Energy management method for electric vehicles and greenhouse planting considering tourism synergy

Abstract

The application discloses an energy management method of electric vehicles and greenhouse planting considering tourism coordination, and the method comprises the following steps: acquiring basic parameters; the basic parameters comprise greenhouse planting structure parameters, tourism electric vehicle parameters and photovoltaic power generation parameters; a greenhouse planting power consumption model is established; the greenhouse planting power consumption model is configured to be able to obtain greenhouse planting power consumption according to the greenhouse planting structure parameters; an electric vehicle charging and discharging model under a tourism route condition is established; the electric vehicle charging and discharging model is configured to be able to obtain the state of charge of the electric vehicle according to the tourism electric vehicle parameters; a collaborative optimization operation model of the electric vehicle and the greenhouse planting is established, and an objective function of the collaborative optimization operation model is to minimize the operation cost determined according to the photovoltaic power generation parameters, the greenhouse planting power consumption and the state of charge of the electric vehicle; and the collaborative optimization operation model of the electric vehicle and the greenhouse planting is solved to obtain an optimal collaborative operation scheme.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle energy management technology, and more particularly to an energy management method for electric vehicles and greenhouse cultivation that takes into account tourism synergy. Background Technology

[0002] In recent years, the integration of rural areas and tourism has developed rapidly, becoming a promising solution for improving the economic and living standards of rural areas. Rural tourism areas possess unique electricity loads, such as greenhouse cultivation, which provides a suitable growing environment for crops, thereby enhancing the tourist experience. Electric tourist buses are considered an energy resource connected to the power system, as their ability to store electrical energy allows them to coordinate with the power system for power dispatch. However, current technologies do not integrate greenhouse cultivation and electric tourist buses, nor do they consider energy management methods for combining electric vehicles with greenhouse cultivation in 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 cultivation that takes into account tourism synergy.

[0004] The technical solution of this invention is implemented as follows: This invention provides an energy management method for electric vehicles and greenhouse cultivation that considers tourism synergy, comprising the following steps:

[0005] Obtain basic parameters; these basic parameters include: greenhouse planting structure parameters, electric vehicle tourism parameters, and photovoltaic power generation parameters.

[0006] A power consumption model for greenhouse cultivation is established; the power consumption model for greenhouse cultivation is configured to obtain the power consumption of greenhouse cultivation based on the structural parameters of greenhouse cultivation.

[0007] A charging and discharging model for electric vehicles under tourism route conditions is established; the electric vehicle charging and discharging model is configured to obtain the state of charge of the electric vehicle based on the parameters of the tourism electric vehicle.

[0008] Establish a collaborative optimization operation model for electric vehicles and greenhouse cultivation; when establishing the collaborative optimization operation model for electric vehicles and greenhouse cultivation, construct an objective function to minimize the operating costs of electric vehicles and greenhouse cultivation; the objective function is: to minimize the operating costs determined based on photovoltaic power generation parameters, greenhouse cultivation power consumption, and the state of charge of electric vehicles;

[0009] Solve the collaborative optimization operation model of electric vehicles and greenhouse planting to obtain the optimal collaborative operation scheme.

[0010] Furthermore, the mathematical expression for the electricity consumption model in greenhouse cultivation is:

[0011]

[0012]

[0013]

[0014]

[0015] in, This represents the total electricity consumption of the greenhouse during time period t. It refers to the number of lighting fixtures. It is the power of the lighting equipment, k. c LF is the illuminance conversion factor, U is the luminous flux of the lighting equipment, L and W are the length and width of the greenhouse, H is the installation height of the lighting equipment, and I is the illuminance conversion factor. ad Additional light provided by the lighting equipment in the greenhouse ad , It refers to the light intensity under suitable growing conditions for crops. It is the total solar radiation received by the greenhouse.

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

[0017]

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

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

[0020]

[0021]

[0022] in, and This indicates that the electric tourist vehicle is located at node n at time t and time t+1.

[0023] Furthermore, the state of charge calculation for electric vehicles satisfies the following conditions:

[0024]

[0025] State of charge:

[0026] in, It refers to the state of charge of the electric tourist vehicle at time t. It refers to the state of charge of the electric tourist vehicle at time t+1. This is the maximum battery capacity for electric vehicles used for tourism. It refers to charge / discharge efficiency. It is a time interval, when =1 indicates that the electric tourist vehicle is on the driving route, when =1 indicates that they are located at nodes. It represents the minimum state of charge of the electric vehicle for tourism at time t. This represents the maximum state of charge of the electric vehicle for tourism at time t. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. This refers to the capacity loss caused by the operation of electric vehicles for tourism.

[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]

[0031] in, It is a binary variable representing the charging state at node n. It is a binary variable representing the discharge state at node n. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. This is the maximum charging power for electric vehicles used for tourism. This is the maximum discharge power of electric vehicles for tourism.

[0032] Furthermore, the optimal collaborative operation model for electric vehicles and greenhouse cultivation is solved to obtain the optimal collaborative operation scheme. Specifically, this includes solving the objective function of operating cost using constraints to obtain the optimal collaborative operation scheme.

[0033] Furthermore, the expression for the objective function of the operating cost of the synergistic optimization operation model of electric vehicles and greenhouse cultivation is as follows:

[0034]

[0035]

[0036]

[0037] Where C is the total operating cost, C pv C is the operating cost of photovoltaic power generation. g The cost of purchasing electricity from the power grid. It is the unit cost of photovoltaic power generation output. It is photovoltaic power generation output. This is the unit purchase price of electricity. This is the unit price of electricity. This refers to the amount of electricity purchased from the power grid. It refers to the electricity sold to the power grid.

[0038] Furthermore, the constraints of the collaborative optimization operation model for electric vehicles and greenhouse cultivation are as follows:

[0039]

[0040]

[0041]

[0042] in, It is photovoltaic power generation output. It refers to the installed capacity of photovoltaic power generation. It is the maximum power transmitted from the grid at time t. It is the total electricity consumption of a rural tourism area at time t. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. It is a time-to-purchase electricity network.

[0043] The present invention also discloses an electronic device, comprising:

[0044] At least one processor; and

[0045] A memory communicatively connected to the at least one processor; wherein,

[0046] The memory stores one or more computer programs that can be executed by the at least one processor, the one or more of the computer programs being executed by the at least one processor to enable the at least one processor to perform the energy management method for electric vehicles and greenhouse cultivation that takes into account tourism collaboration as described above.

[0047] The present invention also discloses a computer-readable medium storing a computer program, wherein the program, when executed by a processor, implements the steps in the energy management method for electric vehicles and greenhouse cultivation that takes into account tourism collaboration as described above.

[0048] Compared with the prior art, the present invention has the following beneficial effects: The present invention takes into account the coordinated operation of greenhouse planting and electric tourist buses, and takes into account the impact of electric vehicle operation, aiming to promote the output of photovoltaic power generation, reduce the amount of electricity purchased from the grid, and at the same time reduce the operating cost of the system.

[0049] This invention addresses the problem of coordinated energy optimization in tourism by proposing an optimized power system operation model based on greenhouse agriculture and electric tourist buses. The model calculates power consumption based on meteorological conditions (i.e., solar radiation) and establishes a spatiotemporal model of the electric buses in conjunction with tourist travel plans. Ultimately, the practical operational problems are solved by optimizing the power system operation model. Compared to scenarios that only consider electric bus charging, this model can increase photovoltaic power generation, reduce the power supply to the public grid, and simultaneously lower system operating costs. Attached Figure Description

[0050] Figure 1 A flowchart illustrating an energy management method for electric vehicles and greenhouse cultivation that considers tourism collaboration, provided for embodiments of this disclosure;

[0051] Figure 2 This is a comparison chart of electricity consumption by residents in tourist-oriented rural communities according to embodiments of this disclosure;

[0052] Figure 3 This is a comparison chart of electricity consumption in agricultural greenhouses in the embodiments of this disclosure;

[0053] Figure 4 This is a diagram showing the charging and discharging results of electric tourist buses 1, 4, and 7 in this embodiment of the present disclosure;

[0054] Figure 5 These are diagrams showing the photovoltaic power generation output results under two different scenarios in the embodiments of this disclosure;

[0055] Figure 6 The diagram shows the power variation with the power grid under two different scenarios in the embodiments of this disclosure;

[0056] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0057] To enable those skilled in the art to better understand the technical solutions of this disclosure, the disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0059] In the various figures, the same elements are represented by similar reference numerals. For clarity, not all parts in the figures are drawn to scale. Furthermore, some well-known parts may not be shown in the figures.

[0060] Many specific details of this disclosure are described below to provide a clearer understanding of it. However, as those skilled in the art will understand, this disclosure may be implemented without following these specific details.

[0061] Figure 1 A flowchart illustrating an energy management method for electric vehicles and greenhouse cultivation that considers tourism synergy, provided as an embodiment of this disclosure. Figure 1 As shown, this invention provides an energy management method for electric vehicles and greenhouse cultivation that considers tourism synergy, comprising the following steps:

[0062] Obtain basic parameters; these basic parameters include: greenhouse planting structure parameters, electric vehicle tourism parameters, and photovoltaic power generation parameters.

[0063] A power consumption model for greenhouse cultivation is established; the power consumption model for greenhouse cultivation is configured to obtain the power consumption of greenhouse cultivation based on the structural parameters of greenhouse cultivation.

[0064] A charging and discharging model for electric vehicles under tourism route conditions is established; the electric vehicle charging and discharging model is configured to obtain the state of charge of the electric vehicle based on the parameters of the tourism electric vehicle.

[0065] A collaborative optimization operation model for electric vehicles and greenhouse cultivation is established. The objective of this model is to minimize the operating cost determined based on the photovoltaic power generation parameters, greenhouse cultivation power consumption, and the state of charge of the electric vehicles.

[0066] Solve the collaborative optimization operation model of electric vehicles and greenhouse planting to obtain the optimal collaborative operation scheme.

[0067] Furthermore, the mathematical expression for the electricity consumption model in greenhouse cultivation is:

[0068]

[0069]

[0070]

[0071]

[0072] in, This represents the total electricity consumption of the greenhouse during time period t. It refers to the number of lighting fixtures. It is the power of the lighting equipment, k. c LF is the illuminance conversion factor, U is the luminous flux of the lighting equipment, L and W are the length and width of the greenhouse, H is the installation height of the lighting equipment, and I is the illuminance conversion factor. ad Additional light provided by the lighting equipment in the greenhouse ad , It refers to the light intensity under suitable growing conditions for crops. It is the total solar radiation received by the greenhouse.

[0073] Furthermore, the electric vehicle charging and discharging model incorporates a spatiotemporal model of the electric vehicle, which is as follows:

[0074]

[0075] r t,bus r is a binary variable t,bus =1 indicates that the electric tourist vehicle is traveling on route r at time t, where R is the set of routes; Ensure that the electric tourist bus can only travel on one route during its journey.

[0076] If an electric vehicle reaches the destination of node n at time t, then it must start from node n at time t+1 and travel on the next route. This constraint can be described by the following formula:

[0077]

[0078]

[0079] in, and This indicates that the electric tourist vehicle is located at node n at time t and time t+1.

[0080] Furthermore, the state of charge calculation for electric vehicles satisfies the following conditions:

[0081]

[0082] State of charge:

[0083] in, It refers to the state of charge of the electric tourist vehicle at time t. It refers to the state of charge of the electric tourist vehicle at time t+1. This is the maximum battery capacity for electric vehicles used for tourism. It refers to charge / discharge efficiency. It is a time interval, when =1 indicates that the electric tourist vehicle is on the driving route, when =1 indicates that they are located at nodes. It represents the minimum state of charge of the electric vehicle for tourism at time t. This represents the maximum state of charge of the electric vehicle for tourism at time t. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. This refers to the capacity loss caused by the operation of electric vehicles for tourism.

[0084] Furthermore, the charging and discharging of electric vehicles in the electric vehicle charging and discharging model should meet the following conditions:

[0085]

[0086]

[0087]

[0088] in, It is a binary variable representing the charging state at node n. It is a binary variable representing the discharge state at node n. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. This is the maximum charging power for electric vehicles used for tourism. This is the maximum discharge power of electric vehicles for tourism. This indicates that the electric tourist bus cannot be charged and discharged simultaneously.

[0089] In some embodiments, establishing a collaborative optimization operation model for electric vehicles and greenhouse cultivation specifically includes: using a collaborative group optimization algorithm to establish a collaborative optimization operation model for electric vehicles and greenhouse cultivation, the specific process of which is as follows:

[0090] (1) Population initialization method:

[0091] X=rand(N,D)*(UB-LB)+LB

[0092] Where N represents the solution, D represents the dimension or variable of the given problem, and UB and LB represent the upper and lower bounds of each dimension of the problem space, respectively. Generate random positions for each particle in the population. Initialize the velocity to zero. Display the initial positions, set the global best position to empty, 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.

[0093] (2) Update candidate solutions:

[0094] X new (i,j)=X (i,j)+u(i,j)

[0095] Xnew(i,j) represents the i-th candidate solution C. i The new optimized position, X(i,j) represents the current position of the ì-th candidate solution, and u(i,j) represents the position j of the ith candidate solution value.

[0096] A dynamic attraction equation is introduced, which influences the movement of solutions towards more promising regions in the search space. This equation adaptively guides particles based on both local and global attraction at their location.

[0097] u new (i,j)=IWV+ PBC +GBC +DAC + ANIC + DMC

[0098] IWV represents inertial weight:

[0099] IWV=w(t)*u(i,j)

[0100] Where w(t) is the inertia weight parameter, it promotes the concentrated exploration of the swarm and makes the swarm converge more effectively. An equation based on the fitness value to dynamically adjust the strength of the interaction between particles is introduced:

[0101] w(t+1)=w(t)*(1-erp(k*t))

[0102] Where k is a constant that determines the rate of inertial weight loss, and t represents the current iteration.

[0103] PBC represents the individual best coefficient: PBC = r1 * (eps * rand(pbest) - X) i )

[0104] Where r1 is a random value of 1, eps is a small value, rand(pbest) is a random solution of the current candidate solution, and Xi is the number of solutions i;

[0105] GBC represents the globally optimal coefficient:

[0106] GBC=r2*gbest-X i

[0107] Where r2 is a random value of 2, gbest l X is the best global solution to date (number of iterations t). i Let i be the solution number;

[0108] DAC represents the dynamic attraction coefficient:

[0109] DAC = r3 * attract i / c1-X i

[0110] Where r3 is a random value of 3, attract i This represents the location with the highest local attraction value near the i-th particle, where c1 is the additional acceleration coefficient of the dynamic attraction term, and X... i Let i represent the solution number;

[0111] ANIC represents the adaptive neighborhood interaction coefficient:

[0112] ANIC = r4 * rand(bestf) - bestf i

[0113] rand(best) is the random fitness value in the current fitness solution, and bestf i Let be the fitness value of the i-th solution.

[0114] Fitness is defined as follows:

[0115]

[0116] DMC is the diversity preservation coefficient.

[0117] DMC=r5*diversity i / c2-X i

[0118] Where r5 is a random value and c2 is the additional acceleration coefficient of the diversity term. i This represents the location of maximum diversity near the i-th particle in the population.

[0119] (3) Final result:

[0120] Display the global optimal position j0 and the optimal candidate solution C. min And best fitness (bestf).

[0121] Furthermore, the optimal collaborative operation model for electric vehicles and greenhouse cultivation is solved to obtain the optimal collaborative operation scheme. Specifically, this includes solving the objective function using constraints to obtain the optimal collaborative operation scheme.

[0122] Furthermore, the power supply for rural communities comes from the power grid and photovoltaic power generation. The electricity consumption for greenhouse cultivation and the charging of electric vehicles both require the use of grid-purchased electricity and photovoltaic power generation.

[0123] The objective function for the operating cost of the synergistic optimization model of electric vehicles and greenhouse cultivation is expressed as follows:

[0124]

[0125]

[0126]

[0127] Where C is the total operating cost, C pv C is the operating cost of photovoltaic power generation. g The cost of purchasing electricity from the power grid. It is the unit cost of photovoltaic power generation output. It is photovoltaic power generation output. This is the unit purchase price of electricity. This is the unit price of electricity. and It is the amount of power exchanged with the power grid.

[0128] Photovoltaic power generation parameters include the unit cost of photovoltaic power generation output and the installed capacity of photovoltaic power generation.

[0129] Furthermore, the constraints of the collaborative optimization operation model for electric vehicles and greenhouse cultivation are as follows:

[0130]

[0131]

[0132]

[0133] in, It is photovoltaic power generation output. It refers to the installed capacity of photovoltaic power generation. It is the maximum power transmitted from the grid at time t. It is the total electricity consumption of a rural tourism area at time t. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. This is the power purchased from the t-network. The output of photovoltaic power generation cannot exceed the installed capacity.

[0134] Taking a rural area comprising multiple rural tourism communities as an example, this paper analyzes the effectiveness and feasibility of the proposed operational model. The rural area consists of four rural communities. There are three tourist pick-up points in the rural tourism area. Electric tourist buses stop at the pick-up points to pick up tourists and then transport them to the rural tourism communities. Fourteen routes of varying lengths (R1 to R14) connect the pick-up points of the rural communities, and the electric tourist buses can choose different routes to transport tourists. Electric tourist buses departing from pick-up points 1 and 2 have two routes to choose from, while those departing from pick-up point 3 have three routes to choose from. After arriving at the rural community, tourists will spend some time sightseeing. During the tourist experience, the electric tourist buses will recharge and discharge locally, participating in the operation of the power system. After the tour, the electric tourist buses will return the tourists to the pick-up points and then return to the rural community to continue recharging and discharging. Each of these rural tourism communities has its own residential electricity consumption, such as... Figure 2 As shown in the image. They also feature greenhouse-style crop picking areas for visitors to experience. The electricity consumption of greenhouses within rural tourism areas is calculated based on the light intensity provided by their lighting equipment, as shown in the image. Figure 3 As shown in Table 1, the power consumption trends of all greenhouses are generally consistent, but there are some differences in time and value. All greenhouses' lighting equipment is used to provide light for the crops, therefore power consumption peaks between 1:00 and 5:00 and between 18:00 and 24:00. This is because during these periods, the greenhouses cannot obtain sufficient sunlight from solar energy and must rely on artificial lighting to maintain optimal growth conditions for the crops. Furthermore, the greenhouses consume virtually no electricity between 9:00 and 15:00, as the solar light during this period is sufficient to meet the crop growth needs, eliminating the need for additional lighting equipment. The structural parameters of the greenhouses are shown in Table 1.

[0135] Figure 4 The charging and discharging results of electric tour buses 1, 4, and 7 are shown. It can be seen that all three buses recharge while parked, as their batteries were depleted during previous passenger transport. The difference lies in the charging time, which varies depending on the duration of each stop. Furthermore, all three electric tour buses discharge power overnight, contributing to the operation of the power system.

[0136] The comparison settings are as follows:

[0137] 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 the power load of rural tourism areas.

[0138] Case 2: Using the proposed operating model, in this case, the electric tourist bus is not only considered as an electrical load, but also supplies power to the system during discharge.

[0139] The photovoltaic power output and power exchange results with the grid in the two cases are as follows: Figure 5 and Figure 6 As shown, the photovoltaic power output in Case 2 (i.e., the proposed operating model) is greater than that in Case 1. Case 1 purchases more electricity from the grid than Case 2, but sells less electricity to the grid. This is because the electric tourist buses promote photovoltaic power output, thereby reducing dependence on grid electricity.

[0140] Table 4 shows the costs of the power system under the two cases. It can be seen that the operating cost of photovoltaic power generation in Case 1 is 801.21 yuan, lower than that in Case 2. However, the cost of the power grid in Case 1 is 3513.33 yuan, higher than that in Case 2. Therefore, the total cost of the case is 4314.54 yuan, higher than that in Case 2.

[0141] Table 1. Parameters of Greenhouse Planting Structure in Rural Tourism Communities

[0142]

[0143] Table 2 Parameter Table for Tourist Electric Buses

[0144]

[0145] The unit operating cost of photovoltaic power generation is 0.03 yuan / kW.

[0146] Table 3. Schedule and Route of Tourist Electric Buses

[0147]

[0148]

[0149]

[0150] Table 4. Cost Table of Power System under Two Cases

[0151]

[0152] Based on the comparisons across different scenarios, a power system optimization model based on greenhouse agriculture and electric tourist buses is proposed to address the issue of coordinated energy optimization in tourism. This model calculates power consumption based on meteorological conditions (i.e., solar radiation) and establishes a spatiotemporal model of the electric buses by incorporating tourist travel plans. Ultimately, the practical operational problems are solved by optimizing the power system operation model. Compared to scenarios that only consider electric bus charging, this model can increase photovoltaic power generation, reduce the amount of power supplied to the public grid, and simultaneously lower system operating costs.

[0153] Based on the same inventive concept, this disclosure also provides an electronic device. Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. Figure 7 As 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 that, when executed by the one or more processors, cause the one or more processors to implement an energy management method for electric vehicles and greenhouse cultivation considering tourism collaboration, as described in any of the above embodiments; the one or more I / O interfaces 103 are connected between the processors and the memory and configured to enable information interaction between the processors and the memory.

[0154] 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 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).

[0155] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0156] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0157] Based on the same inventive concept, this disclosure also provides a computer-readable medium. This computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the energy management methods for electric vehicles and greenhouse cultivation considering tourism collaboration described in the above embodiments.

[0158] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined above in the system of this disclosure.

[0159] It should be noted that the computer-readable medium disclosed herein 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, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this 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, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0160] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0161] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. An energy management method for electric vehicles and greenhouse cultivation that considers tourism synergy, characterized in that, Includes the following steps: Obtain basic parameters; these basic parameters include: greenhouse planting structure parameters, electric vehicle tourism parameters, and photovoltaic power generation parameters. A power consumption model for greenhouse cultivation is established; the power consumption model for greenhouse cultivation is configured to obtain the power consumption of greenhouse cultivation based on the structural parameters of greenhouse cultivation. The mathematical expression for the electricity consumption model in greenhouse cultivation is: in, This represents the total electricity consumption of the greenhouse over time period t. It refers to the number of lighting fixtures. It is the power of the lighting equipment, k. c LF is the illuminance conversion factor, U is the luminous flux of the lighting equipment, L and W are the length and width of the greenhouse, H is the installation height of the lighting equipment, and I is the illuminance conversion factor. ad Additional light provided by the lighting equipment in the greenhouse ad , It refers to the light intensity under suitable growing conditions for crops. This is the total solar radiation received by the greenhouse; A charging and discharging model for electric vehicles under tourism route conditions is established; the electric vehicle charging and discharging model is configured to obtain the state of charge of the electric vehicle based on the parameters of the tourism electric vehicle. The electric vehicle charging and discharging model incorporates a spatiotemporal model of the electric vehicle, which is as follows: r t,bus r is a binary variable t,bus =1 indicates that the electric tourist vehicle is traveling on route r at time t, where R is the set of routes; If an electric vehicle reaches the destination node n at time t, then it must satisfy the constraint that it starts from node n and travels on the next route at time t+1. This constraint can be described by the following formula: in, and This indicates that the electric tourist vehicle is located at node n at time t and time t+1; The state of charge calculation for electric vehicles must satisfy the following conditions: State of charge: in, It refers to the state of charge of the electric tourist vehicle at time t. It refers to the state of charge of the electric tourist vehicle at time t+1. This is the maximum battery capacity for electric vehicles used for tourism. It refers to charge / discharge efficiency. It is a time interval, when =1 indicates that the electric tourist vehicle is on the driving route, when =1 indicates that they are located at nodes. It represents the minimum state of charge of the electric vehicle for tourism at time t. This represents the maximum state of charge of the electric vehicle for tourism at time t. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. This refers to the capacity lost during the operation of electric vehicles for tourism. Establish a collaborative optimization operation model for electric vehicles and greenhouse cultivation; when establishing the collaborative optimization operation model for electric vehicles and greenhouse cultivation, construct an objective function to minimize the operating costs of electric vehicles and greenhouse cultivation; the objective function is: to minimize the operating costs determined based on photovoltaic power generation parameters, greenhouse cultivation power consumption, and the state of charge of electric vehicles; Solve the collaborative optimization operation model of electric vehicles and greenhouse planting to obtain the optimal collaborative operation scheme.

2. The energy management method for electric vehicles and greenhouse cultivation considering tourism synergy according to claim 1, characterized in that: In the electric vehicle charging and discharging model, the charging and discharging of electric vehicles should meet the following conditions: in, It is a binary variable representing the charging state at node n. It is a binary variable representing the discharge state at node n. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. This is the maximum charging power for electric vehicles used for tourism. This is the maximum discharge power of electric vehicles for tourism.

3. The energy management method for electric vehicles and greenhouse cultivation considering tourism synergy according to claim 1, characterized in that: Solving the collaborative optimization operation model of electric vehicles and greenhouse planting to obtain the optimal collaborative operation scheme includes: solving the objective function of operation cost using constraints to obtain the optimal collaborative operation scheme.

4. The energy management method for electric vehicles and greenhouse cultivation considering tourism synergy according to claim 3, characterized in that: The objective function for the operating cost of the synergistic optimization model of electric vehicles and greenhouse cultivation is expressed as follows: Where C is the total operating cost, C pv C is the operating cost of photovoltaic power generation. g The cost of purchasing electricity from the power grid. It is the unit cost of photovoltaic power generation output. It is photovoltaic power generation output. This is the unit purchase price of electricity. This is the unit price of electricity. This refers to the amount of electricity purchased from the power grid. It refers to the electricity sold to the power grid.

5. The energy management method for electric vehicles and greenhouse cultivation considering tourism synergy according to claim 3, characterized in that: The constraints of the collaborative optimization operation model for electric vehicles and greenhouse cultivation are as follows: in, It is photovoltaic power generation output. It refers to the installed capacity of photovoltaic power generation. It is the maximum power transmitted from the grid at time t. It is the total electricity consumption of a rural tourism area at time t. It is the charging power of the electric vehicle for tourism at time t. It is the discharge power of the electric vehicle for tourism at time t. It is a time-to-purchase electricity network.

6. 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, the one or more of the computer programs being executed by the at least one processor to enable the at least one processor to perform an energy management method for electric vehicles and greenhouse cultivation that takes into account tourism synergy as described in any one of claims 1-5.

7. A computer-readable medium, characterized in that, The computer-readable medium stores a computer program, wherein when executed by a processor, the program implements the steps in the energy management method for electric vehicles and greenhouse cultivation that takes into account tourism synergy as described in any one of claims 1-5.

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