A pricing method for electric vehicle charging in a park considering carbon emissions

Abstract

The present invention discloses a method for pricing electric vehicle charging in a park considering carbon emissions, including the following steps: obtaining required data; constructing a model for the cost of electric vehicle electricity consumption according to the objective function of carbon emissions and photovoltaic power generation and power supply in the park; establishing a model for making electric vehicle charging plans according to charging needs; S3, formulating a time-of-use electricity price pricing model according to the comprehensive benefits of both the park and the user; formulating a corresponding charging plan according to the power situation when the electric vehicle is connected to the park; generating a park time-of-use electricity price according to the required data, calculating the carbon emissions and economic benefits generated by the charging of the electric vehicle, and constructing an evaluation target by using the constructed model; using the evaluation target as fitness, using a genetic algorithm, iterating, and finding the optimal solution; obtaining the optimal time-of-use electricity price in the park, and executing the charging plan. The present invention can formulate an electric vehicle charging pricing plan for the park, and reduce the carbon emissions generated by the charging of electric vehicles in the park.

Description

Technical Field

[0001] The present invention relates to the field of new energy electric vehicle charging, and in particular to a method for pricing electric vehicle charging in a park taking carbon emissions into consideration. Background Art

[0002] At present, the electricity required for charging electric vehicles is still provided by the power grid, but thermal power generation still accounts for a certain proportion. Nowadays, electric vehicle charging mainly adopts charging upon access, and the electric vehicle starts charging immediately after connecting to the charging pile, with maximum power, and strives to charge to full power in a short time. Only the distribution network can be competent for this task, but it will inevitably produce indirect and large amounts of carbon emissions in a short time. However, in some parks, electric vehicle owners start work in the morning and get off work at the specified time, and the time they stay can be fully utilized, and the photovoltaic facilities in the park can also provide part of the electricity required for charging, but considering that electric vehicle owners may have temporary travel needs, mandatory charging and dispatch charging should be adopted together. With the continuous development of the power market, the power supply form of the park is also diversified, from a single power grid power supply to a composite power supply of the power grid, energy suppliers and self-built facilities in the park, so the park should also set up its own time-of-use electricity price. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a pricing method for charging electric vehicles in a park that takes carbon emissions into consideration, to formulate reasonable time-of-use electricity prices for the park, and to optimize the scheduling of orderly charging of electric vehicles in the park during different time periods.

[0004] Technical solution: The park electric vehicle charging pricing method of the present invention comprises the following steps:

[0005] S1, obtain the required data, including the real-time power generation of photovoltaic equipment, historical load data of the park, the power consumption of electric vehicles when connected to the park, and the time-of-use electricity price of the power grid;

[0006] S2, based on the objective functions of carbon emissions, photovoltaic power generation, and power grid power supply within the park, constructs a cost model for electric vehicle electricity consumption; and establishes an electric vehicle charging plan formulation model based on charging demand;

[0007] S3, formulate a time-of-use electricity price pricing model based on the comprehensive benefits of both the park and the users;

[0008] S4, formulate a corresponding charging plan according to the power status of the electric vehicle when it is connected to the park;

[0009] S5, based on the data obtained in step S1, using the electric vehicle charging plan formulation model and the time-of-use electricity price pricing model, generate the park time-of-use electricity price and calculate the carbon emissions and economic benefits generated by electric vehicle charging, and construct an evaluation target;

[0010] S6, taking the evaluation target as fitness, using genetic algorithm, iterates to find the optimal solution;

[0011] S7, obtain the optimal time-of-use electricity price in the park and implement the charging plan.

[0012] Further, in step S1, the charging time of the electric vehicle is divided into multiple time periods, starting at a time point of End time is Assume that the real-time power generation of the photovoltaic equipment in a unit time period is The total number of electric vehicles participating in the optimized scheduling in the park is m.

[0013] Further, in step S2, the objective function of carbon emissions and photovoltaic and in-park power grid power supply is constructed as follows:

[0014]

[0015]

[0016] Among them, C ev is the objective function, which represents the carbon emissions generated by charging of all electric vehicles in the park, in kilograms; ω1 is the carbon emission factor of photovoltaics, in kilograms CO2 / kWh; ω2 is the carbon emission factor of the power grid, in kilograms CO2 / kWh; It represents the amount of electricity provided by the power grid in the park during the tth period, in kilowatt-hours; N t It represents the total number of time periods of the park work, N is the time step, and the unit is hours;

[0017] Cost model for electric vehicle electricity consumption:

[0018]

[0019]

[0020] Among them, Cost all It represents the total cost required to charge electric vehicles in the park; represents the time-of-use electricity price of the power grid; μ1 represents the unit price of photovoltaic power sold to the power grid, in kWh / yuan; It represents the remaining amount of photovoltaic power generation in the park during period t. represents the amount of electricity provided by photovoltaic power generation when the electric vehicle is charged during the t period, in kilowatt-hours; μ2 represents the operation and maintenance cost of photovoltaic equipment generated during photovoltaic power generation, in kilowatt-hours / yuan; μ3 represents the subsidy price that each household participating in the park subsidy scheduling needs to pay per day, in yuan; m represents the total number of electric vehicles participating in the optimized scheduling in the park;

[0021] Combining the output power of photovoltaic power in different time periods with the power supply capacity of the power grid to meet the charging requirements of electric vehicles in different time periods, the electric vehicle charging plan formulation model is established as follows:

[0022]

[0023]

[0024]

[0025]

[0026] in, It indicates that there are K electric vehicles in the park with charging power at time T, in kilowatts; It represents the output power of the power grid at time T, in kilowatts; It represents the output power of photovoltaic power at time T, in kilowatts; When the power grid is not needed to provide the power difference required for charging electric vehicles, It indicates the remaining power of photovoltaic output at time T, in kilowatts; time T belongs to time period t;

[0027]

[0028]

[0029] Respectively represent the upper and lower limits of the grid output power, They represent the upper and lower limits of photovoltaic output power respectively.

[0030] Furthermore, in step S3, a time-of-use electricity price pricing model is formulated based on the comprehensive benefits of the park and the user as follows:

[0031]

[0032]

[0033]

[0034] Among them, t = 1, 2, 3, respectively represent the peak period, valley period, and normal period; λ1 is the peak electricity price floating coefficient, λ2 is the valley electricity price floating coefficient, represents the time-of-use electricity price of the park, K min and K max Respectively represent the upper and lower limits of the electricity price ratio;

[0035]

[0036] g2 = earn2-earn1

[0037] in, and They represent the fees that user i who joins the charging plan needs to pay before the charging plan is implemented, and the fees that user i who joins the charging plan needs to pay after the charging plan is implemented; earn1 and earn2 represent the electricity sales revenue of the park before the charging plan is implemented, and the electricity sales revenue of the park after the charging plan is implemented;

[0038] earn all =g2-Cost all

[0039] Among them, earn all is the objective function, which represents the economic profit obtained by the park from charging electric vehicles.

[0040] Further, in step S4, a corresponding charging plan is formulated according to the power status of the electric vehicle when it is connected to the park:

[0041]

[0042] in, It is represented by the total number of charging periods required for the i-th vehicle during the working hours of the park, It represents the percentage of the remaining battery capacity of the i-th electric vehicle when charging is finished. It represents the percentage of the initial power of the i-th car when it is connected to the park to the rated battery capacity. represents the rated battery capacity of the i-th electric vehicle, It is represented by the charging capacity of the i-th electric vehicle in a unit time period;

[0043] To meet the minimum travel target demand of electric vehicle owners, a mandatory charging plan is formulated for electric vehicles:

[0044]

[0045]

[0046]

[0047] in, It is represented by the total number of time slots required for the i-th electric vehicle to charge to meet the minimum travel demand during the working hours of the park. It represents the percentage of the remaining battery capacity of the i-th electric vehicle that is charged to meet the minimum travel demand to the rated battery capacity; is a 0-1 variable, 1 means the i-th vehicle is charged in the t-th time period, and 0 means the i-th vehicle is not charged in the t-th time period;

[0048] When all electric vehicles in the park meet the minimum travel demand, an orderly charging plan will be formulated:

[0049]

[0050]

[0051]

[0052]

[0053]

[0054] in, N represents the time period required for the i-th electric vehicle to meet the minimum travel demand and charge to the target power during the working hours of the park. t Indicates the total number of time periods that the park is working.

[0055] Further, in step S5, the expression of the evaluation target is:

[0056] F=αC ev +βearn all

[0057] Among them, F represents the evaluation target, α is the weight of the optimized target carbon emissions, and β is the weight of the total benefits obtained from charging the optimized target electric vehicles.

[0058] Furthermore, in step S6, on the premise of meeting the charging demand, the charging options of all electric vehicles in the park at different time periods are iterated and solved to obtain the optimal option.

[0059] Further, in step S7, after the maximum number of iterations is met, the optimal park time-of-use electricity price is selected and the electric vehicle charging plan is executed.

[0060] Compared with the prior art, the present invention has the following significant effects:

[0061] The present invention proposes an orderly charging scheduling strategy by establishing an electric vehicle charging plan formulation model and a time-of-use electricity price pricing model. Under the goal of satisfying the minimum travel demand that may be generated by users in the park and the completion of electric vehicle charging as much as possible, the charging time of electric vehicles in the park is optimized and scheduled within the day, which greatly reduces the carbon emissions generated by the charging of electric vehicles in the park, reduces the consumption brought by user charging, absorbs the photovoltaic power generation in the park, reduces the demand for interaction between the park and the power grid, and formulates its own time-of-use electricity price for the park, which increases the economic and environmental benefits brought by the charging of electric vehicles in the park, which is of great significance to reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a flow chart of the present invention;

[0063] Figure 2 It is a curve diagram of the time-of-use electricity price in the park during the day in the embodiment of the present invention;

[0064] Figure 3 A bar chart comparing economic benefits and carbon emissions generated by a charging plan with and without considering carbon emissions in an embodiment of the present invention;

[0065] Figure 4 A charging plan time curve diagram with and without considering carbon emissions in an embodiment of the present invention;

[0066] Figure 5 A time curve diagram of photovoltaic charging and grid charging in consideration of carbon emissions in an embodiment of the present invention;

[0067] Figure 6 This is a curve diagram comparing the amount of electricity at the beginning and end of charging of electric vehicles in a park in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0069] The present invention aims at the two problems of high carbon emissions and high consumption caused by the disorderly charging of electric vehicles. Taking into account the characteristics of park operation and the temporary travel needs that may arise from electric vehicle owners, the present invention proposes an orderly charging scheduling strategy.

[0070] like Figure 1 As shown, the charging pricing method for electric vehicles in a park of the present invention comprises the following steps:

[0071] Step 1: Obtain the required data, including the real-time power generation of photovoltaic equipment, historical load data of the park, power consumption of electric vehicles when connected to the park, time-of-use electricity price of the power grid, etc.;

[0072] The charging time of electric vehicles is divided into multiple time periods, starting at the time point End time is Assume that the real-time power generation of the photovoltaic equipment in a unit time period is It represents the percentage of the initial power of the i-th car when it is connected to the park to the rated battery capacity, and the total number of electric vehicles participating in the optimal scheduling in the park is m.

[0073] Step 2: Based on the objective functions of carbon emissions, photovoltaic power generation, and power supply within the park, a cost model for electric vehicle electricity consumption is constructed, and a model for making electric vehicle charging plans is established based on charging needs. The detailed implementation steps are as follows:

[0074] The charging of electric vehicles in the park is divided into multiple time periods, and the objective function of carbon emissions, photovoltaic power generation, and power supply of the power grid in the park is constructed:

[0075]

[0076]

[0077] Among them, C ev is the objective function, which represents the carbon emissions generated by charging all electric vehicles in the park, in kilograms; ω1 is the carbon emission factor of photovoltaics. Since the construction and manufacturing process of photovoltaic power generation systems involves certain energy consumption and carbon emissions, the carbon emission factor of photovoltaic power generation is not zero, in kilograms of CO2 / kWh; ω2 is the carbon emission factor of the power grid, which is obtained from the document "Notice on Doing a Good Job in the Management of Greenhouse Gas Emission Reports of Power Generation Enterprises from 2023 to 2025" issued by the Ministry of Ecology and Environment, in kilograms of CO2 / kWh; It represents the amount of electricity provided by the power grid in the park during the tth period, in kilowatt-hours; N t It indicates the total number of working periods in the park, N is the time step, and the unit is hour.

[0078] Cost model for electric vehicle electricity consumption:

[0079]

[0080]

[0081] Among them, Cost all represents the total cost of charging electric vehicles in the park; μ1 represents the unit price of photovoltaic power sold to the grid, in kWh / yuan; Indicates the time-of-use electricity price of the power grid; It represents the remaining amount of photovoltaic power generation in the park during period t. It represents the amount of electricity provided by photovoltaic power generation when the electric vehicle is charged during the period t, in kilowatt-hours; μ2 represents the operation and maintenance cost of photovoltaic equipment generated during photovoltaic power generation, in kilowatt-hours / yuan; μ3 represents the subsidy price that each household participating in the scheduling of the park subsidy needs to pay per day, in yuan.

[0082] A charging plan formulation model for electric vehicles is established based on charging demand: combining the output power of photovoltaic power in different time periods with the power supply capacity of the power grid to meet the charging requirements of electric vehicles in different time periods, the charging process of all electric vehicles in the park in a certain time period is:

[0083]

[0084]

[0085]

[0086]

[0087] in, It indicates that there are K electric vehicles in the park with charging power at time T, in kilowatts; It represents the output power of the power grid at time T, in kilowatts; It represents the output power of photovoltaic power at time T, in kilowatts; When the power grid is not needed to provide the power difference required for charging electric vehicles, It indicates the remaining power of photovoltaic output at time T, in kilowatts; time T belongs to time period t.

[0088]

[0089]

[0090] in, Respectively represent the upper and lower limits of the grid output power, Respectively represent the upper and lower limits of photovoltaic output power.

[0091] Step 3: Formulate a time-of-use electricity price pricing model based on the comprehensive benefits of the park and users;

[0092] Time-of-use electricity pricing models include:

[0093]

[0094]

[0095]

[0096] Among them, t = 1, 2, 3, respectively represent the peak, valley, and normal periods, λ1 is the peak electricity price floating coefficient, λ2 is the valley electricity price floating coefficient, represents the time-of-use electricity price of the park, K min and K max They represent the upper and lower limits of the electricity price ratio respectively.

[0097] In order to ensure that the fees that users need to pay after the implementation of the time-of-use electricity price in the park are no more than before the implementation of the time-of-use electricity price, the following constraints are constructed:

[0098]

[0099] g2=earn2-earn1 (15)

[0100] in, and They respectively represent the fees that user i who joins the charging plan needs to pay before the implementation of the charging plan and the fees that user i who joins the charging plan needs to pay after the implementation of the charging plan. earn1 and earn2 respectively represent the electricity sales revenue of the park before the implementation of the charging plan and the electricity sales revenue of the park after the implementation of the charging plan.

[0101] earn all =g2-Cost all (16)

[0102] Among them, earn all Cost is the objective function, which represents the economic profit obtained by the park from charging electric vehicles; all Represents the total cost required for charging electric vehicles in the park.

[0103] Step 4: formulate a corresponding charging plan based on the power status of the electric vehicle when it is connected to the park;

[0104] Charging plans include:

[0105]

[0106] in, It is represented by the total number of charging periods required for the i-th vehicle during the working hours of the park, It represents the percentage of the remaining battery capacity of the i-th electric vehicle when charging is finished. represents the rated battery capacity of the i-th electric vehicle, It is represented as the charging capacity of the i-th electric vehicle in a unit time period.

[0107] In order to meet the minimum travel target demand that electric vehicle owners may have, a mandatory charging plan is established for electric vehicles:

[0108]

[0109]

[0110]

[0111] in, It is represented by the total number of time slots required for the i-th electric vehicle to be charged to meet the minimum travel demand during the working hours of the park. It represents the percentage of the remaining battery capacity of the i-th electric vehicle that is charged to meet the minimum travel demand to the rated battery capacity; It is a 0-1 variable, 1 means that the i-th vehicle is charged in the t-th time period, and 0 means that the i-th vehicle is not charged in the t-th time period.

[0112] When all electric vehicles in the park meet the minimum travel demand, an orderly charging plan will be formulated:

[0113]

[0114]

[0115]

[0116]

[0117]

[0118] in, It represents the number of time periods required for the i-th electric vehicle to meet the minimum travel demand and charge to the target power during the working hours of the park.

[0119] Step 5: Based on the data obtained in step 1, the charging demand constructed in step 2 is used to establish an electric vehicle charging plan formulation model and the time-of-use electricity price pricing model constructed in step 3 to generate the park time-of-use electricity price and calculate the carbon emissions and economic benefits generated by electric vehicle charging; the expression for constructing the evaluation target is as follows:

[0120] F=αC ev +β earn all (26)

[0121] Among them, F represents the evaluation target, which is the fitness value of the optimization algorithm; α is the weight of the optimized target carbon emissions, and β is the weight of the total benefits obtained from charging the optimized target electric vehicles.

[0122] Step 6, using genetic algorithm to iterate and find the optimal solution;

[0123] Using genetic algorithms, the charging options of all electric vehicles in the park at different times are iterated and solved under the premise of meeting the charging demand as much as possible, so as to obtain the optimal option.

[0124] Step 7: Obtain the optimal time-of-use electricity price for the park and implement the charging plan.

[0125] After the maximum number of iterations is met, the optimal park time-of-use electricity price is selected and the electric vehicle charging plan is implemented.

[0126] In this example, a genetic algorithm is used to set the park to be in working period from 8 am to 6 pm, with a time step of 30 minutes, and the working time of the day is divided into 20 periods. Since there are a large number of photovoltaic equipment in the park, in order to encourage photovoltaic power consumption, 22:00-7:00 and 12:00-14:00 can be divided into valley hours, 15:00-17:00 is peak hours, and the rest is normal hours.

[0127] It is assumed that there are 100 electric vehicles in the park, and the initial power of the electric vehicles meets the normal distribution with a mean of 0.4 and a variance of 0.1. The battery capacity of the electric vehicle is 40 kWh, the charging efficiency is 8 kWh / h, the target power of the electric vehicle is 90% when the electric vehicle is charged, and the minimum travel demand power is 50%.

[0128] The time-of-use electricity price of the power grid and the real-time power generation of photovoltaic equipment are data for a certain place.

[0129] Depend on Figure 2 It can be seen that the park has time-of-use electricity prices during the day.

[0130] Depend on Figure 3 It can be seen that the charging plan taking carbon emissions into consideration can significantly reduce the carbon emissions generated by charging and improve economic benefits.

[0131] Depend on Figure 4 It can be seen that in the charging plan taking carbon emissions into consideration, in order to meet the minimum travel demand, the distribution network will charge electric vehicles with less than 50% power from the beginning of the park operation, and then start to implement the charging plan according to the optimal individual that wins in the algorithm; without considering the carbon emission plan, all electric vehicles will be directly charged from the time they are connected to the network until they are fully charged and no more charging will be done.

[0132] Figure 5 It shows the output of photovoltaic and grid charging during the working hours of the park. In order to meet the minimum travel demand, the park starts charging electric vehicles with less than 50% power at the beginning of work. During this period, the photovoltaic power generation is small, so the charging demand is mainly borne by the grid; after the minimum travel demand is met, orderly charging begins. Since the time is close to the peak of photovoltaic power generation, the charging task is mainly borne by photovoltaics.

[0133] Figure 6 It shows the charging process of all electric vehicles in the park, and all electric vehicles meet the target power after charging.

[0134] In summary, the present invention optimizes the scheduling of the electric vehicle charging process while meeting the requirements of electric vehicle charging demand, taking into account the output of new energy equipment in the park, and formulates an exclusive time-of-use electricity price for the park, thereby achieving low carbon and economic win-win results.

Claims

1. A method for charging electric vehicles in a park considering carbon emissions, characterized in that: The steps include: S1, obtain the required data, including the real-time power generation of photovoltaic equipment, historical load data of the park, the power consumption of electric vehicles when connected to the park, and the time-of-use electricity price of the power grid; S2, based on the objective functions of carbon emissions, photovoltaic power generation, and power grid power supply within the park, constructs a cost model for electric vehicle electricity consumption; and establishes an electric vehicle charging plan formulation model based on charging demand; S3, formulate a time-of-use electricity price pricing model based on the comprehensive benefits of both the park and the users; S4, formulate a corresponding charging plan according to the power status of the electric vehicle when it is connected to the park; S5, based on the data obtained in step S1, using the electric vehicle charging plan formulation model and the time-of-use electricity price pricing model, generate the park time-of-use electricity price and calculate the carbon emissions and economic benefits generated by electric vehicle charging, and construct an evaluation target; S6, under the premise of meeting the charging demand, takes the evaluation target as the fitness, uses the genetic algorithm to iterate the charging options of all electric vehicles in the park at different time periods, and calculates the value of the evaluation target to obtain the optimal option; S7, obtain the optimal time-of-use electricity price in the park and implement the charging plan; In step S1, the charging time of the electric vehicle is divided into multiple time periods, starting at End time is Assume that the real-time power generation of the photovoltaic equipment in a unit time period is The total number of electric vehicles participating in the optimization dispatch in the park is m; In step S2, the objective function of carbon emissions and photovoltaic and in-park power grid power supply is constructed as follows: Among them, C ev is the objective function, which represents the carbon emissions generated by charging of all electric vehicles in the park, in kilograms; ω1 is the carbon emission factor of photovoltaics, in kilograms CO2 / kWh; ω2 is the carbon emission factor of the power grid, in kilograms CO2 / kWh; It represents the amount of electricity provided by the power grid in the park during the tth period, in kilowatt-hours; N t It represents the total number of time periods of the park work, N is the time step, and the unit is hours; Cost model for electric vehicle electricity consumption: Among them, Cost all It represents the total cost required to charge electric vehicles in the park; represents the time-of-use electricity price of the power grid; μ1 represents the unit price of photovoltaic power sold to the power grid, in kWh / yuan; It represents the remaining amount of photovoltaic power generation in the park during period t. represents the amount of electricity provided by photovoltaic power generation when the electric vehicle is charged during the t period, in kilowatt-hours; μ2 represents the operation and maintenance cost of photovoltaic equipment generated during photovoltaic power generation, in kilowatt-hours / yuan; μ3 represents the subsidy price that each household participating in the park subsidy scheduling needs to pay per day, in yuan; m represents the total number of electric vehicles participating in the optimized scheduling in the park; Combining the output power of photovoltaic power in different time periods with the power supply capacity of the power grid to meet the charging requirements of electric vehicles in different time periods, the electric vehicle charging plan formulation model is established as follows: in, It indicates that there are K electric vehicles in the park with charging power at time T, in kilowatts; It represents the output power of the power grid at time T, in kilowatts; It represents the output power of photovoltaic power at time T, in kilowatts; When the power grid is not needed to provide the power difference required for charging electric vehicles, It indicates the remaining power of photovoltaic output at time T, in kilowatts; time T belongs to time period t; Respectively represent the upper and lower limits of the grid output power, Respectively represent the upper and lower limits of photovoltaic output power; In step S3, a time-of-use electricity price pricing model is formulated based on the comprehensive benefits of the park and the user as follows: Among them, t = 1, 2, 3, respectively represent the peak period, valley period, and normal period; λ1 is the peak electricity price floating coefficient, λ2 is the valley electricity price floating coefficient, represents the time-of-use electricity price of the park, K min and K max Respectively represent the upper and lower limits of the electricity price ratio; g2 = earn2 - earn1 in, and They represent the fees that user i who joins the charging plan needs to pay before the charging plan is implemented, and the fees that user i who joins the charging plan needs to pay after the charging plan is implemented; earn1 and earn2 represent the electricity sales revenue of the park before the charging plan is implemented, and the electricity sales revenue of the park after the charging plan is implemented; earn all =g2-Cost all Among them, earn all is the objective function, representing the economic profit obtained by the park from charging electric vehicles; In step S4, a corresponding charging plan is formulated according to the power status of the electric vehicle when it is connected to the park: in, It is represented by the total number of charging periods required for the i-th vehicle during the working hours of the park, It represents the percentage of the remaining battery capacity of the i-th electric vehicle when charging is finished. It represents the percentage of the initial power of the i-th car when it is connected to the park to the rated battery capacity. represents the rated battery capacity of the i-th electric vehicle, It is represented by the charging capacity of the i-th electric vehicle in a unit time period; To meet the minimum travel target demand of electric vehicle owners, a mandatory charging plan is formulated for electric vehicles: in, It is represented by the total number of time slots required for the i-th electric vehicle to be charged to meet the minimum travel demand during the working hours of the park. It represents the percentage of the remaining battery capacity of the i-th electric vehicle that is charged to meet the minimum travel demand to the rated battery capacity; is a 0-1 variable, 1 means the i-th vehicle is charged in the t-th time period, and 0 means the i-th vehicle is not charged in the t-th time period; When all electric vehicles in the park meet the minimum travel demand, an orderly charging plan will be formulated: in, N represents the time period required for the i-th electric vehicle to meet the minimum travel demand and charge to the target power during the working hours of the park. t Indicates the total number of time periods for park work; In step S5, the expression of the evaluation target is: F=αC ev +βearn all Among them, F represents the evaluation target, α is the weight of the optimized target carbon emissions, and β is the weight of the total benefits obtained from the optimized target electric vehicle charging; C ev Represents the carbon emissions generated by charging all electric vehicles in the park, in kilograms; earn all Represents the economic profit obtained by the park from charging electric vehicles.

2. The method for charging electric vehicles in a park considering carbon emissions according to claim 1 is characterized in that: In step S7, after the maximum number of iterations is met, the optimal park time-of-use electricity price is selected and the electric vehicle charging plan is executed.

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