Optimal control method and system for DC microgrid with electric vehicle virtual energy storage

Abstract

The present invention discloses an optimization control method and system for a DC microgrid containing virtual energy storage for electric vehicles, comprising: guiding the transfer of characteristic curves of charging and discharging demands of electric vehicles according to electricity price strategies, constructing a virtual energy storage model for electric vehicles to simulate the load shifting effect of the virtual energy storage system of electric vehicles; based on the virtual energy storage model for electric vehicles, integrating source, load and storage resources into collaborative operation optimization, solving the intraday economic optimal scheduling strategy according to a particle swarm algorithm, and establishing a microgrid energy management model for the DC microgrid containing virtual energy storage for electric vehicles; based on the microgrid energy management model, generating an optimization control strategy for optimal energy storage configuration and power distribution of the DC microgrid according to the introduction of the optimal virtual capacitance value in the model; the source, load and storage optimization control strategy for the DC microgrid containing virtual energy storage for electric vehicles proposed by the present invention enables the integration of resources on both the supply and demand sides, improves the interactive flexibility of system regulation, and significantly reduces the system operation cost.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization control of a DC microgrid, and in particular to an optimization control method and system for a DC microgrid containing virtual energy storage for electric vehicles. Background Art

[0002] Although electric vehicles are energy storage batteries, as controllable loads, current control strategies are usually simply treated as switching on or off, and cannot achieve dynamic and continuous coordination with energy storage. If the electric vehicle control parameters are different from the operating parameters of the energy storage device in the system, how to comprehensively evaluate the system energy reserve and how to detect its operating status and coordinate with the energy storage need to be explored. Due to inconsistent parameters, there is a lack of key parameters that can reflect the operating status of the called equipment, resulting in insufficient measurement information processing capabilities. At present, although the optimized scheduling of electric vehicles takes into account the charging and discharging characteristics of electric vehicle batteries, it does not deeply consider the coordinated scheduling between them and energy storage and controllable source-load devices. As a result, the storage and release performance of electric vehicles is not fully developed and utilized, so the economic efficiency is not optimal and there is a large room for improvement. Therefore, there is an urgent need for an optimized control method and system for a DC microgrid containing electric vehicle virtual energy storage to solve the above technical problems. Summary of the Invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide an optimization control method and system for a DC microgrid containing virtual energy storage for electric vehicles, establish an economic model with the goal of maximizing the benefits of the microgrid, and propose an intraday economic scheduling strategy with virtual capacitance value as a unified control parameter and virtual state of charge as a system status evaluation indicator to improve the economic benefits of system operation.

[0004] To achieve the above technical objectives, the present application provides an optimization control method for a DC microgrid with electric vehicle virtual energy storage, wherein the DC microgrid is composed of a permanent magnet direct-drive wind turbine, a hybrid energy storage device, a controllable load processed by an asynchronous motor, and an electric vehicle, comprising the following steps:

[0005] According to the electricity price strategy, the characteristic curve of electric vehicle charging and discharging demand is guided and shifted, and an electric vehicle virtual energy storage model is constructed to simulate the load shifting effect of the electric vehicle virtual energy storage system;

[0006] Based on the electric vehicle virtual energy storage model, by integrating source, load and storage resources into the coordinated operation optimization, the particle swarm algorithm is used to solve the intraday economic optimal scheduling strategy, and a microgrid energy management model for the DC microgrid with electric vehicle virtual energy storage is established;

[0007] Based on the microgrid energy management model and the introduction of the optimal virtual capacitor value in the model, the optimal energy storage configuration and power distribution optimization control strategy of the DC microgrid are generated.

[0008] Preferably, in the process of constructing a virtual energy storage model of an electric vehicle, based on the Monte Carlo simulation method, the charging and discharging form of the electric vehicle is set, and according to the fluctuation of electricity prices, a virtual capacitance value for measuring the virtual energy storage reserve of the electric vehicle and a virtual state of charge for evaluating the operating status index of the electric vehicle are introduced to construct a virtual energy storage model of the electric vehicle.

[0009] Preferably, according to the fluctuation of electricity price, by obtaining the start and end time of electric vehicle virtual energy storage charging and discharging, the virtual energy storage charging and discharging amount E of electric vehicle in different time periods is generated. ev (t) is expressed as:

[0010]

[0011] Preferably, in the process of obtaining the charge and discharge start time, the electric vehicle virtual energy storage charging start time T is obtained according to the electricity price fluctuation. chars and the starting time of electric vehicle virtual energy storage discharge T dchars Expressed as:

[0012] T chars (i) = t f (i)(0≤t f (i)<T zs )

[0013] T dchars (i) = t f (i)(T ws <t f (i)≤24)

[0014] T dchars (i) = T ws (T zs ≤t f (i)≤T ws )

[0015] Where i represents the i-th electric vehicle, T zs Indicates the end time of the early electricity price valley, T ws represents the starting time of the peak electricity price in the evening and the return time of the electric vehicle’s last trip t f The probability density function follows the normal distribution t f (i)~(μ f ,σ f ).

[0016] Preferably, in the process of obtaining the charge and discharge end time, the electric vehicle virtual energy storage charging end time T chare and the end time of electric vehicle virtual energy storage discharge T dchare Expressed as:

[0017]

[0018] Among them, P E is the charging and discharging power of electric vehicles, T dchar represents the discharge time of the electric vehicle’s virtual energy storage, w represents the electric vehicle’s power consumption per kilometer, and the electric vehicle’s daily mileage S follows S(i)~(μ s , σ s ) is a lognormal distribution.

[0019] Preferably, the virtual energy storage discharge time T of the electric vehicle is obtained by setting the remaining power to meet the user's daily travel needs and the discharge amount shall not exceed the maximum discharge depth of the electric vehicle. dchar Expressed as:

[0020]

[0021] Among them, C is the total capacity of the electric vehicle's power battery, r is the maximum discharge depth per kilometer of the electric vehicle, It is the state of charge limit of electric vehicle battery cells.

[0022] Preferably, in the process of constructing the electric vehicle virtual energy storage model, the virtual capacitance value C ve (t) and virtual state of charge SOC ve (t) is expressed as:

[0023]

[0024]

[0025] Among them, U C is the DC bus terminal voltage, η v It is the battery charge and discharge efficiency.

[0026] Preferably, in the process of integrating source, load and storage resources into the coordinated operation optimization, according to the economic benefit value W of the microgrid total Maximization is the goal, and the objective function is established;

[0027] Set power balance constraints according to the operating status of controllable equipment;

[0028] Set energy storage state constraints according to the operating status of controllable equipment;

[0029] According to the target, the objective function, power balance constraints, and energy storage state constraints are established to perform collaborative operation optimization.

[0030] The objective function is expressed as:

[0031]

[0032] The power balance constraint is expressed as:

[0033] P w (t)+P B (t)+P C (t) = P L (t)+P E (t);

[0034] The energy storage state constraint is expressed as:

[0035]

[0036] Where W l is the system controllable load benefit, W e is the charging and discharging benefit of electric vehicles, S bess is the operation and maintenance cost of the hybrid energy storage equipment, S B is the battery life loss cost, S ev is the peak load-shaving cost paid to electric vehicle users, k l is the controllable load benefit per unit of electricity, p t is the electric vehicle transaction electricity price, k bess is the operation and maintenance cost per unit of electricity of the hybrid energy storage device, k ev is the grid-connected subsidy price for electric vehicles per unit discharge capacity, K b is the total investment cost of the battery, A is the linear aging coefficient of the battery, SOH min It is the health status value of the battery at the end of its life, SOC B is the battery state of charge, P L is the controllable load power, P B is the battery charging and discharging power, P C is the supercapacitor charging and discharging power, P w is wind power, SOH B Is the battery health status, SOC C is the state of charge of the supercapacitor.

[0037] The present invention discloses an optimization control system for a DC microgrid containing electric vehicle virtual energy storage, comprising:

[0038] A virtual energy storage construction module is used to guide the shift of the characteristic curve of electric vehicle charging and discharging demand based on the electricity price strategy, build an electric vehicle virtual energy storage model, and simulate the load shifting effect of the electric vehicle virtual energy storage system;

[0039] A microgrid energy management model building module is used to build a microgrid energy management model for a DC microgrid with electric vehicle virtual energy storage by integrating source, load and storage resources into collaborative operation optimization and solving the intraday economic optimal scheduling strategy based on the particle swarm algorithm.

[0040] The optimization strategy generation module is used to generate the optimal control strategy for the optimal energy storage configuration and power distribution of the DC microgrid based on the microgrid energy management model and the introduction of the optimal virtual capacitor value in the model.

[0041] Preferably, the DC microgrid includes a permanent magnet direct-drive wind turbine generator set, a hybrid energy storage device consisting of a battery and a supercapacitor, a controllable load handled by an asynchronous motor, and an electric vehicle with mobile energy storage characteristics;

[0042] The permanent magnet direct drive wind turbine generator set is connected to the DC bus of the DC microgrid through the voltage type AC / DC converter WVSC;

[0043] The hybrid energy storage device is connected to the DC bus through two bidirectional DC / DC converters CVSC and BVSC respectively;

[0044] The controllable load is connected to the DC bus through the AC / DC converter LVSC in the form of a continuously adjustable asynchronous motor;

[0045] Electric vehicles are connected to the DC bus through a bidirectional DC / DC converter EVSC.

[0046] The present invention discloses the following technical effects:

[0047] This paper addresses the energy management challenges of DC microgrids by proposing a source-load-storage optimization control strategy that incorporates electric vehicle virtual energy storage. This strategy integrates resources on both the supply and demand sides, enhancing the interactive flexibility of system regulation. In this optimized control, virtual capacitance values, under comprehensive real-time monitoring of operating conditions, not only assess the regulation potential of electric vehicle virtual energy storage but also facilitate the unified call-up of energy reserves, simplifying optimized operation and significantly reducing system operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 This is a flow chart of the DC microgrid optimization control method according to the present invention;

[0050] Figure 2 This is a simulation topology diagram of the DC microgrid described in the present invention;

[0051] Figure 3 The power forecast and time-of-use electricity price change diagram for a typical working day according to the present invention;

[0052] Figure 4 This is a diagram showing the power changes of various components of the system under the energy optimization control strategy described in the present invention;

[0053] Figure 5 Parameter diagram of virtual energy storage capacitor at different times according to the present invention;

[0054] Figure 6 This is a graph showing changes in total load power under the optimization control strategy described in the present invention;

[0055] Figure 7 This is a diagram of the change in microgrid revenue and cost under the optimization control strategy described in the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0057] like Figure 1-7 As shown, the present invention provides an optimization control method and system for a DC microgrid containing virtual energy storage for electric vehicles. Figure 1 Flowchart of the method for optimizing control of a DC microgrid including electric vehicle virtual energy storage according to an embodiment of the present invention. Figure 1 As shown, the present invention provides a method for optimizing control of a DC microgrid containing virtual energy storage of electric vehicles, the method comprising:

[0058] Step 101: Simulate the load shifting effect of similar energy storage devices based on the characteristic curve of the electric vehicle charging and discharging demand guided by the electricity price strategy, and establish a virtual energy storage model for the electric vehicle;

[0059] Step 102: Integrate source, load, and storage resources into the coordinated operation optimization based on the virtual energy storage system modeling, and establish an economic optimization problem description with the goal of maximizing benefits;

[0060] Step 103: Solve the intraday economic optimal dispatch strategy based on the particle swarm algorithm and establish a microgrid energy management model including electric vehicle virtual energy storage;

[0061] Step 104: Based on the introduction of the optimal virtual capacitance value in the model, an optimization control strategy for optimal energy storage configuration and power distribution including the coordination of electric vehicle virtual energy storage is established.

[0062] Figure 2 The simulation topology diagram of the DC microgrid according to the embodiment of the present invention is shown in FIG. Figure 2 As shown, the DC microgrid system with electric vehicle virtual energy storage provided by the present invention includes a permanent magnet direct-drive wind turbine, a hybrid energy storage device, a controllable load processed by an asynchronous motor, and an electric vehicle. The permanent magnet direct-drive wind turbine is used on the DC bus for power transmission through a unidirectional AC / DC WVSC converter. The hybrid energy storage device includes supercapacitors and batteries, which are used to smooth the power fluctuations generated by the system and are connected to the DC bus through CVSC and BVSC bidirectional DC / DC converters respectively. The controllable load processed by the asynchronous motor is connected through the AC / DC converter of LVSC to share the power regulation pressure of the hybrid energy storage device. The electric vehicle is connected to the bus through a bidirectional DC / DC converter EVSC. In order to evaluate the ability of electric vehicles to provide virtual energy storage, Figure 2 The model shown is used for simulation analysis.

[0063] In step 101, the characteristic curve of the electric vehicle charging and discharging demand is guided by the electricity price strategy to simulate the load shifting effect of similar energy storage equipment, and establish an electric vehicle virtual energy storage model. Assuming that the electric vehicle is charged and discharged in a conventional slow manner, according to the Monte Carlo simulation method, the electric vehicle daily mileage S obeys S(i)~(μ s ,σ s ) is a log-normal distribution, and the return time of the electric vehicle’s last trip is t f The probability density function follows the normal distribution t f (i)~(μ f ,σ f ).

[0064] By comparing the return time of the i-th electric vehicle trip with the end time of the early electricity price valley T zs , the starting time of the peak price of late electricity ws Before the end of the morning electricity price valley, the electric vehicle virtual energy storage is charged to absorb the redundant power, and after the evening electricity price peak, the electric vehicle virtual energy storage is discharged to reduce the original load power impact. Reasonably arrange the starting time T of the electric vehicle virtual energy storage charging chars and the starting time of electric vehicle virtual energy storage discharge T dchars Expressed as:

[0065]

[0066] The discharge time of the electric vehicle's virtual energy storage must ensure that its remaining power can meet the user's daily travel needs, and the discharge amount must not exceed the electric vehicle's maximum discharge depth. The discharge time of the electric vehicle's virtual energy storage is T dchar Expressed as:

[0067]

[0068] Where, C is the total capacity of the electric vehicle power battery, P E is the charging and discharging power of the electric vehicle, w and r are the power consumption per kilometer and the maximum discharge depth of the electric vehicle respectively. It is the state of charge limit of electric vehicle battery cells.

[0069] The charging time of electric vehicles is determined by the discharge amount of electric vehicle virtual energy storage and the power loss during travel. Combined with the constraints on electric vehicles participating in virtual energy storage at the peak and valley times of corresponding electricity prices, the end time T of electric vehicle virtual energy storage charging within different return times can be obtained according to the starting charging and discharging time and discharge duration of electric vehicle virtual energy storage. chare and the end time of electric vehicle virtual energy storage discharge T dchare Expressed as:

[0070]

[0071] According to the start and end time of the electric vehicle virtual energy storage charge and discharge, the electric vehicle virtual energy storage charge and discharge power E in different periods can be obtained respectively. ev (t) is expressed as:

[0072]

[0073] According to the electric vehicle's participation in source-load level regulation, the power consumption is simulated as virtual capacitor charge and discharge, and a unified capacitor parameter instruction is used to participate in the optimization scheduling. The virtual capacitance value C is introduced to measure the virtual energy storage reserve of electric vehicles. ve (t) and the virtual state of charge (SOC) which is an indicator for evaluating the operating status of electric vehicles ve (t), respectively expressed as:

[0074]

[0075] Where U C is the DC bus terminal voltage, η v It is the battery charge and discharge efficiency.

[0076] In step 102, the EV virtual energy storage model is incorporated into the economic optimization solution model. An optimal algorithm is used to calculate forecast data for controllable load power, hybrid energy storage system output, and EV virtual energy storage output for each daily scheduling period. By comprehensively considering the technical characteristics of controllable equipment and day-ahead market electricity prices, a globally optimized scheduling solution is developed over a long timescale, with the goal of maximizing the economic benefits of the microgrid.

[0077] 1) Economic optimization objective function:

[0078]

[0079] Where W l is the system controllable load benefit, W e is the charging and discharging benefit of electric vehicles, S bess is the operation and maintenance cost of the hybrid energy storage equipment, S b is the battery life loss cost, S ev is the peak load-shaving cost paid to electric vehicle users, k l is the controllable load benefit per unit of electricity, p t is the electric vehicle transaction electricity price, k bess is the operation and maintenance cost per unit of electricity of the hybrid energy storage device, k ev is the grid-connected subsidy price for electric vehicles per unit discharge capacity, K b is the total investment cost of the battery, A is the linear aging coefficient of the battery, SOH min It is the health status value of the battery at the end of its life, SOC B is the battery state of charge, P L is the controllable load power, P B is the battery charging and discharging power, P C is the supercapacitor charging and discharging power.

[0080] 2) Power balance constraints:

[0081] P w (t)+P B (t)+P C (t) = P L (t)+P E (t) (7)

[0082] Where, P w is the wind power, P E It is the virtual energy storage charging and discharging power of electric vehicles.

[0083] 3) Energy storage state constraints:

[0084]

[0085] Where, SOC B、SOH B Is the battery state of charge and health, SOC C is the supercapacitor state of charge, SOC ve It is the virtual energy storage charge state of electric vehicles.

[0086] In step 103, according to the electricity price R=[r1,r2,…r 24 ], wind power output power forecast value P w =[P w1 ,P w2 ,…P w24 ]. Set the parameters related to the virtual energy storage model formula and its adjustment range, etc. Set the algorithm parameters, including particle size M, maximum number of iterations j, inertia factor f, learning factors c1, c2, random numbers n1, n2;

[0087] Generate the initial electric vehicle virtual capacitor group, set the virtual capacitor position and update speed, and set the virtual capacitor value of the i-th electric vehicle in time period t to C ve (t) i , the speed is v i ;

[0088] The virtual capacitance values ​​of the electric vehicle virtual capacitance group are calculated in turn according to the objective function. The function with a larger value has a higher fitness. Determine the individual extreme value p i Group extreme value p g ;

[0089] Update the position and speed of the virtual capacitor value to determine the individual extreme value and group extreme value with higher fitness;

[0090]

[0091] The algorithm process ends according to the convergence accuracy requirement or the number of iterations limit, and the output result is the maximum benefit value of the system and the optimal virtual capacitance value C of the electric vehicle corresponding to different time periods. ve (t), by adjusting the optimal virtual capacitance value to regulate the charging and discharging amount of electric vehicles in different periods, the daily optimal operation decision of investing in electric vehicle virtual energy storage is obtained.

[0092] In step 104, based on the introduction of the optimal virtual capacitance value in the model, the optimal energy storage configuration and power distribution optimization control strategy including the virtual energy storage of electric vehicles can be obtained, such as Figure 4 、 Figure 5 shown. Figure 4 The power change diagrams of each device in the microgrid with and without virtual energy storage are compared and analyzed. Figure 5It is the virtual capacitance value and virtual state of charge value under the optimal economic dispatch. The load power is relatively stable from 2:00 to 6:00. In the absence of virtual energy storage, supercapacitor charging and discharging are used to ensure the energy storage balance power margin, while the gradually increasing virtual capacitance value of electric vehicles is used to reasonably absorb the redundant power at night. After 17:00, in the absence of virtual energy storage, the disorderly charging of electric vehicle users leads to "peak on peak", which increases the regulation burden of the system, causes frequent discharge of batteries, and significantly increases battery deterioration. When electric vehicles participate in virtual energy storage, idle electric vehicles discharge to supply the load, and the negative virtual capacitance value directly offsets part of the battery regulation capacity. At this time, the battery hardly moves.

[0093] Figure 6 This graph shows the total load power variation under the optimized control strategy of an embodiment of the present invention. It's clear that disordered charging of electric vehicles without virtual energy storage is the most severe cause of the peak-to-valley load variation. EV virtual energy storage stores power during low-load valleys and discharges it during peak loads, significantly reducing the peak-to-valley load variation by 14.2MW. This reduces the variability of energy demands, smoothes the power curve, and improves system stability.

[0094] Figure 7 This figure shows the change in microgrid revenue and cost under the optimized control strategy of an embodiment of the present invention. A comparative analysis shows that the integration of virtual energy storage reduces the microgrid's overall costs by 67%, significantly offsetting the revenue differential caused by virtual energy storage. The introduction of the virtual energy storage system generates 2.7 times the net revenue of the microgrid compared to without virtual energy storage. The analysis shows that while load revenue is reduced with virtual energy storage due to its integration with traditional energy storage charging and discharging, the overall operating costs of the microgrid are significantly reduced, resulting in a significant net profit advantage.

[0095] The method for optimizing and controlling a DC microgrid containing virtual energy storage for electric vehicles provided by the present invention, taking into account the dual characteristics of electric vehicles as mobile loads and energy storage, formulates a virtual energy storage charging and discharging model in combination with the electricity price mechanism to form a virtual energy storage model for electric vehicles. The virtual energy storage model is integrated into the economic optimization strategy, and an energy collaborative optimization control strategy is formed with the virtual capacitance value and virtual state of charge in each time period as operating parameters with the goal of maximizing economic benefits. The method for optimizing and controlling a DC microgrid containing virtual energy storage for electric vehicles provided by the present invention, under comprehensive real-time monitoring of the operating status, can not only evaluate the regulation potential of the virtual energy storage of electric vehicles through the virtual capacitance value, but also facilitate the unified call of energy reserves, thereby simplifying the optimized operation and significantly reducing the system operating costs.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0098] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An optimization control method for a DC microgrid with electric vehicle virtual energy storage, characterized in that: The DC microgrid is composed of a permanent magnet direct-drive wind turbine, a hybrid energy storage device, a controllable load processed by an asynchronous motor, and an electric vehicle, and includes the following steps: According to the electricity price strategy, the characteristic curve of electric vehicle charging and discharging demand is guided and shifted, and an electric vehicle virtual energy storage model is constructed to simulate the load shifting effect of the electric vehicle virtual energy storage system; Based on the electric vehicle virtual energy storage model, by integrating source, load and storage resources into the coordinated operation optimization, the particle swarm algorithm is used to solve the intraday economic optimal scheduling strategy, and a microgrid energy management model for the DC microgrid with electric vehicle virtual energy storage is established; Based on the microgrid energy management model and the introduction of the optimal virtual capacitance value in the model, an optimization control strategy for the optimal energy storage configuration and power distribution of the DC microgrid is generated. That is, the virtual capacitance value and the virtual state of charge are introduced into the objective function, and the economic optimization objective function is obtained as follows: Where W l is the system controllable load benefit, W e is the charging and discharging benefit of electric vehicles, S bess is the operation and maintenance cost of the hybrid energy storage equipment, S b is the battery life loss cost, S ev is the peak load-shaving cost paid to electric vehicle users, k l is the controllable load benefit per unit of electricity, p t is the electric vehicle transaction electricity price, k bess is the operation and maintenance cost per unit of electricity of the hybrid energy storage device, k ev is the grid-connected subsidy price for electric vehicles per unit discharge capacity, K b is the total investment cost of the battery, A is the linear aging coefficient of the battery, SOH min It is the health status value of the battery at the end of its life, SOC B is the battery state of charge, P L is the controllable load power, P B is the battery charging and discharging power, P C is the supercapacitor charging and discharging power.

2. The optimization control method for a DC microgrid with electric vehicle virtual energy storage according to claim 1, characterized in that: In the process of constructing a virtual energy storage model for electric vehicles, based on the Monte Carlo simulation method, the charging and discharging form of the electric vehicle is set. According to the fluctuation of electricity prices, the virtual capacitance value for measuring the virtual energy storage reserve of the electric vehicle and the virtual state of charge for evaluating the operating status index of the electric vehicle are introduced to construct the virtual energy storage model of the electric vehicle.

3. The optimization control method for a DC microgrid with electric vehicle virtual energy storage according to claim 2, characterized in that: According to the electricity price fluctuation, by obtaining the start and end time of the electric vehicle virtual energy storage charging and discharging, the electric vehicle virtual energy storage charging and discharging power E in different time periods is generated. ev (t) is expressed as:

4. The optimization control method for a DC microgrid with electric vehicle virtual energy storage according to claim 3 is characterized in that: In the process of obtaining the charge and discharge start time, according to the electricity price fluctuation, the electric vehicle virtual energy storage charging start time T is obtained. chars and the starting time of electric vehicle virtual energy storage discharge T dchars Expressed as: T chars (i)=t f (i) (0≤t f (i)<T zs ) T dchars (i)=t f (i) (T ws <t f (i)≤24) T dchars (i)=T ws (T zs ≤t f (i)≤T ws ) Where i represents the i-th electric car, T zs Indicates the end time of the early electricity price valley, T ws represents the starting time of the peak electricity price in the evening and the return time of the electric vehicle’s last trip t f The probability density function follows the normal distribution t f (i)~(μ f ,σ f ).

5. The optimization control method for a DC microgrid with electric vehicle virtual energy storage according to claim 4 is characterized in that: In the process of obtaining the end time of charging and discharging, the electric vehicle virtual energy storage charging end time T chare and the end time of electric vehicle virtual energy storage discharge T dchare Expressed as: Among them, P E is the charging and discharging power of electric vehicles, T dchar represents the discharge time of the electric vehicle’s virtual energy storage, w represents the electric vehicle’s power consumption per kilometer, and the electric vehicle’s daily mileage S follows S(i)~(μ s ,σ s ) is a lognormal distribution.

6. The optimization control method for a DC microgrid with electric vehicle virtual energy storage according to claim 5, characterized in that: By setting the remaining power to meet the user's daily travel needs and the discharge amount must not exceed the maximum discharge depth of the electric vehicle, the virtual energy storage discharge time T of the electric vehicle is obtained. dchar Expressed as: Among them, C is the total capacity of the electric vehicle's power battery, r is the maximum discharge depth per kilometer of the electric vehicle, It is the state of charge limit of electric vehicle battery cells.

7. The optimization control method for a DC microgrid with electric vehicle virtual energy storage according to claim 6, characterized in that: In the process of constructing the electric vehicle virtual energy storage model, the virtual capacitance value C ve (t) and virtual state of charge SOC ve (t) is expressed as: Among them, U C is the DC bus terminal voltage, η v It is the battery charge and discharge efficiency.

8. The optimization control method for a DC microgrid with electric vehicle virtual energy storage according to claim 7, characterized in that: In the process of integrating source, load and storage resources into the coordinated operation optimization, according to the economic benefit value W of the microgrid, total Maximization is the goal, and the objective function is established; Set power balance constraints according to the operating status of controllable equipment; Set energy storage state constraints according to the operating status of controllable equipment; According to the target, an objective function, the power balance constraint condition, and the energy storage state constraint condition are established to perform collaborative operation optimization, wherein: The objective function is expressed as: The power balance constraint is expressed as: P w (t)+P B (t)+P C (t)=P L (t)+P E (t); The energy storage state constraint condition is expressed as: Where W l is the system controllable load benefit, W e is the charging and discharging benefit of electric vehicles, S bess is the operation and maintenance cost of the hybrid energy storage equipment, S b is the battery life loss cost, S ev is the peak load-shaving cost paid to electric vehicle users, k l is the controllable load benefit per unit of electricity, p t is the electric vehicle transaction electricity price, k bess is the operation and maintenance cost per unit of electricity of the hybrid energy storage device, k ev is the grid-connected electricity price of electric vehicles per unit discharge capacity, K b is the total investment cost of the battery, A is the linear aging coefficient of the battery, SOH min It is the health status value of the battery at the end of its life, SOC B is the battery state of charge, P L is the controllable load power, P B is the battery charging and discharging power, P C is the supercapacitor charging and discharging power, P w is wind power, SOH B Is the battery health status, SOC C is the state of charge of the supercapacitor.

9. An optimization control system for a DC microgrid containing electric vehicle virtual energy storage, used to implement the optimization control method for a DC microgrid containing electric vehicle virtual energy storage according to any one of claims 1 to 8, characterized in that: include: A virtual energy storage construction module is used to guide the shift of the characteristic curve of electric vehicle charging and discharging demand based on the electricity price strategy, build an electric vehicle virtual energy storage model, and simulate the load shifting effect of the electric vehicle virtual energy storage system; A microgrid energy management model building module is used to build a microgrid energy management model for a DC microgrid with electric vehicle virtual energy storage by integrating source, load and storage resources into collaborative operation optimization and solving the intraday economic optimal scheduling strategy based on the particle swarm algorithm. The optimization strategy generation module is used to generate an optimization control strategy for the optimal energy storage configuration and power distribution of the DC microgrid based on the microgrid energy management model and the introduction of the optimal virtual capacitance value in the model.

10. The optimization control system of a DC microgrid with electric vehicle virtual energy storage according to claim 9, characterized in that: The DC microgrid includes a permanent magnet direct-drive wind turbine generator set, a hybrid energy storage device composed of a battery and a supercapacitor, a controllable load handled by an asynchronous motor, and an electric vehicle with mobile energy storage characteristics; The permanent magnet direct-drive wind turbine generator set is connected to the DC bus through a voltage-type AC / DC converter WVSC, the hybrid energy storage device is connected to the DC bus and grid through two bidirectional DC / DC converters CVSC and BVSC respectively, the controllable load is in the form of a continuously adjustable asynchronous motor connected to the DC bus through an AC / DC converter LVSC, and the electric vehicle is connected to the DC bus through a bidirectional DC / DC converter EVSC.

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