CHARGING AND DISCHARGING SCHEDULING METHOD FOR ELECTRIC VEHICLES IN MICROGRID UNDER TIME-OF-USE PRICE

Abstract

A charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price includes: determining the system structure of the microgrid and the characters of each unit; establishing the optimal scheduling objective function of the microgrid considering the depreciation cost of the electric vehicle (EV) battery under time-of-use price; determining the constraints of each distributed generator and EV battery, and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid; determining the amount, starting and ending time, starting and ending charge state, and other basic calculating data of the EV accessing the microgrid under time-of-use price; determining the charge and discharge power of the EV when accessing the grid, by solving the optimal scheduling model of the microgrid with a particle swarm optimization algorithm.

Claims

1. A charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price, characterized in that, the method comprises: step 1, determining the system structure of the microgrid and the characters of each unit; step 2, establishing an optimal scheduling objective function of the microgrid considering the depreciation cost of the electric vehicle battery under time-of-use price; step 3, determining the constraint conditions of each distributed generator and the electric vehicle battery; and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid; step 4, determining the amount, the starting and ending time, the starting and ending state of charge, and other basic calculating data of the electric vehicle accessing the grid under time-of-use price; step 5, determining the charge and discharge power of the electric vehicle when accessing the grid, by solving the optimal scheduling model of the microgrid with the particle swarm optimization algorithm, wherein the system structure of the microgrid in the step 1 comprises: photovoltaic unit PV, wind turbine unit WT, diesel generator DG, micro turbine MT, electric vehicle EV, the characters of each unit comprise: the output power P.sub.PV of the PV, is obtained through equation (1): $\begin{array}{cc}{P}_{\mathrm{PV}}=P_{\mathrm{STC}}.Math.\frac{G_{\mathrm{ING}}}{G_{\mathrm{STC}}}\left[1+k\left(T_c-T_r\right)\right]& \left(1\right)\end{array}$ in equation (1), G.sub.ING is the actual light intensity received by the PV, G.sub.STC is the light intensity received by the PV under the standard test condition, P.sub.STC is the maximum output power of the PV under the standard test condition, k is the coefficient of power generation temperature of the PV, T.sub.c is the actual battery temperature of the PV, T.sub.r is the rated battery temperature of the PV; the output power P.sub.WT of the WT, is obtained through equation (2): $\begin{array}{cc}{P}_{\mathrm{WT}}=\{\begin{array}{cc}0& V<V_{\mathrm{ci}}\\ a\times V^3-b\times P_r& V_{\mathrm{ci}}<V<V_r\\ P_r& V_r<V<V_{\mathrm{co}}\\ 0& V>V_{\mathrm{co}}\end{array}& \left(2\right)\end{array}$ in equation (2), a and b represent the coefficient of the output power P.sub.WT of the WT, respectively; and $a=\frac{P_r}{V_r^3-V_{\mathrm{ci}}^3},b=\frac{V_{\mathrm{ci}}^3}{V_r^3-V_{\mathrm{ci}}^3};$ V.sub.ci, V.sub.r and V.sub.co represent the cut-in wind speed, the rated wind speed and the cut-out wind speed of the WT, respectively; P.sub.r is the rated output power of the WT; the fuel cost C.sub.DG of the DG, is obtained through equation (3):
C.sub.DG=Σ(α+βP.sub.DG(t)+γP.sub.DG.sup.2(t))Δt  (3) in equation (3), α, β and γ are parameters of the DG; P.sub.DG(t) is the output power of the DG at time t; Δt is the duration of each time interval; the efficiency function η.sub.MT of the MT, is obtained through equation (4): $\begin{array}{cc}{\eta }_{\mathrm{MT}}={x\left(\frac{P_{\mathrm{MT}}}{P_R}\right)}^3+{y\left(\frac{P_{\mathrm{MT}}}{P_R}\right)}^2+z\left(\frac{P_{\mathrm{MT}}}{P_R}\right)+c& \left(4\right)\end{array}$ in equation (4), x, y, z and c are parameters of the MT; P.sub.R and P.sub.MT are the rated power and output power of the MT, respectively; the cost function C.sub.MT of the MT, is obtained through equation (5): $\begin{array}{cc}{C}_{\mathrm{MT}}=\frac{C_{\mathrm{GAS}}}{\mathrm{LHV}}.Math..Math.\frac{P_{\mathrm{MT}}\left(t\right).Math.\Delta .Math..Math.t}{{\eta }_{\mathrm{MT}}\left(t\right)}& \left(5\right)\end{array}$ in equation (5), C.sub.GAS is the price of the natural gas supplied to the MT; LHV is the low heating value of the natural gas; P.sub.MT(t) is the output power of the MT at time t; η.sub.MT(t) is the power generation efficiency of the MT at time t; and wherein the optimal scheduling objective function of the microgrid in the step 2 is: $\begin{array}{cc}\min .Math..Math.C=\underset{i=1}{\overset{N}{.Math.}}.Math..Math.\underset{t=1}{\overset{T}{.Math.}}.Math..Math.\left[F_i\left(P_i\left(t\right)\right)+{\mathrm{OM}}_i\left(P_i\left(t\right)\right)\right]+C_{\mathrm{GRID}}+C_{\mathrm{BAT}}& \left(6\right)\end{array}$ in equation (6), C is the total operation cost of the microgrid; N is the total amount of the distributed generators within the microgrid; T is the total amount of the time intervals of a scheduling cycle of the microgrid; t is the number of the time intervals; P.sub.i(t) is the output power of distributed generator i within time interval t; F.sub.i(P.sub.i(t)) is the fuel cost of distributed generator i within time interval t; OM.sub.i(P.sub.i(t)) is the operation and maintenance cost of distributed generator i within time interval t, and is obtained through equation (7):
OM.sub.i(P.sub.i(t))=K.sub.OM.sub.iP.sub.i(t)  (7) in equation (7), K.sub.OM.sub.i is an operation and maintenance cost coefficient of distributed generator i; in equation (6), C.sub.GRID is the transaction cost of the microgrid with the main grid, and is obtained through equation (8): $\begin{array}{cc}{C}_{\mathrm{GRID}}=\underset{t=1}{\overset{T}{.Math.}}.Math..Math..Math.P_{\mathrm{GRID}}\left(t\right).Math..Math.S_t& \left(8\right)\end{array}$ in equation (8), P.sub.GRID(t) is the interactive energy of the microgrid with the main grid within time interval t; S.sub.t represents the electricity price of the main grid within time interval t; in equation (6), C.sub.BAT is the battery depreciation cost of the electric vehicle EV, and is obtained through equation (9): $\begin{array}{cc}{C}_{\mathrm{BAT}}=\underset{j=1}{\overset{n}{.Math.}}.Math..Math.\left(\frac{C_{\mathrm{REP}}}{E_{\mathrm{PUT}}}.Math.{\int }_{t_{j.Math..Math.1}}^{t_{j.Math..Math.2}}.Math..Math.P_j^{\mathrm{EV}}\left(t\right).Math..Math.\mathrm{dt}\right)& \left(9\right)\end{array}$ in equation (9), n is the amount of the EVs accessing the microgrid, C.sub.REP is the battery replacement cost of the EV, E.sub.PUT is the total energy throughput of the EV during the lifetime of the battery thereof, t.sub.j1 and t.sub.j2 are the starting and ending time of EV j accessing the microgrid, P.sub.j.sup.EV(t) is the charge or discharge power of the battery of EV j within time interval t after accessing the microgrid.

2-3. (canceled)

4. The charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price according to claim 1, characterized in that, the constraint conditions of the distributed generator and the EV batteries in the step 3 are: $\begin{array}{cc}\underset{i=1}{\overset{N}{.Math.}}.Math..Math.P_i+P_{\mathrm{GRID}}+P_{\mathrm{EV}}=P_{\mathrm{LOAD}}& \left(10\right)\end{array}$
P.sub.i.sup.min≦P.sub.i≦P.sub.i.sup.max  (11)
|P.sub.i(t)−P.sub.i(t−1)|≦r.sub.i  (12)
SOC.sub.j.sup.min≦SOC.sub.j≦SOC.sub.j.sup.max  (13)
P.sub.j.sup.min≦P.sub.j≦P.sub.j.sup.max  (14)
SOC.sub.t.sub.j2≧SOC.sub.t.sub.j2.sup.min  (15)
P.sub.L.sup.min≦P.sub.GRID(t)≦P.sub.L.sup.max  (16) equation (10) represents power equilibrium constraint; P.sub.i is the actual output power of distributed generator i; P.sub.GRID is the actual interactive energy of the microgrid with the main grid; P.sub.EV is the net output power of all the EVs in the microgrid; P.sub.LOAD is the total load demand of the microgrid users; equation (11) represents the constraint of the own power generation capacity of the distributed generator i, wherein P.sub.i.sup.max and P.sub.i.sup.min are the upper and lower limit of the output power of the distributed generator i, respectively; equation (12) represents the ramping-rate constraint of the distributed generator i, wherein P.sub.i(t−1) is the output power of the distributed generator i within time interval t−1; r.sub.i is the maximum ramping rate of the distributed generator i; equation (13) represents the state of charge constraint of EV j; SOC.sub.j represents the state of charge of the battery of the EV j; SOC.sub.j.sup.max and SOC.sub.j.sup.min represent the upper and lower limit of the state of charge of the battery of the EV j, respectively; equation (14) represents the charge and discharge power constraint of the EV; P.sub.j.sup.max represents the upper limit of the discharge power of the EV j; P.sub.j.sup.min represents the lower limit of the charge power of the EV j; equation (15) represents the state of charge constraint of the EV j at the ending time of accessing the microgrid; SOC.sub.t.sub.j2 is the state of charge of the EV j when leaving the microgrid at time t.sub.j2, SOC.sub.t.sub.j2.sup.min is the minimum state of charge which meets the driving demands when the EV j leaves the microgrid; equation (16) is the transmission capacity constraint of the connection lines between the microgrid and the main grid; P.sub.L.sup.min is the lower limit of the transaction power from the microgrid to the grid, P.sub.L.sup.max is the upper limit of the transaction power from the grid to the microgrid.

5. The charging and discharging scheduling method for electric vehicles in microgrid under time-of-use according to claim 1, characterized in that, the step 4 comprises: step 4.1, dividing 24 hours of one day into three time periods: peak time period, flat time period and valley time period according to the peak-valley time-of-use price applied by the main grid; step 4.2, determining the total load demand P.sub.LOAD and output power of the PV and the WT, respectively; step 4.3, determining the total energy throughput E.sub.PUT of the EV during the lifetime of the battery thereof, the battery replacement cost C.sub.REP of the EV, the amount n of the EVs accessing the microgrid, the starting and ending time t.sub.j1 and t.sub.j2 of EV j accessing the microgrid, the state of charge SOC.sub.t.sub.j1 when accessing and the minimum state of charge SOC.sub.t.sub.j2.sup.min required when leaving the microgrid, respectively; step 4.4, determining the parameters α, β and γ of the DG, the parameters x, y, z, c and the rated power P.sub.R of the MT, the price of natural gas C.sub.GAS, the low heating value of the natural gas LHV and the operation and maintenance cost coefficient K.sub.OM.sub.i of distributed generator i which has accessed the microgrid.

6. The charging and discharging scheduling method for electric vehicles in microgrid under time-of-use according to claim 1, characterized in that, the step 5 comprises: step 5.1, taking the output powers of the DG and the MT, the interactive power of the main grid and the microgrid, and the exchanged power of each EV with the microgrid in every moment, as one dimension of particle k, such that the number of the dimension T(n+3) of particle k is obtained; step 5.2, initializing each parameter of the particle swarm optimization algorithm, the parameters including: the total number M of the particles, the number of iterations L, the maximum number of iterations L.sub.max, the speed updating parameters c.sub.1 and c.sub.2, 1≦L≦L.sub.max, and initializing L=1; step 5.3, determining the actual value of each constraint condition in the step 3 and the basic parameters in the step 4, and substituting them into the constraint conditions and objective functions of the particle swarm optimization algorithm, respectively; step 5.4, producing the initial population, obtaining the position and speed of particle k of generation L, and modifying the position and speed of the particles according to the constraint conditions in the step 3; step 5.5, calculating the fitness value of particle k in accordance with objective function minC, and selecting the maximum fitness value among M particles of generation L as the group extreme value of generation L; step 5.6, calculating respectively the position and speed of particle k of generation L+1 according to the position and speed of particle k of generation L, and modifying the position and speed of the particles according to the constraint conditions in the step 3, so as to obtain the positions and speeds of M particles in the particle group of generation L+1; step 5.7, recalculating the fitness value of particle k of generation L+1, and comparing it with the fitness value of particle k of generation L, selecting the larger fitness value as the individual extreme value of particle k of generation L+1; and selecting the maximum fitness value among the individual extreme values of M particles of generation L+1, as the group extreme value of generation L+1; step 5.8, assigning L+1 to L, and judging whether L<L.sub.max, if yes, proceeding to the step 5.6; otherwise stopping the iterations, and obtaining the group extreme value of generation L.sub.max; step 5.9, taking the scheduling solution corresponding to the group extreme value of generation L.sub.max, as the optimal scheduling solution, such that the charge and discharge powers of the EV accessing the microgrid at different times under the constraint conditions are obtained.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0062] FIG. 1 illustrates the whole structure of the present invention;

[0063] FIG. 2 is a flowchart illustrating the particle swarm optimization algorithm of the present invention.

DETAILED DESCRIPTION

[0064] In this embodiment, a charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price comprises:

[0065] Step 1, determining the system structure of the microgrid and the characters of each unit:

[0066] As shown in FIG. 1, the system structure of the microgrid comprises: a photovoltaic unit PV, a wind turbine unit WT, a diesel generator DG, a micro turbine MT, electric vehicles EVs;

[0067] The characters of each unit comprise:

[0068] The output power P.sub.PV of the PV, is obtained through equation (1):

[00010]$\begin{array}{cc}{P}_{\mathrm{PV}}=P_{\mathrm{STC}}.Math.\frac{G_{\mathrm{ING}}}{G_{\mathrm{STC}}}\left[1+k\left(T_c-T_r\right)\right]& \left(1\right)\end{array}$

[0069] In equation (1), G.sub.ING is the actual light intensity received by the PV, G.sub.STC is the light intensity received by the PV under the standard test condition, P.sub.STC is the maximum output power of PV under the standard test condition, k is the coefficient of power generation temperature of the PV, T.sub.c is the actual battery temperature of the PV, T.sub.r is the rated battery temperature of the PV;

[0070] The output power P.sub.WT of the WT, is obtained through equation (2):

[00011]$\begin{array}{cc}{P}_{\mathrm{WT}}=\{\begin{array}{cc}0& V<V_{\mathrm{ci}}\\ a\times V^3-b\times P_r& V_{\mathrm{ci}}<V<V_r\\ P_r& V_r<V<V_{\mathrm{co}}\\ 0& V>V_{\mathrm{co}}\end{array}& \left(2\right)\end{array}$

[0071] In equation (2), a and b are the coefficient of the output power P.sub.WT of the WT; and

[00012]$a=\frac{P_r}{V_r^3-V_{\mathrm{ci}}^3},b=\frac{V_{\mathrm{ci}}^3}{V_r^3-V_{\mathrm{ci}}^3};$

V.sub.ci, V.sub.r and V.sub.co represent the cut-in wind speed, the rated wind speed and the cut-out wind speed of the WT, respectively; P.sub.r is the rated output power of the WT;

[0072] Wherein, the PV and the WT both apply the control method of maximum power tracking output, which enables to make full use of solar energy and wind energy;

[0073] The fuel cost C.sub.DG of the DG, is obtained through equation (3):

C.sub.DG=Σ(α+βP.sub.DG(t)+γP.sub.DG.sup.2(t))Δt  (3)

[0074] In equation (3), α, β and γ are parameters of the DG, and are determined by the type of the DG, for example, the fuel cost function of a certain DG is C.sub.DG=Σ(150+0.12P.sub.DG(t)+00.00085P.sub.DG.sup.2(t))Δt; P.sub.DG(t) is the output power of the DG at time t; Δt is the duration of each time interval;

[0075] The efficiency function η.sub.MT of the MT, is obtained through equation (4):

[00013]$\begin{array}{cc}{\eta }_{\mathrm{MT}}={x\left(\frac{P_{\mathrm{MT}}}{P_R}\right)}^3+{y\left(\frac{P_{\mathrm{MT}}}{P_R}\right)}^2+z\left(\frac{P_{\mathrm{MT}}}{P_R}\right)+c& \left(4\right)\end{array}$

[0076] In equation (4), x, y, z and c are parameters of the MT, and are obtained by fitting the efficiency curves provided from the manufacturers, different parameters are obtained by fitting different types of turbine, for example, the efficiency function of a certain MT is

[00014]${\eta }_{\mathrm{MT}}=0.0753.Math.{\left(\frac{P_{\mathrm{MT}}}{65}\right)}^3-0.3095.Math.{\left(\frac{P_{\mathrm{MT}}}{65}\right)}^2+0.4174.Math.\left(\frac{P_{\mathrm{MT}}}{65}\right)+0.1068;$

P.sub.R and P.sub.MT are the rated power and output power of the MT, respectively;

[0077] The cost function C.sub.MT of the MT, is obtained through equation (5):

[00015]$\begin{array}{cc}{C}_{\mathrm{MT}}=\frac{C_{\mathrm{GAS}}}{\mathrm{LHV}}.Math..Math.\frac{P_{\mathrm{MT}}\left(t\right).Math.\Delta .Math..Math.t}{{\eta }_{\mathrm{MT}}\left(t\right)}& \left(5\right)\end{array}$

[0078] In equation (5), C.sub.GAS is the price of the natural gas supplied to the MT, the gas cost per unit could be C.sub.GAS=0.4; LHV is the low heating value of the natural gas, which normally is 9.73 kwh/m.sup.3; the low heating value refers to the heat released when the vapor generated by burning the fuel containing hydrogen keeps a gaseous state; P.sub.MT(t) is the output power of the MT at time t; η.sub.MT(t) is the power generation efficiency of the MT at time t.

[0079] Step 2, establishing an optimal scheduling objective function of the microgrid considering the depreciation cost of the EV battery under time-of-use price;

[0080] The optimal scheduling objective function of the microgrid is:

[00016]$\begin{array}{cc}\min .Math..Math.C=\underset{i=1}{\overset{N}{.Math.}}.Math..Math.\underset{t=1}{\overset{T}{.Math.}}.Math..Math.\left[F_i\left(P_i\left(t\right)\right)+{\mathrm{OM}}_i\left(P_i\left(t\right)\right)\right]+C_{\mathrm{GRID}}+C_{\mathrm{BAT}}& \left(6\right)\end{array}$

[0081] In equation (6), C is the total operation cost of the microgrid; N is the total amount of the distributed generators within the microgrid; T is the total amount of the time intervals of a scheduling cycle of the microgrid; t is the number of the time intervals; P.sub.i(t) is the output power of distributed generator i within time interval t; F.sub.i(P.sub.i(t)) is the fuel cost of distributed generator i within time interval t; OM.sub.i(P.sub.i(t)) is the operation and maintenance cost of distributed generator i within time interval t, and is obtained through equation (7):

OM.sub.i(P.sub.i(t))=K.sub.OM.sub.iP.sub.i(t)  (7)

[0082] In equation (7), K.sub.OM.sub.i is an operation and maintenance cost coefficient of distributed generator i, and the table below is the operation and maintenance cost coefficients of a certain set of distributed generators:

TABLE-US-00001 TYPE PV WT DG MT Operation and Maintenance 0.0096 0.0296 0.088 0.0293 Cost Coefficient K.sub.OM/Yuan .Math. (kWh).sup.−1

[0083] In equation (6), C.sub.GRID is the transaction cost of the microgrid with the main grid, and is obtained through equation (8):

[00017]$\begin{array}{cc}{C}_{\mathrm{GRID}}=\underset{i=1}{\overset{T}{.Math.}}.Math..Math..Math.P_{\mathrm{GRID}}\left(t\right).Math..Math.S_t& \left(8\right)\end{array}$

[0084] In equation (8), P.sub.GRID(t) is the interactive energy of the microgrid with the main grid within time interval t; S.sub.t represents the electricity price of the main grid within time interval t, the positive value thereof represents the purchasing price of electricity, and the negative value represents the selling price of electricity;

[0085] In equation (6), C.sub.BAT is the battery depreciation cost of an EV, wherein under normal circumstances, as the discharge depth increases, the number of times of recyclable charge and discharge of an EV battery reduces, which brings difficulty in calculating cycle times, but since the total recyclable charge and discharge amount of the battery basically keeps constant, the charge and discharge depreciation cost of the EV battery is obtained through equation (9):

[00018]$\begin{array}{cc}{C}_{\mathrm{BAT}}=\underset{j=1}{\overset{n}{.Math.}}.Math..Math.\left(\frac{C_{\mathrm{REP}}}{E_{\mathrm{PUT}}}.Math.{\int }_{t_{j.Math..Math.1}}^{t_{j.Math..Math.2}}.Math..Math.P_j^{\mathrm{EV}}\left(t\right).Math..Math.\mathrm{dt}\right)& \left(9\right)\end{array}$

[0086] In equation (9), n is the amount of the EVs accessing the microgrid, C.sub.REP is the battery replacement cost of the EV, E.sub.PUT is the total energy throughput of the EV during the lifetime of the battery thereof,

[00019]$E_{\mathrm{PUT}}=\frac{\underset{l=1}{\overset{m}{.Math.}}.Math..Math.E_r.Math.h_{{\mathrm{DOD}}_i}.Math.M_l.Math.\mathrm{•2}}{m},$

wherein, E.sub.r is the rated capacity of the EV battery, m is the amount of the EV batteries tested at different discharge depth, h.sub.DOD.sub.i is the discharge depth of the EV battery in test l, M.sub.l is the total cycle times of the EV battery in test l, wherein the cycle times of different discharge depths and the corresponding total energy throughput are provided by the manufacturer; t.sub.j1 and t.sub.j2 are the starting and ending time of EV j accessing the microgrid, P.sub.j.sup.EV(t) is the charge and discharge power of the battery of EV j within time interval t after accessing the microgrid, wherein a positive value represents discharge, and a negative value represents charge.

[0087] Step 3, determining the constraint conditions of each distributed generator and EV battery; and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid;

[0088] The constraint conditions of the distributed generators and the EV batteries are:

[00020]$\begin{array}{cc}\underset{i=1}{\overset{N}{.Math.}}.Math..Math.P_i+P_{\mathrm{GRID}}+P_{\mathrm{EV}}=P_{\mathrm{LOAD}}& \left(10\right)\end{array}$
P.sub.i.sup.min≦P.sub.i≦P.sub.i.sup.max  (11)

|P.sub.i(t)−P.sub.i(t−1)|≦r.sub.i  (12)

SOC.sub.j.sup.min≦SOC.sub.j≦SOC.sub.j.sup.max  (13)

P.sub.j.sup.min≦P.sub.j≦P.sub.j.sup.max  (14)

SOC.sub.t.sub.j2≧SOC.sub.t.sub.j2.sup.min  (15)

P.sub.L.sup.min≦P.sub.GRID(t)≦P.sub.L.sup.max  (16)

[0089] Equation (10) represents power equilibrium constraint; P.sub.i is the actual output power of distributed generator i; P.sub.GRID is the actual interactive energy of the microgrid with the main grid; P.sub.EV is the net output power of all the EVs in the microgrid; P.sub.LOAD is the total load demand of the microgrid users;

[0090] Equation (11) represents the constraint of the own power generation capacity of distributed generator i, wherein P.sub.i.sup.max and P.sub.i.sup.min are the upper and lower limit of the output power of the distributed generator i, respectively;

[0091] Equation (12) represents the ramping-rate constraint of distributed generator i, wherein, P.sub.i(t−1) is the output power of the distributed generator i within time interval t−1; r.sub.i is the maximum ramping rate of the distributed generator i;

[0092] Equation (13) represents the state of charge constraint of EV j; SOC.sub.j represents the state of charge of the battery of the EV j; SOC.sub.j.sup.max and SOC.sub.j.sup.min represent the upper and lower limit of the state of charge of the battery of the EV j, respectively;

[0093] Equation (14) represents the charge and discharge power constraint of the EV; P.sub.j.sup.max represents the upper limit of the discharge power of EV j; P.sub.j.sup.min represents the lower limit of the charge power of EV j, which is normally determined by the type of the EV battery;

[0094] Equation (15) represents the state of charge constraint of EV j at the ending time of accessing the microgrid; SOC.sub.t.sub.j2 is the state of charge of the EV j when leaving the microgrid at time t.sub.j2, SOC.sub.t.sub.j2.sup.min is the minimum state of charge which meets the driving demands when the EV j leaves the microgrid, which is set independently according to the demands by the owner of the EV;

[0095] Equation (16) is the transaction capacity constraint of the connection lines between the microgrid and the main grid; P.sub.L.sup.min is the lower limit of the transaction power from the microgrid to the grid, P.sub.L.sup.max is the upper limit of the transaction power from the grid to the microgrid.

[0096] Step 4, determining the amount, the starting and ending time, the starting and ending state of charge, and other basic calculating data of the EV accessing the microgrid under time-of-use price;

[0097] Step 4.1, dividing 24 hours of one day into three time periods: peak time period, flat time period and valley time period according to the peak-valley time-of-use price applied by the main grid;

[0098] Step 4.2, determining the total load demand P.sub.LOAD and output power of the PV and the WT, respectively;

[0099] Step 4.3, determining the total energy throughput E.sub.PUT of the EV during the lifetime of the battery thereof, the battery replacement cost C.sub.REP of the EV, the amount n of the EVs accessing the microgrid, the starting and ending time t.sub.j1, and t.sub.j2 of EV j accessing the microgrid, the state of charge SOC.sub.t.sub.j1 when accessing and the minimum state of charge SOC.sub.t.sub.j2.sup.min required when leaving the microgrid, respectively;

[0100] Step 4.4, determining the parameters α, β and γ of the DG, the parameters x, y, z, c and the rated power P.sub.R of the MT, the price of natural gas C.sub.GAS, the low heating value of the natural gas LHV and the operation and maintenance cost coefficient K.sub.OM.sub.i of distributed generator i which has accessed the microgrid.

[0101] Step 5, as shown in FIG. 2, determining the charge and discharge power of the EV when accessing the microgrid, by solving the optimal scheduling model of the microgrid with the PSO algorithm.

[0102] Step 5.1, taking the output powers of the DG and the MT, the interactive power of the main grid and the microgrid, and the exchanged power of each EV with the microgrid in every moment, as one dimension of particle k, such that the number of the dimension T(n+3) of particle k is obtained;

[0103] Step 5.2, initializing each parameter of the PSO, the parameters including: the total number M of the particles, the number of iterations L, the maximum number of iterations L.sub.max, the speed updating parameters c.sub.1 and c.sub.2, 1≦L≦L.sub.max, and initializing L=1;

[0104] Step 5.3, determining the actual value of each constraint condition in Step 3 and the basic parameters in Step 4, and substituting them into the constraint conditions and objective functions of the PSO, respectively;

[0105] Step 5.4, producing the initial population, obtaining the position and speed of particle k of generation L, and modifying the position and speed of the particles according to the constraint conditions in Step 3;

[0106] Step 5.5, calculating the fitness value of particle k in accordance with objective function minC, and selecting the maximum fitness value among M particles of generation L as the group extreme value of generation L;

[0107] Step 5.6, calculating respectively the position and speed of particle k of generation L+1 according to the position and speed of particle k of generation L, and modifying the position and speed of the particles according to the constraint conditions in Step 3, so as to obtain the positions and speeds of M particles in the particle group of generation L+1;

[0108] Step 5.7, recalculating the fitness value of particle k of generation L+1, and comparing it with the fitness value of particle k of generation L, selecting the larger fitness value as the individual extreme value of particle k of generation L+1; and selecting the maximum fitness value among the individual extreme values of M particles of generation L+1, as the group extreme value of generation L+1;

[0109] Step 5.8, assigning L+1 to L, and judging whether L<L.sub.max, if yes, proceeding to Step 5.6; otherwise stopping the iterations, and obtaining the group extreme value of generation L.sub.max;

[0110] Step 5.9, taking the scheduling solution corresponding to the group extreme value of generation L.sub.max, as the optimal scheduling solution, such that the charge and discharge powers of EV accessing the microgrid at different times under the constraint conditions are obtained.