Flexible power distribution network distributed robust operation method and system

Abstract

The application discloses a kind of flexible power distribution network distribution robust operation method and system. Including: according to electric vehicle charging load parameter information and intelligent soft switch parameter information, construct the operation constraint of electric vehicle aggregator based on virtual energy storage;According to flexible power distribution network parameter information and electric vehicle aggregator operation constraint, establish flexible power distribution network deterministic operation optimization model;Distributed photovoltaic and electric vehicle uncertainty scenario set are constructed;According to distributed photovoltaic and electric vehicle uncertainty scenario set and flexible power distribution network deterministic operation optimization model, establish the distributed robust optimization model of considering distributed photovoltaic and electric vehicle uncertainty of flexible power distribution network;Flexible power distribution network distribution robust optimization model is solved, and flexible power distribution network operation strategy data are obtained. It can effectively reduce the mismatch between the amount of power supply and the actual electric vehicle power demand under uncertain environment, while meeting the demand of electric vehicle users, ensure the economic and efficient operation of distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution technology, and in particular to a flexible power distribution network distributed bar operation method and system, a flexible power distribution network distributed bar optimization model and its establishment method. Background Technology

[0002] With the explosive growth of electric vehicle charging stations, the electricity demand of electric vehicles is increasing daily. The energy storage characteristics and diverse charging and discharging characteristics of electric vehicles will inevitably lead to further complexity in the energy flow of the power distribution network. To ensure the safe and stable operation of the power distribution network while meeting the electricity demand of electric vehicles, it is urgent to conduct research on flexible power distribution network operation optimization methods adapted to large-scale charging load access. The rapid advancements in flexible power distribution technology in recent years have provided an opportunity to solve this problem. Intelligent soft switches are power electronic devices installed at traditional tie switches, enabling normalized flexible connections and flexible power transmission control between feeders. They feature continuous and precise adjustment, faster response speed, lower operating costs, and less impact from faults, greatly improving the flexibility and speed of power distribution system control. This provides technical support for the high power quality and high power supply reliability requirements of power distribution systems.

[0003] Research on the optimization of power distribution network operation considering electric vehicle access has been carried out both domestically and internationally. The main focus is on the optimization and control of smart power distribution networks for orderly charging of electric vehicles. However, there are still shortcomings in considering the uncertainties of distributed photovoltaic and electric vehicles, mitigating the impact of large-scale charging load access, achieving energy saving and loss reduction in power distribution networks, and reducing the cost of purchasing electricity for electric vehicles. There is no technical solution that can simultaneously consider the behavioral characteristics of different types of electric vehicles and the flexible power regulation of electric vehicles through intelligent soft switching. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a flexible distribution network split-rod operation method and system, a flexible distribution network split-rod optimization model and its establishment method.

[0005] In a first aspect, embodiments of the present invention provide a method for operating a flexible distribution network with distributed baffles, comprising: Collect flexible distribution network parameter information, electric vehicle charging load parameter information, and intelligent soft switch parameter information; the flexible distribution network parameter information includes annual distributed photovoltaic power output data, and the electric vehicle charging load parameter information includes annual electric vehicle arrival time distribution data for charging stations. Based on electric vehicle charging load parameters and intelligent soft switch parameters, operational constraints for electric vehicle aggregators based on virtual energy storage are constructed. Based on the parameter information of the flexible distribution network and the operating constraints of electric vehicle aggregators based on virtual energy storage, a deterministic operation optimization model for the flexible distribution network is established. Based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data at charging stations, a set of uncertainty scenarios for distributed photovoltaic and electric vehicles is constructed. Based on the uncertainty scenario set of distributed photovoltaic and electric vehicles and the deterministic operation optimization model of flexible distribution network, a sub-Bluer bar optimization model of flexible distribution network considering the uncertainty of distributed photovoltaic and electric vehicles is established. The flexible distribution network sub-bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles is solved to obtain the operation strategy data; The power distribution network is operated based on the operational strategy data.

[0006] Secondly, embodiments of the present invention provide a flexible distribution network distributed rod operation system, comprising: The parameter information acquisition module is used to collect parameter information of flexible distribution network, electric vehicle charging load, and intelligent soft switch; the parameter information of flexible distribution network includes annual distributed photovoltaic power output data, and the parameter information of electric vehicle charging load includes annual electric vehicle arrival time distribution data at charging stations. The aggregator operation constraint construction module is used to construct the electric vehicle aggregator operation constraints based on virtual energy storage, according to the electric vehicle charging load parameter information and the intelligent soft switch parameter information. The operation optimization model establishment module is used to establish a deterministic operation optimization model for the flexible distribution network based on the parameter information of the flexible distribution network and the operation constraints of the electric vehicle aggregator based on virtual energy storage. The sub-Bluerg bar optimization model building module is used to construct a set of uncertain scenarios for distributed photovoltaic and electric vehicles based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data for charging stations; and to establish a sub-Bluerg bar optimization model for flexible distribution networks that considers the uncertainties of distributed photovoltaic and electric vehicles based on the set of uncertain scenarios for distributed photovoltaic and electric vehicles and the deterministic operation optimization model of flexible distribution networks. The operation strategy solution module is used to solve the flexible distribution network sub-bar optimization model that considers the uncertainties of distributed photovoltaic and electric vehicles to obtain operation strategy data; and to operate the distribution network according to the operation strategy data.

[0007] Based on the same inventive concept, embodiments of the present invention provide a method for establishing a flexible distribution network distributed bar optimization model, including: Collect flexible distribution network parameter information, electric vehicle charging load parameter information, and intelligent soft switch parameter information; the flexible distribution network parameter information includes annual distributed photovoltaic power output data, and the electric vehicle charging load parameter information includes annual electric vehicle arrival time distribution data for charging stations. Based on electric vehicle charging load parameters and intelligent soft switch parameters, operational constraints for electric vehicle aggregators based on virtual energy storage are constructed. Based on the parameter information of the flexible distribution network and the operating constraints of electric vehicle aggregators based on virtual energy storage, a deterministic operation optimization model for the flexible distribution network is established. Based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data at charging stations, a set of uncertainty scenarios for distributed photovoltaic and electric vehicles is constructed. Based on the uncertainty scenario set of distributed photovoltaic and electric vehicles and the deterministic operation optimization model of flexible distribution network, a sub-Bluer bar optimization model of flexible distribution network considering the uncertainty of distributed photovoltaic and electric vehicles is established.

[0008] Based on the same inventive concept, this invention provides a flexible distribution network distributed bar optimization model, which is established by the aforementioned method.

[0009] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following: It can solve the problem of improving the operational economy of flexible distribution networks. It fully considers the charging and discharging potential of electric vehicle aggregators based on virtual energy storage and the precise power flow regulation of intelligent soft switching. It establishes a flexible distribution network distributed bar optimization model that takes into account the uncertainties of distributed photovoltaic and electric vehicles, and obtains an intelligent soft switching operation strategy. It effectively reduces the mismatch between the strategy power supply and the actual power demand of electric vehicles under uncertain conditions, and ensures the economical and efficient operation of the distribution network while meeting the needs of electric vehicle users.

[0010] Other features and advantages of the invention will be set forth in the following description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0011] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0012] The accompanying drawings are provided to further illustrate the present invention and form part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, but do not constitute a limitation on the technical solutions of the present invention. In the drawings: Figure 1 This is a flowchart of a flexible distribution network branch-type rod operation method adapted to charging load access in an embodiment of the present invention; Figure 2 This is a schematic diagram of the access location of the multi-terminal intelligent soft-switching port converter in an embodiment of the present invention; Figure 3 This is a schematic diagram of annual photovoltaic power output data in an embodiment of the present invention; Figure 4This is a schematic diagram illustrating the annual arrival time distribution of electric vehicles at charging stations in an embodiment of the present invention; Figure 5 This is a schematic diagram of the time-of-use electricity pricing system in an embodiment of the present invention. Detailed Implementation

[0013] Exemplary embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that the technical solutions of the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0014] To address the problems existing in the prior art, embodiments of the present invention provide a flexible distribution network distributed bar operation method and system, a flexible distribution network distributed bar optimization model and its establishment method.

[0015] Example 1 Embodiment 1 of the present invention provides a method for operating a flexible distribution network with distributed baffles, the process of which is as follows: Figure 1 As shown, it includes the following steps: Step S1: Collect flexible distribution network parameter information, electric vehicle charging load parameter information, and intelligent soft switch parameter information; the flexible distribution network parameter information includes annual distributed photovoltaic power output data, and the electric vehicle charging load parameter information includes annual electric vehicle arrival time distribution data for charging stations.

[0016] Step S2: Based on the electric vehicle charging load parameter information and the intelligent soft switch parameter information, construct the electric vehicle aggregator operation constraints based on virtual energy storage; based on the flexible distribution network parameter information and the electric vehicle aggregator operation constraints based on virtual energy storage, establish a deterministic operation optimization model for the flexible distribution network.

[0017] In some specific embodiments, the operational constraints of the electric vehicle aggregator based on virtual energy storage include: Constraints on electric vehicle user behavior characteristics, electric vehicle aggregator charging and discharging power, electric vehicle aggregator power consumption, and electric vehicle aggregator access constraints based on intelligent soft-switching DC links.

[0018] In some specific embodiments, electric vehicle user behavior can be divided into four categories based on whether they participate in grid regulation and whether the off-grid time is known. The constraints on the characteristics of electric vehicle user behavior include: Class I electric vehicle user behavior characteristics: They do not participate in grid regulation, always charge at maximum power, and leave as soon as they are fully charged, minimizing charging time.

[0019] in, This represents the charging time required for the nth type I electric vehicle user; This represents the expected battery level that the nth type I electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth type I electric vehicle user; This represents the charging power of the nth type I electric vehicle user during time period t; This represents the maximum charging power for the nth type I electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth type I electric vehicle user starts charging; This represents the time when the nth type I electric vehicle user finishes charging and leaves; Class II electric vehicle user behavior characteristics: They do not participate in grid regulation, charge at maximum power until the expected charge level is reached, then stop charging and do not leave immediately, but leave the charging station at the expected departure time.

[0020] in, This represents the charging time required for the nth Class II electric vehicle user; This represents the expected battery level that the nth Class II electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth Class II electric vehicle user; This represents the charging power of the nth type II electric vehicle user during time period t; This represents the maximum charging power of the nth Class II electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class II electric vehicle user begins charging; The time when the nth Class II electric vehicle user finishes charging; This represents the expected departure time of the nth type II electric vehicle user; Category III electric vehicle user behavior characteristics: Participating in grid regulation and responding to electricity price guidance; when connected to a charging station, the charging power is optimized and adjusted according to the grid operating status; when the electricity price is lower than the low electricity price threshold, charging is performed at a power greater than the high power threshold; when the electricity price is higher than the high electricity price threshold, charging is performed at a power less than the low power threshold or no charging is performed.

[0021] in, This represents the charging power of the nth Class III electric vehicle user during time period t; This represents the maximum charging power of the nth Class III electric vehicle user; This represents the expected battery level that the nth Class III electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth Class III electric vehicle user; This represents the capacity of the nth Class III electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class III electric vehicle user begins charging. This indicates the time when the nth Class III electric vehicle user left. Category IV electric vehicle user behavior characteristics: Participating in grid regulation, discharging into the grid when the electricity price is above a high electricity price threshold and charging when the electricity price is below a low electricity price threshold, responding to electricity price guidance and grid operation optimization needs.

[0022]

[0023] in, Let t represent the charging power of the nth Class IV electric vehicle user in time period t, assuming that the power flows from the power distribution network to the electric vehicle in the positive direction of charging. This represents the maximum charging power for the nth Class IV electric vehicle user; This represents the maximum discharge power of the nth Class IV electric vehicle user; The expected battery level required when the nth Class IV electric vehicle user leaves; This represents the initial battery level of the nth Class IV electric vehicle user; This represents the battery level of the nth Class IV electric vehicle user during time period t; This represents the capacity of the nth Class IV electric vehicle user; This represents the lower limit of the battery capacity for the nth Class IV electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class IV electric vehicle user begins charging. The time when the nth Class IV electric vehicle user leaves; The aforementioned behavioral characteristics of electric vehicle users in categories I to IV also include:

[0024] in, The piecewise probability density function representing the time it takes for an electric vehicle to leave a charging station; Indicates the time when the electric vehicle leaves the charging station; This represents the average time it takes for an electric vehicle to leave a charging station. This represents the standard deviation of the time it takes for an electric vehicle to leave a charging station. The probability density function representing the initial charge of an electric vehicle; Indicates the initial charge of the electric vehicle; This represents the average initial charge of the electric vehicle. This represents the standard deviation of the initial charge of an electric vehicle. This indicates the initial battery level of the electric vehicle. This indicates the maximum battery capacity of the electric vehicle.

[0025] In some specific embodiments, the charging and discharging power constraints of the electric vehicle aggregator include:

[0026] in, Let t represent the charging power at node k of the electric vehicle aggregator during time period t, assuming that the power flows from the distribution network to the electric vehicle aggregator in the positive direction. This represents the maximum charging power of electric vehicles during time period t. This represents the maximum discharge power of the electric vehicle aggregate during time period t; Indicates the type of electric vehicle; Represents a set of electric vehicle types. ; n represents the electric vehicle index; Represents the nth Maximum charging power for electric vehicles; Indicating the charging station during time period t Number of electric vehicles; Represents the nth Maximum discharge power of electric vehicles.

[0027] In some specific embodiments, the electric vehicle aggregator's power constraint includes:

[0028] in, This represents the battery level of the electric vehicle aggregator during time period t; Let t represent the charging power of the electric vehicle aggregator during time period t, assuming that the power flows from the power distribution network to the electric vehicle aggregator in the positive direction. Indicates the duration of each scheduling period; This indicates the increased aggregated electricity consumption caused by electric vehicles connecting to the charging station during time period t. This represents the decrease in aggregated electricity volume caused by the electric vehicle leaving the charging station during time period t. Indicates the type of electric vehicle; Represents a set of electric vehicle types. ; n represents the electric vehicle index; Indicates the number of times the charging station is connected during time period t. Number of electric vehicles; Indicates leaving the charging station during time period t. Number of electric vehicles Indicating the charging station during time period t Number of electric vehicles; Represents the nth The initial charge level of the electric vehicle when it connects to the charging station; Represents the nth The battery level of a similar electric vehicle when it leaves a charging station; This represents the maximum battery power of electric vehicles during time period t. Represents the nth The capacity of electric vehicles.

[0029] In some specific embodiments, the electric vehicle aggregator access constraints based on the intelligent soft-switching DC link include:

[0030] in, Let t represent the charging power at node k of the electric vehicle aggregator during time period t, assuming that the power flows from the distribution network to the electric vehicle aggregator in the positive direction. This indicates that the SOP is at node t during time period t. Active power loss of the converter; Indicates the SOP injection node during time period t. The active power; Indicates the SOP injection node during time period t. reactive power; , These represent the SOP injection nodes respectively. The upper and lower limits of active power; These represent the SOP injection nodes respectively. Upper and lower limits of reactive power; Represents a node Loss factor at SOP; Represents a node The capacity of the converter; This indicates the node index that is connected to the SOP. ; This represents the set of nodes that are connected to the SOP. .

[0031] In some specific embodiments, a deterministic operation optimization model for the flexible distribution network is established based on the flexible distribution network parameter information and the operating constraints of electric vehicle aggregators based on virtual energy storage, including: With the objective function of minimizing the overall operating cost of flexible distribution networks and the electricity purchase cost of electric vehicle aggregators, a deterministic operation optimization model for flexible distribution networks is established, taking into account the operating constraints of electric vehicle aggregators based on virtual energy storage, distributed photovoltaic operation constraints, and flexible distribution network operation constraints.

[0032] In some specific embodiments, the objective function is expressed as:

[0033] Where f represents the overall objective function; Indicates the operating cost of the power distribution network; This indicates the electricity purchase cost for electric vehicle aggregators; This represents the cost of active power loss in the distribution network. This indicates the cost of voltage exceeding the limit in the distribution network; Represents the set of branches in a distribution network; Indicates a branch The resistance value; Indicates the branch in time period t The square of the current in; This represents the SOP loss at node i during time period t; This represents the set of nodes that are connected to the SOP; Indicates the duration of each scheduling period; The unit cost representing network loss; Indicates the node in time period t The square of the voltage amplitude; Indicates the total number of nodes in the distribution network; Indicates the total number of time periods; This represents the active load at node i during time period t; Indicates the upper limit of the node voltage; Indicates the lower limit of the node voltage; Indicates the upper limit of the node voltage optimization range; This indicates the lower limit of the node voltage optimization range; when the node voltage is outside the optimization range... At the same time, the degree of voltage deviation from the optimization range can be reduced through system optimization; This represents the unit penalty cost for voltage exceeding the limit at node i during time period t; Represents the set of nodes in a distribution network; This represents the penalty cost parameter; This represents the active power absorbed by aggregator k from the distribution network during time period t; This represents the time-of-use electricity price for period t; Will The linearized expression is as follows:

[0034] in, Indicates voltage auxiliary variable; Indicates the node in time period t The square of the voltage amplitude; Indicates the upper limit of the node voltage optimization range; This indicates the lower limit of the node voltage optimization range; when the node voltage is outside the optimization range... At the same time, the degree of voltage deviation from the optimization range can be reduced through system optimization; This represents the unit penalty cost for voltage exceeding the limit at node i during time period t; This represents the penalty cost parameter.

[0035] In some specific embodiments, the distributed photovoltaic operating constraints include:

[0036] in, This represents the active power output of the distributed power source at node i during time period t; This represents the predicted active power output of the distributed power source at node i during time period t. This represents the reactive power output of the distributed power source at node i during time period t; This represents the upper and lower limits of the reactive power output of the distributed power source at node i during time period t; This represents the capacity of the distributed power source at node i.

[0037] In some specific embodiments, the operational constraints of the flexible distribution network include:

[0038] in, Indicates the node in time period t The square of the voltage amplitude at that point; Indicates the branch in time period t The square of the current amplitude; Indicates the upstream branch of node j, This represents the downstream branch of node j; Represents the set of branches in a distribution network; Representing branch roads Resistance and reactance; Indicates the branch in time period t The active power flowing from node i to node j; Indicates the branch in time period t The reactive power flowing from node i to node j; This represents the active power injected into node i during time period t; This represents the reactive power injected into node i during time period t; This represents the distributed photovoltaic injection node connected to node i during time period t. The active power; This represents the distributed photovoltaic injection node connected to node i during time period t. reactive power; This represents the active power injected into node i by the SOP during time period t; Indicates the SOP injection node during time period t. reactive power; Indicates the node in time period t The active power consumed by the load; This represents the reactive power consumed by the load at node i during time period t; Indicates the node in time period t The voltage amplitude; Indicates the branch in time period t The magnitude of the current flowing from node i to node j; Representing nodes respectively The upper and lower limits of the voltage.

[0039] Step S3: Based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data at charging stations, construct a set of uncertain scenarios for distributed photovoltaic and electric vehicles; based on the set of uncertain scenarios for distributed photovoltaic and electric vehicles and the deterministic operation optimization model of flexible distribution network, establish a flexible distribution network sub-Bluer bar optimization model that considers the uncertainties of distributed photovoltaic and electric vehicles.

[0040] In some specific embodiments, the set of uncertainties regarding distributed photovoltaic and electric vehicles includes:

[0041] in, Represents a set of uncertain scenarios driven by data in distributed photovoltaic and electric vehicle technologies; subscript This represents an index indicating an uncertain scenario, with a value range of [value range missing]. This represents the number of typical scenarios generated by clustering. This represents a typical uncertainty scenario with discrete probabilities generated by clustering; Indicates the first The probability of a typical scenario Represents the set of real numbers representing the probabilities of typical scenarios; Let P represent a column vector consisting of the initial probabilities of each typical scenario; and They represent the first The actual and initial probabilities of a typical scenario; N represents the total number of scenarios in historical data; A confidence set representing the probability of uncertain scenarios in data-driven distributed photovoltaic and electric vehicle projects; and Representing 1-norm and Confidence intervals for norm constraints; and Indicates the credibility of a probability distribution set; and This represents the confidence level of the scenario probability.

[0042] In some specific embodiments, the flexible distribution network distributed bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles includes:

[0043] Where A, B, C, D, E, and F represent the coefficient matrices corresponding to various constraints; represents the constant column vector in various constraints; z represents the set of SOP decision variables; Indicates the first The set of variables to be optimized in a typical scenario includes node voltage variables, line transmission power variables, and electric vehicle charging and discharging power variables that characterize the operating state of the power distribution system. Indicates the first The uncertain variables in this scenario are the power output level of the photovoltaic system and the time distribution of electric vehicles arriving at the charging station.

[0044] Step S4: Solve the flexible distribution network sub-bar optimization model that considers the uncertainties of distributed photovoltaic and electric vehicles to obtain operation strategy data; operate the distribution network according to the operation strategy data.

[0045] In some specific embodiments, the flexible distribution network sub-Bluerg bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles is solved to obtain operation strategy data, including: using column and constraint generation algorithms to solve the flexible distribution network sub-Bluerg bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles to obtain intelligent soft switching operation strategy, aggregator charging strategy, distribution network operation cost and electric vehicle electricity purchase cost.

[0046] In some specific embodiments, a column and constraint generation algorithm is used to solve the flexible distribution network sub-Bluer bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles, including: dividing the solution of the flexible distribution network sub-Bluer bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles into a main problem and sub-problems, and solving them iteratively; The main problem is represented as:

[0047] Given the first stage variables In this case, the subproblem can be represented as:

[0048] The inner subproblem and the outer subproblem can be represented as follows:

[0049] Where the subscript r represents the iteration number index, and its value range is... The total number of iterations required to solve the model; Auxiliary variables introduced to solve the main problem; For the r-th iteration, the th A set of variables to be optimized in a typical scenario; For the first The feasible domain of each scenario; For the r-th iteration, the th The probability of a typical scenario; The SOP execution strategy is calculated for the main problem in the r-th iteration; Indicates the inner sub-problem; Indicates the outer subproblem; The first subproblem obtained from the inner subproblem The optimal objective function value for each scenario Indicates the first The probability of a typical scenario; Set a lower bound for the total running cost of the main problem. The upper bound of the total operating cost of the subproblem and initialize the number of iterations. ; Solve the main problem to obtain the optimal SOP output strategy. Total system operating cost under the optimal SOP (Start of Production) strategy And update the lower bound of the main problem. ; The optimal scheduling strategy for SOP obtained from solving the main problem is substituted into the inner sub-problems to solve the inner sub-problems and obtain the minimum operating cost for each typical scenario. Solve the outer subproblem to obtain the probability distribution of the scenario that maximizes the system's operating cost, and update the upper bound of the subproblem. ; Determine whether the difference between the upper and lower bounds of the main problem and the subproblems is less than the set convergence precision. If the value is less than the calculated total running cost, stop the iteration and output the total running cost; otherwise, update the probability distribution of each scenario in the main problem and add new variables to be optimized in the main problem. and containing variables Inequality constraints and second-order cone constraints, containing variables Inequality constraints with SOP running strategy variable z, including optimization variables and involving uncertain variables Inequality constraints, update iteration count, Then, solve the main problem again.

[0050] For example, in one specific embodiment, a flexible distribution network operating method adapted to charging load access, the process of which is as follows: Figure 1 As shown, it includes the following steps: 1) Based on the selected flexible distribution network, input the flexible distribution network parameter information, including: distribution network topology and parameter information, power load access location and parameter information, distributed photovoltaic access location and annual distributed photovoltaic power output data, and system time-of-use electricity price; input electric vehicle charging load parameter information, including: electric vehicle type, upper and lower limits of charging and discharging power, battery capacity, initial charge, expected charging charge, and annual distribution data of electric vehicle arrival time at charging stations; input intelligent soft switch parameter information, including: port converter capacity, port access location, and active power loss coefficient. For this embodiment, the improved Tianjin power distribution network's line parameters, load parameters, and network topology are first input. This includes 4 feeders, a rated voltage of 10.5kV, a total active load of 9.988MW, and a total reactive power demand of 7.335Mvar. Detailed parameters are shown in Tables 1 and 2. Five distributed photovoltaic (PV) units are then installed in the distribution network, with the installation locations as follows: Figure 2 As shown in the diagram of the PV connection nodes, each distributed photovoltaic (PV) capacity is 1000kW and operates with a constant power factor of 1.0. The annual distributed PV output data is as follows: Figure 3 As shown. The total number of electric vehicles served by the charging station in a single day is set at 300, with a ratio of 1:1:3:5 for the four types of electric vehicles. The maximum charging and discharging power of each electric vehicle is 7kW, and the battery capacity is 40kWh. The annual distribution of electric vehicle arrival times at the charging station is shown below. Figure 4 As shown. The system's time-of-use electricity price is set as follows: Figure 5 As shown in Table 3, the unit cost of network loss and the penalty cost for voltage deviation are also listed. The connection locations of the multi-terminal intelligent soft-switching port converter are set as follows: Figure 2 As shown, each converter has a capacity of 3MVA, the active power loss factor is set to 0.01, and the positive direction is defined as the direction of power injection into the node.

[0051] 2) Based on the electric vehicle charging load parameter information and intelligent soft-switching parameter information provided in step 1), construct the electric vehicle aggregator operation constraints based on virtual energy storage, including: electric vehicle user behavior characteristic constraints, electric vehicle aggregator charging and discharging power constraints, electric vehicle aggregator power constraints, and electric vehicle aggregator access constraints based on the intelligent soft-switching DC link. (1) Electric vehicle user behavior can be divided into four categories according to whether they participate in grid regulation and whether the off-grid time is known. The aforementioned electric vehicle user behavior characteristic constraints can be expressed as: ① Characteristics of Class I electric vehicle users: They do not participate in grid regulation, always charge at maximum power, and leave as soon as they are fully charged, minimizing charging time.

[0052] in, This represents the charging time required for the nth type I electric vehicle user; This represents the expected battery level that the nth type I electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth type I electric vehicle user; This represents the charging power of the nth type I electric vehicle user during time period t; This represents the maximum charging power for the nth type I electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth type I electric vehicle user starts charging; This represents the time when the nth type I electric vehicle user finishes charging and leaves; ② Characteristics of Class II electric vehicle users: They do not participate in grid regulation, charge at maximum power until the expected charge level is reached, then stop charging and do not leave immediately, but leave the charging station at the expected departure time.

[0053] in, This represents the charging time required for the nth Class II electric vehicle user; This represents the expected battery level that the nth Class II electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth Class II electric vehicle user; This represents the charging power of the nth type II electric vehicle user during time period t; This represents the maximum charging power of the nth Class II electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class II electric vehicle user begins charging; The time when the nth Class II electric vehicle user finishes charging; This represents the expected departure time of the nth type II electric vehicle user; ③ Characteristics of Category III Electric Vehicle Users: These vehicles participate in grid regulation and respond to electricity price guidance. When connected to a charging station, the charging power of these vehicles can be optimized and adjusted according to the grid's operating status. They charge at higher power when electricity prices are low and at lower power or not at all when electricity prices are high.

[0054] in, This represents the charging power of the nth Class III electric vehicle user during time period t; This represents the maximum charging power of the nth Class III electric vehicle user; This represents the expected battery level that the nth Class III electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth Class III electric vehicle user; This represents the capacity of the nth Class III electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class III electric vehicle user begins charging. This indicates the time when the nth Class III electric vehicle user left. ④ Characteristics of Category IV Electric Vehicle Users: These vehicles participate in grid regulation, both charging and discharging into the grid. While connected to charging stations, these vehicles can discharge into the grid when electricity prices are high and charge extensively when prices are low, responding to electricity price guidance and grid operation optimization needs, thus achieving peak shaving and valley filling for the system.

[0055] in, Let t represent the charging power of the nth Class IV electric vehicle user in time period t, assuming that the power flows from the power distribution network to the electric vehicle in the positive direction of charging. This represents the maximum charging power for the nth Class IV electric vehicle user; This represents the maximum discharge power of the nth Class IV electric vehicle user; The expected battery level required when the nth Class IV electric vehicle user leaves; This represents the initial battery level of the nth Class IV electric vehicle user; This represents the battery level of the nth Class IV electric vehicle user during time period t; This represents the capacity of the nth Class IV electric vehicle user; This represents the lower limit of the battery capacity for the nth Class IV electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class IV electric vehicle user begins charging. The time when the nth Class IV electric vehicle user leaves;

[0056] All of the above types of electric vehicle users meet the following departure time and initial charge characteristics:

[0057] in, The piecewise probability density function representing the time it takes for an electric vehicle to leave a charging station; Indicates the time when the electric vehicle leaves the charging station; This represents the average time it takes for an electric vehicle to leave a charging station. This represents the standard deviation of the time it takes for an electric vehicle to leave a charging station. The probability density function representing the initial charge of an electric vehicle; Indicates the initial charge of the electric vehicle; This represents the average initial charge of the electric vehicle. This represents the standard deviation of the initial charge of an electric vehicle. This indicates the initial battery level of the electric vehicle. This indicates the maximum battery capacity of the electric vehicle.

[0058] (2) The charging and discharging power constraint of the electric vehicle aggregator can be expressed as:

[0059] in, Let t represent the charging power at node k of the electric vehicle aggregator during time period t, assuming that the power flows from the distribution network to the electric vehicle aggregator in the positive direction. This represents the maximum charging power of electric vehicles during time period t. This represents the maximum discharge power of the electric vehicle aggregate during time period t; Indicates the type of electric vehicle; Represents a set of electric vehicle types. ; n represents the electric vehicle index; Represents the nth Maximum charging power for electric vehicles; Indicating the charging station during time period t Number of electric vehicles; Represents the nth Maximum discharge power of electric vehicles.

[0060] (3) The electric vehicle aggregator power constraint mentioned above can be expressed as:

[0061] in, This represents the battery level of the electric vehicle aggregator during time period t; Let t represent the charging power of the electric vehicle aggregator during time period t, assuming that the power flows from the power distribution network to the electric vehicle aggregator in the positive direction. Indicates the duration of each scheduling period; This indicates the increased aggregated electricity consumption caused by electric vehicles connecting to the charging station during time period t. This represents the decrease in aggregated electricity volume caused by the electric vehicle leaving the charging station during time period t. Indicates the type of electric vehicle; Represents a set of electric vehicle types. ; n represents the electric vehicle index; Indicates the number of times the charging station is connected during time period t. Number of electric vehicles; Indicates leaving the charging station during time period t. Number of electric vehicles Indicating the charging station during time period t Number of electric vehicles; Represents the nth The initial charge level of the electric vehicle when it connects to the charging station; Represents the nth The battery level of a similar electric vehicle when it leaves a charging station; This represents the maximum battery power of electric vehicles during time period t. Represents the nth The capacity of electric vehicles.

[0062] (4) The electric vehicle aggregator access constraint based on the intelligent soft-switching DC link can be expressed as:

[0063] in, Let t represent the charging power at node k of the electric vehicle aggregator during time period t, assuming that the power flows from the distribution network to the electric vehicle aggregator in the positive direction. This indicates that the SOP is at node t during time period t. Active power loss of the converter; Indicates the SOP injection node during time period t. The active power; Indicates the SOP injection node during time period t. reactive power; , These represent the SOP injection nodes respectively. The upper and lower limits of active power; These represent the SOP injection nodes respectively. Upper and lower limits of reactive power; Represents a node Loss factor at SOP; Represents a node The capacity of the converter; This indicates the node index that is connected to the SOP. ; This represents the set of nodes that are connected to the SOP. .

[0064] 3) Based on the flexible distribution network parameter information provided in step 1) and the electric vehicle aggregator operation constraints based on virtual energy storage provided in step 2), establish a deterministic operation optimization model and compact form of the flexible distribution network adapted to the access of large-scale charging loads, including: taking the overall minimum of the flexible distribution network operation cost and the electric vehicle aggregator electricity purchase cost as the objective function, and considering the electric vehicle aggregator operation constraints based on virtual energy storage, the distributed photovoltaic operation constraints, and the flexible distribution network operation constraints. (1) The objective function, which aims to minimize the overall operating cost of the flexible distribution network and the electricity purchase cost of electric vehicle aggregators, can be expressed as:

[0065] Where f represents the overall objective function; Indicates the operating cost of the power distribution network; This indicates the electricity purchase cost for electric vehicle aggregators; This represents the cost of active power loss in the distribution network. This indicates the cost of voltage exceeding the limit in the distribution network; Represents the set of branches in a distribution network; Indicates a branch The resistance value; Indicates the branch in time period t The square of the current in; This represents the SOP loss at node i during time period t; This represents the set of nodes that are connected to the SOP; Indicates the duration of each scheduling period; The unit cost representing network loss; Indicates the node in time period t The square of the voltage amplitude; Indicates the total number of nodes in the distribution network; Indicates the total number of time periods; This represents the active load at node i during time period t; Indicates the upper limit of the node voltage; Indicates the lower limit of the node voltage; Indicates the upper limit of the node voltage optimization range; This indicates the lower limit of the node voltage optimization range; when the node voltage is outside the optimization range... At the same time, the degree of voltage deviation from the optimization range can be reduced through system optimization; This represents the unit penalty cost for voltage exceeding the limit at node i during time period t; Represents the set of nodes in a distribution network; This represents the penalty cost parameter; This represents the active power absorbed by aggregator k from the distribution network during time period t; This represents the time-of-use electricity price for period t; Will The linearized expression is as follows:

[0066] in, Indicates voltage auxiliary variable; Indicates the node in time period t The square of the voltage amplitude; Indicates the upper limit of the node voltage optimization range; This indicates the lower limit of the node voltage optimization range; when the node voltage is outside the optimization range... At the same time, the degree of voltage deviation from the optimization range can be reduced through system optimization; This represents the unit penalty cost for voltage exceeding the limit at node i during time period t; This represents the penalty cost parameter.

[0067] (2) The operating constraints of the distributed photovoltaic system can be expressed as:

[0068] in, This represents the active power output of the distributed power source at node i during time period t; This represents the predicted active power output of the distributed power source at node i during time period t. This represents the reactive power output of the distributed power source at node i during time period t; This represents the upper and lower limits of the reactive power output of the distributed power source at node i during time period t; This represents the capacity of the distributed power source at node i.

[0069] (3) The operational constraints of the flexible distribution network can be expressed as:

[0070] in, Indicates the node in time period t The square of the voltage amplitude at that point; Indicates the branch in time period t The square of the current amplitude; Indicates the upstream branch of node j, This represents the downstream branch of node j; Represents the set of branches in a distribution network; Representing branch roads Resistance and reactance; Indicates the branch in time period t The active power flowing from node i to node j; Indicates the branch in time period t The reactive power flowing from node i to node j; This represents the active power injected into node i during time period t; This represents the reactive power injected into node i during time period t; This represents the distributed photovoltaic injection node connected to node i during time period t. The active power; This represents the distributed photovoltaic injection node connected to node i during time period t. reactive power; This represents the active power injected into node i by the SOP during time period t; Indicates the SOP injection node during time period t. reactive power; Indicates the node in time period t The active power consumed by the load; This represents the reactive power consumed by the load at node i during time period t; Indicates the node in time period t The voltage amplitude; Indicates the branch in time period t The magnitude of the current flowing from node i to node j; Representing nodes respectively The upper and lower limits of the voltage.

[0071] 4) Based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data of the input in step 1), construct a data-driven set of uncertain scenarios for distributed photovoltaic and electric vehicles; (1) The set of uncertain scenarios for data-driven distributed photovoltaic and electric vehicles can be represented as:

[0072] in, Represents a set of uncertain scenarios driven by data in distributed photovoltaic and electric vehicle technologies; subscript This represents an index indicating an uncertain scenario, with a value range of [value range missing]. This represents the number of typical scenarios generated by clustering. This represents a typical uncertainty scenario with discrete probabilities generated by clustering; Indicates the first The probability of a typical scenario Represents the set of real numbers representing the probabilities of typical scenarios; Let P represent a column vector consisting of the initial probabilities of each typical scenario; and They represent the first The actual and initial probabilities of a typical scenario; N represents the total number of scenarios in historical data; A confidence set representing the probability of uncertain scenarios in data-driven distributed photovoltaic and electric vehicle projects; and Representing 1-norm and Confidence intervals for norm constraints; and Indicates the credibility of a probability distribution set; and This represents the confidence level of the scenario probability.

[0073] 5) Based on the uncertainty scenario set of distributed photovoltaic and electric vehicles obtained in step 4) and the deterministic operation optimization model of flexible distribution network adapted to large-scale charging load access obtained in step 3), establish a flexible distribution network sub-Bluer bar optimization model considering the uncertainty of distributed photovoltaic and electric vehicles. (1) The flexible distribution network distributed bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles can be expressed as:

[0074] Where A, B, C, D, E, and F represent the coefficient matrices corresponding to various constraints; z represents the constant column vector in various constraints; z represents the set of SOP decision variables; Indicates the first The set of variables to be optimized in a typical scenario includes node voltage variables, line transmission power variables, and electric vehicle charging and discharging power variables that characterize the operating state of the power distribution system. Indicates the first The uncertain variables in this scenario are the power output level of the photovoltaic system and the time distribution of electric vehicles arriving at the charging station.

[0075] 6) Based on the flexible distribution network sub-bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles obtained in step 5), the column and constraint generation algorithm is used to solve the flexible distribution network sub-bar optimization model to obtain the intelligent soft switch operation strategy, aggregator charging strategy, distribution network operation cost and electric vehicle electricity purchase cost.

[0076] Solving the flexible distribution network sub-Bluer bar optimization model can be represented as follows: The original problem is a two-stage, three-layer optimization problem. The solution uses a column and constraint generation algorithm to divide the model into a main problem and sub-problems, and then iteratively solves the problem.

[0077] ①Main Problem

[0078] ② Subproblems Given the first stage variables In this case, the subproblem can be represented as:

[0079] The inner subproblem and the outer subproblem can be represented as follows:

[0080] Where the subscript r represents the iteration number index, and its value range is... The total number of iterations required to solve the model; Auxiliary variables introduced to solve the main problem; For the r-th iteration, the th A set of variables to be optimized in a typical scenario; For the first The feasible domain of each scenario; For the r-th iteration, the th The probability of a typical scenario; The SOP execution strategy is calculated for the main problem in the r-th iteration; Indicates the inner sub-problem; Indicates the outer subproblem; The first subproblem obtained from the inner subproblem The optimal objective function value for each scenario Indicates the first The probability of a typical scenario; The specific solution steps for the model are as follows: (1) Set the lower bound of the total operating cost of the main problem. The upper bound of the total operating cost of the subproblem and initialize the number of iterations. ; (2) Solve the main problem to obtain the optimal SOP output strategy. Total system operating cost under the optimal SOP (Start of Production) strategy And update the lower bound of the main problem. ; (3) Substitute the SOP optimal scheduling strategy obtained from solving the main problem into the inner sub-problem, solve the inner sub-problem (17.a), and obtain the minimum operating cost for each typical scenario; (4) Solve the outer subproblem (17.b) to obtain the probability distribution of the scenario that maximizes the system's operating cost, and update the upper bound of the subproblem. ; (5) Determine whether the difference between the upper and lower bounds of the main problem and the subproblems is less than the set convergence precision. If the value is less than the calculated total running cost, stop the iteration and output the total running cost; otherwise, update the probability distribution of each scenario in the main problem and add new variables to be optimized in the main problem. and containing variables Inequality constraints and second-order cone constraints, containing variables Inequality constraints with SOP running strategy variable z, including optimization variables and involving uncertain variables Inequality constraints, update iteration count, Return to step (2) and solve the main problem again.

[0081] To fully verify the advancement of the method of the present invention, this embodiment adopts the following three schemes for comparative analysis: Option 1: Neither the electric vehicle nor the smart soft switch is controlled, thus obtaining the initial operating state of the power distribution network; Option 2: Ignoring the uncertainties of photovoltaics and electric vehicles, a deterministic output strategy for intelligent soft switching is obtained; Option 3: Considering the uncertainties of photovoltaics and electric vehicles, a smart soft-switching distributed power output strategy is obtained.

[0082] We randomly selected 100 scenarios from the annual data and compared and analyzed the results of the deterministic output strategy and the distributed bar output strategy of the smart soft switch.

[0083] Table 4 shows a comparison of the optimization results of Scheme 1 and Scheme 2, and Table 5 shows a comparison of the optimization results of Scheme 2 and Scheme 3.

[0084] The computer hardware environment for performing the optimization calculations was an Intel(R) Xeon(R) CPU E5-2603 with a clock speed of 1.60GHz and 12GB of memory; the software environment was a Windows 10 operating system.

[0085]

[0086]

[0087]

[0088] A comparison of Schemes 1 and 2 shows that the virtual energy storage characteristics of electric vehicles and the flexible adjustment characteristics of intelligent soft switching can effectively reduce network losses in the distribution network, prevent voltage overruns, ensure the safe and reliable operation of the power grid, and reduce the electricity purchase cost of electric vehicles. A comparison of Schemes 2 and 3 shows that considering the uncertain power supply strategy of distributed photovoltaics and electric vehicles effectively reduces the penalty costs caused by the mismatch between the strategy's power supply and actual demand, thereby reducing the total costs on both the user side and the system side and improving social benefits.

[0089] The method described in this embodiment can solve the problem of improving the operational economy of flexible distribution networks. It fully considers the charging and discharging potential of electric vehicle aggregators based on virtual energy storage and the precise power flow regulation of intelligent soft switching. It establishes a flexible distribution network sub-bar optimization model that considers the uncertainties of distributed photovoltaics and electric vehicles, and obtains an intelligent soft switching operation strategy. This effectively reduces the mismatch between the strategy power supply and the actual power demand of electric vehicles under uncertain conditions, and ensures the economical and efficient operation of the distribution network while meeting the needs of electric vehicle users.

[0090] Those skilled in the art can change the above order without departing from the scope of protection of this invention.

[0091] Example 2 Embodiment 2 of the present invention provides a flexible distribution network distributed rod operation system, comprising: The parameter information acquisition module is used to collect parameter information of flexible distribution network, electric vehicle charging load, and intelligent soft switch; the parameter information of flexible distribution network includes annual distributed photovoltaic power output data, and the parameter information of electric vehicle charging load includes annual electric vehicle arrival time distribution data at charging stations. The aggregator operation constraint construction module is used to construct the electric vehicle aggregator operation constraints based on virtual energy storage, according to the electric vehicle charging load parameter information and the intelligent soft switch parameter information. The operation optimization model establishment module is used to establish a deterministic operation optimization model for the flexible distribution network based on the parameter information of the flexible distribution network and the operation constraints of the electric vehicle aggregator based on virtual energy storage. The sub-Bluerg bar optimization model building module is used to construct a set of uncertain scenarios for distributed photovoltaic and electric vehicles based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data for charging stations; and to establish a sub-Bluerg bar optimization model for flexible distribution networks that considers the uncertainties of distributed photovoltaic and electric vehicles based on the set of uncertain scenarios for distributed photovoltaic and electric vehicles and the deterministic operation optimization model of flexible distribution networks. The operation strategy solution module is used to solve the flexible distribution network sub-bar optimization model that considers the uncertainties of distributed photovoltaic and electric vehicles to obtain operation strategy data; and to operate the distribution network according to the operation strategy data.

[0092] In this embodiment, the problem of improving the operational economy of flexible distribution networks can be solved. It fully considers the charging and discharging potential of electric vehicle aggregators based on virtual energy storage and the precise power flow regulation of intelligent soft switching. A flexible distribution network distributed bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles is established to obtain the intelligent soft switching operation strategy. This effectively reduces the mismatch between the strategy power supply and the actual power demand of electric vehicles under uncertain conditions, ensuring the economical and efficient operation of the distribution network while meeting the needs of electric vehicle users.

[0093] Based on the same inventive concept, this invention also provides a method for establishing a flexible distribution network sub-Bluer bar optimization model, comprising: collecting flexible distribution network parameter information, electric vehicle charging load parameter information, and smart soft switch parameter information; the flexible distribution network parameter information includes annual distributed photovoltaic power output data, and the electric vehicle charging load parameter information includes annual electric vehicle arrival time distribution data at charging stations; constructing electric vehicle aggregator operation constraints based on virtual energy storage based on the electric vehicle charging load parameter information and the smart soft switch parameter information; establishing a deterministic operation optimization model for the flexible distribution network based on the flexible distribution network parameter information and the electric vehicle aggregator operation constraints based on virtual energy storage; constructing a set of uncertain scenarios for distributed photovoltaic and electric vehicles based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data at charging stations; and establishing a flexible distribution network sub-Bluer bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles based on the set of uncertain scenarios for distributed photovoltaic and electric vehicles and the deterministic operation optimization model for the flexible distribution network.

[0094] Based on the same inventive concept, this embodiment of the invention also provides a flexible distribution network distributed bar optimization model, which is established by the aforementioned flexible distribution network distributed bar optimization model establishment method.

[0095] Regarding the flexible distribution network distributed broom operation system, flexible distribution network distributed broom optimization model and its establishment method in the above embodiments, the specific implementation methods have been described in detail in the embodiments of the flexible distribution network distributed broom operation method, and will not be elaborated here.

[0096] Any modifications, additions, and equivalent substitutions made within the scope of the principles of this invention shall still fall within the patent coverage of this invention.

Claims

1. A method for operating flexible distribution networks using distributed rods, characterized in that, include: Collect parameter information of flexible distribution networks, electric vehicle charging load parameters, and intelligent soft switches; The flexible distribution network parameter information includes annual distributed photovoltaic power output data, and the electric vehicle charging load parameter information includes annual electric vehicle arrival time distribution data at charging stations. Based on electric vehicle charging load parameters and intelligent soft switch parameters, operational constraints for electric vehicle aggregators based on virtual energy storage are constructed. Based on the parameter information of the flexible distribution network and the operating constraints of electric vehicle aggregators based on virtual energy storage, a deterministic operation optimization model for the flexible distribution network is established. Based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data at charging stations, a set of uncertainty scenarios for distributed photovoltaic and electric vehicles is constructed. Based on the uncertainty scenario set of distributed photovoltaic and electric vehicles and the deterministic operation optimization model of flexible distribution network, a sub-Bluer bar optimization model of flexible distribution network considering the uncertainty of distributed photovoltaic and electric vehicles is established. The flexible distribution network sub-bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles is solved to obtain the operation strategy data; The power distribution network is operated based on the operational strategy data.

2. The method as described in claim 1, characterized in that, The operational constraints of the electric vehicle aggregator based on virtual energy storage include: Constraints on electric vehicle user behavior characteristics, electric vehicle aggregator charging and discharging power, electric vehicle aggregator power consumption, and electric vehicle aggregator access constraints based on intelligent soft-switching DC links.

3. The method as described in claim 2, characterized in that, Electric vehicle user behavior is categorized into four types based on whether they participate in grid regulation and whether their off-grid time is known. The constraints on these electric vehicle user behavior characteristics include: Class I electric vehicle user behavior characteristics: They do not participate in grid regulation, always charge at maximum power, and leave as soon as they are fully charged, minimizing charging time. in, This represents the charging time required for the nth type I electric vehicle user; This represents the expected battery level that the nth type I electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth type I electric vehicle user; This represents the charging power of the nth type I electric vehicle user during time period t; This represents the maximum charging power for the nth type I electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth type I electric vehicle user starts charging; This represents the time when the nth type I electric vehicle user finishes charging and leaves; Class II electric vehicle user behavior characteristics: They do not participate in grid regulation, charge at maximum power until the expected charge level is reached, then stop charging and do not leave immediately, but leave the charging station at the expected departure time. in, This represents the charging time required for the nth Class II electric vehicle user; This represents the expected battery level that the nth Class II electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth Class II electric vehicle user; This represents the charging power of the nth type II electric vehicle user during time period t; This represents the maximum charging power of the nth Class II electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class II electric vehicle user begins charging; The time when the nth Class II electric vehicle user finishes charging; This represents the expected departure time of the nth type II electric vehicle user; Category III electric vehicle user behavior characteristics: Participating in grid regulation and responding to electricity price guidance; when connected to a charging station, the charging power is optimized and adjusted according to the grid operating status; when the electricity price is lower than the low electricity price threshold, charging is performed at a power greater than the high power threshold; when the electricity price is higher than the high electricity price threshold, charging is performed at a power less than the low power threshold or no charging is performed. in, This represents the charging power of the nth Class III electric vehicle user during time period t; This represents the maximum charging power of the nth Class III electric vehicle user; This represents the expected battery level that the nth Class III electric vehicle user needs to reach before leaving; This represents the initial battery level of the nth Class III electric vehicle user; This represents the capacity of the nth Class III electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class III electric vehicle user begins charging. This indicates the time when the nth Class III electric vehicle user left. Category IV electric vehicle user behavior characteristics: Participating in grid regulation, discharging into the grid when the electricity price is above a high electricity price threshold and charging when the electricity price is below a low electricity price threshold, responding to electricity price guidance and grid operation optimization needs. in, Let t represent the charging power of the nth Class IV electric vehicle user in time period t, assuming that the power flows from the power distribution network to the electric vehicle in the positive direction of charging. This represents the maximum charging power for the nth Class IV electric vehicle user; This represents the maximum discharge power of the nth Class IV electric vehicle user; The expected battery level required when the nth Class IV electric vehicle user leaves; This represents the initial battery level of the nth Class IV electric vehicle user; This represents the battery level of the nth Class IV electric vehicle user during time period t; This represents the capacity of the nth Class IV electric vehicle user; This represents the lower limit of the battery capacity for the nth Class IV electric vehicle user; Indicates the duration of each scheduling period; This indicates the time when the nth Class IV electric vehicle user begins charging. The time when the nth Class IV electric vehicle user leaves; The aforementioned behavioral characteristics of electric vehicle users in categories I to IV also include: in, The piecewise probability density function representing the time it takes for an electric vehicle to leave a charging station; Indicates the time when the electric vehicle leaves the charging station; This represents the average time it takes for an electric vehicle to leave a charging station. This represents the standard deviation of the time it takes for an electric vehicle to leave a charging station. The probability density function representing the initial charge of an electric vehicle; Indicates the initial charge of the electric vehicle; This represents the average initial charge of the electric vehicle. This represents the standard deviation of the initial charge of an electric vehicle. This indicates the initial battery level of the electric vehicle. This indicates the maximum battery capacity of the electric vehicle.

4. The method as described in claim 2, characterized in that, The charging and discharging power constraints of the electric vehicle aggregator include: in, Let t represent the charging power at node k of the electric vehicle aggregator during time period t, assuming that the power flows from the distribution network to the electric vehicle aggregator in the positive direction. This represents the maximum charging power of electric vehicles during time period t. This represents the maximum discharge power of the electric vehicle aggregate during time period t; Indicates the type of electric vehicle; Represents a set of electric vehicle types. ; n represents the electric vehicle index; Represents the nth Maximum charging power for electric vehicles; Indicating the charging station during time period t Number of electric vehicles; Represents the nth Maximum discharge power of electric vehicles.

5. The method as described in claim 2, characterized in that, The electric vehicle aggregator's power constraints include: in, This represents the battery level of the electric vehicle aggregator during time period t; Let t represent the charging power of the electric vehicle aggregator during time period t, assuming that the power flows from the power distribution network to the electric vehicle aggregator in the positive direction. Indicates the duration of each scheduling period; This indicates the increased aggregated electricity consumption caused by electric vehicles connecting to the charging station during time period t. This represents the decrease in aggregated electricity volume caused by the electric vehicle leaving the charging station during time period t. Indicates the type of electric vehicle; Represents a set of electric vehicle types. ; n represents the electric vehicle index; Indicates the number of times the charging station is connected during time period t. Number of electric vehicles; Indicates leaving the charging station during time period t. Number of electric vehicles Indicating the charging station during time period t Number of electric vehicles; Represents the nth The initial charge level of the electric vehicle when it connects to the charging station; Represents the nth The battery level of a similar electric vehicle when it leaves a charging station; This represents the maximum battery power of electric vehicles during time period t. Represents the nth The capacity of electric vehicles.

6. The method as described in claim 2, characterized in that, The electric vehicle aggregator access constraints based on the intelligent soft-switching DC link include: in, Let t represent the charging power at node k of the electric vehicle aggregator during time period t, assuming that the power flows from the distribution network to the electric vehicle aggregator in the positive direction. This indicates that the SOP is at node t during time period t. Active power loss of the converter; Indicates the SOP injection node during time period t. The active power; Indicates the SOP injection node during time period t. reactive power; , These represent the SOP injection nodes respectively. The upper and lower limits of active power; These represent the SOP injection nodes respectively. Upper and lower limits of reactive power; Represents a node Loss factor at SOP; Represents a node The capacity of the converter; This indicates the node index that is connected to the SOP. ; This represents the set of nodes that are connected to the SOP. .

7. The method as described in any one of claims 1 to 6, characterized in that, Based on the parameter information of the flexible distribution network and the operational constraints of electric vehicle aggregators based on virtual energy storage, a deterministic operation optimization model for the flexible distribution network is established, including: With the objective function of minimizing the overall operating cost of flexible distribution networks and the electricity purchase cost of electric vehicle aggregators, a deterministic operation optimization model for flexible distribution networks is established, taking into account the operating constraints of electric vehicle aggregators based on virtual energy storage, distributed photovoltaic operation constraints, and flexible distribution network operation constraints.

8. The method as described in claim 7, characterized in that, The objective function is expressed as: Where f represents the overall objective function; Indicates the operating cost of the power distribution network; This indicates the electricity purchase cost for electric vehicle aggregators; This represents the cost of active power loss in the distribution network. This indicates the cost of voltage exceeding the limit in the distribution network; Represents the set of branches in a distribution network; Indicates a branch The resistance value; Indicates the branch in time period t The square of the current in; This represents the SOP loss at node i during time period t; This represents the set of nodes that are connected to the SOP; Indicates the duration of each scheduling period; The unit cost representing network loss; Indicates the node in time period t The square of the voltage amplitude; Indicates the total number of nodes in the distribution network; Indicates the total number of time periods; This represents the active load at node i during time period t; Indicates the upper limit of the node voltage; Indicates the lower limit of the node voltage; Indicates the upper limit of the node voltage optimization range; This indicates the lower limit of the node voltage optimization range; when the node voltage is outside the optimization range... At the same time, the degree of voltage deviation from the optimization range can be reduced through system optimization; This represents the unit penalty cost for voltage exceeding the limit at node i during time period t; Represents the set of nodes in a distribution network; This represents the penalty cost parameter; This represents the active power absorbed by aggregator k from the distribution network during time period t; This represents the time-of-use electricity price for period t; Will The linearized expression is as follows: in, Indicates voltage auxiliary variable; Indicates the node in time period t The square of the voltage amplitude; Indicates the upper limit of the node voltage optimization range; This indicates the lower limit of the node voltage optimization range; when the node voltage is outside the optimization range... At the same time, the degree of voltage deviation from the optimization range can be reduced through system optimization; This represents the unit penalty cost for voltage exceeding the limit at node i during time period t; This represents the penalty cost parameter.

9. The method as described in claim 7, characterized in that, The operational constraints of the distributed photovoltaic system include: in, This represents the active power output of the distributed power source at node i during time period t; This represents the predicted active power output of the distributed power source at node i during time period t. This represents the reactive power output of the distributed power source at node i during time period t; This represents the upper and lower limits of the reactive power output of the distributed power source at node i during time period t; This represents the capacity of the distributed power source at node i.

10. The method as described in claim 7, characterized in that, The operational constraints of the flexible distribution network include: in, Indicates the node in time period t The square of the voltage amplitude at that point; Indicates the branch in time period t The square of the current amplitude; Indicates the upstream branch of node j, This represents the downstream branch of node j; Represents the set of branches in a distribution network; Representing branch roads Resistance and reactance; Indicates the branch in time period t The active power flowing from node i to node j; Indicates the branch in time period t The reactive power flowing from node i to node j; This represents the active power injected into node i during time period t; This represents the reactive power injected into node i during time period t; This represents the distributed photovoltaic injection node connected to node i during time period t. The active power; This represents the distributed photovoltaic injection node connected to node i during time period t. reactive power; This represents the active power injected into node i by the SOP during time period t; Indicates the SOP injection node during time period t. reactive power; Indicates the node in time period t The active power consumed by the load; This represents the reactive power consumed by the load at node i during time period t; Indicates the node in time period t The voltage amplitude; Indicates the branch in time period t The magnitude of the current flowing from node i to node j; Representing nodes respectively The upper and lower limits of the voltage.

11. The method as described in claim 1, characterized in that, The set of uncertainties for distributed photovoltaic and electric vehicles includes: in, Represents a set of uncertain scenarios driven by data in distributed photovoltaic and electric vehicle technologies; subscript This represents an index indicating an uncertain scenario, with a value range of [value range missing]. This represents the number of typical scenarios generated by clustering. This represents a typical uncertainty scenario with discrete probabilities generated by clustering; Indicates the first The probability of a typical scenario Represents the set of real numbers representing the probabilities of typical scenarios; Let P represent a column vector consisting of the initial probabilities of each typical scenario; and They represent the first The actual and initial probabilities of a typical scenario; N represents the total number of scenarios in historical data; A confidence set representing the probability of uncertain scenarios in data-driven distributed photovoltaic and electric vehicle projects; and Representing 1-norm and Confidence intervals for norm constraints; and Indicates the credibility of a probability distribution set; and This represents the confidence level of the scenario probability.

12. The method as described in claim 1, characterized in that, The flexible distribution network sub-bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles includes: Where A, B, C, D, E, and F represent the coefficient matrices corresponding to various constraints; z represents the constant column vector in various constraints; z represents the set of SOP decision variables; Indicates the first The set of variables to be optimized in a typical scenario includes node voltage variables, line transmission power variables, and electric vehicle charging and discharging power variables that characterize the operating state of the power distribution system. Indicates the first The uncertain variables in this scenario are the power output level of the photovoltaic system and the time distribution of electric vehicles arriving at the charging station.

13. The method as described in claim 1, characterized in that, The flexible distribution network sub-bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles is solved to obtain operation strategy data, including: The column and constraint generation algorithm is used to solve the flexible distribution network sub-bar optimization model that considers the uncertainties of distributed photovoltaic and electric vehicles, and the results are obtained: intelligent soft switching operation strategy, aggregator charging strategy, distribution network operation cost and electric vehicle electricity purchase cost.

14. The method as described in claim 1, characterized in that, The column and constraint generation algorithm is used to solve the flexible distribution network sub-Bruker optimization model considering the uncertainties of distributed photovoltaic and electric vehicles, including: The solution to the flexible distribution network distributed bar optimization model considering the uncertainties of distributed photovoltaic and electric vehicles is divided into a main problem and sub-problems, and solved iteratively. The main problem is represented as: in, A , B , C , D , E , F Represents the coefficient matrix corresponding to various constraints; , , , , , , z represents the constant column vector in various constraints; z represents the set of SOP decision variables; For the first The feasible domain of each scenario; It indicates that at the r-th iteration, the... The variables to be optimized in a typical scenario include node voltage variables, line transmission power variables, and electric vehicle charging and discharging power variables that characterize the operating state of the power distribution system. This represents the number of typical scenarios generated by clustering; As an auxiliary variable; For the first Scenario during the next iteration The probability of; Indicates the first Uncertain variables in the scenario, namely the output level of photovoltaic power and the time distribution of electric vehicles arriving at charging stations; Given the first stage variables In this case, the subproblem is represented as: The inner subproblem and the outer subproblem are represented as follows: Where the subscript r represents the iteration number index, and its value range is... The total number of iterations required to solve the model; Auxiliary variables introduced to solve the main problem; For the r-th iteration, the th A set of variables to be optimized in a typical scenario; For the first The feasible domain of each scenario; For the r-th iteration, the th The probability of a typical scenario; The SOP execution strategy is calculated for the main problem in the r-th iteration; Indicates the inner sub-problem; Indicates the outer subproblem; The first subproblem obtained from the inner subproblem The optimal objective function value for each scenario Indicates the first The probability of a typical scenario; A confidence set representing the probability of uncertain scenarios in data-driven distributed photovoltaic and electric vehicle projects; Set a lower bound for the total running cost of the main problem. The upper bound of the total operating cost of the subproblem and initialize the number of iterations. ; Solve the main problem to obtain the optimal SOP output strategy. Total system operating cost under the optimal SOP (Start of Production) strategy And update the lower bound of the main problem. ; The optimal scheduling strategy for SOP obtained from solving the main problem is substituted into the inner sub-problems to solve the inner sub-problems and obtain the minimum operating cost for each typical scenario. Solve the outer subproblem to obtain the probability distribution of the scenario that maximizes the system's operating cost, and update the upper bound of the subproblem. ; Determine whether the difference between the upper and lower bounds of the main problem and the subproblems is less than the set convergence precision. If the value is less than the calculated total running cost, stop the iteration and output the total running cost; otherwise, update the probability distribution of each scenario in the main problem and add new variables to be optimized in the main problem. and containing variables Inequality constraints and second-order cone constraints, containing variables Inequality constraints with SOP running strategy variable z, including optimization variables and involving uncertain variables Inequality constraints, update iteration count, Then, solve the main problem again.

15. A flexible distribution network distributed rod operation system, characterized in that, include: The parameter information acquisition module is used to collect parameter information of flexible distribution networks, electric vehicle charging load parameters, and intelligent soft switches. The flexible distribution network parameter information includes annual distributed photovoltaic power output data, and the electric vehicle charging load parameter information includes annual electric vehicle arrival time distribution data at charging stations. The aggregator operation constraint construction module is used to construct the electric vehicle aggregator operation constraints based on virtual energy storage, according to the electric vehicle charging load parameter information and the intelligent soft switch parameter information. The operation optimization model establishment module is used to establish a deterministic operation optimization model for the flexible distribution network based on the parameter information of the flexible distribution network and the operation constraints of the electric vehicle aggregator based on virtual energy storage. The sub-Bluerg bar optimization model building module is used to construct a set of uncertain scenarios for distributed photovoltaic and electric vehicles based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data for charging stations; and to establish a sub-Bluerg bar optimization model for flexible distribution networks that considers the uncertainties of distributed photovoltaic and electric vehicles based on the set of uncertain scenarios for distributed photovoltaic and electric vehicles and the deterministic operation optimization model of flexible distribution networks. The operation strategy solution module is used to solve the flexible distribution network sub-bar optimization model that considers the uncertainties of distributed photovoltaic and electric vehicles to obtain operation strategy data; and to operate the distribution network according to the operation strategy data.

16. A method for establishing a flexible distribution network distributed bar optimization model, characterized in that, include: Collect parameter information of flexible distribution networks, electric vehicle charging load parameters, and intelligent soft switches; The flexible distribution network parameter information includes annual distributed photovoltaic power output data, and the electric vehicle charging load parameter information includes annual electric vehicle arrival time distribution data at charging stations. Based on electric vehicle charging load parameters and intelligent soft switch parameters, operational constraints for electric vehicle aggregators based on virtual energy storage are constructed. Based on the parameter information of the flexible distribution network and the operating constraints of electric vehicle aggregators based on virtual energy storage, a deterministic operation optimization model for the flexible distribution network is established. Based on the annual distributed photovoltaic power output data and the annual electric vehicle arrival time distribution data at charging stations, a set of uncertainty scenarios for distributed photovoltaic and electric vehicles is constructed. Based on the uncertainty scenario set of distributed photovoltaic and electric vehicles and the deterministic operation optimization model of flexible distribution network, a sub-Bluer bar optimization model of flexible distribution network considering the uncertainty of distributed photovoltaic and electric vehicles is established.

17. A flexible distribution network distributed bar optimization model, characterized in that, The flexible distribution network sub-bar optimization model is established by the method described in claim 16.

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