CN109050284B - Electric automobile charging and discharging electricity price optimization method considering V2G - Google Patents
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Abstract
The invention discloses a V2G-considered electric vehicle charging and discharging electricity price optimization method, which comprises the following steps: establishing a mathematical model according to the basic structure of the multi-microgrid system, and obtaining the charge and discharge cost of the electric automobile, the operation cost of the multi-microgrid system and the optimization constraint condition according to the mathematical model; and establishing a double-ring optimization model of the multi-microgrid system, wherein the optimal charging and discharging power of the electric automobile obtained by optimizing the inner ring is used for calculating the operation cost of the multi-microgrid system in the outer ring optimization, the optimal charging and discharging electricity price of the electric automobile obtained by optimizing the outer ring is used for calculating the charging and discharging cost of the electric automobile, and the double-ring optimization model is operated to enable the inner ring optimization and the outer ring optimization to be cycled for multiple times to obtain the minimum charging and discharging cost of the electric automobile, the optimal charging and discharging power of the electric automobile, the minimum operation cost of the multi-microgrid system and the optimal charging and discharging electricity price of. The invention further improves the economy of the overall operation of the system by considering the game among different micro-grids in the regional micro-grid system.
Description
Technical Field
The invention belongs to the field of economic operation of a multi-microgrid system, and particularly relates to a V2G-considered method for optimizing the charge and discharge electricity price of an electric vehicle.
Background
As an important way to ensure energy safety and transformation low-carbon economy, research on electric vehicles and related contents thereof is receiving wide attention from countries in the world. The traditional fuel oil automobile can generate a large amount of pollution due to the combustion of fossil energy, and can cause bad influence on the environment; and the electric automobile is driven by electric energy, so that the problems of carbon emission and pollution emission are well solved. However, the large amount of electric vehicles connected to the power grid may cause a large impact on the safe and stable operation of the power system, and therefore, it is necessary to adopt a proper manner to schedule the electric vehicles, so as to reduce the negative impact of the charging of the electric vehicles on the power grid.
On the other hand, with the enhancement of energy saving and environmental protection awareness and the gradual maturity of related technologies, a micro-grid system constructed by using renewable energy as a main energy source is being widely applied. The micro-grid has small scale and mainly adopts renewable energy sources such as wind power, photovoltaic and the like, so the micro-grid has the characteristics of low carbon, environmental protection and flexible operation. However, the renewable energy has the characteristics of unstable output and large influence of weather, so that expensive energy storage equipment needs to be equipped, and the economical efficiency of system operation is reduced.
In order to solve the above problems, electric vehicle network access technology (V2G) has attracted considerable attention. The electric automobile is used as energy storage equipment, and meanwhile, the energy is moved between a plurality of micro-grids in a space-time mode by utilizing the mobile energy storage characteristic of the electric automobile. At present, a great deal of research is devoted to optimizing the operation of the electric vehicle by controlling the charging and discharging power of the electric vehicle so as to improve the stability and the economy of the operation of the system, but the focused targets of the research are limited to scheduling and controlling the charging and discharging of the electric vehicle by one side, and the charging and discharging behaviors of the electric vehicle are guided by adjusting the charging and discharging quotations of each micro-grid to the electric vehicle, so that the economy of the overall operation of the system is further improved. Therefore, the prior art has the technical problem of poor economical efficiency of the overall operation of the system.
Disclosure of Invention
In view of the above defects or improvement needs in the prior art, the invention provides a method for optimizing the charge and discharge electricity price of an electric vehicle by considering V2G, thereby solving the technical problem of poor economy of the overall operation of the system in the prior art.
In order to achieve the above object, the present invention provides a method for optimizing charge/discharge electricity prices of an electric vehicle considering V2G, comprising:
(1) establishing a mathematical model according to the basic structure of the multi-microgrid system, and obtaining the charge and discharge cost of the electric automobile, the operation cost of the multi-microgrid system and the optimization constraint condition according to the mathematical model;
(2) the method comprises the steps that the charging and discharging cost of the electric automobile is the minimum, the charging and discharging power of all electric automobiles in each micro-grid system in each hour is used as an optimization variable, the electric automobile is economically scheduled, and the optimal charging and discharging power of the electric automobile is obtained;
(3) obtaining an initial discharge electricity price of the electric automobile and an initial charge electricity price of the electric automobile according to the purchase and sale electric energy quotation between the power distribution network and the multi-microgrid system, taking the discharge electricity price of the electric automobile and the charge electricity price of the electric automobile as optimization variables, and optimizing the discharge electricity price of the electric automobile and the charge electricity price of the electric automobile to obtain the optimal charge and discharge electricity price of the electric automobile by taking the minimum running cost of the multi-microgrid system as a target on the premise of meeting optimization constraint conditions;
(4) and (3) taking the step (2) as inner ring optimization and the step (3) as outer ring optimization, establishing a double-ring optimization model of the multi-microgrid system, calculating the operation cost of the multi-microgrid system in the outer ring optimization by using the charging and discharging power of the optimal electric vehicle obtained by the inner ring optimization, calculating the charging and discharging cost of the electric vehicle by using the charging and discharging electricity price of the optimal electric vehicle obtained by the outer ring optimization, and operating the double-ring optimization model to enable the inner ring optimization and the outer ring optimization to be circulated for multiple times to obtain the charging and discharging cost of the target electric vehicle, the charging and discharging power of the target electric vehicle, the operation cost of the target multi-microgrid system and the charging.
Further, the step (1) comprises:
establishing a mathematical model according to a basic structure of the multi-microgrid system, wherein the mathematical model comprises the following steps: the method comprises the steps that a charging and discharging model, a wind power generation prediction model and a photovoltaic power generation prediction model of the electric automobile are used for obtaining an optimization constraint condition, the charging and discharging cost of the electric automobile and the operation cost of the multi-microgrid system according to the charging and discharging model of the electric automobile, the investment cost of a wind driven generator is obtained according to the wind power generation prediction model, and the investment cost of a photovoltaic module is obtained according to the photovoltaic power generation prediction model;
the charging and discharging model of the electric automobile comprises a charging and discharging model of the electric automobile connected to the microgrid when the electric automobile is connected to the multi-microgrid system and a charging and discharging model of the electric automobile not connected to the microgrid when the electric automobile is in a running state.
Further, the charge and discharge costs of the electric vehicle are:
wherein, CEVtlFor the charging and discharging costs of electric vehicles, PEVBi，j，tThe power p charged and discharged at the moment t for the jth electric vehicle of the ith micro-gridEViFor charging and discharging quotation of the ith microgrid, NEViThe number of the electric automobiles in the ith microgrid is N, the number of the microgrids is N, and the scheduling time is T.
Further, the operation cost of the multi-microgrid system is as follows:
CTCi＝min(CPVi+CWTi+CEVi+CGi)
wherein, CTCMSFor operating costs of multi-microgrid systems, CTCiFor the operating cost of the ith microgrid, CPVi、GWTi、CEVi、CGiAnd respectively representing the investment cost of a photovoltaic module of the ith microgrid, the investment cost of a wind driven generator, the operation cost of an electric automobile and the electric network electric energy transaction cost, and obtaining the electric network electric energy transaction cost of the ith microgrid according to the exchange power and the electricity price of the distribution network and the ith microgrid.
Further, the optimization constraints include: network power flow balance constraint, electric vehicle capacity constraint, electric vehicle power constraint, energy exchange power constraint of a micro-grid and a power distribution network, charge and discharge quotation constraint of the micro-grid on the electric vehicle,
the network power flow balance constraint is as follows: pPVi，t+PWTi，t+PEVBi，t+PGi，t＝PLi，t
The capacity constraint of the electric automobile is as follows: eEVB min≤EEVBi，j，t≤EEVB max
The power constraint of the electric automobile is as follows: pEVB min≤PEVBi，j，t≤PEVB max
The energy exchange power constraint of the power distribution network is as follows: pEVB min≤PGi，t≤PEVB max
The charging and discharging quotation constraint of the micro-grid on the electric automobile is as follows: pDE≥ηCEVηDEVpCE
Wherein, PPVi，tFor the generating power of the photovoltaic module of the ith microgrid at the moment t, PWTi，tThe generated power of the wind driven generator at the moment t for the ith microgrid, PEVBi，tThe power P of charging and discharging all electric vehicles of the ith microgrid at the moment tLi，tLoad of ith microgrid at time t, EEVB minIs the minimum value of electric quantity of the electric vehicle, EEVB maxIs the maximum value of electric quantity, P, of the electric vehicleEVB minIs the minimum power, P, of the electric vehicleEVB maxIs the maximum power of the electric vehicle, pDEIs an electric steamDischarge price of vehicle, pCEFor charging electric vehicles, PEVBi，j，tThe power charged and discharged at the moment t for the jth electric vehicle of the ith micro-grid, EEVBi，j，tIt represents the electric quantity of the jth electric vehicle of the ith microgrid at the moment t, ηCEVFor the charging efficiency of electric vehicles, ηDEVFor the discharge efficiency of electric vehicles, PGi，tAnd exchanging power between the power distribution network and the ith micro-grid at the time t.
Further, the step (2) comprises:
(2-1) forming particles of a particle swarm algorithm by using the charging and discharging power of all electric vehicles per hour of each microgrid in the multi-microgrid system as an optimization variable of the particle swarm algorithm;
(2-2) calculating the charge and discharge cost of the electric vehicle of each particle, and taking the minimum value of the charge and discharge cost of the electric vehicle in all the particles as the global optimal value of the first cycle of the inner ring;
(2-3) updating the speed and the position of the particles by using an update formula of the position and the speed of the particle swarm algorithm, calculating the charge-discharge cost of the electric automobile of the updated particles, comparing the charge-discharge cost with the global optimal value of the previous cycle of the inner ring, and taking the minimum value as the new global optimal value of the inner ring;
and (2-4) repeating the step (2-3) for multiple times to obtain the charge and discharge cost of the optimal electric automobile and the charge and discharge power of the optimal electric automobile corresponding to the charge and discharge cost.
Further, the step (3) comprises:
(3-1) taking the charging and discharging electricity price of each microgrid for the electric vehicle as the value of each dimension of each particle in the particle swarm algorithm to obtain a particle swarm, obtaining the initial discharging electricity price of the electric vehicle and the initial charging electricity price of the electric vehicle for initializing the particle swarm according to the purchase and sale electric energy quotation between the power distribution network and the multi-microgrid system, calculating the operation cost of the multi-microgrid system of each particle by using the optimal charging and discharging power of the electric vehicle, and taking the minimum value of the operation cost of the multi-microgrid system of all the particles as the initial global optimal value of an outer ring;
(3-2) updating the speed and the position of the particles by using an update formula of the position and the speed of the particle swarm algorithm, calculating the running cost of the multi-microgrid system of the updated particles, comparing the running cost with the previous global optimal value of the outer ring, and taking the minimum value as the new global optimal value of the outer ring;
and (3-3) repeating the step (3-2) for multiple times to obtain the operation cost of the optimal multi-microgrid system and the corresponding optimal charging and discharging electricity price of the electric automobile.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the invention optimizes the charging and discharging cost of the electric automobile and the operation cost of the multi-microgrid system, and simultaneously considers games among different microgrids in the multi-microgrid system. Each micro-grid adopts a certain strategy to adjust the charge and discharge electricity price of the micro-grid, so that the economic efficiency of the overall operation of the system is further improved.
(2) The charge and discharge cost optimization of the electric automobile and the operation cost optimization of the multi-microgrid system are a double-ring collaborative optimization model. The inner ring of the model carries out economic dispatching on the electric automobile, so that the running cost of the electric automobile reaches the optimum under the condition of a certain electricity price; the outer ring optimizes the charge and discharge price of the electric automobile, and reduces the operation cost of each micro-grid by optimizing and adjusting the charge and discharge quotation of each micro-grid under the condition that a plurality of micro-grids compete with each other, thereby reducing the total operation cost of the system. The inner ring and the outer ring are combined with each other to optimize the operation of a plurality of micro-grids and an integrated system of the electric vehicle.
(3) The double-ring co-evolution method for charging and discharging electricity price optimization and economic dispatching is suitable for a regional micro-grid system comprising a plurality of micro-grids. The electric automobile can carry out energy transfer across time and space in the system, can utilize the change of the electricity price in different time periods, also can utilize different electricity prices of each micro-grid to provide charging and discharging service for the system, thereby reducing the cost of the whole system to a certain extent. Meanwhile, the micro-grid can adjust the electricity price per se according to the operation data of the power grid, and the electric automobile is guided to charge and discharge to a certain extent, so that more profits are obtained. The method can be used for guiding charging and discharging pricing of different micro-grids in the regional micro-grid system, so that the operation efficiency of the system is improved, and the operation cost of the system is reduced.
Drawings
Fig. 1 is a flowchart of a method for optimizing charging and discharging electricity prices of an electric vehicle considering V2G according to an embodiment of the present invention;
fig. 2 is a structural diagram of a multi-piconet system according to an embodiment of the present invention;
FIG. 3 is a flow chart of an inner loop economic dispatch provided by an embodiment of the present invention;
fig. 4 is a flowchart of outer loop optimization according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, a method for optimizing charge and discharge electricity prices of an electric vehicle considering V2G includes:
(1) establishing a mathematical model according to the basic structure of the multi-microgrid system, and obtaining the charge and discharge cost of the electric automobile, the operation cost of the multi-microgrid system and the optimization constraint condition according to the mathematical model;
(2) the method comprises the steps that the charging and discharging cost of the electric automobile is the minimum, the charging and discharging power of all electric automobiles in each micro-grid system in each hour is used as an optimization variable, the electric automobile is economically scheduled, and the optimal charging and discharging power of the electric automobile is obtained;
(3) obtaining an initial discharge electricity price of the electric automobile and an initial charge electricity price of the electric automobile according to the purchase and sale electric energy quotation between the power distribution network and the multi-microgrid system, taking the discharge electricity price of the electric automobile and the charge electricity price of the electric automobile as optimization variables, and optimizing the discharge electricity price of the electric automobile and the charge electricity price of the electric automobile to obtain the optimal charge and discharge electricity price of the electric automobile by taking the minimum running cost of the multi-microgrid system as a target on the premise of meeting optimization constraint conditions;
(4) and (3) taking the step (2) as inner ring optimization and the step (3) as outer ring optimization, establishing a double-ring optimization model of the multi-microgrid system, calculating the operation cost of the multi-microgrid system in the outer ring optimization by using the charging and discharging power of the optimal electric vehicle obtained by the inner ring optimization, calculating the charging and discharging cost of the electric vehicle by using the charging and discharging electricity price of the optimal electric vehicle obtained by the outer ring optimization, and operating the double-ring optimization model to enable the inner ring optimization and the outer ring optimization to be circulated for multiple times to obtain the charging and discharging cost of the target electric vehicle, the charging and discharging power of the target electric vehicle, the operation cost of the target multi-microgrid system and the charging.
As shown in fig. 2, the multi-microgrid system considered in the present invention includes a plurality of relatively independent microgrids, which are classified into a residential area microgrid and an office area (business area) microgrid according to their locations and functions. Each micro-grid is provided with a photovoltaic power generation module (PV) and a small wind driven generator (WT) with certain capacity, and a plurality of electric automobile bidirectional charging and discharging facilities. Each micro-grid is connected with the large power grid, and electric energy can be exchanged with the large power grid, so that the power supply stability is ensured. The two types of micro-grids have similar structures, but have different electricity prices from those used for exchanging electric energy between large grids. The charging and discharging quotation of the micro-grid and the operation of the electric automobile are optimized through a double-ring optimization method, so that the total cost of system operation is reduced.
Specifically, the step (1) comprises the following steps: establishing a mathematical model according to a basic structure of the multi-microgrid system, wherein the mathematical model comprises the following steps: the method comprises the steps that a charging and discharging model, a wind power generation prediction model and a photovoltaic power generation prediction model of the electric automobile are used for obtaining an optimization constraint condition, the charging and discharging cost of the electric automobile and the operation cost of the multi-microgrid system according to the charging and discharging model of the electric automobile, the investment cost of a wind driven generator is obtained according to the wind power generation prediction model, and the investment cost of a photovoltaic module is obtained according to the photovoltaic power generation prediction model;
the charging and discharging model of the electric automobile comprises a charging and discharging model of the electric automobile connected to the microgrid when the electric automobile is connected to the multi-microgrid system and a charging and discharging model of the electric automobile not connected to the microgrid when the electric automobile is in a running state.
The charging and discharging model of the electric automobile connected to the microgrid is as follows:
wherein, PEVBi，j，tThe power P of charging and discharging of the jth electric vehicle of the ith micro-grid at the time tEVBi，j，tGreater than 0 indicates discharge, and PEVBi，j，tLess than 0 indicates charging; eEVBi，j，tThen represents the electric quantity, sigma, of the jth electric vehicle of the ith microgrid at the moment tEVFor self-discharge rate, Δ t is the scheduled time interval, ηCEVFor the charging efficiency of electric vehicles, ηDEVThe discharge efficiency of the electric automobile.
The charging and discharging model of the electric automobile without accessing the microgrid is as follows:
wherein d is the running time of the electric automobile,
the distance traveled by the jth electric vehicle of the ith microgrid at time t, CdThe electric consumption (kWh/km) of the electric automobile per unit distance is calculated.
The charge and discharge cost of the electric automobile is as follows:
wherein, CEVtlFor the charging and discharging costs of electric vehicles, PEVBi，j，tThe power p charged and discharged at the moment t for the jth electric vehicle of the ith micro-gridEViFor charging and discharging quotation of the ith microgrid when an electric vehicle is chargedWhen the electric vehicle is charged, the charging electricity price is adopted, and when the electric vehicle is discharged, the discharging electricity price is adopted; n is a radical ofEViThe number of the electric automobiles in the ith microgrid is N, the number of the microgrids is N, and the scheduling time is T.
The operation cost of the multi-microgrid system is as follows:
CTCi＝min(CPVi+CWTi+CEVi+CGi)
wherein, CTCMSFor operating costs of multi-microgrid systems, CTCiFor the operating cost of the ith microgrid, CPVi、GWTi、CEVi、CGiAnd respectively representing the investment cost of a photovoltaic module of the ith microgrid, the investment cost of a wind driven generator, the operation cost of an electric automobile and the electric network electric energy transaction cost, and obtaining the electric network electric energy transaction cost of the ith microgrid according to the exchange power and the electricity price of the distribution network and the ith microgrid.
The running cost of the electric automobile is as follows:
wherein N isEViNumber of electric vehicles for i-th microgrid, CconstConversion of a single electric vehicle charging and discharging device to daily investment costs, CBDiThe cost of depreciation of the battery generated in the process of charging and discharging the electric automobile is reduced.
The electric network electric energy transaction cost of the ith micro-grid is as follows:
wherein, CgiIs the electricity price, P, of the distribution network and the tth micro-gridGi，tFor exchanging power between the distribution network and the ith microgrid at time t, CECGi，tAnd (5) punishing cost for the power distribution network and the ith micro-grid environment at the time t.
The optimization constraint conditions comprise: network power flow balance constraint, electric vehicle capacity constraint, electric vehicle power constraint, energy exchange power constraint of a micro-grid and a power distribution network, charge and discharge quotation constraint of the micro-grid on the electric vehicle,
the network power flow balance constraint is as follows: pPVi，t+PWTi，t+PEVBi，t+PGi，t＝PLi，t
The capacity constraint of the electric automobile is as follows: eEVB min≤EEVBi，j，t≤EEVB max
The power constraint of the electric automobile is as follows: pEVB min≤PEVBi，j，t≤PEVB max
The energy exchange power constraint of the power distribution network is as follows: pEVB min≤PGi，t≤PEVB max
The charging and discharging quotation constraint of the micro-grid on the electric automobile is as follows: p is a radical ofDE≥ηCEVηDEVPCE
Wherein, PPVi，tFor the generating power of the photovoltaic module of the ith microgrid at the moment t, PWTi，tThe generated power of the wind driven generator at the moment t for the ith microgrid, PEVBi，tThe power P of charging and discharging all electric vehicles of the ith microgrid at the moment tLi，tLoad of ith microgrid at time t, EEVB minIs the minimum value of electric quantity of the electric vehicle, EEVB maxIs the maximum value of electric quantity, P, of the electric vehicleEVB minIs the minimum power, P, of the electric vehicleEVB maxIs the maximum power of the electric vehicle, pDEFor the discharge price of electric vehicles, pCEThe charging price of the electric automobile is obtained.
As shown in fig. 3, step (2) includes:
(2-1) inputting processing data and electricity price of the photovoltaic and wind power generation system, and forming particles of a particle swarm algorithm by using the charging and discharging power of all electric vehicles per hour of each microgrid in the multi-microgrid system as optimization variables of the particle swarm algorithm
Wherein, IMGiIs the power matrix of the ith microgrid, IMG1Is the power matrix of the 1 st microgrid, IMG2Is the power matrix of the 2 nd microgrid, IMGNThe power matrix of the Nth micro-grid is that for the photovoltaic module and the wind driven generator, the power is constantly more than or equal to 0; for the electric automobile, when the power is greater than 0, the electric automobile discharges to the microgrid, otherwise, the electric automobile charges; and when the exchange power is larger than 0, the micro-grid purchases electricity to the power distribution network, otherwise, the micro-grid sells the electric energy to the power distribution network. The individual optimum value (power of charging and discharging of the optimum electric vehicle) and the global optimum value (cost of charging and discharging of the optimum electric vehicle) are initialized, and the initial values are set to larger values.
(2-2) initializing particle swarm optimization particles, calculating the charge and discharge cost of the electric vehicle of each particle, and taking the minimum value of the charge and discharge cost of the electric vehicle in all the particles as the global optimal value of the first cycle of the inner ring;
(2-3) updating the speed and the position of the particles by using an update formula of the position and the speed of the particle swarm algorithm, calculating the charge-discharge cost of the electric automobile of the updated particles, comparing the charge-discharge cost with the global optimal value of the previous cycle of the inner ring, and taking the minimum value as the new global optimal value of the inner ring; the updating formula of the speed of the particle swarm optimization is as follows:
the updating formula of the position of the particle swarm algorithm is as follows:
in the formula, Vi kVelocity in the kth iteration for the ith particle; pbest，i kIs the individual optimal solution of the ith particle at the kth iteration; gbest kIs the global optimal solution of all particles in the population at the kth iteration; xi kIs the position of the ith particle after the kth iteration; w is a weighting factor, the size of which determines the inheritance of the current speed, and the value is generally between 0.1 and 0.9; c. C1、c2Called learning factor, generally takes c1＝c 22, ξ and η are pseudo random numbers between (0, 1), and the position and the speed of the particles are limited in a certain range.
(2-4) repeating the step (2-3) for multiple times until the cycle number reaches the upper limit of the iterative computation number set by the particle swarm algorithm, so as to obtain the optimal scheduling strategy: and the charging and discharging cost of the optimal electric automobile and the charging and discharging power of the corresponding optimal electric automobile.
The step (3) comprises the following steps:
(3-1) taking the charging and discharging price of each microgrid on the electric vehicle as the value of each dimension of each particle in the particle swarm algorithm to obtain a particle swarm:
according to the minimum value of buying and selling electric energy quotation between the power distribution network and the micro-grid
And maximum value
Obtaining the initial discharge electricity price p of the electric automobileDEAnd initial charging price p of electric vehicleCE，
Wherein k is1Is the first coefficient, k2Is the second coefficient.
Obtaining an initial discharge electricity price of the electric automobile and an initial charge electricity price of the electric automobile for initializing the particle swarm according to the purchase and sale electricity price between the power distribution network and the multi-microgrid system, calculating the operation cost of the multi-microgrid system of each particle by using the charge and discharge power of the optimal electric automobile, and taking the minimum value of the operation cost of the multi-microgrid system of all the particles as an initial global optimal value of an outer ring;
(3-2) updating the speed and the position of the particles by using an update formula of the position and the speed of the particle swarm algorithm, calculating the running cost of the multi-microgrid system of the updated particles, comparing the running cost with the previous global optimal value of the outer ring, and taking the minimum value as the new global optimal value of the outer ring;
and (3-3) repeating the step (3-2) for multiple times to obtain the operation cost (global optimal value) of the optimal multi-microgrid system and the corresponding charge and discharge electricity price (individual optimal value) of the optimal electric vehicle.
As shown in fig. 4, running the dual loop optimization model includes:
inputting system parameters, initializing an individual optimal value and a global optimal value, setting an initial value to be a larger value, initializing outer ring particle swarm algorithm particles, setting the outer ring cycle number to be 1, initializing inner ring particle swarm algorithm particles, setting the inner ring cycle number to be 1, calculating the charge and discharge cost of the electric automobile of each particle, updating the individual optimal value (the charge and discharge power of the optimal electric automobile) and the global optimal value by using the minimum value of the charge and discharge costs of the electric automobiles in all the particles, updating the position and the speed of an inner ring particle swarm according to the individual optimal value (the charge and discharge power of the optimal electric automobile) and the global optimal value when the inner ring cycle number is less than or equal to an inner ring cycle preset value, and then performing inner ring cycle again; when the inner ring circulation frequency is larger than the inner ring circulation preset value, an individual optimal value and a global optimal value corresponding to the outer ring particle swarm are set according to the result of the inner ring particle swarm algorithm, when the outer ring circulation frequency is smaller than or equal to the outer ring circulation predicted value, the position and the speed of the outer ring particle swarm are updated according to the individual optimal value and the global optimal value of the outer ring, and then outer ring circulation is performed again. When the outer ring cycle number is larger than the outer ring cycle predicted value, obtaining an economic dispatching result of the electric automobile and an optimization result of charging and discharging quotation of the microgrid, namely: the charging and discharging cost of the target electric automobile, the charging and discharging power of the target electric automobile, the operation cost of the target multi-microgrid system and the charging and discharging electricity price of the target electric automobile.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A method for optimizing the charge and discharge electricity price of an electric automobile considering V2G is characterized by comprising the following steps:
(1) establishing a mathematical model according to the basic structure of the multi-microgrid system, and obtaining the charge and discharge cost of the electric automobile, the operation cost of the multi-microgrid system and the optimization constraint condition according to the mathematical model;
(2) the method comprises the steps that the charging and discharging cost of the electric automobile is the minimum, the charging and discharging power of all electric automobiles in each micro-grid system in each hour is used as an optimization variable, the electric automobile is economically scheduled, and the optimal charging and discharging power of the electric automobile is obtained;
(3) obtaining an initial discharge electricity price of the electric automobile and an initial charge electricity price of the electric automobile according to the purchase and sale electric energy quotation between the power distribution network and the multi-microgrid system, taking the discharge electricity price of the electric automobile and the charge electricity price of the electric automobile as optimization variables, and optimizing the discharge electricity price of the electric automobile and the charge electricity price of the electric automobile to obtain the optimal charge and discharge electricity price of the electric automobile by taking the minimum running cost of the multi-microgrid system as a target on the premise of meeting optimization constraint conditions;
(4) and (3) taking the step (2) as inner ring optimization and the step (3) as outer ring optimization, establishing a double-ring optimization model of the multi-microgrid system, calculating the operation cost of the multi-microgrid system in the outer ring optimization by using the charging and discharging power of the optimal electric vehicle obtained by the inner ring optimization, calculating the charging and discharging cost of the electric vehicle by using the charging and discharging electricity price of the optimal electric vehicle obtained by the outer ring optimization, and operating the double-ring optimization model to enable the inner ring optimization and the outer ring optimization to be circulated for multiple times to obtain the charging and discharging cost of the target electric vehicle, the charging and discharging power of the target electric vehicle, the operation cost of the target multi-microgrid system and the charging.
2. The method for optimizing the charge and discharge electricity price of the electric vehicle considering the V2G according to claim 1, wherein the step (1) comprises:
establishing a mathematical model according to a basic structure of the multi-microgrid system, wherein the mathematical model comprises the following steps: the method comprises the steps that a charging and discharging model, a wind power generation prediction model and a photovoltaic power generation prediction model of the electric automobile are used for obtaining an optimization constraint condition, the charging and discharging cost of the electric automobile and the operation cost of the multi-microgrid system according to the charging and discharging model of the electric automobile, the investment cost of a wind driven generator is obtained according to the wind power generation prediction model, and the investment cost of a photovoltaic module is obtained according to the photovoltaic power generation prediction model;
the charging and discharging model of the electric automobile comprises a charging and discharging model when the electric automobile is connected to the multi-microgrid system and a charging and discharging model when the electric automobile is not connected to the microgrid when the electric automobile is in a running state.
3. The method for optimizing the charge and discharge electricity price of the electric vehicle considering the V2G is characterized in that the charge and discharge cost of the electric vehicle is as follows:
wherein, CEVtlFor the charging and discharging costs of electric vehicles, PEVBi,j,tThe power p charged and discharged at the moment t for the jth electric vehicle of the ith micro-gridEViFor charging and discharging quotation of the ith microgrid, NEViThe number of the electric automobiles in the ith microgrid is N, the number of the microgrids is N, and the scheduling time is T.
4. The method for optimizing the charging and discharging electricity price of the electric vehicle considering the V2G according to claim 1 or 2, wherein the operation cost of the microgrid system is as follows:
CTCi＝min(CPVi+CWTi+CEVi+CGi)
wherein, CTCMSFor operating costs of multi-microgrid systems, CTCiFor the operating cost of the ith microgrid, CPVi、CWTi、CEVi、CGiAnd respectively representing the investment cost of a photovoltaic module of the ith microgrid, the investment cost of a wind driven generator, the operation cost of an electric automobile and the electric network electric energy transaction cost, and obtaining the electric network electric energy transaction cost of the ith microgrid according to the exchange power and the electricity price of the distribution network and the ith microgrid.
5. The method for optimizing the charge and discharge electricity price of the electric vehicle considering the V2G according to claim 1 or 2, wherein the optimization constraint condition includes: network power flow balance constraint, electric vehicle capacity constraint, electric vehicle power constraint, energy exchange power constraint of a micro-grid and a power distribution network, charge and discharge quotation constraint of the micro-grid on the electric vehicle,
the network power flow balance constraint is as follows: pPVi,t+PWTi,t+PEVBi,t+PGi,t＝PLi,t
The capacity constraint of the electric automobile is as follows: eEVBmin≤EEVBi,j,t≤EEVBmax
The power constraint of the electric automobile is as follows: pEVBmin≤PEVBi,j,t≤PEVBmax
The energy exchange power constraint of the power distribution network is as follows: pEVBmin≤PGi,t≤PEVBmax
The charging and discharging quotation constraint of the micro-grid on the electric automobile is as follows: p is a radical ofDE≥ηCEVηDEVpCE
Wherein, PPVi,tFor the generating power of the photovoltaic module of the ith microgrid at the moment t, PWTi,tThe generated power of the wind driven generator at the moment t for the ith microgrid, PEVBi,tThe power P of charging and discharging all electric vehicles of the ith microgrid at the moment tLi,tLoad of ith microgrid at time t, EEVBminIs the minimum value of electric quantity of the electric vehicle, EEVBmaxIs the maximum value of electric quantity, P, of the electric vehicleEVBminIs the minimum power, P, of the electric vehicleEVBmaxIs the maximum power of the electric vehicle, pDEFor the discharge price of electric vehicles, pCEFor charging electric vehicles, PEVBi,j,tThe power charged and discharged at the moment t for the jth electric vehicle of the ith micro-grid, EEVBi,j,tIt represents the electric quantity of the jth electric vehicle of the ith microgrid at the moment t, ηCEVFor the charging efficiency of electric vehicles, ηDEVFor the discharge efficiency of electric vehicles, PGi,tAnd exchanging power between the power distribution network and the ith micro-grid at the time t.
6. The method for optimizing the charge and discharge electricity price of the electric vehicle considering the V2G according to claim 1 or 2, wherein the step (2) comprises:
(2-1) forming particles of a particle swarm algorithm by using the charging and discharging power of all electric vehicles per hour of each microgrid in the multi-microgrid system as an optimization variable of the particle swarm algorithm;
(2-2) calculating the charge and discharge cost of the electric vehicle of each particle, and taking the minimum value of the charge and discharge cost of the electric vehicle in all the particles as the global optimal value of the first cycle of the inner ring;
(2-3) updating the speed and the position of the particles by using an update formula of the position and the speed of the particle swarm algorithm, calculating the charge-discharge cost of the electric automobile of the updated particles, comparing the charge-discharge cost with the global optimal value of the previous cycle of the inner ring, and taking the minimum value as the new global optimal value of the inner ring;
and (2-4) repeating the step (2-3) for multiple times to obtain the charge and discharge cost of the optimal electric automobile and the charge and discharge power of the optimal electric automobile corresponding to the charge and discharge cost.
7. The method for optimizing the charge and discharge electricity price of the electric vehicle considering the V2G according to claim 1 or 2, wherein the step (3) comprises:
(3-1) taking the charging and discharging electricity price of each microgrid for the electric vehicle as the value of each dimension of each particle in the particle swarm algorithm to obtain a particle swarm, obtaining the initial discharging electricity price of the electric vehicle and the initial charging electricity price of the electric vehicle for initializing the particle swarm according to the purchase and sale electric energy quotation between the power distribution network and the multi-microgrid system, calculating the operation cost of the multi-microgrid system of each particle by using the optimal charging and discharging power of the electric vehicle, and taking the minimum value of the operation cost of the multi-microgrid system of all the particles as the initial global optimal value of an outer ring;
(3-2) updating the speed and the position of the particles by using an update formula of the position and the speed of the particle swarm algorithm, calculating the running cost of the multi-microgrid system of the updated particles, comparing the running cost with the previous global optimal value of the outer ring, and taking the minimum value as the new global optimal value of the outer ring;
and (3-3) repeating the step (3-2) for multiple times to obtain the operation cost of the optimal multi-microgrid system and the corresponding optimal charging and discharging electricity price of the electric automobile.
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| CN110138006B (en) * | 2019-05-22 | 2020-12-01 | 南京邮电大学 | Multi-microgrid coordinated optimization scheduling method considering new energy electric vehicle |
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