Commercial electric bus operation scheduling method and system under vehicle-network combination

Abstract

The invention provides a commercial electric bus operation scheduling method and system under vehicle-network combination, and belongs to the field of electric bus resource optimization configuration, and the method comprises the steps: building a coupling network model of a power distribution network and a traffic network and a corresponding bus timetable; constructing an electric bus fleet optimization problem model and a constraint condition model in the coupling network of the power distribution network and the traffic network; obtaining electricity price, bus battery parameters and service income; and based on the bus operation time table, inputting the electricity price, the electric bus parameters and the service income information into the electric bus fleet optimization problem model, solving to obtain the optimized operation parameters of the electric bus fleet and the scheduling parameters of the power distribution network, and obtaining an electric bus scheduling scheme. And meanwhile, the flexibility of a power grid and the operation benefit of an electric bus commercial fleet are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electric bus resource optimization configuration, and particularly relates to a commercial electric bus operation scheduling method and system under the combination of vehicles and networks. BACKGROUND

[0002] The access of high-proportion new energy (such as photovoltaic and wind power) to the power grid brings many uncertainties to the operation of the power grid; on the other hand, with the increasing popularity of electric buses, the proportion of their electricity consumption load is also rising, which exacerbates the vulnerability of the operation of the power grid. Electric buses have high operation flexibility, and their batteries can be regarded as a kind of energy storage resource. If reasonable control measures are implemented, electric buses can serve as mobile energy storage units to provide abundant flexible resources for the power grid.

[0003] Among various types of electric buses, electric buses have larger battery capacity and are usually operated in a centralized mode, that is, managed by a single commercial company or entity. During the pre-planned regular operation period, electric buses serve as a means of transportation to meet the travel demand of the transportation network; and during the remaining idle period, they can serve as mobile energy storage units to provide operation flexibility services for the power grid, such as peak clipping, frequency regulation and backup support. Therefore, electric buses will become an ideal medium for promoting the coordination of the power system and the transportation system and improving the flexibility of the power grid. SUMMARY

[0004] In order to jointly configure the power distribution network and the electric bus fleet to improve electricity efficiency and operation benefits, the application provides a commercial electric bus operation scheduling method and system under the combination of vehicles and networks.

[0005] In order to achieve the above-mentioned purpose, the application provides the following technical solutions: A commercial electric bus operation scheduling method under the combination of vehicles and networks, comprising the following steps: Obtaining charging station information, power grid capacity and electric bus fleet information, establishing a coupled network model of the power distribution network and the transportation network based on the charging station information, the power grid capacity and the electric bus fleet information, and constructing a bus operation timetable based on the coupled network model; Constructing an electric bus fleet optimization problem model in the coupled network of the power distribution network and the transportation network with the goal of minimizing the total operation cost and maximizing the backup service benefits; the electric bus fleet optimization problem model includes an optimization problem objective function and a constraint condition model; the constraint condition model is used to constrain the range of the solution of the objective function; The electricity price, electric bus battery parameter and service benefit information are obtained; the electricity price, electric bus parameter and service benefit information are input into the electric bus fleet optimization problem model based on the bus operation timetable, and the optimized operation parameter of the electric bus fleet and the scheduling parameter of the distribution network are obtained by solving, and the electric bus operation scheduling and the distribution network adjustable standby capacity allocation are performed based on the optimized operation parameter of the electric bus fleet and the scheduling parameter of the distribution network.

[0006] Preferably, the optimization problem objective function, in particular: ; ; ; ; Wherein, is the electricity cost; is the battery attenuation cost, is the standby service benefit, represents the battery discharge depth, represents the time-of-use electricity price, is the total active power injected by the distribution network root node, represents the unit price cost of the battery represents the cumulative discharge power of the electric bus fleet, represents the life cycle of the battery, represents the cumulative adjustable capacity provided by the electric bus fleet, , represents the standby time-of-use electricity price.

[0007] Preferably, the constraint condition model specifically includes distribution network operation constraints, electric bus operation constraints and standby boundary constraints.

[0008] Preferably, the distribution network operation constraints specifically include: ; ; Wherein, and represent the total active power and reactive power injected by the distribution network root node, and represent the active and reactive power flowing out of the distribution network root node, and represent the resistance and reactance, represents the line current flowing through the distribution network root node to node j, represents the set of all sub-nodes connected to the distribution network root node.

[0009] Preferably, the electric bus operation constraints include access signal constraints, charging / discharging constraints, energy constraints and driving power consumption constraints, and the access signal constraints are specifically: ; ; ; wherein, represents the charging / discharging / access signal of the electric bus at the i-th node, represents the electric bus commuting time set, represents the location parameter of the charging station; represents the set of distribution network nodes, represents the index of the electric bus; The charging / discharging power constraints are specifically: ; ; ; wherein, and represent the charging and discharging power of the electric bus, respectively, and represent the charging and discharging efficiency, respectively, and represent the upper limit of charging and discharging, respectively; The energy constraints are specifically: ; ; ; ; wherein, represents the battery energy level SOC of the electric bus, and represent the upper and lower limits of the SOC of the electric bus, respectively, represents the initial electric quantity of the electric bus, represents the traffic power consumption of the electric bus in the time period, and represent the charging and discharging power of the electric bus, respectively; represents the battery energy of the electric bus at the time, represents the minimum energy level required for the electric bus to participate in grid auxiliary services; The driving power consumption constraints are specifically: ; ; wherein, represents the unit energy consumption rate, represents the charging station distance matrix.

[0010] Preferably, the backup boundary constraint includes a total supply constraint, a distribution network boundary constraint, and an electric bus real-time charging and discharging boundary constraint, and the total supply constraint, in particular, ; wherein, and respectively represent the upper and lower bounds of the adjustable capacity; The distribution network boundary constraint, in particular, ; ; wherein, represents the set of child nodes connected to the root node of the distribution network, and j represents the index of the child node connected to the root node of the distribution network, represents the upper / lower limit value of the line current from the root node of the distribution network to the child node j, represents the upper / lower limit value of the active power flowing through the root node of the distribution network to the child node j, represents the upper / lower limit value of the reactive power flowing through the root node of the distribution network to the child node j, represents the reactance between the root node of the distribution network and the child node j, represents the resistance between the root node of the distribution network and the child node j; The electric bus real-time charging and discharging boundary constraint, in particular, ; ; wherein, represents the access signal of the electric bus at the i node, represents the minimum value of the total power that the electric bus fleet can discharge, represents the maximum value of the total power that the electric bus fleet can discharge, represents the maximum cumulative discharge power of the charging pile, represents the maximum cumulative charging power of the charging pile, represents the electric bus charging power, represents the electric bus discharging power.

[0011] Preferably, for the unit energy consumption rate in the driving power consumption constraint, an opportunity constraint reconstruction model is constructed, in particular, ; ; ; wherein, and are auxiliary non-negative decision variables, denotes the inverse function of the Gaussian distribution, denotes the confidence level, is a fourth-order variable, denotes the minimum allowable power of the electric bus, denotes the maximum power of the electric bus, denotes the battery capacity, denotes the auxiliary variable.

[0012] The application also provides a commercial electric bus operation scheduling system under the combination of vehicle and network, specifically comprising: An electrical traffic coupling module is configured to acquire charging station information, power grid capacity and vehicle fleet information of the electric bus, establish a coupling network model of the power distribution network and the traffic network based on the charging station information, the power grid capacity and the vehicle fleet information of the electric bus, and construct a bus operation timetable based on the coupling network model.

[0013] A model construction module is configured to construct an electric bus fleet optimization problem model in the coupling network of the power distribution network and the traffic network with the target of minimizing the total operation cost and maximizing the standby service benefit; the electric bus fleet optimization problem model comprises an optimization problem objective function and a constraint condition model; the constraint condition model is configured to constrain the range of the solution of the objective function.

[0014] An optimization scheduling module is configured to acquire electricity price, electric bus battery parameter and service benefit information; based on the bus operation timetable, input the electricity price, electric bus parameter and service benefit information into the electric bus fleet optimization problem model, solve to obtain the optimized operation parameters of the electric bus fleet and the scheduling parameters of the power distribution network, and perform electric bus operation scheduling and power distribution network standby capacity allocation based on the optimized operation parameters of the electric bus fleet and the scheduling parameters of the power distribution network.

[0015] The application also provides a computer device comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to realize the steps in the commercial electric bus operation scheduling method under the combination of vehicle and network.

[0016] The application also provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program can execute the steps in the commercial electric bus operation scheduling method under the combination of vehicle and network when loaded by a processor.

[0017] The commercial electric bus operation scheduling method provided by the application has the following beneficial effects: The application establishes an "electrical-traffic" coupling network, models the electric bus as a dual function of a traffic tool and a mobile energy storage unit, meets the travel demand, considers the characteristics of the mobile energy storage and the power grid interaction, improves the economic and applicable benefits, optimizes the solution to give full play to the dual function of the electric bus, and the electric bus has uncertainty in the running process, and the constraint condition model is constructed for the uncertainty, the uncertainty problem is solved, and the method has practical significance. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the application and the design scheme thereof, the drawings required by the embodiments will be briefly introduced as follows. The drawings in the following description are only part of the embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings.

[0019] Figure 1 The flow chart of the commercial electric bus operation scheduling method under the combination of the vehicle and the network.

[0020] Figure 2 The "electrical-traffic" coupling network structure diagram of the embodiment of the application.

[0021] Figure 3 The charging station group distance matrix in the "electrical-traffic" coupling network of the embodiment of the application.

[0022] Figure 4 The unit electricity price and backup capacity price curve diagram of the embodiment of the application.

[0023] Figure 5 The power curve and power boundary curve diagram in different modes of the embodiment of the application. DETAILED DESCRIPTION

[0024] In order to make those skilled in the art better understand the technical scheme of the application and can be implemented, the application will be described in detail below in combination with the drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical scheme of the application, and cannot be used to limit the protection scope of the application.

[0025] EMBODIMENT The application provides a commercial electric bus operation scheduling method under the combination of the vehicle and the network, as shown in Figure 1 The specific steps include the following steps: S1, establish a coupled network model of the power distribution network and the traffic network, and construct a bus operation timetable based on the coupled network model; and obtain the adjustable capacity of the bus when there is no travel according to the bus operation timetable.

[0026] The information such as electricity price, backup capacity price, charging pile information, power grid capacity, electric bus information and the like is used to establish the coupled network model of the power distribution network and the traffic network, which is the basis of the topology network of the entire optimization problem and determines the number of power grid nodes I, the charging station link position N, the traffic network node position, the charging station distance and the optimized path planning and the like. The coupled network model is established based on the IEEE-33 node standard power distribution network and the Sinox-Falls 24 node standard traffic network, as shown in Figure 2 .

[0027] The bus travel route timetable under the "electricity-traffic" coupled network is established. In the travel time, the electric bus meets the travel demand, and in the non-travel time, the electric bus participates in providing the adjustable capacity of the power grid, i.e. affecting the feasible region of the optimization problem. That is, in the working period, the electric vehicle performs the traffic task to meet the traffic demand; and in the non-working period, the electric vehicle accesses the power grid to provide the adjustable backup service.

[0028] In the coupled network, the electric bus still serves as a traffic tool to meet the travel demand of users. According to the actual travel data and travel path of residents, five common routes are fitted. The specific time and path are shown in Table 1:

[0029] Table 1 Electric bus travel timetable and route table of "electricity-traffic" coupled network S2, establish a distance matrix of the charging station group and a unit time-of-use electricity price of the electricity and backup electricity , a unit electricity price , and a unit backup electricity price The charging pile distance matrix refers to the existing charging technology, power grid capacity, and the battery capacity and daily driving mileage of the electric bus, and the specific setting is shown in Figure 3 .

[0030] The electricity price refers to the existing large industrial electricity price in a certain place and the ratio of real-time backup price to electricity price in the PJM electricity market auxiliary service market in the United States, and the average value of each period is calculated, including peak value, flat value and valley value. The daily curves of the two prices are shown in Figure 4 .

[0031] S3, construct an electric bus fleet optimization problem model based on minimizing the total operating cost and maximizing the backup service revenue; the electric bus fleet optimization problem model includes an optimization problem objective function and a constraint condition model.

[0032] An objective function of the optimization problem is established, aiming to minimize the total operating cost and maximize the backup service revenue. The total operating cost is composed of two parts, i.e., the overall electricity cost of the electric bus fleet and the battery degradation cost, and the backup service revenue is considered in terms of the overall power provided by the electric bus and the unit backup service price. The objective function is shown as follows:

[0033] In the formula, is the electricity cost, which is calculated by the unit electricity price and the total power consumption; represents the battery discharge depth; is the battery degradation cost, which is calculated by the battery capacity, the manual handling cost, the power cycle times, and the discharge amount; is the backup service revenue, which is calculated by the discharge amount and the discharge price; represents the total active power injected by the distribution network root node; represents the unit price cost of the battery; represents the battery capacity; represents the manual cost of replacing the battery; represents the cumulative discharge power of the electric bus fleet; represents the life cycle of the battery (i.e., the cycle times); represents the adjustable capacity provided by the electric bus fleet; represents for any time.

[0034] S4, a constraint condition model of the optimization problem is established. The constraint conditions include the distribution network operation constraints, the electric bus operation constraints, and the backup boundary constraints.

[0035] Among them, the distribution network operation constraints include power constraints (i.e., active and reactive power constraints), power constraints of charging stations, and voltage and current capacity constraints, which are shown as follows: In the formula, respectively represent the reactive power injected by the distribution network root node, and represent the active and reactive power flowing out of the root node, and respectively represent the resistance and reactance, represents the line current flowing through the root node to node j,​​​​​​​ denotes the set of all child nodes connected to the root node.

[0036] The power flow calculation of each node in the distribution network is as follows: wherein, and denote the active and reactive power injected from node k to node i respectively, denotes the net power flowing to the electric bus, denotes the set of all nodes in the power grid, denotes the set of child nodes connected to node i, and the line current is calculated as follows: wherein, denotes the node voltage, which should satisfy the following constraints: wherein, and denote the upper and lower limits of the node voltage respectively, denotes the upper limit of the line current.

[0037] The operating constraints of the electric bus include access signal constraints, charging / discharging constraints, energy constraints, and driving power consumption constraints. Among them, the access signal constraint is represented as follows:

[0038] wherein, denotes the charging / discharging / access signal of the electric bus at node i, which is a 0-1 decision variable, denotes the set of electric bus commuting times (i.e., performing traffic tasks and cannot participate in power grid auxiliary services), denotes the location parameter of the charging station, which is 1 if node i has a charging station; otherwise, it is 0. The charging / discharging power constraint is represented as follows:

[0039] ​​​​​​​​​​​​; where, and denote the charging and discharging power of the electric bus, and denote the charging and discharging efficiency, and denote the upper limit of charging and discharging. The energy constraints are as follows:

[0040] ; ; ; ; where, denotes the battery energy level SOC (State-of-charge) of the electric bus, and denote the upper and lower limits of the electric bus SOC, which cannot be lower than the required level , denotes the initial electric quantity of the electric bus, denotes the traffic energy consumption of the electric bus in the time period, denotes the traffic energy consumption, which needs to satisfy the following constraints: ; ; where, denotes the energy consumption per unit distance, denotes the charging station distance matrix. denotes the position of the electric bus n at time t, i.e., the distance traveled by the electric bus from t-1 to t.

[0041] The standby boundary constraints include total standby supply constraints, active and reactive power boundary constraints of the distribution network, and real-time charging and discharging boundary constraints of the electric bus, etc. The total supply constraint is as follows:

[0042] ; where, and denote the upper and lower limits of the adjustable capacity. The distribution network boundary constraints are as follows:

[0043] ; ; ; ; ; ; ; ; The real-time charging and discharging boundary constraints of the electric bus are represented as follows:

[0044] ; .

[0045] S5, Since the electric bus operation constraints in the above steps need to consider the energy loss of the electric bus during driving, which is an uncertain parameter. Establish the uncertainty operation model and chance constraint reconstruction model of the electric bus.

[0046] The trip energy consumption of the electric bus fleet depends on the unit energy consumption rate, which directly affects the charging and discharging power of the charging station and the backup power supply capacity of the electric vehicle fleet. Therefore, the unit trip energy consumption rate is an important factor affecting the scheduling results of the electric bus fleet and its coupled network. This energy consumption rate is usually affected by many factors, including but not limited to driving speed, passenger load, and energy consumption of on-board equipment. Set this parameter to conform to the Gaussian distribution, and the uncertainty operation model is represented as follows:

[0047] ; and represent the mean and variance of energy consumption, respectively. For this uncertain parameter, a two-sided constraint original optimization model based on the SOC of the electric bus is constructed, represented as follows:

[0048] ; , represents the confidence level, represents the minimum allowed electric quantity of the electric bus; represents the maximum electric quantity of the electric bus. The two-sided probability constraint is equivalently reconstructed to obtain the chance constraint reconstruction model, which is specifically:

[0049] ; ; ; ; and are auxiliary non-negative decision variables, represents the inverse function of the Gaussian distribution. In this formula, ​​​is a fourth-order variable, which makes the constraint a high-order constraint and cannot be solved. It needs to be reduced by equivalent transformation and expressed as follows:

[0050] where, , is a vector with dimension 1*I (I represents the number of charging stations), is a matrix with dimension I*I, is a vector with dimension 1 , is a vector with dimension 1 . Through this formula, the original bilinear decision variable term can be changed into a first-order decision variable term. The original unsolvable high-order constraint is equivalent to a second-order cone constraint, which is expressed as follows:

[0051] where, and are auxiliary non-negative variables.

[0052] S6, the electric bus fleet optimization problem model is solved by using a solver (such as Gurobi), and a scheduling scheme is obtained, which specifically includes an electric bus scheduling scheme, a power grid power flow scheduling scheme, and a standby capacity scheduling scheme. The electric bus scheduling scheme specifically includes bus charging / discharging, bus location, and electric quantity decision variables; the power grid power flow scheduling scheme specifically includes node phase voltage, line current, node input / output active and reactive power decision variables; the standby capacity scheduling scheme specifically includes node phase voltage, line current, active and reactive power upper and lower limit decision variables, and the optimized operation curve of each unit in the optimization period and the corresponding index calculation result are obtained. Among them, after the solver is solved, the bus location decision variable solving process is obtained is a vector with dimension 1 , which is first equivalent to , a matrix with dimension I*I, and then the following expression is used to obtain the location decision variable of the electric bus at t-1 and t:

[0053] =​​​​​​​​​​ .

[0054] where, and are the position variables of the electric bus at time t-1 and t.

[0055] Six modes are established as shown in Table 2. Mode 1 adopts the above method. Mode 2 also adopts the above method, but ignores uncertainty. In mode 3, the electric bus fleet does not provide backup service. Mode 4 does not consider the spatial scheduling of the electric bus fleet, and the electric bus goes to the nearest charging station immediately after completing the commuter service. Mode 5 neither considers spatial scheduling nor backup service. Mode 6 is the benchmark model, in which the electric bus charges immediately after completing the traffic task at the nearest charging station.

[0056] Table 2 Mode setting After solving the different mode schemes, the experimental results are obtained. A cost analysis table is established to compare the above six modes, as shown in Table 3, it can be seen that the proposed method can significantly improve the overall operating income (cost is negative means positive income). Through the income analysis of providing backup capacity, it shows that the method can greatly improve the backup capacity of the power grid, thereby enhancing the flexibility of the power grid.

[0057] Table 3 Comparison table of cost and income results of different modes As can be seen from the table, mode 1 obtains the maximum income (8665.89 yuan), so it can be seen that the proposed optimization operation technology of commercial electric bus for improving the flexibility of the power grid has very high applicability and economic value. In addition, the power and adjustable capacity curves of the six modes can be obtained, as shown in Figure 5 It can be seen that the power curve of mode one conforms to the actual operation situation, and the adjustable capacity range is the widest, therefore, the proposed model significantly enhances the operation flexibility of the distribution network.

[0058] In the face of the increasingly complex application environment of new power systems and the increasing number of uncertain factors in operation, the huge potential of large-scale resources of electric buses and their charging and swapping facilities for improving the resilience of urban power grids, and the leading advantage of virtual power plants in aggregating multiple source heterogeneous distributed resources, and considering the characteristics of different types of electric buses and traffic behaviors, the focus is on the modeling of the aggregation of multiple types of electric buses and charging facilities participating in virtual power plants for improving the resilience of urban power grids, scheduling optimization and resilience response strategy research. The mechanism of multiple types of electric buses and their charging and discharging facilities for improving the resilience of power grids under extreme events is summarized, the regulation capacity is evaluated, the preventive operation scheduling method and post-disaster power supply recovery strategy of multiple types of electric buses and their charging and discharging facilities participating in virtual power plant services are researched, through the coordinated scheduling of multiple types of electric buses and charging and discharging facilities, a flexible, reliable and efficient virtual power plant load regulation network is constructed, which supports the resilience of the power system, and provides specific decision tools for virtual power plants participating in power grid auxiliary services.

[0059] The application also provides a commercial electric bus operation scheduling system under vehicle network combination, specifically comprising: An electrical traffic coupling module is configured to obtain charging station information, power grid capacity and electric bus fleet information, establish a coupling network model of the power distribution network and the traffic network based on the charging station information, the power grid capacity and the electric bus fleet information, and construct a bus operation timetable based on the coupling network model.

[0060] A model construction module is configured to construct an electric bus fleet optimization problem model of the coupling network of the power distribution network and the traffic network with the target of minimizing total operating cost and maximizing standby service revenue; the electric bus fleet optimization problem model comprises an optimization problem objective function and a constraint condition model; the constraint condition model is configured to constrain the range of the solution of the objective function.

[0061] An optimization scheduling module is configured to obtain electricity price, electric bus battery parameter and service revenue information; based on the bus operation timetable, the electricity price, the electric bus parameter and the service revenue information are input into the electric bus fleet optimization problem model to obtain optimized operation parameters of the electric bus fleet and scheduling parameters of the power distribution network, and the electric bus operation scheduling and the distribution of adjustable standby capacity of the power distribution network are performed based on the optimized operation parameters of the electric bus fleet and the scheduling parameters of the power distribution network.

[0062] The above-mentioned modules in the commercial electric bus operation scheduling system under vehicle network combination can be realized by software, hardware and their combinations. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules.

[0063] The application further provides a computer device, comprising a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to implement the steps in the method embodiment of the commercial electric bus operation scheduling method under the combination of vehicle and network. The specific implementation method can be referred to the method embodiment, which will not be repeated here.

[0064] Further, the application further provides a non-temporary computer readable storage medium comprising instructions, and the storage medium stores a computer program. For example, the storage medium comprises instructions, and the above instructions can be executed by the processor of the computer device to complete the above method. For example, the non-temporary computer readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk and an optical data storage device, etc. When the computer program is executed by the processor, the steps in the method embodiment of the commercial electric bus operation scheduling method under the combination of vehicle and network can be implemented. The specific implementation method can be referred to the method embodiment, which will not be repeated here.

[0065] Those skilled in the art should understand that the embodiments of the application can provide a method, a system or a computer program product. Therefore, the application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the application can adopt a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0066] The application is described with reference to flowcharts and / or block diagrams of the method, device (system) and computer program product according to the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks.

[0067] These computer program instructions can also be stored in a computer readable storage medium which can guide the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks.

[0068] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0069] It should be noted that the above-mentioned detailed embodiments enable those skilled in the art to have a more comprehensive understanding of the present application, but do not limit the present application in any way. Therefore, although the present application has been described in detail in the specification and examples, those skilled in the art should understand that the present application can still be modified or replaced by equivalents; all technical solutions and improvements that do not deviate from the spirit and scope of the present application are covered by the protection scope of the present application. Any reference signs in the claims should not be considered as limiting the claims. Simple changes or equivalent replacements of technical solutions that are obvious to those skilled in the art within the scope of the present disclosure are within the protection scope of the present application.

Claims

1. A method for scheduling operation of a commercial electric bus under vehicle-network integration, characterized in that, The method comprises the following steps: obtaining charging station information, power grid capacity and electric bus fleet information, establishing a coupled network model of the power distribution network and the traffic network based on the charging station information, the power grid capacity and the electric bus fleet information, and constructing a bus operation timetable based on the coupled network model; constructing an electric bus fleet optimization problem model in the coupled network of the power distribution network and the traffic network with the objective of minimizing total operating costs and maximizing standby service benefits; the electric bus fleet optimization problem model comprises an optimization problem objective function and a constraint condition model; the constraint condition model is used to constrain the range of the solution of the objective function; obtaining electricity prices, electric bus battery parameters and service benefit information; inputting the electricity prices, electric bus parameters and service benefit information into the electric bus fleet optimization problem model based on the bus operation timetable, and solving to obtain optimized operation parameters of the electric bus fleet and scheduling parameters of the power distribution network, and performing electric bus operation scheduling and power distribution network adjustable standby capacity allocation based on the optimized operation parameters of the electric bus fleet and the scheduling parameters of the power distribution network.

2. The method according to claim 1, wherein, The optimization problem objective function is specifically: ； ； ； ； wherein, is the cost of electricity, is the battery degradation cost, is the revenue of backup service, denotes the battery depth of discharge, denotes the time-of-use electricity price, denotes the total active power injected at the grid root node, denotes the unit price cost of the battery, denotes the cumulative discharge power of the electric bus fleet, denotes the life cycle of the battery, denotes the adjustable capacity provided by the electric bus fleet cumulatively, denotes for any time, denotes the backup time-of-use electricity price.

3. The method according to claim 1, wherein, The constraint condition model specifically comprises power distribution network operation constraints, electric bus operation constraints and standby boundary constraints.

4. The method according to claim 3, wherein, The power distribution network operation constraints are specifically: ； ； wherein, and Pj and Qj represent the total active and reactive power injected at the root node of the distribution network, and Pj and Qj represent the active and reactive power flowing out of the root node of the distribution network, and R and X represent the resistance and reactance, respectively, Ij represents the line current flowing through the root node of the distribution network to node j, Nj represents the set of all child nodes connected to the root node of the distribution network.

5. The method according to claim 3, wherein, The electric bus operation constraints comprise access signal constraints, charging and discharging constraints, energy constraints and driving power consumption constraints, and the access signal constraints are specifically: ； ； ； wherein, represents the charging / discharging / switching signal of the electric bus at the i-th node, represents the set of electric bus commuting times, represents the location parameter of the charging station; represents the set of distribution network nodes, represents the index index of the electric bus. The charging and discharging power constraints are specifically: ； ； ； wherein and Pcharge and Pdischarge represent the charging and discharging power of the electric bus, respectively, and ηcharge and ηdischarge represent the charging and discharging efficiency, respectively, and Pmaxcharge and Pmaxdischarge represent the upper limit of the charging and discharging, respectively. The energy constraints are specifically: ； ； ； ； in, This indicates the State of Charge (SOC) of the battery in an electric bus. and These represent the upper and lower limits of the State of Charge (SOC) for electric buses. This indicates the initial charge level of the electric bus. Indicates electric buses Traffic energy consumption during a given time period and These represent the charging and discharging power of the electric bus, respectively. Indicates electric buses Battery level at any time This indicates the minimum energy level required for electric buses to participate in grid ancillary services; The driving power consumption constraints are specifically: ； ； wherein, represents the unit travel energy consumption rate, represents the charging station distance matrix.

6. The method according to claim 3, wherein, The standby boundary constraints comprise total supply constraints, power distribution network boundary constraints and electric bus real-time charging and discharging boundary constraints, and the total supply constraints are specifically: ； wherein, and respectively represent the upper and lower bounds of the adjustable capacity; The power distribution network boundary constraints are specifically: ； ； wherein, denotes the set of child nodes connected to the root node of the distribution network, j denotes the index of the child node connected to the root node of the distribution network, denotes the upper / lower limit value of the line current from the root node of the distribution network to the child node j, denotes the upper / lower limit value of the active power flowing through the root node of the distribution network to the child node j, denotes the upper / lower limit value of the reactive power flowing through the root node of the distribution network to the child node j, denotes the reactance between the root node of the distribution network and the child node j, denotes the resistance between the root node of the distribution network and the child node j; The electric bus real-time charging and discharging boundary constraints are specifically: ； ； wherein, represents the access signal of the electric bus at the i node, represents the minimum value of the total power that the electric bus fleet is able to discharge, represents the maximum value of the total power that the electric bus fleet is able to discharge, represents the maximum value of the accumulated discharge power of the charging post, represents the maximum value of the accumulated charge power of the charging post, represents the charge power of the electric bus, represents the discharge power of the electric bus.

7. The method according to claim 5, wherein, An opportunity constraint reconstruction model is constructed for the unit distance energy consumption rate in the driving power consumption constraints, and the opportunity constraint reconstruction model is specifically: ； ； ； wherein and are auxiliary non-negative decision variables, denotes the inverse function of a Gaussian distribution, denotes a confidence level, is a fourth order variable, denotes the minimum allowed electric quantity of the electric bus, denotes the maximum electric quantity of the electric bus, denotes the battery capacity, denotes an auxiliary variable.

8. A commercial electric bus operation scheduling system under vehicle-network integration, characterized in that, It comprises: An electrical traffic coupling module is configured to obtain charging station information, power grid capacity and electric bus fleet information, establish a coupled network model of the power distribution network and the traffic network based on the charging station information, the power grid capacity and the electric bus fleet information, and construct a bus operation timetable based on the coupled network model; A model construction module is configured to construct an electric bus fleet optimization problem model in the coupled network of the power distribution network and the traffic network with the objective of minimizing total operating costs and maximizing standby service benefits; the electric bus fleet optimization problem model comprises an optimization problem objective function and a constraint condition model; the constraint condition model is used to constrain the range of the solution of the objective function; An optimization scheduling module is configured to obtain electricity prices, electric bus battery parameters and service benefit information; input the electricity prices, electric bus parameters and service benefit information into the electric bus fleet optimization problem model based on the bus operation timetable, and solve to obtain optimized operation parameters of the electric bus fleet and scheduling parameters of the power distribution network, and perform electric bus operation scheduling and power distribution network adjustable standby capacity allocation based on the optimized operation parameters of the electric bus fleet and the scheduling parameters of the power distribution network.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-8. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when loaded by the processor, is capable of executing the steps of the method of any one of claims 1 to 7.

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