A system for collaborative interaction between an electric vehicle and an active distribution network and its operation method

Abstract

The present invention belongs to the field of new energy and energy-saving technologies, and specifically discloses a system for collaborative interaction between an electric vehicle and an active distribution network and its operation method, which includes a resource device layer, a resource sensing and control layer, a system information layer, an energy management layer, and a human-computer interaction layer connected in sequence; the resource device layer includes schedulable electric vehicles and an active distribution network system; the resource sensing and control layer includes a data acquisition and transmission unit and a resource device control unit; the system information layer includes a data processing module and an information storage module; the energy management layer includes an energy management system and a digital twin system; the human-computer interaction layer includes a human-computer interaction interface and a configuration platform. The present invention builds a digital twin based on data-driven technology, simulates the operation of the digital twin, verifies and improves the scheduling instructions of the energy management system, enhances the rationality and executability of the scheduling scheme, and solves the problems of lack of overall system simulation, few human-computer interactions, and imperfect optimization scheduling models in the current collaborative assistance system.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy and energy conservation, and particularly relates to a system for collaborative interaction between an electric vehicle and an active distribution network and an operation method thereof. Background Art

[0002] With the continuous acceleration of the energy transformation process, the installed capacity of clean energy mainly composed of wind turbines and photovoltaics has been increasing year by year. The diverse behaviors on the consumer side such as electric vehicle charging will lead to greater difficulties in power load forecasting and coordination. The interaction and interweaving of the power source, grid, and load will exacerbate the power system fluctuations, and the "double high" and "double peak" characteristics of the power system are prominent, bringing major challenges to the safe and stable operation of the power grid. The orderly coordinated interaction between electric vehicles and active distribution networks can enhance the balance regulation ability of the power system and suppress the fluctuations of the power grid. Electric vehicles can be used as a distributed energy storage resource and have great potential for interaction with the power grid. Through orderly charging technology and V2G (vehicle-to-grid) technology, the peak load value of centralized electric vehicle charging can be reduced, the impact of large-scale electric vehicle charging on the power grid can be alleviated, and auxiliary services such as peak shaving and frequency modulation, new energy consumption, and standby can be provided for the power grid, effectively improving the operation quality of the power system.

[0003] The problems existing in the current collaborative interaction system between electric vehicles and active distribution networks are as follows: There is a lack of overall simulation analysis of the interaction system between electric vehicles and active distribution network systems; the human-machine interaction interface can often only display data and parameters and cannot directly control the system in real time through the interface, and the human-machine interaction function is less realized; the optimization scheduling model is not perfect enough, and the optimal allocation of resources is not accurate enough. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a system for collaborative interaction between an electric vehicle and an active distribution network and an operation method thereof, so as to solve the problems of lack of overall system simulation, less human-machine interaction, and imperfect optimization scheduling model in the current collaborative interaction system.

[0005] In order to achieve the above purpose, the present invention is implemented by adopting the following technical solutions:

[0006] On the one hand, the present invention provides a system for collaborative interaction between an electric vehicle and an active distribution network, including a resource device layer, a resource sensing and control layer, a system information layer, an energy management layer, and a human-machine interaction layer connected in sequence;

[0007] The resource device layer includes dispatchable electric vehicles and an active distribution network system;

[0008] The resource sensing and control layer includes a data acquisition and transmission unit and a resource device control unit;

[0009] The system information layer includes a data processing module and an information storage module;

[0010] The energy management layer includes an energy management system and a digital twin system;

[0011] The human-machine interaction layer includes a human-machine interaction interface and a configuration platform.

[0012] Furthermore, the active distribution network system includes a distribution network, distributed photovoltaic, distributed motors, energy storage devices, power connection lines, transformers, and user loads; the distributed photovoltaic is connected to the distribution network through a transformer and a power connection line; the distributed motors are connected to the distribution network through a transformer and a power connection line; the energy storage device and the user load are respectively connected to the distribution network through a power connection line.

[0013] Furthermore, the data acquisition and transmission unit includes intelligent sensors for data acquisition, cameras, electronic tags inside the equipment, wireless modules, intelligent gateways, and optical fibers, which collect the original data information of the resource equipment layer and transmit the original data information to the system information layer; the resource equipment control unit includes an equipment controller for executing the scheduling scheme issued by the energy management layer and regulating the operating state of the equipment in the resource equipment layer.

[0014] Furthermore, the data processing module is responsible for processing and transmitting the original data information collected by the resource sensing and control layer to the energy management layer; the information storage module stores the original data information by constructing multiple databases and transmits the original data information to the energy management layer; the multiple databases include a real-time information database, a historical information database, a fault information database, and a digital twin system operation information database.

[0015] Furthermore, the energy management system is composed of an optimal scheduling module, a power generation prediction module, and a load prediction module; the optimal scheduling module obtains the optimal scheduling instruction of the system by solving the energy management model and transmits it to the digital twin system; the power generation prediction module trains a neural network using meteorological information and power generation information and realizes the prediction of the power generation of each distributed power source based on the neural network; the load prediction module trains a neural network using the load information in the historical information database and the real-time information database and can realize the long-term and short-term prediction of the load based on the neural network;

[0016] The digital twin system includes a creation module and a simulation operation module; the creation module of the digital twin system creates digital twins of resource devices based on the electric vehicle unit and the active distribution network system, simultaneously establishes a spatial environment information set corresponding to the resource devices, and uses 3D visualization technology for display; the digital twins of the resource devices include the overall appearance, internal structure, and device operation characteristics of the resource devices, and the device operation characteristics include: power generation characteristics, power consumption characteristics, charging and discharging characteristics of the electric vehicle unit, etc.; the simulation operation module of the digital twin system regulates the digital twins of the resource devices to perform simulation operation according to the optimal scheduling instructions transmitted by the energy management system, and sends the operation results to the energy management system;

[0017] The energy management model consists of three parts: an objective function, constraint conditions, and a solution algorithm; the objective function includes economy, environmental protection, and new energy consumption; the constraint conditions include various distributed power sources, electric vehicles, and energy conservation constraint conditions involved; the inputs of the energy management model include load prediction power, photovoltaic power generation prediction power, wind turbine power generation prediction power, and device parameters; the outputs of the energy management model include battery charging and discharging power, electric vehicle charging power, and the interaction power with the large power grid; the solution algorithm uses the non-dominated sorting genetic algorithm, sets data such as the maximum number of iterations, population size, crossover rate, and mutation rate, and performs optimal solution for the distribution network to generate a decision-making plan.

[0018] Furthermore, the human-machine interaction layer uses a monitoring configuration platform to develop a human-machine interaction interface, which has several data display functions, and the several data display functions include data display, status display, trend chart, and bar chart; the human-machine interaction layer uses the monitoring configuration platform as the development environment to implement the primitive design, graphic interface drawing, graphic animation design, associated real-time data measurement points, and browsing real-time pictures in the human-machine interaction interface, and finally presents them in the form of graphs, tables, or curves; the human-machine interaction layer can provide the operation state monitoring results and the operation conditions of the scheduling instructions to the user through the human-machine interaction interface for viewing and interaction.

[0019] Furthermore, the economic objective function in the objective function is as follows:

[0020] f1 = γ1 * f Grid + γ2 * f EV + γ3 * f PV + γ4 * f WT +(1 - γ1 - γ2 - γ3 - γ4) * f si

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Among them, f1 is the operation cost of the distribution network, f Grid is the cost of interacting with the power grid, f EV is the electric vehicle dispatching cost, f PV is the photovoltaic operation and maintenance cost, f WT is the wind turbine operation and maintenance cost, f si is the energy storage operation and maintenance cost, and γ1, γ2, γ3, and γ4 are weight coefficients respectively; is the power selling power of the distribution network at time t, is the power purchasing power of the distribution network at time t; C sell (t) is the power selling price at time t, C buy (t) is the power purchasing price at time t; is the charging power of the electric vehicle cluster before dispatching, is the charging power of the electric vehicle cluster after dispatching, C EV (t) is the dispatching subsidy coefficient at time t; P PV (t) is the output of the photovoltaic at time t, C PV is the power generation cost coefficient of the photovoltaic; P WT (t) is the output of the wind turbine at time t, C WT is the power generation cost coefficient of the wind turbine; P si (t) is the charge and discharge power of the energy storage battery at time t, C si is the output cost coefficient of the energy storage;

[0027] The environmental protection objective function in the said objective function is as follows:

[0028]

[0029] Among them, f2 is the pollutant treatment cost, ε e is the pollution treatment cost coefficient;

[0030] The new energy consumption objective function in the said objective function is as follows:

[0031]

[0032] Among them, f3 is the new energy consumption index, is the upper limit of the photovoltaic output prediction, is the upper limit of the wind turbine output prediction, P PV (t) is the actual output of the photovoltaic in the t period, P WT (t) is the actual output of the wind turbine in the t period.

[0033] Furthermore, the power balance constraint condition in the above-mentioned constraint conditions is as follows:

[0034]

[0035] In the formula, P load is the user load;

[0036] The wind power generation output constraint is as follows:

[0037]

[0038] In the formula, is the upper limit of the wind turbine output;

[0039] The photovoltaic power generation output constraint is as follows:

[0040]

[0041] In the formula, is the upper limit of the photovoltaic output;

[0042] The energy storage constraint is as follows:

[0043] The charge and discharge power constraint of the energy storage battery

[0044]

[0045] The energy constraint of the energy storage battery

[0046] SOC si (t) = SOC si (t - 1) + μ si P si (t)

[0047]

[0048] The energy storage battery ramp constraint

[0049] P si (t + 1) - P si (t) ≤ M

[0050] In the formula: is the maximum charge and discharge power of the energy storage battery, μ si is the charge and discharge coefficient of the energy storage battery, SOC si (t) is the SOC of the energy storage battery at time t, are respectively the upper and lower limits of the SOC of the energy storage battery, and M is the maximum ramp ratio;

[0051] The electric vehicle constraint is as follows:

[0052] The charge and discharge power constraint of the electric vehicle at each time period

[0053]

[0054] Electric vehicle power constraint

[0055] SOC EV，i (t) = SOC EV，i (t - 1)+μ ev P EV，i (t)

[0056]

[0057]

[0058] Aggregate power of electric vehicles

[0059]

[0060] In the formula: is the maximum charging power of the electric vehicle, P EV，i (t) is the charging and discharging power of the electric vehicle at time t, μ ev is the charging coefficient of the electric vehicle, SOC EV，i (t) is the SOC of the electric vehicle at time t, are the upper and lower limits of the electric vehicle's SOC respectively, C i is the electric vehicle power, is the total power of the electric vehicle during the last charging period, P EV，sum is the power of the aggregated electric vehicles.

[0061] Furthermore, the process of the solution algorithm is as follows: Initialize the population, perform genetic operations to generate a temporary population, merge the initial population and the temporary population, sort the individuals in the merged population using the dominance relationship and crowding degree, select the dominant individuals to form the next generation population, and finally end the iteration and output the optimization result when the conditions are met.

[0062] On the other hand, the present invention provides an operation method for a collaborative interaction system between an electric vehicle and an active distribution network. Based on the system for collaborative interaction between an electric vehicle and an active distribution network described in any one of the above, it includes:

[0063] Step 1: Use intelligent sensors, cameras, and tag - based radio frequency technology to collect various types of original data information from the resource devices of the collaborative interaction system between the electric vehicle and the active distribution network in the resource device layer in real time, including: voltage, frequency, power, and transmit the original data information to the real - time database resource sensing layer;

[0064] Step 2: The system information layer processes the raw data information collected by the resource sensing and control layer, including outlier removal and mean calculation, and uploads and stores the processed information.

[0065] Step 3: Based on the historical data and real-time data in the system information layer, the creation module of the digital twin system uses data-driven technology to establish a digital twin of the interactive system between the electric vehicle and the active distribution network system.

[0066] Step 4: The optimization scheduling module of the energy management system obtains the preliminary scheduling instructions of the system by solving the energy management model.

[0067] Step 5: According to the preliminary scheduling instructions of the energy management system, the digital twin system performs virtual simulation operation and transmits the virtual operation results to the energy management system.

[0068] Step 6: The energy management system adjusts the preliminary operation optimization plan based on the information fed back after the digital twin system executes the preliminary operation optimization plan, adjusts the preliminary scheduling instructions to generate the final scheduling instructions, and transmits them to the resource sensing and control layer.

[0069] Step 7: The resource device control unit in the resource sensing and control layer controls the corresponding energy devices in the resource device layer according to the scheduling instructions issued by the energy management layer.

[0070] The present invention has at least the following beneficial effects:

[0071] 1. The present invention utilizes the digital twin concept, builds a digital twin based on data-driven technology, simulates the operation of the digital twin, verifies and improves the scheduling instructions of the energy management system, and enhances the rationality and feasibility of the scheduling plan, solving the problems of lack of overall system simulation, few human-computer interactions, and imperfect optimization scheduling models in the current collaborative assistance system.

[0072] 2. The human-computer interaction interface of the present invention has the function of issuing scheduling instructions, and can directly control the entity and the virtual interactive system between the electric vehicle and the active distribution network through the visual interface, improving the convenience and real-time of the control operation.

[0073] 3. The present invention uses a dedicated monitoring configuration platform as the development environment, which can well realize the work links such as primitive design, graphic interface drawing, graphic animation design, associated real-time data measurement points, and browsing real-time pictures in the human-computer interaction interface, and finally provides the operation state monitoring results, scheduling instruction operation conditions, etc. to the user for viewing and interaction in various presentation forms (charts, curves, etc.). Description of the Drawings

[0074] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic embodiments and descriptions thereof of the invention are used to explain the invention and do not unduly limit the invention. In the drawings:

[0075] Figure 1 It is a framework diagram of a collaborative interaction system between an electric vehicle and an active distribution network based on digital twin;

[0076] Figure 2 It is a connection diagram of system equipment between an electric vehicle and an active distribution network;

[0077] Figure 3 It is a flowchart of a multi-objective optimization algorithm;

[0078] Figure 4 It is an operation mechanism diagram of a collaborative interaction system between an electric vehicle and an active distribution network based on digital twin. Detailed implementation manners

[0079] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0080] The following detailed descriptions are all exemplary descriptions, aiming to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms adopted by the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the present invention are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention.

[0081] Embodiment 1

[0082] As Figure 1 shown, a system for collaborative interaction between an electric vehicle and an active distribution network includes: a resource device layer, a resource sensing layer, a system information layer, an energy management layer, and a human-computer interaction layer.

[0083] The resource device layer includes a schedulable electric vehicle unit and an active distribution network system, where the active distribution network system includes a distribution network, distributed photovoltaics, distributed motors, energy storage devices, power connection lines, transformers, and user loads; As Figure 2As shown in the figure, the distributed photovoltaic power is connected to the distribution network through transformers and power connection lines; the distributed motors are connected to the distribution network through transformers and power connection lines; the energy storage devices and user loads are respectively connected to the distribution network through power connection lines; the distributed photovoltaic power and distributed motors deliver the electricity generated by the distributed photovoltaic power and distributed motors to the user loads, electric vehicles and energy storage devices through transformers and power connection lines; when the distributed photovoltaic power and distributed motors generate more electricity, the surplus electricity can be fed into the grid, and the distribution network delivers the electricity from the grid to the user loads, electric vehicles and energy storage devices through transformers and power connection lines. The energy storage devices store the electricity from the wind turbines, photovoltaic power and the grid, and can also deliver the stored electricity to the electric vehicles, user loads and the grid.

[0084] The resource sensing and control layer includes a data acquisition and transmission unit and a resource device control unit. The data acquisition and transmission unit includes intelligent sensors for data acquisition, cameras, electronic tags inside the devices, as well as devices such as wireless modules, intelligent gateways and optical fibers for data transmission. It collects the original data information of the resource device layer and transmits the original data information to the system information layer; the resource device control unit mainly includes a device controller, which is used to execute the scheduling scheme issued by the energy management layer and regulate the operating state of the devices in the resource device layer.

[0085] The system information layer includes a data processing module and an information storage module. The data processing module is responsible for processing the original data information collected by the resource sensing and control layer and transmitting it to the energy management layer; the information storage module stores the original data information by constructing multiple databases, which is convenient for query management and invocation, and transmits the original data information to the energy management layer; the multiple databases include a real-time information database, a historical information database, a fault information database and a digital twin system operation information database.

[0086] The energy management layer includes an energy management system and a digital twin system; the energy management system is mainly composed of an optimization scheduling module, a power generation prediction module and a load prediction module; the optimization scheduling module obtains the optimized scheduling instructions of the system by solving the energy management model and transmits them to the digital twin system; the power generation prediction module trains a neural network using meteorological information and power generation information, and realizes the prediction of the power generation of each distributed power source based on the neural network; the load prediction module trains a neural network using the load information in the historical information database and the real-time information database, and can realize the long-term and short-term prediction of the load based on the neural network.

[0087] The digital twin system includes a creation module and a simulation operation module; the creation module of the digital twin system creates digital twins of resource devices based on the electric vehicle unit and the active distribution network system, simultaneously establishes the spatial environment information set corresponding to the resource devices, and uses 3D visualization technology for display; the digital twins of the resource devices include the overall appearance, internal structure, and device operation characteristics of the resource devices, and the device operation characteristics include: power generation characteristics, power consumption characteristics, charging and discharging characteristics of the electric vehicle unit, etc.; the simulation operation module of the digital twin system regulates the digital twins of the resource devices to perform simulation operation according to the optimized scheduling instructions transmitted by the energy management system, and sends the operation results to the energy management system.

[0088] The energy management model consists of three parts: an objective function, constraint conditions, and a solution algorithm. Among them, the objective function includes economy, environmental protection, and new energy consumption; the constraint conditions include the distributed power sources, electric vehicles, and energy conservation constraint conditions involved; the inputs of the energy management model include load forecast power, photovoltaic power generation forecast power, wind turbine power generation forecast power, and device parameters, etc.; the outputs of the energy management model, that is, decision variables, include battery charging and discharging power, electric vehicle charging power, and interaction power with the large power grid; in terms of the solution algorithm, the non-dominated sorting genetic algorithm is used to set data such as the maximum number of iterations, population size, crossover rate, and mutation rate, etc., to optimize and solve the distribution network to generate a decision-making plan.

[0089] The economic objective function in the objective function is as follows:

[0090] f1 = γ1 * f Grid + γ2 * f EV + γ3 * f PV + γ4 * f WT +(1 - γ1 - γ2 - γ3 - γ4) * f si

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] In the formula, f1 is the operation cost of the distribution network, f Grid is the interaction cost with the power grid, f EV is the scheduling cost of the electric vehicle, f PV is the operation and maintenance cost of the photovoltaic, f WT is the operation and maintenance cost of the wind turbine, f siLet \(C_{om}\) be the energy storage operation and maintenance cost, and \(\gamma_1\), \(\gamma_2\), \(\gamma_3\), \(\gamma_4\) be the weight coefficients respectively; Let \(P_{s}(t)\) be the selling electric power of the distribution network at time \(t\), and \(P_{b}(t)\) be the buying electric power of the distribution network at time \(t\); \(C_{s}\) sell (t) is the selling electricity price at time \(t\), \(C_{b}\) buy (t) is the buying electricity price at time \(t\); Let \(P_{e1}\) be the charging power of the electric vehicle cluster before scheduling, and \(P_{e2}\) be the charging power of the electric vehicle cluster after scheduling, \(C_{sub}\) EV (t) is the scheduling subsidy coefficient at time \(t\); \(P_{pv}\) PV (t) is the output power of the photovoltaic at time \(t\), \(C_{pv}\) PV is the power generation cost coefficient of the photovoltaic; \(P_{w}\) WT (t) is the output power of the wind turbine at time \(t\), \(C_{w}\) WT is the power generation cost coefficient of the wind turbine; \(P_{es}\) si (t) is the charge and discharge power of the energy storage battery at time \(t\), \(C_{es}\) si is the output cost coefficient of the energy storage.

[0097] The environmental protection objective function in the objective function is as follows:

[0098]

[0099] In the formula, \(f_2\) is the pollutant treatment cost, \(\varepsilon\) e is the pollution treatment cost coefficient. The pollutant emissions sources of the distribution network mainly come from purchasing electricity from the grid and natural gas power generation. Among them, it is assumed that all electricity purchased from the grid is coal-fired power generation. The pollutant components mainly consider CO2, SO2 and NO x three emission gases.

[0100] The new energy consumption objective function in the objective function is as follows:

[0101]

[0102] In the above formula, \(f_3\) is the new energy consumption index, is the upper limit of the photovoltaic output power prediction, is the upper limit of the wind turbine output power prediction, \(P_{pv}\) PV (t) is the actual output power of the photovoltaic in the \(t\) period, \(P_{w}\) WT (t) is the actual output power of the wind turbine in the \(t\) period.

[0103] The power balance constraint condition in the constraint conditions is as follows:

[0104]

[0105] In the above formula, \(P_{load}\) load为 is the user load.

[0106] The constraints on the power output of the wind turbine are as follows:

[0107]

[0108] In the formula, is the upper limit of the wind turbine output.

[0109] The constraints on the power output of the photovoltaic power generation are as follows:

[0110]

[0111] In the formula, is the upper limit of the photovoltaic output.

[0112] The constraints on the energy storage are as follows:

[0113] Constraints on the charge and discharge power of the energy storage battery

[0114]

[0115] Constraints on the battery capacity of the energy storage battery

[0116] SOC si (t)=SOC si (t - 1)+μ si P si (t)

[0117]

[0118] Constraints on the ramp rate of the energy storage battery

[0119] P si (t + 1)-P si (t)≤M

[0120] In the formula: is the maximum charge and discharge power of the energy storage battery, and μ si is the charge and discharge coefficient of the energy storage battery. SOC si (t) is the SOC of the energy storage battery at time t. are the upper and lower limits of the SOC of the energy storage battery respectively, and M is the maximum ramp rate.

[0121] SOC, the full name is State of Charge, which represents the state of charge of the battery, also known as the remaining power. It represents the ratio of the remaining dischargeable power of the battery after being used for a period of time or left unused for a long time to the fully charged state power, usually expressed as a percentage. It is represented by one byte, that is, two - digit hexadecimal (the value range is 0 - 100), meaning the remaining power is 0% - 100%. When SOC = 0, it means the battery is fully discharged, and when SOC = 100%, it means the battery is fully charged.

[0122] The constraints on the electric vehicle are as follows:

[0123] Charging and discharging power constraints of electric vehicles at different times

[0124]

[0125] Battery capacity constraints of electric vehicles

[0126] SOC EV，i (t) = SOC EV，i (t - 1)+μ ev P EV，i (t)

[0127]

[0128]

[0129] Aggregate power of electric vehicles

[0130]

[0131] In the formula: is the maximum charging power of the electric vehicle, P EV，i (t) is the charging and discharging power of the electric vehicle at time t, μ ev is the charging coefficient of the electric vehicle, SOC EV，i (t) is the SOC of the electric vehicle at time t, are the upper and lower limits of the SOC of the electric vehicle respectively, C i is the battery capacity of the electric vehicle, is the total battery capacity during the last charging period of the electric vehicle, P EV，sum is the power of the aggregated electric vehicles.

[0132] The flow of the solution algorithm is as Figure 3 shown: Initialize the population, perform genetic operations to generate a temporary population, merge the initial population and the temporary population, sort the individuals in the merged population using the dominance relationship and crowding degree, select the dominant individuals to form the next generation population, and finally end the iteration and output the optimization result when the conditions are met.

[0133] The human-machine interaction layer includes a human-machine interaction interface and a configuration platform. A dedicated monitoring and configuration platform is used for the development of the human-machine interaction interface, which can cover the operations of key devices in the monitoring system and has several data display functions, enabling various data displays. The several data display functions include data display, status display, trend charts, and bar charts, etc. The human-machine interaction layer uses a dedicated monitoring and configuration platform as the development environment, which can well implement the primitive design, graphic interface drawing, graphic animation design, association with real-time data measurement points, and browsing of real-time images in the work process of the human-machine interaction interface, and finally presents in the form of graphs, tables, or curves, etc. The human-machine interaction layer can provide the operation status monitoring results and dispatching instruction operation conditions to users through the human-machine interaction interface for viewing and interaction.

[0134] Embodiment 2

[0135] Figure 4 A method for operating an electric vehicle and active distribution network collaborative interaction system includes:

[0136] Step 1: Various types of original data information (system operation characteristic parameters such as voltage, frequency, power, etc.) of the electric vehicle and active distribution network collaborative interaction system resources in the resource device layer are collected in real time by intelligent sensors, cameras, and tag-based radio frequency technology, and the original data information is transmitted to the real-time database resource sensing and control layer;

[0137] Step 2: The system information layer processes the original data information collected by the resource sensing and control layer (removing outliers and calculating the mean), and uploads and stores the processed information;

[0138] Step 3: Based on the historical data and real-time data in the system information layer, the creation module of the digital twin system uses data-driven technology to establish a digital twin of the electric vehicle and active distribution network system interaction system;

[0139] Step 4: The optimal dispatching module of the energy management system obtains the preliminary dispatching instructions of the system by solving the energy management model;

[0140] Step 5: The digital twin system performs virtual simulation operation according to the preliminary dispatching instructions of the energy management system and transmits the virtual operation results to the energy management system;

[0141] Step 6: The energy management system adjusts the preliminary operation optimization plan based on the information fed back after the digital twin system executes the preliminary operation optimization plan, adjusts the preliminary dispatching instructions to generate the final dispatching instructions, and transmits them to the resource sensing and control layer;

[0142] Step 7: The resource device control unit in the resource sensing and control layer regulates the corresponding energy devices in the resource device layer according to the dispatching instructions issued by the energy management layer;

[0143] Step 8: By subscribing to the services published by the energy management layer, the human-computer interaction layer can obtain various operation information of the collaborative interaction system, and design and reference software through programming and other means to complete the visualization processing of data. In addition, the operator can issue scheduling instructions to the server of the energy management layer based on the observed operation information and his own experience, so as to realize the control of the collaborative interaction system of the physical and virtual electric vehicles and the active distribution network.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A system for collaborative interaction between an electric vehicle and an active distribution network, characterized in that, It includes a resource device layer, a resource sensing and control layer, a system information layer, an energy management layer, and a human-machine interaction layer connected in sequence; The resource device layer includes schedulable electric vehicles and an active distribution network system; The resource sensing and control layer includes a data acquisition and transmission unit and a resource device control unit; The system information layer includes a data processing module and an information storage module; The energy management layer contains an energy management system and a digital twin system; The human-machine interaction layer includes a human-machine interaction interface and a configuration platform; The energy management system is composed of an optimal scheduling module, a power generation prediction module, and a load prediction module; the optimal scheduling module obtains the optimal scheduling instructions of the system by solving the energy management model and transmits them to the digital twin system; the power generation prediction module uses meteorological information and power generation information to train a neural network and realizes the prediction of the power generation of each distributed power source based on the neural network; the load prediction module uses the load information in the historical information database and the real-time information database to train a neural network and realizes the long-term and short-term prediction of the load based on the neural network; The digital twin system includes a creation module and a simulation operation module; the creation module of the digital twin system creates a digital twin of the resource device based on the electric vehicle unit and the active distribution network system, establishes the spatial environment information set corresponding to the resource device at the same time, and displays it using three-dimensional visualization technology; The digital twin of the resource device includes the overall appearance, internal structure, and device operation characteristics of the resource device. The device operation characteristics include: power generation characteristics, power consumption characteristics, and charge and discharge characteristics of the electric vehicle unit; the simulation operation module of the digital twin system controls the digital twin of the resource device to perform simulation operation according to the optimal scheduling instructions transmitted by the energy management system and sends the operation results to the energy management system; The energy management model consists of an objective function, constraint conditions, and a solution algorithm; The objective function includes economy, environmental protection, and new energy consumption; the constraint conditions include the distributed power sources, electric vehicles, and energy conservation constraint conditions involved; the input of the energy management model includes the predicted load power, predicted photovoltaic power generation, predicted wind turbine power generation, and device parameters; the output of the energy management model includes the charge and discharge power of the battery, the charging power of the electric vehicle, and the interaction power with the large power grid; the solution algorithm uses the non-dominated sorting genetic algorithm, sets data such as the maximum number of iterations, population size, crossover rate, and mutation rate, and optimally solves the distribution network to generate a decision-making plan.

2. The system for collaborative interaction between an electric vehicle and an active distribution network according to claim 1, wherein The active distribution network system includes a distribution network, distributed photovoltaics, distributed motors, energy storage devices, power liaison lines, transformers, and user loads; the distributed photovoltaics are connected to the distribution network through transformers and power liaison lines; the distributed motors are connected to the distribution network through transformers and power liaison lines; the energy storage device and the user load are respectively connected to the distribution network through power liaison lines.

3. A system for collaborative interaction between an electric vehicle and an active distribution network according to claim 1, characterized in that, The data acquisition and transmission unit contains intelligent sensors for data acquisition, cameras, electronic tags in the device, wireless modules, intelligent gateways, and optical fibers, collects the original data information of the resource device layer, and transmits the original data information to the system information layer; The resource device control unit includes a device controller for executing the scheduling scheme issued by the energy management layer and regulating the operating states of the devices in the resource device layer.

4. The system for collaborative interaction between an electric vehicle and an active distribution network according to claim 3, characterized in that, The data processing module is responsible for processing and transmitting the raw data information collected by the resource sensing layer to the energy management layer; the information storage module stores the raw data information by constructing multiple databases and transmits the raw data information to the energy management layer; The multiple databases include a real-time information database, a historical information database, a fault information database, and a digital twin system operation information database.

5. The system for collaborative interaction between an electric vehicle and an active distribution network according to claim 1, wherein The human-machine interaction layer uses a monitoring configuration platform to develop a human-machine interaction interface, which has several data display functions; the several data display functions include data display, status display, trend chart, and bar chart; the human-machine interaction layer takes the monitoring configuration platform as the development environment to realize the primitive design, graphic interface drawing, graphic animation design, associated real-time data measurement points, and browsing of real-time pictures in the human-machine interaction interface, and finally presents them in the form of graphs, tables, or curves; the human-machine interaction layer can provide the operation state monitoring results and the operation conditions of the scheduling instructions to the user through the human-machine interaction interface for viewing and interaction.

6. The system for collaborative interaction between an electric vehicle and an active distribution network according to claim 1, characterized in that, The economic objective function in the objective function is as follows: Wherein, is the operation cost of the distribution network, is the cost of interacting with the power grid, is the dispatching cost of electric vehicles, is the operation and maintenance cost of photovoltaic, is the operation and maintenance cost of wind turbines, is the operation and maintenance cost of energy storage, , , , are the weight coefficients respectively; is the selling power of the distribution network at time t, is the purchasing power of the distribution network at time t; is the selling price at time t, is the purchasing price at time t; is the charging power of the electric vehicle cluster before dispatching, is the charging power of the electric vehicle cluster after dispatching, is the dispatching subsidy coefficient at time t; is the output of photovoltaic at time t, is the power generation cost coefficient of photovoltaic; is the output of wind turbines at time t, is the power generation cost coefficient of wind turbines; is the charge and discharge power of the energy storage battery at time t, is the output cost coefficient of energy storage; The environmental protection objective function in the objective function is as follows: In the formula, is the pollution treatment cost, is the pollution treatment physical cost coefficient; The new energy consumption objective function in the objective function is as follows: In the formula, is the new energy consumption index, is the upper limit of the predicted PV output, is the upper limit of the predicted wind turbine output, is the PV actual output during the period, is the wind turbine actual output during the period.

7. The system for collaborative interaction between an electric vehicle and an active distribution network according to claim 1, characterized in that, The power balance constraint condition in the constraint conditions is as follows: In the formula, is the user load; The wind power generation output constraint is as follows: In the formula, is the upper limit of the fan output; The photovoltaic power generation output constraint is as follows: Wherein, is the upper limit of the output of the photovoltaic; The energy storage constraint is as follows: The charge and discharge power constraint of the energy storage battery The state of charge constraint of the energy storage battery The ramp rate constraint of the energy storage battery Wherein: is the maximum charge-discharge power of the energy storage battery, is the charge-discharge coefficient of the energy storage battery, is the SOC of the energy storage battery at time t, and are the upper and lower limits of the SOC of the energy storage battery respectively, is the maximum ramp ratio; The electric vehicle constraint is as follows: The charge and discharge power constraint of the electric vehicle at each time period The state of charge constraint of the electric vehicle The aggregated power of the electric vehicle is the maximum charging power of the electric vehicle, is the charging and discharging power of the electric vehicle at time t, is the charging coefficient of the electric vehicle, is the SOC of the electric vehicle at time t, 、 are the upper and lower limits of the SOC of the electric vehicle respectively, is the battery level of the electric vehicle, is the total battery level during the last charging period of the electric vehicle, is the power of the aggregated electric vehicles.

8. A system for collaborative interaction between an electric vehicle and an active distribution network according to claim 1, characterized in that, The process of the solution algorithm is as follows: initialize the population, execute genetic operations to generate a temporary population, merge the initial population and the temporary population, sort the individuals in the merged population using the dominance relationship and crowding degree, select the dominant individuals to form the next generation population, and finally end the iteration and output the optimization result when the conditions are met.

9. An operation method for a collaborative interaction system between an electric vehicle and an active distribution network, based on the system for collaborative interaction between an electric vehicle and an active distribution network according to any one of claims 1-8, characterized in that, It includes: Step 1: Real-time collect various raw data information of the electric vehicle and the active distribution network collaborative interaction system resources in the resource device layer from intelligent sensors, cameras, and tag-based radio frequency technology, including voltage, frequency, and power, and transmit the raw data information to the resource sensing layer of the real-time database; Step 2: The system information layer processes the raw data information collected by the resource sensing layer, including outlier removal and mean calculation, and uploads and stores the processed information; Step 3: Based on the historical data and real-time data in the system information layer, the creation module of the digital twin system uses data-driven technology to establish a digital twin of the electric vehicle and the active distribution network system interaction system; Step 4: The optimal scheduling module of the energy management system obtains the preliminary scheduling instructions of the system by solving the energy management model; Step 5: The digital twin system performs virtual simulation operation according to the preliminary scheduling instructions of the energy management system and transmits the virtual operation results to the energy management system; Step 6: The energy management system adjusts the preliminary operation optimization plan based on the information fed back after executing the preliminary operation optimization plan by the digital twin system, generates the final scheduling instruction by adjusting the preliminary scheduling instruction, and transmits it to the resource sensing layer; Step 7: The resource device control unit in the resource sensing layer controls the corresponding energy devices in the resource device layer according to the scheduling instruction issued by the energy management layer.

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