CN111242362A - Electric vehicle real-time charging scheduling method based on charging station comprehensive state prediction - Google Patents
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Abstract
The invention discloses an electric vehicle real-time charging scheduling method based on charging station comprehensive state prediction, which comprises the steps of establishing a charging station comprehensive state prediction model of charging station functions, an idle state and a comprehensive charging progress; on the basis, based on a vehicle scheduling theory, a multi-objective scheduling model is established by taking the owner satisfaction degree, the maximum total income of the charging station and the minimum fluctuation of the total variance of the load of the power grid as targets, the model is optimized to obtain an optimal scheduling strategy, and an effective solution is provided for reducing the utilization imbalance of the charging station, improving the owner satisfaction degree, improving the income of the charging station and reducing the fluctuation of the load of the power grid.
Description
Technical Field
The invention belongs to the field of electric vehicle charging, and relates to an electric vehicle real-time scheduling method based on comprehensive state prediction of a charging station.
Background
The electric vehicle charging scheduling problem is one of basic problems in the electric vehicle charging research field, and electric vehicle real-time charging scheduling generally predicts a charging station state and guides an electric vehicle with a charging demand to an optimal adaptive charging station by acquiring real-time information of the charging station, the electric vehicle, a power grid and the like. The scheduling decision is influenced by comprehensive states of the charging station, such as action, idle state, charging progress and the like, the influence of state changes and coupling relations of the charging station on the scheduling decision is not comprehensively considered in the traditional electric vehicle real-time scheduling method, only partial states are analyzed or predicted, the scheduling method and strategy are unreasonable, and further the problems of unbalanced charging requirement of the electric vehicle and the supply of the charging station, large power grid load fluctuation, low satisfaction degree of an owner and the like are caused. Under the background, the invention makes up the defects of the traditional real-time scheduling method, and the provided real-time charging scheduling method based on the comprehensive state prediction of the charging station can solve the problems of unbalanced utilization of the charging station, low satisfaction of an owner, large load fluctuation of a power grid and the like.
Disclosure of Invention
Aiming at the defects of the existing method, the invention provides a real-time charging scheduling method of an electric vehicle based on the comprehensive state prediction of a charging station. Specifically, a charging station comprehensive state prediction model is established through the charging station coupling effect, the idle state and the charging progress comprehensive state prediction; on the basis, a multi-target scheduling model is established by taking the owner satisfaction degree, the maximum total income of the charging station and the minimum fluctuation rate of the charging load in the adjacent time period as targets, an optimal scheduling strategy is obtained, and meanwhile, an effective solution is provided for balancing the utilization rate of the charging station, improving the owner satisfaction degree and reducing the load fluctuation of a power grid.
The concrete steps
The method comprises the following steps:
predicting charging station coupling
① establishing interaction factors of the charging station and the electric vehicle:
where Σ is the sum symbol in the mathematical formula, Dj(k) Represents the average distance, D, from the electric vehicle, which selects charging station j at time k, to charging station js,j(k) The distance between the electric vehicle s selecting the charging station j at the moment k and the charging station j is shown, and m represents the number of vehicles selecting the charging station j at the moment k;
② establishing interaction factors between charging stations;
first, an interaction model between two charging stations is established:
νij(k)＝β1ui(k)+β2τi(k)
in the above formula, vij(k) Represents the degree of action, u, of the charging station i on the charging station j at the moment ki(k) Is the control quantity of the charging station i at the moment k, the charging station can select the real-time electricity price, the charging power and the parking fee as the control quantity β1、β2As positive scalar weights, β2+β2＝1，τi(k) The proportion of vehicles entering charging station i at time k is expressed as follows:
wherein M (k) is the total number of vehicles to be charged at time k, Mi(k) The number of vehicles for which charging station i is selected;
further, an interaction model of neighboring charging stations is obtained:
in the formula, zj(k) Representing the interaction of charging station j with all neighbouring charging stations at time k, NiIndicating the number of adjacent charging stations;
③, establishing a charging station coupling action prediction model;
wherein the content of the first and second substances,ρ1、ρ2representing the weights, p, for a known positive scalar1+ρ2＝1；
(II) predicting the charging station idle state
Wherein the content of the first and second substances,
for the moment k +1 the charging station idle state prediction, muj0、μj1、μj2、μj3For known positive scalar weights, obtained by historical data training, dj(k) Representing random disturbances, x, caused by unscheduled vehicles entering a charging stationj(k) Representing the idle state of the charging station at the moment k;
(III) predicting charging progress of charging station
Wherein ol,jη is the current charging progress of the first charging pile in the charging station jl,jThe charging efficiency of the charging pile is improved; q is the number of charging piles;
and (IV) the comprehensive state prediction of the charging station is obtained by combining the prediction of the coupling effect, the idle state and the charging progress of the charging station:
wherein epsilonj(k +1) is a charging station comprehensive state prediction quantity, b1、b2、b3For known positive scalar weights, b1+b2+b31, respectively in the range of [0,1]；
(V) establishing a multi-target model for electric vehicle real-time dispatching based on comprehensive state prediction of charging station
① vehicle owner satisfaction objective function based on the charging station comprehensive state prediction;
based on the benefit of the vehicle owner and the actual requirement constraint, a target function J of the satisfaction degree of the vehicle owner is set1(k +1) is as follows:
wherein min {. is symbol for minimum value, rjIs the parking fee unit price, T, of the charging station jcIs the consumption time, w, of the electric vehicle charging to the target electric quantityTj(k +1) is the charging latency factor at time k +1, TdIs the compensation time converted from the electric quantity consumed by the electric vehicle to the charging station, α1，α2Weight factor for vehicle owner cost, α1+α21, sigma is the time value of the owner, and n is the total number of charging stations;
in the above formula, the time T is consumedcTime of supplement TdThe following equations were respectively obtained:
wherein SOC is the state of charge of the electric vehicle, E is the battery capacity, D is the distance from the vehicle to the charging station, P is the charging power of the vehicle owner, and theta is the power consumption per kilometer wherein wTj(k +1) is obtained according to the comprehensive state of the charging station, and is specifically expressed as follows:
② charging station revenue objective function based on charging station state of integration prediction;
considering the comprehensive state of the charging stations, the income maximization objective function J of all the charging stations at the moment k2(k +1) is represented by:
wherein max {. is the symbol for maximum value, Isj(k +1) is the cost of the electric vehicle s at charging station j at time k, TsjRepresenting the charging time of the electric vehicle s at a charging station j, g is the number of the electric vehicles to be dispatched, n is the number of the charging stations of the operator, e is a positive scalar weight, and is obtained through historical data training, and pj(k) Representing the real-time electricity price of the charging station at the moment k;
③ power grid load objective function based on charging station integrated state prediction;
according to the comprehensive state prediction of the charging station, the target function J is set by combining the power grid load fluctuation theory and aiming at reducing the charge load fluctuation rate in the adjacent time period3(k +1) is as follows:
wherein, PEV(k) And h is a positive scalar weight and is obtained through historical data training.
(VI) solving real-time scheduling multi-target model of electric automobile
And carrying out optimization solution on the electric vehicle real-time scheduling multi-target model to obtain an optimal scheduling strategy, and realizing the electric vehicle real-time charging scheduling method based on the charging station comprehensive state prediction.
Has the advantages that: the electric vehicle real-time charging scheduling method based on the charging station comprehensive state prediction overcomes the defects of the traditional electric vehicle real-time charging scheduling method, provides a charging station comprehensive state prediction method considering the coupling effect, the idle state, the charging progress and the like of a charging station and a real-time scheduling multi-target model based on the comprehensive state prediction, and can better realize the multi-target optimization of balancing the resource utilization rate of the charging station, improving the satisfaction degree of an owner and reducing the load fluctuation of a power grid through the scheduling method.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
As shown in fig. 1, the solution of the electric vehicle real-time charging scheduling method based on the charging station comprehensive state prediction of the present invention includes the following steps:
the method comprises the following specific implementation steps:
predicting charging station coupling
And acquiring the number of vehicles entering the charging station and the distance real-time data between the electric vehicle and the charging station, and predicting the coupling effect of the charging station.
① establishing interaction factors of the charging station and the electric vehicle;
where Σ is the sum symbol in the mathematical formula, Dj(k) Represents the average value of the distances from the electric vehicle to the charging station j at the moment ks,j(k) The distance between the electric vehicle s selecting the charging station j at the moment k and the charging station j is shown, and m represents the number of vehicles selecting the charging station j at the moment k;
② establishing interaction factors between charging stations;
firstly, establishing an interaction degree model between two charging stations:
νij(k)＝β1ui(k)+β2τi(k)
in the above formula, vij(k) Represents the degree of action, u, of the charging station i on the charging station j at the moment ki(k) Is the control quantity of the charging station i at the moment k, the charging station can select factors such as real-time electricity price, charging power, parking fee and the like as the control quantity β1、β2As positive scalar weights, β2+β2Where 1 is taken as 0.5, τ respectivelyi(k) The proportion of vehicles entering charging station i at time k is expressed as follows:
wherein M (k) is the total number of vehicles to be charged at time k, Mi(k) The number of vehicles for which charging station i is selected;
further, an interaction model of neighboring charging stations is obtained:
in the formula, zj(k) Represents the degree of interaction, N, of charging station j with all neighboring charging stations at time kiRepresenting the number of adjacent charging stations, it is contemplated in this patent to refer to charging stations within 5 km of each other as adjacent charging stations;
③, establishing a charging station action prediction model;
where ρ is1、ρ2Representing the weights, p, for a known positive scalar1+ρ2Each 0.5 is taken as 1.
(II) predicting the charging station idle state
And acquiring real-time data of non-scheduled vehicles entering the charging station, and predicting the idle state of the charging station. The charging station idle state is pre-measured as
With the charging station control amount u at the previous momentj(k)、Interaction z of charging station jj(k) Random disturbance d caused by non-scheduled vehicle entering charging station jj(k) In this regard, the charging station idle state prediction value is expressed as follows:
wherein, muj0、μj1、μj2、μj3The weight is known as the positive scalar weight and can be obtained through historical data training;
(III) predicting charging progress of charging station
And acquiring the charging progress of the charging pile and the charging efficiency real-time data of the charging pile, and predicting the charging progress of the charging station.
Wherein ol,jFor the current charging schedule of the ith charging pile in charging station j, ηl,jThe charging efficiency of the charging piles is obtained, and q is the number of the charging piles;
(IV) forecasting the comprehensive state of the charging station by combining the coupling effect, the idle state and the prediction of the charging progress of the charging station
Wherein epsilonj(k +1) is a charging station comprehensive state prediction quantity, b1、b2、b3For known positive scalar weights, b1+b2+b31, respectively in the range of [0,1]；
(V) establishing a multi-target model for electric vehicle real-time dispatching based on comprehensive state prediction of charging station
① vehicle owner satisfaction objective function based on the charging station comprehensive state prediction;
based on the benefit of the vehicle owner and the actual requirement constraint, a target function J of the satisfaction degree of the vehicle owner is set1(k +1) is as follows:
wherein min {. is symbol for minimum value, rjIs the parking fee unit price, T, of the charging station jcIs the consumption time, w, of the electric vehicle charging to the target electric quantityTj(k +1) is the waiting time factor for the user at time k +1, TdIs the replenishment time converted from the amount of electricity lost during charging, α1，α2Weight factor for vehicle owner cost, α1+α2Taking 0.5 as 1, wherein sigma is the time value of the owner, and n is the total number of charging stations;
in the above formula, the time T is consumedcTime of supplement TdCan be obtained by the following equation:
wherein SOC is the state of charge of the electric vehicle, E is the battery capacity, D is the distance from the vehicle to the charging station, P is the charging power of the vehicle owner, theta is the power consumption per kilometer, and the charging waiting time factor w of the vehicle owner at the moment of k +1Tj(k +1) can be obtained from the charging station state, which is specifically expressed as follows:
② charging station revenue objective function based on charging station state of integration prediction;
considering the comprehensive state of the charging stations, the income maximization objective function J of all the charging stations at the moment k2(k +1) can be represented as:
wherein max {. is the symbol for maximum value, Isj(k +1) is the cost of the electric vehicle s at charging station j at time k, TsjThe method comprises the steps that the charging duration of an electric vehicle s at a charging station j is represented, g is the number of the electric vehicles to be dispatched, n is the number of the charging stations of an operator, and e is a positive scalar weight and can be obtained through historical data training;
③ power grid load objective function based on charging station integrated state prediction;
according to the comprehensive state prediction of the charging station, the target function J is set by combining the power grid load fluctuation theory and aiming at reducing the charge load fluctuation rate in the adjacent time period3(k +1) is as follows:
wherein, PEV(k) The weight is the total load requirement of the electric automobile at the moment k, and h is a positive scalar weight and can be obtained through historical data training.
(VI) solving real-time scheduling multi-target model of electric automobile
The method comprises the steps of obtaining real-time data such as power grid load and electric vehicle charge state, solving a real-time electric vehicle scheduling model to obtain an optimal scheduling strategy, and achieving the electric vehicle real-time charging scheduling method based on comprehensive state prediction of a charging station.
Claims (1)
1. The electric vehicle real-time charging scheduling method based on the charging station comprehensive state prediction is characterized by comprising the following steps of:
one) predicting charging station coupling
① establishing interaction factors of the charging station and the electric vehicle:
where Σ is the sum symbol in the mathematical formula, Dj(k) Represents the average distance, D, from the electric vehicle, which selects charging station j at time k, to charging station js,j(k) The distance between the electric vehicle s selecting the charging station j at the moment k and the charging station j is shown, and m represents the number of vehicles selecting the charging station j at the moment k;
② establishing interaction factors between charging stations;
first, an interaction model between two charging stations is established:
νij(k)＝β1ui(k)+β2τi(k)
in the above formula, vij(k) Represents the degree of action, u, of the charging station i on the charging station j at the moment ki(k) The control quantity of the charging station i at the moment k is selected by the charging station, the real-time electricity price, the charging power and the parking fee are used as the control quantity β1、β2As positive scalar weights, β2+β2＝1，τi(k) The proportion of vehicles entering charging station i at time k is expressed as follows:
wherein M (k) is the total number of vehicles to be charged at time k, Mi(k) The number of vehicles for which charging station i is selected;
further, an interaction model of neighboring charging stations is obtained:
in the formula, zj(k) To representk interaction of charging station j with all neighboring charging stations, NiIndicating the number of adjacent charging stations;
③, establishing a charging station coupling action prediction model;
where ρ is1、ρ2Representing the weights, p, for a known positive scalar1+ρ2＝1；
(II) predicting the charging station idle state
Wherein the content of the first and second substances,
for the moment k +1 the charging station idle state prediction, muj0、μj1、μj2、μj3For known positive scalar weights, obtained by historical data training, dj(k) Representing random disturbances, x, caused by unscheduled vehicles entering a charging stationj(k) Representing the idle state of the charging station at the moment k;
(III) predicting charging progress of charging station
Wherein ol,jη is the current charging progress of the first charging pile in the charging station jl,jThe charging efficiency of the charging pile is improved; q is the number of charging piles;
and (IV) the comprehensive state prediction of the charging station is obtained by combining the prediction of the coupling effect, the idle state and the charging progress of the charging station:
wherein epsilonj(k +1) is a charging station comprehensive state prediction quantity, b1、b2、b3For known positive scalar weights, b1+b2+b31, respectively in the range of [0,1]；
(V) establishing a multi-target model for electric vehicle real-time dispatching based on comprehensive state prediction of charging station
① vehicle owner satisfaction objective function based on the charging station comprehensive state prediction;
based on the benefit of the vehicle owner and the actual requirement constraint, a target function J of the satisfaction degree of the vehicle owner is set1(k +1) is as follows:
wherein min {. is symbol for minimum value, rjIs the parking fee unit price, T, of the charging station jcIs the consumption time, w, of the electric vehicle charging to the target electric quantityTj(k +1) is the charging latency factor at time k +1, TdIs the compensation time converted from the electric quantity consumed by the electric vehicle to the charging station, α1，α2Weight factor for vehicle owner cost, α1+α21, sigma is the time value of the owner, and n is the total number of charging stations;
in the above formula, the time T is consumedcTime of supplement TdThe following equations were respectively obtained:
wherein SOC is the state of charge of the electric vehicle, E is the battery capacity, D is the distance from the vehicle to the charging station, P is the charging power of the vehicle owner,
for power consumption per kilometer wTj(k +1) according to charging stationThe overall state is obtained as follows:
② charging station revenue objective function based on charging station state of integration prediction;
considering the comprehensive state of the charging stations, the income maximization objective function J of all the charging stations at the moment k2(k +1) is represented by:
wherein max {. is the symbol for maximum value, Isj(k +1) is the cost of the electric vehicle s at charging station j at time k, TsjRepresenting the charging time of the electric vehicle s at a charging station j, g is the number of the electric vehicles to be dispatched, n is the number of the charging stations of the operator, e is a positive scalar weight, and is obtained through historical data training, and pj(k) Representing the real-time electricity price of the charging station at the moment k;
③ power grid load objective function based on charging station integrated state prediction;
according to the comprehensive state prediction of the charging station, the target function J is set by combining the power grid load fluctuation theory and aiming at reducing the charge load fluctuation rate in the adjacent time period3(k +1) is as follows:
wherein, PEV(k) H is a positive scalar weight and is obtained through historical data training, wherein the h is the total load requirement of the electric automobile at the moment k;
(VI) solving real-time scheduling multi-target model of electric automobile
And carrying out optimization solution on the electric vehicle real-time scheduling multi-target model to obtain an optimal scheduling strategy, and realizing the electric vehicle real-time charging scheduling method based on the charging station comprehensive state prediction.
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| CN113650515A (en) * | 2021-07-07 | 2021-11-16 | 广州杰赛科技股份有限公司 | Electric vehicle charging control method and device, terminal equipment and storage medium |
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