Robust optimization method for solar-energy charging stations considering travel behavior deviation of charging users

Abstract

This application discloses a robust optimization method for photovoltaic charging stations with energy storage, taking into account the travel behavior biases of charging users. By collecting multi-dimensional data from the power and transportation networks in real time, a randomized user equilibrium hybrid traffic flow allocation model is used to characterize the path selection and charging behavior of electric vehicle users under different perception biases. The transportation and power networks are coupled through the spatiotemporal constraints of the transportation-power coupling network. A two-stage robust optimization model for photovoltaic charging stations is established, integrating the multi-dimensional uncertainty of the charging station's photovoltaic output and the perceived biases of travel user behavior in the transportation network. An efficient solution algorithm is designed for the specific model, ultimately generating the optimal operating strategy for the distribution network and photovoltaic charging station equipment under the worst-case scenario. This method achieves a dynamic optimal balance between the economic efficiency and robustness of the distribution network's operation, reduces operating costs while ensuring system safety margins, and significantly enhances the coordinated control capabilities of complex coupled systems.

Description

Technical Field

[0001] The present application relates to the technical field of power-transportation coupling system management, and in particular to a robust optimization method for photovoltaic charging stations that takes into account travel behavior deviations of charging users. Background Art

[0002] The field of power-transport coupled system management aims to achieve the intelligent integration of energy, transportation, and information flows through deep collaboration between power and transportation networks. Its core lies in building an integrated "vehicle-road-network-station" architecture. Leveraging key technologies such as multi-energy coordinated dispatch, this approach addresses the challenges of temporal and spatial volatility brought about by the large-scale integration of electric vehicles, while also tapping into the potential of mobile energy storage and load capacity within transportation vehicles.

[0003] In a power-transport coupled system that considers biases in charging user travel behavior, the robust optimization operation method for photovoltaic (PV)-storage charging stations aims to address the operational risks of integrated PV-storage-charging stations in this coupled power-transport system, caused by biases in charging user travel perception and uncertainty in PV output, through modeling and robust optimization of travel user behavior. By fine-tuning the perceived characteristics of travel user behavior, a robust decision-making model is constructed to optimize the coordinated scheduling strategy for PV-storage-charging, thereby preventing voltage overload and line overload in the distribution network caused by excessive charging loads due to random user selection of charging stations.

[0004] Most existing technologies assume that user charging behavior follows a fixed probability distribution, and fail to consider user psychological factors, group game behavior, and the dynamic impact of traffic congestion on charging decisions. They are unable to capture the negative feedback effect of "congestion-charging demand transfer-local overload of the distribution network." Secondly, most technologies only focus on optimizing the risk of a single dimension of new energy output fluctuations or changes in travel demand, and lack an in-depth analysis of the spatiotemporal reconstruction of loads and the spatiotemporal fluctuations of photovoltaic output caused by deviations in user path selection and charging decision-making behavior. This defect makes it difficult for existing models to accurately depict the spatiotemporal evolution of charging loads under the constraints of road network topology, which will significantly weaken the potential for coordinated operation of photovoltaic storage charging station equipment. Summary of the Invention

[0005] The embodiments of the present application achieve a dynamic optimal balance between the economic efficiency and robustness of distribution network operation by providing a robust optimization method for photovoltaic charging stations that takes into account the travel behavior deviations of charging users.

[0006] In order to achieve the above-mentioned purpose, the technical solution of the embodiment of the present invention is:

[0007] On the first aspect, an embodiment of the present invention provides a robust optimization method for photovoltaic charging stations that takes into account the travel behavior deviations of charging users, including: based on a vehicle-road-station-network collaborative architecture, real-time collection of multi-dimensional data of the power network and the traffic network; based on the collected OD demand data and road congestion characteristics of the traffic network, a random user equilibrium mixed traffic flow allocation model is used to characterize the path selection and charging behavior of electric vehicle users under different perception deviations, and the spatiotemporal distribution of charging vehicles is deduced in combination with the equilibrium characteristics of the traffic network; based on the spatiotemporal distribution of charging vehicles, a spatiotemporal correlation constraint of the traffic-power coupling network is constructed to couple the traffic and power networks. , determine the charging load distribution of each charging station; based on the random user equilibrium mixed traffic flow distribution model, obtain the multi-dimensional uncertainty interval of the user behavior perception deviation of the traffic network travel, and at the same time combine the photovoltaic output fluctuation to construct a multi-dimensional uncertainty fluctuation interval set; based on the multi-dimensional uncertainty fluctuation interval set, construct a two-stage robust optimization optimal operation model containing photovoltaic storage charging stations in the power-transportation coupling system, and determine the constraints and objective function of the optimal operation state; solve the two-stage robust optimization optimal operation model based on the preset nested outer approximation C&CG algorithm, and obtain the optimal operation strategy of the distribution network and photovoltaic storage charging station equipment in the worst scenario.

[0008] In some possible implementations, based on the vehicle-road-station-network collaborative architecture, multi-dimensional data of the power network and transportation network are collected in real time, including: on the traffic side, regional traffic topology is obtained through map software, and vehicle OD demand, maximum road network capacity and free flow travel time are obtained through on-board terminals and roadside units; on the power side, distribution network topology parameters, photovoltaic output forecast curve, energy storage capacity, operating boundaries, node voltage, line load margin, maximum capacity of charging stations and average charging time information are synchronously accessed; on the environmental side, weather forecast data is collected through sensors.

[0009] In some possible implementations, based on the collected OD demand data and road congestion characteristics of the traffic network, a random user equilibrium mixed traffic flow allocation model is used to characterize the path selection and charging behavior of electric vehicle users under different perception biases, and the spatiotemporal distribution of charging vehicles is deduced in combination with the equilibrium characteristics of the traffic network, including: describing the traffic topology as a directed graph expression; establishing a mixed traffic flow allocation model by introducing random utility theory based on OD demand data, the maximum traffic capacity of the road network and the free flow travel time, and allocating OD demand to all travel paths using a random user equilibrium model based on the user's perception of travel cost; wherein, the final equilibrium state of the random user equilibrium model is described as a fixed point problem of path flow; based on the established fixed point problem, it is converted into a convex optimization model through the first-order optimality condition; and the convex optimization model is solved to obtain the spatiotemporal distribution of charging vehicles under the equilibrium characteristics of the traffic network.

[0010] In some possible implementations, the convex optimization model is expressed as:

[0011] ;

[0012] in, is the objective function of the random user equilibrium model, For road sections traffic flow, For road sections The road section travel time, The service time of the user at the charging station, is the path identifier, is a set of valid paths, is the perception coefficient, is a regular road segment set, is a set of virtual road segments, For all types of user OD pairs, is a set of user OD pairs of a single user type, For OD The path below of traffic, is an integral variable used to represent the flow from 0 to The process of flow.

[0013] In some possible implementations, based on the spatiotemporal distribution of charging vehicles, the spatiotemporal correlation constraints of the traffic-power coupling network are constructed, the traffic and power networks are coupled, and the charging load distribution of each charging station is determined, including: assuming that each PV-ESS-CS is powered by the nearest distribution network node, and there is a linear mapping relationship between the traffic flow and the charging load at the charging station, when the traffic flow at the charging station is given, the distribution network node to which the charging station belongs is obtained. Charging load:

[0014]

[0015] in, For distribution network nodes The charging station load of the PV-ESS-CS, is the charging power of a single electric vehicle, is the set of charging station nodes in the distribution network, for The traffic volume of the virtual road section a corresponding to the charging station at each moment is the traffic volume of the charging station.

[0016] In some possible implementations, based on the random user equilibrium mixed traffic flow allocation model, a multi-dimensional uncertainty interval of the user behavior perception deviation of the traffic network travel is obtained, and at the same time, combined with the photovoltaic output fluctuation, a multi-dimensional uncertainty fluctuation interval set is constructed, including: Based on the random user equilibrium mixed traffic flow allocation model, a polyhedron uncertainty interval set of charging loads with different user perception levels is constructed. , expressed as:

[0017] ;

[0018] in, , Represents different perception coefficients Down The maximum and minimum values ​​of traffic flow in the virtual road section of the charging station at each moment, express Deviation range of traffic flow during a period; It is a binary variable. When it takes the value of 1, the uncertain variable in the corresponding period takes the value of the interval boundary; is the uncertainty adjustment parameter of the charging load, which indicates the total number of periods in which the corresponding uncertain variable reaches the boundary value of the fluctuation range within the scheduling period. It is used to adjust the conservatism of the robust optimal solution. is the upper boundary value of the perception coefficient, is the lower boundary value of the perception coefficient, The objective function of the stochastic user equilibrium model for mixed traffic flow allocation is, is the scheduling period;

[0019] Multidimensional uncertainty fluctuation interval set Expressed as:

[0020] ;

[0021] in, The maximum fluctuation deviation allowed for photovoltaic output; It is a binary variable. When it takes the value of 1, the uncertain variable in the corresponding period takes the value of the interval boundary. Access Node The photovoltaic injection power, is the photovoltaic reference power, is the uncertainty adjustment parameter for photovoltaics.

[0022] In some possible implementations, based on a set of multidimensional uncertainty fluctuation intervals, a two-stage robust optimization optimal operation model for a power-transportation coupled system with a photovoltaic storage charging station is constructed to determine the constraints and objective function of the optimal operation state, including: using a directed graph G E =[E N ,EL ] describes the distribution network topology, where E N and E L Respectively represent the node set and line set of the distribution network; in determining the topological structure G E and electrical parameters of the circuit and When , the second-order cone-relaxed DistFlow power flow model is used to describe the operation constraints of PDS, where and For the line The resistance and reactance of the power grid are taken into account; considering the safe and stable operation of the power grid and the power quality requirements, the constraints include the construction of voltage and current boundary constraints, the upper power grid power purchase constraints, the energy storage operation constraints, and the static VAR compensation device operation constraints; the objective function of the two-stage robust optimization optimal operation model is the operation cost F PDS Minimum, F PDS The energy storage charging and discharging cost C ESS , the cost of electricity purchased by the upper power grid C GRID and network loss cost C LOSS composition.

[0023] In some possible implementations, a two-stage robust optimization optimal operation model is solved based on a preset nested outer approximation C&CG algorithm to obtain the optimal operation strategy of the distribution network and photovoltaic storage charging station equipment under the worst scenario, including: based on the nested outer approximation C&CG algorithm, the two-stage robust optimization optimal operation model is decomposed into a main problem MP and a sub-problem SP, and solved using a solver to obtain the optimal operation strategy of the distribution network and photovoltaic storage charging station equipment under the worst scenario; the optimal operation strategy includes: traffic flow distribution, charging load distribution, vehicle travel time cost, and vehicle charging time cost of the transportation network; node voltage, branch current, upstream power purchase, and branch transmission active and reactive power of the distribution network; the worst distribution of photovoltaic and charging loads of the photovoltaic energy storage charging station, energy storage charging and discharging power, energy storage charge state, and SVC compensation power.

[0024] In a second aspect, an embodiment of the present invention provides a robust optimization device for a photovoltaic charging station that takes into account the travel behavior deviation of charging users, characterized by comprising:

[0025] The data acquisition module is used to collect multi-dimensional data of the power network and transportation network in real time based on the vehicle-road-station-network collaborative architecture;

[0026] A first determination module is configured to use a random user equilibrium mixed traffic flow allocation model to characterize the path selection and charging behavior of electric vehicle users under different perception biases based on the collected OD demand data and road congestion characteristics of the traffic network, and to deduce the spatiotemporal distribution of charging vehicles in combination with the equilibrium characteristics of the traffic network;

[0027] A second determination module is configured to construct a spatiotemporal correlation constraint of a traffic-power coupling network based on the spatiotemporal distribution of the charging vehicles, couple the traffic and power networks, and determine the charging load distribution of each charging station;

[0028] A multi-dimensional uncertainty fluctuation interval construction module is used to obtain the multi-dimensional uncertainty interval of the user behavior perception deviation of the traffic network travel based on the random user equilibrium mixed traffic flow allocation model, and at the same time, combine the photovoltaic output fluctuation to construct a multi-dimensional uncertainty fluctuation interval set;

[0029] A model building module is used to build a two-stage robust optimization optimal operation model of a power-transportation coupled system including a photovoltaic storage charging station based on the multi-dimensional uncertainty fluctuation interval set, and determine the constraints and objective function of the optimal operation state;

[0030] The model solving module is used to solve the two-stage robust optimization optimal operation model based on the preset nested outer approximation C&CG algorithm to obtain the optimal operation strategy of the distribution network and photovoltaic storage charging station equipment under the worst-case scenario.

[0031] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the method provided in the first aspect of the present invention when executing the executable instructions or computer programs stored in the memory.

[0032] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0033] In an embodiment of the present invention, a random user equilibrium mixed traffic flow distribution model based on bounded rationality perception bias is constructed. By characterizing the interactive mechanism between user path selection and charging decision-making, it breaks through the limitation of traditional inventions in insufficient quantification of the randomness of charging load behavior. The model achieves accurate modeling of the spatiotemporal distribution of charging demand through analysis of the equilibrium characteristics of the traffic network, and creates a method for characterizing the uncertainty of charging load intervals driven by perception bias, providing a more realistic load forecasting tool for coupled system analysis. Compared with the invention of the traditional single-stage optimization method, the model of the present invention achieves a dynamic optimal balance between the economic efficiency and robustness of distribution network operation, reduces operating costs while ensuring the safety margin of the system, and significantly improves the coordinated regulation capability of complex coupled systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A schematic flow chart of an embodiment of a robust optimization method for a photovoltaic charging station that considers travel behavior deviations of charging users provided by the present invention;

[0036] Figure 2 Schematic diagram of the vehicle-road-station-network collaborative architecture in an embodiment of the present invention;

[0037] Figure 3 Schematic diagram of the topological structure of the electric-transport coupling system in an embodiment of the present invention;

[0038] Figure 4 Schematic diagram of the topological structure of the electric-transport coupling system in an embodiment of the present invention;

[0039] Figure 5 Schematic diagram of charging load fluctuation range of each charging station in an embodiment of the present invention;

[0040] Figure 6 This is a flow chart of an algorithm for solving the two-stage robust optimization optimal operation model in an embodiment of the present invention;

[0041] Figure 7 is a power balance diagram of each charging station under the optimal strategy in an embodiment of the present invention;

[0042] Figure 8 This is a diagram of device output under the optimal strategy of each charging station in an embodiment of the present invention;

[0043] Figure 9 Schematic diagram of the structure of a robust optimization device for a solar-energy storage charging station that takes into account travel behavior deviations of charging users in an embodiment of the present invention;

[0044] Figure 10 FIG. 4 is a schematic structural diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0046] In the relevant description of this embodiment, the terms "including, containing, having" and the like are open terms and are generally understood to include but not be limited to; the term "at least one" is generally understood to mean one or more, where "plurality" refers to two or more; the term "at least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items, for example, "at least one of a, b or c", or "at least one of a, b and c", can all represent: a, b, c, ab (i.e., a and b), ac, bc, or abc, where a, b, c can be single or multiple respectively; the symbol "A / B" is used to describe the selection relationship of associated objects, generally indicating an "or" relationship before and after.

[0047] In the following description of the present embodiment, the terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0048] Those skilled in the art should understand that in the following description of the embodiments of the present application, the order of serial numbers does not mean the order of execution, some or all of the steps can be executed in parallel or sequentially, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0049] It will be understood by those skilled in the art that the numerical ranges in the examples of the present application are to be understood as also specifically disclosing each intermediate value between the upper and lower limits of the ranges. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the range is also included in the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded in the scope.

[0050] Unless otherwise indicated, the technical / scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which this application belongs. Although this application describes only preferred methods and materials, any methods and materials similar or equivalent to those herein may also be used in the implementation or testing of this application. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

[0051] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0052] The field of power-transport coupled system management aims to achieve the intelligent integration of energy, transportation, and information flows through deep collaboration between power and transportation networks. Its core lies in building an integrated "vehicle-road-network-station" architecture. Leveraging key technologies such as multi-energy coordinated dispatch, this approach addresses the challenges of temporal and spatial volatility brought about by the large-scale integration of electric vehicles, while also tapping into the potential of mobile energy storage and load capacity within transportation vehicles.

[0053] In a power-transport coupled system that considers biases in charging user travel behavior, the robust optimization operation method for photovoltaic (PV)-storage charging stations aims to address the operational risks of integrated PV-storage-charging stations in this coupled power-transport system, caused by biases in charging user travel perception and uncertainty in PV output, through modeling and robust optimization of travel user behavior. By fine-tuning the perceived characteristics of travel user behavior, a robust decision-making model is constructed to optimize the coordinated scheduling strategy for PV-storage-charging, thereby preventing voltage overload and line overload in the distribution network caused by excessive charging loads due to random user selection of charging stations.

[0054] Most existing technologies assume that user charging behavior follows a fixed probability distribution, and fail to consider user psychological factors, group game behavior, and the dynamic impact of traffic congestion on charging decisions. They are unable to capture the negative feedback effect of "congestion-charging demand transfer-local overload of the distribution network." Secondly, most technologies only focus on optimizing the risk of a single dimension of new energy output fluctuations or changes in travel demand, and lack an in-depth analysis of the spatiotemporal reconstruction of loads and the spatiotemporal fluctuations of photovoltaic output caused by deviations in user path selection and charging decision-making behavior. This defect makes it difficult for existing models to accurately depict the spatiotemporal evolution of charging loads under the constraints of road network topology, which will significantly weaken the potential for coordinated operation of photovoltaic storage charging station equipment.

[0055] Based on this, an embodiment of the present invention provides a robust optimization method for a photovoltaic charging station that takes into account the travel behavior deviations of charging users, thereby achieving a dynamic optimal balance between the economic efficiency and robustness of the distribution network operation.

[0056] Figure 1 A schematic diagram of an embodiment of a robust optimization method for a photovoltaic charging station considering the travel behavior deviation of charging users provided by the present invention is provided in FIG. Figure 1 As shown, the above method may include:

[0057] S101, based on a vehicle-road-station-network collaborative architecture, collects multi-dimensional data of power and transportation networks in real time;

[0058] Among them, the vehicle-road-station-network collaborative architecture can be found in Figure 2 As shown, in the embodiment of the present invention, based on Figure 2 The vehicle-road-station-network collaborative architecture shown here collects multi-dimensional data from the power grid and transportation network in real time, including at least:

[0059] On the traffic side, regional traffic topology is obtained through map software, and vehicle OD requirements, maximum road network capacity, and free-flow travel time are obtained through vehicle terminals and roadside units.

[0060] On the power side, it simultaneously accesses distribution network topology parameters, photovoltaic output forecast curves, energy storage capacity, operating boundaries, node voltage, line load margin, maximum capacity of charging stations, and average charging time information;

[0061] On the environmental side, weather forecast data is collected through sensors.

[0062] S102: Based on the collected OD demand data and road congestion characteristics of the traffic network, a random user equilibrium mixed traffic flow assignment model is used to characterize the path selection and charging behavior of electric vehicle users under different perception biases. The spatiotemporal distribution of charging vehicles is deduced in combination with the equilibrium characteristics of the traffic network.

[0063] In some embodiments, the above step S102 specifically includes:

[0064] S1021, describes the traffic topology as a directed graph representation;

[0065] Specifically, by introducing the concept of a virtual road, a traffic expansion network is established for charging stations to operate in the traffic network, such as Figure 3 As shown, the traffic network topology is defined as G T =[T N ,T A ], T N is a node set, T N = ∩ , is a set of regular road nodes, is the virtual road node set; T A is the set of traffic network segments, T A = ∩ , is a regular road segment set, is a set of virtual road segments. w represents the valid path set, k∈K w The origin-destination (OD) analysis method is used to describe the travel demand of the transportation network. The travel demand of vehicles with similar departure and arrival areas during the same period is divided into two categories: electric vehicle users with charging needs and OD for w e OD of electric vehicles and fuel vehicles without charging requirements g , the set of all types of user OD pairs is W={w e ,w g}.

[0066] S1022: Based on OD demand data, the maximum capacity of the road network, and free-flow travel time, a random user equilibrium mixed traffic flow allocation model is established by introducing random utility theory. OD demand is allocated to all travel paths using the random user equilibrium mixed traffic flow allocation model based on user perception of travel costs. The final equilibrium state of the random user equilibrium mixed traffic flow allocation model is described as a fixed point problem for path flow.

[0067] In some embodiments, the random user equilibrium mixed traffic flow allocation model uses a Logit discrete choice model to describe user decisions. The Logit discrete choice model aims to predict the probability that a decision maker will choose a specific option when faced with a limited set of mutually exclusive options. In an embodiment of the present invention, the Logit discrete choice model assumes that users choose routes based on perceived costs that include actual costs and random errors, so that no user can reduce their perceived costs by unilaterally changing the route. This equilibrium takes into account the uncertainty in user decisions, such as incomplete information or individual preference differences. Specifically:

[0068] use Indicates OD pair The traveler pairs between The perceived travel and charging costs are expressed as:

[0069] ;

[0070] in, For OD Path between The actual travel and charging costs, are independent random variables that obey the Gumbel distribution, It is a sensitivity parameter that determines the intensity of randomness and is inversely proportional to the understanding of travel and charging costs. It is a measure of the degree of understanding of the route and charging costs by travelers.

[0071] The mathematical model of a conventional road is expressed as:

[0072] ;

[0073] The model is an existing road impedance function, which describes the relationship between the vehicle travel time in the road section and the strictly increasing traffic flow of the road section. and Road sections Traffic flow and road section capacity; and Road sections The road section travel time and free flow travel time.

[0074] The mathematical model of the charging station virtual section is:

[0075] ;

[0076] It is a Davidson function developed based on queuing theory that describes the relationship between the waiting time of users participating in charging services and traffic flow; and are the user’s service time at the charging station and the charging time respectively. is the shape parameter of the function, is the capacity of the charging station.

[0077] The relationship constraint between the segment flow and the path flow is:

[0078] ;

[0079] in Indicates the path and road sections The correlation matrix of the road segment On the path superior, , otherwise 0.

[0080] All travel demands must be fully allocated, subject to the following constraints:

[0081]

[0082] For OD travel needs.

[0083] Travel user path The actual cost is the sum of the regular road section time and the charging service time, which can be expressed as:

[0084] ;

[0085] According to the properties of Gumbel distribution, the above Logit discrete choice model can be expressed as follows:

[0086] ;

[0087] The above formula of Logit discrete choice model expresses the traveler's choice of path The probability of choosing is the probability that its utility is higher than all other path options. →∞ time, When it approaches 1, all travelers rationally choose the path with the lowest travel cost. When the value is small, the traveler's perception error is large, and the traveler will choose multiple paths.

[0088] Since the path travel cost is related to the traffic, the path selection probability is no longer a constant, but a function related to the traffic. At the same time, the change of the path selection probability leads to the change of the path traffic, which affects the path travel cost function. This cycle is interdependent and finally reaches the random user equilibrium condition, that is, the path The probability of being selected is the probability that the user understands that the travel and charging costs are minimized, that is:

[0089] ;

[0090] The final equilibrium state of the random user equilibrium is usually described as a fixed point problem of path flow, which is expressed as:

[0091] ;

[0092] S1023, based on the established fixed point problem, it is converted into a convex optimization model through the first-order optimality condition;

[0093] Based on the established fixed point problem, it is transformed into a convex optimization problem for mixed traffic flow assignment considering fuel and electric vehicles (HTAP-SUE) model through the first-order optimality condition: In some embodiments, the convex optimization model is expressed as:

[0094] ;

[0095] in, is the objective function of the random user equilibrium model, For road sections traffic flow, For road sections The road section travel time, The service time of the user at the charging station, is the path identifier, is a set of valid paths, is the perception coefficient, is a regular road segment set, is a set of virtual road segments, For all types of user OD pairs, is a set of user OD pairs of a single user type, For OD The path below of traffic, is an integral variable used to represent the flow from 0 to x a The process of flow.

[0096] The above convex optimization model contains the following constraints:

[0097] ;

[0098] ;

[0099] ;

[0100] ;

[0101] .

[0102] S1024, solving the convex optimization model to obtain the spatiotemporal distribution of charging vehicles under the equilibrium characteristics of the transportation network.

[0103] like Figure 4 As shown, different perception coefficients can be obtained by solving the above model Accurate spatiotemporal distribution of charging vehicles under the equilibrium characteristics of the traffic network.

[0104] S103, based on the spatiotemporal distribution of charging vehicles, constructing spatiotemporal correlation constraints for the traffic-power coupling network, coupling the traffic and power networks, and determining the charging load distribution of each charging station;

[0105] In some embodiments, the charging load is usually determined by the user's travel perception. The PV-ESS-CS can be used to couple the transportation network with the power distribution network. Assuming that each PV-ESS-CS is powered by the nearest distribution network node, and that there is a linear mapping relationship between the traffic flow at the charging station and the charging load, when the traffic flow at the charging station is given, the distribution network node to which the charging station belongs can be obtained. Charging load:

[0106]

[0107] in, For distribution network nodes The charging station load of the PV-ESS-CS, is the charging power of a single electric vehicle, is the set of charging station nodes in the distribution network, is the traffic volume of the virtual road section a corresponding to the charging station at time t, that is, the traffic volume of the charging station.

[0108] S104: Based on the random user equilibrium mixed traffic flow allocation model, a multi-dimensional uncertainty interval of the user behavior perception deviation of the traffic network is obtained. At the same time, combined with the photovoltaic output fluctuation, a multi-dimensional uncertainty fluctuation interval set is constructed;

[0109] Specifically, based on the random user equilibrium mixed traffic flow allocation model, a polyhedron uncertainty interval set of charging loads with different user perception levels is constructed. , expressed as:

[0110] ;

[0111] in, , Represents different perception coefficients Down The maximum and minimum values ​​of traffic flow in the virtual road section of the charging station at each moment, express Deviation range of traffic flow during a period; It is a binary variable. When it takes the value of 1, the uncertain variable in the corresponding period takes the value of the interval boundary; is the uncertainty adjustment parameter of the charging load, which indicates the total number of periods in which the corresponding uncertain variable reaches the boundary value of the fluctuation range within the scheduling period. It is used to adjust the conservatism of the robust optimal solution. is the upper boundary value of the perception coefficient, is the lower boundary value of the perception coefficient, The objective function of the random user equilibrium model for mixed traffic flow allocation is: is the scheduling period;

[0112] Multidimensional uncertainty fluctuation interval set Expressed as:

[0113] ;

[0114] in, The maximum fluctuation deviation allowed for photovoltaic output; It is a binary variable. When it takes the value of 1, the uncertain variable in the corresponding period takes the value of the interval boundary. Access Node The photovoltaic injection power, is the photovoltaic reference power, This is the uncertainty adjustment parameter for photovoltaics, representing the total number of periods within the scheduling cycle when the corresponding uncertain variable reaches the boundary of the fluctuation range. This parameter can be used to adjust the conservatism of the optimal solution. A larger value indicates a more conservative solution, while a smaller value indicates a more risky solution. In practical applications, the uncertainty set parameters can be set based on historical forecast deviations.

[0115] In some embodiments, the fluctuation interval of the uncertainty optimization problem constructed based on the determination of the multi-dimensional uncertainty fluctuation interval set is as follows: Figure 5 shown.

[0116] S105: Based on a set of multi-dimensional uncertainty fluctuation intervals, a two-stage robust optimization optimal operation model for a power-transportation coupled system with a photovoltaic storage charging station is constructed to determine the constraints and objective function for the optimal operation state.

[0117] For details, see Figure 2As shown, using the directed graph G E =[E N ,E L ] describes the distribution network topology, where E N and E L Respectively represent the node set and line set of the distribution network; in determining the topological structure G E and electrical parameters of the circuit and When , the DistFlow power flow model with second-order cone relaxation is used to describe the operation constraints of PDS, which is expressed as:

[0118] ;

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] express Time flow into the line Active power and reactive power at the head end; and For the line resistance and reactance; and express Time outflow node The active power and reactive power, is a node downstream nodes. and is the node injection power. 、 Indicates the active power and reactive power injected into the distribution network from the upper power grid; , QPV j,t represents the access node The photovoltaic injection power, 、 Indicates access node Energy storage discharge power and charging power. and Indicates access node Conventional active and reactive loads; Indicates access node Charging load; Indicates access node Charging load; Representation node To the node outgoing current; and respectively Time Node and nodes The voltage amplitude.

[0126] Considering the safe and stable operation of the power grid and the power quality requirements, the voltage and current boundaries must meet the following constraints:

[0127] ;

[0128] ;

[0129] Among them, U min 、U max The minimum and maximum values ​​allowed for the node voltage during operation; I max It is the maximum value allowed for the line current during operation.

[0130] The power purchase constraints of the upper-level power grid meet the following constraints:

[0131] ;

[0132] ;

[0133] In the formula and It is the minimum value of the active power and reactive power of the power purchased by the upper level. and It is the maximum value of active power and reactive power purchased from the upper level.

[0134] Energy storage operation constraints meet the following constraints:

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] in, and A 0, 1 variable representing the charge and discharge status of the energy storage; and Indicates the maximum charging and discharging power of energy storage; 、 are the minimum and maximum values ​​of the energy storage state of charge, respectively. Representation node The connected storage The state of charge at the moment, is the maximum capacity of energy storage; 、 The charging and discharging efficiency of energy storage;

[0142] The operation constraints of the static VAR compensation device meet the following constraints:

[0143] ;

[0144] in, Indicates the maximum compensation capacity of the static VAR compensation device, the node of the static VAR compensation device The connected storage The compensation amount of the static VAR compensation device at the moment.

[0145] Based on the established uncertainty set, the optimal operation problem of the distribution network is established as a two-stage robust problem. The objective function of the two-stage robust optimization optimal operation model is the operation cost F PDS Minimum, F PDS The energy storage charging and discharging cost C ESS , the cost of electricity purchased by the upper power grid C GRID and network loss cost C LOSS Composition, expressed as:

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] in, is the unit charging and discharging cost of the energy storage system, The time-of-use electricity purchase price of the upper power grid, is the unit network loss penalty cost coefficient. The variables in the first stage are , including the energy storage charging and discharging state and energy storage charging and discharging power, the second stage variables are , including the optimal power flow state variables of the distribution network and the output of the SVC. The uncertainty set is , including the uncertainty set of photovoltaic and charging loads. In order to more conveniently explain the optimization model and solution method, the present invention provides a compact form of the model, which is expressed as:

[0151] ;

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] in, 、 is the coefficient vector corresponding to the objective function; A, B, C, D, E, F, G, K are the coefficient matrices of the variables under the corresponding constraints, 、 、 、 is a constant column vector

[0158] S106, based on the preset nested outer approximation C&CG algorithm, solve the two-stage robust optimization optimal operation model to obtain the optimal operation strategy of the distribution network and photovoltaic storage charging station equipment under the worst scenario.

[0159] See also Figure 6 As shown, Figure 6 This is a flowchart of the algorithm for solving the two-stage robust optimization optimal operation model in an embodiment of the present invention. Based on the C&CG algorithm with nested outer approximation, the two-stage robust optimization optimal operation model is decomposed into a main problem MP and a subproblem SP, specifically including:

[0160] The main problem is expressed as:

[0161] ;

[0162] in, is the current iteration number; For the Variables at the iteration; For the The uncertain variables under the “worst scenario” obtained after iterations The value of .

[0163] The specific form of the sub-problem is expressed as:

[0164] ;

[0165] The sub-problem is a "maximum-minimum" bi-level programming problem. Given a set of In the case of , the inner min problem becomes a second-order cone programming problem. According to the strong duality theory, it can be transformed into the max form of the dual problem and combined with the outer max problem as shown below:

[0166] ;

[0167] ;

[0168] in: 、 are the dual variables corresponding to the equality constraints and inequality constraints respectively, 、 is the dual variable corresponding to the second-order cone constraint.

[0169] There are bilinear terms in the objective function , contains a large number of 0, 1 variables in the uncertain set, the model is nonlinear and non-convex, and there are difficulties in solving it. For this problem, the big M method can be used to deal with it, but this method has certain requirements on the value of big M, which is difficult to select.

[0170] Based on this, according to the constructed main problem and sub-problems, the present invention designs a CC&G algorithm with a nested outer approximation technique (OA) algorithm to solve the problem of difficulty in solving nonlinear non-convex problems, specifically including:

[0171] Algorithm 1: OA-C&CG algorithm, including:

[0172] (1) Initialization: Set the iteration counter k1=0, the convergence tolerance ɛ1>0, and the lower limit , upper limit UB1=+∞;

[0173] (2) Solve the main problem MP: Set the optimal solution of the main problem MP for the k1th iteration ( ) is equal to the lower boundary value LB1= , the target value of the sub-problem SP is ;

[0174] (3) Solve the subproblem SP: Solve the main problem Substitute into SP. Apply Algorithm 2 to solve SP and get the optimal solution and the worst case ( ). Update UB1=min ;

[0175] (4) Determine the convergence condition: If UB1-LB1<ɛ1, output the optimal solution and terminate the iteration; otherwise:

[0176] (a) If <+∞, introduce new variables into MP and add new constraints. Update k1=k1+1 and return to step (2).

[0177] (b) If = +∞, then introduce new variables and add only some constraints in MP. Update k1 = k1 + 1 and return to step (2).

[0178] Algorithm 2: OA algorithm, including:

[0179] (1) Initialization: Set the inner loop iteration counter k2=0, the convergence tolerance ɛ2>0, and the lower limit , upper limit UB2=+∞. Input the first stage variables obtained from the main problem in Algorithm 1 , and choose the initial uncertainty

[0180] (2) Solve the subproblem ,let( ) represents the optimal solution. Update the lower bound

[0181] (3) Solve the main problem O( ), define the linearization operator (λ,u). Through the variables in the SP problem ( ) for the bilinear term λ T The linearization performed by Du is as follows:

[0182] ;

[0183] ;

[0184] Get the optimal solution ( ), and update the upper bound, expressed as:

[0185] UB2=O( );

[0186] (4) Determine convergence. If UB2-LB2<ɛ2, terminate the inner loop and output the result. At this time, the optimal solution of SP is Otherwise, increment the iteration counter k2=k2+1 and update the uncertain variable Return to step (2) and continue iterating until convergence.

[0187] Based on the above solution algorithm, the solver is used to solve the problem and obtain the optimal operation strategy of the distribution network and the photovoltaic charging station equipment under the worst scenario; the present invention takes the coupling system of the 5-node distribution network and the transportation network as an example, and its topology is as follows Figure 3 As shown, the nodes T2, T3, and T4 in the traffic network are established as virtual sections of charging stations to simulate the charging and queuing conditions of the photovoltaic storage and fast charging integrated power stations C1, C2, and C3 of the distribution network nodes E4, E2, and E5. The present invention assumes that fuel vehicles and electric vehicles without charging needs in the coupled system account for 80% of the total travel demand, and the available paths are 1-2, 3-4, and 5-6; electric vehicle users with charging needs account for 20% of the total travel demand, and the available paths are 1-7-2, 3-8-4, and 5-9-6. Each user can complete the charging task and leave within a period of time. Based on the mathematical model and solution algorithm established by the present invention, the two-stage robust optimization model is solved by using a solver, as shown in FIG. Figure 7 、 Figure 8 As shown in the figure, the optimal operation strategy under the worst-case scenario is obtained. This includes: traffic flow distribution, charging load distribution, vehicle travel time cost, and vehicle charging time cost of the transportation network; node voltage, branch current, upstream power purchase, and branch transmission active and reactive power of the distribution network; and the worst-case distribution of photovoltaic and charging loads, energy storage charging and discharging power, energy storage state of charge, and SVC compensation power of the photovoltaic energy storage charging station.

[0188] In an embodiment of the present invention, a random user equilibrium mixed traffic flow distribution model based on bounded rationality perception bias is constructed. By characterizing the interactive mechanism of user path selection and charging decision-making, it breaks through the limitation of traditional inventions that the randomness of charging load behavior is insufficiently quantified. This model realizes the accurate modeling of the spatiotemporal distribution of charging demand through the analysis of the equilibrium characteristics of the traffic network, and creates a method for characterizing the uncertainty of charging load intervals driven by perception bias, providing a more realistic load forecasting tool for coupled system analysis. Compared with the invention of the traditional single-stage optimization method, the model of the present invention realizes the dynamic optimal balance between the economic efficiency and robustness of distribution network operation, reduces the operating cost while ensuring the system safety margin, and significantly improves the coordinated control capability of complex coupled systems. In addition, the C&CG algorithm of the nested outer approximation algorithm of the present invention solves the bottleneck of solution efficiency caused by the non-convex nonlinearity of the model through the dynamic decision-making of the main problem and the coordinated iteration mechanism of the linearization of the sub-problem. It provides innovative methodological support for the optimization of large-scale power-transportation coupled systems.

[0189] Based on the same inventive concept, an embodiment of the present application also provides a robust optimization device for a photovoltaic charging station that takes into account the travel behavior deviations of charging users. Figure 9 This is a structural diagram of a robust optimization device for a solar-storage charging station that considers the travel behavior deviation of charging users in an embodiment of the present invention. Figure 9As shown, the robust optimization device 900 for a solar-energy charging station considering the travel behavior deviation of charging users may include:

[0190] Data acquisition module 901, used to collect multi-dimensional data of power network and transportation network in real time based on vehicle-road-station-network collaborative architecture;

[0191] A first determination module 902 is configured to use a random user equilibrium mixed traffic flow allocation model to characterize the path selection and charging behavior of electric vehicle users under different perception biases based on the collected OD demand data and road congestion characteristics of the traffic network, and to deduce the spatiotemporal distribution of charging vehicles in combination with the equilibrium characteristics of the traffic network;

[0192] A second determination module 903 is configured to construct a spatiotemporal correlation constraint of a traffic-power coupling network based on the spatiotemporal distribution of the charging vehicles, couple the traffic and power networks, and determine the charging load distribution of each charging station;

[0193] A multi-dimensional uncertainty fluctuation interval construction module 904 is used to obtain a multi-dimensional uncertainty interval of the user behavior perception deviation of the traffic network based on the random user equilibrium mixed traffic flow allocation model, and to construct a multi-dimensional uncertainty fluctuation interval set in combination with the photovoltaic output fluctuation;

[0194] A model building module 905 is used to build a two-stage robust optimization optimal operation model for a power-transport coupled system including a photovoltaic storage charging station based on the multi-dimensional uncertainty fluctuation interval set, and determine the constraints and objective function of the optimal operation state;

[0195] The model solving module 906 is used to solve the two-stage robust optimization optimal operation model based on the preset nested outer approximation C&CG algorithm to obtain the optimal operation strategy of the distribution network and the photovoltaic storage charging station equipment under the worst scenario.

[0196] Based on the same inventive concept, an embodiment of the present application provides an electronic device, which can be consistent with the robust optimization method of the photovoltaic charging station that takes into account the travel behavior deviation of charging users in one or more of the above embodiments. Figure 10 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention, see Figure 10 As shown, the electronic device 1000 can adopt general computer hardware, including a processor 1001 and a memory 1002.

[0197] In some possible implementations, the at least one processor may comprise any physical device having circuitry that performs logical operations on one or more inputs. For example, the at least one processor may comprise one or more integrated circuits, including application-specific integrated circuits, microchips, microcontrollers, microprocessors, all or part of a central processing unit, a graphics processing unit, a digital signal processor, a field programmable gate array, or other circuitry suitable for executing instructions or performing logical operations. The instructions executed by the at least one processor may, for example, be preloaded into a memory integrated with or embedded in the controller, or may be stored in a separate memory. The memory may include random access memory, read-only memory, a hard disk, an optical disk, magnetic media, flash memory, other permanent, fixed, or volatile memory, or any other mechanism capable of storing instructions. In some embodiments, the at least one processor may comprise more than one processor. Each processor may have a similar structure, or the processors may have different configurations that are electrically connected or disconnected from each other. For example, the processors may be separate circuits or integrated into a single circuit. When more than one processor is used, the processors may be configured to operate independently or collaboratively. The processors may be coupled electrically, magnetically, optically, acoustically, mechanically, or by other means that allow them to interact.

[0198] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments.

[0199] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A robust optimization method for solar-storage charging stations considering the travel behavior deviation of charging users, characterized by: include: Based on the vehicle-road-station-network collaborative architecture, multi-dimensional data of power grid and transportation network are collected in real time; Based on the collected OD demand data and road congestion characteristics of the traffic network, a random user equilibrium mixed traffic flow allocation model is used to characterize the path selection and charging behavior of electric vehicle users under different perception biases, and the spatiotemporal distribution of charging vehicles is deduced in combination with the equilibrium characteristics of the traffic network; Based on the spatiotemporal distribution of the charging vehicles, a spatiotemporal correlation constraint of the traffic-power coupling network is constructed, the traffic and power networks are coupled, and the charging load distribution of each charging station is determined; Based on the random user equilibrium hybrid traffic flow allocation model, the multi-dimensional uncertainty interval of the user behavior perception deviation of the traffic network is obtained. At the same time, combined with the photovoltaic output fluctuation, a multi-dimensional uncertainty fluctuation interval set is constructed, including: Based on the random user equilibrium mixed traffic flow allocation model, a polyhedron uncertainty interval set of charging loads with different user perception levels is constructed. , expressed as: ； in, for The charging station corresponds to the virtual road section at any time The traffic volume, that is, the traffic volume of the charging station, is the perception coefficient, , Represents different perception coefficients Down The maximum and minimum values ​​of traffic flow in the virtual road section of the charging station at each moment, express Deviation range of traffic flow during a period; It is a binary variable. When it takes the value of 1, the uncertain variable in the corresponding period takes the value of the interval boundary; is the uncertainty adjustment parameter of the charging load, which indicates the total number of periods in which the corresponding uncertain variable reaches the boundary value of the fluctuation range within the scheduling period. It is used to adjust the conservatism of the robust optimal solution. is the upper boundary value of the perception coefficient, is the lower boundary value of the perception coefficient, Stochastic user equilibrium model for mixed traffic flow allocation, is the scheduling period, For distribution network nodes The charging station load of the PV-ESS-CS, is the charging power of a single electric vehicle; Multidimensional uncertainty fluctuation interval set Expressed as: ； in, The maximum fluctuation deviation allowed for photovoltaic output; It is a binary variable. When it takes the value of 1, the uncertain variable in the corresponding period takes the value of the interval boundary. Access Node The photovoltaic injection power, is the photovoltaic reference power, Adjust parameters for PV uncertainty; Based on the multi-dimensional uncertainty fluctuation interval set, a two-stage robust optimization optimal operation model for the electric-transportation coupled system with a photovoltaic storage charging station is constructed to determine the constraints and objective function of the optimal operation state; Based on the preset nested outer approximation C&CG algorithm, the two-stage robust optimization optimal operation model is solved to obtain the optimal operation strategy of the distribution network and photovoltaic storage charging station equipment under the worst scenario.

2. The method according to claim 1, characterized in that The vehicle-road-station-network collaborative architecture collects multi-dimensional data of the power network and transportation network in real time, including: On the traffic side, regional traffic topology is obtained through map software, and vehicle OD requirements, maximum road network capacity, and free-flow travel time are obtained through vehicle terminals and roadside units. On the power side, it simultaneously accesses distribution network topology parameters, photovoltaic output forecast curves, energy storage capacity, operating boundaries, node voltage, line load margin, maximum capacity of charging stations, and average charging time information; On the environmental side, weather forecast data is collected through sensors.

3. The method according to claim 2, characterized in that Based on the collected OD demand data and road congestion characteristics of the traffic network, a random user equilibrium mixed traffic flow allocation model is used to characterize the path selection and charging behavior of electric vehicle users under different perception biases, and the spatiotemporal distribution of charging vehicles is deduced in combination with the equilibrium characteristics of the traffic network, including: Describing the traffic topology as a directed graph representation; Based on the OD demand data, the maximum capacity of the road network, and the free-flow travel time, a hybrid traffic flow allocation model is established by introducing random utility theory. OD demand is allocated to all travel paths using a stochastic user equilibrium model based on user perception of travel costs. The final equilibrium state of the stochastic user equilibrium model is described as a fixed point problem for path flow. Based on the established fixed point problem, it is transformed into a convex optimization model through the first-order optimality condition; The convex optimization model is solved to obtain the spatiotemporal distribution of charging vehicles under the equilibrium characteristics of the transportation network.

4. The method according to claim 3, characterized in that The convex optimization model is expressed as: ； in, is the objective function of the random user equilibrium model, For road sections traffic flow, For road sections The road section travel time, The service time of the user at the charging station, is the path identifier, is a set of valid paths, is a regular road segment set, is a set of virtual road segments, For all types of user OD pairs, is a set of user OD pairs of a single user type, For OD The path below of traffic, is an integral variable used to represent the flow from 0 to The process of flow.

5. The method according to claim 4, characterized in that The method of constructing a traffic-power coupling network spatiotemporal correlation constraint based on the spatiotemporal distribution of the charging vehicles, coupling the traffic and power networks, and determining the charging load distribution of each charging station includes: Assuming that each PV-ESS-CS is powered by the nearest distribution network node, and there is a linear mapping relationship between the traffic flow of the charging station and the charging load, when the traffic flow of the charging station is given, the distribution network node to which the charging station belongs is obtained. Charging load: ； in, It is the collection of charging station nodes in the distribution network.

6. The method according to claim 5, characterized in that Based on the multi-dimensional uncertainty fluctuation interval set, a two-stage robust optimization optimal operation model of a power-transportation coupling system including a photovoltaic storage charging station is constructed to determine the constraints and objective function of the optimal operation state, including: Using the directed graph G E =[E N ,E L ] describes the distribution network topology, where E N and E L Respectively represent the node set and line set of the distribution network; in determining the topological structure G E and electrical parameters of the circuit and When , the second-order cone-relaxed DistFlow power flow model is used to describe the operation constraints of PDS, where and For the line The resistance and reactance of the grid are taken into account. Considering the safe and stable operation of the grid and the power quality requirements, the constraints include voltage and current boundary constraints, power purchase constraints of the upper grid, energy storage operation constraints, and static VAR compensation device operation constraints. The objective function of the optimal operation model of the two-stage robust optimization is the operation cost F PDS Minimum, F PDS The energy storage charging and discharging cost C ESS , the cost of electricity purchased by the upper power grid C GRID and network loss cost C LOSS composition.

7. The method according to claim 6, characterized in that The preset nested outer approximation C&CG algorithm is used to solve the two-stage robust optimization optimal operation model to obtain the optimal operation strategy of the distribution network and the photovoltaic storage charging station equipment under the worst scenario, including: Based on the nested outer approximation C&CG algorithm, the two-stage robust optimization optimal operation model is decomposed into a main problem MP and a subproblem SP, which are solved using a solver to obtain the optimal operation strategy for the distribution network and photovoltaic energy storage charging station equipment under the worst-case scenario; the optimal operation strategy includes: traffic flow distribution, charging load distribution, vehicle travel time cost, and vehicle charging time cost of the transportation network; node voltage, branch current, upstream power purchase amount, and branch transmission active and reactive power of the distribution network; the worst-case distribution of photovoltaic and charging loads, energy storage charging and discharging power, energy storage state of charge, and SVC compensation power of the photovoltaic energy storage charging station.

8. A robust optimization device for a solar-storage charging station that considers the travel behavior deviation of charging users, characterized in that: include: The data acquisition module is used to collect multi-dimensional data of the power network and transportation network in real time based on the vehicle-road-station-network collaborative architecture; A first determination module is configured to use a random user equilibrium mixed traffic flow allocation model to characterize the path selection and charging behavior of electric vehicle users under different perception biases based on the collected OD demand data and road congestion characteristics of the traffic network, and to deduce the spatiotemporal distribution of charging vehicles in combination with the equilibrium characteristics of the traffic network; A second determination module is configured to construct a spatiotemporal correlation constraint of a traffic-power coupling network based on the spatiotemporal distribution of the charging vehicles, couple the traffic and power networks, and determine the charging load distribution of each charging station; The multi-dimensional uncertainty fluctuation interval construction module is used to obtain the multi-dimensional uncertainty interval of the user behavior perception deviation of the traffic network travel based on the random user equilibrium mixed traffic flow allocation model, and at the same time, combine the photovoltaic output fluctuation to construct a multi-dimensional uncertainty fluctuation interval set, including: Based on the random user equilibrium mixed traffic flow allocation model, a polyhedron uncertainty interval set of charging loads with different user perception levels is constructed. , expressed as: ； in, for The charging station corresponds to the virtual road section at any time The traffic volume, that is, the traffic volume of the charging station, is the perception coefficient, , Represents different perception coefficients Down The maximum and minimum values ​​of traffic flow in the virtual road section of the charging station at each moment, express Deviation range of traffic flow during a period; It is a binary variable. When it takes the value of 1, the uncertain variable in the corresponding period takes the value of the interval boundary; is the uncertainty adjustment parameter of the charging load, which indicates the total number of periods in which the corresponding uncertain variable reaches the boundary value of the fluctuation range within the scheduling period. It is used to adjust the conservatism of the robust optimal solution. is the upper boundary value of the perception coefficient, is the lower boundary value of the perception coefficient, Stochastic user equilibrium model for mixed traffic flow allocation, is the scheduling period, For distribution network nodes The charging station load of the PV-ESS-CS, is the charging power of a single electric vehicle; Multidimensional uncertainty fluctuation interval set Expressed as: ； in, The maximum fluctuation deviation allowed for photovoltaic output; It is a binary variable. When it takes the value of 1, the uncertain variable in the corresponding period takes the value of the interval boundary. Access Node The photovoltaic injection power, is the photovoltaic reference power, Adjust parameters for PV uncertainty; A model building module is used to build a two-stage robust optimization optimal operation model of a power-transportation coupled system including a photovoltaic storage charging station based on the multi-dimensional uncertainty fluctuation interval set, and determine the constraints and objective function of the optimal operation state; The model solving module is used to solve the two-stage robust optimization optimal operation model based on the preset nested outer approximation C&CG algorithm to obtain the optimal operation strategy of the distribution network and photovoltaic storage charging station equipment under the worst-case scenario.

9. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the method according to any one of claims 1 to 7 when executing the executable instructions or computer program stored in the memory.

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