CN105160451A - Electric-automobile-contained micro electric network multi-target optimization scheduling method - Google Patents

Abstract

The invention relates to a multi-objective optimal dispatching method for a micro-grid including electric vehicles, which is characterized in that the method includes the following steps: 1) Determine the mode for electric vehicles to access the micro-grid, and use a single electric vehicle under different access modes The distribution characteristics of discharge and charge loads of electric vehicles are superimposed to obtain the distribution characteristics of discharge and charge loads of electric vehicles. Optimal dispatching model of microgrid for vehicle access; 3) Solve the optimal dispatching model of microgrid considering large-scale electric vehicle access by using particle swarm optimization algorithm based on automatic reorganization mechanism, and compare and analyze microgrid dispatching under various dispatching strategies Economical, so as to get the optimal scheduling strategy. Compared with the prior art, the present invention has the advantages of being comprehensive, effective and feasible.

Description

A multi-objective optimal scheduling method for microgrid with electric vehicles

technical field

The invention relates to the field of micro-grid dispatching, in particular to a multi-objective optimal dispatching method for a micro-grid containing electric vehicles.

Background technique

As a new type of distributed power network management and supply technology, microgrid can facilitate the access of renewable energy power generation systems to the distribution network, and realize the effective management of demand-side energy and the efficient utilization of main grid power energy.

The optimal dispatching problem of microgrid is similar to that of traditional large power grid, but it has its particularity and complexity. The main goal of optimal scheduling problem is to achieve the lowest cost in the power grid operation process. As the problem of environmental pollution has attracted people's attention, many researchers now put the cost of environmental pollution into the problem of scheduling optimization. In the optimal dispatching of the microgrid, reducing operating costs and reducing pollutant emissions is also of great significance to the energy saving and emission reduction of the entire power grid.

In recent years, with the strengthening and implementation of the government's energy-saving, environmental protection and high-tech related policies, the number of users using electric vehicles (Electric Vehicle, EV) has been increasing, and the electric energy storage capacity of these electric vehicles has been considerable. However, the access of electric vehicles to the grid is flexible and decentralized, not limited by space and time. This feature will increase the instability of the grid and affect the power quality of the grid. Similar to distributed power, if electric vehicles are connected to the microgrid, the impact of electric vehicles directly connected to the grid can be avoided or reduced.

At present, scholars at home and abroad have carried out a series of research work on the multi-objective optimal dispatching of microgrid and large-scale access of electric vehicles, and achieved some theoretical and practical results. Chen Dawei and Zhu Guiping established an optimal scheduling model for microgrids that takes environmental factors into account, but they only multiply the sum of the two objective functions of the lowest operating cost and the lowest pollution treatment cost by fixed weights. In fact, it is still a single-objective optimal scheduling model. Yang Qi et al. mainly studied four hardware structures of microgrid economic dispatching systems involving grid-connected and island-operated microgrids, and analyzed the role of energy storage units. S.W.Hadley et al. studied the statistical law of the last return time of EVs and the daily driving distance, established a statistical model of EVs charging demand, and analyzed the impact of EVs random charging on the grid load. Han Haiying et al. considered the charging and discharging process of electric vehicles according to time intervals, and established an optimal scheduling model for microgrids containing large-scale grid-connectable EVs, and obtained the combined output of a single generating unit. However, these processing methods are relatively simple, and many aspects need further research.

Contents of the invention

The purpose of the present invention is to provide a comprehensive, effective and feasible multi-objective optimal dispatching method for a microgrid including electric vehicles in order to overcome the above-mentioned defects in the prior art.

The purpose of the present invention can be achieved through the following technical solutions:

A method for multi-objective optimal dispatching of a microgrid containing electric vehicles, characterized in that the method comprises the following steps:

1) Determine the mode of electric vehicle access to the microgrid, and obtain the discharge and charge distribution characteristics of electric vehicles by superimposing the discharge and charge distribution characteristics of a single electric vehicle under different access modes;

2) Add electric vehicles as microgrid scheduling objects to the microgrid optimal dispatching, and establish a microgrid optimal dispatching model considering the large-scale electric vehicle access according to the distribution characteristics of electric vehicles' discharging and charging loads;

3) The particle swarm optimization algorithm based on the automatic reorganization mechanism is used to solve the optimal dispatching model of the microgrid considering the access of large-scale electric vehicles, and the dispatching economics of the microgrid under various dispatching strategies are compared and analyzed to obtain the optimal dispatching strategy.

The modes in step 1) include the VOG mode of unidirectional disorderly charging and the V2G mode of bidirectional orderly charging and discharging.

The optimization objective function of the microgrid optimal dispatching model considering the access of large-scale electric vehicles in step 2) is:

Microgrid scheduling operation cost Obj ₁ is the smallest:

The treatment cost of discharged pollutants Obj ₂ is the smallest:

The comprehensive cost of microgrid dispatching is the lowest:

minObj ₃ =m ₁ Obj ₁ +m ₂ Obj ₂

Among them, C _G is the fuel cost of distributed power generation, C _OM is the operation and maintenance cost, C _DP is the depreciation cost of the power generation unit, C _Grid is the power exchange cost between the micro-grid and the large power grid, and C _EV is the power exchange between the micro-grid and electric vehicles Cost, C _L is the load outage compensation cost, ΔT _t is, T is, j is the number of the distributed generation unit in the microgrid, t is the operation period, k is the type of pollutants discharged, C _k is the emission per kilogram The cost of pollutants, γ _jk (P _jt ) is the weight of pollutant k generated when the power generation unit j in the microgrid outputs P _jt electric energy, γ _gridk (P _gridt ) is the pollution generated when the distribution network outputs P _gridt electric energy The weight of object k, m ₁ and m ₂ are the weights of operating cost and emission cost;

The constraints are:

Power balance constraints:

Cold/Hot Power Balance Constraints:

W _Load = W _MT + W _FC

The upper and lower limits of the active power P _jt of the distributed generation unit:

Limit constraints of the exchange power P _grid between the microgrid and the main grid:

The power P _SBt constraint and charge SOC _SBt constraint of the energy storage unit:

P _{SBmin ≤} P _{SBt ≤} P _SBmax

SOC _SBmin ≤ SOC _SBt ≤ SOC _SBmax

SOC _end = SOC ₀

Power P _Evt constraints and charge SOC _EVt constraints of electric vehicles:

P _{EVmin ≤P EVt ≤P EVmax}

SOC _EVmin ≤ SOC _EVt ≤ SOC _EVmax

Among them, P _jt is the power generated by the power generation unit j in the microgrid during the t period, P _gridt is the power transmitted from the distribution network to the microgrid during the t period, P _batteryt is the power generated by the battery during the t period, and P _loadt is the power in the microgrid during the t period. W _Load is the cooling/heating load demand of the entire microgrid system, W _MT is the cooling/heating power provided by the waste heat flue gas of the micro gas turbine, W _FC is the cooling/heating power provided by the heat generated by the fuel cell, is the minimum output power of distributed generation unit j, is the maximum output power of the distributed generation unit j, is the minimum power that can be transmitted by the common point line between the microgrid and the distribution network, is the maximum power that can be transmitted by the common point line between the microgrid and the distribution network, _PSBmin and _PSBmax are the minimum power and maximum power of the battery charging and discharging respectively, and SOC _SBmin and SOC _SBmax are the minimum value of the state of charge of the battery respectively and the maximum value, SOC ₀ and SOC _end are the state of charge of the storage battery at the initial time 0 and the end time 24 in the one-day scheduling cycle, P _EVmin , P _EVmax are the minimum power and maximum power of electric vehicle charging and discharging respectively, SOC _min , SOC _max are the minimum and maximum values of the electric vehicle battery state of charge, respectively.

In the V0G mode of unidirectional disorderly charging, the power demand of electric vehicles in the discharge and charge load distribution characteristics of electric vehicles is:

E _EV ＝η _EV ·d

Among them, η _EV is the electricity demand coefficient per unit mileage of electric vehicles, and the mileage d of each electric vehicle obeys the logarithmic normal distribution, and its probability density function is:

The charging time f(x) satisfies the normal distribution:

Among them, μ _d and μ _s are expected values, and σ _d and σ _s are standard deviations. .

In the V2G mode of bidirectional orderly charge and discharge, the discharge duration T _disc1 in the discharge and charge load distribution characteristics of electric vehicles is:

Combined with the charging power of the electric vehicle, the charging duration can be obtained, so that the continuous charging time T _disc2 can be obtained as:

The charging load required by a single electric vehicle is the total energy consumed in a day, that is, P _EV is:

Among them, T _all-disc is the total discharge duration required to discharge the electric vehicle to the lower limit of the state of charge when the electric vehicle is fully charged, P _disc is the discharge power of the electric vehicle, SOC _max and SOC _min are the upper and lower limits of the battery state of charge, respectively, and D is The daily driving mileage of electric vehicles, W ₁₀₀ is the power consumption of electric vehicles per 100 kilometers, T _{end_disc} is the discharge end time, T _{start_disc} is the grid discharge time, P _c is the charging power of electric vehicles.

The multiple different scheduling strategies in step 2) include a microgrid grid-connected operation strategy and a microgrid island operation strategy.

Described step 3) specifically comprises the following steps:

31) According to the access mode of electric vehicles, set the model parameters, objective function parameters and constraint parameters of each distributed power generation unit in the microgrid, and introduce uncontrollable and predictable distributed power output and cooling power load parameters;

32) Use the controllable output unit micro gas turbine, fuel cell, diesel generator, storage battery and main grid interchangeable power as five-dimensional particles, and set the parameters of the particle swarm algorithm, including the number of particles, the dimension of the solution space, the maximum number of iterations, particle maximum velocity and recombination index r;

33) Calculate the fitness of each particle, and record the current individual extremum and corresponding objective function value of each particle, and then obtain the overall extremum and corresponding objective function value, and select the individual optimal value and global The optimal value;

34) The current number of iterations is increased by one, and the position and speed of the particle swarm are updated;

35) Judging whether the result meets the premature convergence criterion and whether the number of reorganizations reaches the preset value, if the result meets the premature convergence criterion and the number of reorganizations does not reach the preset value, then reorganize the particle swarm and the reorganization index r=r+ 1, and return to step 33), otherwise proceed to step 36);

36) Judging whether the convergence condition is satisfied, if so, then obtain the global optimum or reach the maximum number of iterations, and end the iterative process; if not, return to step 33) to continue the iterative operation.

Compared with the prior art, the present invention has the following advantages:

Considering the charging and discharging characteristics of electric vehicles and the usage habits of car owners, electric vehicles are respectively connected to the microgrid in the one-way disordered V0G mode and the two-way orderly V2G mode, thus establishing a system that considers the mass access of electric vehicles. The mathematical model of micro-grid optimal dispatching uses three optimization objectives of minimum operation and maintenance cost, highest environmental benefit and lowest comprehensive cost at the same time, and compares and analyzes the impact of different electric vehicle access methods on the economic operation of micro-grid under six preset dispatching strategies In order to verify the effectiveness and feasibility of the microgrid optimal dispatch model built by electric vehicles connected in V2G mode and V0G mode.

Description of drawings

Figure 1 is a flow chart of microgrid optimal scheduling analysis under electric vehicle random charging mode.

Figure 2 is a flowchart of the calculation of electric vehicle charging load based on the Monte Carlo simulation method.

Fig. 3 is a load curve diagram of an electric vehicle under a one-way disordered V0G mode.

Fig. 4 is a load curve diagram of electric vehicles under the bidirectional and orderly V2G mode.

Figure 5 shows the charging and discharging strategy of the battery under grid-connected operation.

Figure 6 is the predicted power curves of PV and WT.

Fig. 7 is a diagram of the optimized output of each power generation unit under the optimization goal 3 of strategy 1.

Fig. 8 is a diagram of the optimized output of each power generation unit under the optimization objective 3 of strategy 3.

Fig. 9 is a diagram of the optimized output of each power generation unit under the optimization objective 3 of strategy 4.

Detailed ways

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

Example:

The invention aims at the influence of the electric vehicle on the optimal dispatch after it is added to the micro grid. For this reason, the present invention includes distributed power sources including photovoltaic (Photovoltaic, PV), wind power (WindTurbine, WT), fuel cell (FuelCell, FC), micro gas turbine (MicroTurbine, MT), diesel engine (DieselGenerator, DSG) and energy storage The unit includes a battery (Battery, Bat), and the access of electric vehicles is also considered.

The optimal dispatching problem of the microgrid is an economic operation optimization problem, and the objective functions are different due to the different emphasis on the consideration of the problem. The invention establishes a micro-grid multi-objective economic scheduling model that considers operating costs, environmental benefits, and the comprehensive cost of both as optimization targets.

1. Optimization goals

(1) The operating cost of microgrid dispatching is the smallest

The present invention is mainly aimed at the economic scheduling cost during the operation of the microgrid, so the initial investment and construction costs of each distributed power supply are not included in the scheduling cost category, but only consider the operation and maintenance costs and fuel costs of the distributed power supply scheduling output; in addition , Electric vehicles belong to the owner's private property, and the purchase and maintenance costs of electric vehicles are calculated and borne by the owner themselves, and are not included in the operating cost of the microgrid. However, this part of the cost is an influencing factor when formulating the electricity price for purchasing electricity from electric vehicles. Therefore, the optimization target of microgrid operation cost includes fuel cost C _G of distributed power generation, operation and maintenance cost C _OM , depreciation cost C _DP of power generation unit, power exchange cost C _Grid between microgrid and large grid, and electric energy between microgrid and electric vehicle. The exchange cost C _EV and the load outage compensation cost C _L are expressed as:

(2) The treatment cost of discharged pollutants is minimal

The operation of micro gas turbine MT, fuel cell FC and diesel generator DG in the microgrid and the power generation of the large grid unit will generate CO ₂ , SO ₂ , NO _X and other pollutants, which will result in the cost of pollutant emission treatment. The objective function for the minimum cost of microgrid emission treatment can be expressed as:

In the formula, j is the number of distributed generation units in the microgrid, 1~N; t is the operating period, 1~T; k is the type of pollutants emitted (CO ₂ , SO ₂ , NO _X , etc.); C _k is The cost of treating each kilogram of pollutants discharged (yuan/kg); γ _jk (P _jt ) is the weight of pollutant k produced when the power generation unit j in the microgrid outputs P _jt electric energy (kg/kW); γ _gridk (P _gridt ) The weight (kg/kW) of pollutant k produced when the distribution network outputs P _gridt electric energy.

(3) The comprehensive cost of microgrid dispatching is the lowest

minObj ₃ =m ₁ Obj ₁ +m ₂ Obj ₂ (3)

In the formula, Obj ₃ is the comprehensive cost of microgrid; Obj ₁ is the operating cost; Obj ₂ is the cost of pollutant discharge treatment; m ₁ and m ₂ are the weights of operating cost and emission cost respectively, in order to balance the impact of energy and environment. Assuming that the operation cost and the emission cost have the same weight, m ₁ =m ₂ =1 is taken here.

2. Constraints

(1) Power balance constraints

P _jt is the power (kW) generated by the power generation unit j in the microgrid during the t period; P _gridt is the power (kW) transmitted from the distribution network to the microgrid during the t period, and a negative value indicates that the power is reversely transmitted from the microgrid to the distribution network. grid; P _batteryt is the power (kW) generated by the battery during the t period, and the negative value indicates the absorbed power of the battery; P _loadt is the load demand (kW) in the microgrid during the t period.

(2) Cooling/heating power balance constraints

Cooling/heating and power cogeneration units need to meet the cooling and heating load constraints:

W _Load = W _MT + W _FC (5)

In the formula: W _Load is the cooling/heating load demand of the entire microgrid system, kW; W _MT is the cooling/heating power provided by the waste heat flue gas of the micro gas turbine, kW; W _FC is the cooling/heating power provided by the heat generated by the fuel cell , kW.

(3) The upper and lower limits of the active power of the generating unit

In the formula, is the minimum output power of the distributed generation unit j; is the maximum output power of distributed generation unit j.

(4) Limit constraints of power exchange between microgrid and main grid

In the formula, are the minimum power and maximum power that can be transmitted by the common point line between the microgrid and the distribution network, respectively.

(5) Power constraints and charge constraints of the energy storage unit

The charging and discharging power and state of charge (SOC) constraints that the battery system should meet are:

P _{SBmin ≤} P _{SBt ≤} P _SBmax (8)

SOC _SBmin ≤ SOC _SBt ≤ SOC _SBmax (9)

In the formula, _PSBmin and _PSBmax are the minimum power and maximum power of charging and discharging the battery, respectively; SOC _SBt is the state of charge of the battery in period t; SOC _SBmin and SOC _SBmax are the minimum and maximum values of the battery state of charge, respectively.

In addition, since the optimal scheduling of the battery by the microgrid presents a dynamic periodicity, this paper assumes that the SOC of the battery is consistent throughout the day's scheduling cycle, that is, the constraints are satisfied:

SOC _end = SOC ₀ (10)

Among them, SOC ₀ and SOC _end are the state of charge of the battery at the initial time 0 and the end time 24 of the one-day dispatching period, respectively.

(6) Power constraints and charge constraints of electric vehicles

For electric vehicles, the charging and discharging power and state of charge constraints that should also be satisfied are:

P _{EVmin ≤} P _{EVt ≤} P _EVmax (11)

SOC _EVmin ≤ SOC _EVt ≤ SOC _EVmax (12)

In the formula, P _EVmin and P _EVmax are the minimum power and maximum power of electric vehicle charging and discharging, respectively; SOC _EVt is the state of charge of the electric vehicle battery in the period t; SOC _min and SOC _max are the minimum state of charge of the electric vehicle battery, respectively. value and maximum.

3. Electric vehicle model

(1) Power characteristics of electric vehicles during disorderly charging

The charging power of electric vehicles is related to uncertain factors such as the mileage and charging time of electric vehicles. Therefore, it is necessary to simulate electric vehicles one by one by generating random events that obey statistical laws. The total charging power curve can be obtained by superimposing the power curves of all electric vehicles, and the process is shown in Figure 1.

For each electric vehicle, there is about 14% probability that it will not travel. If it travels, its daily mileage d approximately obeys the logarithmic normal distribution, and its probability density function is:

In the formula: distribution parameters μ _d = 3.2, σ _d = 0.88, the distribution is the average value and standard deviation of the daily mileage of electric vehicles. The corresponding electric vehicle charging electricity demand is E _EV :

E _EV ＝η _EV ·d(14)

In the VOG disorderly access mode, since most car owners will charge their electric vehicles immediately after returning home, assuming that the last return time of the day is the time to start charging, the time to start charging satisfies the normal distribution:

In the formula: μ _s =17.6; σ _s =3.4.

(2) Power characteristics of electric vehicles during orderly charging and discharging

The orderly charging and discharging of electric vehicles refers to the charging and discharging of electric vehicles under the premise of first satisfying the usage habits of electric vehicle users through policies such as electricity price guidance and the use of time-of-use electricity prices to control electric vehicles when a large number of electric vehicles are connected. The discharge is scheduled in an orderly manner. When connected to the grid, it is discharged during the peak period of electricity price and charged during the period of low electricity price; when it is isolated, it is discharged during the peak period of load and charged during the period of low load.

From the daily mileage S, the daily energy consumption of the electric vehicle can be obtained, so as to obtain the state of charge of the electric vehicle battery when it is connected to the grid:

In the formula, C is the total capacity of the electric vehicle battery.

The discharge duration is:

In the formula: T _all-disc is the total discharge duration required to discharge the electric vehicle to the lower limit of the state of charge when the electric vehicle is fully charged; T _disc is the actual discharge duration; P _disc is the discharge power of the electric vehicle; S _OC,max and S _{OC, min} are the upper and lower limits of the battery state of charge, respectively.

The grid-connected discharge time T _{start_disc} is obtained by judging the last return time t ₀ and the energy demand of the microgrid.

The discharge end time T _{end_disc} is jointly determined by the discharge start time and the discharge duration, and the upper limit is 24:00 at the end of the day.

The discharge period is T _{start_disc} ~ T _{end_disc} , during this period, electric vehicles are discharged according to the discharge power, and the discharge loads of N electric vehicles are accumulated to obtain the total discharge power in the discharge period.

Due to the limitation of discharge time, some electric vehicles are not fully discharged, so the initial state of charge when charging starts is different due to different discharge conditions, and the initial state of charge is uniquely determined by the above discharge conditions. The charging load required by a single electric vehicle is the total energy consumed in a day, that is,

Combined with the charging power of electric vehicles, the charging duration can be obtained, and thus the charging load distribution can be obtained.

(3) Monte Carlo simulation method to solve

The Monte Carlo method is a statistical method for estimating mathematical functions based on random sampling and random simulation. The basic solution idea is: for the problem to be solved, according to the statistical law of the physical phenomenon itself, or artificially construct a suitable probability model dependent on random variables, so that the statistics of some random variables are solutions to the problem. The Monte Carlo method is based on the following two theories:

①The law of large numbers: within the domain [a, b] of the function f(x), randomly select N numbers xi with a uniform probability distribution, and the arithmetic mean of the sum of the function values converges to the expected value of the function. After drawing enough random samples, the Monte Carlo estimate of the integral will converge to the correct result of the integral, i.e. the random variable statistic is:

②Central limit theorem: A random variable formed by the accumulation of a large number of weak factors obeys a single normal distribution. The error ε of the Monte Carlo method depends on the standard deviation σ and the number of samples N, and is proportional to the standard deviation σ and inversely proportional to the square root of the N side of the sample number, namely:

Monte Carlo sampling average approximation is an effective method for solving stochastic optimization, also known as stochastic simulation method, also known as statistical test method, it provides an effective way to verify the constraints in the form of probability, it is mainly used in solving mathematics, For engineering applications and production management, the basic idea of the Monte Carlo sampling average approximation method is: first establish a probability model, make one of its parameters equal to the solution of the problem, and then select random variables according to the assumed distribution Specific values (this process is also called sampling) to construct a deterministic model and calculate the results; then through the results of multiple sampling experiments, the statistical characteristics of the parameters are obtained, and finally the approximate value of the solution is calculated. The calculation process of electric vehicle charging load based on Monte Carlo simulation is shown in Figure 2.

N in the figure is the number of electric vehicles simulated, and n is the electric vehicle calculated by the current simulation. The system input information includes the total scale of electric vehicles, the probability distribution of various charging behaviors, the probability distribution of possible charging periods and initial charging time, and the initial SOC probability distribution corresponding to different types of charging behaviors from multi-duration constraints. For the calculation of the charging load of a single electric vehicle, the charging behavior of the vehicle must first be determined. If the vehicle has multiple charging behaviors, a random number that satisfies the uniform distribution of U(0,1) elaborated by the system, according to the occurrence of different charging behaviors A probability distribution that determines the charging behavior of the vehicle.

(4) Electric vehicle charging and discharging case

The proposed model is verified by taking a microgrid formed by a residential area as an example. There are 400 households in this residential area, and the following assumptions are given:

① On average, every two households own one electric vehicle, that is, the community has 200 electric vehicles;

② The BYD E6 model is selected in this paper, and the parameters are as follows: capacity Q = 60kW h; charging power P _dh = 10kW; power consumption S _1kWh = 4.762km/(kW h); discharge conversion efficiency η = 85%;

③The average daily travel distance of each electric vehicle is 34.76km, and the daily charging capacity of all electric vehicles is 1460kW·h.

④ In order to encourage car owners to participate in the orderly charge and discharge plan of electric vehicles under the guidance of electricity prices, the price of electricity purchased from electric vehicles by the microgrid is proposed to be the price of electricity sold to conventional loads in real time, so that owners of electric vehicles can benefit by about 0.6 yuan for every 1kWh discharged by electric vehicles (The electricity price during off-peak hours when EVs are charging is 0.37 yuan/kWh, and the electricity price during peak hours when EVs are discharging is 1.03 yuan/kWh). With the maturity of the market and the improvement of technology, microgrid managers can appropriately reduce the price of electricity purchased from electric vehicles to recover the cost of modifying charging and discharging devices.

The present invention simulates the orderly charging and discharging load characteristics of electric vehicles of different scales connected to the power grid, because the main function of electric vehicles is still as a means of transportation, taking into account the user's car habits, orderly charging and discharging, 07:00-17 :00 time period, the calculation of charge and discharge load that does not participate in the scheduling of orderly charge and discharge is different from that of disordered charge and discharge in Figure 3, and its simulated daily load curves are shown in Figure 4 respectively.

4. Optimal scheduling strategy for microgrid operation:

The microgrid scheduling objects considered in the present invention include photovoltaic power generation equipment (Photovoltaic, PV), wind turbine (WindTurbine, WT), micro gas turbine (MicroTurbine, MT), diesel engine (DieselGenerator, DG), fuel cell (FuelCell, FC) , storage batteries (StorageBattery, SB), electric vehicles (electricvehicles, EVs) and the main grid for exchanging electric energy. The time-of-use electricity price model is used to formulate the optimal dispatching strategy of the microgrid, and the 24 hours of the day are divided into valley periods (00:00-07:00 and 23:00-24:00) and normal periods (07:00) according to the load conditions of the external power grid. -10:00, 15:00-18:00 and 21:00-23:00) and peak hours (10:00-15:00 and 18:00-21:00). Microgrid scheduling takes 1 hour as a scheduling unit period. First, it predicts the power load and cooling and heating load at the current scheduling time, as well as the output of photovoltaic power generation equipment and wind turbines, and monitors the state of charge of the battery to During the dispatching period, the minimum operating cost of the microgrid, the maximum environmental benefit and the minimum comprehensive cost are the optimization goals. According to different dispatching strategies, the optimization results under different dispatching strategies are obtained, and the active power output status of the controllable power generation units in the microgrid is determined. , The charging and discharging power curve of the battery and the active power exchanged with the main network.

(1) Basic scheduling strategy for microgrid optimal scheduling

①Scheduling strategies for photovoltaic and wind power

Since solar energy and wind power are clean energy and do not cause pollution to the environment, the electricity generated by photovoltaic power generation equipment and wind turbines is given priority, and storage batteries are arranged to stabilize their output power fluctuations, so that their actual output is more in line with the predicted output curve.

②Battery charge and discharge scheduling strategy

The present invention mainly considers the role of the storage battery in the microgrid dispatching system from three aspects:

First, stabilize the fluctuation of wind turbine and photovoltaic output to ensure that they can output according to the predicted output curve;

Second, when the microgrid is connected to the grid, arrange for the battery to be charged during the valley period, disconnected during the normal period, and discharged during the peak period, as shown in Figure 5. Considering that the state of charge of the battery is a periodic cycle every day, that is, after the end of a day's charge and discharge, it will return to the state of charge at the beginning of the day. At the same time, considering the impact of battery charge and discharge on its service life, the lower limit of charge is 20% and the upper limit is 100%;

Third, when the microgrid is operating in an isolated island, when the output of photovoltaic and wind power meets the demand of the power load and there is a surplus, the battery can store excess power; when the output of photovoltaic and wind power cannot meet the power load, the battery can be discharged to supplement Insufficient power supply. Here, the periodicity of battery charge and discharge is also taken into account, and the lower limit of charge is 20%, and the upper limit is 90%.

(2) Microgrid Optimal Dispatch Strategy

Because electric vehicles have the characteristics of storing electric energy, after being connected to a large number of microgrids, they can be regarded as mobile energy storage devices. Through reasonable arrangements, they can replace conventional energy storage devices or backup generator sets to a certain extent, thereby improving the utilization rate of electric vehicles and reducing energy consumption. Investment in building microgrids. The research focus of the present invention is to obtain a feasible micro-grid scheduling strategy, and reasonably arrange electric vehicles to connect to the micro-grid to achieve greater operating economy. Therefore, this paper formulates six optimal scheduling strategies for microgrids considering the integration of electric vehicles.

Strategy 1: The grid-connected operation of the microgrid, the distributed power generation units PV, WT, MT, DG, FC and the main grid jointly participate in optimal scheduling, and the priority is given to the output of photovoltaic power generation equipment PV and wind turbine WT. The micro gas turbine MT operates under the mode of "constant power by heat", the battery SB is discharged during the peak period and charged during the off-peak period, and the diesel generator DG fills up the remaining shortage to jointly meet the power consumption and cooling and heating loads . Electric energy can be exchanged bidirectionally between the microgrid and the main grid.

Strategy 2: Micro-grid island operation, prioritizing the output of photovoltaic power generation equipment PV and wind turbine WT, fuel cell FC operates in the mode of "constant heat by electricity", and micro gas turbine MT operates in the mode of "constant power by heat". When the generated electric energy exceeds the electric load, the battery SB stores excess electric energy; when the generated electric energy cannot meet the electric load, the output power of the diesel generator DG and the battery SB supplements the insufficient power supply;

Strategy 3: The microgrid is connected to the grid, and the electric vehicle adopts the V0G mode of one-way disorderly charging. Only consider the charging situation of electric vehicles EV, that is, regard electric vehicles as pure electricity loads. Electricity loads (conventional loads + electric vehicles) are supplied with electricity by distributed power generation units PV, WT, MT, DG, FC, batteries SB and the main grid.

Strategy 4: The microgrid is connected to the grid, and the electric vehicle adopts the V2G mode of two-way orderly charging. Considering the discharge characteristics of electric vehicles EVs, electric vehicles EVs and batteries SB are charged and stored at a low price during the valley period of the main grid. , FC, and the main grid provide electric energy; during the normal period, the electric vehicle EV is not charged and discharged as a means of transportation, and the regular load is provided by the distributed power generation units PV, WT, MT, DG, FC, battery SB, and the main grid; while in the peak period During the period, electric vehicles start to discharge according to a certain probability to provide electric energy for conventional loads. The distributed power generation units PV, WT, MT, DG, FC and battery SB work together to provide electric energy. If there is excess electric energy, it will be fed back to the main grid.

Strategy 5: The microgrid operates in an isolated island, and electric vehicles adopt the V0G mode of one-way disorderly charging. Only consider the charging situation of electric vehicles EV, that is, regard electric vehicles as pure electricity loads. Electricity loads (conventional loads + electric vehicles) are provided by distributed power generation units PV, WT, MT, DG, FC and battery SB to provide electric energy. When the maximum output of the distributed generation units PV, WT, MT, DG, FC and battery SB cannot meet the power demand of the load, the active power supply and demand of the entire microgrid can be balanced by temporarily removing the interruptible load.

Strategy 6: The microgrid operates in an isolated island, and electric vehicles adopt the V2G mode of two-way orderly charging. Considering the discharge characteristics of electric vehicles EV, when the load is low, the electric vehicle EV is charged and stored in a centralized way to store the electric energy generated by renewable energy. The output provides electric energy; when the load is flat, the electric vehicle EV is not charged and discharged as a means of transportation, and the conventional load is provided by the distributed power generation unit PV, WT, MT, DG, FC and the battery SB to provide electric energy; and when the load peaks, the electric vehicle Discharge starts according to a certain probability, and at the same time, the conventional load is provided by the coordinated output of distributed power generation units PV, WT, MT, DG, FC and battery SB. When the power demand of the load cannot be met, the active power supply and demand of the entire microgrid can be balanced by temporarily removing the interruptible load.

4. Case analysis

(1) Parameter setting

The research time of this case is one day in summer, and the operation plan for the period of 00:00-24:00 of the day is formulated according to the time interval of 1 hour. Figure 6 shows the power generation prediction curves of photovoltaic and wind energy in 24 time sections; the relevant data of each distributed power source are shown in Table 1. Table 2 shows the real-time electricity load demand of the microgrid, and Table 3 shows the real-time cooling load demand of the microgrid; Table 4 shows the segmented electricity price of the distribution network; Table 5 shows the pollutant emission coefficient of each power generation unit .

Table 1 Parameters of distributed power sources in microgrid

Table 2 Microgrid real-time electrical load demand (kW)

Table 3 Microgrid real-time cooling load demand (kW)

Table 4 Microgrid and distribution network electricity price scheme

Note: peak hours: 10:00～15:00, 18:00～21:00; normal hours: 7:00～10:00, 15:00～18:00, 21:00～23:00; Valley hours: 23:00～24:00, 0:00～7:00.

Table 5 Pollutant emission coefficients of each power generation unit

(2) Result analysis and discussion

The total cost of optimal scheduling under different scheduling strategies and different objective functions is shown in Table 6.

Table 6 Comparison of total cost of optimized scheduling results

According to the data in Table 6, make an analysis:

1) In the comparison of scheduling strategies 1, 3, and 4, in the same grid-connected operation state, the total operation cost under the optimization objectives 1, 2, and 3 of the V2G access mode decreased by 8.2% and 8.2%, respectively, compared with that before EVs access. 7.9% and 8.0%, which are 12.8%, 12.0% and 12.4% lower than those using the V0G mode. It shows that in the V0G mode, the electric vehicle simply increases the power load, which only increases the operating cost of the micro-grid; while in the V2G mode, the electric vehicle absorbs the excess energy of the distributed power source as a mobile energy storage device and makes full use of the main power supply. The difference in electricity prices during the peak and valley periods of the grid, centralized charging when the electricity price is low, and peak-shaving discharge when the electricity price is high, reduce the output burden of distributed power sources and the dependence on the main network, as shown in Figure 7-9.

2) In the comparison of scheduling strategies 2, 5, and 6, in the same island operation state, the total operation cost under the optimization targets 1, 2, and 3 of V0G mode increased by 7.6% and 6.2% respectively compared with before EVs access and 7.5%, while the total operating costs under the optimization objectives 1, 2, and 3 of the V2G access mode increased by 9.8%, 12.4%, and 12.9% respectively compared with those before EVs access. It shows that under the island operation, the access of electric vehicles will increase the operating cost of the microgrid. This is because the cost of distributed power output is higher than the cost of purchasing electricity from the main network. At the same time, due to the excessive load of a large number of electric vehicles, Its effect on peak-shaving and valley-filling of conventional electric loads is not enough to make up for the additional operation costs of distributed power sources. Therefore, under short-term island operation, it is necessary to study a more reasonable electric vehicle charging plan.

3) In the comparison of dispatching strategies 3 and 5, the electric vehicles are both in the V0G access mode, and the total cost of the optimization objectives 1, 2, and 3 in the island operation is 20.6% higher than that in the grid-connected operation. 27.3% and 30.5%, this is because the cost of generating electricity from distributed power sources during island operation is higher than the cost of purchasing electricity from the main grid.

4) In the comparison of dispatching strategies 4 and 6, the electric vehicles are also in the V2G access mode, and the total cost of the optimization objectives 1, 2, and 3 in the island operation is also 41.0% higher than that in the grid-connected operation. 53.2% and 56.5%, this is because the power generation cost of distributed power in island operation is higher than the cost of purchasing power from the main network, but it is much more than the higher ratio in V0G mode in analysis (3), This is because the increased load of EVs centralized charging is too large, and a large number of diesel generators are used to output power, which increases the total cost of island operation, especially under the optimization goals 2 and 3 that take into account environmental benefits, the increase reaches 53.2% respectively and 56.5%.

Claims (7)

1. A micro-grid multi-objective optimal scheduling method containing electric vehicles, is characterized in that the method may further comprise the steps: 1) Determine the mode of electric vehicle access to the microgrid, and obtain the discharge and charge distribution characteristics of electric vehicles by superimposing the discharge and charge distribution characteristics of a single electric vehicle under different access modes; 2) Add electric vehicles as micro-grid scheduling objects to micro-grid optimal dispatch, and establish a micro-grid optimal dispatch model considering the large-scale electric vehicle access according to the distribution characteristics of electric vehicles' discharging and charging loads; 3) The particle swarm optimization algorithm based on the automatic reorganization mechanism is used to solve the optimal dispatching model of the microgrid considering the access of large-scale electric vehicles, and the dispatching economics of the microgrid under various dispatching strategies are compared and analyzed to obtain the optimal dispatching strategy. 2. A multi-objective optimization scheduling method for a microgrid including electric vehicles according to claim 1, characterized in that the modes in step 1) include VOG mode of unidirectional disorderly charging and bidirectional ordered charging V2G mode of discharge. 3. A multi-objective optimal dispatching method for a microgrid containing electric vehicles according to claim 1, characterized in that, in said step 2), the optimization objective of the microgrid optimal dispatching model for large-scale electric vehicle access is considered The function is: Microgrid scheduling operation cost Obj ₁ is the smallest: ${\mathrm{<span\; class="notranslate">\; minObj</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; T</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;T</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; E.</span>}\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; \&rsqb;</span>$ The treatment cost of discharged pollutants Obj ₂ is the smallest: ${\mathrm{<span\; class="notranslate">\; minObj</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; T</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;T</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&lsqb;</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&gamma;</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span>$ The comprehensive cost of microgrid dispatching is the lowest: minObj ₃ =m ₁ Obj ₁ +m ₂ Obj ₂ Among them, C _G is the fuel cost of distributed power generation, C _OM is the operation and maintenance cost, C _DP is the depreciation cost of the power generation unit, C _Grid is the power exchange cost between the micro-grid and the large power grid, and C _EV is the power exchange between the micro-grid and electric vehicles C _L is the load outage compensation cost, ΔT _t is the unit time period, T is the dispatch cycle, j is the number of the distributed generation unit in the microgrid, t is the operation period, k is the type of pollutants emitted, C _k In order to deal with the cost of discharging pollutants per kilogram, γ _jk (P _jt ) is the weight of pollutant k produced when the power generation unit j in the microgrid outputs P _jt electric energy, and γ _gridk (P _gridt ) is the distribution network output P _gridt The weight of pollutant k produced by electric energy, m ₁ and m ₂ are the weights of operating cost and emission cost; The constraints are: Power balance constraints: $\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; the\; y</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; t</span>}}$ Cold/Hot Power Balance Constraints: W _Load = W _MT + W _FC The upper and lower limits of the active power P _jt of the distributed generation unit: ${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}^{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}$ Limit constraints of the exchange power P _grid between the microgrid and the main grid: ${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}}^{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; d</span>}}^{\mathrm{<span\; class="notranslate">\; max</span>}}$ The power P _SBt constraint and charge SOC _SBt constraint of the energy storage unit: P _{SBmin ≤} P _{SBt ≤} P _SBmax SOC _SBmin ≤ SOC _SBt ≤ SOC _SBmax SOC _end = SOC ₀ Power P _Evt constraints and charge SOC _EVt constraints of electric vehicles: P _{EVmin ≤P EVt ≤P EVmax} SOC _EVmin ≤ SOC _EVt ≤ SOC _EVmax Among them, P _jt is the power generated by the power generation unit j in the microgrid during the t period, P _gridt is the power transmitted from the distribution network to the microgrid during the t period, P _batteryt is the power generated by the battery during the t period, and P _loadt is the power in the microgrid during the t period. W _Load is the cooling/heating load demand of the entire microgrid system, W _MT is the cooling/heating power provided by the waste heat flue gas of the micro gas turbine, W _FC is the cooling/heating power provided by the heat generated by the fuel cell, is the minimum output power of distributed generation unit j, is the maximum output power of the distributed generation unit j, is the minimum power that can be transmitted by the common point line between the microgrid and the distribution network, is the maximum power that can be transmitted by the common point line between the microgrid and the distribution network, _PSBmin and _PSBmax are the minimum power and maximum power of the battery charging and discharging respectively, and SOC _SBmin and SOC _SBmax are the minimum value of the state of charge of the battery respectively and the maximum value, SOC ₀ and SOC _end are the state of charge of the storage battery at the initial time 0 and the end time 24 in the dispatching cycle of a day respectively, P _EVmin and P _EVmax are the minimum power and maximum power of electric vehicle charging and discharging respectively, SOC _min , SOC _max are the minimum and maximum values of the electric vehicle battery state of charge, respectively. 4. a kind of micro-grid multi-objective optimization scheduling method containing electric vehicles according to claim 2 is characterized in that, under the VOG mode of unidirectional disorderly charging, the discharge and charge load distribution characteristics of electric vehicles The power requirement is: E _EV ＝η _EV ·d Among them, η _EV is the electricity demand coefficient per unit mileage of electric vehicles, and the mileage d of each electric vehicle obeys the logarithmic normal distribution, and its probability density function is: $\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; d\&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; ln</span>}\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}$ The charging time f _s (x) satisfies the normal distribution: ${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\frac{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}<span\; class="notranslate">\; ,</span>& <span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}\\ \frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\frac{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; <</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>\end{array}\right.$ Among them, μ _d and μ _s are expected values, and σ _d and σ _s are standard deviations. 5. A multi-objective optimal scheduling method for a microgrid containing electric vehicles according to claim 2, characterized in that, in the V2G mode of bidirectional orderly charging and discharging, the discharge in the discharge and charge load distribution characteristics of electric vehicles lasts Time T _disc1 is: ${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; DW</span>}}_{\mathrm{<span\; class="notranslate">\; 100</span>}}}{\mathrm{<span\; class="notranslate">\; 100</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}}}$ ${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; SOC</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; SOC</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}}}$ Combined with the charging power of the electric vehicle, the charging duration can be obtained, so that the continuous charging time T _disc2 can be obtained as: ${\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; E.</span>}\mathrm{<span\; class="notranslate">\; V</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; c</span>}}}$ The charging load required by a single electric vehicle is the total energy consumed in a day, that is, P _EV is: ${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; E.</span>}\mathrm{<span\; class="notranslate">\; V</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; DW</span>}}_{\mathrm{<span\; class="notranslate">\; 100</span>}}}{\mathrm{<span\; class="notranslate">\; 100</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; )</span>$ Among them, T _all-disc is the total discharge duration required to discharge the electric vehicle to the lower limit of the state of charge when the electric vehicle is fully charged, P _disc is the discharge power of the electric vehicle, SOC _max and SOC _min are the upper and lower limits of the battery state of charge, respectively, and D is The daily driving mileage of electric vehicles, W ₁₀₀ is the power consumption of electric vehicles per 100 kilometers, T _{end_disc} is the discharge end time, T _{start_disc} is the grid discharge time, P _c is the charging power of electric vehicles. 6. A multi-objective optimal dispatching method for a microgrid including electric vehicles according to claim 1, characterized in that the various dispatching strategies in step 2) include microgrid grid-connected operation strategy and microgrid Grid island operation strategy. 7. A multi-objective optimal dispatching method for a microgrid containing electric vehicles according to claim 1, wherein said step 3) specifically includes the following steps: 31) According to the access mode of electric vehicles, set the model parameters, objective function parameters and constraint parameters of each distributed power generation unit in the microgrid, and introduce uncontrollable and predictable distributed power output and cooling power load parameters; 32) Use the controllable output unit micro gas turbine, fuel cell, diesel generator, battery and main grid interchangeable power as five-dimensional particles, and set the parameters of the particle swarm algorithm, including the number of particles, the dimension of the solution space, the maximum number of iterations, The maximum particle velocity and the recombination index r; 33) Calculate the fitness of each particle, and record the current individual extremum and the corresponding objective function value of each particle, and then obtain the overall extremum and the corresponding objective function value, and select the individual optimal value and the global The optimal value; 34) Add one to the current number of iterations, and update the position and speed of the particle swarm; 35) Judging whether the result meets the premature convergence standard and whether the number of reorganizations reaches the preset value, if the result meets the premature convergence standard and the number of reorganizations does not reach the preset value, then reorganize the particle swarm and the reorganization index r=r+ 1, and return to step 33), otherwise go to step 36); 36) Judging whether the convergence condition is satisfied, if so, obtain the global optimum or reach the maximum number of iterations, end the iterative process, if not, return to step 33), and continue the iterative operation.