CN108764554B - Robust optimization method for guiding orderly charging of electric automobile - Google Patents

Robust optimization method for guiding orderly charging of electric automobile Download PDF

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CN108764554B
CN108764554B CN201810489716.0A CN201810489716A CN108764554B CN 108764554 B CN108764554 B CN 108764554B CN 201810489716 A CN201810489716 A CN 201810489716A CN 108764554 B CN108764554 B CN 108764554B
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于艾清
王惠洲
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Shanghai University of Electric Power
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Abstract

The invention relates to a robust optimization method for guiding orderly charging of an electric automobile, which comprises the following steps: (1) establishing a time-of-use electricity price responsivity model of an electric automobile user; (2) establishing an electric vehicle charging load model after implementing time-of-use electricity price; (3) establishing an electric automobile ordered charging model considering demand side response and wind-light output; (4) the wind-solar output uncertainty is considered, and an electric automobile ordered charging robust optimization model is obtained; (5) optimizing and solving the models in the steps (3) and (4) by using a quantum particle swarm algorithm to obtain each time interval division and the corresponding time-of-use electricity price; (6) and guiding the user to charge according to the time interval division and the corresponding time-of-use electricity price. Compared with the prior art, the method can effectively guide the user to select the initial charging time, optimize the charging load of the electric automobile and realize the effect of filling the valley of the regional power grid.

Description

Robust optimization method for guiding orderly charging of electric automobile
Technical Field
The invention relates to a method for guiding charging of an electric automobile, in particular to a robust optimization method for guiding orderly charging of the electric automobile.
Background
Electric vehicles and new energy power generation develop rapidly in recent years, new opportunities are brought to human beings for solving energy crisis and environmental pollution problems, and however, the uncertainty of charging time and space of electric vehicles and the uncertainty of wind and light output also bring new challenges to the operation of a power grid. On one hand, the time-space distribution of renewable energy sources such as wind power, solar energy and the like has strong volatility and intermittence, and as the permeability of the renewable energy sources is continuously improved, the uncertainty of the output power of the renewable energy sources is stabilized, and the intermittent energy sources are efficiently absorbed, which is urgent. On the other hand, when the electric automobile is connected to a power grid in a large scale for charging, phenomena such as 'peak-up and peak-up', increase of system operation cost, reduction of power quality and the like occur in the power grid.
In order to solve the two problems, orderly charging of the electric vehicle and new energy power generation need to be cooperatively combined and considered, and the existing field of cooperative scheduling of new energy and electric vehicles has two modes of direct control and indirect control. The direct control is that on the basis of meeting the basic charging requirements of electric vehicle users, a charging station operator or an area distribution network control center directly controls the charging power and the charging starting time of the electric vehicle by adopting a technical means. Indirect control is the adoption of a power price mechanism to guide the user to actively adjust the charging behavior. The direct control requires frequent adjustment of the charging power of the charging pile, which has a negative effect on the life of the battery and the charger, and thus it is difficult to obtain the support and practical application of the user.
In the aspect of processing uncertainty of new energy output, methods such as probability estimation or historical data deduction analysis are mostly adopted, and the methods have the defects of large calculation amount, difficulty in ensuring calculation accuracy and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a robust optimization method for guiding the orderly charging of an electric vehicle.
The purpose of the invention can be realized by the following technical scheme:
a robust optimization method for guiding orderly charging of an electric automobile comprises the following steps:
(1) establishing a time-of-use electricity price responsivity model of an electric automobile user;
(2) establishing an electric vehicle charging load model after implementing time-of-use electricity price;
(3) establishing an electric automobile ordered charging model considering demand side response and wind-light output;
(4) the wind and light output uncertainty is considered, and the electric automobile ordered charging model is optimized to obtain an electric automobile ordered charging robust optimization model;
(5) performing optimization solution on the electric vehicle ordered charging model and the electric vehicle ordered charging robust optimization model by using a quantum particle swarm algorithm to obtain each time interval division and corresponding time-of-use electricity price;
(6) and guiding the user to charge according to the time interval division and the corresponding time-of-use electricity price.
The step (1) is specifically as follows:
dividing the electricity price into three periods of peak, flat and valley, and establishing an electric automobile user time-of-use electricity price responsivity model, wherein the time-of-use electricity price responsivity model is a time-of-use electricity price responsivity curve, the time-of-use electricity price responsivity curve takes the electricity price difference of the peak period, the flat period and the valley period as a horizontal coordinate, and takes the electric automobile user time-of-use electricity price responsivity as a vertical coordinate, and the electric automobile user time-of-use electricity price responsivity specifically comprises the following steps: the ratio of the transfer amount of the charging load of the electric automobile from the high electricity price period to the low electricity price period to the charging load of the original time period under the condition of single electricity price.
The responsivity model of the electric automobile user time-of-use electricity price is specifically as follows:
Figure GDA0003245667590000021
wherein r is the time-of-use electricity price responsivity of the electric automobile user, d is the electricity price difference, and d is1As a power price difference dead zone threshold, d1Indicating the difference in electricity prices at the beginning of the response of the user of the electric vehicle, d2Is the power price difference saturation threshold, d2The electric price difference when the user of the electric automobile does not respond any more is shown, k is the slope of the linear region of the time-of-use electric price responsivity curve, rmaxThe maximum value of the responsivity of the time-of-use electricity price of the user of the electric automobile.
The step (2) is specifically as follows:
dividing one day into 24 time intervals, dividing each time interval into peak time interval, flat time interval and valley time interval, and defining attribute v of ith time intervali：
vi∈{1,2,3}，i＝1,2,…,24，
viWhen 1, the i-th period is a peak period, viWhen 2, the i-th period is a normal period，viWhen the value is 3, the ith time interval is a valley time interval;
the electric vehicle charging load model after the time-of-use electricity price is determined according to the division of the peak time period, the flat time period and the valley time period specifically comprises the following steps:
Figure GDA0003245667590000031
Pito implement the fitting load for the i-th period after the time of use, TpIs a peak period, TfAt the usual time, TvIn the valley period, Pini,iFor carrying out the measured load in the i-th period before the time of use of electricity, PpTo implement the total load of the time-of-use price pre-peak period, PfTo implement the total load of the preceding flat period of the time of day, rpfFor the electric vehicle user's electrovalence responsivity shifted from peak period to flat period, rpvFor the electric vehicle user's electrovalence responsivity, r, shifted from peak period to valley periodfvThe user electricity price responsiveness of the electric automobile is transferred from the normal time period to the valley time period.
The ordered charging model of the electric automobile in the step (3) is specifically as follows:
an objective function:
Figure GDA0003245667590000032
Figure GDA0003245667590000033
wherein F is the sum of squares of deviations of the load curve, Pl,iFor the original load, P, of the regional power grid to be optimized in the ith periodw,iWind power output power P of the region to be optimized in the ith time periods,iFor the photovoltaic output power, P, of the region to be optimized in the ith periodiTo implement the fitting load for the i-th period after the time-of-use price, PavThe average value of the load in the optimization period is T, and the optimization total period is T;
constraint conditions are as follows:
(a) the electricity price difference inequality constrains:
dpv.min≤dpv≤dpv.max，
dpf.min≤dpf≤dpf.max，
dfv.min≤dfv≤dfv.max，
(b) the electricity price difference equation constraint is as follows:
dpv＝dpf+dfv，
(c) and (3) charging load constraint:
minPi≥0，
wherein d ispvIs the peak-to-valley time interval of the valence difference, dpfIs the peak mean time section valence difference, dfvThe mean valley time interval of the electric valence difference, dpv.minIs the minimum value of electricity price difference in peak-valley period, dpv.maxMaximum value of electricity price difference at peak-valley time, dpf.minIs the minimum value of the peak mean time section electric valence difference, dpf.maxIs the maximum value of the peak mean time section electric valence difference, dfv.minIs the minimum value of the electricity price difference in the valley leveling period, dfv.maxThe maximum value of the electricity price difference in the valley leveling period.
The step (4) is specifically as follows:
considering wind and light output uncertainty, dividing the wind and light output uncertainty into a plurality of scenes according to the difference of wind power output power and photovoltaic output power of the region to be optimized in each time period;
aiming at different scenes, the method for determining the electric automobile ordered charging robust optimization model specifically comprises the following steps:
an objective function:
minτ，
constraint conditions are as follows:
Figure GDA0003245667590000043
where τ is a relatively robust parameter, FΩFor the objective function in the ordered charging model of the electric automobile in the corresponding scene,
Figure GDA0003245667590000042
to be corresponding toAnd solving the obtained objective function value through the electric automobile ordered charging model under the scene.
The step (5) is specifically as follows:
aiming at each scene, firstly, an electric vehicle ordered charging model using a quantum particle swarm algorithm is adopted to solve to obtain each time interval division and a corresponding time-of-use electricity price preliminary value, and then, an electric vehicle ordered charging robust optimization model is utilized to perform optimization solution to obtain each time interval division and corresponding time-of-use electricity price meeting all different scenes.
Compared with the prior art, the invention has the following advantages:
(1) the method can effectively divide the peak-to-valley period and the corresponding time-of-use electricity price, guide a user to select the initial charging time, optimize the charging load of the electric automobile and realize the effect of filling the valley of the regional power grid;
(2) the invention adopts a robust optimization method to process the uncertainty of wind-light output, can ensure the good operation of a power system under different wind-light output scenes, effectively processes the fluctuation of uncertain parameters, and simultaneously plays a role in peak clipping and valley filling on a load curve;
(3) the method has flexibility, can autonomously select relative robust parameters in case of a severe wind-solar output scene, reduces conservatism of robust optimization, makes a reasonable electricity price scheme, reduces the dispersion square sum and peak-valley difference rate of a load curve, and stabilizes fluctuation of wind-solar output uncertainty on a power grid.
Drawings
FIG. 1 is a block flow diagram of a robust optimization method for guiding orderly charging of an electric vehicle according to the present invention;
FIG. 2 is a time-of-use electricity price responsivity curve diagram of the electric vehicle user of the present invention;
FIG. 3 is a flow chart of a quantum-behaved particle swarm algorithm of the present invention;
FIG. 4 is a load curve before and after the electric vehicle is charged in disorder in different scenarios according to an embodiment of the present invention;
FIG. 5 is a charge price and time division diagram for different scenarios of the present invention;
FIG. 6 is a charge price and time period division diagram for the relatively robust optimization case of the present invention;
FIG. 7 is a load curve before and after the ordered charging robust optimization in different scenarios of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
As shown in fig. 1, a robust optimization method for guiding orderly charging of an electric vehicle includes the following steps:
(1) establishing a time-of-use electricity price responsivity model of an electric automobile user;
(2) establishing an electric vehicle charging load model after implementing time-of-use electricity price;
(3) establishing an electric automobile ordered charging model considering demand side response and wind-light output;
(4) the wind and light output uncertainty is considered, and the electric automobile ordered charging model is optimized to obtain an electric automobile ordered charging robust optimization model;
(5) performing optimization solution on the electric vehicle ordered charging model and the electric vehicle ordered charging robust optimization model by using a quantum particle swarm algorithm to obtain each time interval division and corresponding time-of-use electricity price;
(6) and guiding the user to charge according to the time interval division and the corresponding time-of-use electricity price.
The step (1) is specifically as follows:
dividing the electricity price into three periods of peak, flat and valley, and establishing an electric automobile user time-of-use electricity price responsivity model, wherein the time-of-use electricity price responsivity model is a time-of-use electricity price responsivity curve, the time-of-use electricity price responsivity curve takes the electricity price difference of the peak period, the flat period and the valley period as a horizontal coordinate, and takes the electric automobile user time-of-use electricity price responsivity as a vertical coordinate, and the electric automobile user time-of-use electricity price responsivity specifically comprises the following steps: the ratio of the transfer amount of the charging load of the electric automobile from the high electricity price period to the low electricity price period to the charging load of the original time period under the condition of single electricity price.
As shown in fig. 2, when the electric vehicle is charged to the time-of-use electricity price, the user selects a corresponding charging period according to the difference in charging prices between the three periods of the peak and the valley. The time-of-use electricity price responsivity of the electric automobile user is defined as the ratio of the transfer amount of the electric automobile charging load from a high electricity price time period to a low electricity price time period to the original time charging load under the condition of single electricity price; and the piecewise linear function is used for expressing the time-of-use electricity price responsivity of the electric automobile user, the abscissa of the responsivity curve is the price difference (three conditions of peak-valley, peak-valley and flat-valley) in the peak-valley period, and the ordinate is the responsivity of the user to the electricity price difference in different conditions.
The responsivity model of the electric automobile user time-of-use electricity price is specifically as follows:
Figure GDA0003245667590000061
wherein r is the time-of-use electricity price responsivity of the electric automobile user, d is the electricity price difference, and d is1As a power price difference dead zone threshold, d1Indicating the difference in electricity prices at the beginning of the response of the user of the electric vehicle, d2Is the power price difference saturation threshold, d2The electric price difference when the user of the electric automobile does not respond any more is shown, k is the slope of the linear region of the time-of-use electric price responsivity curve, rmaxThe maximum value of the responsivity of the time-of-use electricity price of the user of the electric automobile.
The step (2) is specifically as follows:
dividing one day into 24 time intervals, dividing each time interval into peak time interval, flat time interval and valley time interval, and defining attribute v of ith time intervali：
vi∈{1,2,3}，i＝1,2,…,24，
viWhen 1, the i-th period is a peak period, viWhen 2, the i-th period is a normal period, viWhen the value is 3, the ith time interval is a valley time interval;
the electric vehicle charging load model after the time-of-use electricity price is determined according to the division of the peak time period, the flat time period and the valley time period specifically comprises the following steps:
Figure GDA0003245667590000071
Pito implement the fitting load for the i-th period after the time of use, TpIs a peak period, TfAt the usual time, TvIn the valley period, Pini,iFor carrying out the measured load in the i-th period before the time of use of electricity, PpTo implement the total load of the time-of-use price pre-peak period, PfTo implement the total load of the preceding flat period of the time of day, rpfFor the electric vehicle user's electrovalence responsivity shifted from peak period to flat period, rpvFor the electric vehicle user's electrovalence responsivity, r, shifted from peak period to valley periodfvThe user electricity price responsiveness of the electric automobile is transferred from the normal time period to the valley time period.
The ordered charging model of the electric automobile in the step (3) is specifically as follows:
an objective function:
Figure GDA0003245667590000072
Figure GDA0003245667590000073
wherein F is the sum of squares of deviations of the load curve, Pl,iFor the original load, P, of the regional power grid to be optimized in the ith periodw,iWind power output power P of the region to be optimized in the ith time periods,iFor the photovoltaic output power, P, of the region to be optimized in the ith periodiTo implement the fitting load for the i-th period after the time-of-use price, PavThe average value of the load in the optimization period is T, and the optimization total period is T;
constraint conditions are as follows:
(a) the electricity price difference inequality constrains:
dpv.min≤dpv≤dpv.max，
dpf.min≤dpf≤dpf.max，
dfv.min≤dfv≤dfv.max，
(b) the electricity price difference equation constraint is as follows:
dpv＝dpf+dfv，
(c) and (3) charging load constraint:
minPi≥0，
wherein d ispvIs the peak-to-valley time interval of the valence difference, dpfIs the peak mean time section valence difference, dfvThe mean valley time interval of the electric valence difference, dpv.minIs the minimum value of electricity price difference in peak-valley period, dpv.maxMaximum value of electricity price difference at peak-valley time, dpf.minIs the minimum value of the peak mean time section electric valence difference, dpf.maxIs the maximum value of the peak mean time section electric valence difference, dfv.minIs the minimum value of the electricity price difference in the valley leveling period, dfv.maxThe maximum value of the electricity price difference in the valley leveling period.
The step (4) is specifically as follows:
considering wind and light output uncertainty, dividing the wind and light output uncertainty into a plurality of scenes according to the difference of wind power output power and photovoltaic output power of the region to be optimized in each time period;
aiming at different scenes, the method for determining the electric automobile ordered charging robust optimization model specifically comprises the following steps:
an objective function:
minτ，
constraint conditions are as follows:
Figure GDA0003245667590000083
where τ is a relatively robust parameter, FΩFor the objective function in the ordered charging model of the electric automobile in the corresponding scene,
Figure GDA0003245667590000082
and solving the objective function value obtained by the electric automobile ordered charging model in the corresponding scene.
The step (5) is specifically as follows:
aiming at each scene, firstly, an electric vehicle ordered charging model using a quantum particle swarm algorithm is adopted to solve to obtain each time interval division and a corresponding time-of-use electricity price preliminary value, and then, an electric vehicle ordered charging robust optimization model is utilized to perform optimization solution to obtain each time interval division and corresponding time-of-use electricity price meeting all different scenes.
In the solving process, in order to further reduce the conservative property of robust optimization and avoid the condition that the effective solution of the electricity price scheme cannot be solved due to a severe wind power output scene, a maximum tau which can be tolerated under different scenes can be specifiedmax，τmax≥τ*，τ*Solving the minimum tau obtained by the electric vehicle ordered charging robust optimization model, and then solving a feasible solution on the basis of the following constraint conditions:
Figure GDA0003245667590000092
decision variables of the robust optimization model constructed by the invention comprise time-of-use charging price difference and charging peak-to-valley time interval division, wherein the value of the price difference can be decimal, and the peak-to-valley time interval is expressed by an integer; the optimization problem is therefore a non-linear mixed integer optimization problem. The invention adopts quantum particle swarm algorithm to solve the objective function, compared with the basic particle swarm algorithm, the algorithm adopts quantum bits to encode the current position of the particle, uses quantum revolving gates to realize the particle search, uses quantum NOT gates to realize the particle variation to avoid premature convergence, and the flow chart of the quantum particle swarm algorithm is shown in figure 3.
In this embodiment, values of parameters of the electric vehicle user time-of-use electricity price responsivity curve at peak valley, peak average and valley average time periods are as follows: the slope of the linear region is 1, the dead zone threshold value is 0 yuan/kWh, the saturation threshold value is 1 yuan/kWh, and the maximum responsiveness of an electric vehicle user is 1. The method comprises the following steps that an original power grid basic load without considering wind and light output adopts a typical daily load of a certain city, a new energy output scene set is constructed on the basis of historical wind and light output data of the city, and the minimum wind and light output, the medium wind and light output and the maximum wind and light output are respectively scene 1, scene 2 and scene 3; assuming that the charging power is constant power, 2.5kW is taken, and a monte carlo simulation method is adopted to simulate the situation of disordered charging of 100 ten thousand electric vehicles, so as to obtain load curves before and after disordered charging of the electric vehicles in different scenes, as shown in fig. 4, fig. 4(a), fig. 4(b) and fig. 4(c) respectively correspond to the load curves before and after disordered charging of the electric vehicles in scene 1, scene 2 and scene 3.
According to the ordered charging robust optimization technology, charging prices and time period division under different scenes can be obtained, as shown in FIG. 5; the charging price and time period division diagram in the case of relatively robust optimization is shown in fig. 6.
Fig. 7(a), fig. 7(b) and fig. 7(c) respectively correspond to load curves before and after the ordered charging robust optimization in three different scenes, namely scene 1, scene 2 and scene 3, and it can be seen from fig. 7 that, in the case of the unordered charging, the charging load demand peak of the electric vehicle will appear near the peak of the original load, causing the phenomenon of "peak-to-peak overlapping"; after the electric vehicle ordered charging robust optimization technology provided by the invention is adopted, the peak value and fluctuation of a load curve are obviously reduced relative to the unordered charging condition in each scene; with the increase of the relative robust parameters, the peak value and the fluctuation of the load curve are improved, but the situation is still better than the situation of the disordered charging of the electric automobile. Therefore, reasonable electricity price is formulated through a robust optimization technology, charging guidance is carried out on electric vehicle users, uncertainty of wind and light output can be well processed, and good peak clipping and valley filling effects are achieved on regional loads, so that the model and the algorithm are robust and effective. In addition, the robust optimization model provided by the invention is an adjustable relative robust model, and in practical engineering application, acceptable relative robust parameters can be selected according to needs, so that the conservation of robust optimization is reasonably reduced, and the condition that effective electricity price cannot be obtained under a severe wind-light output scene is avoided, therefore, the invention has engineering practical value.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.

Claims (4)

1. A robust optimization method for guiding orderly charging of an electric vehicle is characterized by comprising the following steps:
(1) establishing a time-of-use electricity price responsivity model of an electric automobile user;
(2) establishing an electric vehicle charging load model after implementing time-of-use electricity price;
(3) establishing an electric automobile ordered charging model considering demand side response and wind-light output;
(4) the wind and light output uncertainty is considered, and the electric automobile ordered charging model is optimized to obtain an electric automobile ordered charging robust optimization model;
(5) performing optimization solution on the electric vehicle ordered charging model and the electric vehicle ordered charging robust optimization model by using a quantum particle swarm algorithm to obtain each time interval division and corresponding time-of-use electricity price;
(6) guiding a user to charge according to the time interval division and the corresponding time-of-use electricity price;
the step (2) is specifically as follows:
dividing one day into 24 time intervals, dividing each time interval into peak time interval, flat time interval and valley time interval, and defining attribute v of ith time intervali：
vi∈{1,2,3}，i＝1,2,…,24，
viWhen 1, the i-th period is a peak period, viWhen 2, the i-th period is a normal period, viWhen the value is 3, the ith time interval is a valley time interval;
the electric vehicle charging load model after the time-of-use electricity price is determined according to the division of the peak time period, the flat time period and the valley time period specifically comprises the following steps:
Figure FDA0003245667580000011
Pito implement the fitting load for the i-th period after the time of use, TpIs a peak period, TfAt the usual time, TvIn the valley period, Pini,iMeasured load of the ith time period before time-of-use electricity price implementation，PpTo implement the total load of the time-of-use price pre-peak period, PfTo implement the total load of the preceding flat period of the time of day, rpfFor the electric vehicle user's electrovalence responsivity shifted from peak period to flat period, rpvFor the electric vehicle user's electrovalence responsivity, r, shifted from peak period to valley periodfvThe user electricity price responsivity of the electric automobile is transferred from the ordinary time period to the valley time period;
the ordered charging model of the electric automobile in the step (3) is specifically as follows:
an objective function:
Figure FDA0003245667580000021
Figure FDA0003245667580000022
wherein F is the sum of squares of deviations of the load curve, Pl,iFor the original load, P, of the regional power grid to be optimized in the ith periodw,iWind power output power P of the region to be optimized in the ith time periods,iFor the photovoltaic output power, P, of the region to be optimized in the ith periodiTo implement the fitting load for the i-th period after the time-of-use price, PavThe average value of the load in the optimization period is T, and the optimization total period is T;
constraint conditions are as follows:
(a) the electricity price difference inequality constrains:
dpv.min≤dpv≤dpv.max，
dpf.min≤dpf≤dpf.max，
dfv.min≤dfv≤dfv.max，
(b) the electricity price difference equation constraint is as follows:
dpv＝dpf+dfv，
(c) and (3) charging load constraint:
min Pi≥0，
wherein d ispvIs the peak-to-valley time interval of the valence difference, dpfIs the peak mean time section valence difference, dfvThe mean valley time interval of the electric valence difference, dpv.minIs the minimum value of electricity price difference in peak-valley period, dpv.maxMaximum value of electricity price difference at peak-valley time, dpf.minIs the minimum value of the peak mean time section electric valence difference, dpf.maxIs the maximum value of the peak mean time section electric valence difference, dfv.minIs the minimum value of the electricity price difference in the valley leveling period, dfv.maxThe maximum value of the electricity price difference in the valley leveling period;
the step (4) is specifically as follows:
considering wind and light output uncertainty, dividing the wind and light output uncertainty into a plurality of scenes according to the difference of wind power output power and photovoltaic output power of the region to be optimized in each time period;
aiming at different scenes, the method for determining the electric automobile ordered charging robust optimization model specifically comprises the following steps:
an objective function:
min τ，
constraint conditions are as follows:
Figure FDA0003245667580000031
where τ is a relatively robust parameter, FΩFor the objective function in the ordered charging model of the electric automobile in the corresponding scene,
Figure FDA0003245667580000032
and solving the objective function value obtained by the electric automobile ordered charging model in the corresponding scene.
2. The robust optimization method for guiding orderly charging of the electric vehicle according to claim 1, wherein the step (1) is specifically as follows:
dividing the electricity price into three periods of peak, flat and valley, and establishing an electric automobile user time-of-use electricity price responsivity model, wherein the time-of-use electricity price responsivity model is a time-of-use electricity price responsivity curve, the time-of-use electricity price responsivity curve takes the electricity price difference of the peak period, the flat period and the valley period as a horizontal coordinate, and takes the electric automobile user time-of-use electricity price responsivity as a vertical coordinate, and the electric automobile user time-of-use electricity price responsivity specifically comprises the following steps: the ratio of the transfer amount of the charging load of the electric automobile from the high electricity price period to the low electricity price period to the charging load of the original time period under the condition of single electricity price.
3. The robust optimization method for guiding orderly charging of the electric vehicle as claimed in claim 2, wherein the responsiveness model of the user time-of-use electricity price of the electric vehicle is specifically:
Figure FDA0003245667580000033
wherein r is the time-of-use electricity price responsivity of the electric automobile user, d is the electricity price difference, and d is1As a power price difference dead zone threshold, d1Indicating the difference in electricity prices at the beginning of the response of the user of the electric vehicle, d2Is the power price difference saturation threshold, d2The electric price difference when the user of the electric automobile does not respond any more is shown, k is the slope of the linear region of the time-of-use electric price responsivity curve, rmaxThe maximum value of the responsivity of the time-of-use electricity price of the user of the electric automobile.
4. The robust optimization method for guiding orderly charging of electric vehicles according to claim 1, wherein the step (5) is specifically as follows:
aiming at each scene, firstly, an electric vehicle ordered charging model using a quantum particle swarm algorithm is adopted to solve to obtain each time interval division and a corresponding time-of-use electricity price preliminary value, and then, an electric vehicle ordered charging robust optimization model is utilized to perform optimization solution to obtain each time interval division and corresponding time-of-use electricity price meeting all different scenes.
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