阅读说明：本技术 一种区域微网中v2g电动汽车智能充放电管理方法 (Intelligent charging and discharging management method for V2G electric vehicle in regional microgrid ) 是由 于会群 尹申 彭道刚 张�浩 于 2020-07-06 设计创作，主要内容包括：本发明提供了一种区域微网中V2G电动汽车智能充放电管理方法,属于电动车辆充电领域。本发明包括如下步骤：确定微网系统的基本参数；建立可再生能源模块出力模型来预测可再生能源模块出力,建立储能装置负荷模型来预测储能装置负荷状态；建立电动汽车负荷特性模型并获得所有电动汽车的荷电状态；建立优化模型并求解,使得电动汽车用户的满意度最大为前提,微网系统运行的总成本最低；根据优化模型求解的结果对电动汽车进行调动。本发明不仅能在提升需求侧电动汽车的满意度和用户响应能力,同时还抑制了微网系统的负荷波动、降低了运行成本。(The invention provides an intelligent charging and discharging management method for a V2G electric vehicle in a regional microgrid, and belongs to the field of electric vehicle charging. The invention comprises the following steps: determining basic parameters of the microgrid system; establishing a renewable energy module output model to predict the renewable energy module output, and establishing an energy storage device load model to predict the energy storage device load state; establishing an electric automobile load characteristic model and obtaining the charge states of all electric automobiles; establishing an optimization model and solving to ensure that the maximum satisfaction degree of the electric vehicle user is the premise, and the total running cost of the microgrid system is the lowest; and transferring the electric automobile according to the result of the optimization model solution. The method and the device can improve the satisfaction degree and the user response capability of the electric automobile on the demand side, inhibit the load fluctuation of the microgrid system and reduce the operation cost.)

1. An intelligent charging and discharging management method for V2G electric vehicles in regional micro-grids is used for managing a micro-grid system, and the micro-grid system comprises: the intelligent measurement system comprises an intelligent measurement module, a micro-grid energy management center, an electric vehicle charge-discharge module, a renewable energy module and a micro-grid response main body, wherein the micro-grid response main body comprises a plurality of electric vehicles and energy storage devices, each electric vehicle is provided with a power battery, the renewable energy module comprises a fan power generation unit and a photovoltaic power generation unit, and the intelligent measurement system is characterized by comprising the following steps:

step 1, determining basic parameters of a microgrid system, wherein the basic parameters comprise: the number of the electric vehicles, the satisfaction degree of the initial electric vehicle user, the capacity of the energy storage device, the upper limit of the state of charge of the energy storage device and the lower limit of the state of charge of the electric vehicle;

step 2, establishing a renewable energy module output model to predict the renewable energy module output, and establishing an energy storage device load model to predict the energy storage device load state;

step 3, establishing an electric vehicle load characteristic model and obtaining the charge states of all the electric vehicles;

step 4, establishing an optimization model and solving, so that the maximum satisfaction degree of the electric automobile user is the premise, and the total running cost of the microgrid system is the lowest;

and 5, mobilizing the electric automobile according to the result of the solution of the optimization model.

2. The intelligent charging and discharging management method for the V2G electric vehicles in the regional microgrid according to claim 1, characterized in that:

wherein the renewable energy module output model comprises:

the wind power random output model is used for calculating the output power of the wind power generation unit; and

and the solar radiation random output model is used for calculating the output power of the photovoltaic power generation unit.

3. The intelligent charging and discharging management method for the V2G electric vehicles in the regional microgrid according to claim 2, characterized in that:

wherein the wind random output model is

In the formula, P_w(t) is the output power of the wind power generation unit, v (t) represents the wind speed at time t, v_rIs the rated wind speed, v, of the wind power unit_inFor the cut-in wind speed, v, of the wind power unit_outIs the cut-out wind speed, P, of the wind power unit_rAnd rated output power is provided for the wind power generation unit.

4. The intelligent charging and discharging management method for the V2G electric vehicles in the regional microgrid according to claim 2, characterized in that:

wherein the random output model of solar radiation is

P_PV(k)＝P_peak×η×D(k)×10^-3

In the formula, P_PV(k) For the photovoltaic power generation unit to output power, P_peakPeak watt power of the photovoltaic power generation unit, η efficiency of the photovoltaic power generation unit, and d (k) peak watt hours of the photovoltaic power generation unit.

5. The intelligent charging and discharging management method for the V2G electric vehicles in the regional microgrid according to claim 1, characterized in that:

wherein the energy storage device load model is

In the formula, soc (t) is the remaining capacity of the energy storage device at time t; i is_BSThe variable quantity of the electric quantity of the energy storage device from the moment t-1 to the moment t; q_BSAs the capacity of the storage battery,

the constraint condition of the energy storage device load model is

P_SBmin≤P_SB(t)≤P_SBmax

SOC_SBmin≤SOC_SB(t)≤SOC_SBmax

In the formula, P_SBminMinimum power for charging and discharging the energy storage device, P_SBmaxMaximum power for charging and discharging energy storage device, SOC_SB(t) is the state of charge, SOC, of the energy storage device during time t_SBminIs the minimum value of the state of charge, SOC, of the energy storage device_SBmaxIs the maximum value of the state of charge of the energy storage device.

6. The intelligent charging and discharging management method for V2G electric vehicles in regional micro-grids as claimed in claim 1,

wherein the electric vehicle load characteristic model comprises an electric vehicle load state model, an electric vehicle charging load model and an electric vehicle discharging load model,

the electric automobile load state model is

In the formula, S_l(t) is the state of charge of the electric vehicle l at time t, P_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_l(t) is a function of the charge/discharge state of the electric vehicle l, η_lFor efficiency of electric energy conversion, Q_s,lIs the total charge capacity, S, of the electric vehicle_0,lIs the initial charge state of the electric automobile when the electric automobile is connected to the network,

the charging load model of the electric automobile is

In the formula, P_c(t) is the charging load of the electric vehicle, N (t) is the total number of the electric vehicles responding in the period of t, P_c,l(t) is the charging rated power of the electric automobile l in the sampling point t,

the discharge load model of the electric automobile is

In the formula, P_d(t) is the discharging load of the electric automobileThe load, N (t) is the total number of the electric vehicles responding in the t period, P_d,l(t) is the discharge rated power of the electric automobile l in the t period,

the constraint conditions of the electric vehicle discharge load model are as follows:

in the formula, P_d,l(t) is the value of discharge power in the electric automobile in the period of t, Q_V2GIs the available discharge capacity of a single electric automobile,

Q_V2G＝(Q_s,l-Q_r,l-Q_d,l-Q_Loss)

in the formula, Q_s,lTotal capacity, Q, of said power cells for a single electric vehicle_r,lReserve capacity, Q, of the power cell for a single electric vehicle_d,lThe daily use capacity, Q, of the power battery for a single electric vehicle_LossThe power battery of a single electric vehicle consumes energy,

in the formula, P_c,lRated power, P, for charging the electric vehicle_d,lRated power for discharging the electric vehicle, η_lThe efficiency of the conversion of electric energy is expressed,

S_min,l≤S_l(t)≤S_max,l

in the formula, S_min,lIs the minimum value of the electric vehicle charge state, S_max,lIs the maximum value of the state of charge of the electric automobile,

0≤S_0,l<1

0≤S_E,l<1

in the formula, S_E,lThe state of charge desired by the user of the electric vehicle l,

r_l≤r_max,l

in the formula, r_lBreakage rate, r, of the power battery_max,lThe maximum breakage rate of the power battery acceptable for the user of the electric automobile,

in the formula, L_B(t) is the normal load of the microgrid system; mu.s_c,l(t)、μ_d,l(t) is the charging and discharging state function of the electric vehicle l in the t period, and the charging and discharging state function and the discharging state function are opposite, and the value of the charging and discharging state function is 0 or 1.

7. The intelligent charging and discharging management method for V2G electric vehicles in regional micro-grids as claimed in claim 1,

wherein the optimization model is

In the formula, F₁A satisfaction model for the electric vehicle user, F_1maxA maximum value, omega, of a satisfaction model of the electric vehicle user₁Is F₁Weight coefficient of (1), F₂(ii) a load variance model for the electric vehicle participating in the scheduling, F_2maxParticipating in scheduling a maximum value, ω, of a load equation model for the electric vehicle₂Is F₂Weight coefficient of (1), F₃A daily running total cost model for the microgrid system, F_3maxA maximum value, omega, of a total daily running cost model of the microgrid system₃Is F₃The weight coefficient of (2).

8. The intelligent charging and discharging management method for V2G electric vehicles in regional micro-grids as claimed in claim 7,

wherein the satisfaction model is

In the formula (I), the compound is shown in the specification,for the travel satisfaction model of the single electric vehicle user,for the electricity price satisfaction model of the single electric vehicle user,

the travel satisfaction model is

In the formula (I), the compound is shown in the specification,is the state of charge, Q, of the electric vehicle at the moment of the I off-grid_r,lFor reserving capacity, Q, of said electric vehicle l_d,lFor the daily use capacity of the electric vehicle l,

the electricity price satisfaction degree model is

In the formula, C_l(t) the price of electricity charged by the electric vehicle l in the period of t, C_l(t_min) Minimum charge for charging cost of the electric vehicle, C_l(t_max) Respectively charging the electric vehicle l with the highest charge, P_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_l(t) is the charge-discharge state function of the electric vehicleNumber of

The battery loss satisfaction model is as follows:

in the formula, P_l(t) is the value of the charge-discharge power of the electric vehicle l at the time t, mu_d,l(t) is a function of the discharge state of the electric vehicle l in the period t.

9. The intelligent charging and discharging management method for V2G electric vehicles in regional micro-grids as claimed in claim 7,

wherein the load variance model is

In the formula, L_B(t) is the normal load of the microgrid system, P_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_l(t) is a function of the charge-discharge state of the electric vehicle l, μ_T ^HIs the average load of the microgrid system,the charging load of the electric vehicle l.

10. The intelligent charging and discharging management method for V2G electric vehicles in regional micro-grids as claimed in claim 7,

wherein the total daily operating cost model is

In the formula, C_EV,sub(t) a reward fee obtained when the electric vehicle participates in the auxiliary service of the microgrid within the time period t, C_R,op(t) is the total operation and maintenance cost of the microgrid system, C_BES,cd(t) is the energy storageCharging and discharging loss cost of the device, C_EV,cd(t) is the charge and discharge cost of the electric vehicle user in the t period, C_RES,sub(t) is the power generation subsidy of the renewable energy source module in the time period of t,

the calculation method of the obtained reward fee of the electric automobile participating in the micro-grid auxiliary service comprises the following steps:

wherein N (t) is the total number of responding electric vehicles in the period of t, P_d,l(t) is the discharge rated power of the electric automobile in the t period, I_EV,subReward fee criteria for the electric vehicle participating in the microgrid auxiliary service within the time period t,

the method for calculating the total operation and maintenance cost of the microgrid system comprises the following steps:

in the formula (I), the compound is shown in the specification,

the method for calculating the charge and discharge loss cost of the energy storage device comprises the following steps:

C_BES,cd(t)＝P_B(t)·ρ_B(t)·△t

in the formula, ρ_B(t) is as followsThe cost and expense standard converted from the loss of the energy storage device,

the method for calculating the charge and discharge cost of the electric automobile in the t period comprises the following steps:

wherein N (t) is the total number of responding electric vehicles in the period of t, P_l(t) is the value of the charge-discharge power at time t of the electric vehicle l, ρ_EV(t) is the fee charged by the micro-grid system in the charging and discharging process of the electric automobile within the time period t,

the calculation method of the renewable energy module power generation subsidy in the time period t comprises the following steps:

C_RES,sub(t)＝(P_WT,valid(t)I_WT,sub+P_PV,valid(t)I_PV,sub)·△t

in the formula, P_WT,valid(t) the wind power unit output, P, taken up during the period t_PV,valid(t) the photovoltaic power generation unit output which is absorbed in the time period t, I_WT,subSubsidizing a cost standard for each generation of one degree of electricity by the wind power generation unit, I_PV,subAnd subsidizing the cost standard for each electricity generation of one degree of the photovoltaic power generation unit.

Technical Field

The invention relates to the field of electric vehicle charging, in particular to an intelligent charging and discharging management method for a V2G electric vehicle in an area micro-grid.

Background

Energy is an important precondition for human survival and development, along with the continuous development of social science and technology and economy, the traditional fossil energy is exhausted day by day, the problems of global warming, environmental pollution and the like are aggravated day by day, and the improvement of the utilization rate of clean energy is an important research topic; the wind-solar power generation system has great dependence on weather conditions, so that the intermittence and uncertainty of the power generation capacity of the wind-solar power generation system are caused, and the stability of the wind-solar power generation system is greatly influenced if the wind-solar power generation system is directly connected to a power grid. The microgrid can efficiently solve the problem of large-scale distributed access of Renewable Energy (RE) to the ground, and the flexibility of a microgrid system is enhanced by the grid-connected and isolated operation modes of the microgrid.

In recent years, Electric Vehicles (EVs) have been rapidly developed, and the value of mobile energy storage characteristics thereof has been gradually recognized. EV is always regarded as a reasonable utilization of clean energy and is an effective way for solving the energy environmental problem; the development of EVs has also driven the development of lithium ion batteries, so that electric vehicles with V2G (Vehicle-to-Grid, V2G) function have become increasingly popular. The V2G means that the electric automobile can be used as a mobile energy storage unit, electric energy is injected into a power grid during peak load, and electric energy is supplemented from the power grid during valley load, so that bidirectional flow of energy is realized, the standby requirement is reduced, the system operation cost is reduced, and the system operation economy is improved; therefore, large-scale V2G electric vehicles enter the microgrid, and participation in the joint scheduling of the microgrid is a necessary development trend in the future.

Disclosure of Invention

The invention is made to solve the above problems, and an object of the invention is to provide an intelligent charge and discharge management method for a V2G electric vehicle in an intelligent green environment-friendly regional microgrid.

The invention provides an intelligent charging and discharging management method for V2G electric vehicles in a regional microgrid, which is used for managing a microgrid system, and the microgrid system comprises: the intelligent measurement module, the microgrid energy management center, the electric automobile charge-discharge module, the renewable energy module and the microgrid response main body, the microgrid response main body comprises a plurality of electric automobiles and an energy storage device, each electric automobile is provided with a power battery, the renewable energy module comprises a fan power generation unit and a photovoltaic power generation unit, the characteristics are that the intelligent measurement module comprises the following steps: step 1, determining basic parameters of the microgrid system, wherein the basic parameters comprise: the number of electric vehicles, the initial satisfaction of electric vehicle users, the capacity of the energy storage device, the upper limit of the state of charge of the energy storage device and the lower limit of the state of charge of the energy storage device; step 2, establishing a renewable energy module output model to predict the renewable energy module output, and establishing an energy storage device load model to predict the energy storage device load state; step 3, establishing an electric vehicle load characteristic model and obtaining the charge states of all electric vehicles; step 4, establishing an optimization model and solving, so that the maximum satisfaction degree of the electric automobile user is the premise, and the total running cost of the microgrid system is the lowest; and 5, mobilizing the electric automobile according to the result of the solution of the optimization model.

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein, the renewable energy module output model comprises: the wind power random output model is used for calculating the output power of the wind power generation unit; and the solar radiation random output model is used for calculating the output power of the photovoltaic power generation unit.

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein the wind random output model is

In the formula, P_w(t) is the output power of the wind power generation unit, v (t) represents the wind speed at time t, v_rRated wind speed, v, of the wind power unit_inFor the cut-in wind speed, v, of the wind power unit_outCut-out wind speed, P, for a wind power unit_rThe rated output power of the wind power generation unit.

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein the random output model of solar radiation is

P_PV(k)＝P_peak×η×D(k)×10^-3

In the formula, P_PV(k) For photovoltaic power generation unit output power, P_peakPeak watt power of the photovoltaic power generation unit, η efficiency of the photovoltaic power generation unit, and d (k) peak watt hours of the photovoltaic power generation unit.

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein the energy storage device load model is

In the formula, soc (t) is the remaining capacity of the energy storage device at time t; i is_BSThe variable quantity of the electric quantity of the energy storage device from the moment t-1 to the moment t; q_BSAs the capacity of the storage battery,

the constraint condition of the load model of the energy storage device is

P_{SB min}≤P_SB(t)≤P_{SB max}

SOC_{SB min}≤SOC_SB(t)≤SOC_{SB max}

In the formula, P_{SB min}Minimum power for charging and discharging the energy storage device, P_{SB max}Maximum power for charging and discharging energy storage device, SOC_SB(t) is the state of charge, SOC, of the energy storage device during time t_{SB min}Is the minimum value of the state of charge, SOC, of the energy storage device_{SB max}Is the maximum value of the state of charge of the energy storage device.

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein the electric vehicle load characteristic model comprises an electric vehicle load state model, an electric vehicle charging load model and an electric vehicle discharging load model,

the electric automobile load state model is

In the formula, S_l(t) is the state of charge of the electric vehicle l at time t, P_l(t) is electric steamValue of charging and discharging power, mu, of vehicle l at time t_l(t) is a function of the charge/discharge state of the electric vehicle l, η_lFor efficiency of electric energy conversion, Q_s,lIs the total charge capacity, S, of the electric vehicle_0,lIs the initial charge state of the electric automobile when the electric automobile is connected to the network,

the charging load model of the electric automobile is

In the formula, P_c(t) is the charging load of the electric vehicle, N (t) is the total number of the electric vehicles responding in the period of t, P_c,l(t) is the charging rated power of the electric automobile l in the sampling point t,

the discharge load model of the electric automobile is

In the formula, P_d(t) is the discharge load of the electric vehicle, N (t) is the total number of the electric vehicles responding in the period of t, P_d,l(t) is the discharge rated power of the electric automobile l in the t period,

the constraint conditions of the electric vehicle discharge load model are as follows:

in the formula, P_d,l(t) is the value of discharge power in the electric automobile in the period of t, Q_V2GIs the available discharge capacity of a single electric automobile,

Q_V2G＝(Q_s,l-Q_r,l-Q_d,l-Q_Loss)

in the formula, Q_s,lIs the total capacity, Q, of the power battery of a single electric vehicle_r,lFor reserve capacity, Q, of a single electric vehicle's power battery_d,lIs the daily use capacity, Q, of the power battery of a single electric automobile_LossEnergy is lost to the power battery of a single electric vehicle,

in the formula, P_c,lRated power, P, for charging electric vehicles_d,lRated power for discharging electric vehicles, η_lThe efficiency of the conversion of electric energy is expressed,

S_min,l≤S_l(t)≤S_max,l

in the formula, S_min,lIs the minimum value of the state of charge of the electric vehicle, S_max,lIs the maximum value of the charge state of the electric automobile,

0≤S_0,l<1

0≤S_E,l<1

in the formula, S_E,lThe state of charge desired by the user of the electric vehicle l,

r_l≤r_max,l

in the formula, r_lBreakage rate, r, of power battery_max,lThe maximum breakage rate of the power battery acceptable for the user of the electric automobile,

in the formula, L_B(t) is the conventional load of the microgrid system; mu.s_c,l(t)、μ_d,l(t) is the charging and discharging state function of the electric vehicle l in the t period, and the charging and discharging state function and the discharging state function are opposite, and the value of the charging and discharging state function is 0 or 1.

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein the optimization model is

In the formula, F₁For users of electric vehiclesSatisfaction model, F_1maxMaximum value of satisfaction model, omega, for electric vehicle users₁Is F₁Weight coefficient of (1), F₂Load variance model for electric vehicle participation scheduling, F_2maxParticipating in scheduling the maximum, ω, of the load equation model for electric vehicles₂Is F₂Weight coefficient of (1), F₃A daily running total cost model for the microgrid system, F_3maxThe maximum value, omega, of a total daily running cost model of the microgrid system₃Is F₃The weight coefficient of (2).

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein the satisfaction model is

In the formula (I), the compound is shown in the specification,for a travel satisfaction model of a single electric vehicle user,for a model of electricity price satisfaction for a single electric vehicle user,a power battery loss satisfaction model for a single electric vehicle user,

the travel satisfaction model is

In the formula (I), the compound is shown in the specification,is the charge state, Q, of the electric vehicle at the moment of I off-grid_r,lReserve capacity, Q, for electric vehicles l_d,lFor the daily use capacity of the electric automobile I,

the satisfaction degree model of electricity price is

In the formula, C_l(t) the price of electricity charged by the electric vehicle l in the period of t, C_l(t_min) Minimum charge for charging cost of electric vehicle, C_l(t_max) The highest charge, P, for the charging cost of the electric vehicle l_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_l(t) is a function of the charging/discharging state of the electric vehicle

The battery loss satisfaction model is

In the formula, P_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_d,lAnd (t) is a discharge state function of the electric vehicle l in the t period.

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein the load variance model is

In the formula, L_B(t) is the normal load of the microgrid system, P_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_l(t) is a function of the charge-discharge state of the electric vehicle l, μ_T ^HIs the average load, P, of the microgrid system_l ^EV(t) represents a charging load of the electric vehicle l.

In the intelligent charging and discharging management method for the V2G electric vehicle in the regional microgrid, the method provided by the invention can also have the following characteristics: wherein the content of the first and second substances,

the total daily operating cost model is

In the formula, C_EV,sub(t) a reward fee obtained when the electric vehicle participates in the auxiliary service of the microgrid within the time period t, C_R,op(t) is the total operation and maintenance cost of the microgrid system, C_BES,cd(t) cost of charging and discharging losses of energy storage device, C_EV,cd(t) is the charge and discharge cost of the electric vehicle user in the t period, C_RES,sub(t) is the power generation subsidy of the renewable energy source module in the time period of t,

the calculation method of the obtained reward fee of the electric automobile participating in the micro-grid auxiliary service comprises the following steps:

wherein N (t) is the total number of responding electric vehicles in the period of t, P_d,l(t) is the discharge rated power of the electric automobile in the t period, I_EV,subThe fee standard is awarded for the electric automobile participating in the micro-grid auxiliary service in the time period t,

the method for calculating the total operation and maintenance cost of the microgrid system comprises the following steps:

in the formula (I), the compound is shown in the specification,the cost coefficient of operation and maintenance of the fan power generation unit,

For the operation and maintenance cost coefficient of the photovoltaic power generation unit,

is the operation and maintenance cost coefficient, P, of the energy storage device_w(t) is the output power of the fan unit, P_PV(t) is the output power of the photovoltaic power generation unit, P_B(t) is the output power of the energy storage device,

the method for calculating the charge and discharge loss cost of the energy storage device comprises the following steps:

C_BES,cd(t)＝P_B(t)·ρ_B(t)·△t

in the formula, ρ_B(t) is a cost-expense criterion converted from energy storage device losses,

the method for calculating the charge and discharge cost of the electric automobile in the t period comprises the following steps:

wherein N (t) is the total number of responding electric vehicles in the period of t, P_l(t) is the value of the charge-discharge power at time t of the electric vehicle l, ρ_EV(t) is the fee charged by the micro-grid system in the charging and discharging process of the electric automobile within the time period t,

the calculation method of the renewable energy module power generation subsidy in the time period t comprises the following steps:

C_RES,sub(t)＝(P_WT,valid(t)I_WT,sub+P_PV,valid(t)I_PV,sub)·△t

in the formula, P_WT,valid(t) the wind power unit output which is absorbed in the time period t, P_PV,valid(t) the photovoltaic power generation unit output consumed in the time period t, I_WT,subSubsidizing the cost standard for each generation of one degree of electricity of the wind power generation unit, I_PV,subAnd the cost standard is subsidized for each generation of one-degree electricity of the photovoltaic power generation unit.

Action and Effect of the invention

According to the intelligent charging and discharging management method for the V2G electric vehicles in the regional microgrid, because an optimization model is established and a three-stage intelligent charging and discharging method for the electric vehicles is adopted, the method can be used for load peak clipping and valley filling in the microgrid system and reducing the running cost of the microgrid, and can also be used for greatly improving the response capability of users in the microgrid system in a visible time region, so that the energy storage property of the microgrid system is greatly improved under the precondition that the microgrid system contains V2G electric vehicles of a certain scale.

Drawings

Fig. 1 is a schematic structural diagram of a microgrid system in embodiment 1 of the present invention;

fig. 2 is a flowchart of an intelligent charge and discharge management method for V2G electric vehicles in an area microgrid in embodiment 2 of the present invention;

fig. 3 is a graph illustrating a change of a total load of the microgrid system with time before and after an implementation of an intelligent charging and discharging management method for V2G electric vehicles in an area microgrid in embodiment 2 of the present invention; and

fig. 4 is a graph of changes in the corresponding number of electric vehicles with time after the method for intelligent charging and discharging management of V2G electric vehicles in the regional microgrid in embodiment 2 of the present invention is implemented.

Detailed Description

In order to make the technical means, the creation features, the achievement purposes and the effects of the invention easy to understand, the invention is specifically described below by combining the embodiment and the attached drawings of the specification.

< example 1>

Fig. 1 is a schematic structural diagram of a microgrid system in embodiment 1 of the present invention.

As shown in fig. 1, the present embodiment provides a microgrid system 100, including: the system comprises an intelligent measuring module 10, a microgrid energy management center 20, an electric vehicle charging and discharging module 30, a renewable energy module 40, a microgrid response main body 50 and a conventional load module 60.

The intelligent measurement module 10 is an advanced measurement information structure, AMI for short. The intelligent measurement module 10 is configured to monitor and collect the output and demand side load level information of the renewable energy module 40 in real time, and upload the collected information to the microgrid energy management center 20 in time.

The microgrid energy management center 20 is a main control unit for the microgrid system to perform real-time control on the optimized operation, and a microgrid system 100 operation optimization real-time control strategy is formulated through two stages of dynamic optimization and real-time power distribution on the basis of the load demand uploaded by the intelligent measurement module 10, the output information of the renewable energy module 40 and the power information of the microgrid response main body 50.

The electric vehicle charging and discharging module 30 is a final execution unit for real-time power control of the electric vehicle. In the embodiment, the charging and discharging facility is specifically an electric automobile charging and discharging facility. Specifically, the electric vehicle charging and discharging module 30 is a centralized bidirectional charging pile. In another embodiment, the charging and discharging facilities may be other electric vehicle charging and discharging facilities, and may be centralized electric vehicle charging and discharging facilities or distributed electric vehicle charging and discharging facilities.

The renewable energy module 40 includes a fan power generation unit 41 and a photovoltaic power generation unit 42, and in other embodiments, the renewable energy module 40 may also include other renewable energy power generation units, such as a biological power generation unit, a tidal power generation unit, and the like, according to actual situations. In the present embodiment, the renewable energy module 40 includes a 600MW wind power generation system and a photovoltaic power generation system.

The microgrid response main body 50 includes an electric vehicle cluster 51(EV cluster) and an energy storage device 52 (BES). The electric vehicle cluster 51 includes a plurality of electric vehicles, each of which has a power battery. The energy storage device 52 is a storage battery in this embodiment, and a device such as a super capacitor may be used in other embodiments. In the present embodiment, the electric vehicle cluster 51 includes 480 electric vehicles having a V2G function, and the energy storage device 52 is a chemical energy storage system with a capacity of 500 kWh. The capacity of the power battery of each electric automobile is 30 kWh.

The conventional load module 60 is a conventional power consuming device connected to the microgrid system 10.

< example 2>

Fig. 2 is a flowchart of an intelligent charge and discharge management method for V2G electric vehicles in an area microgrid in embodiment 2 of the present invention.

As shown in fig. 2, this embodiment provides an intelligent charging and discharging management method for a V2G electric vehicle in an area microgrid, which is used for performing intelligent charging and discharging management on a V2G electric vehicle in a microgrid system in embodiment 1, and includes the following steps:

s1, determining basic parameters of the microgrid system, wherein the basic parameters comprise: the number of electric vehicles, the initial satisfaction of electric vehicle users, the capacity of the energy storage device, the upper limit of the state of charge of the energy storage device and the lower limit of the state of charge of the electric vehicle;

s2, establishing a renewable energy module output model to predict the renewable energy module output, in this embodiment, the renewable energy module output model includes: a wind power random output model and a solar radiation random output model.

In the present embodiment, the wind random contribution model is established for the wind turbine. The wind driven generator converts wind energy into mechanical energy through fan blades, and then converts the mechanical energy into electric energy through a generator.

The short-term probability characteristic of wind speed obeys Weibull distribution (Weibull distribution)

Wherein v is the actual wind speed, k is the shape parameter, and c is the scale parameter.

A nonlinear relation exists between the output of the fan and the wind speed, so that the random output model of the wind power is

In the formula, P_w(t) is the output power of the wind power generation unit, v (t) represents the wind speed at time t, v_rRated wind speed, v, of the wind power unit_inFor the cut-in wind speed, v, of the wind power unit_outCut-out wind speed, P, for a wind power unit_rThe rated output power of the wind power generation unit.

Photovoltaic power generation and wind power generation are the same, and are easily influenced by external environments, such as seasons, geographical positions, landform and topography, and are also random variables with nonlinear characteristics, and assuming that the amount of solar radiation obeys Beta distribution (Beta distribution) at any time t, the probability distribution characteristic of short-term solar illumination intensity can be described as follows:

where () is the gamma function, r (t) is the amount of solar radiation per sampling period, r_maxFor maximum solar radiation, α and β are the shape parameters of beta distribution, respectively_sR (t) represents the percentage of the total daily radiation dose of the radiation dose of each sampling period from sunrise to sampling point t, and r represents the total daily radiation dose.

If the value of the total radiation r is known, then r (t) can be described by a normal distribution:

σ_R＝d₀+d₁S_L

in the formula (d)₀And d₁Are all constant coefficients, S_LIt is daily long.

D(t)＝H(t)×0.0116

Wherein D (t) is peak watt-hour, 0.0116 is a conversion coefficient, and H (k) is the solar radiation amount in each sampling period.

P_PV(t)＝P_peak×η×D(t)×10^-3

In the formula, P_PV(t) is the output power of the photovoltaic power generation unit, P_peakPeak watt power of the photovoltaic power generation unit, η efficiency of the photovoltaic power generation unit, and d (k) peak watt hours of the photovoltaic power generation unit.

The storage battery is used as a commonly used energy storage unit in the microgrid, although the probability density is not higher than that of a super capacitor, the storage battery is relatively large, and the storage battery can be used as relatively stable energy storage equipment in a new energy microgrid.

The residual capacity of the storage battery at the time t is equal to the residual capacity of the storage battery at the time t-1, the charging quantity of the storage battery from the time t-1 to the time t or the discharging quantity of the storage battery from the time t-1 to the time t is subtracted. The mathematical model mainly considers the relationship between the storage capacity and the charging and discharging power of the storage battery, so the state of charge model of the storage battery at the time t is as follows:

wherein SOC (t) is the time t of the batteryThe remaining amount of power of; i is_BSRepresenting the variation of the electric quantity of the storage battery from the time t-1 to the time t; q_BSIs the battery capacity.

The charge-discharge power and state-of-charge (SOC) constraints that a battery system should satisfy are:

P_{SB min}≤P_SB(t)≤P_{SB max}

SOC_{SB min}≤SOC_SB(t)≤SOC_{SB max}

in the formula, P_{SB min}、P_{SB max}Respectively the minimum power and the maximum power of the charging and discharging of the storage battery; SOC_SB(t) is the state of charge of the battery over time t; SOC_{SB min}、SOC_{SB max}Respectively, the minimum and maximum battery state of charge.

And S3, establishing an electric vehicle load characteristic model through Monte Carlo simulation so as to extract the charge states of all electric vehicles. In the embodiment, the electric vehicle load characteristic model comprises an electric vehicle load state model, an electric vehicle charging load model and an electric vehicle discharging load model,

the electric automobile load state model is

In the formula, S_l(t) is the state of charge of the electric vehicle l at time t, P_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_l(t) is a function of the charge/discharge state of the electric vehicle l, η_lFor efficiency of electric energy conversion, Q_s,lIs the total charge capacity, S, of the electric vehicle_0,lThe initial charge state of the electric vehicle is when the electric vehicle is connected to the network. When an electric vehicle load characteristic model is established, meeting the travel requirements of users is an important method for improving the satisfaction degree of the users. When the battery SOC is lower than the charging threshold S_L,l(t) a charging demand is generated. The charge threshold here is actually a value that changes with time, electricity price, and user habit, and is set to a constant value S for simplifying the model_LIn particular in the present implementationIn the examples, take S_L＝0.2。

Specifically, in the present embodiment, the charge-discharge state function of the electric vehicle l is as follows:

the charging load model of the electric automobile is

In the formula, P_c(t) is the charging load of the electric vehicle, N (t) is the total number of the electric vehicles responding in the period of t, P_c,l(t) is the charging rated power of the electric automobile l in the sampling point t,

the discharge load model of the electric automobile is

In the formula, P_d(t) is the discharge load of the electric vehicle, N (t) is the total number of the electric vehicles responding in the period of t, P_d,l(t) is the discharge rated power of the electric automobile l in the t period,

the constraint conditions of the electric vehicle discharge load model are as follows:

in the formula, P_d,l(t) is the value of discharge power in the electric automobile in the period of t, Q_V2GIs the available discharge capacity of a single electric automobile,

Q_V2G＝(Q_s,l-Q_r,l-Q_d,l-Q_Loss)

in the formula, Q_s,lIs the total capacity, Q, of the power battery of a single electric vehicle_r,lFor reserve capacity, Q, of a single electric vehicle's power battery_d,lIs the daily use capacity, Q, of the power battery of a single electric automobile_LossAs a single electricityThe power battery of the electric vehicle loses energy,

in the formula, P_c,lRated power, P, for charging electric vehicles_d,lRated power for discharging electric vehicles, η_lThe efficiency of the conversion of electric energy is expressed,

S_min,l≤S_l(t)≤S_max,l

in the formula, S_min,lIs the minimum value of the state of charge of the electric vehicle, S_max,lIn the embodiment, S is the maximum value of the state of charge of the electric vehicle_min,l＝0.2，S_max,l＝0.9。

0≤S_0,l<1

0≤S_E,l<1

In the formula, S_E,lA state of charge expected by a user of the electric vehicle l;

r_l≤r_max,l

in the formula, r_lBreakage rate, r, of power battery_max,lThe maximum breakage rate of the power battery acceptable for the user of the electric automobile,

in the formula, L_B(t) is the conventional load of the microgrid system; mu.s_c,l(t)、μ_d,l(t) is the charging and discharging state function of the electric vehicle l in the t period, and the charging and discharging state function and the discharging state function are opposite, and the value of the charging and discharging state function is 0 or 1.

And S4, establishing an optimization model and solving, so that the total running cost of the microgrid system is the lowest under the conditions of the maximum user satisfaction degree of the electric automobile and the minimum scheduling load variance of the electric automobile.

In this embodiment, the optimization model is:

in the formula, F₁Satisfaction model for electric vehicle users, F_1maxMaximum value of satisfaction model, omega, for electric vehicle users₁Is F₁Weight coefficient of (1), F₂Load variance model for electric vehicle participation scheduling, F_2maxParticipating in scheduling the maximum, ω, of the load equation model for electric vehicles₂Is F₂Weight coefficient of (1), F₃A daily running total cost model for the microgrid system, F_3maxThe maximum value, omega, of a total daily running cost model of the microgrid system₃Is F₃The weight coefficient of (2).

Specifically, the three-stage optimized intelligent charging and discharging method adopted in the embodiment includes the following steps:

s4-1, establishing a satisfaction model which is

In the formula (I), the compound is shown in the specification,

for a travel satisfaction model of a single electric vehicle user,for a model of electricity price satisfaction for a single electric vehicle user,a power battery loss satisfaction model for a single electric vehicle user,

the travel satisfaction model is

In the formula (I), the compound is shown in the specification,is the charge state, Q, of the electric vehicle at the moment of I off-grid_r,lReserve capacity, Q, for electric vehicles l_d,lFor the daily capacity of the electric vehicle, in this embodiment, when the electric vehicle is used

The value is smaller than the charge state required by the daily driving of the user, the satisfaction degree of the user on the trip is greatly discounted, and the satisfaction degree at the time is set to be 0; when the value of the travel satisfaction index can meet the daily travel requirement of the user but cannot meet the travel requirement of the owner in some special occasions, the travel satisfaction index of the user is set as m; when the value reaches the state of charge expected by the user, 0 is

The travel satisfaction of the user at this time takes a maximum value of 1.

The satisfaction degree model of electricity price is

In the formula, C_l(t) the price of electricity charged by the electric vehicle l in the period of t, C_l(t_min) Minimum charge for charging cost of electric vehicle, C_l(t_max) The highest charge, P, for the charging cost of the electric vehicle l_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_l(t) is a function of the charge-discharge state of the electric vehicle l,between 0 and 1, when the user is scheduled to charge at a low electricity rate period,the value of (a) increases, and the maximum value is 1.

The battery loss satisfaction model is

In the formula, P_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_d,lAnd (t) is a discharge state function of the electric vehicle l in the t period. In the time period when the electric automobile is connected to the power grid, the energy which is reversely transmitted to the microgrid by the electric automobile participating in the microgrid energy regulation belongs to extra loss to the power battery, when the energy value transmitted back is more, the extra loss brought to the electric automobile battery is more, the aging rate of the battery is accelerated, and certain negative effects are formed on the functional attributes of the battery.

Solving the satisfaction degree model to maximize the satisfaction degree of the electric automobile user;

s4-2, establishing a load variance model for electric vehicle dispatching,

the load variance model is

In the formula, L_B(t) is the normal load of the microgrid system, P_l(t) is the value of the charging and discharging power of the electric vehicle l at the time t, mu_l(t) is a function of the charge-discharge state of the electric vehicle l, μ_T ^HIs the average load, P, of the microgrid system_l ^EV(t) is a charging load of the electric vehicle l.

Solving the load variance model by taking the user satisfaction degree not lower than S4-1 as constraint so as to minimize the load variance;

s4-3, establishing a total daily running cost model of the microgrid system,

the total daily operating cost model is

In the formula, C_EV,sub(t) a reward fee obtained when the electric vehicle participates in the auxiliary service of the microgrid within the time period t, C_R,op(t) is the total operation and maintenance cost of the microgrid system, C_BES,cd(t) cost of charging and discharging losses of energy storage device, C_EV,cd(t) is the charge and discharge cost of the electric vehicle user in the t period, C_RES,sub(t) is the power generation subsidy of the renewable energy source module in the time period of t,

the calculation method of the obtained reward fee of the electric automobile participating in the micro-grid auxiliary service comprises the following steps:

wherein N (t) is the total number of responding electric vehicles in the period of t, P_d,l(t) is the discharge rated power of the electric automobile in the t period, I_EV,subThe fee standard is awarded for the electric automobile participating in the micro-grid auxiliary service in the time period t,

the method for calculating the total operation and maintenance cost of the microgrid system comprises the following steps:

in the formula (I), the compound is shown in the specification,

the cost coefficient of operation and maintenance of the fan power generation unit,For the operation and maintenance cost coefficient of the photovoltaic power generation unit,

is the operation and maintenance cost coefficient, P, of the energy storage device_w(t) is the output power of the fan unit, P_PV(k) For photovoltaic power generation unit output power, P_B(t) is the output power of the energy storage device,

the method for calculating the charge and discharge loss cost of the energy storage device comprises the following steps:

C_BES,cd(t)＝P_B(t)·ρ_B(t)·△t

in the formula, ρ_B(t) is a cost-expense criterion converted from energy storage device losses,

the method for calculating the charge and discharge cost of the electric automobile in the t period comprises the following steps:

wherein N (t) is the total number of responding electric vehicles in the period of t, P_l(t) is the value of the charge-discharge power at time t of the electric vehicle l, ρ_EV(t) is the fee charged by the micro-grid system in the charging and discharging process of the electric automobile within the time period t,

the calculation method of the renewable energy module power generation subsidy in the time period t comprises the following steps:

C_RES,sub(t)＝(P_WT,valid(t)I_WT,sub+P_PV,valid(t)I_PV,sub)·△t

in the formula, P_WT,valid(t) the wind power unit output which is absorbed in the time period t, P_PV,valid(t) the photovoltaic power generation unit output consumed in the time period t, I_WT,subSubsidizing the cost standard for each generation of one degree of electricity of the wind power generation unit, I_PV,subAnd the cost standard is subsidized for each generation of one-degree electricity of the photovoltaic power generation unit.

And solving the daily running total cost model by taking the load variance not greater than S4-2 as a constraint, so that the running total cost of the microgrid system is the lowest.

And S5, issuing an electric vehicle dispatching plan according to the solution result of the optimization model in the S4, the output of the renewable energy module and the existing microgrid response main body, and dispatching the electric vehicles in the microgrid system. For example, when the wind and light output reaches the maximum value, the electric vehicle is charged, the wind and light output in the microgrid is consumed as much as possible, and in the valley period of the wind and light output, the conventional load in the microgrid is just in the peak value or secondary peak value state, so that the electric vehicle is discharged.

S6, counting the actual value of the daily load curve of the micro-grid system, and comparing the actual value with the daily total load curve of the micro-grid system before management by the method;

fig. 3 is a graph showing changes in total load of the microgrid system with time before and after the implementation of the intelligent charging and discharging management method for V2G electric vehicles in the regional microgrid in embodiment 2 of the present invention.

As shown in fig. 3, in the valley period, the load difference before and after optimization is not large, but in the peak period, the load after optimization is greatly reduced compared with that before optimization, which indicates that the load pressure in the peak period can be effectively relieved by using the method.

And S7, counting the number of the electric automobiles responding to the microgrid energy management center in the microgrid system after a long time (1 year in the embodiment).

Fig. 4 is a graph of changes in the corresponding number of electric vehicles with time after the method for intelligent charging and discharging management of V2G electric vehicles in the regional microgrid in embodiment 2 of the present invention is implemented.

As shown in fig. 4, as time goes on, the number of electric vehicles responding to the microgrid scheduling plan in the system also increases gradually from the first 100 or so to more than 300 vehicles in a year. This shows that the present invention can effectively increase the enthusiasm of electric vehicle users.