CN114583729A - Light-storage electric vehicle charging station scheduling method considering full-life-cycle carbon emission - Google Patents

Light-storage electric vehicle charging station scheduling method considering full-life-cycle carbon emission Download PDF

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CN114583729A
CN114583729A CN202111599655.1A CN202111599655A CN114583729A CN 114583729 A CN114583729 A CN 114583729A CN 202111599655 A CN202111599655 A CN 202111599655A CN 114583729 A CN114583729 A CN 114583729A
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charging station
energy storage
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罗平
张嘉昊
曾睿原
杨晴
吕强
高慧敏
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Hangzhou Dianzi University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/008—Circuit arrangements for AC mains or AC distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]

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Abstract

本发明公开了一种考虑全生命周期碳排放的光‑储电动汽车充电站调度方法，本发明首先根据电网提供的各时刻新能源发电占比的历史数据，充电站光伏发电历史数据和天气预报数据对未来一日的火力发电占比、光伏出力分别进行预测。其次以接入电网的每辆EV的充放电状态及储能设备的充放电状态为优化变量，建立考虑全生命周期碳排放以及充电站运营成本的充电站优化调度模型。使用改进型的非支配排序遗传算法求解得到充电站日前调度计划。充电站运行过程中优先使用PV和储能设备为EV供电，当PV和储能设备无法满足EV充电需求时，充电站从电网购电以保证EV充电需求，最后收集数据修正原有模型。

Figure 202111599655

The invention discloses a solar-storage electric vehicle charging station scheduling method that considers carbon emissions in the whole life cycle. The invention firstly provides historical data of the proportion of new energy power generation at each moment provided by the power grid, historical data of photovoltaic power generation of charging stations and weather forecast. The data predicts the proportion of thermal power generation and photovoltaic output in the next day. Secondly, taking the charging and discharging status of each EV connected to the power grid and the charging and discharging status of energy storage equipment as the optimization variables, a charging station optimization scheduling model is established that considers the carbon emissions of the whole life cycle and the operating cost of the charging station. The day-ahead scheduling plan of the charging station is obtained by using an improved non-dominated sorting genetic algorithm. During the operation of the charging station, PV and energy storage equipment are preferentially used to supply power for EVs. When the PV and energy storage equipment cannot meet the EV charging demand, the charging station purchases electricity from the grid to ensure EV charging demand, and finally collects data to correct the original model.

Figure 202111599655

Description

考虑全生命周期碳排放的光-储电动汽车充电站调度方法Scheduling method of light-storage electric vehicle charging station considering carbon emissions in the whole life cycle

技术领域technical field

本发明涉及电动汽车充放电服务技术，具体涉及考虑全生命周期碳排放的含光伏电 动汽车充电站调度方法。The invention relates to a charging and discharging service technology for electric vehicles, in particular to a scheduling method for a photovoltaic electric vehicle charging station that takes into account the carbon emission of the whole life cycle.

背景技术Background technique

虽然电动汽车(Electric Vehicle，EV)在运行过程中不产生碳排放，但现阶段我国的发电结构仍以火力发电为主，由其产生的越来越大的充电需求会导致发电系统产生大量碳排放。另外光伏发电、风力发电等新能源发电在发电过程中不产生碳排放，但是 其设备在生产制造、回收的过程中均会产生碳排放。针对该问题提出一种考虑全生命周 期碳排放的光-储EV充电站调度方法。传统的调度方法通常以价格为引导，引导用户在 电价较低的时间段进行充电，尽可能的降低用户、充电站成本。在考虑碳排放问题后， 充电站在满足EV用户充电需求的情况下优先使用充电站光伏(photovoltaic，PV)、 储能设备为EV充电，充电站储能设备在系统新能源发电占比较高的时间段进行充电， 促进新能源发电消纳。由于新能源发电占比较高的时间段不一定是电价较低的时间段， 因此充电站会损失一部分利益以促进新能源消纳，降低碳排放。Although Electric Vehicles (EVs) do not produce carbon emissions during their operation, thermal power generation is still the main power generation structure in my country at this stage, and the increasing charging demand generated by them will cause the power generation system to generate a large amount of carbon emissions. emission. In addition, new energy power generation such as photovoltaic power generation and wind power generation does not produce carbon emissions in the process of power generation, but its equipment will produce carbon emissions in the process of manufacturing and recycling. Aiming at this problem, a light-storage EV charging station scheduling method considering the carbon emission of the whole life cycle is proposed. The traditional scheduling method is usually guided by price, guiding users to charge in the time period when the electricity price is low, and reducing the cost of users and charging stations as much as possible. After considering the issue of carbon emissions, charging stations will give priority to using photovoltaic (PV) charging stations and energy storage devices to charge EVs when they meet the charging needs of EV users. It is charged during the time period to promote the consumption of new energy power generation. Since the time period when the proportion of new energy power generation is high is not necessarily the time period when the electricity price is low, the charging station will lose a part of the benefits to promote new energy consumption and reduce carbon emissions.

发明内容SUMMARY OF THE INVENTION

本发明以减少EV运行过程中间接产生的碳排放为主要目的，提供一种综合考虑碳排 放以及充电站运行成本的优化调度方法。The main purpose of the present invention is to reduce the carbon emissions indirectly generated in the EV operation process, and provides an optimal scheduling method that comprehensively considers the carbon emissions and the operating cost of the charging station.

本发明假定充电站可以获取用户充电需求、用户接受充电站充放电调度、充电站可 从电网获取电网运行时各时刻新能源发电占比数据。首先根据电网提供的各时刻新能源 发电占比的历史数据，充电站光伏发电历史数据和天气预报数据对未来一日的火力发电 占比、光伏出力分别进行预测。其次以接入电网的每辆EV的充放电状态及储能设备的充放电状态为优化变量，建立考虑全生命周期碳排放以及充电站运营成本的充电站优化调度模型。使用改进型的非支配排序遗传算法求解得到充电站日前调度计划。充电站运 行过程中优先使用PV和储能设备为EV供电，当PV和储能设备无法满足EV充电需求时， 充电站从电网购电以保证EV充电需求。储能设备优先在电网新能源发电占比较高的时 段进行充电。充电站在运行过程中收集EV充电数据、PV出力数据、各时刻新能源占比 数据，并将这些数据加入相应数据集，修正原有模型。The present invention assumes that the charging station can obtain the user's charging demand, the user accepts the charging and discharging schedule of the charging station, and the charging station can obtain the data on the proportion of new energy power generation at each moment during the operation of the grid from the power grid. Firstly, according to the historical data of the proportion of new energy power generation at each moment provided by the power grid, the historical data of photovoltaic power generation of charging stations and weather forecast data, the proportion of thermal power generation and photovoltaic output in the future are predicted respectively. Secondly, taking the charging and discharging status of each EV connected to the power grid and the charging and discharging status of energy storage equipment as the optimization variables, a charging station optimization scheduling model is established that considers the carbon emissions of the whole life cycle and the operating cost of the charging station. The day-ahead scheduling plan of the charging station is obtained by using an improved non-dominated sorting genetic algorithm. During the operation of the charging station, the PV and energy storage equipment are preferentially used to supply power to EVs. When the PV and energy storage equipment cannot meet the EV charging demand, the charging station purchases electricity from the grid to ensure the EV charging demand. The energy storage equipment is preferentially charged in the period when the new energy generation of the grid accounts for a high proportion. The charging station collects EV charging data, PV output data, and new energy ratio data at each moment during operation, and adds these data to the corresponding data set to correct the original model.

具体按照以下步骤实施：Specifically, follow the steps below:

步骤1、新能源发电占比预测；Step 1. Predict the proportion of new energy power generation;

步骤2、充电站PV出力预测；Step 2. Predict the PV output of the charging station;

步骤3、构建EV、储能设备的充放电模型；Step 3. Build the charging and discharging model of EV and energy storage equipment;

步骤4、构建以碳排放最小为目标的调度策略；Step 4. Build a scheduling strategy with the goal of minimizing carbon emissions;

设备的生命周期主要分为生产、使用、报废三个阶段，其三个过程中均有可能产生碳排放，对于PV设备和储能设备而言运行过程中没有碳排放，其碳排放主要在生产建 造以及回收处理过程中。基于费用等年值法设备生产和回收过程中的碳排放的N分钟折 现如式(1)所示[1]。The life cycle of equipment is mainly divided into three stages: production, use, and scrapping. All three processes may generate carbon emissions. For PV equipment and energy storage equipment, there is no carbon emission during operation, and its carbon emissions are mainly in production. during construction and recycling. The N-minute discount of carbon emissions in the process of equipment production and recycling based on the cost-equivalent annual value method is shown in formula (1) [1] .

Figure BDA0003432770890000021
Figure BDA0003432770890000021

其中，L0为碳排放每N分钟折现值；I1为折现率；m1为设备使用年限；ψmade为生产过程 中的碳排放；ψre为回收过程中的碳排放。Among them, L 0 is the discounted value of carbon emission per N minutes; I 1 is the discount rate; m 1 is the service life of the equipment; ψ made is the carbon emission in the production process; ψ re is the carbon emission in the recycling process.

EV和传统燃油汽车在生产、运行、报废回收阶段均会产生碳排放，本发明以充电站运行过程中减少碳排放为研究目标故不考虑汽车在生产和报废回收阶段的碳排放。由于EV的渗透率与充电负荷直接相关，因此考虑燃油车在运行过程中产生的碳排放，其具体 计算如式(2)所示：EVs and traditional fuel vehicles will produce carbon emissions during production, operation, and scrap recycling. The present invention takes carbon emissions reduction during the operation of the charging station as the research goal, so the carbon emissions of automobiles during production and scrap recycling are not considered. Since the penetration rate of EVs is directly related to the charging load, the carbon emissions generated by fuel vehicles during operation are considered, and the specific calculation is shown in equation (2):

Lv＝γ·AGC·M (2)L v =γ·AGC·M (2)

其中，Lv为燃油车运行过程中产生的碳排放；γ为燃油车碳排放因子；AGC为单位里程 油耗；M为行驶里程。Among them, L v is the carbon emission generated during the operation of the fuel vehicle; γ is the carbon emission factor of the fuel vehicle; AGC is the fuel consumption per unit mileage; M is the mileage.

由于充电站PV容量有限且PV受日照影响有较大的波动性，因此在PV供给不足的情况下，充电站还需要从大电网购电以满足EV用户充电需求。充电站在运行过程中的碳 排放具体如式(3)所示。Due to the limited PV capacity of the charging station and the large fluctuation of PV due to the influence of sunlight, the charging station also needs to purchase electricity from the large power grid to meet the charging needs of EV users when the PV supply is insufficient. The carbon emission of the charging station during operation is shown in formula (3).

Figure BDA0003432770890000022
Figure BDA0003432770890000022

其中，L为充电站运行时产生的碳排放；Lfix为充电站设备全寿命周期碳排放的折现值， 其具体计算如式(4)所示；m表示燃油车数量，Lv,i表示第i辆燃油车的碳排放；α表示表示火力机组发电的碳排放强度；βt表示t时刻发电系统中火电的占比，假定其值可 以获得；Pbuy表示向大电网购电量；Ppv表示充电站PV出力；Pbat表示充电站储能系统出 力；PV2G表示EV用户参与V2G的放电功率。Among them, L is the carbon emission generated during the operation of the charging station; L fix is the discounted value of the carbon emission in the whole life cycle of the charging station equipment, and its specific calculation is shown in formula (4); m is the number of fuel vehicles, L v, i represents the carbon emission of the i-th fuel vehicle; α represents the carbon emission intensity of thermal power generation; β t represents the proportion of thermal power in the power generation system at time t, which is assumed to be available; P buy represents the purchase of electricity from the large power grid; P pv represents the PV output of the charging station; P bat represents the output of the energy storage system of the charging station; P V2G represents the discharge power of EV users participating in V2G.

Figure BDA0003432770890000031
Figure BDA0003432770890000031

其中，Ppv,max为PV最大装机容量；Pbat,max为储能设备最大容量；ψpv、ψbat为单位容量PV、 储能设备的全生命周期碳排放；n为充电桩数目；ψpv为一个充电桩全生命周期碳排放；ψcs为充电站基础建设和拆除回收过程产生碳排放；Among them, P pv,max is the maximum installed capacity of PV; P bat,max is the maximum capacity of energy storage equipment; ψ pv and ψ bat are the carbon emissions per unit capacity of PV and energy storage equipment in the whole life cycle; n is the number of charging piles; ψ pv is the carbon emission in the whole life cycle of a charging pile; ψ cs is the carbon emission generated by the infrastructure construction and dismantling and recycling process of the charging station;

步骤5、以充电站运营成本最低为目标的调度策略；Step 5. The scheduling strategy aiming at the lowest operating cost of the charging station;

充电站运营成本具体表示如式(5)所示。The specific expression of the operating cost of the charging station is shown in Equation (5).

F＝c1Ppv+c2Pbat+cbPg,b-csPg,s+cvPev,d-c3Pev,c (5)F=c 1 P pv +c 2 P bat +c b P g,b -c s P g,s +c v P ev,d -c 3 P ev,c (5)

其中，c1，c2分别为PV和储能设备单位功率出力成本；cb、cs为充电站向电网购电、售电 的电价；cv为充电站补贴用户参与V2G的成本；c3为充电站售电价格，Pg,b表示充电站向电网购电量，Pg,s表示电网向充电站的售电量；Pev,c为EV充电功率；Pev,d为EV参与V2G 放电功率。Among them, c 1 and c 2 are the unit power output costs of PV and energy storage equipment respectively; c b and c s are the electricity prices of the charging station to purchase and sell electricity from the grid; c v is the cost of the charging station to subsidize users to participate in V2G; c 3 is the electricity selling price of the charging station, P g,b is the electricity purchased by the charging station from the grid, P g,s is the electricity sold by the grid to the charging station; P ev,c is the EV charging power; P ev,d is the EV participation in V2G discharge power.

步骤6、调度策略约束条件构建Step 6. Construction of scheduling policy constraints

(1)功率约束(1) Power constraints

Ppv+Pg+Pbat＝Pev,c-Pev,d (6)P pv +P g +P bat =P ev,c -P ev,d (6)

其中，Ppv为充电站PV出力，Pg为充电站和电网间的交换功率，充电站从购电时其值为 正，充电站向电网馈电时其值为负；Pbat为充电站储能设备功率，储能设备放电时其值为正，充电时其值为负；Pev,c为EV充电功率；Pev,d为EV参与V2G放电功率。Among them, P pv is the PV output of the charging station, P g is the exchange power between the charging station and the grid, the value is positive when the charging station purchases electricity, and the value is negative when the charging station feeds power to the grid; P bat is the charging station. The power of the energy storage device is positive when the energy storage device is discharging and negative when it is charging; P ev,c is the EV charging power; P ev,d is the EV participating V2G discharge power.

(2)储能设备约束(2) Energy storage device constraints

Smin≤St≤Smax (7)S min ≤S t ≤S max (7)

St＝St-1+(ηbat,cλbat,cPbat,c-λbat,dPbat,d/ηbat,d)Δt (8)S t =S t-1 +(η bat,c λ bat,c P bat,c −λ bat,d P bat,d /η bat,d )Δt (8)

λbat,c·λbat,d＝0,λbat,c,λbat,d∈{0,1} (9)λ bat,c ·λ bat,d =0,λ bat,c ,λ bat,d ∈{0,1} (9)

其中，St为t时刻储能设备的电量；Smin为储能设备电量最小值，Smax为储能设备电量最 大值，ηbat,c为储能设备充电效率；Pbat,c储能设备充电功率；ηbat,d为储能设备放电效率； Pbat,d储能设备放电功率；Δt为间隔时间；λbat,c，λbat,d为充放电状态量，当储能设备处于充电状态时λbat,c为1，λbat,d为0；处于放电状态时，λbat,d为1；λbat,c为0。Among them, S t is the power of the energy storage device at time t; S min is the minimum power of the energy storage device, S max is the maximum power of the energy storage device, η bat,c is the charging efficiency of the energy storage device; P bat, c energy storage Equipment charging power; η bat,d is the discharge efficiency of the energy storage device; P bat,d is the discharge power of the energy storage device; Δt is the interval time; In the charging state, λ bat,c is 1, and λ bat,d is 0; in the discharging state, λ bat,d is 1; λ bat,c is 0.

(3)EV电池约束(3) EV battery constraints

Figure BDA0003432770890000041
Figure BDA0003432770890000041

Figure BDA0003432770890000042
Figure BDA0003432770890000042

λev,c·λev,d＝0,λev,c,λev,d∈{0,1} (12)λ ev,c ·λ ev,d =0,λ ev,c ,λ ev,d ∈{0,1} (12)

其中，

Figure BDA0003432770890000043
为t时刻EV电量；Ecap为EV电池容量；Emin为EV电池电量最小值；为EV接入充电站时的最小电量；为EV离开充电站时的电量；Emax为EV电池电量最大值，ηev,c为储能设备充电效率；Pev,c储能设备充电功率；ηev,d为储能设备放电效率；Pev,d储能设 备放电功率；Δt为间隔时间；λev,c，λev,d为充放电状态量，当储能设备处于充电状态 时λev,c为1，λev,d为0；处于放电状态时，λev,d为1；λev,c为0。in,
Figure BDA0003432770890000043
is EV power at time t; E cap is EV battery capacity; E min is the minimum value of EV battery power; is the minimum power when EV is connected to the charging station; is the power when EV leaves the charging station; E max is the maximum value of EV battery power , η ev,c is the charging efficiency of the energy storage device; P ev,c is the charging power of the energy storage device; η ev,d is the discharge efficiency of the energy storage device; P ev,d is the discharge power of the energy storage device; Δt is the interval time; λ ev ,c , λ ev,d is the charge and discharge state quantity, when the energy storage device is in the charging state, λ ev,c is 1, and λ ev,d is 0; when it is in the discharging state, λ ev,d is 1; λ ev, c is 0.

步骤7、调度策略求解方法Step 7. Scheduling strategy solution method

针对上述多目标问题采用基于d2距离(解距离参考向量的欧式距离)改进型的非支配 排序遗传算法(NSGA-II)进行求解，首先初始化种群，然后计算种群对应目标函数值，并对其进行非支配排序以及基于d2距离的非支配层个体选择，同时通过选择、交叉、变 异产生子代种群，并将父代和子代总群合并得到新种群，重复上述操作直至满足终止条 件。根据本发明方法可进一步研究EV渗透率、用户参与V2G比例、电网中可再生能源 的比例对于结果的的影响，进而为节能减排起到一定的参考作用。Aiming at the above multi-objective problem, an improved non-dominated sorting genetic algorithm (NSGA-II) based on d 2 distance (the Euclidean distance of the solution distance to the reference vector) is used to solve the problem. First, the population is initialized, and then the corresponding objective function value of the population is calculated, and its Carry out non-dominated sorting and individual selection of non-dominated layers based on d 2 distance, and at the same time generate offspring populations through selection, crossover, and mutation, and combine the parent and offspring total populations to obtain a new population, and repeat the above operations until the termination conditions are met. According to the method of the present invention, the influence of EV penetration rate, user participation in V2G ratio, and the ratio of renewable energy in the power grid on the results can be further studied, thereby playing a certain reference role for energy conservation and emission reduction.

步骤8、模型修正Step 8. Model Correction

在实际运行过程中充电站收集当日EV充电数据、PV出力数据、当日天气数据、电网各时刻新能源发电占比数据，并将数据加入到相应数据集，修正步骤1、步骤2中的 预测模型，进一步使模型更加准确。During the actual operation, the charging station collects the EV charging data, PV output data, weather data of the day, and the proportion of new energy power generation at each moment of the grid, and adds the data to the corresponding data set to modify the prediction models in steps 1 and 2. , further making the model more accurate.

作为优选，所述的新能源发电占比预测；具体为：Preferably, the forecast of the proportion of new energy power generation; specifically:

由于各时刻新能源发电占比是时间序列，选择对时间序列具有良好处理能力的长短 时记忆网络进行预测其中LSTM具备3个隐藏层，每层有个300个LSTM单元网络，隐藏层使用ReLU函数作为激活函数；网络输入为前30天各时刻的新能源发电占比数据，输出为未 来一天各时刻新能源发电占比数据。Since the proportion of new energy power generation at each moment is a time series, a long and short-term memory network with good processing capability for time series is selected for prediction. LSTM has 3 hidden layers, each layer has 300 LSTM unit networks, and the hidden layer uses the ReLU function As an activation function; the network input is the data on the proportion of new energy power generation at each moment in the previous 30 days, and the output is the data on the proportion of new energy power generation at each moment in the next day.

作为优选，充电站PV出力预测具体为Preferably, the PV output prediction of the charging station is specifically as follows

在充电站建设初期由于缺乏PV历史数据，根据光伏电池模型对其出力进行预测，光伏 电池模型具体如式(13)所示。In the early stage of the construction of the charging station, due to the lack of PV historical data, the output of the charging station is predicted according to the photovoltaic cell model. The photovoltaic cell model is specifically shown in formula (13).

Figure BDA0003432770890000051
Figure BDA0003432770890000051

其中，I为光伏电池输出电流；U为光伏电池的输出电压；S为光照强度，标况下为1000W/m2； T为电池表面温度；Tref为标况下参考温度；Isc为光伏电池短路电流；I0为二极管反向饱 和电流；q为电荷电量；n为二极管排放系数；K为波尔兹曼常数；Rsh为电池内部等效电阻；Among them, I is the output current of the photovoltaic cell; U is the output voltage of the photovoltaic cell; S is the light intensity, 1000W/m 2 under the standard condition; T is the battery surface temperature; T ref is the reference temperature under the standard condition; Isc is the photovoltaic cell Short-circuit current; I 0 is the reverse saturation current of the diode; q is the charge quantity; n is the diode discharge coefficient; K is the Boltzmann constant; Rsh is the internal equivalent resistance of the battery;

在充电站运行过程中，PV数据越来越充足，考虑使用深度学习的方法对PV出力进行 预测PV出力受天气因素影响较大且有很强的时间相关性，使用LSTM对PV出力进行预测。其中LSTM具备3个隐藏层，每层有个300个LSTM单元网络，隐藏层使用ReLU函数作为激活 函数。网络选择对其影响较大的辐照度、温度、湿度、散射度以及前七日各时刻PV出力 历史数据作为输入。网络的输出即为未来一天各时刻PV出力情况。During the operation of the charging station, the PV data is becoming more and more abundant. Consider using the deep learning method to predict the PV output. The PV output is greatly affected by weather factors and has a strong time correlation. LSTM is used to predict the PV output. Among them, LSTM has 3 hidden layers, each layer has a network of 300 LSTM units, and the hidden layer uses the ReLU function as the activation function. The network selects the irradiance, temperature, humidity, scattering degree, and the historical PV output data at each time of the previous seven days as input. The output of the network is the PV output at each moment in the next day.

作为优选，构建EV、储能设备的充放电模型；具体为：As an option, build a charging and discharging model of EV and energy storage equipment; specifically:

EV及储能设备充放电模型由式(14)表示：The charging and discharging model of EV and energy storage equipment is represented by equation (14):

Figure BDA0003432770890000052
Figure BDA0003432770890000052

其中，

Figure BDA0003432770890000053
为EV或储能设备在t时刻的荷电状态；Pt c与Pt d分别为EV或储能设备在t时刻 的额定充电功率与放电功率；E为EV电池容量或储能设备容量；ηc,ηd分别为EV或储能设备相应的充电，放电效率；Δt为时间间隔。in,
Figure BDA0003432770890000053
is the state of charge of the EV or energy storage device at time t; P t c and P t d are the rated charging power and discharge power of the EV or energy storage device at time t, respectively; E is the EV battery capacity or energy storage device capacity; η c , η d are the corresponding charging and discharging efficiencies of EVs or energy storage devices, respectively; Δt is the time interval.

本发明具有的优点及有益结果为：The advantages and beneficial results that the present invention has are:

(1)本发明综合考虑了EV充电站运行过程中的碳排放以及充电站运营成本。(1) The present invention comprehensively considers the carbon emission during the operation of the EV charging station and the operating cost of the charging station.

(2)本发明可以进一步研究主要影响因素对结果的影响，为减小碳排放提供更全面的 参考价值。(2) The present invention can further study the influence of main influencing factors on the results, and provide a more comprehensive reference value for reducing carbon emissions.

(3)本发明使用的基于d2距离改进型NSGA-II算法可以提升样本多样性，进而提升全局搜索能力。(3) The improved NSGA-II algorithm based on the d 2 distance used in the present invention can improve the diversity of samples, thereby improving the global search ability.

(4)本发明不断更新、完善相应数据集，并对模型做出修正，在运行过程中模型准确 度会进一步提升。(4) The present invention continuously updates and improves the corresponding data set, and makes corrections to the model, and the accuracy of the model will be further improved during the running process.

附图说明Description of drawings

图1新能源发电占比预测LSTM结构图；Figure 1 LSTM structure diagram for the prediction of the proportion of new energy power generation;

图2基于d2距离改进型NSGA-II算法流程。Fig. 2 The improved NSGA-II algorithm flow based on d 2 distance.

具体实施方式Detailed ways

步骤1、新能源发电占比预测；Step 1. Predict the proportion of new energy power generation;

如图1所示，由于各时刻新能源发电占比是时间序列，选择对时间序列具有良好处理 能力的长短时记忆网络进行预测其中LSTM具备3个隐藏层，每层有个300个LSTM单元网络，隐藏层使用ReLU函数作为激活函数；网络输入为前30天各时刻的新能源发电占比数据，输出为未来一天各时刻新能源发电占比数据。As shown in Figure 1, since the proportion of new energy power generation at each moment is a time series, a long and short-term memory network with good processing capability for time series is selected for prediction. The LSTM has 3 hidden layers, and each layer has a network of 300 LSTM units. , the hidden layer uses the ReLU function as the activation function; the network input is the data of the proportion of new energy power generation at each moment in the previous 30 days, and the output is the data of the proportion of new energy power generation at each moment in the next day.

步骤2、充电站PV出力预测；Step 2. Predict the PV output of the charging station;

在充电站建设初期由于缺乏PV历史数据，根据光伏电池模型对其出力进行预测，光伏 电池模型具体如式(1)所示。In the early stage of the construction of the charging station, due to the lack of PV historical data, the output of the charging station is predicted according to the photovoltaic cell model. The photovoltaic cell model is specifically shown in formula (1).

Figure BDA0003432770890000061
Figure BDA0003432770890000061

其中，I为光伏电池输出电流；U为光伏电池的输出电压；S为光照强度，标况下为1000W/m2； T为电池表面温度；Tref为标况下参考温度；Isc为光伏电池短路电流；I0为二极管反向饱 和电流；q为电荷电量；n为二极管排放系数；K为波尔兹曼常数；Rsh为电池内部等效电阻；Among them, I is the output current of the photovoltaic cell; U is the output voltage of the photovoltaic cell; S is the light intensity, 1000W/m 2 under the standard condition; T is the battery surface temperature; T ref is the reference temperature under the standard condition; Isc is the photovoltaic cell Short-circuit current; I 0 is the reverse saturation current of the diode; q is the charge quantity; n is the diode discharge coefficient; K is the Boltzmann constant; Rsh is the internal equivalent resistance of the battery;

在充电站运行过程中，PV数据越来越充足，考虑使用深度学习的方法对PV出力进行 预测PV出力受天气因素影响较大且有很强的时间相关性，使用LSTM对PV出力进行预测。其中LSTM具备3个隐藏层，每层有个300个LSTM单元网络，隐藏层使用ReLU函数作为激活 函数。网络选择对其影响较大的辐照度、温度、湿度、散射度以及前七日各时刻PV出力 历史数据作为输入。网络的输出即为未来一天各时刻PV出力情况。During the operation of the charging station, the PV data is becoming more and more abundant. Consider using the deep learning method to predict the PV output. The PV output is greatly affected by weather factors and has a strong time correlation. LSTM is used to predict the PV output. Among them, LSTM has 3 hidden layers, each layer has a network of 300 LSTM units, and the hidden layer uses the ReLU function as the activation function. The network selects the irradiance, temperature, humidity, scattering degree, and the historical PV output data at each time of the previous seven days as input. The output of the network is the PV output at each moment in the next day.

步骤3、构建EV、储能设备的充放电模型；Step 3. Build the charging and discharging model of EV and energy storage equipment;

EV及储能设备充放电模型由式(2)表示：The charging and discharging model of EV and energy storage equipment is represented by equation (2):

Figure BDA0003432770890000062
Figure BDA0003432770890000062

其中，

Figure BDA0003432770890000063
为EV或储能设备在t时刻的荷电状态；Pt c与Pt d分别为EV或储能设备在t时刻 的额定充电功率与放电功率；E为EV电池容量或储能设备容量；ηc,ηd分别为EV或储能设备相应的充电，放电效率；Δt为时间间隔。in,
Figure BDA0003432770890000063
is the state of charge of the EV or energy storage device at time t; P t c and P t d are the rated charging power and discharge power of the EV or energy storage device at time t, respectively; E is the EV battery capacity or energy storage device capacity; η c , η d are the corresponding charging and discharging efficiencies of EVs or energy storage devices, respectively; Δt is the time interval.

步骤4、构建以碳排放最小为目标的调度策略；Step 4. Build a scheduling strategy with the goal of minimizing carbon emissions;

设备的生命周期主要分为生产、使用、报废三个阶段，其三个过程中均有可能产生碳排放，对于PV设备和储能设备而言运行过程中没有碳排放，其碳排放主要在生产建 造以及回收处理过程中。基于费用等年值法设备生产和回收过程中的碳排放的N分钟折 现如式(3)所示[1]。The life cycle of equipment is mainly divided into three stages: production, use, and scrapping. All three processes may generate carbon emissions. For PV equipment and energy storage equipment, there is no carbon emission during operation, and its carbon emissions are mainly in production. during construction and recycling. The N-minute discount of carbon emissions in the process of equipment production and recycling based on the cost-equivalent annual value method is shown in Equation (3) [1].

Figure BDA0003432770890000071
Figure BDA0003432770890000071

其中，L0为碳排放每N分钟折现值；I1为折现率；m1为设备使用年限；ψmade为生产过程 中的碳排放；ψre为回收过程中的碳排放。Among them, L 0 is the discounted value of carbon emission per N minutes; I 1 is the discount rate; m 1 is the service life of the equipment; ψ made is the carbon emission in the production process; ψ re is the carbon emission in the recycling process.

EV和传统燃油汽车在生产、运行、报废回收阶段均会产生碳排放，本发明以充电站运行过程中减少碳排放为研究目标故不考虑汽车在生产和报废回收阶段的碳排放。由于EV的渗透率与充电负荷直接相关，因此考虑燃油车在运行过程中产生的碳排放，其具体 计算如式(4)所示：EVs and traditional fuel vehicles will produce carbon emissions during production, operation, and scrap recycling. The present invention takes carbon emissions reduction during the operation of the charging station as the research goal, so the carbon emissions of automobiles during production and scrap recycling are not considered. Since the penetration rate of EV is directly related to the charging load, the carbon emission generated by the fuel vehicle during operation is considered, and its specific calculation is shown in formula (4):

Lv＝γ·AGC·M (4)L v =γ·AGC·M (4)

其中，Lv为燃油车运行过程中产生的碳排放；γ为燃油车碳排放因子；AGC为单位里程 油耗；M为行驶里程。Among them, L v is the carbon emission generated during the operation of the fuel vehicle; γ is the carbon emission factor of the fuel vehicle; AGC is the fuel consumption per unit mileage; M is the mileage.

由于充电站PV容量有限且PV受日照影响有较大的波动性，因此在PV供给不足的情况下，充电站还需要从大电网购电以满足EV用户充电需求。充电站在运行过程中的碳 排放具体如式(5)所示。Due to the limited PV capacity of the charging station and the large fluctuation of PV due to the influence of sunlight, the charging station also needs to purchase electricity from the large power grid to meet the charging needs of EV users when the PV supply is insufficient. The carbon emission of the charging station during operation is shown in formula (5).

Figure BDA0003432770890000072
Figure BDA0003432770890000072

其中，L为充电站运行时产生的碳排放；Lfix为充电站设备全寿命周期碳排放的折现值， 其具体计算如式(6)所示；m表示燃油车数量，Lv,i表示第i辆燃油车的碳排放；α表示表示火力机组发电的碳排放强度；βt表示t时刻发电系统中火电的占比，假定其值可 以获得；Pbuy表示向大电网购电量；Ppv表示充电站PV出力；Pbat表示充电站储能系统出 力；PV2G表示EV用户参与V2G的放电功率。Among them, L is the carbon emission generated during the operation of the charging station; L fix is the discounted value of the carbon emission in the whole life cycle of the charging station equipment, and its specific calculation is shown in formula (6); m is the number of fuel vehicles, L v, i represents the carbon emission of the i-th fuel vehicle; α represents the carbon emission intensity of thermal power generation; β t represents the proportion of thermal power in the power generation system at time t, which is assumed to be available; P buy represents the purchase of electricity from the large power grid; P pv represents the PV output of the charging station; P bat represents the output of the energy storage system of the charging station; P V2G represents the discharge power of EV users participating in V2G.

Figure BDA0003432770890000073
Figure BDA0003432770890000073

其中，Ppv,max为PV最大装机容量；Pbat,max为储能设备最大容量；ψpv、ψbat为单位容量PV、 储能设备的全生命周期碳排放；n为充电桩数目；ψpv为一个充电桩全生命周期碳排放；ψcs为充电站基础建设和拆除回收过程产生碳排放；Among them, P pv,max is the maximum installed capacity of PV; P bat,max is the maximum capacity of energy storage equipment; ψ pv and ψ bat are the carbon emissions per unit capacity of PV and energy storage equipment in the whole life cycle; n is the number of charging piles; ψ pv is the carbon emission in the whole life cycle of a charging pile; ψ cs is the carbon emission generated by the infrastructure construction and dismantling and recycling process of the charging station;

步骤5、以充电站运营成本最低为目标的调度策略；Step 5. The scheduling strategy aiming at the lowest operating cost of the charging station;

充电站运营成本具体表示如式(7)所示。The specific expression of the operating cost of the charging station is shown in Equation (7).

F＝c1Ppv+c2Pbat+cbPg,b-csPg,s+cvPev,d-c3Pev,c (7)F=c 1 P pv +c 2 P bat +c b P g,b -c s P g,s +c v P ev,d -c 3 P ev,c (7)

其中，c1，c2分别为PV和储能设备单位功率出力成本；cb、cs为充电站向电网购电、售电 的电价；cv为充电站补贴用户参与V2G的成本；c3为充电站售电价格，Pg,b表示充电站向电网购电量，Pg,s表示电网向充电站的售电量；Pev,c为EV充电功率；Pev,d为EV参与V2G 放电功率。Among them, c 1 and c 2 are the unit power output costs of PV and energy storage equipment respectively; c b and c s are the electricity prices of the charging station to purchase and sell electricity from the grid; c v is the cost of the charging station to subsidize users to participate in V2G; c 3 is the electricity selling price of the charging station, P g,b is the electricity purchased by the charging station from the grid, P g,s is the electricity sold by the grid to the charging station; P ev,c is the EV charging power; P ev,d is the EV participation in V2G discharge power.

步骤6、调度策略约束条件构建Step 6. Construction of scheduling policy constraints

(1)功率约束(1) Power constraints

Ppv+Pg+Pbat＝Pev,c-Pev,d (8)P pv +P g +P bat =P ev,c -P ev,d (8)

其中，Ppv为充电站PV出力，Pg为充电站和电网间的交换功率，充电站从购电时其值为 正，充电站向电网馈电时其值为负；Pbat为充电站储能设备功率，储能设备放电时其值为正，充电时其值为负；Pev,c为EV充电功率；Pev,d为EV参与V2G放电功率。Among them, P pv is the PV output of the charging station, P g is the exchange power between the charging station and the grid, the value is positive when the charging station purchases electricity, and the value is negative when the charging station feeds power to the grid; P bat is the charging station. The power of the energy storage device is positive when the energy storage device is discharging and negative when it is charging; P ev,c is the EV charging power; P ev,d is the EV participating V2G discharge power.

(2)储能设备约束(2) Energy storage device constraints

Smin≤St≤Smax (9)S min ≤S t ≤S max (9)

St＝St-1+(ηbat,cλbat,cPbat,c-λbat,dPbat,d/ηbat,d)Δt (10)S t =S t-1 +(η bat,c λ bat,c P bat,c −λ bat,d P bat,d /η bat,d )Δt (10)

λbat,c·λbat,d＝0,λbat,c,λbat,d∈{0,1} (11)λ bat,c ·λ bat,d =0,λ bat,c ,λ bat,d ∈{0,1} (11)

其中，St为t时刻储能设备的电量；Smin为储能设备电量最小值，Smax为储能设备电量最 大值，ηbat,c为储能设备充电效率；Pbat,c储能设备充电功率；ηbat,d为储能设备放电效率； Pbat,d储能设备放电功率；Δt为间隔时间；λbat,c，λbat,d为充放电状态量，当储能设备处于充电状态时λbat,c为1，λbat,d为0；处于放电状态时，λbat,d为1；λbat,c为0。Among them, S t is the power of the energy storage device at time t; S min is the minimum power of the energy storage device, S max is the maximum power of the energy storage device, η bat,c is the charging efficiency of the energy storage device; P bat, c energy storage Equipment charging power; η bat,d is the discharge efficiency of the energy storage device; P bat,d is the discharge power of the energy storage device; Δt is the interval time; In the charging state, λ bat,c is 1, and λ bat,d is 0; in the discharging state, λ bat,d is 1; λ bat,c is 0.

(3)EV电池约束(3) EV battery constraints

Figure BDA0003432770890000081
Figure BDA0003432770890000081

Figure BDA0003432770890000082
Figure BDA0003432770890000082

λev,c·λev,d＝0,λev,c,λev,d∈{0,1} (14)λ ev,c ·λ ev,d =0,λ ev,c ,λ ev,d ∈{0,1} (14)

其中，

Figure BDA0003432770890000083
为t时刻EV电量；Ecap为EV电池容量；Emin为EV电池电量最小值；为EV接入充电站时的最小电量；为EV离开充电站时的电量；Emax为EV电池电量最大值，ηev,c为储能设备充电效率；Pev,c储能设备充电功率；ηev,d为储能设备放电效率；Pev,d储能设 备放电功率；Δt为间隔时间；λev,c，λev,d为充放电状态量，当储能设备处于充电状态 时λev,c为1，λev,d为0；处于放电状态时，λev,d为1；λev,c为0。in,
Figure BDA0003432770890000083
is EV power at time t; E cap is EV battery capacity; E min is the minimum value of EV battery power; is the minimum power when EV is connected to the charging station; is the power when EV leaves the charging station; E max is the maximum value of EV battery power , η ev,c is the charging efficiency of the energy storage device; P ev,c is the charging power of the energy storage device; η ev,d is the discharge efficiency of the energy storage device; P ev,d is the discharge power of the energy storage device; Δt is the interval time; λ ev ,c , λ ev,d is the charge and discharge state quantity, when the energy storage device is in the charging state, λ ev,c is 1, and λ ev,d is 0; when it is in the discharging state, λ ev,d is 1; λ ev, c is 0.

步骤7、调度策略求解方法Step 7. Scheduling strategy solution method

针对上述多目标问题采用基于d2距离(解距离参考向量的欧式距离)改进型的非支配 排序遗传算法(NSGA-II)进行求解，首先初始化种群，然后计算种群对应目标函数值，并对其进行非支配排序以及基于d2距离的非支配层个体选择，同时通过选择、交叉、变 异产生子代种群，并将父代和子代总群合并得到新种群，重复上述操作直至满足终止条 件。基于d2距离改进型的NSGA-II具体流程如图2所示。根据本发明方法可进一步研究 EV渗透率、用户参与V2G比例、电网中可再生能源的比例对于结果的的影响，进而为节 能减排起到一定的参考作用。Aiming at the above multi-objective problem, an improved non-dominated sorting genetic algorithm (NSGA-II) based on d 2 distance (the Euclidean distance of the solution distance to the reference vector) is used to solve the problem. First, the population is initialized, and then the corresponding objective function value of the population is calculated, and its Carry out non-dominated sorting and individual selection of non-dominated layers based on d 2 distance, and at the same time generate offspring populations through selection, crossover, and mutation, and combine the parent and offspring total populations to obtain a new population, and repeat the above operations until the termination conditions are met. The specific process of the improved NSGA-II based on the d 2 distance is shown in Figure 2. According to the method of the present invention, the influence of EV penetration rate, user participation in V2G ratio, and the ratio of renewable energy in the power grid on the results can be further studied, thereby playing a certain reference role for energy conservation and emission reduction.

步骤8、模型修正Step 8. Model Correction

在实际运行过程中充电站收集当日EV充电数据、PV出力数据、当日天气数据、电网各时刻新能源发电占比数据，并将数据加入到相应数据集，修正步骤1、步骤2中的 预测模型，进一步使模型更加准确。During the actual operation, the charging station collects the EV charging data, PV output data, weather data of the day, and the proportion of new energy power generation at each moment of the grid, and adds the data to the corresponding data set to modify the prediction models in steps 1 and 2. , further making the model more accurate.

参考文献references

[1]矫舒美,乔学博,李勇,姚天宇,曹一家.计及综合能源系统全寿命周期碳排放和 碳交易的电转气设备和光伏联合优化配置[J].电力自动化设备,2021,41(09):156-163.[1] Jiao Shumei, Qiao Xuebo, Li Yong, Yao Tianyu, Cao Jiajia. Combined optimal configuration of power-to-gas equipment and photovoltaics considering carbon emissions and carbon trading in the entire life cycle of an integrated energy system [J]. Electric Power Automation Equipment, 2021, 41(09):156-163.

Claims (4)

1. The light-storage electric vehicle charging station scheduling method considering the full-life-cycle carbon emission is characterized by comprising the following steps:
step 1, predicting the power generation proportion of new energy;
step 2, PV output prediction of a charging station;
step 3, constructing charging and discharging models of the EV and the energy storage equipment;
step 4, constructing a scheduling strategy taking minimum carbon emission as a target;
the N minutes of carbon emission in the production and recovery process of equipment based on the cost equal-year-value method is represented by the formula (3);
Figure FDA0003432770880000011
wherein L is0The carbon emissions per N minutes turned off; i is1The discount rate is obtained; m is a unit of1The service life of the equipment; psimadeCarbon emission in the production process; psireCarbon emission in the recovery process;
since the permeability of EV is directly related to the charging load, considering the carbon emissions generated during the operation of the fuel vehicle, the specific calculation is as shown in equation (4):
Lv＝γ·AGC·M (2)
wherein L isvCarbon emission generated in the running process of the fuel vehicle; gamma is a carbon emission factor of the fuel vehicle; AGC is unit mileage oil consumption; m is the driving mileage;
due to the limited PV capacity of the charging station and the large fluctuation of PV due to sunlight, the charging station needs to purchase power from the large power grid to meet the charging demand of EV users in case of insufficient PV supply; the carbon emission of the charging station in the operation process is specifically shown as a formula (5);
Figure FDA0003432770880000012
wherein L is carbon emission generated when the charging station operates; l isfixThe specific calculation of the conversion value of the carbon emission of the whole life cycle of the charging station equipment is shown as the formula (6); m represents the number of fuel vehicles, Lv,iRepresents the carbon emissions of the ith fuel vehicle; alpha represents the carbon emission intensity of the thermal power unit for power generation; beta is atRepresenting the proportion of thermal power in the power generation system at the time t; pbuyIndicating that the electricity is purchased to the large power grid; ppvRepresents the PV output of the charging station; pbatRepresenting the output of the energy storage system of the charging station; p isV2GDischarge power representing EV user participation V2G;
Figure FDA0003432770880000013
wherein, Ppv,maxPV maximum installed capacity; pbat,maxMaximum capacity for energy storage devices; psipv、ψbatIs the unit capacity PV, the full life cycle carbon emission of the energy storage equipment; n is the number of charging piles; psipvCarbon emission for a full life cycle of a charging pile; psicsCarbon emission is generated in the process of building, dismantling and recycling the charging station foundation;
step 5, a scheduling strategy with the lowest operation cost of the charging station as a target;
the operation cost of the charging station is specifically represented as formula (7);
F＝c1Ppv+c2Pbat+cbPg,b-csPg,s+cvPev,d-c3Pev,c (5)
wherein, c1，c2Respectively the unit power output cost of the PV and the energy storage equipment; c. Cb、csPurchase to the power grid for charging stationsElectricity, electricity prices for selling electricity; c. CvSubsidizing the cost of the user participating in V2G for the charging station; c. C3For selling electricity at a charging station, Pg,bIndicate charging station to purchase electric quantity, P, to the electric networkg,sRepresenting the amount of electricity sold by the power grid to the charging station; pev,cCharging power for the EV; pev,dParticipating in V2G discharge power for the EV;
step 6, construction of scheduling policy constraint conditions
(1) Power constraint
Ppv+Pg+Pbat＝Pev,c-Pev,d (6)
Wherein, PpvFor charging station PV contribution, PgThe value of the power exchange between the charging station and the power grid is positive when the charging station purchases power, and the value of the power exchange between the charging station and the power grid is negative when the charging station feeds power to the power grid; pbatThe power of the energy storage equipment of the charging station is determined, the value of the energy storage equipment is positive when the energy storage equipment is discharged, and the value of the energy storage equipment is negative when the energy storage equipment is charged; pev,cCharging power for the EV; pev,dParticipating in V2G discharge power for EV;
(2) energy storage device restraint
Smin≤St≤Smax (7)
St＝St-1+(ηbat,cλbat,cPbat,c-λbat,dPbat,d/ηbat,d)Δt (8)
λbat,c·λbat,d＝0,λbat,c,λbat,d∈{0,1} (9)
Wherein S istThe electric quantity of the energy storage equipment at the moment t; sminIs the minimum value of the energy storage device, SmaxIs the maximum value of the electric quantity of the energy storage equipment, etabat,cCharging efficiency for the energy storage device; pbat,cEnergy storage device charging power; etabat,dDischarging efficiency for the energy storage device; pbat,dThe discharge power of the energy storage device; Δ t is the interval time; lambda [ alpha ]bat,c，λbat,dFor the charge-discharge state quantity, lambda when the energy storage device is in the charging statebat,cIs 1, λbat,dIs 0; in the discharge state, λbat,dIs 1; lambdabat,cIs 0;
(3) EV battery restraint
Figure FDA0003432770880000021
Figure FDA0003432770880000022
λev,c·λev,d＝0,λev,c,λev,d∈{0,1} (12)
Wherein,
Figure FDA0003432770880000023
EV electric quantity at t moment; ecapIs the EV battery capacity; eminThe electric quantity of the EV battery is the minimum value; minimum electric quantity when the EV is accessed into a charging station; the electric quantity when the EV leaves the charging station; emaxMaximum charge of EV battery, etaev,cCharging efficiency for the energy storage device; pev,cEnergy storage device charging power; etaev,dDischarging efficiency for the energy storage device; pev,dThe discharge power of the energy storage device; Δ t is the interval time; lambda [ alpha ]ev,c，λev,dFor charging and discharging quantities, lambda when the energy storage device is in the charging stateev,cIs 1, λev,dIs 0; in the discharge state, λev,dIs 1; lambda [ alpha ]ev,cIs 0;
step 7, scheduling strategy solving method
Using a base d for the above-mentioned multiple objective problems2Solving a distance improved non-dominated sorting genetic algorithm, firstly initializing a population, then calculating a corresponding objective function value of the population, and carrying out non-dominated sorting on the objective function value and based on d2Selecting non-dominant layer individuals of the distance, generating offspring populations through selection, crossing and variation, combining the parent population and the offspring population to obtain a new population, and repeating the operation until a termination condition is met;
step 8, model correction
In the actual operation process, the charging station collects EV charging data, PV output data, weather data of the same day and new energy power generation ratio data of the power grid at all times, adds the data into a corresponding data set, modifies the prediction models in the step 1 and the step 2, and further enables the models to be more accurate.
2. The method of claim 1, wherein the method comprises: predicting the power generation proportion of the new energy; the method specifically comprises the following steps:
because the new energy power generation ratio at each moment is a time sequence, a long-time memory network with good processing capacity for the time sequence is selected to predict, wherein the LSTM has 3 hidden layers, each layer has 300 LSTM unit networks, and the hidden layers use a ReLU function as an activation function; the network inputs the new energy power generation ratio data of each time in the previous 30 days, and outputs the new energy power generation ratio data of each time in the next day.
3. The method of claim 1, wherein the method comprises: charging station PV output prediction
Predicting the output of the photovoltaic cell model according to the photovoltaic cell model, wherein the photovoltaic cell model is specifically shown as a formula (1);
Figure FDA0003432770880000031
wherein I is the output current of the photovoltaic cell; u is the output voltage of the photovoltaic cell; s is the illumination intensity and is 1000W/m under the standard condition2(ii) a T is the surface temperature of the battery; t isrefIs a reference temperature under standard conditions; isc is the short-circuit current of the photovoltaic cell; i is0Is a diode reverse saturation current; q is the charge capacity; n is the diode emission coefficient; k is Boltzmann constant; rsh is the battery internal equivalent resistance;
in the operation process of the charging station, PV data are more and more sufficient, the PV output is predicted by using an LSTM (least squares maximum Transmission technology) in consideration of the fact that the PV output is greatly influenced by weather factors and has strong time correlation by using a deep learning method, and the PV output is predicted by using the LSTM; the LSTM is provided with 3 hidden layers, each layer is provided with 300 LSTM unit networks, and the hidden layers use a ReLU function as an activation function; the network selects irradiance, temperature, humidity and scattering degree which have great influence on the network and PV output historical data of each moment in the previous seven days as input; the output of the network is the PV output situation at each time of the future day.
4. The method of claim 1, wherein the method comprises: establishing a charge and discharge model of the EV and the energy storage equipment; the method comprises the following specific steps:
the EV and energy storage device charge-discharge model is represented by equation (2):
Figure FDA0003432770880000032
wherein,
Figure FDA0003432770880000033
the state of charge of the EV or the energy storage equipment at the moment t; pt cAnd Pt dRated charging power and discharging power of the EV or the energy storage equipment at the moment t are respectively; e is the EV battery capacity or the energy storage equipment capacity; etac,ηdCorrespondingly charging and discharging efficiencies of the EV or the energy storage equipment respectively; Δ t is the time interval.
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