CN112308373A - Electric automobile ordered charging and discharging scheduling model and algorithm based on space-time double-layer optimization - Google Patents
Electric automobile ordered charging and discharging scheduling model and algorithm based on space-time double-layer optimization Download PDFInfo
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Abstract
The invention relates to a time-space double-layer optimization-based electric automobile ordered charging and discharging scheduling model, which is characterized in that: the system comprises an upper-layer time scale model and a lower-layer space scheduling model, the charging and discharging electric quantity is used as a connection variable of upper-layer time scheduling optimization and lower-layer space scheduling optimization, and the number of charging vehicles is distributed according to the charging and discharging quantity of each time interval of a daily plan when real-time space distribution scheduling is carried out, so that the supply and demand balance of the electric quantity is realized. The charging and discharging optimization control of the electric automobile in real time at 24 time intervals is realized in time; and the V2G scheduling plan is embodied to each access point spatially, and a proper node is selected as a charging and discharging access point according to a charging and discharging plan formulated by time and upper-layer time, so that the network loss of the power distribution system is minimized.
Description
Technical Field
The invention belongs to the field of electric vehicle charging and discharging, and relates to an electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization and an algorithm thereof.
Background
In recent years, under the support of policies of governments at all levels in the center and in the local, the electric automobile industry in China is vigorously developed. The electric automobile using clean electric power as a power source can effectively reduce the use of fossil fuel and reduce the level of carbon emission. According to the research report of the development strategy of electric vehicles of the department of industry and informatization, the holding quantity of electric vehicles in China in 2030 is up to 8000 thousands. If the large-scale electric automobile adopts an unordered charging mode, a great deal of adverse effects are brought to the power grid, such as increased power grid burden, increased system grid loss, reduced power quality and the like.
Therefore, a charging load model of the electric automobile needs to be researched, double-layer scheduling and control of time and space dimensions are carried out on the charging behavior of the electric automobile, bidirectional energy exchange and interaction between the electric automobile and a power grid are achieved, the load characteristic of the power grid is improved, and the peak-valley difference of a demand side is reduced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization and an algorithm thereof.
The technical problem to be solved by the invention is realized by the following technical scheme:
the utility model provides an electric automobile is charge-discharge scheduling model in order based on double-deck optimization of space-time which characterized in that: the system comprises an upper-layer time scale model and a lower-layer space scheduling model, wherein the upper-layer time scale model is used for considering the cluster time distribution characteristic of the electric automobile and determining the charge and discharge power quantity of the electric automobile at different optimization periods by taking the minimum system equivalent load fluctuation and the maximum charge and discharge income of an electric automobile owner as targets;
and the lower-layer space scheduling model is used for making an ordered charging and discharging distribution scheduling plan of each electric automobile according to the charging and discharging space distribution characteristics of the electric automobiles by taking the minimization of the active network loss of the system as a target on the basis of the result of the upper-layer time scale model.
Moreover, the upper-layer time scale model comprises an objective function and a constraint condition;
2 objective functions are provided, wherein the objective function is used for minimizing the equivalent load fluctuation of the system, and the charge and discharge benefits of the user side are maximized as the objective function; the constraint conditions comprise four conditions, namely battery charging and discharging power constraint, battery state of charge (SOC) constraint, user travel time constraint and electric vehicle charging and discharging quantity constraint in each time period.
Moreover, the objective function for minimizing the system equivalent load fluctuation is as follows:
in the ordered charge and discharge regime, the parameters are defined as follows: duration of day: t; total number of sampling points in one day:
Nki.e., 24; average load power: pav(t); equivalent load power of the system in the period t: ps(t)；
Load power of the system during the period t: p (t); and (3) charging and discharging power of the electric automobile of the system at the t period: pev(t)；
The charge and discharge income objective function of the maximized user side is as follows:
wherein the parameters are as follows: and (3) the electricity price when the electric vehicle owner participates in the discharge of V2G in the t period: q (t); t period charge and discharge time per period: Δ t; price of charging of discharging electric automobile owner of the electric automobile at the time t: r (t);
and the amount of charge:Pdis(t) and Pch(t); the electric automobile participates in the battery loss compensation cost of V2G, and the cost is fixed: closs；
The constraint conditions of the charge and discharge power of the battery are as follows:
in the formula, the parameters are defined as follows: charge and discharge power per hour of the electric vehicle: p (t); maximum charging power: p (t)c,maxTaking 12 kW; maximum discharge power: p (t)d,max30 kW;
the constraint conditions of the state of charge value of the battery are as follows:
SOCmin≤SOC(i)≤SOCmax
Qm·SOC(t+1)＝Qm·SOC(t)+Pc(t)·Δt·ηc-Pd(t)·Δt/ηd (4)
in the formula, the parameters are defined as follows:
the charge state of the ith electric vehicle is as follows: SOC (i); upper and lower limits of the electric vehicle state of charge value: SOCmaxAnd SOCmin(ii) a Battery capacity of electric vehicle: qm(ii) a Charging and discharging efficiency of the electric vehicle battery: etacAnd ηd；
The user travel time constraint conditions are as follows:
setting the electric automobiles to be in a running off-grid state at 8:00-9:00 and 17:00-18:00 in one day, and not participating in interaction with a power grid;
the constraint conditions of the number of the electric vehicles charged and discharged in each time period are as follows:
in the formula, the parameters are defined as follows:
number of electric vehicles in charge and discharge state during period t: n is a radical ofc(t) and Nd(t); electricity of t periodTotal number of automobiles: n is a radical ofmax。
Moreover, the objective function of the lower spatial scheduling model is a minimum network loss rate objective function, which is:
in the formula, the parameters are defined as follows: total number of branches of lines in the power distribution network: l; resistance value of line i in power distribution network: rl(ii) a Current value flowing through line i in the power distribution network:
the sampling and the regulation time period are long: at.
The constraint conditions of the lower layer space scheduling model are composed of three conditions, namely node power flow equation constraint, branch power flow constraint and node voltage amplitude constraint;
the constraint conditions of the node power flow equation are as follows:
in the formula, the parameters are defined as follows:
active and reactive power unbalance amount delta P of node iiAnd Δ Qi: voltage amplitude of node i: u shapei(ii) a Total number of nodes of the power distribution network: n is a radical ofb(ii) a Active and reactive power injected by node i: piAnd Qi(ii) a Conductance, susceptance and phase angle difference G between i and jij、Bij、δij；
The branch power flow constraint conditions are as follows:
in the formula, the parameters are defined as follows:
active and reactive power transmitted by branch l: pl、Ql(ii) a The active and reactive power upper and lower limits of branch transmission power: pl max、Pl min、
Active and reactive power variation values of branch l: delta PlAnd Δ Ql；
The node voltage amplitude constraint conditions are as follows:
in the formula, the parameters are defined as follows:
the voltage amplitude of the node i and the upper and lower limits thereof: u shapei、
The constraint conditions of the charging and discharging quantity of the electric automobile with each node are as follows:
in the formula, the parameters are defined as follows:
the number and the upper limit value of the electric vehicles participating in the power grid interaction at the node i in the period t: n is a radical ofi(t) and
a method for solving an electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization is characterized by comprising the following steps: the method comprises a charging load solving method and solving methods of an upper-layer time scale model and a lower-layer space scheduling model, wherein the charging load solving method is realized by a Monte Carlo method, and the solving methods of the upper-layer time scale model and the lower-layer space scheduling model are realized by a multi-objective genetic algorithm.
Moreover, the method for solving the charging load by using the monte carlo method comprises the following steps: the method comprises the following steps:
and 2, for a single electric vehicle, determining corresponding battery capacity and charging mode according to probability distribution of different types of electric vehicles, determining charging duration by extracting initial charging time and daily driving mileage to obtain daily charging power demand of the single electric vehicle, accumulating the charging loads of n electric vehicles obtained through calculation, and performing statistical processing on the result to obtain a daily total electric vehicle charging load curve.
Moreover, the solving method of the upper-layer time scale model comprises the following steps:
the programming calculation is carried out by adopting MATLAB2014b simulation environment, the optimization calculation is carried out by adopting a genetic algorithm toolbox compiled by Sheffield university in England when the ordered charging and discharging model of the electric automobile is solved, and the convergence precision epsilon of the algorithm is set to be 10-5The population size pop is 150, the evolution algebra gen is 400, the cross probability Pc is 0.9, and the variation probability Pm is 0.05;
the solving method of the lower layer space scheduling model comprises the following steps:
the MATLAB2014b simulation environment is adopted for programming calculation, and genetic algorithm parameters are selected as follows: the maximum genetic algebra MAXGEN is 300, the groove GGAP is 0.9, the cross probability Pc is 0.7, and the mutation probability Pm is 0.05.
The invention has the advantages and beneficial effects that:
the electric vehicle ordered charging and discharging scheduling model based on the space-time double-layer optimization and the algorithm thereof are used for reducing adverse effects of charging loads of electric vehicles on a power grid and guiding users to reasonably and effectively participate in power grid interaction, the electric vehicle ordered charging and discharging control model based on the space-time double-dimensionality is established, the space-time distribution characteristic of the electric vehicles is fully considered, the model is decoupled into an optimized scheduling sub-model based on a time scale and a space scale, and the model is solved by adopting a genetic algorithm and forward-backward-generation tide calculation. The calculation example shows that the model can realize real-time charging and discharging scheduling of the electric automobile: the electric automobile discharges and feeds back energy to the system during the peak time and peak hour electricity price periods of the power grid, and the electric automobile charges and stores electric energy during the relatively low valley time and valley hour electricity price periods of the power grid, so that the double optimization aims of stabilizing the fluctuation of equivalent load and improving the charging and discharging benefits of users are realized; and the V2G scheduling plan is embodied to each access point spatially, and a proper node is selected as a charging and discharging access point according to a charging and discharging plan formulated by time and upper-layer time, so that the network loss of the power distribution system is minimized.
Drawings
FIG. 1 is a flow chart of an electric vehicle charging load calculation based on Monte Carlo simulation according to the present invention;
FIG. 2 is a time-space united dispatching electric vehicle ordered charging and discharging flow chart.
Detailed Description
The present invention is further illustrated by the following specific examples, which are intended to be illustrative, not limiting and are not intended to limit the scope of the invention.
An electric automobile ordered charging and discharging scheduling model based on space-time double-layer optimization is characterized in that: the system comprises an upper-layer time scale model and a lower-layer space scheduling model, wherein the upper-layer time scale model is used for considering the cluster time distribution characteristic of the electric automobile and determining the charge and discharge power quantity of the electric automobile at different optimization periods by taking the minimum system equivalent load fluctuation and the maximum charge and discharge income of an electric automobile owner as targets;
and the lower-layer space scheduling model is used for making an ordered charging and discharging distribution scheduling plan of each electric automobile according to the charging and discharging space distribution characteristics of the electric automobiles by taking the minimization of the active network loss of the system as a target on the basis of the result of the upper-layer time scale model.
The upper-layer time scale model comprises an objective function and a constraint condition;
2 objective functions are provided, wherein the objective function is used for minimizing the equivalent load fluctuation of the system, and the charge and discharge benefits of the user side are maximized as the objective function; the constraint conditions comprise four conditions, namely battery charging and discharging power constraint, battery state of charge (SOC) constraint, user travel time constraint and electric vehicle charging and discharging quantity constraint in each time period.
The objective function of minimizing the system equivalent load fluctuation is as follows:
in the ordered charge and discharge regime, the parameters are defined as follows: duration of day: t; total number of sampling points in one day: n is a radical ofkI.e., 24; average load power: pav(t); equivalent load power of the system in the period t: ps(t); load power of the system during the period t: p (t); and (3) charging and discharging power of the electric automobile of the system at the t period: pev(t)；
And taking the charge and discharge benefits of the maximized user side as an objective function II. The electric vehicle load belongs to transferable load classification in controllable load, and when a power grid dispatching side needs an electric vehicle user to complete a certain interactive task, the power grid pays corresponding cost for the load, namely the power grid side demand response dispatching cost. From the perspective of the user side, however, the larger the charging and discharging benefit is, the higher the positivity of the user to participate in the scheduling of the power grid V2G is. The user's V2G profit consists of charge-discharge cost difference and battery loss compensation.
The charge and discharge income objective function of the maximized user side is as follows:
wherein the parameters are as follows: and (3) the electricity price when the electric vehicle owner participates in the discharge of V2G in the t period: q (t); t period charge and discharge time per period: Δ t; price of charging of discharging electric automobile owner of the electric automobile at the time t: r (t);
and the amount of charge: pdis(t) and Pch(t); battery loss of electric automobile participating in V2GCompensation cost, being fixed cost: closs；
The constraint conditions of the charge and discharge power of the battery are as follows:
in the formula, the parameters are defined as follows: charge and discharge power per hour of the electric vehicle: p (t); maximum charging power: p (t)c,maxTaking 12 kW; maximum discharge power: p (t)d,max30 kW;
the SOC value is the ratio of the residual electric quantity to the rated capacity under the same condition when the battery is at a certain discharge rate. The residual electric quantity of the battery can be well reflected through the SOC value, and the SOC value of the battery has certain limitation in the charging and discharging process.
The constraint conditions of the state of charge value of the battery are as follows:
SOCmin≤SOC(i)≤SOCmax
Qm·SOC(t+1)＝Qm·SOC(t)+Pc(t)·Δt·ηc-Pd(t)·Δt/ηd (4)
in the formula, the parameters are defined as follows:
the charge state of the ith electric vehicle is as follows: SOC (i); upper and lower limits of the electric vehicle state of charge value: SOCmaxAnd SOCmin(ii) a Battery capacity of electric vehicle: qm(ii) a Charging and discharging efficiency of the electric vehicle battery: etacAnd ηd；
The user travel time constraint conditions are as follows:
setting the electric automobiles to be in a running off-grid state at 8:00-9:00 and 17:00-18:00 in one day, and not participating in interaction with a power grid;
the constraint conditions of the number of the electric vehicles charged and discharged in each time period are as follows:
in the formula, the parameters are defined as follows:
number of electric vehicles in charge and discharge state during period t: n is a radical ofc(t) and Nd(t); total number of electric vehicles in t period: n is a radical ofmax。
Moreover, the objective function of the lower spatial scheduling model is a minimum network loss rate objective function, which is:
in the formula, the parameters are defined as follows: total number of branches of lines in the power distribution network: l; resistance value of line i in power distribution network: rl(ii) a Current value flowing through line i in the power distribution network:
the sampling and the regulation time period are long: at.
The constraint conditions of the lower layer space scheduling model are composed of three conditions, namely node power flow equation constraint, branch power flow constraint and node voltage amplitude constraint;
the constraint conditions of the node power flow equation are as follows:
in the formula, the parameters are defined as follows:
active and reactive power unbalance amount delta P of node iiAnd Δ Qi: voltage amplitude of node i: u shapei(ii) a Total number of nodes of the power distribution network: n is a radical ofb(ii) a Active and reactive power injected by node i: piAnd Qi(ii) a Conductance, susceptance and phase angle difference G between i and jij、Bij、δij；
The branch power flow constraint conditions are as follows:
in the formula, the parameters are defined as follows:
active and reactive power transmitted by branch l: pl、Ql(ii) a The active and reactive power upper and lower limits of branch transmission power: pl max、Pl min、
Active and reactive power variation values of branch l: delta PlAnd Δ Ql；
The node voltage amplitude constraint conditions are as follows:
in the formula, the parameters are defined as follows:
the voltage amplitude of the node i and the upper and lower limits thereof: u shapei、
The charging of a plurality of electric vehicles at the same node may cause the overload of the power distribution network, and the voltage drops and even exceeds the limit, so the upper and lower limit constraints of the voltage amplitude of each node need to be considered.
The constraint conditions of the charging and discharging quantity of the electric automobile with each node are as follows:
in the formula, the parameters are defined as follows:
the number and the upper limit value of the electric vehicles participating in the power grid interaction at the node i in the period t: n is a radical ofi(t) and
a method for solving an electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization is characterized by comprising the following steps: the method comprises a charging load solving method and solving methods of an upper-layer time scale model and a lower-layer space scheduling model, wherein the charging load solving method is realized by a Monte Carlo method, and the solving methods of the upper-layer time scale model and the lower-layer space scheduling model are realized by a multi-objective genetic algorithm.
The Monte Carlo method, or computer random simulation method, is a "random number" based calculation method. The process for predicting the charging load demand of the large-scale electric automobile connected to the power grid based on the Monte Carlo method is shown in figure 1:
the charging load solving method is realized by a Monte Carlo method and comprises the following steps: the method comprises the following steps:
and 2, for a single electric vehicle, determining corresponding battery capacity and charging mode according to probability distribution of different types of electric vehicles, determining charging duration by extracting initial charging time and daily driving mileage to obtain daily charging power demand of the single electric vehicle, accumulating the charging loads of n electric vehicles obtained through calculation, and performing statistical processing on the result to obtain a daily total electric vehicle charging load curve.
The genetic algorithm is an intelligent random search algorithm based on biological natural selection and a natural genetic mechanism, and an optimal solution is searched by simulating a natural evolution process. And solving the upper layer time model and the lower layer space model by adopting a genetic algorithm.
The solving method of the upper-layer time scale model comprises the following steps:
considering that the charging and discharging load of the electric automobile at the node k of the power distribution network in the t-th period is Ck,tThe decision variables are real number decision variables, and the variable coding mode adopts common binary coding, thereby facilitating the genetic operations such as crossing, variation and the like. The number C of electric vehicles with the node k participating in V2G is coded by binaryk,tAs chromosomes:
the programming calculation is carried out by adopting MATLAB2014b simulation environment, and the optimization calculation is carried out by adopting a genetic algorithm toolbox written by Sheffield university in England when the ordered charging and discharging model of the electric automobile is solved. The convergence accuracy ε of the algorithm is set to 10-5The population size pop is 150, the evolution algebra gen is 400, the cross probability Pc is 0.9, and the variation probability Pm is 0.05. Through the charging and discharging scheduling of the time dimension electric automobile, the electric automobile feeds back energy to the system during the peak time period of the power grid, and the electric automobile is charged and stored in the relative valley time period of the power grid, so that the dual optimization aims of stabilizing the fluctuation of equivalent load and improving the charging and discharging benefits of users are fulfilled.
The solving method of the lower layer space scheduling model comprises the following steps:
considering the number Q of nodes k of the electric automobile connected to the power distribution network in the t-th periodk,tThe decision variables are real number decision variables, and the variable coding mode adopts common binary coding, thereby facilitating the genetic operations such as crossing, variation and the like. The number Q of electric vehicles with the node k participating in V2G is coded by binaryk,tAs chromosomes:
the programming calculations were performed using the MATLAB2014b simulation environment. The genetic algorithm parameters are selected as follows: the maximum genetic algebra MAXGEN is 300, the groove GGAP is 0.9, the cross probability Pc is 0.7, and the mutation probability Pm is 0.05. The network loss of the charging load of the electric automobile connected to the root node in the chain type power distribution network is minimum, and the network loss is increased as the access point moves from the root node to the tail end node. In addition, the longer the path of power transmission, the longer the electrical distance, and the greater the active loss. Therefore, the charging access point of the electric vehicle selects a front end node close to the feeder line of the power distribution network in terms of system operation economy and reliability of line operation. When the electric vehicle is discharged as a temporary 'power supply', the network loss of the end node of the access network is minimum, and the network loss is gradually increased as the access point moves from the end node to the root node. And the loss increases as the line distance from the power supply point to the end node increases. Therefore, the end node close to the power distribution system is selected as a discharge access point of the electric automobile. The lower-layer space model selects different nodes as charging and discharging access nodes of the electric automobile through optimized scheduling of the charging and discharging load nodes of the electric automobile in space dimensionality, and loss minimization of a power distribution system is achieved.
According to the electric automobile ordered charge-discharge space-time two-dimensional scheduling model shown in the figure, the charge-discharge quantity optimized by time is used as a connection variable of the upper-layer time scheduling optimization and the lower-layer space scheduling optimization, and the number of the charged vehicles is distributed according to the charge-discharge quantity of each time period of a daily plan when real-time space distribution scheduling is carried out, so that the supply and demand balance of electric quantity is realized. The working principle is as follows:
establishing an upper-layer time scale model by taking the minimum system equivalent load fluctuation and the maximum charge-discharge income of an electric vehicle owner as targets;
solving the upper time scale model by adopting a genetic algorithm, and meeting a termination condition to obtain an optimal solution, namely an ordered charging and discharging sequence based on a time dimension;
on the basis of upper-layer time optimization, a lower-layer space scheduling model is established by taking the minimization of the active network loss of the system as a target;
carrying out load flow calculation and space load optimization with the network loss as the minimum target by adopting a genetic algorithm, meeting termination conditions to obtain an optimal solution, and storing the optimal configuration result of the charge and discharge quantity of the electric vehicle of each load node;
extracting the initial charging time, the charging amount, the charging mode and the like of the electric automobile by a Monte Carlo simulation method to obtain the total charging load of the electric automobile in each time period in the disordered charging mode;
compared with the double-layer optimized ordered charge-discharge curve, the disordered charge load curve can effectively reduce the peak-valley difference and achieve the effect of peak clipping and valley filling.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. The utility model provides an electric automobile is charge-discharge scheduling model in order based on double-deck optimization of space-time which characterized in that: the system comprises an upper-layer time scale model and a lower-layer space scheduling model, wherein the upper-layer time scale model is used for considering the cluster time distribution characteristic of the electric automobile and determining the charge and discharge power quantity of the electric automobile at different optimization periods by taking the minimum system equivalent load fluctuation and the maximum charge and discharge income of an electric automobile owner as targets;
and the lower-layer space scheduling model is used for making an ordered charging and discharging distribution scheduling plan of each electric automobile according to the charging and discharging space distribution characteristics of the electric automobiles by taking the minimization of the active network loss of the system as a target on the basis of the result of the upper-layer time scale model.
2. The electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization according to claim 1 is characterized in that: the upper-layer time scale model comprises an objective function and a constraint condition;
2 objective functions are provided, wherein the objective function is used for minimizing the equivalent load fluctuation of the system, and the charge and discharge benefits of the user side are maximized as the objective function; the constraint conditions comprise four conditions, namely battery charging and discharging power constraint, battery state of charge (SOC) constraint, user travel time constraint and electric vehicle charging and discharging quantity constraint in each time period.
3. The electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization according to claim 2 is characterized in that: the objective function of minimizing the system equivalent load fluctuation is as follows:
in the ordered charge and discharge regime, the parameters are defined as follows: duration of day: t; total number of sampling points in one day: n is a radical ofkI.e., 24; average load power: pav(t); equivalent load power of the system in the period t: ps(t); load power of the system during the period t: p (t); and (3) charging and discharging power of the electric automobile of the system at the t period: pev(t); the charge and discharge income objective function of the maximized user side is as follows:
wherein the parameters are as follows: and (3) the electricity price when the electric vehicle owner participates in the discharge of V2G in the t period: q (t); the price of charging for the electric vehicle owner at the time t: r (t); charge and discharge time per period: Δ t; electric quantity of electric vehicle discharged and charged during t period: pdis(t) and Pch(t); the electric automobile participates in the battery loss compensation cost of V2G, and the cost is fixed: closs。
4. The electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization according to claim 2 is characterized in that: the constraint conditions of the charge and discharge power of the battery are as follows:
in the formula, the parameters are defined as follows: charge and discharge power per hour of the electric vehicle: p (t); maximum charging power: p (t)c,maxTaking 12 kW; maximum discharge power: p (t)d,max30 kW;
the constraint conditions of the state of charge value of the battery are as follows:
SOCmin≤SOC(i)≤SOCmax
Qm·SOC(t+1)＝Qm·SOC(t)+Pc(t)·Δt·ηc-Pd(t)·Δt/ηd (4)
in the formula, the parameters are defined as follows:
the charge state of the ith electric vehicle is as follows: SOC (i); upper and lower limits of the electric vehicle state of charge value: SOCmaxAnd SOCmin(ii) a Battery capacity of electric vehicle: qm(ii) a Charging and discharging efficiency of the electric vehicle battery: etacAnd ηd；
The user travel time constraint conditions are as follows:
setting the electric automobiles to be in a running off-grid state at 8:00-9:00 and 17:00-18:00 in one day, and not participating in interaction with a power grid;
the constraint conditions of the number of the electric vehicles charged and discharged in each time period are as follows:
in the formula, the parameters are defined as follows:
number of electric vehicles in charge and discharge state during period t: n is a radical ofc(t) and Nd(t); total number of electric vehicles in t period: n is a radical ofmax。
5. The electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization according to claim 2 is characterized in that: the objective function of the lower layer space scheduling model is a minimum network loss rate objective function, and the objective function is as follows:
in the formula, the parameters are defined as follows: total number of branches of lines in the power distribution network: l; resistance value of line i in power distribution network: rl(ii) a Current value flowing through line i in the power distribution network:
the sampling and the regulation time period are long: at.
6. The electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization according to claim 2 is characterized in that: the constraint conditions of the lower layer space scheduling model are composed of three conditions, namely node power flow equation constraint, branch power flow constraint and node voltage amplitude constraint;
the constraint conditions of the node power flow equation are as follows:
in the formula, the parameters are defined as follows:
active and reactive power unbalance amount delta P of node iiAnd Δ Qi: voltage amplitude of node i: u shapei(ii) a Total number of nodes of the power distribution network: n is a radical ofb(ii) a Active and reactive power injected by node i: piAnd Qi(ii) a Conductance, susceptance and phase angle difference G between i and jij、Bij、δij；
The branch power flow constraint conditions are as follows:
in the formula, the parameters are defined as follows:
active and reactive power transmitted by branch l: pl、Ql(ii) a The active and reactive power upper and lower limits of branch transmission power: pl max、Pl min、
Active and reactive power variation values of branch l: delta PlAnd Δ Ql；
The node voltage amplitude constraint conditions are as follows:
in the formula, the parameters are defined as follows:
the voltage amplitude of the node i and the upper and lower limits thereof: u shapei、
The constraint conditions of the charging and discharging quantity of the electric automobile with each node are as follows:
in the formula, the parameters are defined as follows:
the number and the upper limit value of the electric vehicles participating in the power grid interaction at the node i in the period t: n is a radical ofi(t) and
7. a method for solving an electric vehicle ordered charging and discharging scheduling model based on space-time double-layer optimization is characterized by comprising the following steps: the method comprises a charging load solving method and solving methods of an upper-layer time scale model and a lower-layer space scheduling model, wherein the charging load solving method is realized by a Monte Carlo method, and the solving methods of the upper-layer time scale model and the lower-layer space scheduling model are realized by a multi-objective genetic algorithm.
8. The method for solving the ordered charging and discharging scheduling model of the electric vehicle based on the space-time double-layer optimization is characterized by comprising the following steps of: the charging load solving method is realized by a Monte Carlo method and comprises the following steps: the method comprises the following steps:
step 1, inputting basic information of an electric automobile, wherein the basic information of the electric automobile comprises but is not limited to: the specification number of the electric automobile, the probability distribution of the initial charging time, and the battery capacity and charging mode of the three types of electric automobiles are selected;
and 2, for a single electric vehicle, determining corresponding battery capacity and charging mode according to probability distribution of different types of electric vehicles, determining charging duration by extracting initial charging time and daily driving mileage to obtain daily charging power demand of the single electric vehicle, accumulating the charging loads of n electric vehicles obtained through calculation, and performing statistical processing on the result to obtain a daily total electric vehicle charging load curve.
9. The method for solving the ordered charging and discharging scheduling model of the electric vehicle based on the space-time double-layer optimization is characterized by comprising the following steps of: the solving method of the upper-layer time scale model comprises the following steps:
the programming calculation is carried out by adopting MATLAB2014b simulation environment, the optimization calculation is carried out by adopting a genetic algorithm toolbox compiled by Sheffield university in England when the ordered charging and discharging model of the electric automobile is solved, and the convergence precision epsilon of the algorithm is set to be 10-5The population size pop is 150, the evolution algebra gen is 400, the cross probability Pc is 0.9, and the variation probability Pm is 0.05;
the solving method of the lower layer space scheduling model comprises the following steps:
the MATLAB2014b simulation environment is adopted for programming calculation, and genetic algorithm parameters are selected as follows: the maximum genetic algebra MAXGEN is 300, the groove GGAP is 0.9, the cross probability Pc is 0.7, and the mutation probability Pm is 0.05.
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