CN112003314B - Ordered charging patch scheduling method for electric automobile - Google Patents

Ordered charging patch scheduling method for electric automobile Download PDF

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CN112003314B
CN112003314B CN202010964396.7A CN202010964396A CN112003314B CN 112003314 B CN112003314 B CN 112003314B CN 202010964396 A CN202010964396 A CN 202010964396A CN 112003314 B CN112003314 B CN 112003314B
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charging
time
electric automobile
user
power grid
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CN112003314A (en
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侯慧
张誉宝
黄俊力
郭冰新
刘海军
黄亮
张清勇
王建建
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Wuhan University of Technology WUT
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
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    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
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    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
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Abstract

The invention provides an ordered charging patch scheduling method for an electric automobile. According to the invention, the time disorder of electric vehicle access power grid charging is researched based on time-of-use electricity price, and an electric vehicle charging load model is built. Constructing a mutual benefit win-win gain model considering the user and the power grid gain; taking the mutual benefit win-win benefits as a preferable target, and solving by using a particle swarm algorithm to obtain the optimized initial charging time of the electric automobile; constructing a stimulation subsidy model and an electric vehicle optimal charging subsidy scheduling model, and optimizing by adopting a traversal method to obtain the subsidy price of the power grid for a user responding to the strategy in a certain time; and solving the optimal charging patch scheduling model by adopting a particle swarm algorithm according to the responsiveness of the optimized user to the patch policy and the patch price, so as to obtain the optimal charging starting time of the electric automobile. According to the invention, the benefits of both power grid users are fully considered, the highest benefits of mutual benefits and win-win are solved by utilizing a particle swarm algorithm, and the electric automobile scheduling strategy which is satisfactory to both the power grid and the users is obtained.

Description

Ordered charging patch scheduling method for electric automobile
Technical Field
The invention belongs to the field of energy Internet, and particularly relates to an ordered charging patch scheduling method for an electric automobile.
Background
Electric vehicles are an environmentally friendly alternative to internal combustion vehicles, and although electric vehicles are environmentally friendly, the large power requirements due to the popularity of the intended electric vehicles may jeopardize the safe and economical operation of the power grid. A large number of grid-connected vehicles may cause a problem of grid overload, which causes a problem of capacity expansion of an expensive power distribution network and increases energy consumption. Because the resident community has unordered charging habit of 'stopping immediately', a reasonable electric vehicle charging strategy is formulated aiming at the resident community to reduce damage to the power grid, improve the utilization rate of electric energy and maintain stable and safe operation of the power grid.
Disclosure of Invention
Aiming at the defects and optimization requirements of the existing research, the invention aims to provide an ordered charging patch scheduling strategy of an electric automobile, which is designed for residential community residents, is scientific and reasonable and has good applicability.
The technical scheme of the invention is an ordered charging patch scheduling method for an electric automobile, which is characterized by comprising the following steps:
an ordered charging patch scheduling method for an electric automobile is characterized by comprising the following steps:
step 1: dividing 24 hours a day into a plurality of time periods, and further defining the electricity price of each time period;
step 2: and (5) researching the random distribution characteristic of the charging time of the electric automobile connected into the power grid in the residential community, and establishing a charging load model of the electric automobile.
Step 3: constructing a user electricity utilization mode profit model, a user electricity charge expenditure profit model, a user profit model and a power grid load variance model, and finally constructing a mutual benefit win-win profit model;
step 4: taking the mutual benefit win-win benefits as a preferable target, and optimizing and solving by using a particle swarm algorithm to obtain the optimized initial charging time of the electric vehicle;
step 5: calculating the human body feeling quantity and the objective stimulation quantity, and establishing a stimulation patch model;
step 6: constructing an optimal charging subsidy scheduling model of the electric automobile, and optimizing by adopting a traversing method in combination with the responsiveness of a user to a subsidy policy to obtain the subsidy price of the optimized power grid at a certain time for the user willing to change the charging starting time;
step 7: determining the quantity of the electric vehicle willing to change the charging starting time according to the responsiveness of the optimized user to the subsidy policy in the step 6; the patch price of the electric automobile of the ith user at the mth period of time is that according to the optimized electric automobile willing to change the charging starting time wherein ui ∈(0,τ * N)，m∈(0,X)，τ * N represents the number of the optimized users willing to change the charging starting time, and the electricity price of the mth time period is obtained again by bringing the optimized users into the time-sharing electricity price in the step 1, namely wherein ui ∈(0,τ * N), m E (0, X); solving the optimal charging patch scheduling model of the electric automobile by adopting the particle swarm algorithm in the step 4 through the electricity expense income of the user in the step 3, namely theta, to obtain the optimal charging starting time of the ith electric automobile, namely +.>Wherein i is E [1, N]N is the number of electric vehicles, and X is the number of time periods in a day.
Preferably, the number of time periods in the day in the step 1 is X, and the length of each time period is h;
the electricity price of each time period in the step 1 is as follows:
the electricity price of the m-th time period is c x,m M represents the m-th time period, m.epsilon.1, X]；
Preferably, the specific steps of establishing the load level model in the step 2 are as follows:
the charging start time of the electric automobile is t 0,i A travel distance d i ，i∈[1,N]；
The initial charging time for randomly generating a plurality of electric vehicles through the normal distribution probability function is as follows:
wherein i is E [1, N]N is the number of electric vehicles, mu d Is the expected value, sigma d Is standard deviation, t 0,i The initial charging time of the ith electric automobile;
charging time period K of ith electric automobile i The method comprises the following steps:
INT is a rounding function, t 0,i And h is the initial charging time of the ith electric automobile, and h is the length of each time period.
The calculated state of charge of the electric automobile is as follows:
E i ＝(1-d i /D i )×100％
wherein i is E [1, N]N is the number of electric vehicles, E i Is the charge state of the ith electric automobile battery, d i Day drive for the ith electric vehicle stateMileage D i The maximum driving mileage of the ith electric vehicle in the pure electric state;
the calculated charging time length of the electric automobile is as follows:
wherein i is E [1, N]，T C,i The charging time length d of the ith electric automobile i The daily driving mileage of the ith electric automobile; w (W) 100,i Power consumption of the ith electric automobile for hundred kilometers, p c,i The charging power of the ith electric automobile is represented by lambda, and the charging efficiency is represented by lambda;
number of charging periods beta of the ith electric vehicle K,i The method comprises the following steps:
INT is a rounding function, T C,i And h is the length of each time period for the charging time length of the ith electric automobile.
The charging end time of the electric automobile is calculated as follows:
T e,i ＝t 0,i +T c,i
wherein i is E [1, N]N is the number of electric vehicles, t 0,i For the initial charging time of the ith electric automobile, T C,i For the charging time length T of the ith electric automobile e,i The charging end time of the electric automobile is;
if T e,i > 24, T e,i ＝T e,i -24；
The total charging load P of the electric automobile in the m time period ev,m The method comprises the following steps:
0≤N m ≤N
wherein i is E [1, N]，N m The number of the electric vehicles in the m time period is obtained by traversing the charging time periods of N electric vehicles; p is p c,i,m Load charged in m time period of ith electric automobile, if the load is not charged in the time period, p c,i,m ＝0；P ev,m The total charging load of the electric automobile in the m time period and the original load P of the micro-grid load in the m time period load0,m Adding to obtain micro-grid load P at m moment after disordered charging of electric automobile load,m X is the number of time periods;
preferably, in the step 3, the user electricity consumption mode benefit model is as follows:
wherein ,in order to implement the change value of the total charging power of the electric automobile user in each time period of time-of-use electricity price after the ordered charging strategy, P ev,m For the total charge load of the electric vehicle in the m time period, < > for>In order to optimize the total charging load of the electric automobile at m time, namely the solving variable to be optimized, X is the number of time periods, epsilon is the income of the user in the power utilization mode, and is a function of the charging starting time of the electric automobile, epsilon [0,1 ]]；
And step 3, defining a user electricity fee expenditure income model as follows:
wherein θ is the electricity fee expenditure income of the user, C i To pay for electricity charges for the ith electric vehicle one day before the orderly charging strategy is implemented,i-th electric automobile after implementing ordered charging strategyOne day of electricity fee expenditure.
The calculation formula of the electricity charge expenditure of the ith electric automobile in one day is as follows:
and 3, the user profit model is as follows:
Y＝γ 1 ε+γ 2 θ
γ 1 +γ 2 ＝1
wherein Y is the user benefit; gamma ray 1 For the weight value of the user power consumption mode income, gamma 2 A weight value of the income of the expense for the user;
step 3, the power grid load variance model reflects a power grid load stability index, and is specifically defined as follows:
wherein ,σ2 The power grid load variance, X is the number of time periods, N is the number of electric vehicles and P 0,m For the conventional electricity load of residents in the Mth time period, P i,m For the charging load of the ith electric automobile in the mth time period, P Avg The average value of the total load of the power grid in one day;
the power grid load fluctuation income reflects the degree of power grid load fluctuation, and the larger the power grid fluctuation income is, the more stable the power grid load is, and is specifically defined as:
in the formula ：for the power grid fluctuation income->Solving variable to be optimized for the optimized power grid load variance +.>In order to eliminate the user side, the minimum value of the power grid load variance, # in the case of an absolute order of the electric vehicle>In order to avoid the user side, the maximum value of the power grid load variance is +.> and />Can be optimized by a particle swarm algorithm;
the mutual benefit win-win gain model in the step 3 represents the overall satisfaction degree of the power grid and the user on the charging strategy, and is specifically defined as follows:
wherein Q is the mutual benefit win-win benefit, Y is the user benefit,for grid load fluctuation benefit, gamma 1 The gain weight coefficient of the power grid;
preferably, in the step 4, the initial charging time of the electric vehicle when the highest mutual benefit win-win gain is solved by using a particle swarm algorithm is as follows:
the preferred targets in step 4 are as follows:
updating the velocity v of the particles iter,i And an initial charging time t iter,i Defining a maximum initial charge time t iter,i,max =24, defining a minimum initial charge time t iter,i,min ＝0。
v iter,i ＝w*v iter,i +c1*rand*(y iter-1,i -t iter,i )+c2*rand*(pg-t iter,i )
t iter,i ＝t iter,i +v iter,i
Wherein c1 is a first learning factor, c2 is a second learning factor, w is an inertial weight, pg is globally optimal, y iter-1,i Initial charge time t for the previous iteration of particles iter-1,i Velocity v iter,i And an initial charging time t iter,i The iter represents the number of iterations of the particle.
In the optimization model, the following steps are: the mutual benefit and win-win income Q is about the charging starting time of the electric automobileThe i-th electric vehicle charging start time t 0,i T is the initial solution of the particle swarm algorithm iter,i And (5) optimizing the initial charging time of the ith electric automobile for the ith iteration of the particle swarm. INT is a rounding function, < > and->In order to optimize the initial charging time of the ith electric automobile, h is the length of each time period,/for the ith electric automobile>The method comprises the steps that the charging starting time period of an ith electric automobile is optimized for the ith iteration of a particle swarm, and X is the number of time periods;
employing particle swarm algorithm to win-win benefit Q * Maximizing the initial charging time of the ith electric automobile after optimization by using the optimized target
Preferably, the human body feeling amount calculated in the step 5 is:
s＝kln(R)+s 0
wherein k is a Weber coefficient, s 0 S is human body feeling quantity, R is objective stimulation quantity; the s reflects the feeling degree of the vehicle owner on the subsidy price, and the R reflects the reduction degree of the grid on the valley electricity price;
the objective stimulation amount in the step 5 is as follows:
wherein R is objective stimulation, R p,m The subsidy price of the power grid for the mth period is the variable to be solved, R 0,m To the electricity price of the m time period before the patch, R' m The electricity price of the m time period after the patch is the electricity price;
wherein, tau is the responsiveness of the user to the subsidy policy, namely the proportion of the user willing to change the self-charging policy according to the subsidy price; r is R M In order to be the smallest of the perceived differences,s 0 for the stimulus constant, k is the Weber coefficient, R p,i,m,j The subsidy price of the power grid in the mth period is calculated; r is R 0,m The electricity price of the m-th period before the patch is completed.
The stimulus patch model determines the extent of response of the participant, the stronger the participant (the smaller k, s 0 The greater) the higher the benefit it can obtain in the game, but it may also result in a secondary peak when Gu Shibian is peaked, which is detrimental to the grid.
Preferably, in the step 6, the constructing an optimal charging patch scheduling model of the electric automobile is as follows:
wherein W is the operation profit of the local distribution network, W min For the minimum operation profit acceptable by the local distribution network, X is the number of time periods, tau j N is the number of the j-th iteration user willing to change the starting time of charging, u i For the ith user willing to change the charge start time, R M To minimize perceived difference, Q op For optimum charge return, Q * Epsilon for the mutual benefit win-win benefit solved in step 4 op For the optimal electricity utilization mode of users, theta op For the optimal user electricity fee expenditure gain,the wave income of the optimal power grid is obtained; τ j The responsiveness of the user to the subsidy policy is that the proportion of the user who is willing to change the self-charging policy according to the subsidy price is the to-be-solved variable; />The method comprises the steps that the subsidy price of an electric automobile of an ith user in an mth period of time, which is willing to change the charging starting time, of a jth iterative power grid is used as a variable to be solved;
and step 6, optimizing by a traversal method according to the responsiveness of the user to the subsidy policy to obtain the subsidy price of the optimized power grid for the user willing to change the charging starting time in a certain time, wherein the subsidy price is specifically as follows:
taking the initial charging time of the electric vehicle after optimization in the step 4 as a known quantity to be brought into the optimal charging subsidy scheduling model of the electric vehicle, and updating the optimal user electricity utilization mode gain, the optimal user electricity charge expenditure gain and the optimal power grid fluctuation gain, and solving the power grid pair (u) when the mutual benefit win-win gain is maximum in the step 6 by adopting a traversing method i Patch price of electric vehicle in mth period
Note that, the subsidy price at the mth period for the ith user willing to change the charging start timeWhen no patch is applied ∈ ->
According to the (u) i Patch price of mth period of electric vehicleThe responsiveness of the user to the patch policy after the stimulation patch model in the step 5 is solved and optimized, namely tau * ；
According to the optimal stimulated subsidy model, each time the subsidy price is determined, the user has determined responsiveness and pursues the maximum income of the user, so that the ordered charging strategy is responded, and the power grid load is further stable; the power grid side sets subsidy price according to the user response, so that the power grid load is stable, and the economic benefit of the power grid is improved.
The invention has the beneficial effects that: the method fully considers the benefits of both power grid users, solves the highest mutual benefit win-win benefits by utilizing a particle swarm algorithm, and gives consideration to the electric automobile scheduling strategy which is satisfactory to both the power grid and the users.
Drawings
Fig. 1: the flow chart of the method of the invention;
fig. 2: the method is used for preliminarily optimizing the daily load curve of the electric automobile after the charging time without being patched;
fig. 3: the invention provides a daily load curve with the highest mutual benefit win-win benefit after the patch.
Detailed Description
In order to facilitate the understanding and practice of the invention, those of ordinary skill in the art will now make further details with reference to the drawings and examples of implementation, it being understood that the examples of implementation described herein are intended to illustrate and explain the invention and are not intended to limit the invention.
Embodiments of the present invention are described below in conjunction with fig. 1-3, with the following specific steps:
an ordered charging patch scheduling method for an electric automobile is characterized by comprising the following steps:
step 1: dividing 24 hours a day into a plurality of time periods, and defining electricity prices for each time period;
the number of time periods in the day in the step 1 is X, and x=24; each time period is h, and h=1 hour;
the electricity price of each time period in the step 1 is as follows:
the electricity price of the m-th time period is c x,m M represents the m-th time period, m.epsilon.1, X]；
See table 1 for details:
table 1: time-of-use electricity price meter
Step 2: the method comprises the steps of researching the random distribution characteristic of the charging time of the electric automobile of the residential community when being connected into a power grid, and establishing a charging load model of the electric automobile by considering the charging state, the initial charging time and the individual and group charging characteristics of the electric automobile after the electric automobile returns home.
The specific steps of establishing the load level model in the step 2 are as follows:
the charging start time of the electric automobile is t 0,i A travel distance d i ，i∈[1,N]；
The initial charging time for randomly generating a plurality of electric vehicles through the normal distribution probability function is as follows:
wherein i is E [1, N]N is the number of electric vehicles, n=509; mu (mu) d =17.47 is the expected value, σ d =3.41 is standard deviation, t 0,i The initial charging time of the ith electric automobile;
charging time period K of ith electric automobile i The method comprises the following steps:
INT is a rounding function, t 0,i And h is the initial charging time of the ith electric automobile, and h is the length of each time period.
The calculated state of charge of the electric automobile is as follows:
E i ＝(1-d i /D i )×100％
wherein i is E [1, N]N is the number of electric vehicles, E i Is the charge state of the ith electric automobile battery, d i D is the daily driving mileage of the ith electric automobile state i The maximum driving mileage of the ith electric vehicle in the pure electric state;
the calculated charging time length of the electric automobile is as follows:
wherein i is E [1, N]，T C,i The charging time length d of the ith electric automobile i The daily driving mileage of the ith electric automobile; w (W) 100,i Power consumption of the ith electric automobile for hundred kilometers, p c,i The charging power of the ith electric automobile, λ is the charging efficiency, λ=90%;
number of charging periods beta of the ith electric vehicle K,i The method comprises the following steps:
INT is a rounding function, T C,i And h is the length of each time period for the charging time length of the ith electric automobile.
The charging end time of the electric automobile is calculated as follows:
T e,i ＝t 0,i +T c,i
wherein i is E [1, N]N is the number of electric vehicles, t 0,i For the initial charging time of the ith electric automobile, T C,i For the charging time length T of the ith electric automobile e,i The charging end time of the electric automobile is;
if T e,i > 24, T e,i ＝T e,i -24；
The total charging load P of the electric automobile in the m time period ev,m The method comprises the following steps:
0≤N m ≤N
wherein i is E [1, N]，N m The number of the electric vehicles in the m time period is obtained by traversing the charging time periods of N electric vehicles; p is p c,i,m Load charged in m time period of ith electric automobile, if the load is not charged in the time period, p c,i,m ＝0；P ev,m The total charging load of the electric automobile in the m time period and the original load P of the micro-grid load in the m time period load0,m Adding to obtain micro-grid load P at m moment after disordered charging of electric automobile load,m X is the number of time periods;
step 3: constructing a user electricity utilization mode profit model, a user electricity charge expenditure profit model, a user profit model and a power grid load variance model, and finally constructing a mutual benefit win-win profit model;
in the step 3, the user electricity utilization mode profit model is as follows:
wherein ,for the time-sharing electricity price of the electric automobile user and the total charging power of each period after the ordered charging strategy is implementedRate of change, P ev,m For the total charge load of the electric vehicle in the m time period, < > for>In order to optimize the total charging load of the electric automobile at m time, namely the solving variable to be optimized, X is the number of time periods, epsilon is the income of the user in the power utilization mode, and is a function of the charging starting time of the electric automobile, epsilon [0,1 ]]；
And step 3, defining a user electricity fee expenditure income model as follows:
wherein θ is the electricity fee expenditure income of the user, C i To pay for electricity charges for the ith electric vehicle one day before the orderly charging strategy is implemented,and the electric charge expenditure of the ith electric automobile in one day after the ordered charging strategy is implemented.
The calculation formula of the electricity charge expenditure of the ith electric automobile in one day is as follows:
and 3, the user profit model is as follows:
Y＝γ 1 ε+γ 2 θ
γ 1 +γ 2 ＝1
wherein Y is the user benefit; gamma ray 1 For the weight value of the user power consumption mode income, gamma 1 ＝0.4，γ 2 Weight of income for user fee expenditure, gamma 2 ＝0.4；
Step 3, the power grid load variance model reflects a power grid load stability index, and is specifically defined as follows:
wherein ,σ2 The power grid load variance, X is the number of time periods, N is the number of electric vehicles and P 0,m For the conventional electricity load of residents in the Mth time period, P i,m For the charging load of the ith electric automobile in the mth time period, P Avg The average value of the total load of the power grid in one day;
the power grid load fluctuation income reflects the degree of power grid load fluctuation, and the larger the power grid fluctuation income is, the more stable the power grid load is, and is specifically defined as:
in the formula ：for the power grid fluctuation income->Solving variable to be optimized for the optimized power grid load variance +.>In order to eliminate the user side, the minimum value of the power grid load variance, # in the case of an absolute order of the electric vehicle>In order to avoid the user side, the maximum value of the power grid load variance is +.> and />Can be optimized by a particle swarm algorithm;
the mutual benefit win-win gain model in the step 3 represents the overall satisfaction degree of the power grid and the user on the charging strategy, and is specifically defined as follows:
wherein Q is the mutual benefit win-win benefit, Y is the user benefit,for grid load fluctuation benefit, gamma 1 Is the gain weight coefficient of the power grid, gamma 1 ＝0.2；
Step 4: taking the mutual benefit win-win benefits as a preferable target, and optimizing and solving by using a particle swarm algorithm to obtain the optimized initial charging time of the electric vehicle;
in the step 4, when the highest mutual benefit win-win gain is solved by using the particle swarm algorithm, the initial charging time of the electric automobile is as follows:
the preferred targets in step 4 are as follows:
updating the velocity v of the particles iter,i And an initial charging time t iter,i Defining a maximum initial charge time t iter,i,max =24, defining a minimum initial charge time t iter,i,min ＝0。
v iter,i ＝w*v iter,i +c1*rand*(y iter-1,i -t iter,i )+c2*rand*(pg-t iter,i )
t iter,i ＝t iter,i +v iter,i
Wherein c1 is a first learning factor, c1=1.5; c2 is a second learning factor, c2=2.5; w is inertial weight, w=0.5; pg is globally optimal, y iter-1,i Initial charge time t for the previous iteration of particles iter-1,i Velocity v iter,i And an initial charging time t iter,i Item represents the number of iterations of the particle, item=100.
In the optimization model, the following steps are: the mutual benefit and win-win income Q is about the charging starting time of the electric automobileThe i-th electric vehicle charging start time t 0,i T is the initial solution of the particle swarm algorithm iter,i And (5) optimizing the initial charging time of the ith electric automobile for the ith iteration of the particle swarm. INT is a rounding function, < > and->In order to optimize the initial charging time of the ith electric automobile, h is the length of each time period,/for the ith electric automobile>The method comprises the steps that the charging starting time period of an ith electric automobile is optimized for the ith iteration of a particle swarm, and X is the number of time periods;
employing particle swarm algorithm to win-win benefit Q * Maximizing the initial charging time of the ith electric automobile after optimization by using the optimized targetThe daily load curve after optimization is shown in fig. 2. .
Step 5: calculating the human body feeling quantity and the objective stimulation quantity, and establishing a stimulation patch model;
in the step 5, the human body feeling quantity is calculated as follows:
s＝kln(R)+s 0
wherein k is weber coefficient, k=1; s is(s) 0 S is the stimulation constant 0 =1; s is human body feeling quantity and R is objective stimulation quantity; the s reflects the feeling degree of the vehicle owner on the subsidy price, and the R reflects the reduction degree of the grid on the valley electricity price;
the objective stimulation amount in the step 5 is as follows:
wherein R is objective stimulation, R p,m The subsidy price of the power grid for the mth period is the variable to be solved, R 0,m To the electricity price of the m time period before the patch, R' m The electricity price of the m time period after the patch is the electricity price;
wherein, tau is the responsiveness of the user to the subsidy policy, namely the proportion of the user willing to change the self-charging policy according to the subsidy price; r is R M In order to be the smallest of the perceived differences,s 0 for the stimulus constant, k is the Weber coefficient, R p,i,m,j The subsidy price of the power grid in the mth period is calculated; r is R 0,m The electricity price of the m-th period before the patch is completed.
The stimulus patch model determines the extent of response of the participant, the stronger the participant (the smaller k, s 0 The greater) the higher the benefit it can obtain in the game, but it may also result in a secondary peak when Gu Shibian is peaked, which is detrimental to the grid.
Step 6: constructing an optimal charging subsidy scheduling model of the electric automobile, and optimizing by adopting a traversing method in combination with the responsiveness of a user to a subsidy policy to obtain the subsidy price of the optimized power grid at a certain time for the user willing to change the charging starting time;
in the step 6, the construction of the optimal charging patch scheduling model of the electric automobile is as follows:
according to the subsidy information of the power grid, a user actively adjusts the self-charging plan from the perspective of self benefit so as to achieve the minimum charging cost, so that the self-income is maximized, and in the process of dynamic game, the user and the power grid always pursue the goal of the maximum self-income.
Wherein W is the operation profit of the local distribution network, W min For the minimum operation profit acceptable by the local distribution network, X is the number of time periods, tau j N is the number of the j-th iteration user willing to change the starting time of charging, u i For the ith user willing to change the charge start time, R M To minimize perceived difference, Q op For optimum charge return, Q * Epsilon for the mutual benefit win-win benefit solved in step 4 op For the optimal electricity utilization mode of users, theta op For the optimal user electricity fee expenditure gain,the wave income of the optimal power grid is obtained; τ j The responsiveness of the user to the subsidy policy is that the proportion of the user who is willing to change the self-charging policy according to the subsidy price is the to-be-solved variable; />The method comprises the steps that the subsidy price of an electric automobile of an ith user in an mth period of time, which is willing to change the charging starting time, of a jth iterative power grid is used as a variable to be solved;
and step 6, optimizing by a traversal method according to the responsiveness of the user to the subsidy policy to obtain the subsidy price of the optimized power grid for the user willing to change the charging starting time in a certain time, wherein the subsidy price is specifically as follows:
taking the initial charging time of the electric vehicle after optimization in the step 4 as a known quantity to be brought into the optimal charging subsidy scheduling model of the electric vehicle, and updating the optimal user electricity utilization mode gain, the optimal user electricity charge expenditure gain and the optimal power grid fluctuation gain, and solving the power grid pair (u) when the mutual benefit win-win gain is maximum in the step 6 by adopting a traversing method i Patch price of electric vehicle in mth periodHere the (u) i The patch prices of the electric vehicles at the mth period are assumed to be consistent,the time period was 24,1,2,3,4,5,6,7.
Note that, the subsidy price at the mth period for the ith user willing to change the charging start timeWhen no patch is applied ∈ ->
The specific details of the time-sharing electricity price after patch are shown in Table 2:
table 2: time-of-use electricity price meter after patch
According to the (u) i Patch price of mth period of electric vehicleThe responsiveness of the user to the patch policy after the stimulation patch model in the step 5 is solved and optimized, namely tau * ，τ * ＝37.39％；
Step 7: determining the quantity of the electric vehicle willing to change the charging starting time according to the responsiveness of the optimized user to the subsidy policy in the step 6; the patch price of the electric automobile of the ith user at the mth period of time is that according to the optimized electric automobile willing to change the charging starting time wherein ui ∈(0,τ * N)，m∈(0,X)，τ * N represents the number of the optimized users willing to change the charging starting time, and the electricity price of the mth time period is obtained again by bringing the optimized users into the time-sharing electricity price in the step 1, namely wherein ui ∈(0,τ * N), m E (0, X); solving the optimal charging patch scheduling model of the electric automobile by adopting the particle swarm algorithm in the step 4 through the electricity expense income of the user in the step 3, namely theta, to obtain the optimal charging starting time of the ith electric automobile, namely +.>Wherein i is E [1, N]N is the number of electric vehicles, and X is the number of time periods in a day;
the grid daily load curve is shown in fig. 3.
According to the optimal stimulated subsidy model, each time the subsidy price is determined, the user has determined responsiveness and pursues the maximum income of the user, so that the ordered charging strategy is responded, and the power grid load is further stable; the power grid side sets subsidy price according to the user response, so that the power grid load is stable, and the economic benefit of the power grid is improved.
It should be understood that the foregoing description of the preferred embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that those skilled in the art can make substitutions or modifications without departing from the scope of the invention as set forth in the appended claims.

Claims (1)

1. An ordered charging patch scheduling method for an electric automobile is characterized by comprising the following steps:
step 1: dividing 24 hours a day into a plurality of time periods, and further defining the electricity price of each time period;
step 2: researching the random distribution characteristic of the charging time of the resident community electric automobile connected to the power grid, and establishing a charging load model of the electric automobile;
step 3: constructing a user electricity utilization mode profit model, a user electricity charge expenditure profit model, a user profit model and a power grid load variance model, and finally constructing a mutual benefit win-win profit model;
step 4: taking the mutual benefit win-win benefits as a preferable target, and optimizing and solving by using a particle swarm algorithm to obtain the optimized initial charging time of the electric vehicle;
step 5: calculating the human body feeling quantity and the objective stimulation quantity, and establishing a stimulation patch model;
step 6: constructing an optimal charging subsidy scheduling model of the electric automobile, and optimizing by adopting a traversing method in combination with the responsiveness of a user to a subsidy policy to obtain the subsidy price of the optimized power grid for the user willing to change the charging starting time in a certain time;
step 7: determining the quantity of the electric vehicle willing to change the charging starting time according to the responsiveness of the optimized user to the subsidy policy in the step 6; the patch price of the electric automobile of the ith user at the mth period of time is that according to the optimized electric automobile willing to change the charging starting time wherein ui ∈(0,τ * N)，m∈(0,X)，τ * N represents the number of charging start time which the user is willing to change after optimization, and the electricity price of the mth time period is re-obtained by bringing the electricity price into the time-sharing electricity price in the step 1, namely +.> wherein ui ∈(0,τ * N), m E (0, X); solving the optimal charging patch scheduling model of the electric automobile by adopting the particle swarm algorithm in the step 4 through the electricity expense income of the user in the step 3, namely theta, to obtain the optimal charging starting time of the ith electric automobile, namely +.>Wherein i is E [1, N]N is the number of electric vehicles, and X is the number of time periods in a day;
the number of time periods in the day in the step 1 is X, and the length of each time period is h;
the electricity price of each time period in the step 1 is as follows:
the electricity price of the m-th time period is c x,m M represents the m-th time period, m.epsilon.1, X]；
The specific steps of building the load level model in the step 2 are as follows:
the charging start time of the electric automobile is t 0,i A travel distance d i ，i∈[1,N]；
The initial charging time for randomly generating a plurality of electric vehicles through the normal distribution probability function is as follows:
wherein i is E [1, N]N is the number of electric vehicles, mu d Is the expected value, sigma d Is standard deviation, t 0,i The initial charging time of the ith electric automobile;
charging time period K of ith electric automobile i The method comprises the following steps:
INT is a rounding function, t 0,i The initial charging time of the ith electric automobile is h, and the length of each time period is h;
the calculated state of charge of the electric automobile is as follows:
E i ＝(1-d i /D i )×100％
wherein i is E [1, N]N is the number of electric vehicles, E i Is the charge state of the ith electric automobile battery, d i D is the daily driving mileage of the ith electric automobile state i The maximum driving mileage of the ith electric vehicle in the pure electric state;
the calculated charging time length of the electric automobile is as follows:
wherein i is E [1, N]，T C,i The charging time length d of the ith electric automobile i The daily driving mileage of the ith electric automobile; w (W) 100,i Power consumption of the ith electric automobile for hundred kilometers, p c,i The charging power of the ith electric automobile is represented by lambda, and the charging efficiency is represented by lambda;
number of charging periods beta of the ith electric vehicle K,i The method comprises the following steps:
INT is a rounding function, T C,i The charging time length of the ith electric automobile is h, and the length of each time period is h;
the charging end time of the electric automobile is calculated as follows:
T e,i ＝t 0,i +T c,i
wherein i is E [1, N]N is the number of electric vehicles, t 0,i For the initial charging time of the ith electric automobile, T C,i For the charging time length T of the ith electric automobile e,i The charging end time of the electric automobile is;
if T e,i > 24, T e,i ＝T e,i -24；
The total charging load P of the electric automobile in the m time period ev,m The method comprises the following steps:
0≤N m ≤N
wherein i is E [1, N]，N m The number of the electric vehicles in the m time period is obtained by traversing the charging time periods of N electric vehicles; p is p c,i,m Load charged in m time period of ith electric automobile, if the load is not charged in the time period, p c,i,m ＝0；P ev,m The total charging load of the electric automobile in the m time period and the original load P of the micro-grid load in the m time period load0,m Adding to obtain micro-grid load P at m moment after disordered charging of electric automobile load,m X is the number of time periods;
in the step 3, the user electricity utilization mode profit model is as follows:
wherein ,in order to implement the change value of the total charging power of the electric automobile user in each time period of time-of-use electricity price after the ordered charging strategy, P ev,m For the total charge load of the electric vehicle in the m time period, < > for>In order to optimize the total charging load of the electric automobile at m time, namely the solving variable to be optimized, X is the number of time periods, epsilon is the income of the user in the power utilization mode, and is a function of the charging starting time of the electric automobile, epsilon [0,1 ]]；
And step 3, defining a user electricity fee expenditure income model as follows:
wherein θ is the electricity fee expenditure income of the user, C i To pay for electricity charges for the ith electric vehicle one day before the orderly charging strategy is implemented,the electricity charge expenditure of the ith electric automobile in one day after the ordered charging strategy is implemented;
the calculation formula of the electricity charge expenditure of the ith electric automobile in one day is as follows:
and 3, the user profit model is as follows:
Y＝γ 1 ε+γ 2 θ
γ 1 +γ 2 ＝1
wherein Y is the user benefit; gamma ray 1 For the weight value of the user power consumption mode income, gamma 2 A weight value of the income of the expense for the user;
step 3, the power grid load variance model reflects a power grid load stability index, and is specifically defined as follows:
wherein ,σ2 The power grid load variance, X is the number of time periods, N is the number of electric vehicles and P 0,m For the conventional electricity load of residents in the Mth time period, P i,m For the charging load of the ith electric automobile in the mth time period, P Avg The average value of the total load of the power grid in one day;
the power grid load fluctuation income reflects the degree of power grid load fluctuation, and the larger the power grid fluctuation income is, the more stable the power grid load is, and is specifically defined as:
in the formula ：for the power grid fluctuation income->Solving variable to be optimized for the optimized power grid load variance +.>In order to avoid consideration of the user side, the electric vehicle is the most in the power grid load variance under the condition of absolute orderSmall value (S)>In order to avoid the user side, the maximum value of the power grid load variance is +.> and />Can be optimized by a particle swarm algorithm;
the mutual benefit win-win gain model in the step 3 represents the overall satisfaction degree of the power grid and the user on the charging strategy, and is specifically defined as follows:
wherein Q is the mutual benefit win-win benefit, Y is the user benefit,for grid load fluctuation benefit, gamma 1 The gain weight coefficient of the power grid;
in the step 4, when the highest mutual benefit win-win gain is solved by using the particle swarm algorithm, the initial charging time of the electric automobile is as follows:
the preferred targets in step 4 are as follows:
updating the velocity v of the particles iter,i And an initial charging time t iter,i Defining a maximum initial charge time t iter,i,max =24, defining a minimum initial charge time t iter,i,min ＝0；
v iter,i ＝w*v iter,i +c1*rand*(y iter-1,i -t iter,i )+c2*rand*(pg-t iter,i )
t iter,i ＝t iter,i +v iter,i
Wherein c1 is a first learning factor, c2 is a second learning factor, w is an inertial weight, pg is globally optimal, y iter-1,i Initial charge time t for the previous iteration of particles iter-1,i Velocity v iter,i And an initial charging time t iter,i Item represents the number of iterations of the particle;
in the optimization model, the following steps are: the mutual benefit and win-win income Q is about the charging starting time of the electric automobileThe i-th electric vehicle charging start time t 0,i T is the initial solution of the particle swarm algorithm iter,i The initial charging time of the ith electric automobile after the particle swarm is subjected to the iterative optimization for the ith time; INT is a rounding function, < > and->In order to optimize the initial charging time of the ith electric automobile, h is the length of each time period,/for the ith electric automobile>The method comprises the steps that the charging starting time period of an ith electric automobile is optimized for the ith iteration of a particle swarm, and X is the number of time periods;
employing particle swarm algorithm to win-win benefit Q * Maximizing the initial charging time of the ith electric automobile after optimization by using the optimized target
In the step 5, the human body feeling quantity is calculated as follows:
s＝kln(R)+s 0
wherein k is a Weber coefficient, s 0 S is human body feeling quantity, R is objective stimulation quantity; the s reflects the feeling degree of the vehicle owner on the subsidy price, and the R reflects the reduction degree of the grid on the valley electricity price;
the objective stimulation amount in the step 5 is as follows:
wherein R is objective stimulation, R p,m The subsidy price of the power grid for the mth period is the variable to be solved, R 0,m To the electricity price of the m time period before the patch, R' m The electricity price of the m time period after the patch is the electricity price;
wherein, tau is the responsiveness of the user to the subsidy policy, namely the proportion of the user willing to change the self-charging policy according to the subsidy price; r is R M In order to be the smallest of the perceived differences,s 0 for the stimulus constant, k is the Weber coefficient, R p,i,m,j The subsidy price of the power grid in the mth period is calculated; r is R 0,m The electricity price of the m-th period before the patch is completed;
in the step 6, the construction of the optimal charging patch scheduling model of the electric automobile is as follows:
wherein W is the operation profit of the local distribution network, W min For the minimum operation profit acceptable by the local distribution network, X is the number of time periods, tau j N is the number of the j-th iteration user willing to change the starting time of charging, u i For the ith user willing to change the charge start time, R M To minimize perceived difference, Q op For optimum charge return, Q * Epsilon for the mutual benefit win-win benefit solved in step 4 op For the optimal electricity utilization mode of users, theta op For the optimal user electricity fee expenditure gain,the wave income of the optimal power grid is obtained; τ j The responsiveness of the user to the subsidy policy is that the proportion of the user who is willing to change the self-charging policy according to the subsidy price is the to-be-solved variable; />The method comprises the steps that the subsidy price of an electric automobile of an ith user in an mth period of time, which is willing to change the charging starting time, of a jth iterative power grid is used as a variable to be solved;
and step 6, optimizing by a traversal method according to the responsiveness of the user to the subsidy policy to obtain the subsidy price of the optimized power grid for the user willing to change the charging starting time in a certain time, wherein the subsidy price is specifically as follows:
taking the initial charging time of the electric vehicle after optimization in the step 4 as a known quantity to be brought into the optimal charging subsidy scheduling model of the electric vehicle, and updating the optimal user electricity utilization mode gain, the optimal user electricity charge expenditure gain and the optimal power grid fluctuation gain, and solving the power grid pair (u) when the mutual benefit win-win gain is maximum in the step 6 by adopting a traversing method i Patch price of electric vehicle in mth period
Note that, the subsidy price at the mth period for the ith user willing to change the charging start timeWhen no patch is applied ∈ ->
According to the (u) i Patch price of mth period of electric vehicleThe responsiveness of the user to the patch policy after the stimulation patch model in the step 5 is solved and optimized, namely tau * ；
According to the optimal stimulated subsidy model, each time the subsidy price is determined, the user has determined responsiveness and pursues the maximum income of the user, so that the ordered charging strategy is responded, and the power grid load is further stable;
the power grid side sets subsidy price according to the user response, so that the power grid load is stable, and the economic benefit of the power grid is improved.
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