Power distribution network voltage regulation and control method and system based on electric vehicle cluster aggregation modeling

Abstract

The invention relates to the technical field of power distribution network regulation and control, and discloses a power distribution network voltage regulation and control method and system based on electric vehicle cluster aggregation modeling, and the method comprises the steps: obtaining the multi-source data of a power distribution network and electric vehicles; constructing a feasible region of a single electric vehicle based on the charging demand of an electric vehicle user; the feasible regions of the single electric vehicles are aggregated into the feasible region of the charging station through space-time coupling; based on the two feasible regions, taking the minimum sum of squares of voltage deviation of the whole network as a target, and constructing a voltage optimization model adopting aggregation regulation and control of the electric vehicles; and when monitoring that the node voltage exceeds the limit, dynamically adjusting the charging and discharging power of the electric vehicle through a voltage optimization model. According to the invention, the electric vehicle cluster participates in scheduling as a whole, the individual charging demand is reserved through the single vehicle feasible region, and the resource regulation and control capability and stability are improved through the charging station feasible region; and voltage control is carried out by adopting an electric vehicle cluster aggregation regulation and control mode, and flexible resources are precisely regulated and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network voltage regulation, and in particular to a distribution network voltage regulation method and system based on aggregated modeling of electric vehicle clusters. Background Art

[0002] With the high penetration of distributed new energy and the emergence of new loads such as electric vehicles, the power flow distribution of traditional distribution networks has been greatly changed, resulting in problems such as high and low voltages and local overvoltages in the distribution network, which puts higher requirements on the voltage regulation of the distribution network.

[0003] Traditional distribution network voltage regulation is divided into two categories: one depends on on-load tap-changing transformers, capacitor banks and distributed reactive power compensation devices. The static response characteristics of these devices limit their adaptability in the face of dynamic voltage problems. The other adopts a centralized optimization regulation scheme, which conducts unified scheduling by collecting the information of the whole network. Although it can provide high regulation accuracy, the complex communication and calculation requirements reduce its practicability. At the same time, due to the characteristics of fast response and flexible controllability, the impact of electric vehicles (EV) on the power grid and their controllability have become research hotspots. On the one hand, as a new type of load, electric vehicles are very likely to cause an additional peak during the load peak, increasing the risk of local low voltage violation in the distribution network. On the other hand, electric vehicles can be regarded as controllable load resources to participate in the distribution network scheduling, providing a new regulation means for the distribution network voltage regulation. However, due to the scattered distribution, small capacity and strong randomness of electric vehicles, it is difficult to schedule them. At present, there are those that build models for electric vehicle loads, and there are also those that approximate the electric vehicle load as an equivalent model of a storage battery, but they all adopt a data-driven idea, with numerous model parameters and complex calculation and solution, and do not make full use of the historical data of electric vehicle charging stations and cannot be applied to the distribution network voltage regulation. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a distribution network voltage regulation method and system based on aggregated modeling of electric vehicle clusters to solve the problems that the existing technology's deployment strategy for electric vehicles in the distribution network cannot adaptively adjust dynamically, the complex communication and calculation requirements cannot respond quickly, the model parameters of electric vehicles are numerous and the solution is complex, and the historical data of electric vehicle charging stations cannot be fully utilized and applied to the distribution network voltage regulation.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a distribution network voltage regulation method based on aggregated modeling of electric vehicle clusters, including: obtaining multi-source data of the distribution network and electric vehicles; Construct the feasible region of a single electric vehicle based on the charging demands of electric vehicle users; Aggregate the feasible regions of single electric vehicles through spatio-temporal coupling to generate the feasible region of a charging station; Based on the feasible region of the single electric vehicle and the feasible region of the charging station, with the goal of minimizing the sum of the squares of the voltage deviations across the network, construct a voltage optimization model that employs the aggregated regulation of electric vehicles; When it is detected that the node voltage exceeds the limit, dynamically adjust the charging and discharging power of the electric vehicle through the voltage optimization model.

[0007] As a preferred embodiment of the method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster according to the present invention, wherein: constructing the feasible region of a single electric vehicle based on the charging demands of electric vehicle users includes: Obtain the charging demand parameters of the user; Extract the rated power, charging efficiency, charging start and end time periods, and demanded power parameters based on the obtained parameters; Based on the extracted parameter data, form the feasible region of a single electric vehicle by constructing constraints on charging power limit, dynamic accumulation of charging amount, battery capacity boundary, and power smoothing.

[0008] As a preferred embodiment of the method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster according to the present invention, wherein: aggregating the feasible regions of single electric vehicles through spatio-temporal coupling to generate the feasible region of a charging station includes: Aggregate the feasible regions of single electric vehicles to obtain the Minkowski sum of the polyhedra of the feasible region of the charging station; Perform constraint projection on the Minkowski sum through the elimination method to solve for the feasible region of the charging station.

[0009] As a preferred embodiment of the method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster according to the present invention, wherein: based on the feasible region of the single electric vehicle and the feasible region of the charging station, with the goal of minimizing the sum of the squares of the voltage deviations across the network, constructing a voltage optimization model that employs the aggregated regulation of electric vehicles specifically includes: Take the sum of the squares of the deviations between the voltages of all nodes and the reference voltage as the objective function; Construct system safe operation constraints, AC power flow constraints, and aggregated regulation constraints of electric vehicles.

[0010] As a preferred embodiment of the method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster according to the present invention, wherein: the aggregated regulation constraints of electric vehicles include: Select different paths through the feasible region of the charging station to construct energy change boundary constraints and energy state continuity constraints, so as to obtain the regulation constraints of the second-order approximate feasible region of the charging station; Among them, the energy change boundary constraint means that for any two consecutive moments within the scheduling period, the total energy change of the charging station within the time period between the two consecutive moments is between the preset upper and lower bounds of the energy change; The energy state continuity constraint is that the stored energy at the current moment is equal to the stored energy at the previous moment plus the product of the aggregated charging power within the current time period and the scheduling time interval.

[0011] As a preferred solution of the distribution network voltage regulation method based on electric vehicle cluster aggregation modeling according to the present invention, wherein: when it is monitored that the node voltage exceeds the limit, the charging and discharging power of the electric vehicle is dynamically adjusted through the voltage optimization model, including: When it is monitored that the grid voltage exceeds the preset deviation range, multi-source heterogeneous data of the grid is obtained in real time; Based on the current grid topology and the access status of electric vehicles, the constraints of the voltage optimization model are dynamically updated; Taking the minimum sum of squares of the voltage deviations of the whole network as the optimization goal, the voltage optimization model is solved through an optimization algorithm to obtain the optimal distribution scheme of the charging and discharging power of the electric vehicle cluster within the aggregated feasible region.

[0012] As a preferred solution of the distribution network voltage regulation method based on electric vehicle cluster aggregation modeling according to the present invention, wherein: multi-source data of the distribution network and electric vehicles are obtained, including: load data, line data, topology data, operation data and user data.

[0013] In a second aspect, the present invention provides a distribution network voltage regulation system based on electric vehicle cluster aggregation modeling, including: An acquisition module, configured to acquire multi-source data of the distribution network and electric vehicles; A first region construction module, configured to construct a feasible region for a single electric vehicle based on the charging requirements of electric vehicle users; A second region construction module, configured to aggregate and generate a feasible region for the charging station by spatio-temporal coupling of the feasible regions of single electric vehicles; A model construction module, configured to construct a voltage optimization model adopting electric vehicle aggregation regulation with the minimum sum of squares of the voltage deviations of the whole network as the goal based on the feasible region of the single electric vehicle and the feasible region of the charging station; A solution module, configured to dynamically adjust the charging and discharging power of the electric vehicle through the voltage optimization model when it is monitored that the node voltage exceeds the limit.

[0014] In a third aspect, the present invention provides an electronic device, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distribution network voltage regulation method based on the aggregated modeling of electric vehicle clusters are implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which implement the steps of the distribution network voltage regulation method based on the aggregated modeling of electric vehicle clusters when executed by a processor.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention takes the electric vehicle cluster as an aggregated whole to participate in scheduling. By modeling the single-vehicle feasible region, the individual charging demands are retained, which can avoid the grid impact caused by disorderly charging while ensuring user satisfaction. Also, through the aggregation of the charging station feasible regions, the dispersed EV resources are transformed into schedulable energy storage units, improving the flexible resource regulation ability and stability. By real-time monitoring the voltage state of the distribution network, when local overvoltage or low voltage problems occur, the voltage control is carried out by means of the aggregated regulation of the electric vehicle cluster, and precise regulation is performed through fast-response flexible resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic diagram of the overall process of the distribution network voltage regulation method based on the aggregated modeling of electric vehicle clusters according to an embodiment of the present invention.

[0019] Figure 2 It is a schematic diagram of the binary tree of the calculation path set of successive elimination in the distribution network voltage regulation method based on the aggregated modeling of electric vehicle clusters according to an embodiment of the present invention.

[0020] Figure 3 It is a schematic diagram of the improved IEEE33-node distribution network structure in the distribution network voltage regulation method based on the aggregated modeling of electric vehicle clusters according to an embodiment of the present invention.

[0021] Figure 4 It is a graph of the node voltage change of node 32 under voltage regulation in the distribution network voltage regulation method based on the aggregated modeling of electric vehicle clusters according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe in detail the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0023] Example 1. Referring to Figure 1 , which is an embodiment of the present invention, provides a method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster, including: S100: Obtain multi-source data of the distribution network and electric vehicles; S200: Construct a feasible region for a single electric vehicle based on the charging demand of electric vehicle users; S300: Aggregate the feasible regions of single electric vehicles through spatio-temporal coupling to generate a feasible region for a charging station; S400: Based on the feasible region of a single electric vehicle and the feasible region of a charging station, with the goal of minimizing the sum of the squares of the voltage deviations of the entire network, construct a voltage optimization model that adopts the aggregated regulation of electric vehicles; S500: When it is detected that the node voltage exceeds the limit, dynamically adjust the charging and discharging power of the electric vehicle through the voltage optimization model.

[0024] It should be noted that the current participation of electric vehicles in the voltage regulation of the distribution network mainly has a contradiction between the dynamic response ability and the dispersion of electric vehicles. Its spatial random distribution characteristics make it difficult to match the coverage range of the regulation unit with the voltage-sensitive area of the distribution network. Currently, some studies have established mathematical models for electric vehicle loads to simulate the charging demands of electric vehicles under different power markets. For example, aggregating electric vehicles into virtual energy storage to provide information guidance for the distribution network planning, or approximately aggregating the electric vehicle load into an equivalent model of a storage battery, and then using regression analysis to obtain the parameters of the equivalent model of the storage battery; these models need to process 6-8 dimensional characteristic parameters such as charging time, SOC state, and user behavior, and the solution dimension increases exponentially with the scale. Approximately 40% of the regular charging behaviors in the actual historical data of charging stations have not been effectively extracted, and the excessive dependence on real-time data has increased the computational burden; at the same time, the dynamic mapping relationship between the charging demand elasticity and the voltage has not been established.

[0025] Therefore, to address these specific problems, through the above solutions of S100-S500, multi-source heterogeneous data is fused to obtain the grid operation status and user behavior characteristics. The feasible region of a single electric vehicle can avoid grid shocks caused by disorderly charging while ensuring user satisfaction; through the aggregation of the feasible regions of charging stations, the dispersed EV resources are transformed into schedulable energy storage units, and the corresponding regulation constraint model is constructed, which can effectively suppress voltage over-limit while improving the flexibility of resource regulation, forming a closed-loop of dynamic regulation.

[0026] Example 2, referring to Figure 1 - Figure 2 , which is an embodiment of the present invention. Based on the above embodiments, a method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster is provided.

[0027] In the embodiment of the present invention, the acquisition of multi-source data of the distribution network and electric vehicles in step S100 includes: load data, line data, topology data, operation data, and user data; Specifically, the load data may include: power demand, peak-valley characteristics; the topology data may include network structure, node connection relationship; the line parameters may include impedance, capacity constraint; the operation state may include node voltage, power flow distribution; the user data may include charging demand response sensitivity, typical charging time period distribution probability.

[0028] In the embodiment of the present invention, the construction of the feasible region of a single electric vehicle based on the charging demand of electric vehicle users in step S200 includes the following steps A1 - A3: A1: Obtain user charging demand parameters; A2: Extract rated power, charging efficiency, charging start and end time periods, and required charge parameters based on the obtained parameters; Specifically, it can be expressed as: rated power and charging efficiency , as well as the time period of arriving at the charging pile , the time period of leaving the charging pile and the required charge .

[0029] A3: Based on the extracted parameter data, form the feasible region of a single electric vehicle by constructing charging power limit, dynamic accumulation of charging amount, battery capacity boundary, and power smoothing constraint.

[0030] Specifically, it can be assumed that the electric vehicle starts charging at the time period, ends charging at the time period. Then, a feasible region model is constructed for a single electric vehicle. The above charging power limit, dynamic accumulation of charging amount, battery capacity boundary, and power smoothing constraint can be expressed as: Charging power limit: The charging power of the EV is limited by the rated power and satisfies: (1) Dynamic accumulation of charging amount: The charging energy of the EV changes with time, specifically: (2) Battery capacity boundary: The cumulative charging amount of the EV satisfies: (3) Power smoothing constraint: The charging power change of the EV satisfies: (4) (5) (6) (7) (8) In the formula, is the charging power of the electric vehicle in the time period ; is the minimum limit value of the charging power of the electric vehicle; is the maximum limit value of the charging power of the electric vehicle; is the index of the scheduling time period; is the scheduling time interval; is the starting time period of the scheduling; is the rated power of the electric vehicle; The cumulative charging amount of the electric vehicle in the time period ; is the charging efficiency of the electric vehicle; is the minimum cumulative charging amount boundary of the time period ; is the maximum cumulative charging amount boundary of the time period ;

[0031] It should be noted that an electric vehicle (EV) is a flexible mobile energy storage unit, and its charging and discharging behaviors are affected by various factors, such as battery type, vehicle type, user travel pattern, etc.; therefore, it is necessary to comprehensively analyze these factors to obtain the key parameters of the EV so as to characterize the EV operation domain; the above steps A1 - A3 take the electric vehicle cluster as an aggregated whole to participate in the scheduling, and first characterize the operation domain of a single electric vehicle through characteristic parameters such as the arrival time, departure time, and expected power of the electric vehicle.

[0032] In the embodiment of the present invention, step S300 aggregates the feasible domains of single electric vehicles through space - time coupling to generate the feasible domain of the charging station, including the following steps B1 - B2: B1: Aggregate the feasible domains of single electric vehicles to obtain the Minkowski sum of the polyhedra of the feasible domain of the charging station; Specifically, the Minkowski sum of the polyhedra of the feasible domain of the charging station can be expressed as: (9) In the formula, is the feasible domain of the charging station; is the th operation domain of the electric vehicle; represents the Minkowski sum operation, is the number of electric vehicles.

[0033] B2: Perform constrained projection on the Minkowski sum through the elimination method to obtain the feasible region of the charging station; Preferably, in step B2, since directly solving the Minkowski sum has extremely high complexity in high-dimensional space, constrained projection can be performed through the Fourier-Motzkin elimination method; Exemplarily, the implementation steps are as follows: Represent the operating domain of each electric vehicle in the charging station in the form of linear constraints: (10) In the formula, is the decision variable of the th electric vehicle; is the charging power; is the cumulative charging amount; is the th electric vehicle's constraint matrix, representing the linear constraint conditions of the charging power and the cumulative charging amount; is the right-side constant vector of the constraint condition, representing the upper or lower limit of the charging power and the cumulative charging amount.

[0034] By successively eliminating the variables of individual electric vehicles and retaining the global variable ; Among them, the set of calculation paths for successive elimination is represented as follows: (11) In the formula, is the set of all paths of the complete binary tree. The complete binary tree is as Figure 2 shown; is the coefficient vector on the path ; and represent the lower bound and upper bound of the path respectively; is the global variable of the cumulative charging amount, representing the total charging amount of all electric vehicles in the charging station.

[0035] The calculation formulas for the parameters and are as follows: When the number of non-zero nodes in the path is odd: (12) (13) Among them, is the upper limit of the battery capacity of the th electric vehicle; is the lower limit of the battery capacity of the th electric vehicle; is the path The starting index point; is the path The ending index point; is the path The starting time mapping of the battery state; is the path The ending time mapping of the battery state; is the scheduling time interval; Used to represent the path The cumulative impact on the charging power; is the Upper limit of the charging power of the is the Lower limit of the charging power of the When the number of non-zero nodes in the path is even: (14) (15) Wherein, the angled brackets represent the matrix inner product operation; Calculated according to the following formula: (16) In the formula, is the path At the time point The cumulative coefficient vector; is the scheduling period of the electric vehicle; is the path At the time point The coefficient vector.

[0036] In another alternative embodiment, the elimination method in step B2 can also be the Gaussian elimination method.

[0037] It should be noted that step S300 integrates the feasible region of a single electric vehicle into the feasible region of the charging station, which can achieve the global optimal allocation of charging resources.

[0038] In the embodiment of the present invention, step S400 constructs a voltage optimization model using electric vehicle aggregation regulation based on the feasible region of a single electric vehicle and the feasible region of the charging station, with the goal of minimizing the sum of the squares of the voltage deviations across the network, specifically including the following steps C1 - C2: C1: Taking the sum of the squares of the deviations between all node voltages and the reference voltage as the objective function; Specifically, the objective function can be expressed as: (17) In the formula, is the node The voltage; is the reference voltage; is the set of all nodes in the distribution network.

[0039] C2: Construct system secure operation constraints, AC power flow constraints, and electric vehicle aggregation regulation constraints; Specifically, the above-mentioned system secure operation constraints and AC power flow constraints can be specifically expressed as: System secure operation constraints: (18) In the formula, is the minimum allowable value of the node voltage; is the maximum allowable value of the node voltage.

[0040] AC power flow constraints: (19) (20) In the formula, and are respectively the active power and reactive power of node ; and are respectively the active power and reactive power of the load at node ; and are respectively the admittance matrix elements of node and node ; is the voltage of node ; is the voltage phase angle difference between node and .

[0041] In the embodiment of the present invention, in step C2, the electric vehicle aggregation regulation constraints include: Select different paths through the feasible region of the charging station, and construct energy change boundary constraints and energy state continuity constraints to obtain the regulation constraints of the second-order approximate feasible region of the charging station; Among them, for the energy change boundary constraints, for any two consecutive moments within the scheduling period, the total energy change of the charging station within the time period of the two consecutive moments is between the preset upper and lower bounds of energy change; Specifically, the energy change boundary constraints can be expressed as: (21) The energy state continuity constraint is that the stored energy at the current moment is equal to the stored energy at the previous moment plus the product of the aggregated charging power within the current time period and the scheduling time interval; Specifically, the energy state continuity constraint can be expressed as: (22) In formulas (21) and (22), 、 are two moments within the scheduling period and satisfy ; is the number of electric vehicles; and are respectively the lower bound and upper bound of the energy change of the charging station between the moment and ; is the minimum charging power of the th vehicle at the moment ; is the maximum charging power of the th vehicle at the moment ; 、 are respectively the lower limits of the battery capacities of the th electric vehicle at the moments 、 ; are respectively the upper limits of the battery capacities of the th electric vehicle at the moments 、 ; and are respectively the cumulative energy states of the charging station at the moments and ; is the total stored energy of the charging station at the moment ; is the total stored energy of the charging station at the moment ; is the aggregated charging power of the charging station at the moment .

[0042] It should be noted that in steps C1 - C2, by introducing system safe operation constraints, AC power flow constraints, and electric vehicle aggregation regulation constraints, it is ensured that the optimization result meets the safe operation requirements of the power grid and the charging station.

[0043] In the embodiment of the present invention, in step S500, when it is monitored that the node voltage exceeds the limit, the charging and discharging power of the electric vehicle is dynamically adjusted through the voltage optimization model, including the following steps D1 - D3: D1: When it is monitored that the grid voltage exceeds the preset deviation range, multi - source heterogeneous data of the grid is obtained in real time; D2: Based on the current grid topology and the access status of electric vehicles, the constraints of the voltage optimization model are dynamically updated; D3: With the minimum of the sum of the squares of the grid voltage deviations as the optimization objective, solve the voltage optimization model through an optimization algorithm to obtain the optimal allocation scheme of the charging and discharging power of the electric vehicle cluster within the aggregated feasible region.

[0044] Exemplarily, the specific implementation manners of steps D1 - D3 are as follows: When it is monitored that the grid voltage exceeds the preset deviation range (such as ±7%), based on the current grid topology and EV access status, load the AC power flow equation constraints (such as power balance), system security constraints (such as voltage upper and lower limits), and the charging station second - order approximate feasible region constraints generated by S300; with the minimum of the sum of the squares of the grid voltage deviations as the optimization objective, use convex relaxation or distributed optimization algorithms (such as ADMM) to solve the optimal allocation scheme of the EV cluster charging and discharging power, ensuring that the solution is within the aggregated feasible region.

[0045] In summary, the technical solution of the present invention aggregates the feasible regions of single electric vehicles through spatio - temporal coupling to generate the feasible region of the charging station, considering the time and space dimensions, ensuring the reasonable allocation of charging station resources at different times and different nodes, and being able to avoid resource waste or local overload; constructs a voltage optimization model using electric vehicle aggregation regulation. When it is monitored that the node voltage exceeds the limit, the charging and discharging power of the electric vehicle is adjusted in real time through the voltage optimization model to quickly restore the grid voltage stability, realizing dynamic regulation and real - time response, and at the same time improving the flexibility and robustness of the grid.

[0046] Example 3, referring to Figure 3 - Figure 4 Based on the previous example, this example provides an effect case of a distribution network voltage regulation method based on electric vehicle cluster aggregation modeling to illustrate the feasibility and beneficial effects of our solution.

[0047] Analyze with the improved IEEE - 33 - node distribution network, and its topological structure is as Figure 3 shown. Electric vehicle charging stations are connected to nodes 5, 13, and 28 of this distribution network respectively. The capacities of the electric vehicle charging stations EV1, EV2, and EV3 are 0.45MW, 0.36MW, and 0.45MW respectively. The rated power of the charging piles of the charging stations EV1, EV2, and EV3 is set to 7kW, and it is set to cover 100 electric vehicles. Parameters such as the arrival time, departure time, and expected charging power of the electric vehicles are obtained from the operator. The CPU of the simulation device is AMD Ryzen 9 7945HX, the memory is 32GB, and the programming software is Matlab 2023a and Mosek 9.3.

[0048] Since voltage over - limits are likely to occur at the end - node of the distribution network after new energy grid connection, and the voltage safety range is 0.93 - 1.07pu. Select Figure 3 node 32 inFigure 4 as shown

[0049] As can be seen from Figure 4 after aggregating the participating node voltages of the electric vehicle cluster for regulation, the node voltage at the end node 32 of the distribution network feeder is significantly improved. The node voltage fluctuates in the range of 0.946 - 1.022 pu, meeting the voltage deviation requirements and with relatively smooth fluctuations.

[0050] Example 4. The above is a schematic solution of a distribution network voltage regulation method based on electric vehicle cluster aggregation modeling. It should be noted that the technical solution of the distribution network voltage regulation system based on electric vehicle cluster aggregation modeling belongs to the same concept as the technical solution of the above-mentioned distribution network voltage regulation method based on electric vehicle cluster aggregation modeling. For the details not described in detail in the technical solution of the distribution network voltage regulation system based on electric vehicle cluster aggregation modeling in this example, reference can be made to the description of the technical solution of the above-mentioned distribution network voltage regulation method based on electric vehicle cluster aggregation modeling.

[0051] This embodiment also provides a system for a distribution network voltage regulation method based on electric vehicle cluster aggregation modeling, including: An acquisition module, configured to acquire multi-source data of the distribution network and electric vehicles; A first region construction module, configured to construct a feasible region for a single electric vehicle based on the charging demand of electric vehicle users; A second region construction module, configured to aggregate the feasible regions of single electric vehicles through spatio-temporal coupling to generate a feasible region for a charging station; A model construction module, configured to construct a voltage optimization model for electric vehicle aggregation regulation with the goal of minimizing the sum of squares of voltage deviations across the network based on the feasible region of a single electric vehicle and the feasible region of a charging station; A solution module, configured to dynamically adjust the charging and discharging power of electric vehicles through the voltage optimization model when it is detected that the node voltage exceeds the limit.

[0052] This embodiment also provides an electronic device applicable to the situation of distribution network voltage regulation based on electric vehicle cluster aggregation modeling, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the distribution network voltage regulation method based on electric vehicle cluster aggregation modeling as proposed in the above embodiment.

[0053] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the distribution network voltage regulation method based on electric vehicle cluster aggregation modeling as proposed in the above embodiment.

[0054] The storage medium proposed in this embodiment and the method for regulating the voltage of a distribution network based on the aggregation modeling of electric vehicle clusters proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0055] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and the necessary general-purpose hardware, and of course, it can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0056] It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solution of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solution of the present invention, and it should all be covered within the scope of the claims of the present invention.

Claims

1. A method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster, characterized in that, Including: Obtain multi-source data of the distribution network and electric vehicles; Construct the feasible region of a single electric vehicle based on the charging demands of electric vehicle users; Aggregate the feasible regions of single electric vehicles through spatio-temporal coupling to generate the feasible region of the charging station, including: Aggregate the feasible regions of single electric vehicles to obtain the polyhedral Minkowski sum of the feasible region of the charging station; Perform constraint projection on the Minkowski sum through the elimination method to solve and obtain the feasible region of the charging station; Based on the feasible region of the single electric vehicle and the feasible region of the charging station, with the goal of minimizing the sum of the squares of the voltage deviations across the entire network, construct a voltage optimization model using electric vehicle aggregation regulation, specifically including: Taking the sum of the squares of the deviations between the voltages of all nodes and the reference voltage as the objective function; Construct system safe operation constraints, AC power flow constraints, and electric vehicle aggregation regulation constraints; When it is detected that the node voltage exceeds the limit, dynamically adjust the charging and discharging power of the electric vehicle through the voltage optimization model.

2. The method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster as described in claim 1, wherein The construction of the feasible region of a single electric vehicle based on the charging demands of electric vehicle users includes: Obtain user charging demand parameters; Extract rated power, charging efficiency, charging start and end time periods, and demand power parameters based on the obtained parameters; Based on the extracted parameter data, form the feasible region of a single electric vehicle by constructing charging power limit, dynamic accumulation of charging amount, battery capacity boundary, and power smoothing constraints.

3. The method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster according to claim 2, wherein The electric vehicle aggregation regulation constraints include: Select different paths through the feasible region of the charging station to construct energy change boundary constraints and energy state continuity constraints to obtain the regulation constraints of the second-order approximate feasible region of the charging station; Among them, the energy change boundary constraint means that for any two consecutive moments within the scheduling period, the total energy change of the charging station within the time period of the two consecutive moments is between the preset upper and lower bounds of the energy change; The energy state continuity constraint is that the stored energy at the current moment is equal to the stored energy at the previous moment plus the product of the aggregated charging power within the current time period and the scheduling time interval.

4. The method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster according to claim 3, wherein When it is detected that the node voltage exceeds the limit, dynamically adjust the charging and discharging power of the electric vehicle through the voltage optimization model, including: When it is detected that the grid voltage exceeds the preset deviation range, obtain the multi-source heterogeneous data of the grid in real time; Based on the current grid topology and the access status of electric vehicles, dynamically update the constraints of the voltage optimization model; Taking the minimum sum of the squares of the voltage deviations across the entire network as the optimization goal, solve the voltage optimization model through an optimization algorithm to obtain the optimal allocation scheme of the charging and discharging power of the electric vehicle cluster within the aggregated feasible region.

5. The method for regulating the voltage of a distribution network based on the aggregated modeling of an electric vehicle cluster according to claim 1 or 4, characterized in that, Obtain multi-source data of the distribution network and electric vehicles, including: load data, line data, topology data, operation data, and user data.

6. A distribution network voltage regulation system based on aggregated modeling of electric vehicle clusters, applied to the method according to any one of claims 1-5, characterized in that, Including: An acquisition module for obtaining multi-source data of the distribution network and electric vehicles; A first region construction module for constructing the feasible region of a single electric vehicle based on the charging demands of electric vehicle users; A second region construction module for aggregating the feasible regions of single electric vehicles through spatio-temporal coupling to generate the feasible region of the charging station; A model construction module for constructing a voltage optimization model using electric vehicle aggregation regulation with the goal of minimizing the sum of the squares of the voltage deviations across the entire network based on the feasible region of the single electric vehicle and the feasible region of the charging station; A solution module, configured to dynamically adjust the charging and discharging power of electric vehicles through the voltage optimization model when it is detected that the node voltage exceeds the limit.

7. An electronic device, characterized in that, It includes: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distribution network voltage regulation method based on the aggregated modeling of an electric vehicle cluster according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the distribution network voltage regulation method based on the aggregated modeling of an electric vehicle cluster according to any one of claims 1 to 5 are implemented.

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