximum bearing capacity Pareto front boundary curve and the coordinate axis is the continuous photovoltaic-electric vehicle coupled safety domain.

[0037] By processing discrete boundary points through data fitting interpolation, a continuous and closed safety domain is constructed, effectively solving the problem of boundary discontinuities caused by discrete points and ensuring that the safe operation boundary fully covers all feasible points. Specifically, the boundary points are constructed based on the Pareto optimal solution set, using these points as the basic data source to ensure that the safety domain reflects the true optimization results. Discrete boundary points are smoothly connected through data fitting interpolation, and appropriate mathematical methods such as curve fitting are selected to eliminate discrete gaps, achieving a smooth transition between points and avoiding boundary jumps. A continuous Pareto front boundary curve of maximum carrying capacity is formed, generating an uninterrupted curve representing the maximum photovoltaic and electric vehicle carrying capacity combination of the distribution network under safety constraints, accurately defining the carrying capacity limit. The closed region enclosed by the Pareto front boundary curve of maximum carrying capacity and the coordinate axes defines the closed safe operation range, covering all feasible point combinations and improving the comprehensiveness of the evaluation.

[0038] The preferred multi-objective optimization model for the load-bearing capacity of the power distribution network photovoltaic-electric vehicle is as follows:

[0039] in, To maximize the load-bearing capacity of photovoltaic-electric vehicles; The carrying capacity of distributed photovoltaic power generation; For the load capacity of electric vehicles; This refers to the actual active power connected to the distributed photovoltaic system. This refers to the actual active power connected to the electric vehicle. This represents the maximum adjustable capacity of distributed photovoltaic systems. This represents the maximum deployability of electric vehicles. For electric vehicle access nodes; This refers to the set of access nodes for distributed photovoltaic power generation. This refers to the set of access nodes for electric vehicles. This refers to the actual access node for distributed photovoltaic power generation. For time; It is a periodicity.

[0040] This invention also provides a system for constructing a photovoltaic-electric vehicle coupling safety domain in a power distribution network, comprising: The first module is used to establish a multi-objective optimization model for the photovoltaic-electric vehicle carrying capacity of the distribution network with the dual objectives of distributed photovoltaic carrying capacity and electric vehicle carrying capacity, and including the second-order cone relaxation power flow constraint of the distribution network. The second module is used to transform the multi-objective optimization model of the photovoltaic-electric vehicle carrying capacity of the distribution network into multiple single-objective optimization sub-problems based on the Epsilon-constraint method, and to obtain a uniformly distributed Pareto optimal solution set; The third module is used to map the Pareto optimal solution set to a two-dimensional plane with photovoltaic load factor and electric vehicle load factor as coordinate axes. Based on the boundary points of the Pareto optimal solution set, a continuous photovoltaic-electric vehicle coupled security domain is constructed.

[0041] See Figure 1 A method for constructing a photovoltaic-electric vehicle coupled security domain in a power distribution network includes the following steps: (1) Establish a multi-objective optimization model for PV-EV load-bearing capacity assessment To accurately describe the physical constraints of power flow in distribution networks, this invention employs an improved DistFlow branch model and utilizes second-order cone relaxation (SOCP) technology to transform the nonlinear power flow equations into a convex optimization problem. The objective function maximizes the distributed photovoltaic (PV) carrying capacity and the electric vehicle (EV) carrying capacity, comprehensively considering constraints such as DistFlow branch power flow, node power balance, voltage / branch current security, and flexible resource coordination, thus constructing a multi-objective optimization model encompassing PV and EV.

[0042] Specifically, the following steps are included: To maximize the distribution network's capacity to support distributed photovoltaic (PV) and electric vehicle (EV) grid connections, the following multi-objective optimization function is established:

[0043] In the formula, and These represent the photovoltaic load factor and the electric vehicle load factor, respectively. and These are the sets of access nodes for PV and EV, respectively. and This represents the actual active power connected to the grid. and This refers to its installed capacity or maximum adjustable capacity.

[0044] Constraints: 1. Node power balance constraints Considering various flexible resources, the balance equation for node injected power is:

[0045] In the formula, and This represents the net value of active and reactive power injected into node j during time period t. The sum represents the active power of the substation (main transformer) connected to the grid at node j during time period t. and This represents the active and reactive power output of a conventional generator set or distributed power source at node j during time period t. and This represents the actual active and reactive power generated by the distributed photovoltaic system at node j during time period t. This represents the active power of the energy storage system at node j during time period t.

[0046] and This represents the normal load demand at node j during time period t. This represents the active power consumed by the electric vehicle charging station at node j during time period t. This represents the active power of the energy storage system at node j during time period t. and This represents the adjustment of active and reactive power of the controllable load at node j in time period t. This represents the amount of reactive power compensation provided by the reactive power compensation device at node j during time period t.

[0047] 2. Improved DistFlow power flow constraints

[0048] In the formula, Indicates the conjugate of the current. Let J be the total power flowing from node j to downstream node k. The total power injected from node i into node j, It is the branch power loss transmitted from node i to node j.

[0049] , Represents the nth feeder node and nodes voltage amplitude, Indicates the connection to the nth feeder node. and the branch impedance of the node, Indicates a branch Apparent power on Represents a node The injected apparent power at the location, Represents the set of all branches in the network. It represents the set of all nodes in the network.

[0050] This model transforms the distribution network carrying capacity calculation problem, which involves numerous discrete variables and nonlinear power flows, into a mixed-integer convex programming problem using a second-order cone relaxation technique. This significantly improves computational efficiency while ensuring global optimality. The branch power flow constraints after phase angle relaxation are as follows:

[0051] In the formula, Represents a node The amount of active power injected into the nth feeder, Indicates from node The sum of active power flowing to all its child nodes. Indicates from node Flow to Node The active power on the branch line. Represents a node The reactive power injection amount of the nth feeder Indicates from node The sum of reactive power flowing to all its child nodes. Indicates from node Flow to Node Reactive power on the branch line, , In the DistFlow model, to linearize the voltage drop square, it is usually defined as the square of the voltage magnitude. Indicates the reactance value of the branch circuit. This indicates the resistance value of the branch.

[0052] 3. Energy Storage System (ESS) Operational Constraints Energy storage must meet energy conservation and state of charge (SOC) constraints:

[0053] In the formula, This represents the energy state of the energy storage system at node j during time period t. This represents the energy state of the energy storage system at node j during time period t+1. This represents the charge / discharge efficiency coefficient of the energy storage system. This indicates the time step of the calculation. This represents the minimum allowable energy margin for the energy storage system at node j. This represents the maximum allowable energy storage capacity of the energy storage system at node j.

[0054] These represent the charging active power and discharging active power of the energy storage at node j during time period t, respectively. This indicates the maximum rated charge and discharge power limit of the energy storage system. It is a binary 0-1 auxiliary variable. This indicates a mutual exclusion constraint, ensuring that the energy storage system will not perform charging and discharging operations simultaneously.

[0055] 4. Network security operation constraints Ensure the power grid operates within physical safety limits:

[0056] In the formula, It represents the square of the per-unit voltage value of node j during time period t. This indicates the lower and upper limits of the per-unit voltage values ​​allowed for nodes in the distribution network. It represents the square of the per-unit value of the branch current connecting node i and node j during time period t. This indicates the maximum thermally stable current limit that branch ij is allowed to flow through. This represents the total active power of the substation's main transformer connected to the grid during time period t. These represent the permissible reverse power supply limit (if any) and the forward capacity load limit of the main transformer, respectively.

[0057] (2) Based on Epsilon ( Pareto front solved by constraint method To address the competition and coupling relationship between the carrying capacities of PV and EV, instead of the traditional weighted method, a different approach is adopted. - The constraint method uses a specific objective (such as PV occupancy rate) as a constraint condition and sets different step size thresholds. In each At the horizontal level, we find the optimal solution for another objective (such as EV load factor). Compared to the weighted method, this method can effectively handle non-convex Pareto fronts and ensure a uniform distribution of solutions.

[0058] Specifically, the following steps are included: In multi-objective optimization, since PV and EV often compete (for example, when grid capacity is limited, increasing PV access may reduce the charging space for electric vehicles), this invention adopts... - The constraint method transforms a multi-objective problem into a series of single-objective subproblems for solution. Traditional weighted summation methods struggle to handle non-convex Pareto fronts, while this method, by using one of the objective functions as a constraint and setting different step size thresholds, effectively ensures a uniform distribution of solutions. Its core idea and specific solution steps are as follows: Step 1: Determine the boundary extremes of single-objective optimization First, satisfying the set of all physical operation constraints of the distribution network Under the premise of [specific conditions], respectively [the following are] the photovoltaic carrying capacity targets. and electric vehicle carrying capacity target Perform independent single-objective maximization solutions. Through solving, obtain... , The maximum and minimum values ​​are used to determine The effective range of values ​​for the constraint factor.

[0059] Step 2: Division Iteration step size of constraint factor Based on the preset number of iterations or the required accuracy N, the target value range of the non-primary optimization is discretized with equal step sizes to generate a series of different constraint boundary values. Taking the setting of photovoltaic carrying capacity as a constraint as an example, the boundary value of its k-th iteration can be defined as a threshold that gradually increases within the interval.

[0060] Step 3: Construct and solve the parameterized single-objective subproblem Retain one objective function as the primary objective, transform the other objectives into constraints, and apply them according to the conditions set in step 2. The boundary values ​​are solved in multiple rounds. Specifically, it is divided into the following two sub-models. 1. A parametric model with EV as the optimization objective (given PV load capacity, optimize EV load capacity): With a fixed photovoltaic load capacity, solve for the maximum load capacity of the electric vehicle.

[0061] 2. A parametric model with PV as the optimization objective (given EV load capacity, optimize PV load capacity) is used to solve for the maximum photovoltaic load capacity while keeping the electric vehicle load rate constant.

[0062] In the formula, The objective function for photovoltaic load factor is defined as the ratio of the total actual photovoltaic power connected to the grid to the maximum connectable capacity. The objective function for electric vehicle load factor is defined as the ratio of the total actual charging power of the entire network to the maximum load demand of charging stations. The corresponding constraint parameter (Epsilon factor). This is the set of all power flow, voltage, current, and flexible resource operation constraints constructed in the first step.

[0063] Step 4: Extraction and Construction of Pareto Solution Sets After completing all iterations, combine the optimal solutions obtained in each iteration. The data is then summarized. Unsolvable states where the solver is infeasible due to overly strict constraints, as well as dominated inferior solutions, are filtered and eliminated, while all non-dominated solutions are retained. This uniformly distributed set of non-dominated optimal solutions constitutes the Pareto front of the PV-EV carrying capacity of the distribution network in this invention, and provides accurate boundary data support for the next step of constructing the coupled safety domain.

[0064] (3) Constructing a safe domain for PV-EV load-bearing capacity coupling The safety domain is the set of feasible solutions that satisfy all physical constraints. This invention extracts the optimal solution set on the Pareto front and maps the safe operation boundary of the system in a two-dimensional coordinate system (the horizontal axis is PV penetration rate and the vertical axis is EV access capacity). The intersection of the two is the safety domain, thus achieving the collaborative optimization of the two.

[0065] Based on The present invention obtains multiple Pareto optimal solutions using the constraint method, maps the discrete optimization results to a two-dimensional geometric space, and constructs a continuous safe operating domain for the power distribution network by extracting the boundary intersection of the PV and EV carrying capacities.

[0066] 1. Data Mapping and Continuous Boundary Reconstruction The series of non-dominated Pareto solution sets obtained by solving This is mapped to a two-dimensional Cartesian coordinate system with PV load capacity on the x-axis and EV load capacity on the y-axis. Since discrete points cannot directly guide actual continuous operation, this invention employs data fitting interpolation technology to connect and smooth discrete extreme points, generating a continuous Pareto front boundary curve of maximum load capacity. The closed region enclosed by both curves constitutes the initial coupled safety domain.

[0067] 2. Mathematical definition of coupled security region Taking into account the above physical constraints and boundary curves, the cooperative security domain of photovoltaic-electric vehicle distribution network It can be defined as:

[0068] In the formula, For the collaborative security domain of photovoltaic and electric vehicle distribution networks; This refers to the actual scale or penetration rate of distributed photovoltaic power generation. The actual load or demand of electric vehicle charging stations; Indicates a given EV size Below, the maximum photovoltaic carrying capacity boundary is determined by the Pareto front curve; Indicates a given PV size Below, the maximum load-bearing limit of electric vehicles is determined by the Pareto front curve.

[0069] Once this safety domain is constructed, it can be directly applied to the real-time assessment and planning of the distribution network. It collects the actual operating points in the current power grid and determines whether the coordinates of the point are within the domain. If it is within the domain, the system is safe, and the current carrying capacity margin can be quantified by calculating the shortest Euclidean distance from the actual operating point to the Pareto boundary. If the actual operating point approaches the boundary, an early warning is triggered, guiding the dispatch center to activate flexible resources (such as energy storage charging and discharging or adjustable loads) to intervene and pull the operating point back to the center of the safety domain.

[0070] In a specific embodiment of the present invention, a simulation analysis is performed using a standard IEEE 33-node distribution system to verify the effectiveness of the proposed method for constructing a coupled security domain for photovoltaic-electric vehicles in a distribution network.

[0071] See Figure 2 The system reference voltage is 12.66kV, the reference power is 10MVA, the total active load is 3.715MVA, and the total reactive load is 2.3MVA. To simulate the impact of distributed generation and electric vehicle (EV) access on the distribution network's end voltage, the following simulation scenario was set: Distribution network nodes 18, 22, 25, and 33 were selected as centralized access points for distributed photovoltaic (PV) and electric vehicle (EV) charging stations. The node voltage safety range was set to [0.95, 1.05]pu, where pu is a per-unit value; the upper limit of branch thermal stability current was set according to the line model; and the energy storage SOC range was set to [0.1, 0.9]. The proposed distribution network PV-EV coupling safety domain construction method was used, with an iteration step size N set to 50. The second-order cone relaxation model was solved using the CPLEX solver. (See also...) Figure 3 and Figure 4 The system's load-bearing capacity was scanned and solved from two different perspectives, obtaining the Pareto front under one-sided constraints. Improved EV load-bearing capacity supported by photovoltaics: Figure 3The Pareto front curve is shown, obtained by treating distributed photovoltaic (PV) load factor as a fixed parameter (x-axis) and maximizing electric vehicle (EV) load factor (y-axis) as the objective. Improvement of PV load factor under EV integration: Figure 4 The Pareto front curve is shown, obtained by solving the problem with electric vehicle (EV) load factor as a fixed parameter (vertical axis) and maximizing photovoltaic (PV) load factor (horizontal axis) as the objective. See also Figure 5 The diagram shows the final constructed PV-EV coupled safety domain (grey shaded area). This region is the intersection of the EV and PV carrying capacities and also the set of all feasible operating points of the system.

[0072] The safety domain constructed using the method of this invention accurately characterizes the maximum regulation capacity of the distribution network under different source-load ratios. Especially in the medium-to-high proportion of PV access range, the coupling effect is used to expand the feasible operating domain of the system. This safety domain not only provides planners with accurate capacity upper limit data, but also provides dispatchers with a visualized safety boundary early warning reference.

[0073] This invention quantifies and utilizes the synergistic expansion effect of PV and EV through multi-objective optimization and boundary reconstruction techniques. First, a PV-EV multi-objective load-bearing capacity optimization model based on second-order cone relaxation (SOCP) sets the objective function to simultaneously maximize the photovoltaic load-bearing capacity and the electric vehicle load-bearing capacity. The constraints include not only basic network security constraints (voltage and current limits) and energy storage operation constraints, but also an improved DistFlow branch model. Furthermore, the second-order cone relaxation (SOCP) technique is used to transform the nonlinear power flow equations into a mixed-integer convex programming problem. Simultaneously, an improved DistFlow branch model is employed. - The constraint method for solving non-convex Pareto fronts explicitly abandons the traditional linear weighted summation method and adopts... - The constraint method addresses the competitive coupling relationship between PV and EV. Specifically, it involves: first, finding the single-objective extremum to determine the effective interval; then, transforming one of the objective functions (such as PV carrying capacity) into a constraint condition with an increasing step size threshold; finally, by solving the parameterized single-objective subproblem multiple times, filtering and extracting a uniformly distributed non-dominated Pareto solution set; and finally, using a two-dimensional mapping and continuous reconstruction technique for the PV-EV coupled safety domain, mapping the discrete Pareto non-dominated optimal solution set obtained in the previous step to a two-dimensional Cartesian coordinate system with PV carrying capacity as the horizontal axis and EV carrying capacity as the vertical axis. Discrete points are then smoothly connected using data fitting or interpolation techniques to generate a continuous maximum carrying capacity Pareto front boundary curve. The closed region enclosed by this curve and the coordinate axes constitutes the coupled safety domain.

[0074] Based on the same inventive concept, this application provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for constructing a photovoltaic-electric vehicle coupling safety domain in a power distribution network. The memory may include main memory, such as high-speed random access memory, or it may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, an extended industry-standard architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory stores the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0075] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the method for constructing a photovoltaic-electric vehicle coupling safety domain in a power distribution network. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include RAM (Random Access Memory) and / or cache memory, etc. The non-volatile memory may include ROM (Read-Only Memory), hard disk, flash memory, optical disk, magnetic disk, etc.

[0076] Based on the same inventive concept, this application provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer device, cause the computer device to perform the steps of the above-described method for constructing a photovoltaic-electric vehicle coupled security domain in a power distribution network.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM (Compact Disc Read-Only Memory), optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer apparatus or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer device or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer device or other programmable data processing equipment to cause a series of operational steps to be performed on the computer device or other programmable equipment to produce a process implemented by the computer device, thereby providing instructions that execute on the computer device or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0082] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network, characterized in that, Includes the following steps: A multi-objective optimization model for the photovoltaic-electric vehicle carrying capacity of the distribution network is established, with the distributed photovoltaic carrying capacity and electric vehicle carrying capacity as dual objectives, and including the second-order cone relaxation power flow constraint of the distribution network. Based on the Epsilon-constraint method, the multi-objective optimization model of photovoltaic-electric vehicle carrying capacity of distribution network is transformed into multiple single-objective optimization sub-problems for solution, and a uniformly distributed Pareto optimal solution set is obtained; The Pareto optimal solution set is mapped to a two-dimensional plane with photovoltaic load factor and electric vehicle load factor as coordinate axes. Based on the boundary points of the Pareto optimal solution set, a continuous photovoltaic-electric vehicle coupled security domain is constructed.

2. The method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network according to claim 1, characterized in that, The second-order cone relaxation power flow constraint of the distribution network is obtained through the following method: Based on the DistFlow branch power flow model, a second-order cone relaxation method is introduced to convexify the nonlinear terms in the DistFlow branch power flow model.

3. The method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network according to claim 1, characterized in that, Based on the Epsilon-constraint method, the multi-objective optimization model of the photovoltaic-electric vehicle carrying capacity of the distribution network is transformed into multiple single-objective optimization sub-problems for solution, obtaining a uniformly distributed Pareto optimal solution set. The specific steps include: The extreme value ranges of photovoltaic and electric vehicle carrying capacity are determined by solving the problem with the single objective of maximizing photovoltaic carrying capacity and maximizing electric vehicle carrying capacity respectively, under the premise of satisfying the preset set of distribution network operation constraints. The extreme value ranges of photovoltaic load factor and electric vehicle load factor are discretized according to a preset step size to generate continuous constraint boundary values; By taking either the photovoltaic load factor or the electric vehicle load factor as the objective function and the other as a constraint condition with bounded boundary values, a single-objective optimization subproblem is constructed and solved sequentially. Summarize the solutions to all single-objective optimization subproblems, and select non-dominated solutions to obtain a uniformly distributed Pareto optimal solution set.

4. The method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network according to claim 3, characterized in that, The set of constraints for distribution network operation includes node power balance constraints, second-order cone relaxation power flow constraints of the distribution network, energy storage system operation constraints, and network security operation constraints.

5. The method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network according to claim 1, characterized in that, Based on the boundary points of the Pareto optimal solution set, a continuous photovoltaic-electric vehicle coupled safety domain is constructed. Specifically, the discrete boundary points in the Pareto optimal solution set are smoothly connected by data fitting interpolation to form a continuous maximum bearing capacity Pareto front boundary curve. The closed region enclosed by the maximum bearing capacity Pareto front boundary curve and the coordinate axis is the continuous photovoltaic-electric vehicle coupled safety domain.

6. The method for constructing a photovoltaic-electric vehicle coupling security domain in a power distribution network according to claim 1, characterized in that, The multi-objective optimization model for the load-bearing capacity of photovoltaic-electric vehicles in power distribution networks is as follows: in, To maximize the load-bearing capacity of photovoltaic-electric vehicles; The carrying capacity of distributed photovoltaic power generation; For the load capacity of electric vehicles; This refers to the actual active power connected to the distributed photovoltaic system. This refers to the actual active power connected to the electric vehicle. This represents the maximum adjustable capacity of distributed photovoltaic systems. This represents the maximum deployability of electric vehicles. For electric vehicle access nodes; This refers to the set of access nodes for distributed photovoltaic power generation. This refers to the set of access nodes for electric vehicles. This refers to the actual access node for distributed photovoltaic power generation. For time; It is a periodicity.

7. A system for constructing a photovoltaic-electric vehicle coupled safety domain in a power distribution network, characterized in that, include: The first module is used to establish a multi-objective optimization model for the photovoltaic-electric vehicle carrying capacity of the distribution network with the dual objectives of distributed photovoltaic carrying capacity and electric vehicle carrying capacity, and including the second-order cone relaxation power flow constraint of the distribution network. The second module is used to transform the multi-objective optimization model of the photovoltaic-electric vehicle carrying capacity of the distribution network into multiple single-objective optimization sub-problems based on the Epsilon-constraint method, and to obtain a uniformly distributed Pareto optimal solution set; The third module is used to map the Pareto optimal solution set to a two-dimensional plane with photovoltaic load factor and electric vehicle load factor as coordinate axes. Based on the boundary points of the Pareto optimal solution set, a continuous photovoltaic-electric vehicle coupled security domain is constructed.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for constructing a photovoltaic-electric vehicle coupled security domain in a power distribution network as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for constructing a coupled security domain of photovoltaic-electric vehicle in a power distribution network as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method for constructing a coupled security domain for photovoltaic-electric vehicles in a power distribution network as described in any one of claims 1 to 6.

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