A power distribution system-oriented electric vehicle and controllable air conditioner cooperative operation method

Abstract

The application provides a power distribution system-oriented electric vehicle and controllable air conditioner cooperative operation method. The method first constructs a refined space-time model based on power grid topology and geographic information, in combination with real-time load, electric vehicle movement state and charging demand, air conditioner parameters and environmental temperature and other multi-source data; then, an optimization model is established with the minimum comprehensive operation as the target, and the optimization model contains power balance, voltage safety, line capacity, electric vehicle charging completion degree and air conditioner temperature control comfort degree and other constraints; finally, a coordinated scheduling scheme of electric vehicle charging and air conditioner load is obtained through high-efficiency algorithm solving. Compared with the traditional method, the application realizes more actual safe, economic and comfortable cooperative optimization by fusing the power grid structure, electric vehicle mobility and air conditioner thermal dynamic characteristics.

Description

Technical Field

[0001] This application belongs to the field of electric vehicle and air conditioning scheduling, specifically involving a method for the coordinated operation of electric vehicles and controllable air conditioning in a power distribution system. Background Technology

[0002] With the increasing penetration of flexible loads, such as electric vehicles (EVs) and air conditioners, in distribution networks, their large-scale and distributed access presents new challenges to the safe, economical, and efficient operation of traditional distribution networks. On the one hand, EV charging loads exhibit significant spatiotemporal uncertainty and mobility; disordered charging can easily lead to problems such as local grid load peak superposition, line overload, and voltage exceeding limits. On the other hand, air conditioning loads are significantly affected by outdoor weather conditions and user temperature control preferences; their clustered start-up and shutdown behavior may further exacerbate the peak-valley difference in the power grid, affecting the stability of system operation. Against this backdrop, how to achieve spatiotemporal coordinated optimization of distributed load resources through effective scheduling methods, while meeting users' electricity demand and comfort, has become crucial for improving the absorption capacity and operational efficiency of distribution networks.

[0003] Current research on independent scheduling of electric vehicles or air conditioners has made some progress, but most methods still focus on optimizing single-type loads or single time scales, lacking systematic modeling of the coordinated interaction of multiple heterogeneous loads in the spatiotemporal dimension. Furthermore, existing research often simplifies the power grid topology to a single node or ignores its actual geographical structure and electrical constraints, making it difficult to accurately reflect the dynamic impact of load spatiotemporal distribution on network power flow and voltage quality. Therefore, there is an urgent need for a spatiotemporal scheduling modeling method that can deeply integrate the physical characteristics of the distribution network, finely describe the operating characteristics of electric vehicles and air conditioners, and realize their coordinated interaction, in order to fully tap the adjustment potential of flexible resources on the load side and promote the development of the distribution network towards a safer, more economical, and smarter direction. Summary of the Invention

[0004] Firstly, in view of the shortcomings of the existing technology, the purpose of this application is to provide a method for the coordinated operation of electric vehicles and controllable air conditioners in a power distribution system, which can realize a more economical and practical electric vehicle scheduling and air conditioner power scheduling scheme.

[0005] This can be achieved through the following approach: A method for coordinated operation of electric vehicles and controllable air conditioning in a power distribution system includes: Obtain the real-time load of network nodes within the distribution network area; The system receives charging-related information and air conditioning information from electric vehicles to be charged. The charging-related information includes the group of electric vehicles that need to be charged, the time when each vehicle arrives at the charging station, the time when the electric vehicle leaves, the initial state of charge of the electric vehicle, and the maximum capacity of the vehicle battery. The air conditioning information includes the outdoor temperature, the power parameters of each air conditioner, the internal heat load, the indoor thermal resistance, the equivalent heat capacity of the room, and the installation location of the air conditioner. The real-time load of the network node, the charging-related information of the electric vehicle, and the time-related variables in the air conditioning information are discretized, and the discretized data is used as the input data of the optimization model. Based on the input data of the optimization model, the charging power of the electric vehicle charging station and the operating power of the air conditioner are used as decision variables. An optimization model is constructed with the goal of minimizing the overall system operating cost. The optimization model is solved to obtain the spatiotemporal allocation scheme of electric vehicle charging power and the power adjustment strategy of air conditioner load. The overall system operating cost includes the grid operation loss cost and the electric vehicle time and temperature compensation cost paid to guide orderly charging. The constraints of the optimization model include upper and lower limits of node voltage amplitude, distribution network transmission capacity constraints, real-time load size of the network nodes, node active power balance constraints composed of electric vehicle charging power and air conditioning load, electric vehicle location constraints defined by binary variables, electric vehicle spatial scheduling distance constraints, expected state of charge constraints of electric vehicles when leaving the charging station based on the initial state of charge of electric vehicles and the maximum capacity of the vehicle battery, and state of charge constraints of electric vehicles at any time, upper and lower limits of electric vehicle state of charge, dynamic changes in indoor temperature based on internal heat load, indoor thermal resistance and equivalent heat capacity of the room, upper and lower limits of indoor temperature, upper and lower limits of air conditioning load power, and upper limit of average indoor temperature.

[0006] Furthermore, the objective function for the overall operating cost of the system is shown in formula (1.1): The power grid operating loss cost The results are calculated based on formulas (1.2) and (1.3): In the formula, The unit price of electricity. for Time to the side road The corresponding network loss, It is the collection of all branches of the distribution network. This represents the number of time periods contained in a day after the time parameter is discretized. For time step, branch road The resistance, for Time to the side road active power, for Time to the side road reactive power, This is the rated voltage of the power distribution network.

[0007] Furthermore, in the node active power balance constraint, the expression for the active load Pj(t) of node j at time t is shown in formula (1.8): In the formula, for node The baseline load at any given time corresponds to the real-time load of the network node. for node Electric vehicle charging power at any given time for node The air conditioning load at any given time This is a set of nodes for installing electric vehicle charging stations. This is the set of nodes for which air conditioning loads are installed.

[0008] Furthermore, the electric vehicle location constraints are as shown in formula (1.24): In the formula, For electric vehicles, This is the number of the charging station. For the number of charging stations, This is a binary variable representing that each electric vehicle can only choose one charging station for charging.

[0009] The spatial scheduling distance constraint for electric vehicles is shown in formula (1.25): In the formula, For the first The distance from the vehicle's starting point to the charging station. The maximum acceptable scheduling distance.

[0010] Furthermore, the method also includes: receiving destination location information of each electric vehicle, and determining the charging station closest to the destination of the corresponding electric vehicle based on the destination location information of each electric vehicle; The desired state of charge constraint of the electric vehicle when leaving the charging station is shown in formula (1.26): In the formula, For the first The state of charge of an electric vehicle when it leaves a charging station. This represents the initial state of charge of an electric vehicle. For distance from the first The charging power of the nearest charging station to the destination for an electric vehicle. For the arrival time, The moment the electric vehicle leaves. This refers to the maximum capacity of the vehicle's battery. This represents the upper limit of the state of charge of an electric vehicle.

[0011] Furthermore, the state of charge constraint of the electric vehicle at any given time is shown in equation (1.27): In the formula, Indicates the first electric vehicles at all times The state of charge of the battery. For the first Electric vehicles at charging station The charging power, To characterize the first electric vehicles at all times A binary variable indicating whether the device is in a charging state.

[0012] Furthermore, the constraint on the dynamic change of indoor temperature satisfies the discrete difference equation shown in formula (1.30): In the formula, Indoor temperature, Outdoor temperature The cooling capacity provided for the air conditioner For internal heat load, The equivalent heat capacity of the room. This is the equivalent thermal resistance of the room.

[0013] Furthermore, the constraints also include an upper limit constraint on the average indoor temperature and a constraint on the number of charging piles; The upper limit constraint on the average indoor temperature is shown in formula (1.33): In the formula, Describes the upper limit of the average indoor temperature; The constraint on the number of charging piles is shown in formula (1.34): In the formula, the middle (t) is the th m One charging station t The number of electric vehicles connected at any given time. For the first m The number of charging piles at each charging station.

[0014] Secondly, in view of the shortcomings of the existing technology, the purpose of this application is to provide a device for the coordinated operation of electric vehicles and controllable air conditioning in a power distribution system, which can realize a more economical and practical electric vehicle scheduling and air conditioning power scheduling scheme.

[0015] This can be achieved through the following approach: A device for coordinated operation of electric vehicles and controllable air conditioning in a power distribution system includes: The data interaction module is used to obtain the real-time load size of network nodes in the distribution network area, and receive charging-related information and air conditioning information of electric vehicles to be charged. The charging-related information of electric vehicles includes the group of electric vehicles that need to be charged, the time when each vehicle arrives at the charging station, the time when the electric vehicle leaves, the initial state of charge of the electric vehicle, and the maximum capacity of the vehicle battery. The air conditioning information includes the outdoor temperature, the power parameters of each air conditioner, the internal heat load, the indoor thermal resistance, the equivalent heat capacity of the room, and the installation location of the air conditioner. The model building module is used to discretize the real-time load of the network nodes, the charging-related information of the electric vehicles, and the time-related variables in the air conditioning information, and use the discretized data as the input data of the optimization model; it is also used to construct an optimization model based on the input data of the optimization model, with the charging power of the electric vehicle charging station and the operating power of the air conditioner as decision variables, with the goal of minimizing the overall system operating cost, and to set the constraints of the optimization model; The solution control module is used to solve the optimization model to obtain the spatiotemporal allocation scheme of electric vehicle charging power and the power adjustment strategy of air conditioning load; The overall operating cost of the system includes the grid operation loss cost and the electric vehicle time-sharing schedule compensation cost paid to guide orderly charging; The constraints of the optimization model include upper and lower limits of node voltage amplitude, distribution network transmission capacity constraints, real-time load size of the network nodes, node active power balance constraints composed of electric vehicle charging power and air conditioning load, electric vehicle location constraints defined by binary variables, electric vehicle spatial scheduling distance constraints, expected state of charge constraints of electric vehicles when leaving the charging station based on the initial state of charge of electric vehicles and the maximum capacity of the vehicle battery, and state of charge constraints of electric vehicles at any time, upper and lower limits of electric vehicle state of charge, dynamic changes in indoor temperature based on internal heat load, indoor thermal resistance and equivalent heat capacity of the room, upper and lower limits of indoor temperature, upper and lower limits of air conditioning load power, and upper limit of average indoor temperature.

[0016] Thirdly, in view of the shortcomings of the prior art, the purpose of this application is to provide a computer-readable storage medium that can realize a more economical and practical electric vehicle scheduling and air conditioning power scheduling scheme.

[0017] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as described in the first aspect.

[0018] The beneficial effects of this application are: This application integrates the distribution network topology, the spatiotemporal flexibility of electric vehicles, and the adjustability of air conditioning loads. The established model is closer to the actual operating scenario, thereby enabling more economical and efficient scheduling and control while ensuring the safety of power grid operation and the comfort of users' energy consumption. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0021] Figure 2 This is a diagram of a 33-node power distribution system.

[0022] Figure 3 This is the optimized charging timing diagram for electric vehicles.

[0023] Figure 4 This is an optimized graph showing the number of electric vehicles charging at each charging station at each time.

[0024] Figure 5 This is the optimized load power diagram for each air conditioner at various times. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] The following description, in conjunction with the accompanying drawings and embodiments, illustrates a method, apparatus, and computer-readable storage medium for the coordinated operation of an electric vehicle and a controllable air conditioner in a power distribution system, as provided in this application. It should be understood that the following embodiments are used to explain the technical solutions of this application and are not intended to limit the scope of protection of this application; where there is no conflict, the technical features in the following embodiments can be combined with each other.

[0027] Figure 1 This illustration shows a flowchart of a method for the coordinated operation of electric vehicles and controllable air conditioning systems in a power distribution system, according to an embodiment of this application. The execution entity of the method can be a device for the coordinated operation of electric vehicles and controllable air conditioning systems in a power distribution system. The device includes at least a data interaction module, a model building module, and a solution control module. The data interaction module is used to acquire or receive data required by the model; the model building module is used to perform time discretization processing, optimize model construction, and set constraints; and the solution control module is used to solve the optimized model and output the scheduling results.

[0028] In this embodiment, the data interaction module acquires the real-time load of network nodes within the power distribution network area; and receives charging-related information and air conditioning information from electric vehicles to be charged. The charging-related information includes the group of electric vehicles needing charging, the arrival time of each vehicle at the charging station, the departure time of each vehicle, the initial state of charge of the electric vehicles, and the maximum capacity of the vehicle batteries; the air conditioning information includes the outdoor temperature, the power parameters of each air conditioner, the internal heat load, the indoor thermal resistance, the equivalent heat capacity of the room, and the installation location of the air conditioner.

[0029] It should be noted that the real-time load of the network node can be directly obtained data or data obtained based on a dataset that characterizes the operating status of the distribution network area; this application does not impose additional limitations on the specific data source. The above statement is only used to ensure that the real-time load of the network node can be used as input data for the optimization model, and does not limit this application to a specific acquisition system or a specific prediction model; for example, in some cases, it may be obtained through the following methods: (1) Using the balancing node as the root node, the nodes in the network, electric vehicle charging stations, and air conditioning loads are assigned numbers according to their order of occurrence. The numbering process is as follows: Figure 3 As shown, the complete set of nodes of the distribution network is obtained from this.

[0030] (2) By fitting historical data, node load characteristic curves, electric vehicle charging behavior characteristic curves, and air conditioning load operating characteristic curves are obtained. Based on these curves, the input data required for the daily optimization model is generated. The input data includes the real-time load size of network nodes, charging-related information of electric vehicles to be charged, and operating information of the air conditioning load.

[0031] For the real-time load of network nodes, taking node 7 as an example, its active load at 00:00 is 128kW. Subsequently, the active load of node 7 is recorded at one sampling point per minute; for example, the active load at 00:01 is 127.86kW, the active load at 00:02 is 127.72kW, and so on until 23:59, thus obtaining a total of 1440 active load data points for node 7 throughout the day. This allows us to obtain the real-time load of the corresponding network node at each sampling time within a day, which can be used as input data for subsequent discretization processing and optimization model construction.

[0032] Regarding charging-related information for electric vehicles waiting to be charged, taking an example of 200 electric vehicles needing charging in the area, for ease of explanation, it is assumed that the maximum battery capacity of all electric vehicles is 100kWh. Taking electric vehicle number 1 as an example, the time it arrives at the assigned charging station is 8:17, the time it leaves is 13:53, and the initial state of charge is 0.43. Thus, the group of electric vehicles needing charging that day, the arrival time of each vehicle at the charging station, the departure time of each vehicle, the initial state of charge of each vehicle, and the maximum battery capacity of each vehicle can be obtained as input data for the subsequent spatiotemporal allocation of electric vehicle charging power. If, in a specific implementation, it is necessary to determine the charging station closest to the destination of a given electric vehicle, then the destination location information of that electric vehicle is further received; for example, the destination of electric vehicle number 1 is node 4, and the charging station closest to the destination of electric vehicle number 1 is determined based on this destination location information.

[0033] For the operating information of air conditioning load, taking air conditioning load 1 as an example, a large number of independent random samples are taken from the daily start-up time of air conditioning load 1 to obtain a daily start-up time sample set of air conditioning load 1. Then, statistical analysis is performed on the daily start-up time sample set to obtain the probability distribution of the start-up of air conditioning load 1 at different times of the day, the expected start-up time, and its uncertainty range. Subsequently, the start-up time of air conditioning load 1 on the day is randomly predicted based on the probability distribution. Thus, the operating information of the air conditioning load on the day can be obtained as input data for subsequent air conditioning load power adjustment strategies.

[0034] Through the above processing, the method obtains the real-time load size of network nodes, charging-related information of electric vehicles to be charged, and operating information of air conditioning load, respectively. The real-time load size of network nodes is used to construct node active power balance constraints. The charging-related information of electric vehicles to be charged is used to construct electric vehicle location constraints, spatial scheduling distance constraints, state of charge constraints, and expected state of charge constraints when leaving the charging station. The operating information of the air conditioning load is used to determine the time basis for air conditioning load participation in regulation, and together with outdoor temperature, power parameters of each air conditioner, internal heat load, indoor thermal resistance, equivalent heat capacity of the room, and air conditioner installation location, it is used to construct indoor temperature dynamic change constraints and air conditioning load power constraints.

[0035] (3) The model building module discretizes the real-time load of the network nodes, the charging-related information of the electric vehicles, and the time-related variables in the air conditioning information, and uses the discretized data as input data for the optimization model. Through this processing, time-series related information such as network node load, the time when the electric vehicle arrives at the charging station, the time when the electric vehicle leaves, the initial state of charge of the electric vehicle, and the outdoor temperature can all be mapped to discrete time periods, thus providing a unified time scale for the subsequent constraints and objective function represented by time t.

[0036] The real-time load of the network nodes, the charging-related information of the electric vehicles, and the time-related variables in the air conditioning information are discretized. In one specific implementation, a 24-hour day is divided into 96 time periods, each lasting 15 minutes. For the time information of the electric vehicles, the arrival time at the charging station is normalized to the corresponding time period, and the departure time of the electric vehicles is normalized to the corresponding time period. For the outdoor temperature and the real-time load of the network nodes in the air conditioning information, values ​​are taken or matched according to the corresponding time periods. This discretized data is used as the input data for the optimization model. For example, taking electric vehicle No. 1 as an example, its actual arrival time at the charging station is 14:53, which belongs to the time period of 14:45–15:00. Therefore, its arrival time is uniformly recorded as the end time of the time period, 15:00. Similarly, its departure time is also normalized according to the time period, and the start time of the corresponding time period is taken as the departure time. The time processing method for the air conditioning load is the same.

[0037] (4) The model building module constructs an optimization model based on the input data of the optimization model, using the charging power of the electric vehicle charging station and the operating power of the air conditioner as decision variables, with the goal of minimizing the overall system operating cost. The solution control module solves the optimization model to obtain the spatiotemporal allocation scheme of the electric vehicle charging power and the power adjustment strategy of the air conditioner load.

[0038] The overall system operating cost includes grid operation loss costs and electric vehicle temperature compensation costs paid to guide orderly charging. Therefore, the optimization model does not adjust electric vehicle charging or air conditioning load in isolation, but rather coordinates the scheduling of electric vehicle charging power and air conditioning operating power under the combined constraints of distribution network operation, electric vehicle operation, and air conditioning temperature control.

[0039] In this embodiment, the objective function for the overall operating cost of the system is shown in formula (1.1): Formula (1.1) is used to characterize the optimization scheduling objective of minimizing the overall system operating cost; the overall operating cost includes at least the grid operation loss cost and the electric vehicle time-sharing scheduling compensation cost paid to guide orderly charging.

[0040] The power grid operation loss cost is calculated based on formula (1.2): In the formula, The unit price of electricity. Let t be the branch at time t ijThe corresponding network loss is E, which is the set of all branches of the distribution network, NT, which is the number of time periods in a day after the time parameter is discretized, and Δt is the time step.

[0041] The branch at time t ij The corresponding network loss is calculated based on formula (1.3): In the formula, branch road ij The resistance, for t Time to the side road ij active power, for t Time to the side road ij reactive power, This is the rated voltage of the power distribution network.

[0042] In this embodiment, the constraints of the optimization model include upper and lower limits of node voltage amplitude, distribution network transmission capacity constraints, real-time load size of the network nodes, node active power balance constraints composed of electric vehicle charging power and air conditioning load, electric vehicle location constraints defined by binary variables, electric vehicle spatial scheduling distance constraints, expected state of charge constraints of electric vehicles when leaving the charging station based on the initial state of charge of electric vehicles and the maximum capacity of the vehicle battery, and state of charge constraints of electric vehicles at any time, upper and lower limits of electric vehicle state of charge, dynamic changes in indoor temperature based on internal heat load, indoor thermal resistance and equivalent heat capacity of the room, upper and lower limits of indoor temperature, upper and lower limits of air conditioning load power, and upper limit of average indoor temperature.

[0043] For distribution network operation constraints, the line voltage drop constraint is as follows: The upper and lower limits of node voltage amplitude are constrained as follows: in, and These are the lower and upper limits of the voltage amplitude for the distribution network, respectively.

[0044] Distribution network transmission capacity constraints: in This represents the maximum transmission capacity of the distribution network branch.

[0045] The relevant constraint formulas are shown below, ignoring line losses: in, for tTime to the side road jk Transmitted active power, for t Time to the side road ij Transmitted active power, For nodes j exist t Active load at the current time, of which u(j) This represents the set of all nodes connected to node j and located downstream of node j.

[0046] In the node active power balance constraint, the active power load of node j at time t is... The expression is shown in formula (1.8): In the formula, The base load at node j at time t. Let the charging power of the electric vehicle at node j at time t be [the charging power of the electric vehicle at node j]. Let be the air conditioning load at node j at time t. This is a set of nodes for installing electric vehicle charging stations. This is the set of nodes for which air conditioning loads are installed.

[0047] The simplified formula for reactive power transmission constraints in distribution networks is as follows: in, for t Time to the side road jk Transmitted reactive power for t Time to the side road ij Transmitted reactive power For nodes j exist t Reactive load at any given time; among which u(j) This represents the set of all nodes connected to node j and located downstream of node j.

[0048] The simplified formula for voltage drop constraint in distribution networks is as follows: in, for t At any given moment i voltage amplitude, for t At any given moment j voltage amplitude, branch road ij The resistance, branch road ij Reactance, This is the rated voltage of the power distribution network.

[0049] For the operational constraints of electric vehicles, the electric vehicle location constraints are as shown in formula (1.24): In the formula, n is the electric vehicle's ID, m is the charging station's ID, and M is the number of charging stations, a binary variable representing that each electric vehicle can only choose one charging station. Using this binary variable, the optimization model can determine the charging station selection result for each electric vehicle among multiple charging stations.

[0050] The spatial scheduling distance constraint for electric vehicles is shown in formula (1.25): In the formula, Let be the distance from the starting point of the nth car to the charging station. The maximum acceptable dispatch distance means that the dispatch distance cannot exceed the user's intention. This constraint is used to prevent electric vehicles from being dispatched to charging stations that are outside the user's acceptable range.

[0051] In an implementation involving the desired state of charge constraint when an electric vehicle leaves a charging station, the data interaction module also receives the destination location information of each electric vehicle and determines the charging station closest to the destination of the corresponding electric vehicle based on the destination location information of each electric vehicle. By using the destination location information in this implementation, it can be avoided from becoming an isolated input feature in the main process.

[0052] The desired state of charge constraint of the electric vehicle when leaving the charging station is shown in formula (1.26): In the formula, For the first n The state of charge of an electric vehicle when it leaves a charging station. This represents the initial state of charge of an electric vehicle. For distance from the first n The charging power of the nearest charging station to the destination for an electric vehicle. For the arrival time of the electric vehicle, The moment the electric vehicle leaves. For the battery capacity of electric vehicles, This represents the upper limit of the state of charge of an electric vehicle.

[0053] The state of charge constraint of the electric vehicle at any given time is shown in formula (1.27): In the formula, (t) represents the state of charge of the battery of the nth electric vehicle at time t; Let the charging power of the nth electric vehicle at charging station m be denoted by ; in addition, a binary variable is introduced. This is used to characterize whether the nth electric vehicle is charging at time t.

[0054] The upper and lower limits of the electric vehicle's state of charge are constrained as follows: in, This is the limit of the state of charge for electric vehicles. This represents the upper limit of the state of charge (SOC) of an electric vehicle. By constraining the location of the electric vehicle, the spatial scheduling distance of the electric vehicle, the expected SOC of the electric vehicle when leaving the charging station, the SOC of the electric vehicle at any given moment, and the upper and lower limits of the SOC, the feasibility of the electric vehicle in terms of spatial selection, charging time period, and changes in SOC can be constrained.

[0055] Regarding the constraints related to air conditioning load and indoor temperature, the constraint on dynamic changes in indoor temperature is established based on the stability formula of the thermal equation, which is as follows: In the formula, Indoor temperature, Outdoor temperature The cooling capacity provided for the air conditioner Internal heat load (measurable interference). The equivalent heat capacity of the room. This is the equivalent thermal resistance of the room.

[0056] Since continuous-time models cannot be directly solved in a digital optimizer, they are transformed into discrete-time difference equations. In one specific implementation, a 24-hour day is divided into 96 time periods, each lasting 15 minutes. After processing, the dynamic change constraint of indoor temperature satisfies the discrete difference equation shown in formula (1.30): In the formula, Indoor temperature, Outdoor temperature The cooling capacity provided for the air conditioner Let C be the internal heat load, C be the equivalent heat capacity of the room, and R be the equivalent thermal resistance of the room. This discrete difference equation allows the influence of air conditioning operating power on indoor temperature to be calculated by the optimization model within a discrete time period.

[0057] The upper and lower limits of indoor temperature are constrained as follows: In the formula, and These represent the upper and lower limits of indoor temperature, respectively.

[0058] The air conditioning load power has upper and lower bound constraints as follows: In the formula, This indicates the maximum load power of the air conditioning unit.

[0059] The upper limit constraint on the average indoor temperature is shown in formula (1.33): In the formula, This describes the upper limit of the average indoor temperature.

[0060] The constraints also include a constraint on the number of charging piles, as shown in formula (1.34): In the formula, (t) is the th m One charging station t The number of electric vehicles connected at any given time. For the first m The number of charging piles at each charging station. This constraint limits the number of electric vehicles charging at any given time to no more than the number of charging piles at the corresponding charging station.

[0061] In one specific implementation, the model building module transforms constraints such as power balance, node voltage safety, line capacity limitations, electric vehicle charging demand, and air conditioning temperature control comfort into corresponding mathematical expressions, and constructs an optimization problem with the goal of minimizing the overall system operating cost. Subsequently, the solution control module calls Gurobi 12.0.0 to solve the optimization problem, obtaining a spatiotemporal allocation scheme for electric vehicle charging power and a power regulation strategy for air conditioning load that satisfy the constraints.

[0062] In one specific embodiment, the unit electricity price =0.07$ / kWh; Distribution network line parameters can be taken from the standard 33-node example, and the rated voltage of the distribution network. =12.66kV, =0.9, =1.1, =0.1, =0.9; the charging and discharging power of the electric vehicle charging station can be 10kW, and the maximum acceptable dispatch distance is... The range can be 1km, and the number of charging piles in the electric vehicle charging station can be [35, 40, 37, 41]. The above values ​​are used to illustrate the model solution process of this embodiment and are not intended to limit the application of this application under other power distribution network scales, other numbers of charging stations, or other air conditioning conditions.

[0063] Combination Figure 3 As shown, Figure 3 The optimized electric vehicle charging timing results are shown. Figure 3 Using time-discretized time periods as the horizontal time series basis, and the charging status of each electric vehicle as the display object, this method reflects the charging schedule of each electric vehicle within a day after the optimization model is solved. Figure 3 It can be seen that the solution control module does not cause all electric vehicles to be charged immediately upon arrival. Instead, it allocates the charging time of each electric vehicle based on constraints such as the time when each vehicle arrives at the charging station, the time when the electric vehicle leaves, the initial state of charge of the electric vehicle, and the maximum capacity of the vehicle battery, so that the charging load of electric vehicles is distributed in a time dimension.

[0064] Combination Figure 4 As shown, Figure 4 The optimized charging stations show the number of electric vehicles charging at each time point. Figure 4 The number of charging vehicles corresponding to different charging stations changes over time, reflecting the spatial scheduling results of the spatiotemporal allocation scheme for electric vehicle charging power. Figure 4 It can be seen that the optimization model, when determining the charging station to which each electric vehicle is connected and the corresponding charging time period, simultaneously considers the spatial scheduling distance constraints of electric vehicles, the number of charging piles, and the operation constraints of the power distribution network, so that the number of electric vehicles connected to each charging station in each time period is limited by the number of charging piles.

[0065] Combination Figure 5 As shown, Figure 5 The optimized load power of each air conditioner at various times is shown. Figure 5 The power consumption of each air conditioning load changes over time, reflecting the power adjustment strategy for that air conditioning load. Figure 5 It can be seen that when determining the operating power of the air conditioner, the optimization model does not simply maintain a constant power operation. Instead, it adjusts the operating power of each air conditioner at different times under the premise of satisfying the constraints of dynamic changes in indoor temperature, upper and lower limits of indoor temperature, upper and lower limits of air conditioner load power, and upper limit of average indoor temperature.

[0066] Depend on Figures 3 to 5The solution results show that the method described in this embodiment achieves a synergistic relationship between electric vehicle charging arrangements, the number of vehicles connected to charging stations, and air conditioning load power adjustment by simultaneously optimizing the electric vehicle charging power and air conditioning operating power. Specifically, Figure 3 Reflecting the charging timing distribution of electric vehicles over time, Figure 4 It reflects the spatial distribution of electric vehicles' access to different charging stations. Figure 5 It reflects the power adjustment results of the air conditioning load in different time periods.

[0067] It should be noted that, Figures 3 to 5 This is not intended to limit the number of electric vehicles, charging stations, air conditioners, or specific load levels in this application, but rather to exemplify how the optimization model, given the real-time load levels of network nodes within a given distribution network area, charging-related information of the electric vehicles to be charged, and air conditioning information, can output a spatiotemporal allocation scheme for electric vehicle charging power and a power adjustment strategy for air conditioning load. In practical applications, Figures 3 to 5 The results shown will vary depending on the real-time load of the network nodes, the charging information of the electric vehicles to be charged, the air conditioning information, and the constraints of the power distribution network.

[0068] Furthermore, this embodiment also provides a device for the coordinated operation of electric vehicles and controllable air conditioning in a power distribution system. The device includes a data interaction module, a model building module, and a solution control module. The data interaction module is used to acquire the real-time load of network nodes within the power distribution network area and to receive charging-related information and air conditioning information from the electric vehicle to be charged.

[0069] The model building module is used to discretize the real-time load of the network nodes, the charging-related information of the electric vehicles, and the time-related variables in the air conditioning information, and uses the discretized data as the input data of the optimization model; it is also used to construct an optimization model based on the input data of the optimization model, with the charging power of the electric vehicle charging station and the operating power of the air conditioner as decision variables, with the goal of minimizing the overall system operating cost, and to set the constraints of the optimization model.

[0070] The solution control module is used to solve the optimization model to obtain the spatiotemporal allocation scheme of electric vehicle charging power and the power adjustment strategy of air conditioning load. The overall system operating cost includes the grid operation loss cost and the electric vehicle spatiotemporal scheduling compensation cost paid to guide orderly charging; the constraints of the optimization model are the same as those in the above method embodiments, and will not be repeated here.

[0071] Furthermore, this embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the method for coordinated operation of an electric vehicle and a controllable air conditioner for a power distribution system as described in any of the above method embodiments. The computer-readable storage medium is used to provide specification support for the computer-readable storage medium in the claims and is not limited to a specific memory type.

[0072] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0073] The foregoing has shown and described the basic principles, main features, and advantages of this application. Those skilled in the art should understand that this application is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this application. Various changes and modifications can be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of the claims of this application.

Claims

1. A method for coordinated operation of electric vehicles and controllable air conditioning in a power distribution system, characterized in that, include: Obtain the real-time load of network nodes within the distribution network area; The system receives charging-related information and air conditioning information from electric vehicles to be charged. The charging-related information includes the group of electric vehicles that need to be charged, the time when each vehicle arrives at the charging station, the time when the electric vehicle leaves, the initial state of charge of the electric vehicle, and the maximum capacity of the vehicle battery. The air conditioning information includes the outdoor temperature, the power parameters of each air conditioner, the internal heat load, the indoor thermal resistance, the equivalent heat capacity of the room, and the installation location of the air conditioner. The real-time load of the network node, the charging-related information of the electric vehicle, and the time-related variables in the air conditioning information are discretized, and the discretized data is used as the input data of the optimization model. Based on the input data of the optimization model, the charging power of the electric vehicle charging station and the operating power of the air conditioner are used as decision variables. An optimization model is constructed with the goal of minimizing the overall system operating cost. The optimization model is solved to obtain the spatiotemporal allocation scheme of electric vehicle charging power and the power adjustment strategy of air conditioner load. The overall system operating cost includes the grid operation loss cost and the electric vehicle time and temperature compensation cost paid to guide orderly charging. The constraints of the optimization model include upper and lower limits of node voltage amplitude, distribution network transmission capacity constraints, real-time load size of the network nodes, node active power balance constraints composed of electric vehicle charging power and air conditioning load, electric vehicle location constraints defined by binary variables, electric vehicle spatial scheduling distance constraints, expected state of charge constraints of electric vehicles when leaving the charging station based on the initial state of charge of electric vehicles and the maximum capacity of the vehicle battery, and state of charge constraints of electric vehicles at any time, upper and lower limits of electric vehicle state of charge, dynamic changes in indoor temperature based on internal heat load, indoor thermal resistance and equivalent heat capacity of the room, upper and lower limits of indoor temperature, upper and lower limits of air conditioning load power, and upper limit of average indoor temperature.

2. The method according to claim 1, characterized in that, The objective function for the overall operating cost of the system is shown in the following formula: The power grid operating loss cost Calculated based on the following formula: In the formula, The unit price of electricity. for Subway The corresponding network loss, It is the collection of all branches of the distribution network. This represents the number of time periods contained in a day after the time parameter is discretized. For time step, branch road The resistance, for Subway active power, for Subway reactive power, This is the rated voltage of the power distribution network.

3. The method according to claim 1, characterized in that, In the node active power balance constraint, the expression for the active load Pj(t) of node j at time t is shown in the following formula: In the formula, for node The baseline load at any given time corresponds to the real-time load of the network node. for node Electric vehicle charging power at any time for node The air conditioning load at any given time This is a set of nodes for installing electric vehicle charging stations. This is the set of nodes where air conditioning loads are installed.

4. The method according to claim 1, characterized in that, The electric vehicle location constraints are shown in the following formula: In the formula, For electric vehicles, This is the number of the charging station. For the number of charging stations, This is a binary variable representing that each electric vehicle can only choose one charging station for charging. The spatial scheduling distance constraint for electric vehicles is shown in the following formula: In the formula, For the first The distance from the vehicle's starting point to the charging station. The maximum acceptable scheduling distance.

5. The method according to claim 1, characterized in that, The method further includes: receiving destination location information for each electric vehicle, and determining the nearest charging station to the destination of the corresponding electric vehicle based on the destination location information for each electric vehicle. The desired state of charge constraint for the electric vehicle when leaving the charging station is shown in the following formula: In the formula, For the first The state of charge of an electric vehicle when it leaves a charging station. This refers to the initial state of charge of an electric vehicle. For distance from the first The charging power of the nearest charging station to the destination for an electric vehicle. For the arrival time, The moment the electric vehicle leaves. This refers to the maximum capacity of the vehicle's battery. This represents the upper limit of the state of charge of an electric vehicle.

6. The method according to claim 1, characterized in that, The state-of-charge constraint of the electric vehicle at any given moment is shown in the following formula: In the formula, Indicates the first electric vehicles at all times The state of charge of the battery. For the first Electric vehicles at charging station The charging power, To characterize the first electric vehicles at all times A binary variable indicating whether the device is in a charging state.

7. The method according to claim 1, characterized in that, The constraint on the dynamic change of indoor temperature satisfies the discrete difference equation shown in the following formula: In the formula, Indoor temperature, Outdoor temperature The cooling capacity provided for the air conditioner For internal heat load, The equivalent heat capacity of the room. This is the equivalent thermal resistance of the room.

8. The method according to claim 1, characterized in that, The constraints also include an upper limit constraint on the average indoor temperature and a constraint on the number of charging piles. The upper limit constraint on the average indoor temperature is shown in the following formula: In the formula, Describes the upper limit of the average indoor temperature; The constraint on the number of charging piles is shown in the following formula: In the formula, the middle (t) is the th m One charging station t The number of electric vehicles connected at any given time. For the first m The number of charging piles at each charging station.

9. A device for coordinated operation of electric vehicles and controllable air conditioning in a power distribution system, characterized in that, include: The data interaction module is used to obtain the real-time load size of network nodes in the distribution network area, and receive charging-related information and air conditioning information of electric vehicles to be charged. The charging-related information of electric vehicles includes the group of electric vehicles that need to be charged, the time when each vehicle arrives at the charging station, the time when the electric vehicle leaves, the initial state of charge of the electric vehicle, and the maximum capacity of the vehicle battery. The air conditioning information includes the outdoor temperature, the power parameters of each air conditioner, the internal heat load, the indoor thermal resistance, the equivalent heat capacity of the room, and the installation location of the air conditioner. The model building module is used to discretize the real-time load of the network nodes, the charging-related information of the electric vehicles, and the time-related variables in the air conditioning information, and use the discretized data as the input data of the optimization model; it is also used to construct an optimization model based on the input data of the optimization model, with the charging power of the electric vehicle charging station and the operating power of the air conditioner as decision variables, with the goal of minimizing the overall system operating cost, and to set the constraints of the optimization model; The solution control module is used to solve the optimization model to obtain the spatiotemporal allocation scheme of electric vehicle charging power and the power adjustment strategy of air conditioning load; The overall operating cost of the system includes the grid operation loss cost and the electric vehicle time-sharing schedule compensation cost paid to guide orderly charging; The constraints of the optimization model include upper and lower limits of node voltage amplitude, distribution network transmission capacity constraints, real-time load size of the network nodes, node active power balance constraints composed of electric vehicle charging power and air conditioning load, electric vehicle location constraints defined by binary variables, electric vehicle spatial scheduling distance constraints, expected state of charge constraints of electric vehicles when leaving the charging station based on the initial state of charge of electric vehicles and the maximum capacity of the vehicle battery, and state of charge constraints of electric vehicles at any time, upper and lower limits of electric vehicle state of charge, dynamic changes in indoor temperature based on internal heat load, indoor thermal resistance and equivalent heat capacity of the room, upper and lower limits of indoor temperature, upper and lower limits of air conditioning load power, and upper limit of average indoor temperature.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 8.

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