Electric vehicle scheduling method and system for distribution area with light storage, and storage medium

Abstract

The application provides a power distribution area electric vehicle scheduling method, system and storage medium containing a light storage, wherein the scheduling method: firstly, clustering analysis is carried out according to historical data of photovoltaic power generation, a photovoltaic output scene is generated, and photovoltaic power generation power in a day is determined; secondly, an electric storage energy model and an electric vehicle battery loss mathematical model are respectively established; under the condition of fully considering power grid loss and minimum operation cost, taking minimum load peak-valley difference and power purchase and sale cost as an objective function, an electric vehicle coordinated scheduling model of a photovoltaic and storage power generation system is established; then, decision variables and constraint conditions of the electric vehicle coordinated scheduling model of the photovoltaic and storage power generation system are determined; then, a multi-objective particle swarm algorithm is used to optimize and solve the electric vehicle coordinated scheduling model of the photovoltaic and storage power generation system; the application fully utilizes the advantages of the photovoltaic and storage power generation system and a traditional power grid, increases renewable energy consumption, reduces the use of coal and reduces the cost of electric vehicle battery loss.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of new energy technology, and in particular to a power distribution area electric vehicle scheduling method containing a light storage system, a system and a storage medium. BACKGROUND

[0002] With the aggravation of the greenhouse effect and the depletion of fossil energy, developing clean energy has become the consensus of all sectors of society. Distributed energy using renewable energy for power generation has the advantages of low pollution and high efficiency, and has developed rapidly in recent years. At the same time, with the gradual implementation of different electricity price policies in China, the increasing maturity of energy storage technology and the substantial increase in the proportion of distributed power generation technology, the economic dispatching and unit commitment of power grids containing energy storage systems under different electricity price policies have gradually become a research hotspot.

[0003] At present, a lot of research work has been done on the power grid scheduling problem of renewable energy and electric vehicles, but there is less research on the uncertainty of renewable energy output and the role of energy storage in economic dispatching.

[0004] Therefore, there is an urgent need for a power distribution area electric vehicle scheduling method containing a light storage system, which utilizes the respective advantages of the light storage power generation system and the traditional power grid, increases the consumption of renewable energy, reduces the use of coal and reduces the cost of electric vehicle battery loss. SUMMARY

[0005] The main purpose of the application is to provide a power distribution area electric vehicle scheduling method containing a light storage system, which utilizes the respective advantages of the light storage power generation system and the traditional power grid, increases the consumption of renewable energy, reduces the use of coal and reduces the cost of electric vehicle battery loss.

[0006] To achieve the above purpose, the application provides a power distribution area electric vehicle scheduling method containing a light storage system, which comprises the following steps:

[0007] S1: using a multiple linear regression algorithm to perform cluster analysis on historical data of photovoltaic power generation, generating a photovoltaic output scene and determining photovoltaic power generation power in a day;

[0008] S2: respectively establishing an electric energy storage model and an electric vehicle battery loss mathematical model;

[0009] S3: under the condition of fully considering power grid loss and minimum operation cost, taking the minimum load peak-valley difference and power purchase and sale cost as the objective function, establishing an electric vehicle coordinated scheduling model containing a light storage power generation system;

[0010] S4: determining the decision variables and constraint conditions of the electric vehicle coordinated scheduling model containing the light storage power generation system;

[0011] S5: The multi-objective particle swarm optimization algorithm (MOPSO) is used to solve the coordinated scheduling model of the electric vehicle with the photovoltaic storage power generation system.

[0012] As a further optimization of the above scheme, the step S1 of determining the photovoltaic power generation power in a day comprises:

[0013] S11: The Monte Carlo simulation is used to randomly generate a photovoltaic output sampling scenario S;

[0014] S12: The geometric distance between each pair of scenarios s and s' in the photovoltaic output sampling scenario S is calculated;

[0015] S13: The scenario d with the smallest sum of the probability distance from the remaining scenarios is selected;

[0016] S14: The scenario r with the closest geometric distance to the scenario d in the photovoltaic output sampling scenario S is replaced by the scenario d, the probability of d is added to the probability of the scenario r, d is eliminated, and a new S' is formed;

[0017] S15: It is judged whether the number of remaining scenarios meets the requirement; if not, the steps S11 to S13 are repeated; if yes, the scenario reduction is ended.

[0018] As a further optimization of the above scheme, the step S2 comprises:

[0019] S21: An electric storage model is established;

[0020] The electric storage capacity is:

[0021] E ES (t)=(1-τ)E ES (t-1)+[P ES-ch (t)η ch -P ES-dis (t) / η dis ]Δt (1)

[0022] Wherein, E ES (t) is the electric storage capacity at t period; τ is the discharge rate of the electric storage itself; P ES-ch (t) is the charging power of the storage at t period; P ES-dis (t) is the discharging power of the storage at t period; η ch is the charging efficiency of the storage at t period; η dis is the discharging efficiency of the storage at t period;

[0023] S22: An electric vehicle (EV) battery loss model is established;

[0024] The electric vehicle (EV) battery loss formula is:

[0025]

[0026] wherein n v is the number of EVs; is the battery purchase cost of the vth EV; is the number of charge-discharge cycles of the vth EV battery during its lifetime; is the vth EV battery capacity; is the EV available battery discharge depth; is the vth EV discharge power at time t; is the vth EV discharge efficiency; is the vth EV travel distance at time t;E v is the power consumed per unit distance traveled by the EV.

[0027] As a further optimization of the above scheme, the step S3 comprises:

[0028] According to the mathematical model of the electric energy storage and the electric vehicle battery loss established in step S2, an electric vehicle coordinated scheduling model is constructed, with the purchase and sale of electricity cost, EV battery loss cost and demand response cost as the objective function;

[0029] The objective function is:

[0030]

[0031] wherein, is the purchase and sale of electricity cost at time t; is the demand response cost at time t; is the EV battery loss cost at time t; is the transferable load cost at time t.

[0032] As a further optimization of the above scheme, the step S4 comprises:

[0033] S41: According to the electric vehicle coordinated scheduling model of the photovoltaic storage power generation system set by the objective function, the decision variables are determined as: photovoltaic power P PV ; purchase and sale of electricity interrupted load EV battery discharge power electric energy storage system charge-discharge power P ES-ch , P ES-dis ;

[0034] S42: According to the electric vehicle coordinated scheduling model of the photovoltaic storage power generation system set by the objective function, the constraint conditions are determined, including the electric vehicle battery capacity constraint, the electric energy storage system charge-discharge power constraint, the purchase and sale of electricity constraint, and the system power balance constraint.

[0035] As a further optimization of the above scheme, specifically, the constraint condition needs to meet formula (4)~formula (10):

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] wherein, is the upper limit of the vth EV storage capacity; is the vth EV storage capacity; is the lower limit of the vth EV storage capacity; is the charging power of the energy storage system at t time; is the maximum charging power of the energy storage system at t time; is the discharging power of the energy storage system at t time; is the maximum discharging power of the energy storage system at t time; respectively represent whether the electric energy storage is charging or discharging at t time, 1 if yes, otherwise 0; is the power of buying and selling electricity at t time; is the maximum power of buying and selling electricity at t time; is whether to buy and sell electricity at t time, 1 if yes, otherwise 0; is the interrupted load amount in t period; is the power load in t period; is the interrupted load coefficient of the mth stage; is the transferred load amount after the period transfer; is the upper limit of the transferred load amount in t period.

[0044] As a further optimization of the above scheme, the step S5 comprises:

[0045] S51: data initialization, input photovoltaic power generation power, electric energy storage model, electric vehicle battery loss mathematical model and its corresponding parameters, and MOPSO algorithm parameters; at the same time, initialize the particle population, each particle individual in the population corresponds to a scheduling scheme in a scheduling period;

[0046] S52: input the particle individual as a system variable to the simulation model, correct the decision variable which violates the constraint condition, and calculate the electricity purchase and sale cost, EV battery loss cost and demand response cost of the system as the individual fitness value;

[0047] S53: input the individual fitness as an optimization model, and obtain the speed and position of each particle in the offspring population by formula (11);

[0048]

[0049] V i+1 is the speed of the new generation of particles, V i is the speed of the current generation of particles, ω is the inertia weight, c1 and c2 are learning efficiency, X i+1 is the new position of the particle, X i is the current position of the particle, and rand is a random number between 0 and 1, pbest i is the current best position of each particle;

[0050] S54: determine the individual extreme value pbest: take pbest as the initial individual extreme value of the particle, if the current particle dominates pbest, take the current particle as the pbest individual extreme value; if the two cannot be compared, calculate the number of particles that dominate other particles in the group, and the particle that dominates more is taken as the individual extreme value pbest;

[0051] S55: sort the population in layers, store the optimal non-dominated solution Pareto in the external archive set, clear the non-Pareto solution, and judge whether the external archive set exceeds the specified capacity m;

[0052] S56: return to step S53 until the optimal solution set Pareto is found and output.

[0053] As a further optimization of the above scheme, in step S55, if the specified capacity m is exceeded, m particles are selected according to the crowding distance and the roulette method is used to select gbest; if the specified capacity m is not exceeded, the roulette method is directly used to select gbest.

[0054] In addition, to achieve the above purpose, the application also provides a power distribution area electric vehicle scheduling system containing a light storage, which comprises an energy management system, a photovoltaic power generation system and an electric energy storage unit, which are connected with the energy management system, and each electric vehicle connected with the energy management system; the energy management system comprises a memory, a processor and a power distribution area electric vehicle scheduling program stored in the memory and executable on the processor, and the power distribution area electric vehicle scheduling program is executed by the processor to realize the steps of the power distribution area electric vehicle scheduling method containing a light storage as described above.

[0055] In addition, to achieve the above object, the application further provides a storage medium, wherein the storage medium stores a power distribution area electric vehicle scheduling method program, and the power distribution area electric vehicle scheduling program, when executed by a processor, implements the steps of the power distribution area electric vehicle scheduling method with light storage as claimed in any one of the above.

[0056] Due to the above technical scheme, the application has the following beneficial effects:

[0057] The power distribution area electric vehicle scheduling method with light storage, the system and the storage medium have the following advantages. Firstly, the historical data of photovoltaic power generation is clustered and analyzed to generate a photovoltaic output scene and determine the photovoltaic power generation power in a day. Secondly, an electric storage energy model and an electric vehicle battery loss mathematical model are established respectively. Under the condition of fully considering the power grid loss and the minimum operation cost, the load peak-valley difference and the minimum purchase and sale of electricity cost are taken as the objective function to establish the electric vehicle coordinated scheduling model of the light storage power generation system. Then, the decision variables and the constraint conditions of the electric vehicle coordinated scheduling model of the light storage power generation system are determined. Then, the multi-objective particle swarm algorithm is used to optimize and solve the electric vehicle coordinated scheduling model of the light storage power generation system, so as to obtain the optimal solution set of the light storage power generation system and the traditional power grid. The advantages of the light storage power generation system and the traditional power grid are fully utilized to increase the renewable energy consumption and reduce the use of coal and the cost of electric vehicle battery loss. Specifically, the light storage power generation system supplies power to the user side to reduce the output of the traditional power grid, reduce the use of coal and promote the consumption of renewable energy. The user side can also charge the electric vehicle when the electricity price is low, which can realize the effect of peak clipping and valley filling and has good application potential. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from the structures shown in the drawings without creative labor.

[0059] Figure 1 is a flowchart of the power distribution area electric vehicle scheduling method with light storage of the embodiment of the present application;

[0060] Figure 2 is a simple structure topology diagram of the power distribution area electric vehicle scheduling system with light storage of the embodiment of the present application;

[0061] Figure 3is a power distribution area electric vehicle scheduling method with light storage of an embodiment of the present application, a specific implementation flowchart;

[0062] Figure 4 is a transferable load response result graph of an embodiment of the present application;

[0063] Figure 5 is an energy storage optimization result graph under time-of-use pricing of an embodiment of the present application;

[0064] Figure 6 is a BYD E6 scheduling result graph;

[0065] Figure 7 is a Nissan LEAF scheduling result graph.

[0066] The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0068] It should be noted that all directional indications (such as up, down, etc.) in the embodiments of the present application are only used to explain the relative positional relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly.

[0069] In addition, the description such as "first", "second" and the like in the present application is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features.

[0070] In addition, the technical solutions of each embodiment of the present application can be combined with each other, but it must be based on the realization of the ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the scope of protection required by the present application.

[0071] Example 1:

[0072] Referring to Figure 1 The present application provides a power distribution area electric vehicle scheduling method with light storage, the steps of which include:

[0073] S1: clustering analysis of historical data of photovoltaic power generation is performed by using a multiple linear regression algorithm to generate a photovoltaic output scenario and determine photovoltaic power generation in a day;

[0074] S2: an electric energy storage model and an electric vehicle battery loss mathematical model are respectively established;

[0075] S3: under the condition of fully considering power grid loss and minimum operation cost, a coordinated scheduling model of electric vehicles with a photovoltaic energy storage power generation system is established, with minimum load peak-valley difference and power purchase and sale cost as objective functions;

[0076] S4: decision variables and constraint conditions of the coordinated scheduling model of electric vehicles with the photovoltaic energy storage power generation system are determined;

[0077] S5: a multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize and solve the coordinated scheduling model of electric vehicles with the photovoltaic energy storage power generation system. In this way, an optimal solution set about matching of the photovoltaic energy storage power generation system and traditional power grid power consumption can be obtained; the photovoltaic energy storage power generation system and the traditional power grid can be fully utilized to increase renewable energy consumption and reduce coal use and electric vehicle battery loss cost; specifically, the photovoltaic energy storage power generation system supplies power to the user side, reduces the output of the traditional power grid, reduces coal use and promotes renewable energy consumption; the user side can also charge the electric vehicle when the electricity price is low, which can realize peak clipping and valley filling and has good application potential.

[0078] As a preferred embodiment, the step of determining photovoltaic power generation in a day in step S1 includes:

[0079] S11: photovoltaic output sampling scenarios S are randomly generated by using Monte Carlo simulation;

[0080] S12: geometric distances between each pair of scenarios s and s' in the photovoltaic output sampling scenarios S are calculated;

[0081] S13: a scenario d with the smallest sum of probability distance from the remaining scenarios is selected;

[0082] S14: a scenario r with the closest geometric distance to the scenario d in the photovoltaic output sampling scenarios S is selected to replace the scenario d, the probability of the scenario d is added to the probability of the scenario r, the scenario d is eliminated, and a new S' is formed;

[0083] S15: whether the number of remaining scenarios meets the requirement is determined; if not, steps S11 to S13 are repeated; if yes, the scenario reduction is ended; in this embodiment, the scenario reduction is ended when the number of remaining scenarios is equal to 5.

[0084] As a preferred embodiment, step S2 includes:

[0085] S21: Establishing an electrical energy storage model;

[0086] The electrical energy storage capacity is:

[0087] E ES (t) = (1 - τ)E ES (t - 1) + [P ES-ch (t)η ch - P ES-dis (t) / η dis ]Δt (1)

[0088] Wherein, E ES (t) is the electrical energy storage capacity at t period; τ is the electrical energy storage self-discharge rate; P ES-ch (t) is the charging power of the electrical energy storage at t period; P ES-dis (t) is the discharging power of the electrical energy storage at t period; η ch is the charging efficiency of the electrical energy storage at t period; η dis is the discharging efficiency of the electrical energy storage at t period;

[0089] S22: Establishing an electric vehicle (EV) battery loss model;

[0090] The electric vehicle (EV) battery loss formula is:

[0091]

[0092] Wherein, n v is the number of EVs; is the battery purchase cost of the vth EV; is the number of charging and discharging cycles of the vth EV battery within the life cycle; is the battery capacity of the vth EV; is the available battery discharge depth of the EV; is the discharging power of the vth EV at t period; is the discharging efficiency of the vth EV; is the driving distance of the vth EV at t period; E v is the power consumed per unit driving distance of the EV.

[0093] As a preferred embodiment, the step S3 comprises:

[0094] According to the electrical energy storage and electric vehicle battery loss mathematical models established in step S2, an electric vehicle coordinated scheduling model is constructed, with the purchase and sale of electricity cost, EV battery loss cost and demand response cost being the minimum objective function;

[0095] The objective function is:

[0096]

[0097] Wherein, Cost of buying and selling electricity for t period; Demand response cost for t period; EV battery wear cost for t period; Transferable load cost for t period.

[0098] As a preferred embodiment, the step S4 comprises:

[0099] S41: determining the decision variable according to the photovoltaic and energy storage power system electric vehicle coordinated scheduling model set by the objective function, wherein the decision variable is: photovoltaic power P PV ; buying and selling electricity quantity interrupted load quantity EV battery discharge power energy storage system charge and discharge power P ES-ch , P ES-dis ;

[0100] S42: determining the constraint condition according to the photovoltaic and energy storage power system electric vehicle coordinated scheduling model set by the objective function, wherein the constraint condition comprises electric vehicle battery capacity constraint, energy storage system charge and discharge power constraint, buying and selling electricity quantity constraint, and system power balance constraint.

[0101] As a preferred embodiment, specifically, the constraint condition needs to satisfy formula (4) to formula (10):

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] wherein, is the upper limit of the electric quantity of the vth EV; is the electric quantity of the vth EV; is the lower limit of the electric quantity of the vth EV; is the energy storage system charge power at t moment; is the maximum energy storage system charge power at t moment; is the energy storage system discharge power at t moment; is the maximum energy storage system discharge power at t moment; represents whether the electric energy storage charges or discharges at time t, and is 1 if yes, otherwise 0; represents the maximum power of buying and selling electricity at time t; represents the maximum power of buying and selling electricity at time t; represents whether the electric energy storage charges or discharges at time t, and is 1 if yes, otherwise 0; represents the interrupted load at time t; represents the power load at time t; represents the interrupted load coefficient at the mth level; represents the transferred load after time interval; represents the upper limit of the transferred load at time t.

[0110] As a preferred embodiment, reference is made to the accompanying drawings Figure 3 , and the step S5 comprises:

[0111] S51: data initialization, inputting photovoltaic power generation, electric energy storage model, electric vehicle battery loss mathematical model and corresponding parameters, and MOPSO algorithm parameters; at the same time, initializing particle population, setting particle population size as 20, maximum iteration number as 200, and each particle individual in the population corresponding to a scheduling scheme in a scheduling period;

[0112] S52: inputting the particle individual as a system variable into a simulation model, correcting the decision variable violating the constraint condition, and calculating the buying and selling electricity cost, EV battery loss cost and demand response cost of the system as the individual fitness value;

[0113] S53: taking the individual fitness as an input of the optimization model, obtaining the speed and position of each particle in the offspring population through formula (11); in the embodiment, setting c1 and c2 as 1.2; selecting the initial inertia weight ω max as 0.9, and the inertia weight ω min as 0.4 when iteration reaches the maximum number;

[0114]

[0115] wherein, V i+1 is the new generation particle speed, V i is the current generation particle speed, ω is the inertia weight, c1 and c2 are learning efficiency, X i+1 is the new position of the particle, X i is the current position of the particle, and rand is a random number between 0 and 1, pbest i is the current best position of each particle;

[0116] S54: determining individual extreme value pbest: taking pbest as the initial individual extreme value of the particle, if the current particle dominates pbest, then taking the current particle as the pbest individual extreme value; if the two cannot be compared, then calculating the number of particles dominated by the two in the group, and the particle dominating more is taken as the individual extreme value pbest;

[0117] S55: sorting the population in layers, storing the optimal non-dominated solution Pareto into the external archive set, clearing the non-Pareto solution, and judging whether the external archive set exceeds the specified capacity m; if the specified capacity m is exceeded, then selecting m particles according to the crowding distance and using the roulette method to select gbest; if the specified capacity m is not exceeded, then directly using the roulette method to select gbest; wherein gbest is the best position found by all particles in the entire group;

[0118] S56: returning to step S53 until the optimal dominated solution Pareto is found and output.

[0119] In order to better illustrate the implementation steps of the power distribution area electric vehicle scheduling method provided by the application, refer to Figure 1 and Figure 3 ; first, through the photovoltaic output scene, the photovoltaic power generation power in a day is generated as described in Figure 4 , two curves respectively represent the load before photovoltaic transfer in a day and the load after photovoltaic transfer in a day; second, input basic parameters: such as the maximum charge and discharge power of the energy storage device is 1kW, the charging efficiency is 95%, the capacity of the battery pack is 4kW·h; 1000 BYD E6 and Nissan LEAF electric vehicles are selected, the battery capacities are 57 and 24kW / h respectively, the battery costs are 22800 and 9600 yuan, and the initial and final storage capacities are 36.42 / 10.75 and 4.68 / 5.86kW / h respectively; the interruptible load is not more than 20% of the total load; the related parameters of MOPSO are taken as: initializing 20 particle positions, velocities, pbest and gbest, the initial iteration number is inter=0, the maximum iteration number is 200, c1 and c2 are both 1.2; the initial inertia weight ω max is 0.9, and the inertia weight ω minis 0.4; the fitness values of 20 initial populations, i.e. the purchase and sale electricity cost, the EV battery loss cost and the demand response cost, are calculated; then the particle speed, position and dynamic inertia weight are updated, and the population individual extreme value pbest is updated; the pareto optimal solution of the new particle is found, and is stored in the external archive set; the pareto optimal solution in the external archive set is found, and the non-pareto optimal solution is cleared; then it is judged whether the optimal solution quantity of the external set exceeds the specified capacity m, if yes, m particles are selected according to the crowding distance and the roulette method is used to select gbest; if no, the roulette method is directly used to select gbest; wherein gbest is the best position found by all particles in the whole group; iteration is sequentially performed until the optimal dominant solution Pareto is found and is outputted;

[0120] Under the condition of fully considering the minimum grid loss and operation cost, taking the minimum load peak-valley difference and purchase and sale electricity cost as the objective function, the energy storage optimization structure diagram under the time-of-use electricity price as shown in Figure 5 Through the energy storage optimization structure diagram under the time-of-use electricity price as shown in Figure 5 It can be seen that the scheduling method provided by the present application can play a role in peak clipping and valley filling. Specifically, the electric energy storage system is charged at 0:00-8:00 and 14:00-16:00; the electric energy storage system is discharged at 9:00-12:00, 13:00-15:00 and 18:00-20:00, thereby reducing the original load curve peak load; further, the scheduling results of the two types of electric vehicles obtained by the scheduling method of the present application are shown in the scheduling result diagrams as shown in Figure 6 and Figure 7 It can be seen that the electric vehicle battery EV charging time is mostly concentrated in the 6:00-8:00 and 13:00-15:00 time periods, and the discharging time is concentrated in the 9:00-13:00 and 18:00-20:00 time periods; but there are some differences in the optimization results of the two types of electric vehicles, for example, for the selected Nissan LEAF, when the initial electric quantity of the electric vehicle battery EV is low, in order to meet its own driving demand, it is charged at the maximum charging power in the 6:00-8:00 time period until the upper limit of the storage quantity is reached; while the initial electric quantity of the BYD E6 is high, only a small amount of charging time is needed to reach the upper limit of the storage quantity; and when the final electric quantity of the electric vehicle battery EV is low, in addition to the driving consumption and the required final electric quantity, other electric quantities are discharged in the 11:00-13:00 high time period; in summary, the electric vehicle can use the electric energy storage system to charge it in the peak period, thereby reducing the peak-valley difference and also reducing the purchase cost of the electric vehicle.

[0121] Example 2:

[0122] Referring to Figure 2The application further provides a power distribution area electric vehicle scheduling system with light storage, comprising an energy management system, a photovoltaic power generation system and an electric energy storage unit, each of which is connected with the energy management system, and each electric vehicle connected with the energy management system; the energy management system comprises a memory, a processor and a power distribution area electric vehicle scheduling program stored in the memory and executable on the processor, and the power distribution area electric vehicle scheduling program is executed by the processor to realize the steps of the power distribution area electric vehicle scheduling method with light storage as described above.

[0123] Embodiment 3

[0124] The application further provides a storage medium, wherein the storage medium stores a power distribution area electric vehicle scheduling method program, and the power distribution area electric vehicle scheduling method program is executed by a processor to realize the steps of the power distribution area electric vehicle scheduling method with light storage according to any one of the above.

[0125] The storage medium can comprise a high-speed RAM memory, and can further comprise a non-volatile memory, for example, at least one disk memory. It can be understood that the storage medium can be a random access memory (RAM), a disk, a hard disk, a solid state disk (SSD) or a non-volatile memory, and the like various machine readable media that can store program codes.

[0126] Those skilled in the art should understand that the embodiments of the application can be provided as a method or a storage medium. Therefore, the embodiments of the application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the application can be in the form of 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.

[0127] The embodiments of the application are described with reference to flowcharts and / or block diagrams of the method 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, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device for realizing the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for realizing the functions specified in one flow or multiple flows and / or blocks Figure 1 The device for realizing the functions specified in one flow or multiple flows and / or blocks

[0128] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 flow or flows and / or blocks Figure 1 of the flow or flows and / or blocks.

[0129] These computer program instructions can also be loaded onto 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 that are executed on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 flow or flows and / or blocks Figure 1 of the flow or flows and / or blocks.

[0130] The above merely preferred embodiments of the present application and not therefore limit the scope of patent protection of the present application. Any equivalent structure variations or direct / indirect applications in other related technical fields made by using the contents of the present application specification and drawings are included in the patent protection scope of the present application.

Claims

1. A power distribution area electric vehicle scheduling method containing light storage, characterized by the steps of Comprise: S1: using multiple linear regression algorithm to cluster analysis of historical data of photovoltaic power generation, generating photovoltaic output scene, determine photovoltaic power generation in a day; S2: respectively establish electric energy storage model and electric vehicle battery loss mathematical model; The step S2 comprises: S21: establish electric energy storage model; The electric energy storage capacity is: E ES (t) = (1 - τ)E ES (t - 1) + [P ES-ch (t)η ch - P ES-dis (t) / η dis ]△t (1) wherein E ES (t) is the electric energy storage capacity at time t; τ is the self-discharge rate of the electric energy storage; P ES-ch (t) is the charging power of the energy storage at time t; P ES-dis (t) is the discharging power of the energy storage at time t; η ch is the charging efficiency of the energy storage at time t; η dis is the discharging efficiency of the energy storage at time t; S22: establish electric vehicle EV battery loss model; The electric vehicle EV battery loss formula is: Where, n v For EVs: Cbv is the battery purchase cost of the vth EV; Lcv is the charge-discharge cycle count of the vth EV's battery over its lifespan; SEVv is the battery capacity of the vth EV; dDODv is the depth of discharge of the EV's usable battery; gvdv,t is the discharge power of the vth EV at time t; ηvdv is the discharge efficiency of the vth EV; dtrv,t is the driving distance of the vth EV at time t; E v The power consumed per unit distance traveled by an EV; S3: in full consideration of the minimum condition of power grid loss and operation cost, the minimum of load peak valley difference and purchase and sale electricity cost as objective function, establish electric vehicle coordinated scheduling model containing photovoltaic storage power generation system; The step S3 comprises: According to the electric energy storage and electric vehicle battery loss mathematical model established in step S2, the electric vehicle coordinated scheduling model with the minimum of purchase and sale electricity cost, EV battery loss cost and demand response cost as objective function is constructed; The objective function is: Wherein, Fm t is the purchase and sale electricity cost of t period; FDR t is the demand response cost of t period; FEV t is the EV battery loss cost of t period; Fcshift t is the transferable load cost of t period; S4: determine the decision variables and constraint conditions of the electric vehicle coordinated scheduling model containing photovoltaic storage power generation system; The step S4 comprises: S41: Determine the decision variables according to the coordination scheduling model of the photovoltaic power generation system for electric vehicles set by the objective function, respectively: photovoltaic power generation power P PV ; purchase and sale of electricity PBC t, SBC t; interrupted load Lcurt t; EV battery discharge power gvd v, t; electric energy storage system charge and discharge power P ES-ch , P ES-dis ; S42: according to the constraint conditions of the electric vehicle coordinated scheduling model of photovoltaic storage power generation system set by the objective function, including electric vehicle battery capacity constraint, electric energy storage system charging and discharging power constraint, purchase and sale electricity quantity constraint, system power balance constraint; S5: adopt multi-objective particle swarm optimization algorithm to optimize and solve the electric vehicle coordinated scheduling model containing photovoltaic storage power generation system; The step S5 comprises: S51: data initialization, input photovoltaic power generation, electric energy storage model, electric vehicle battery loss mathematical model and corresponding parameters, and MOPSO algorithm parameters; at the same time, initialize particle population, each particle individual in the population corresponds to a scheduling scheme in a scheduling period; S52: input the particle individual as system variable into simulation model, modify the decision variables violating the constraint conditions, and calculate the purchase and sale electricity cost, EV battery loss cost and demand response cost of the system as individual fitness value; S53: take the individual fitness as input of optimization model, get the speed and position of each particle in offspring population through formula (11); where V i+1 is the new generation particle velocity, V i is the current generation particle velocity, ω is the inertia weight, c1, c2 are learning efficiencies, X i+1 is the new position of the particle, X i is the current position of the particle, rand is a random number between 0-1, pbest i is the current best position of each particle; S54: determine individual extreme value pbest: take pbest as initial individual extreme value of particle, if the current particle dominates pbest, take the current particle as pbest individual extreme value; if the two cannot be compared, calculate the number of particles dominating other particles in the group, and the particle dominating more is taken as individual extreme value pbest; S55: sort the population by layer, store the optimal non-dominated solution Pareto in external archive set, and remove non-Pareto solution, and judge whether the external archive set exceeds the specified capacity m; S56: return to step S53, until the optimal solution set Pareto is found and output.

2. The power distribution area electric vehicle dispatching method with light storage according to claim 1, characterized in that, The step of determining photovoltaic power generation in a day in the step S1 comprises: S11: randomly generating photovoltaic output sampling scenarios S by Monte Carlo simulation; S12: calculating the geometric distance between each pair of scenarios s and s' in the photovoltaic output sampling scenarios S; S13: selecting the scenario d with the minimum sum of the probability distance from the remaining scenarios; S14: replacing the scenario d with the scenario r closest to the scenario d in the photovoltaic output sampling scenarios S, adding the probability of d to the probability of r, eliminating d, and forming a new S'; S15: determining whether the number of the remaining scenarios meets the requirement; if not, repeating steps S11 to S13; if yes, ending the scenario reduction.

3. The power distribution area electric vehicle dispatching method with light storage according to claim 1, characterized in that, The constraint condition needs to satisfy formula (4) to formula (10): Wherein, SEV,min v is the upper limit of the vth EV storage capacity; SEV v is the vth EV storage capacity; SEV,max v is the lower limit of the vth EV storage capacity; PESC t is the charging power of the energy storage system at t; PESC,max t is the maximum charging power of the energy storage system at t; PESD t is the discharging power of the energy storage system at t; PESD,max t is the maximum discharging power of the energy storage system at t; μESC t and μESD t respectively represent whether the energy storage is charging or discharging at t, and are 1 if yes, and 0 if no; PBC t and SBC t are the power of buying and selling electricity at t; PBC,max t and SBC,max t are the maximum power of buying and selling electricity at t; μBSC t and μBSD t are whether buying and selling electricity at t, and are 1 if yes, and 0 if no; Lcurt t is the interrupted load in the t period; Lload t is the power load in the t period; kcurt m is the mth interrupted load coefficient; Lshift t is the transferred load after the shift; Lshift,max t is the upper limit of the transferred load at t.

4. The power distribution area electric vehicle dispatching method with light storage according to claim 1, characterized in that, In step S55, if the specified capacity m is exceeded, m particles are selected according to the crowded distance and the roulette method is used to select gbest; if the specified capacity m is not exceeded, the roulette method is directly used to select gbest.

5. A photovoltaic storage containing distribution area electric vehicle dispatching system, characterized in that, The energy management system, the photovoltaic power generation system and the electric energy storage unit are connected with the energy management system, and each electric vehicle is connected with the energy management system; the energy management system comprises a memory, a processor, and a power distribution area electric vehicle scheduling program stored in the memory and executable on the processor; when the power distribution area electric vehicle scheduling program is executed by the processor, the steps of the photovoltaic storage-based power distribution area electric vehicle scheduling method according to any one of claims 1-4 are implemented.

6. A storage medium, characterized by The storage medium stores a photovoltaic storage-based power distribution area electric vehicle scheduling method program, and when the photovoltaic storage-based power distribution area electric vehicle scheduling method program is executed by the processor, the steps of the photovoltaic storage-based power distribution area electric vehicle scheduling method according to any one of claims 1-4 are implemented.

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