CN115860379A - Electric automobile day-ahead scheduling strategy and system based on economic target conversion - Google Patents
Electric automobile day-ahead scheduling strategy and system based on economic target conversion Download PDFInfo
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Abstract
The invention relates to an electric automobile day-ahead scheduling strategy based on economic target conversion, which comprises the following steps: s1: acquiring basic information; s2: establishing an electric automobile charging model; s3: analyzing whether the obtained electric vehicle scheduling time exceeds a constraint condition or not; meanwhile, judging whether the number of the calculated electric vehicles is larger than N, if so, returning to S2 to continue calculating until the scheduling plan of the N electric vehicles is completed; s4: and calculating an objective function of the day-ahead scheduling of the electric automobile to obtain an optimized day-ahead scheduling plan. The invention fully utilizes the flexibility of the electric automobile load, considers the benefits of the power grid side and the user side, limits the objective function by the minimum variance, reduces the negative effects of the electric automobile connected to the power grid, and converts the technical index of the load peak-valley difference of the power grid into the economic index of the minimum user charging cost by the objective function. The strategy ensures the optimal economy of the user, reduces the peak-valley difference of the load of the power grid and verifies the effectiveness of the strategy.
Description
Technical Field
The invention belongs to the technical field of economic dispatching of electric automobiles, and particularly relates to a day-ahead dispatching strategy and system of an electric automobile based on economic target conversion.
Background
With the development of battery technology, the electric automobile is used as a green travel vehicle, the development is very rapid, and the electric automobile is widely popularized in all countries in the world. As a novel load, the charging behavior of the electric automobile has 'multi-space-time-scale discreteness', and when the large-scale electric automobile is connected into a power grid, the large load impact is caused to a power system, so that the safety of the power grid operation is seriously influenced. On one hand, the large number of large-scale electric automobiles is large, the electric automobile load has the characteristics of fluctuation and randomness, a large number of electric automobile charging loads can coincide with the peak time of the power load, the effect of 'adding peaks on peaks' is achieved, and the scheduling pressure of a power system is greatly increased; on the other hand, as a random load, the electric vehicle has a higher requirement on the installed capacity of the power supply side due to disordered charging, the installed capacity is too low to ensure normal operation of the power grid during disordered charging of the electric vehicle, the operating cost of the power grid is increased due to too high installed capacity, and the power grid planning faces the balance problem of economy and reliability. Therefore, the electric vehicle is guided to charge and discharge through reasonable excitation based on the demand response strategy, and the method has important significance for safe operation of a power grid.
In the daily use process of the electric automobile, the charging time period coincides with the peak-time electricity price time period of the power grid, so that the charging cost of a user is too high, and the use cost of the user is improved. The price of electricity when timesharing is the most effective means of management and control electric automobile behavior of charging, can change electric automobile's the action of charging and discharging, and the user is calmed to the millet time period with partial peak charging period, promotes millet time period power consumption. With the development of the interaction technology of the electric automobile and the power grid, the electric automobile can be stimulated and guided to emit a large amount of electric energy to the power grid by adopting the V2G technology in the peak load period of the power grid through the electric charge subsidy, so that the peak clipping and valley filling effects are realized on the whole system, the operation reliability of the power system is improved, and the power generation and operation cost of the power system is reduced. The formulation of the charge and discharge electricity price at the peak valley is very important for the development of the electric automobile, and a reasonable charge and discharge electricity price mechanism enables electric automobile users to actively respond to electric power balance in idle time, so that the safe and efficient operation of a regional power grid of a power system is ensured, the use cost of the electric automobile users is reduced, the acceptance degree of the users to the electric automobile can be further improved, and the practical significance is realized for the popularization and development of the electric automobile. Therefore, how to solve the economic dispatch considering the operation of the electric vehicle is a key problem to be solved urgently.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an electric vehicle day-ahead scheduling strategy based on economic target conversion so as to solve the problems in the background technology.
The technical problem to be solved by the invention is realized by the following technical scheme:
an electric automobile day-ahead scheduling strategy based on economic target conversion is characterized in that: the method comprises the following steps:
s1: acquiring basic information, comprising: power grid base load, N electric vehicles and time-of-use electricity price; simultaneously acquiring Monte Carlo simulation of the driving range, the charging power and the battery capacity of the electric automobile;
s2: establishing an electric vehicle charging model, calculating an initial charge state of the electric vehicle by using the acquired basic information, acquiring electric vehicle dispatching time by combining Monte Carlo simulation of arrival and departure time of the electric vehicle, and solving the day-ahead dispatching problem of the electric vehicle by using CPLEX;
s3: analyzing whether the obtained electric vehicle scheduling time exceeds a constraint condition, and returning to S1 for recalculation if the obtained electric vehicle scheduling time exceeds the constraint condition until all the constraint conditions are met; meanwhile, judging whether the number of the calculated electric vehicles is larger than N, if so, returning to S2 to continue calculating until the scheduling plan of the N electric vehicles is completed;
s4: and calculating an objective function of the day-ahead scheduling of the electric automobile to obtain an optimized day-ahead scheduling plan.
As a further improvement of the present technical solution, the method for establishing the electric vehicle charging model includes:
first, a probability density function of a daily travel distance, a probability density function of electric vehicle arrival time, and a probability density function of electric vehicle departure time per day are obtained.
Further, establishing a day-ahead scheduling model of the electric automobile according to the obtained functions:
s2.1: obtaining the charge state of the electric automobile when the electric automobile arrives according to the driving mileage and the battery parameters of the electric automobile:
wherein S is i，0 The initial charge state when the ith electric automobile arrives; s i，end Is the state of charge at the departure of the ith vehicle; d i The driving mileage of the ith electric automobile; e 100 The power consumption of the electric automobile is hundreds of kilometers; c i Is the battery capacity of the ith vehicle;
s2.2: the day-ahead scheduling is to predict the load curve of the day according to the historical load of a certain area, the 24-hour day of the area is divided into 96 time periods, and each time period is 15 minutes to perform modeling simulation; according to the time distribution law of arrival and departure of the electric vehicle, 13 bj Charging power of the ith vehicle is P ei Suppose that the charging pile charges the electric vehicle with constant power and only during the time period [ t ] between the arrival and departure of the ith electric vehicle arr，i ，t dep，i ]In the process of optimization, the optimization is carried out,
wherein, P ei For charging the ith electric automobile in the scheduling period tRate, P evci For the rated charging power of the ith electric vehicle,
is a variable of 0-1 corresponding to the state of charge.
S2.3: assuming that the load size of the electric automobile charged in the t-th period is P j If there are N electric vehicles in total, then there are:
wherein the power grid load P of the jth time interval sj For loading electric vehicle P j And a base load P bj The superposition of the two or more layers of the film,
P sj ＝P j +P bj j＝1，2，3，...，96
for the charging process of electric vehicles, there are
Wherein eta is the charging efficiency of the electric automobile; c i Is the battery capacity of the ith vehicle; p ei Charging power of the ith electric automobile in a scheduling period t; Δ t is the time interval; s i (t) represents the state of charge of the ith vehicle at time t; s i (t-1) represents the state of charge of the ith vehicle at time t-1.
The model is the electric automobile day-ahead scheduling model.
As a further improvement of the technical scheme, the driving distance of the electric automobile obeys log-normal distribution and the probability density function f of the daily driving distance D (d) Comprises the following steps:
in the formula (f) D (d) A probability density function representing a daily driving distance; sigma D For daily drivingStandard deviation of mileage, and σ D ＝0.88；μ D Is the expected value of daily mileage, and μ D =3.2; d is the daily mileage and is in km.
As a further improvement of the technical solution, the probability density function of the arrival time of the electric vehicle is as follows:
wherein f is arr (t) is the probability density function of the arrival time of the electric vehicle, mu arr Is the expected value of the arrival time of the electric vehicle, and mu arr ＝17.6；σ arr Is the standard deviation of the arrival time of the electric vehicle, and σ arr ＝3.4。
As a further improvement of the technical solution, the probability density function of the electric vehicle per day departure time is:
wherein f is dep (t) is the probability density function of the electric automobile departure time per day, mu dep Is the expected value of the departure time of the electric vehicle, and mu dep ＝8.92；σ dep Standard deviation of departure time of electric vehicle, and σ dep ＝3.24。
As a further improvement of the technical solution, the calculating of the objective function of the electric vehicle day-ahead scheduling policy includes two aspects of technical indicators and economic indicators:
the economic indicators comprise:
the method is characterized in that the lowest charging cost of an electric vehicle user is taken as an objective function, the user is guided to charge in a low-electricity-price period, and the load is unified on a time scale, namely:
in the formula (f) 1 The economic index of the day-ahead scheduling of the electric automobile is represented, c (j) is the charging electricity price of a j time interval, T is the division of 96 time intervals, P ei Charging power for the ith electric vehicle;
the technical indexes comprise:
for the technical indexes of the power grid side, the variance and the peak-valley difference of the power grid load are considered:
a) Variance (variance)
The variance is used for describing the dispersion degree of the power grid load, a smaller load variance indicates that the overall load fluctuation degree is smaller,
wherein, P sj For the grid load of the j-th time period,
is the average grid load over the time period T, and Var is the variance of the grid load.
b) Difference between peak and valley
The purpose of optimizing the load curve can be achieved by reducing the peak-to-valley difference,
p vd ＝max(P sj )-min(P sj )
wherein, P sj Grid load for the jth period, p vd The peak-to-valley difference of the power grid load;
the economic and technical aspects have different dimensions and can be solved by obtaining a Pareto optimal solution.
As a further improvement of the technical solution, the constraint conditions of the electric vehicle day-ahead scheduling policy include:
1) Electric vehicle state of charge constraints
Wherein the content of the first and second substances,Sand
respectively representing the upper limit and the lower limit of the charging state of the battery of the electric automobile;
2) User travel constraints
S i，end ≤S(j)，j＝t dep，i
Wherein S is i，end Is the state of charge at the time of departure of the ith vehicle, t dep，i Is the departure time of the ith vehicle;
3) Charging station capacity constraints
Wherein, C tc Is the rated power of the charging station, P ei The charging power of the ith electric automobile in the dispatching period t.
The utility model provides an electric automobile day-ahead scheduling system based on economic objective of conversion which characterized in that: comprises an information acquisition module, an electric vehicle charging model establishing module, a dispatching plan analyzing and judging module and a day-ahead dispatching plan optimizing module,
the information acquisition module is used for acquiring the basic load of a power grid, N electric vehicles and the time-of-use electricity price; simultaneously acquiring Monte Carlo simulation of the driving range, the charging power and the battery capacity of the electric automobile;
the electric vehicle charging model establishing module is used for calculating the initial charge state of the electric vehicle by using the acquired basic information, acquiring the scheduling time of the electric vehicle by combining Monte Carlo simulation of arrival and departure time of the electric vehicle, and solving the day-ahead scheduling problem of the electric vehicle by using CPLEX;
the scheduling plan analyzing and judging module is used for analyzing whether the obtained electric vehicle scheduling time exceeds a constraint condition; meanwhile, judging whether the number of the calculated electric vehicles is larger than N;
the day-ahead scheduling plan optimization module is used for calculating an objective function of the day-ahead scheduling of the electric vehicle to obtain an optimized day-ahead scheduling plan.
The method for establishing the electric vehicle charging model establishing module comprises the following steps:
firstly, acquiring a probability density function of daily driving distance, a probability density function of electric vehicle arrival time and a probability density function of electric vehicle departure time per day;
then, establishing a day-ahead scheduling model of the electric automobile according to the obtained functions, and specifically comprising the following steps:
s2.1: according to the driving mileage and the battery parameters of the electric automobile, obtaining the charge state when the electric automobile arrives:
wherein S is i，0 The initial state of charge when the ith electric automobile arrives is obtained; s i，end Is the state of charge at the departure of the ith vehicle; d i The driving mileage of the ith electric automobile is calculated; e 100 The power consumption of the electric automobile is hundreds of kilometers; c i Is the battery capacity of the ith vehicle;
s2.2: the day-ahead scheduling is to predict the load curve of the day according to the historical load of a certain area, the 24-hour day of the area is divided into 96 time periods, and each time period is 15 minutes to perform modeling simulation; according to the time distribution law of arrival and departure of the electric vehicle, 13 bj Charging power of the ith vehicle is P ei Suppose that the charging pile charges the electric vehicle with constant power and only during the time period [ t ] between the arrival and departure of the ith electric vehicle arr，i ，t dep，i ]The method (2) is optimized in the (1),
wherein, P ei For charging power of ith electric vehicle in scheduling period t, P evci For the rated charging power of the ith electric vehicle,
a variable of 0-1 corresponding to the charging state;
s2.3: assuming that the load size of the j-th cycle charging electric automobile is P j If there are N electric vehicles in total, then there are:
wherein, the grid load P of the jth time interval sj For electric vehicle loading P j And a base load P bj The superposition of the two components is carried out,
P sj ＝P j +P bj j＝1，2，3，...，96
for the charging process of electric vehicles, there are
Wherein η is the charging efficiency of the electric vehicle; c i Is the battery capacity of the ith vehicle; p ei Charging power of the ith electric automobile in a scheduling period t; Δ t is the time interval; s i (t) represents the state of charge of the ith vehicle at time t; s i (t-1) represents the state of charge of the ith vehicle at time t-1.
The day-ahead scheduling plan optimization module comprises the following contents in two aspects of technical indexes and economic indexes:
the economic index comprises the following contents:
the method is characterized in that the lowest charging cost of an electric vehicle user is taken as an objective function, the user is guided to charge in a low-electricity-price period, and the load is unified on a time scale, namely:
in the formula (f) 1 The economic index of the day-ahead scheduling of the electric automobile is represented, c (j) is the charging electricity price of a j time period, T is the division of 96 time periods, P ei Charging power for the ith electric vehicle;
the technical indexes comprise:
for the technical indexes of the power grid side, the variance and the peak-valley difference of the power grid load are considered:
a) Variance (variance)
The variance is used for describing the dispersion degree of the power grid load, a smaller load variance indicates that the overall load fluctuation degree is smaller,
wherein, P sj Is the grid load for the jth time period,
is the average grid load over the time period T, and Var is the variance of the grid load.
b) Difference between peak and valley
The purpose of optimizing the load curve can be achieved by reducing the peak-to-valley difference,
p vd ＝max(P sj )-min(P sj )
wherein, P sj Grid load for the jth period, p vd The peak-to-valley difference of the power grid load;
the economic and technical aspects have different dimensions and can be solved by obtaining a Pareto optimal solution.
The invention has the advantages and beneficial effects that:
according to the electric vehicle day-ahead scheduling strategy and system based on the economic target conversion, firstly, a Monte Carlo method is adopted to establish an electric vehicle state model according to the probability distribution of the traveling time and distance of an electric vehicle user; then, a day-ahead scheduling model with the minimum user charging cost is established at the time-of-use electricity price; finally, the strategy is verified by a small-sized rapid charging station day-ahead scheduling model solved by CPLEX. The proposed scheduling strategy fully utilizes the flexibility of the electric automobile load, considers the benefits of the power grid side and the user side, limits the objective function by the minimum variance, reduces the negative effects of the electric automobile connected to the power grid, and converts the technical index of the peak-valley difference of the power grid load into the economic index of the minimum user charging cost by the objective function. Compared with the limitation of a scheduling strategy taking the minimum variance as an objective function in load global optimization, the strategy ensures the optimal economy of users, reduces the load peak-valley difference of a power grid and verifies the effectiveness of the strategy.
Drawings
FIG. 1 is a block diagram of a process flow for solving a day-ahead scheduling plan for an electric vehicle according to the present invention;
FIG. 2 is a comparison of the variance of two load curves under economic dispatch of an electric vehicle according to the present invention;
FIG. 3 is a comparison of charging electricity prices and grid loads under economic dispatch for electric vehicles, as contemplated by the present invention;
FIG. 4 is a load curve of an out-of-order charging mode under economic dispatch of an electric vehicle according to the present disclosure;
FIG. 5 is a comparison of load curves before and after optimization under the economic dispatch of electric vehicles studied by the present invention.
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.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Referring to fig. 1, an electric vehicle day-ahead scheduling strategy based on economic objective conversion is characterized by comprising the following steps for realizing the proposed electric vehicle day-ahead scheduling strategy based on economic objective conversion:
s1: acquiring basic information, comprising: power grid base load, N electric vehicles and time-of-use electricity price; simultaneously acquiring Monte Carlo simulation of the driving range, the charging power and the battery capacity of the electric automobile;
s2: establishing an electric vehicle charging model, calculating an initial charge state of the electric vehicle by using the acquired basic information, acquiring electric vehicle dispatching time by combining Monte Carlo simulation of arrival and departure time of the electric vehicle, and solving the day-ahead dispatching problem of the electric vehicle by using CPLEX;
s3: analyzing whether the obtained electric vehicle scheduling time exceeds a constraint condition, and if so, returning to S1 for recalculation until all the constraint conditions are met; meanwhile, judging whether the number of the calculated electric vehicles is larger than N, if so, returning to S2 to continue calculating until the scheduling plan of the N electric vehicles is completed;
s4: and calculating an objective function of the day-ahead scheduling of the electric automobile to obtain an optimized day-ahead scheduling plan.
The time-of-use price of the power grid is specified for non-special loads in a certain area. Generally, the electricity prices during the peak load period are also during the peak load period, and the electricity prices during the valley load period are also during the valley load period. The charging price of the electric automobile adopts a domestic industrial time-of-use price form, and the charging time-of-use price is shown in the following table:
with the disordered access of a large number of electric vehicles to a power grid, in order to incorporate the electric vehicles into a load optimization scheduling model, it is necessary to analyze the driving and charging characteristics of the electric vehicles, and the method for establishing the electric vehicle charging model comprises the following steps:
first, a probability density function of a daily travel distance, a probability density function of electric vehicle arrival time, and a probability density function of electric vehicle departure time per day are obtained.
Further, establishing a day-ahead scheduling model of the electric automobile according to the obtained functions:
s2.1: according to the driving mileage and the battery parameters of the electric automobile, obtaining the charge state when the electric automobile arrives:
wherein S is i，0 The initial charge state when the ith electric automobile arrives; s i，end Is the state of charge at the departure of the ith vehicle; d i The driving mileage of the ith electric automobile; e 100 The power consumption of the electric automobile is hundreds of kilometers; c i Is the battery capacity of the ith vehicle;
s2.2: the day-ahead scheduling is to predict the load curve of the day according to the historical load of a certain area, the 24-hour day of the area is divided into 96 time periods, and each time period is 15 minutes to perform modeling simulation; according to the time distribution law of arrival and departure of the electric vehicle, 13 bj Charging power of the ith vehicle is P ei Suppose that the charging pile charges the electric vehicle with constant power and only during the time period [ t ] between the arrival and departure of the ith electric vehicle arr，i ，t dep，i ]In the process of optimization, the optimization is carried out,
wherein, P ei For charging power of ith electric vehicle in scheduling period t, P evci For the rated charging power of the ith electric automobile,
is a variable of 0-1 corresponding to the state of charge.
S2.3: assuming that the load size of the j-th cycle charging electric automobile is P j If there are N electric vehicles in total, then there are:
wherein the power grid load P of the jth time interval sj For loading electric vehicle P j And a base load P bj The superposition of the two components is carried out,
P sj ＝P j +P bj j＝1，2，3，...，96
for the charging process of electric vehicles, there are
Wherein η is the charging efficiency of the electric vehicle; c i Is the battery capacity of the ith vehicle; p ei The charging power of the ith electric automobile in the dispatching cycle t is obtained; Δ t is the time interval; s i (t) represents the state of charge of the ith vehicle at time t; s i (t-1) represents the state of charge of the ith vehicle at time t-1.
The model is the electric automobile day-ahead scheduling model.
As a further improvement of the technical scheme, the driving distance of the electric automobile obeys log-normal distribution and the probability density function f of the daily driving distance D (d) Comprises the following steps:
in the formula (f) D (d) A probability density function representing a daily driving distance; sigma D Is the standard deviation of the daily mileage, and σ D ＝0.88；μ D Is the expected value of daily mileage, and μ D =3.2; d is the daily mileage and is in km.
As a further improvement of the technical solution, the probability density function of the arrival time of the electric vehicle is as follows:
wherein f is arr (t) is the probability density function of the arrival time of the electric vehicle, mu arr Is the expected value of the arrival time of the electric vehicle, and mu arr ＝17.6；σ arr Is the standard deviation of the arrival time of the electric vehicle, and σ arr ＝3.4。
As a further improvement of the technical solution, the probability density function of the electric vehicle per day departure time is:
wherein f is dep (t) is the probability density function of the electric automobile per day departure time, mu dep Is the expected value of the departure time of the electric vehicle, and mu dep ＝8.92；σ dep Is the standard deviation of the departure time of the electric vehicle, and sigma dep ＝3.24。
As a further improvement of the technical scheme, the objective function for calculating the day-ahead scheduling strategy of the electric vehicle comprises the following contents in the aspects of technical indexes and economic indexes:
the economic index comprises the following contents:
the method is characterized in that the lowest charging cost of an electric vehicle user is taken as an objective function, the user is guided to charge in a low-electricity-price period, and the load is unified on a time scale, namely:
in the formula (f) 1 The economic index of the day-ahead scheduling of the electric automobile is represented, c (j) is the charging electricity price of a j time interval, T is the division of 96 time intervals, P ei Charging power for the ith electric vehicle;
the technical indexes comprise:
for the technical indexes of the power grid side, the variance and the peak-valley difference of the power grid load are considered:
a) Variance (variance)
The variance is used for describing the dispersion degree of the power grid load, a smaller load variance indicates that the overall load fluctuation degree is smaller,
wherein, P sj Is the grid load for the jth time period,
is the average grid load over the time period T, and Var is the variance of the grid load.
But in the case of a power grid, the load curve may not be optimized by using the variance alone as an index. Reducing the variance may only reduce the overall fluctuation of the load curve, but the local load curve may still fluctuate significantly.
Referring to FIG. 2, a comparison of the variance of the daily load curve for month 3 versus the average load curve for month in the area is shown. The variance of line A is 59517kW 2 The variance of line B is 73889kW 2 . The two load curves are substantially similar in shape. Although the variance of line a is small, the load curve does not improve significantly, but rather there is a large load fluctuation over a short period of time. Therefore, the change of the grid load is only considered in the technical index.
b) Difference between peak and valley
The purpose of optimizing the load curve can be achieved by reducing the peak-to-valley difference,
p vd ＝max(P sj )-min(P sj )
wherein, P sj Grid load for the jth period, p vd Peak-to-valley difference of grid load;
if the minimum load peak-valley difference is taken as the target function on the power grid side, the target function becomes a multi-target problem, and the method has different dimensionalities in the aspects of economy and technology and can be solved by obtaining a Pareto optimal solution.
The electricity rate distribution is consistent with the load distribution, the electricity rate is highest at the peak of the load, and the electricity rate is lowest at the low load, please refer to fig. 3.
Since the user charges during the low electricity rate period, it charges during the off-peak period. Therefore, the user is guided to charge at the lowest charging cost, the load in the low valley period can be increased, and the purpose of reducing the peak-valley difference is achieved. Therefore, the technical index can be realized through the converted economic objective function, and the model can be greatly simplified.
Therefore, aiming at the defect of the objective function with the minimum variance, the objective function of the day-ahead scheduling model is used for minimizing the charging cost of the user, the converted economic objective function considers the technical indexes of the load, and compared with the multi-objective function, the solution of the scheduling model is simplified.
As a further improvement of the technical solution, the constraint conditions of the electric vehicle day-ahead scheduling policy include:
1) Electric vehicle state of charge constraints
Wherein the content of the first and second substances,Sand
respectively representing the upper limit and the lower limit of the charging state of the battery of the electric automobile;
2) User travel constraints
S i，end ≤S(j)，j＝t dep，i
Wherein S is i，end Is the state of charge at the time of departure of the ith vehicle, t dep，i Is the departure time of the ith vehicle;
3) Charging station capacity constraints
Wherein, C tc Is the rated power of the charging station, P ei Charging power of the ith electric automobile in a scheduling period t;
without an electric vehicle dispatch strategy, an electric vehicle would charge at constant power as soon as it arrives at a charging station, which is a traditional out-of-order charging mode.
Referring to fig. 4, a blue curve represents a base load excluding a load of the electric vehicle, and a red curve represents a total load including a random charging load of the electric vehicle. It can be seen that the stacking of the disordered charge load and the base charge increases the peak power consumption and increases the load peak-to-valley difference.
According to the method, a day-ahead electric vehicle dispatching model with the minimum charging cost of electric vehicle users as an objective function in the background of time-of-use electricity price is established, and a simulation result is shown in an attached figure 5.
As can be seen from the figure, the load peak value cannot be increased under the day-ahead scheduling strategy, because the electricity price is higher in the peak load period, the user is guided to charge in the low electricity price period through scheduling, the charging load is transferred on the time scale, the load valley value is increased, the load peak-valley difference of the power grid is reduced, and the effectiveness of the proposed strategy is verified.
Compared with the traditional unordered charging of the electric automobile, the day-ahead scheduling strategy based on the economic target conversion can guide a user to charge in a valley period, benefit of the user is improved to the maximum extent, peak-valley difference of power grid load is reduced, only the objective function with the minimum variance has limitation in local optimization of a load curve, and optimization of the objective function with the minimum peak-valley difference ignores economy of electric automobile scheduling. Compared with the multi-objective function, the objective function with the minimum charging cost for the electric vehicle users is simplified under the condition of good optimization effect, and the reasonability and the superiority of the proposed electric vehicle day-ahead scheduling strategy are proved.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure 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 so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. 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.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. An electric automobile day-ahead scheduling strategy based on economic target conversion is characterized in that: the method comprises the following steps:
s1: acquiring basic information, comprising: power grid base load, N electric vehicles and time-of-use electricity price; simultaneously acquiring Monte Carlo simulation of the driving range, the charging power and the battery capacity of the electric automobile;
s2: establishing an electric vehicle charging model, calculating the initial charge state of the electric vehicle by using the acquired basic information, acquiring the scheduling time of the electric vehicle by combining Monte Carlo simulation of arrival and departure time of the electric vehicle, and solving the day-ahead scheduling problem of the electric vehicle by using CPLEX;
s3: analyzing whether the obtained electric vehicle scheduling time exceeds a constraint condition, and returning to S1 for recalculation if the obtained electric vehicle scheduling time exceeds the constraint condition until all the constraint conditions are met; meanwhile, judging whether the number of the calculated electric vehicles is larger than N, if so, returning to S2 to continue calculating until the scheduling plan of the N electric vehicles is completed;
s4: and calculating an objective function of the day-ahead scheduling of the electric automobile to obtain an optimized day-ahead scheduling plan.
2. The electric vehicle day-ahead scheduling strategy based on economic objective conversion according to claim 1, characterized in that: the method for establishing the electric automobile charging model comprises the following steps:
firstly, acquiring a probability density function of daily driving distance, a probability density function of electric vehicle arrival time and a probability density function of electric vehicle departure time per day;
then, establishing a day-ahead scheduling model of the electric automobile according to the obtained functions, and specifically comprising the following steps:
s2.1: according to the driving mileage and the battery parameters of the electric automobile, obtaining the charge state when the electric automobile arrives:
wherein S is i，0 The initial state of charge when the ith electric automobile arrives is obtained; s i，end Is the state of charge at the departure of the ith vehicle; d is a radical of i The driving mileage of the ith electric automobile; e 100 The power consumption of the electric automobile is hundreds of kilometers; c i Is the battery capacity of the ith vehicle;
s2.2: the day-ahead scheduling is to predict the load curve of the day according to the historical load of a certain area, the 24-hour day of the area is divided into 96 time periods, and each time period is 15 minutes to perform modeling simulation; according to the time distribution law of arrival and departure of the electric vehicle, 13 bj Charging power of the ith vehicle is P ei Suppose that the charging pile charges the electric vehicle with constant power and only during the time period [ t ] between the arrival and departure of the ith electric vehicle arr，i ，t dep，i ]In the process of optimization, the optimization is carried out,
wherein, P ei Charging power, P, for the ith electric vehicle in the dispatching cycle t evci For the rated charging power of the ith electric automobile,
a variable of 0-1 corresponding to the charging state;
s2.3: assuming that the load size of the j-th cycle charging electric automobile is P j If there are N electric vehicles in total, then there are:
wherein, the grid load P of the jth time interval sj For loading electric vehicle P j And a base load P bj The superposition of the two components is carried out,
P sj ＝P j +P bj j＝1，2，3，...，96
for the charging process of electric vehicles, there are
Wherein η is the charging efficiency of the electric vehicle; c i Is the battery capacity of the ith vehicle; p is ei Charging power of the ith electric automobile in a scheduling period t; Δ t is the time interval; s i (t) represents the state of charge of the ith vehicle at time t; s i (t-1) represents the state of charge of the ith vehicle at time t-1.
3. The electric vehicle day-ahead scheduling strategy based on economic objective conversion according to claim 2, characterized in that: the driving distance of the electric automobile obeys the logarithmic normal distribution, and the probability density function f of the daily driving distance D (d) Comprises the following steps:
in the formula, f D (d) A probability density function representing a daily driving distance; sigma D Is the standard deviation of the daily mileage, and σ D ＝0.88；μ D Is the expected value of daily mileage, and μ D =3.2; d is the daily mileage and is in km.
4. The electric vehicle day-ahead scheduling strategy based on economic objective conversion according to claim 2, characterized in that: the probability density function of the arrival time of the electric automobile is as follows:
wherein f is arr (t) is the probability density function of the arrival time of the electric vehicle, mu arr Is the expected value of the arrival time of the electric vehicle, and mu arr ＝17.6；σ arr Is the standard deviation of the arrival time of the electric vehicle, and σ arr ＝3.4。
5. The electric vehicle day-ahead scheduling strategy based on economic objective conversion according to claim 2, characterized in that: the probability density function of the electric automobile departure time per day is as follows:
wherein f is dep (t) is the probability density function of the electric automobile per day departure time, mu dep Is a desired value of the departure time of the electric vehicle, and mu dep ＝8.92；σ dep Is the standard deviation of the departure time of the electric vehicle, and sigma dep ＝3.24。
6. The electric vehicle day-ahead scheduling strategy based on economic objective conversion according to claim 1, characterized in that: the objective function for calculating the day-ahead scheduling strategy of the electric automobile comprises the following contents in the aspects of technical indexes and economic indexes:
the economic index comprises the following contents:
the method is characterized in that the lowest charging cost of an electric vehicle user is taken as an objective function, the user is guided to charge in a low-electricity-price period, and the load is unified on a time scale, namely:
in the formula (f) 1 The economic index of the day-ahead scheduling of the electric automobile is represented, c (j) is the charging electricity price of a j time interval, T is the division of 96 time intervals, P ei Charging power for the ith electric vehicle;
the technical indexes comprise:
for the technical indexes of the power grid side, the variance and the peak-valley difference of the power grid load are considered:
a) Variance (variance)
The variance is used for describing the dispersion degree of the power grid load, a smaller load variance indicates that the overall load fluctuation degree is smaller,
wherein, P sj Is the grid load for the jth time period,
is the average grid load over the time period T, and Var is the variance of the grid load.
b) Difference between peak and valley
The purpose of optimizing the load curve can be achieved by reducing the peak-to-valley difference,
p vd ＝max(P sj )-min(P sj )
wherein, P sj Grid load for the jth period, p vd The peak-to-valley difference of the power grid load;
the economic and technical aspects have different dimensions and can be solved by obtaining a Pareto optimal solution.
7. The electric vehicle day-ahead scheduling strategy based on economic objective conversion according to claim 1, characterized in that: the constraint conditions of the electric vehicle day-ahead scheduling strategy comprise:
1) Electric vehicle state of charge constraints
Wherein the content of the first and second substances,Sand
respectively representing the upper limit and the lower limit of the charging state of the battery of the electric automobile;
2) User travel constraints
S i，end ≤S(j)，j＝t dep,i
Wherein S is iend Is the state of charge at the time of departure of the ith vehicle, t dep，i Is the departure time of the ith vehicle;
3) Charging station capacity constraints
Wherein, C tc Is the rated power of the charging station, P ei The charging power of the ith electric automobile in the period t is scheduled.
8. The utility model provides an electric automobile day-ahead scheduling system based on economic objective of conversion which characterized in that: comprises an information acquisition module, an electric vehicle charging model establishing module, a dispatching plan analyzing and judging module and a day-ahead dispatching plan optimizing module,
the information acquisition module is used for acquiring the basic load of a power grid, N electric vehicles and the time-of-use electricity price; simultaneously acquiring Monte Carlo simulation of the driving range, the charging power and the battery capacity of the electric automobile;
the electric vehicle charging model establishing module is used for calculating the initial charge state of the electric vehicle by using the acquired basic information, acquiring the scheduling time of the electric vehicle by combining Monte Carlo simulation of arrival and departure time of the electric vehicle, and solving the day-ahead scheduling problem of the electric vehicle by using CPLEX;
the scheduling plan analyzing and judging module is used for analyzing whether the obtained electric vehicle scheduling time exceeds a constraint condition; meanwhile, judging whether the number of the calculated electric vehicles is larger than N;
the day-ahead scheduling plan optimization module is used for calculating an objective function of the day-ahead scheduling of the electric vehicle to obtain an optimized day-ahead scheduling plan.
9. The electric vehicle day-ahead scheduling system based on economic objective conversion according to claim 8, wherein: the method for establishing the electric vehicle charging model establishing module comprises the following steps:
firstly, acquiring a probability density function of daily driving distance, a probability density function of electric vehicle arrival time and a probability density function of electric vehicle departure time per day;
then, establishing a day-ahead scheduling model of the electric automobile according to the obtained functions, and specifically comprising the following steps:
s2.1: according to the driving mileage and the battery parameters of the electric automobile, obtaining the charge state when the electric automobile arrives:
wherein S is i，0 The initial charge state when the ith electric automobile arrives; s. the i，end Is the state of charge at the departure of the ith vehicle; d i The driving mileage of the ith electric automobile; e 100 Is an electric steamPower consumption of a hundred kilometers of the vehicle; c i Is the battery capacity of the ith vehicle;
s2.2: the day-ahead scheduling is to predict the load curve of the day according to the historical load of a certain area, the 24-hour day of the area is divided into 96 time periods, and each time period is 15 minutes for modeling simulation; according to the time distribution rule of arrival and departure of the electric vehicle, 13 bj Charging power of the ith vehicle is P ei Suppose that the charging pile charges the electric vehicle with constant power and only during the time period [ t ] between the arrival and departure of the ith electric vehicle arr，i ，td ep，i ]In the process of optimization, the optimization is carried out,
wherein, P ei For charging power of ith electric vehicle in scheduling period t, P evci For the rated charging power of the ith electric vehicle,
a variable of 0-1 corresponding to the charging state;
s2.3: assuming that the load size of the j-th cycle charging electric automobile is P j If there are N electric vehicles in total, then there are:
wherein the power grid load P of the jth time interval sj For loading electric vehicle P j And a base load P bj The superposition of the two components is carried out,
P sj ＝P j +P bj j＝1，2，3，...，96
for the charging process of electric vehicles, there are
Wherein η is the charging efficiency of the electric vehicle; c i Is the battery capacity of the ith vehicle; p ei Charging power of the ith electric automobile in a scheduling period t; Δ t is the time interval; s i (t) represents the state of charge of the ith vehicle at time t; s i (t-1) represents the state of charge of the ith vehicle at time t-1.
10. The electric vehicle day-ahead scheduling system based on economic objective conversion according to claim 8, wherein: the day-ahead scheduling plan optimization module comprises the following contents in two aspects of technical indexes and economic indexes:
the economic index comprises the following contents:
the method is characterized in that the lowest charging cost of an electric vehicle user is taken as an objective function, the user is guided to charge in a low-electricity-price period, and the load is unified on a time scale, namely:
in the formula (f) 1 The economic index of the day-ahead scheduling of the electric automobile is represented, c (j) is the charging electricity price of a j time interval, T is the division of 96 time intervals, P ei Charging power for the ith electric vehicle;
the technical indexes comprise:
for the technical indexes of the power grid side, the variance and the peak-valley difference of the power grid load are considered:
a) Variance (variance)
The variance is used for describing the dispersion degree of the power grid load, a smaller load variance indicates that the overall load fluctuation degree is smaller,
wherein, P sj For the grid load of the j-th time period,
is the average grid load over the time period T, and Var is the variance of the grid load.
b) Difference between peak and valley
The purpose of optimizing the load curve can be achieved by reducing the peak-to-valley difference,
p vd ＝max(P sj )-min(P sj )
wherein, P sj Is the grid load of the jth period, p vd The peak-to-valley difference of the power grid load; the economic and technical aspects have different dimensions and can be solved by obtaining a Pareto optimal solution.
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| CN117114367A (en) * | 2023-10-23 | 2023-11-24 | 苏州苏能集团有限公司 | Charging and discharging control method and device for electric automobile |
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| CN116796911A (en) * | 2023-08-25 | 2023-09-22 | 国网江苏省电力有限公司淮安供电分公司 | Medium-voltage distribution network optimization regulation and control method and system based on typical scene generation and on-line scene matching |
| CN117114367A (en) * | 2023-10-23 | 2023-11-24 | 苏州苏能集团有限公司 | Charging and discharging control method and device for electric automobile |
| CN117114367B (en) * | 2023-10-23 | 2024-01-26 | 苏州苏能集团有限公司 | Charging and discharging control method and device for electric automobile |
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