Electric vehicle charging and discharging distributed optimal scheduling method and system containing photovoltaic output

Abstract

The invention belongs to the technical field of new energy and energy conservation, and relates to an electric vehicle charging and discharging distributed optimal scheduling method and system containing photovoltaic output. A power grid mathematical model is formed according to the coupling relation between a photovoltaic-superior power grid system and an electric vehicle; the minimum total cost of the carbon emission cost and the light abandoning cost + the battery degradation cost is considered as an objective function, and the constraint conditions comprise photovoltaic output constraint, tie line power constraint, power balance constraint and electric vehicle state constraint; an electric vehicle is used as energy storage equipment, electric power is stored through charging, smoothing of a power grid load is facilitated, and photovoltaic volatility is matched with the requirement of a power grid; a synchronous alternating direction multiplier method is adopted to realize distributed solution of the two decomposed systems, distributed optimization is performed through electric vehicle charging and discharging scheduling containing photovoltaic output, and different charging strategies are formulated to reduce the loss of renewable energy sources caused by the absorption problem.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy and energy conservation, and relates to a distributed optimal scheduling method and system for electric vehicle charging and discharging with photovoltaic output. Background Art

[0002] At present, the development stage of electric vehicles has transitioned from the R & D stage to the trial operation stage, attempting different business models to achieve the interaction between users and the power grid. It is of great significance to seek an orderly charging method that can meet the needs of both parties. On the other hand, in recent years, the development of photovoltaic has been very rapid, and photovoltaic grid connection has become a hot trend. However, there are still relatively serious problems with photovoltaic at present, and there is a large amount of light curtailment. There is a potential fit between electric vehicle charging and wind power, and electric vehicles, as mobile loads, may consume renewable energy.

[0003] If the characteristics of rapid response of electric vehicles can be utilized, on the premise of meeting the basic charging needs of electric vehicle users, combined with the guidance of a reasonable orderly charging / discharging strategy, the charging time and power of electric vehicles can be changed, and finally peak shaving and valley filling can be achieved, ensuring the coordinated development of both electric vehicles and the power grid, and at the same time realizing the local consumption of photovoltaic. Summary of the Invention

[0004] The purpose of the present invention is to provide a distributed optimal scheduling method and system for electric vehicle charging and discharging with photovoltaic output, so as to solve the technical problem of large losses caused by the consumption problem of renewable energy in the prior art.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: In the first aspect, the present application discloses a distributed optimal scheduling method for electric vehicle charging and discharging with photovoltaic output, including: Obtaining the coupled relationship between the photovoltaic-electric vehicle-superior power grid system, electric vehicle charging and discharging information, photovoltaic output, and power information provided by the superior power grid; Based on the total cost minimum function and constraint conditions of the preset battery degradation cost, carbon emission cost, and light curtailment cost, decoupling the photovoltaic-electric vehicle-superior power grid system to obtain the electric vehicle system and the photovoltaic-superior power grid system; the constraint conditions include: photovoltaic output constraint, tie line power constraint, power balance constraint, and electric vehicle state constraint; Based on the electric vehicle charging and discharging information, photovoltaic output, and power information provided by the superior power grid, respectively performing distributed solution on the electric vehicle system and the photovoltaic-superior power grid system to obtain a scheduling optimization plan.

[0006] Preferably, the specific total cost minimum function is as follows:

[0007] In the formula, is the total cost; is the scheduling duration; is the number of electric vehicles; is the power exchanged between the local distribution network and the superior power grid at time t; is the output of the photovoltaic power generation unit; is the cost of carbon emissions and the cost of light curtailment, is the cost of battery degradation, is the charging and discharging power of the i-th electric vehicle at time t.

[0008] Preferably, the cost of carbon emissions and the cost of light curtailment are related to the power exchanged between the local distribution network and the superior power grid at time t and the output of the photovoltaic power generation unit and are specifically expressed by the following formula:

[0009] The cost of battery degradation is related to the charging and discharging power of the i-th electric vehicle at time t and is specifically expressed by the following formula:

[0010] In the formula: is the cost of processing unit carbon emissions; is the carbon dioxide emissions generated per unit of power generation; is the time interval of scheduling; is the cost of cutting photovoltaic; is the predicted value of the photovoltaic power generation unit; , , are the quadratic term, the linear term and the constant term coefficients of the battery degradation cost respectively.

[0011] Preferably, the photovoltaic output constraint:

[0012] The tie-line power constraint:

[0013] The power balance constraint:

[0014] The electric vehicle state constraint:

[0015]

[0016]

[0017]

[0018] Wherein, is the output of the photovoltaic power generation unit; is the predicted value of the photovoltaic power generation unit; is the power exchanged between the local distribution network at time t and the superior power grid; is the upper limit of the tie-line power; is the load at time t; is the charging and discharging power of the nth electric vehicle at time t; is the charging and discharging power of the ith electric vehicle at time t; indicates whether the ith electric vehicle discharges and charges in the t period. If so, set it to 1, otherwise set it to 0; is the rated charging power of the electric vehicle, is the battery capacity of the ith electric vehicle at time t; η is the charging and discharging efficiency, is the scheduling time interval; is the lower limit of the battery capacity, is the upper limit of the battery capacity.

[0019] Preferably, the synchronous alternating direction multiplier method is used to perform distributed solutions on the electric vehicle system and the photovoltaic-superior power grid system respectively, which specifically includes the following steps: S301: Transmit the charging and discharging information of the electric vehicle to the photovoltaic-superior power grid system. At the same time, transmit the photovoltaic output and the power information provided by the superior power grid back to the electric vehicle system; S302: Based on the constraint conditions, in a parallel computing manner, iteratively optimize the output of the photovoltaic power generation unit at the (k + 1)th time and the power exchanged between the local distribution network at time t and the superior power grid , the load at the kth time t and the charging and discharging power of the nth electric vehicle at time t , until the convergence state; obtain the scheduling optimization scheme.

[0020] Preferably, the synchronous alternating direction multiplier method is used to perform distributed solutions on the electric vehicle system and the photovoltaic-superior power grid system respectively, and the convergence criterion is:

[0021] Wherein, is the number of iterations; is the convergence accuracy; is the output of the photovoltaic power generation unit at the (k + 1)th time; is the power exchanged between the local distribution network at time t and the superior power grid at the (k + 1)th time; is the load at the k-th time t; is the charging and discharging power of the n-th electric vehicle at time t.

[0022] Second, the present application discloses a distributed optimal scheduling system for electric vehicle charging and discharging with photovoltaic output, which is characterized by including: An acquisition unit, configured to acquire the coupling relationship between the photovoltaic-electric vehicle-superior power grid system, the electric vehicle charging and discharging information, and the power information provided by the photovoltaic output and the superior power grid; A decoupling unit, configured to decouple the photovoltaic-electric vehicle-superior power grid system based on a total cost minimum function and constraint conditions of preset battery degradation cost, carbon emission cost, and light abandonment cost, to obtain an electric vehicle system and a photovoltaic-superior power grid system; the constraint conditions include: photovoltaic output constraint, tie line power constraint, power balance constraint, and electric vehicle state constraint; A calculation unit, configured to perform distributed solution on the electric vehicle system and the photovoltaic-superior power grid system respectively based on the electric vehicle charging and discharging information, the photovoltaic output, and the power information provided by the superior power grid, to obtain a scheduling optimization scheme.

[0023] Preferably, the specific total cost minimum function is as follows:

[0024]

[0025]

[0026] In the formula: is the total cost; is the scheduling duration; is the number of electric vehicles; is the power exchanged between the local distribution network and the superior power grid at time t; is the output of the photovoltaic power generation unit; is the carbon emission cost and the light abandonment cost, is the battery degradation cost, is the charging and discharging power of the i-th electric vehicle at time t; is the cost of processing unit carbon emissions; is the carbon dioxide emission per unit power generation; is the scheduling time interval; is the cost of cutting photovoltaic; is the predicted value of the photovoltaic power generation unit; , , are respectively the quadratic term, the linear term, and the constant term coefficients of the battery degradation cost.

[0027] In a third aspect, the present application discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the distributed optimal scheduling method for charging and discharging of an electric vehicle with photovoltaic output described in any one of the above are implemented.

[0028] In a fourth aspect, the present application discloses a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the distributed optimal scheduling method for charging and discharging of an electric vehicle with photovoltaic output described in any one of the above are implemented.

[0029] Compared with the prior art, the present invention has the following beneficial effects: Energy storage and smoothing of grid load: Electric vehicles can be used as energy storage devices. Storing electricity through charging is beneficial for smoothing the grid load and matching the volatility of photovoltaic power with the grid demand.

[0030] Promoting the consumption of renewable energy: As mobile loads, electric vehicles can be distributedly optimized through the charging and discharging scheduling of electric vehicles with photovoltaic output according to the distribution and supply of photovoltaic power, and different charging strategies can be formulated to reduce the losses caused by the consumption problem of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 is a flowchart of the method for the embodiment of the present invention; Figure 2 is a schematic coupling diagram of the photovoltaic-electric vehicle-upper-level power grid system for the embodiment of the present invention; Figure 3 is a schematic decoupling diagram of the photovoltaic-electric vehicle-upper-level power grid system for the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0034] Accordingly, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0035] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not require further definition and explanation in subsequent drawings.

[0036] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the invention product is usually placed during use, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be construed as a limitation to the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0037] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0038] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, if terms such as "set", "installed", "connected", "coupled" are understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0039] The present invention will be further described in detail below with reference to the accompanying drawings: See Figure 1 , this application discloses a distributed optimal scheduling method for electric vehicle charging and discharging with photovoltaic output, including: S1: Obtain the mutually coupled relationship of the photovoltaic-electric vehicle-superior power grid system, electric vehicle charging and discharging information, photovoltaic output, and power information provided by the superior power grid; S2: Based on the total cost minimum function considering the preset battery degradation cost, carbon emission cost, and curtailment cost of light, and the constraint conditions, decouple the photovoltaic-electric vehicle-superior power grid system to obtain the electric vehicle system and the photovoltaic-superior power grid system; the constraint conditions include: photovoltaic output constraint, tie-line power constraint, power balance constraint, and electric vehicle state constraint; S3: Based on the electric vehicle charging and discharging information, photovoltaic output, and power information provided by the superior power grid, perform distributed solutions for the electric vehicle system and the photovoltaic-superior power grid system respectively to obtain the dispatching optimization plan.

[0040] This application forms a power grid mathematical model based on the coupling relationship between the photovoltaic-superior power grid system and the electric vehicle. The objective function of this application is the total cost minimum function considering the carbon emission cost, curtailment cost of light, and battery degradation cost. The constraint conditions include photovoltaic output constraint, tie-line power constraint, power balance constraint, and electric vehicle state constraint; regarding the electric vehicle as an energy storage device, charging and storing electricity is beneficial to smoothing the power grid load and matching the volatility of photovoltaic with the demand of the power grid; the synchronous alternating direction method of multipliers (SADMM) is used to achieve distributed solutions for the two decomposed systems, using the information transfer at the model coupling to replace the constraints at the coupling, and iterating by mutually transferring the solution information of the two subsystems, finally obtaining the solution that satisfies the coupling relationship between the systems. Regarding the electric vehicle as a mobile load, according to the distribution and supply of photovoltaic, distributed optimization can be carried out through the charging and discharging dispatching of electric vehicles including photovoltaic output, and different charging strategies can be formulated to reduce the losses caused by the accommodation problem of renewable energy.

[0041] In some embodiments, the specific total cost minimum function is as follows:

[0042] In the formula, is the total cost; is the dispatching duration; is the number of electric vehicles; is the power exchanged between the local distribution network and the superior power grid at time t; is the output of the photovoltaic power generation unit; is the carbon emission cost and curtailment cost of light, is the battery degradation cost, is the charging and discharging power of the i-th electric vehicle at time t.

[0043] Further preferably, the carbon emission cost and curtailment cost of light is related to the power exchanged between the local distribution network and the superior power grid at time t and the output of the photovoltaic power generation unit are related, and are specifically expressed by the following formula:

[0044] Battery degradation cost is related to the charging and discharging power of the i-th electric vehicle at time t and is specifically expressed by the following formula:

[0045] In the formula: is the cost of processing unit carbon emissions; is the carbon dioxide emissions generated per unit of power generation; is the time interval of scheduling; is the cost of cutting off the photovoltaic; is the predicted value of the photovoltaic power generation unit; , , are the quadratic term, linear term and constant term coefficients of the battery degradation cost respectively.

[0046] In some embodiments, the constraint conditions include: photovoltaic output constraint, tie-line power constraint, power balance constraint and electric vehicle state constraint. Specifically, Photovoltaic output constraint:

[0047] Tie-line power constraint:

[0048] Power balance constraint:

[0049] Electric vehicle state constraint:

[0050]

[0051]

[0052]

[0053] In the formula, is the output of the photovoltaic power generation unit; is the predicted value of the photovoltaic power generation unit; is the power exchanged between the local distribution network and the superior power grid at time t; is the upper limit of the tie-line power; is the load at time t; is the charging and discharging power of the n-th electric vehicle at time t is the charging and discharging power of the \(i\)-th electric vehicle at time \(t\); represents whether the \(i\)-th electric vehicle discharges and charges during the time period \(t\). If so, set it to 1, otherwise set it to 0; is the rated charging power of the electric vehicle, is the battery capacity of the \(i\)-th electric vehicle at time \(t\); \(\eta\) is the charging and discharging efficiency, is the scheduling time interval; is the lower limit of the battery capacity, is the upper limit of the battery capacity.

[0054] In some embodiments, the synchronous alternating direction method of multipliers (SADMM) is used to perform distributed solutions for the electric vehicle system and the PV-upper grid system respectively. The convergence criterion is:

[0055] In the formula, is the number of iterations; is the convergence accuracy.

[0056] In some embodiments, a distributed optimal scheduling method for electric vehicle charging and discharging with PV output Figure 2 shows the mutually coupled relationship of the PV-electric vehicle-upper grid system. To apply the distributed scheduling method for electric vehicles with PV output, it is necessary to decouple the electric vehicle from other systems through the objective function. The decomposition mechanism of the PV-electric vehicle-upper grid system is as Figure 3 shown. The charging and discharging information of the electric vehicle obtained by the electric vehicle scheduling center is transmitted to the PV-upper grid system, and at the same time, the PV output power decided by the distribution network operator and the power information provided by the upper grid are transmitted back to the electric vehicle scheduling center, and then the SADMM algorithm is used to perform distributed solutions for the two decomposed systems.

[0057] The synchronous alternating direction method of multipliers (SADMM) algorithm is widely used to solve convex optimization problems in the field of power system optimal scheduling, and this algorithm has excellent decomposability and convergence. The principle of SADMM is to use the information transfer at the model coupling to replace the constraints at the coupling, and perform iterations by mutually transferring the solution information of the two subsystems, and finally find the solution that satisfies the coupling relationship between the systems.

[0058] The objective function includes the carbon emission cost, the curtailment cost of PV power, and the cost of battery degradation caused by electric vehicle charging and discharging. The specific objective function is as follows: (1) In the formula, is the total cost; is the scheduling duration; is the number of electric vehicles; is the power exchanged between the local distribution network and the superior power grid at time t; is the output of the photovoltaic power generation unit; are the cost of carbon emissions and the cost of light curtailment, is the cost of battery degradation, is the charging and discharging power of the i-th electric vehicle at time t, positive for charging and negative for discharging.

[0059] The cost of dealing with carbon emissions and the calculation of light curtailment are as follows: (2) In the formula: is the cost of dealing with unit carbon emissions; is the carbon dioxide emissions generated per unit of electricity generation; is the time interval of scheduling; is the cost of cutting photovoltaic; is the predicted value of the photovoltaic power generation unit.

[0060] The battery degradation situation is affected by the charging and discharging power, and the battery degradation cost is defined as follows: (3) In the formula: 、 、 are the quadratic term, linear term and constant term coefficients of the battery degradation cost respectively, is the charging and discharging power of the i-th electric vehicle at time t, positive for charging and negative for discharging.

[0061] The constraints include photovoltaic output constraints, tie-line power constraints, power balance constraints and electric vehicle state constraints.

[0062] Photovoltaic output constraints: (4) Tie-line power constraints: (5) In the formula: is the upper limit of the tie-line power, and the lower limit of 0 means that the local distribution network does not supply power to the superior power grid in the reverse direction, and the photovoltaic output is locally consumed.

[0063] Power balance constraints: (6) In the formula, is the load at time t.

[0064] Electric vehicle state constraints: (7) (8) (9) (10) In the formula: Indicates whether the i-th electric vehicle discharges and charges during the t period. If so, set it to 1; otherwise, set it to 0. Is the rated charging power of the electric vehicle. Is the battery capacity of the i-th electric vehicle at time t, and is the charge-discharge efficiency. Is the lower limit of the battery capacity. Is the upper limit of the battery capacity.

[0065] For the scenario where electric vehicles participate in charge-discharge scheduling, the main solution steps of ADMM are as follows: Taking Figure 3 Interconnection as an example, the coupling constraint to be satisfied is the power balance constraint:

[0066] In the formula, Is the output of the photovoltaic power generation unit; Is the power exchanged between the local distribution network and the superior power grid at time t; Is the load at time t; Is the charge-discharge power of the n-th electric vehicle at time t; When using the standard ADMM for decentralized solution, the solution process for each iteration is as follows: (11) In the formula: Represents the number of iterations; Is the introduced Lagrange multiplier vector; Is a constant greater than zero; Is the output of the photovoltaic power generation unit at the (k + 1)-th time; Is the power exchanged between the local distribution network and the superior power grid at time t at the (k + 1)-th time; Is the load at time t at the k-th time; Is the charge-discharge power of the n-th electric vehicle at time t; Is the cost of carbon emissions and the cost of abandoned light; Is the cost of battery degradation; Is the output of the photovoltaic power generation unit at the k-th time; Is the power exchanged between the local distribution network and the superior power grid at time t at the k-th time; Is the charge-discharge power of the n-th electric vehicle at time t at the (k + 1)-th time.

[0067] From the iterative process of ADMM, it can be seen that its principle is a serial iterative method. That is, the optimization value of the previous area is used to enter the subsequent area for optimization and solution. After all areas are optimized, the upper-level coordinator updates the Lagrange multipliers and distributes them to the decentralized areas. Obviously, the iterative speed of this method is relatively slow and not conducive to large-scale calculations. The following is the conversion process from ADMM to SADMM.

[0068] Taking the first equation of Equation (11) as an example, the latter two terms can be expressed as: (12) Let = , and omit the constant term , Equation (12) can be transformed into: (13) Take the average value of the optimization results of each time for two areas , let (14) Then let Equation (14) replace the optimization results of the adjacent areas to be selected next in Equation (13), that is, let replace in the first equation of Equation (13) and in the second equation, and the expression for decentralized solution based on SADMM can be obtained: (15) (16) Based on the SADMM algorithm, the convergence criterion for solution is (17) In the formula: is the number of iterations; is the convergence accuracy; is the output power of the photovoltaic power generation unit at the (k + 1)-th time; is the power exchanged between the local distribution network at the (k + 1)-th time and the superior power grid at time t; is the load at time t of the k-th time; is the charging and discharging power of the n-th electric vehicle at time t.

[0069] Further preferably, the synchronous alternating direction multiplier method is used to perform distributed solutions for the electric vehicle system and the photovoltaic-superior power grid system respectively, and the solution steps are as follows: 1) Input the photovoltaic prediction value and the relevant parameters of the electric vehicle.

[0070] 2) Set the number of iterations = 1, given the convergence accuracy of the algorithm, initialize the power to be optimized in two regions.

[0071] 3) Optimize the DC tie-line power in a parallel computing manner and .

[0072] 4) Determine whether the convergence condition is satisfied according to Equation (17). If so, end the iteration; otherwise, go to step 5).

[0073] 5) Set the number of iterations = + 1, and update update and respectively according to Equations (7)-(9); then go to step 3) for the next iteration calculation.

[0074] This application also discloses a distributed optimal scheduling system for electric vehicle charging and discharging with photovoltaic output, including: An acquisition unit for acquiring the coupled relationship between the photovoltaic-electric vehicle-superior power grid system, electric vehicle charging and discharging information, photovoltaic output, and power information provided by the superior power grid; A decoupling unit for decoupling the photovoltaic-electric vehicle-superior power grid system based on a total cost minimum function and constraint conditions of preset battery degradation cost, carbon emission cost, and light abandonment cost to obtain an electric vehicle system and a photovoltaic-superior power grid system; the constraint conditions include: photovoltaic output constraint, tie-line power constraint, power balance constraint, and electric vehicle state constraint; A calculation unit for performing distributed solutions on the electric vehicle system and the photovoltaic-superior power grid system respectively based on the electric vehicle charging and discharging information, photovoltaic output, and power information provided by the superior power grid to obtain a scheduling optimization scheme.

[0075] In some embodiments, in the decoupling unit, the specific total cost minimum function is as follows:

[0076]

[0077]

[0078] In the formula: is the total cost; is the scheduling duration; is the number of electric vehicles; is the power exchanged between the local distribution network and the superior power grid at time t; is the output of the photovoltaic generator set; is the carbon emission cost and light abandonment cost, is the battery degradation cost, is the charging and discharging power of the i-th electric vehicle at time t; is the cost of dealing with unit carbon emissions; is the carbon dioxide emissions generated by unit power generation; is the scheduling time interval; is the cost of cutting photovoltaic; is the predicted value of the photovoltaic power generation unit; , , are the quadratic term, linear term and constant term coefficients of the battery degradation cost respectively.

[0079] This application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the distributed optimization scheduling method for charging and discharging of electric vehicles with photovoltaic output described in any one of the above are implemented.

[0080] This application also discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the distributed optimization scheduling method for charging and discharging of electric vehicles with photovoltaic output described in any one of the above are implemented.

[0081] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0082] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks

[0083] 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 work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one process or more processes and / or one block or more blocks in the flow. Figure 1 in one process or more processes and / or one block or more blocks Figure 1 specified functions.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or more processes and / or one block or more blocks in the flow. Figure 1 in one process or more processes and / or one block or more blocks Figure 1 specified functions.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A distributed optimization scheduling method for charging and discharging electric vehicles with photovoltaic output, characterized in that: include: Obtain the mutual coupling relationship between photovoltaic-electric vehicle-upper grid system, electric vehicle charging and discharging information, photovoltaic output and power information provided by the upper grid; Based on the preset total cost minimum function and constraints of battery degradation cost, carbon emission cost and abandoned light cost, the photovoltaic-electric vehicle-upper grid system is decoupled to obtain the electric vehicle system and the photovoltaic-upper grid system; the constraints include: photovoltaic output constraint, tie line power constraint, power balance constraint and electric vehicle status constraint; Based on the charging and discharging information of electric vehicles, photovoltaic output and the power information provided by the upper-level power grid, distributed solutions are performed on the electric vehicle system and the photovoltaic-upper-level power grid system respectively to obtain the scheduling optimization plan.

2. A distributed optimization scheduling method for charging and discharging electric vehicles with photovoltaic output according to claim 1, characterized in that: The specific total cost minimum function is as follows: In the formula, is the total cost; The scheduling duration; is the number of electric vehicles; The local distribution network exchanges power with the upper grid at time t; The output of the photovoltaic generator set; The cost of carbon emissions and the cost of abandoned solar power, is the battery degradation cost, is the charging and discharging power of the i-th electric vehicle at time t.

3. A distributed optimization scheduling method for charging and discharging electric vehicles with photovoltaic output according to claim 2, characterized in that: The cost of carbon emissions and the cost of abandoned solar power Exchange power with the upper grid at time t and the output of photovoltaic generators Related, specifically expressed by the following formula: Battery degradation costs and the charging and discharging power of the i-th electric vehicle at time t Related, specifically expressed by the following formula: Where: The cost of processing a unit of carbon emissions; is the amount of carbon dioxide emissions per unit of electricity generated; is the time interval for scheduling; To cut the cost of photovoltaics; Predicted values ​​of photovoltaic generators; , , are the coefficients of the quadratic term, linear term and constant term of the battery degradation cost respectively.

4. A distributed optimization scheduling method for charging and discharging electric vehicles with photovoltaic output according to claim 1, characterized in that: The photovoltaic output constraints are: Tie line power constraints: Power balance constraints: Electric vehicle state constraints: In the formula, The output of the photovoltaic generator set; Predicted values ​​of photovoltaic generators; The local distribution network exchanges power with the upper power grid at time t; is the upper limit of the tie line power; is the load at time t; is the charging and discharging power of the nth electric vehicle at time t; is the charging and discharging power of the i-th electric vehicle at time t; Indicates whether the i-th electric vehicle is discharged and charged in time period t, if yes, set to 1, otherwise set to 0; is the rated charging power of the electric vehicle, is the battery capacity of the ith electric vehicle at time t; η is the charge and discharge efficiency, is the time interval for scheduling; is the lower limit of battery capacity, The upper limit of battery capacity.

5. A distributed optimization scheduling method for charging and discharging electric vehicles with photovoltaic output according to claim 4, characterized in that: The synchronous alternating direction multiplier method is used to perform distributed solutions for the electric vehicle system and the photovoltaic-upper grid system, which specifically includes the following steps: S301: transmitting the electric vehicle charging and discharging information to the photovoltaic-upper grid system, and at the same time, transmitting the photovoltaic output and the power information provided by the upper grid back to the electric vehicle system; S302: Based on the constraints, iteratively optimize the output of the k+1th photovoltaic generator set and the power exchanged between the local distribution network and the upper power grid at time t in a parallel computing manner. , the load at the kth time t and the charging and discharging power of the nth electric vehicle at the time t , to the convergence state; get the scheduling optimization plan.

6. A distributed optimization scheduling method for charging and discharging electric vehicles with photovoltaic output according to claim 5, characterized in that: The synchronous alternating direction multiplier method performs distributed solutions for the electric vehicle system and the photovoltaic-upper grid system respectively, and the convergence criterion is: In the formula, is the number of iterations; is the convergence accuracy; is the output of the k+1th photovoltaic generator set; The k+1th local distribution network exchanges power with the upper power grid at time t; is the load at the kth time t; is the charging and discharging power of the nth electric car at time t.

7. A distributed optimization scheduling system for charging and discharging electric vehicles with photovoltaic output, characterized in that: include: An acquisition unit is used to acquire the mutual coupling relationship between photovoltaic-electric vehicle-upper grid system, the charging and discharging information of electric vehicle, photovoltaic output and power information provided by the upper grid; A decoupling unit is used to decouple the photovoltaic-electric vehicle-upper grid system based on the preset total cost minimum function of battery degradation cost, carbon emission cost and abandoned light cost and constraints, and obtain the electric vehicle system and the photovoltaic-upper grid system; the constraints include: photovoltaic output constraint, tie line power constraint, power balance constraint and electric vehicle state constraint; The calculation unit is used to perform distributed solutions for the electric vehicle system and the photovoltaic-superior power grid system based on the electric vehicle charging and discharging information, photovoltaic output and power information provided by the superior power grid to obtain a scheduling optimization plan.

8. The distributed optimization scheduling system for charging and discharging electric vehicles with photovoltaic power according to claim 7 is characterized in that: The specific total cost minimum function is as follows: Where: is the total cost; The scheduling duration; is the number of electric vehicles; The local distribution network exchanges power with the upper grid at time t; The output of the photovoltaic generator set; The cost of carbon emissions and the cost of abandoned solar power, is the battery degradation cost, is the charging and discharging power of the i-th electric vehicle at time t; The cost of processing a unit of carbon emissions; is the amount of carbon dioxide emissions per unit of electricity generated; is the time interval for scheduling; To cut the cost of photovoltaics; Predicted values ​​of photovoltaic generators; , , are the coefficients of the quadratic term, linear term and constant term of the battery degradation cost respectively.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the distributed optimization scheduling method for charging and discharging of electric vehicles with photovoltaic output as described in any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the distributed optimization scheduling method for charging and discharging of electric vehicles with photovoltaic output as described in any one of claims 1 to 6.

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