Electric vehicle and greenhouse planting energy management method considering tourism collaboration

Abstract

The invention discloses an electric vehicle and greenhouse planting energy management method considering tourism collaboration. The method comprises the following steps: acquiring basic parameters; the basic parameters comprise greenhouse planting structure parameters, travel electric vehicle parameters and photovoltaic power generation parameters; establishing a greenhouse planting power consumption model; the greenhouse planting power consumption model is configured to obtain greenhouse planting power consumption according to greenhouse planting structure parameters; establishing an electric vehicle charging and discharging model under the tourist route condition; the electric vehicle charging and discharging model is configured to obtain the state of charge of the electric vehicle according to the travel electric vehicle parameters; establishing an electric vehicle and greenhouse planting collaborative optimization operation model, wherein the objective function is that the operation cost is minimized according to the photovoltaic power generation parameters, the greenhouse planting power consumption and the charge state of the electric vehicle; and solving the electric vehicle and greenhouse planting collaborative optimization operation model to obtain an optimal collaborative operation scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle energy management, and in particular to an energy management method for electric vehicles and greenhouse planting taking tourism collaboration into consideration. Background Art

[0002] In recent years, the integration of rural areas and tourism has rapidly developed, becoming a promising solution for improving the economy and living standards in rural areas. Rural tourism areas possess unique power loads, such as greenhouse cultivation, which provides a suitable growing environment for crops and enhances the visitor experience. Tourist electric buses are considered an energy resource connected to the power system, as they store electrical energy and can coordinate power dispatch with the power system. However, existing technologies do not integrate greenhouse cultivation with tourist electric buses, nor do they consider energy management methods for electric vehicles and greenhouse cultivation in a coordinated manner for tourism. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes an energy management method for electric vehicles and greenhouse planting taking into account tourism collaboration.

[0004] The technical solution of the present invention is implemented as follows: The present invention provides an energy management method for electric vehicles and greenhouse cultivation taking into account tourism synergy, comprising the following steps:

[0005] Obtaining basic parameters; the basic parameters include: greenhouse planting structure parameters, tourist electric vehicle parameters, and photovoltaic power generation parameters;

[0006] Establishing a greenhouse planting power consumption model; the greenhouse planting power consumption model is configured to obtain greenhouse planting power consumption according to greenhouse planting structural parameters;

[0007] Establishing an electric vehicle charging and discharging model under tourist route conditions; the electric vehicle charging and discharging model is configured to be able to obtain the state of charge of the electric vehicle based on the tourist electric vehicle parameters;

[0008] Establishing a coordinated optimization operation model for electric vehicles and greenhouse planting; when establishing the coordinated optimization operation model for electric vehicles and greenhouse planting, constructing an objective function for minimizing the operating costs of electric vehicles and greenhouse planting; the objective function is: minimizing the operating costs determined based on photovoltaic power generation parameters, greenhouse planting power consumption, and the state of charge of electric vehicles;

[0009] Solve the coordinated optimization operation model of electric vehicles and greenhouse cultivation to obtain the optimal coordinated operation plan.

[0010] Furthermore, the mathematical expression of the greenhouse planting power consumption model is:

[0011]

[0012]

[0013]

[0014]

[0015] in, is the total electricity consumption of the greenhouse in time period t, is the number of lighting devices, P l is the power of the lighting equipment, k c is the illuminance conversion coefficient, LF is the luminous flux of the lighting equipment, U is the lighting utilization coefficient, L and W are the length and width of the greenhouse, H is the installation height of the lighting equipment, I ad is the additional light provided by the lighting equipment in the greenhouse I ad , I set It is the light intensity under conditions suitable for crop growth. is the total solar radiation received by the greenhouse.

[0016] Furthermore, the electric vehicle charging and discharging model incorporates the electric vehicle's spatiotemporal model, which is as follows:

[0017]

[0018] r t,bus is a binary variable, r t,v =1 means that the tourist electric vehicle is traveling on route r at time t, where R is the set of routes;

[0019] If an electric car reaches the end point of node n at time t, then it must start from node n and travel on the next route at time t+1. This restriction can be described by the following formula:

[0020]

[0021]

[0022] Among them, n t,bus and n t+1,bus It means that the tourist electric car is located at node n at time t and time t+1.

[0023] Furthermore, the state of charge calculation of the electric vehicle meets the following conditions:

[0024]

[0025] State of Charge:

[0026] Among them, SOC t，busis the state of charge of the tourist electric vehicle at time t, SOC t+1，bus is the state of charge of the tourist electric vehicle at time t+1, E bus,max is the maximum battery capacity of the tourist electric vehicle, η is the charge and discharge efficiency, ΔT is the time interval, when ΔT = 1 it means that the tourist electric vehicles are on the driving route, when ΔT = 1 it means that they are at the node, is the minimum state of charge of the tourist electric vehicle at time t, is the maximum state of charge of the tourist electric vehicle at time t, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, E bus，d It is the capacity lost in the operation of tourist electric vehicles.

[0027] Furthermore, the charging and discharging of electric vehicles in the electric vehicle charging and discharging model should meet the following conditions:

[0028]

[0029]

[0030] 0≤S n,dis +S n,char ≤1

[0031] Among them, S n,char is a binary variable representing the charge state at node n, S n,dis is a binary variable representing the discharge state at node n, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, is the maximum charging power of tourist electric vehicles, It is the maximum discharge power of tourist electric vehicles.

[0032] Furthermore, the coordinated optimization operation model of electric vehicles and greenhouse cultivation is solved to obtain the optimal coordinated operation plan, which specifically includes: using constraint conditions to solve the operation cost objective function to obtain the optimal coordinated operation plan.

[0033] Furthermore, the operating cost objective function of the coordinated optimization operation model of electric vehicles and greenhouse planting is expressed as follows:

[0034] C=C pv +C g

[0035]

[0036] Among them, C is the total operating cost, C pvis the operating cost of photovoltaic power generation, C g is the cost of purchasing electricity from the grid, c pv is the unit cost of photovoltaic power output, P pv,t is the photovoltaic power output, is the unit purchase price of electricity, is the unit selling price of electricity, The amount of electricity purchased from the power grid. It is the amount of electricity sold to the grid.

[0037] Furthermore, the constraints of the coordinated optimization operation model of electric vehicles and greenhouse cultivation are as follows:

[0038] P pv,t ≤P ins

[0039]

[0040]

[0041] Among them, P pv,t is the photovoltaic power output, P ins is the installed capacity of photovoltaic power generation, is the maximum power transmitted from the grid at time t, P load,t is the total electricity consumption in the rural tourism area at time t, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, P g，t It is time to purchase grid electricity.

[0042] The present invention also discloses an electronic device, comprising:

[0043] at least one processor; and

[0044] a memory communicatively connected to the at least one processor; wherein,

[0045] The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to execute the energy management method of electric vehicles and greenhouse cultivation considering tourism collaboration as described above.

[0046] The present invention also discloses a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps in the energy management method for electric vehicles and greenhouse cultivation considering tourism collaboration as described above are implemented.

[0047] Compared with the existing technology, the present invention has the following beneficial effects: the present invention takes into account the coordinated operation of greenhouse planting and tourist electric buses, takes into account the impact of electric vehicle operation, and aims to promote the output of photovoltaic power generation, reduce the amount of electricity purchased from the power grid, and reduce the operating cost of the system.

[0048] This paper addresses the problem of collaborative optimization of tourism energy resources and proposes an optimized power system operation model based on greenhouse agricultural cultivation and tourist electric buses. This model calculates power consumption based on meteorological conditions (i.e., solar radiation) and establishes a spatiotemporal model of the electric buses, taking into account tourist travel plans. Ultimately, the actual operational issues were resolved by optimizing the power system operation model. Compared to a scenario that only considers electric bus charging, this model can increase photovoltaic power generation, reduce public grid power supply, and reduce system operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of an energy management method for electric vehicles and greenhouse planting considering tourism collaboration provided in an embodiment of the present disclosure;

[0050] Figure 2 A comparison chart of electricity consumption of residents in a tourism-oriented rural community according to an embodiment of the present disclosure;

[0051] Figure 3 A comparison chart of electricity consumption in agricultural greenhouses according to an embodiment of the present disclosure;

[0052] Figure 4 Graph showing the charge and discharge results of tourist electric buses 1, 4, and 7 according to the embodiments of the present disclosure;

[0053] Figure 5 Graphs showing photovoltaic power generation output results under two cases in the embodiments of the present disclosure;

[0054] Figure 6 Graphs showing power changes with the power grid under two cases in the embodiments of the present disclosure;

[0055] Figure 7 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the present disclosure is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0057] Unless otherwise defined, the technical or scientific terms used in this disclosure should have the usual meanings understood by people with ordinary skills in the field to which this disclosure belongs. The "first", "second" and similar words used in this disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, similar words such as "one", "an" or "the" do not indicate a quantitative limitation, but rather indicate the presence of at least one. Similar words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0058] In each accompanying drawing, identical element adopts similar reference numeral to represent.For the sake of clarity, each part in the accompanying drawings is not all drawn to scale.In addition, some well-known parts may not be shown in the drawings.

[0059] Many specific details of the present disclosure are described below to provide a clearer understanding of the present disclosure. However, as will be appreciated by those skilled in the art, the present disclosure may be implemented without following these specific details.

[0060] Figure 1 This is a flow chart of an energy management method for electric vehicles and greenhouse planting considering tourism collaboration provided by an embodiment of the present disclosure. Figure 1 As shown, the present invention provides an energy management method for electric vehicles and greenhouse planting considering tourism synergy, comprising the following steps:

[0061] Obtaining basic parameters; the basic parameters include: greenhouse planting structure parameters, tourist electric vehicle parameters, and photovoltaic power generation parameters;

[0062] Establishing a greenhouse planting power consumption model; the greenhouse planting power consumption model is configured to obtain greenhouse planting power consumption according to greenhouse planting structural parameters;

[0063] Establishing an electric vehicle charging and discharging model under tourist route conditions; the electric vehicle charging and discharging model is configured to be able to obtain the state of charge of the electric vehicle based on the tourist electric vehicle parameters;

[0064] Establishing a coordinated optimization operation model for electric vehicles and greenhouse planting; the goal of the coordinated optimization operation model for electric vehicles and greenhouse planting is to minimize operating costs determined based on the photovoltaic power generation parameters, greenhouse planting power consumption, and the state of charge of the electric vehicles;

[0065] Solve the coordinated optimization operation model of electric vehicles and greenhouse cultivation to obtain the optimal coordinated operation plan.

[0066] Furthermore, the mathematical expression of the greenhouse planting power consumption model is:

[0067]

[0068]

[0069]

[0070]

[0071] in, is the total electricity consumption of the greenhouse in time period t, is the number of lighting devices, P l is the power of the lighting equipment, k c is the illuminance conversion coefficient, LF is the luminous flux of the lighting equipment, U is the lighting utilization coefficient, L and W are the length and width of the greenhouse, H is the installation height of the lighting equipment, I ad is the additional light provided by the lighting equipment in the greenhouse I ad , I set It is the light intensity under conditions suitable for crop growth. is the total solar radiation received by the greenhouse.

[0072] Furthermore, the electric vehicle charging and discharging model incorporates the electric vehicle's spatiotemporal model, which is as follows:

[0073]

[0074] r t,bus is a binary variable, r t,v =1 means that the tourist electric vehicle is traveling on route r at time t, where R is the set of routes; Make sure the tourist electric bus can only travel on one route during the trip.

[0075] If an electric car reaches the end point of node n at time t, then it must start from node n and travel on the next route at time t+1. This restriction can be described by the following formula:

[0076]

[0077]

[0078] Among them, n t,bus and n t+1,bus It means that the tourist electric car is located at node n at time t and time t+1.

[0079] Furthermore, the state of charge calculation of the electric vehicle meets the following conditions:

[0080]

[0081] State of Charge:

[0082] Among them, SOC t，bus is the state of charge of the tourist electric vehicle at time t, SOC t+1，bus is the state of charge of the tourist electric vehicle at time t+1, E bus,max is the maximum battery capacity of the tourist electric vehicle, η is the charge and discharge efficiency, ΔT is the time interval, when ΔT = 1 it means that the tourist electric vehicles are on the driving route, when ΔT = 1 it means that they are at the node, is the minimum state of charge of the tourist electric vehicle at time t, is the maximum state of charge of the tourist electric vehicle at time t, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, E bus，d It is the capacity lost in the operation of tourist electric vehicles.

[0083] Furthermore, the charging and discharging of electric vehicles in the electric vehicle charging and discharging model should meet the following conditions:

[0084]

[0085]

[0086] 0≤S n,dis +S n,char ≤1

[0087] Among them, S n,char is a binary variable representing the charge state at node n, S n,dis is a binary variable representing the discharge state at node n, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, is the maximum charging power of tourist electric vehicles, Is the maximum discharge power of tourist electric vehicles. 0≤S n,dis +S n,char ≤1 indicates that the tourist electric bus cannot be charged and discharged at the same time.

[0088] In some embodiments, establishing a collaborative optimization operation model for electric vehicles and greenhouse planting specifically includes: using a collaborative swarm optimization algorithm to establish a collaborative optimization operation model for electric vehicles and greenhouse planting, and the specific process is as follows:

[0089] (1) Initialization method of population:

[0090] X=rand(N,D)*(UB-LB)+LB

[0091] Where N represents the solution, D represents the dimension or variables of the given problem, and UB and LB represent the vectors of upper and lower bounds for each dimension of the problem space, respectively. Generate a random position for each particle in the population. Initialize the velocity to zero. Display the initial position, set the global best position to NULL, initialize the individual best fitness value to infinity, and set the global best fitness value to infinity. Iterate N times, evaluating the fitness of each particle in the population and updating the individual best position, global best position, and global inertia weight.

[0092] (3) Update candidate solutions:

[0093] X new (i,j)=X(i,j)+u(i,j)

[0094] Xnew(i,j) represents the ìth candidate solution C i The new optimized position of X(i,j) represents the current position of the ìth candidate solution, and u(i,j) represents the position j of the i-th candidate solution value.

[0095] A dynamic attraction equation is introduced that influences the solution to move towards more promising regions in the search space. The equation automatically guides particles adaptively based on the local and global attraction forces at their locations:

[0096] u new (i,j)=IWV+PBC+GBC+DAC+ANIC+DMC

[0097] IWV stands for Inertia Weight:

[0098] IWV=w(t)*u(i,j)

[0099] Where w(t) is the inertia weight parameter, which promotes the concentrated exploration of the group and makes the group converge more effectively. An equation for dynamically adjusting the interaction strength between particles based on the fitness value is introduced: w(t+1)=w(t)*(1-erp(k*t))

[0100] Where k is a constant that determines the rate of inertia reduction, and t represents the current iteration.

[0101] PBC represents personal best coefficient: PBC=r1*(eps*rand(pbest)-X i )

[0102] Where r1 is a random value 1, eps is a small value, rand(pbest) is a random solution of the current candidate solution, and Xi is the solution number i;

[0103] GBC stands for Global Optimization Coefficient:

[0104] GBC=r2*gbest-Xi

[0105] Among them, r2 is a random value 2, gbest l is the best global solution so far (number of iterations t), X i is the number of solutions i;

[0106] DAC stands for Dynamic Attraction Coefficient:

[0107] DAC=r3*attract i / c1-X i

[0108] Among them, r3 is a random value 3, attract i represents the position with the highest local attraction value near the i-th particle, c1 is the additional acceleration coefficient of the dynamic attraction term, X i represents the number of solutions i;

[0109] ANIC stands for Adaptive Neighborhood Interaction Coefficient:

[0110] ANIC=r4*rand(bestf)-bestf i

[0111] rand(best) is the random fitness value in the current fitness solution, bestf i is the fitness value of the i-th solution.

[0112] Fitness is defined as follows:

[0113]

[0114] DMC is the diversity maintenance coefficient:

[0115] DMC=r5*diversity i / c2-X i

[0116] Among them, r5 is a random value, and c2 is the additional acceleration coefficient of the diversity term. i represents the position of maximum diversity around the i-th particle in the population.

[0117] (4) Final result:

[0118] Display the global best position j0 and the best candidate solution C min And the best fitness bestf.

[0119] Furthermore, the coordinated optimization operation model of electric vehicles and greenhouse cultivation is solved to obtain the optimal coordinated operation plan, which specifically includes: using constraint conditions to solve the objective function to obtain the optimal coordinated operation plan.

[0120] Furthermore, the power source for rural communities is the grid and photovoltaic power generation, and both greenhouse cultivation power consumption and electric vehicle charging require the use of purchased grid electricity and photovoltaic power generation.

[0121] The expression of the operating cost objective function of the coordinated optimization operation model of electric vehicles and greenhouse planting is:

[0122] C=C pv +C g

[0123]

[0124]

[0125] Among them, C is the total operating cost, C pv is the operating cost of photovoltaic power generation, C g is the cost of purchasing electricity from the grid, c pv is the unit cost of photovoltaic power output, P pv,t is the photovoltaic power output, is the unit purchase price of electricity, is the unit selling price of electricity, and is the amount of power exchanged with the grid.

[0126] Photovoltaic power generation parameters include the unit cost of photovoltaic power generation output, installed capacity of photovoltaic power generation, etc.

[0127] Furthermore, the constraints of the coordinated optimization operation model of electric vehicles and greenhouse cultivation are as follows:

[0128] P pv,t ≤P ins

[0129]

[0130]

[0131] Among them, P pv,t is the photovoltaic power output, P ins is the installed capacity of photovoltaic power generation, is the maximum power transmitted from the grid at time t, P load,t is the total electricity consumption in the rural tourism area at time t, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, P g，t The output of photovoltaic power generation cannot exceed the installed capacity.

[0132] Taking a rural area containing multiple rural tourism communities as an example, the effectiveness and feasibility of the proposed operation model are analyzed. The rural area consists of four rural communities. There are three tourist pick-up points in the rural tourism area. Tourist electric buses stop at the pick-up points to pick up tourists and then transport them to the rural tourism communities. There are fourteen routes of different lengths (R1 to R14) connecting the pick-up points of the rural communities, and tourist electric buses can choose different routes to transport tourists. Tourist electric buses departing from pick-up points 1 and 2 have two routes to choose from, while tourist electric buses departing from pick-up point 3 have three routes to choose from. After the tourist electric bus arrives at the rural community, tourists will go sightseeing for a period of time. During the tourists' sightseeing experience, the tourist electric bus will charge and discharge locally and participate in the operation of the power system. After the tour, the tourist electric bus will take the tourists back to the pick-up point, and then return to the rural community to continue charging and discharging. These rural tourism communities have their own residents' electricity consumption, such as Figure 2 They also have crop picking areas with greenhouses for tourists to experience. The electricity consumption of greenhouses in rural tourism areas is calculated based on the light intensity provided by their lighting equipment. The results are as follows Figure 3 As shown in the table, the electricity consumption trends for each greenhouse are basically the same, but there are certain differences in time and value. The lighting equipment in all greenhouses is used to provide light for the crops, so the power consumption reaches its peak between 1:00 and 5:00 and 18:00 and 24:00. This is because during these time periods, the greenhouse cannot obtain sufficient light from solar energy and must rely on artificial lighting to maintain optimal growing conditions for the crops. In addition, the greenhouse basically consumes no electricity between 9:00 and 15:00 because the solar light during this period is sufficient to meet the growth needs of the crops, and there is no need to turn on additional lighting equipment. The structural parameters of the greenhouse are shown in Table 1.

[0133] Figure 4 The charge and discharge results for tourist electric buses 1, 4, and 7 are shown. It can be seen that all three buses charge while docked, as their batteries have been depleted by transporting tourists. The difference lies in the varying charging times depending on the duration of the docking. Furthermore, all three buses discharge their batteries at night, contributing to the power system's operation.

[0134] The comparison settings are as follows:

[0135] Case 1: Tourist electric buses only charge and do not discharge to participate in the operation of the power system, that is, tourist electric buses are only regarded as power loads in rural tourism areas.

[0136] Case 2: Using the proposed operation model, in this case, the tourist electric bus is not only regarded as an electric load, but also supplies power to the system during the discharge period.

[0137] The photovoltaic power generation output and power exchange results with the grid in the two cases are as follows: Figure 5 and Figure 6 As shown in Figure 2, the proposed operating model shows that Case 2 (i.e., the proposed operating model) outputs more PV power than Case 1. Case 1 purchases more electricity from the grid and sells less to the grid than Case 2. This is because the tourist electric buses boost PV power output, thereby reducing reliance on grid electricity.

[0138] The power system costs for the two cases are shown in Table 4. It can be seen that the operating cost of photovoltaic power generation in Case 1 is 801.21 yuan, which is lower than that in Case 2. However, the cost of the power grid in Case 1 is 3513.33 yuan, which is higher than that in Case 2. Therefore, the total cost of the case is 4314.54 yuan, which is higher than that in Case 2.

[0139] Table 1. Parameters of greenhouse planting structure in rural tourism communities

[0140] Length (m) Width (m) Height (m) <![CDATA[Area (m 2 )]]> Village 1 50 40 5 2000 Village 2 100 30 4 3000 Village 3 60 30 4 1800 Village 4 50 30 5 1500

[0141] Table 2 Parameters of tourist electric buses

[0142] parameter value Battery capacity (kWh) 200 Maximum charge and discharge power (kW) 20 Charge and discharge efficiency 0.9 Maximum / minimum state of charge 0.9 / 0.1

[0143] The unit operating cost of photovoltaic power generation is 0.03 yuan / kW.

[0144] Table 3 Time and route schedule for tourist electric buses

[0145]

[0146]

[0147] Table 4 Cost table of power system under two cases

[0148] Photovoltaic power generation operating costs Grid-related costs Total cost Case 1 801.21 3513.33 4314.54 Case 2 1001.51 2955.31 3956.82

[0149] Based on the comparison of the above scenarios, we propose a power system optimization model based on greenhouse agricultural cultivation and tourist electric buses to address the problem of coordinated energy optimization in tourism. This model calculates power consumption based on meteorological conditions (i.e., solar insolation) and establishes a spatiotemporal model of the electric buses, taking into account tourist travel plans. Ultimately, the optimization of the power system operation model solves the actual operational issues. Compared to a scenario that only considers electric bus charging, this model can increase photovoltaic power generation, reduce public grid power supply, and reduce system operating costs.

[0150] Based on the same inventive concept, an embodiment of the present disclosure further provides an electronic device. Figure 7 FIG. 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Figure 7As shown, an electronic device includes: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement any of the energy management methods for electric vehicles and greenhouse cultivation that consider tourism collaboration as described in the above embodiments. The one or more I / O interfaces 103 are connected between the processor and the memory and are configured to enable information exchange between the processor and the memory.

[0151] Among them, the processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically such as SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read-write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus), etc.

[0152] In some embodiments, the processor 101 , the memory 102 , and the I / O interface 103 are connected to each other via a bus 104 , and further connected to other components of the computing device.

[0153] In some embodiments, the one or more processors 101 include a field programmable gate array.

[0154] Based on the same inventive concept, embodiments of the present disclosure further provide a computer-readable medium having a computer program stored thereon, wherein when executed by a processor, the program implements the steps of any of the above-described methods for energy management of electric vehicles and greenhouse cultivation considering tourism collaboration.

[0155] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, including a computer program carried on a machine-readable medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the system of the present disclosure are executed.

[0156] It should be noted that the computer-readable medium described in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0157] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0158] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present disclosure, and such modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. An energy management method for electric vehicles and greenhouse planting considering tourism synergy, characterized in that: The steps include: Obtaining basic parameters; the basic parameters include: greenhouse planting structure parameters, tourist electric vehicle parameters, and photovoltaic power generation parameters; Establishing a greenhouse planting power consumption model; the greenhouse planting power consumption model is configured to obtain greenhouse planting power consumption according to greenhouse planting structural parameters; Establishing an electric vehicle charging and discharging model under tourist route conditions; the electric vehicle charging and discharging model is configured to be able to obtain the state of charge of the electric vehicle based on the tourist electric vehicle parameters; Establishing a coordinated optimization operation model for electric vehicles and greenhouse planting; when establishing the coordinated optimization operation model for electric vehicles and greenhouse planting, constructing an objective function for minimizing the operating costs of electric vehicles and greenhouse planting; the objective function is: minimizing the operating costs determined based on photovoltaic power generation parameters, greenhouse planting power consumption, and the state of charge of electric vehicles; Solve the coordinated optimization operation model of electric vehicles and greenhouse cultivation to obtain the optimal coordinated operation plan.

2. The energy management method for electric vehicles and greenhouse farming considering tourism synergy according to claim 1 is characterized by: The mathematical expression of the greenhouse planting electricity consumption model is: in, is the total electricity consumption of the greenhouse in time period t, is the number of lighting devices, P l is the power of the lighting equipment, k c is the illuminance conversion coefficient, LF is the luminous flux of the lighting equipment, U is the lighting utilization coefficient, L and W are the length and width of the greenhouse, H is the installation height of the lighting equipment, I ad is the additional light provided by the lighting equipment in the greenhouse I ad , I set It is the light intensity under conditions suitable for crop growth. is the total solar radiation received by the greenhouse.

3. The energy management method for electric vehicles and greenhouse farming considering tourism synergy according to claim 1 is characterized by: The electric vehicle charging and discharging model incorporates the electric vehicle's spatiotemporal model, which is as follows: r t,bus is a binary variable, r t,v =1 means that the tourist electric vehicle is traveling on route r at time t, where R is the set of routes; If an electric car reaches the end point of node n at time t, then it must start from node n and travel on the next route at time t+1. This restriction can be described by the following formula: Among them, n t,bus and n t+1,bus It means that the tourist electric car is located at node n at time t and time t+1.

4. The energy management method for electric vehicles and greenhouse farming considering tourism synergy according to claim 3 is characterized by: The state of charge calculation of electric vehicles meets the following conditions: State of Charge: Among them, SOC t，bus is the state of charge of the tourist electric vehicle at time t, SOC t+1，bus is the state of charge of the tourist electric vehicle at time t+1, E bus,max is the maximum battery capacity of the tourist electric vehicle, η is the charge and discharge efficiency, ΔT is the time interval, when ΔT = 1 it means that the tourist electric vehicles are on the driving route, when ΔT = 1 it means that they are at the node, is the minimum state of charge of the tourist electric vehicle at time t, is the maximum state of charge of the tourist electric vehicle at time t, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, E bus，d It is the capacity lost in the operation of tourist electric vehicles.

5. The energy management method for electric vehicles and greenhouse planting considering tourism synergy according to claim 1 or 3, characterized in that: The charging and discharging of electric vehicles in the electric vehicle charging and discharging model should meet the following conditions: 0≤S n,dis +S n,char ≤1 Among them, S n,char is a binary variable representing the charge state at node n, S n,dis is a binary variable representing the discharge state at node n, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, is the maximum charging power of tourist electric vehicles, It is the maximum discharge power of tourist electric vehicles.

6. The energy management method for electric vehicles and greenhouse farming considering tourism synergy according to claim 1 is characterized by: Solve the coordinated optimization operation model of electric vehicles and greenhouse cultivation to obtain the optimal coordinated operation plan, specifically including: using constraint conditions to solve the operation cost objective function to obtain the optimal coordinated operation plan.

7. The energy management method for electric vehicles and greenhouse farming considering tourism synergy according to claim 6 is characterized by: The expression of the operating cost objective function of the coordinated optimization operation model of electric vehicles and greenhouse planting is: C=C pv +C g Among them, C is the total operating cost, C pv is the operating cost of photovoltaic power generation, C g is the cost of purchasing electricity from the grid, c pv is the unit cost of photovoltaic power output, P pv,t is the photovoltaic power output, is the unit purchase price of electricity, is the unit selling price of electricity, The amount of electricity purchased from the power grid. It is the amount of electricity sold to the grid.

8. The energy management method for electric vehicles and greenhouse planting considering tourism synergy according to claim 6 is characterized by: The constraints of the coordinated optimization operation model of electric vehicles and greenhouse cultivation are: P pv,t ≤P ins Among them, P pv,t is the photovoltaic power output, P ins is the installed capacity of photovoltaic power generation, is the maximum power transmitted from the grid at time t, P load,t is the total electricity consumption in the rural tourism area at time t, is the charging power of the tourist electric vehicle at time t, is the discharge power of the tourist electric vehicle at time t, P g，t It is time to purchase grid electricity.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, and the one or more computer programs are executed by the at least one processor to enable the at least one processor to execute the energy management method for electric vehicles and greenhouse cultivation considering tourism collaboration as described in any one of claims 1-8.

10. A computer-readable medium, characterized in that The computer-readable medium stores a computer program, wherein when the program is executed by a processor, the steps of the energy management method for electric vehicles and greenhouse planting considering tourism collaboration as described in any one of claims 1 to 8 are implemented.

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