CN112100743B - Electric vehicle virtual machine set regulation and control method suitable for stabilizing new energy output fluctuation - Google Patents
Electric vehicle virtual machine set regulation and control method suitable for stabilizing new energy output fluctuation Download PDFInfo
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Abstract
The invention discloses an electric vehicle virtual machine set regulating and controlling method suitable for stabilizing new energy output fluctuation, belonging to the technical field of power demand response, comprising the following steps: s1, analyzing load characteristics of an electric automobile cluster and predicting upper and lower limits of generating power of a virtual machine set; s2, carrying out virtual machine set planned generation power optimization solution; s3, real-time grouping regulation and control of the electric automobile clusters in the virtual machine set are carried out. The method is suitable for the electric vehicle virtual machine set regulation and control method for stabilizing the new energy output fluctuation, realizes the rule analysis of the arrival of the vehicle at the charging pile and the upper and lower limit prediction of the generated power of the virtual machine set, realizes the planned generated power optimization of the virtual machine set based on a genetic algorithm, further realizes the real-time grouping regulation and control of the electric vehicle cluster, and provides technical support for stabilizing the energy output fluctuation by regulating and controlling the electric vehicle virtual machine set.
Description
Technical Field
The invention belongs to the technical field of power demand response, and particularly relates to a regulating and controlling method of an electric vehicle virtual machine set, which is suitable for stabilizing new energy output fluctuation.
Background
In recent years, the demand response is practiced on a large scale worldwide, and the method has important effects of peak clipping and valley filling, promoting new energy consumption and the like. The electric automobile load is a typical demand response resource, has the dual characteristics of source load, and has huge load adjustable potential on the premise of meeting the demands of users. Therefore, the electric automobile load cluster regulation strategy suitable for various power system scenes (such as peak clipping, valley filling, new energy consumption and the like) becomes an important research topic. The existing research is mainly focused on the electric automobile cluster load to realize peak clipping and valley filling scenes, lacks the research related to stabilizing new energy fluctuation through electric automobile cluster load regulation and control, and lacks technical means to perform unified scheduling control on the electric automobile cluster load, and aiming at the situation, an electric automobile virtual machine set regulation and control method suitable for stabilizing new energy output fluctuation is provided.
Disclosure of Invention
The invention aims to provide the electric vehicle virtual machine set regulating and controlling method suitable for stabilizing the new energy output fluctuation, so that the rule analysis of the arrival of vehicles at a charging pile and the upper and lower limit prediction of the generated power of the virtual machine set are realized, the planned generated power optimization of the virtual machine set is realized based on a genetic algorithm, the real-time grouping regulation and control of the electric vehicle cluster are further realized, and the technical support is provided for stabilizing the new energy output fluctuation by regulating and controlling the electric vehicle virtual machine set.
The aim of the invention can be achieved by the following technical scheme:
The electric automobile virtual machine set regulation and control method suitable for stabilizing the fluctuation of new energy output comprises the following steps:
S1, carrying out electric vehicle cluster load characteristic analysis and virtual machine set power generation upper and lower limit prediction;
S2, carrying out virtual machine set plan generation power optimization solution;
and S3, carrying out real-time grouping regulation and control on the electric automobile clusters in the virtual machine set.
Further, the step S1 performs electric vehicle cluster load characteristic analysis and virtual machine set power generation upper and lower limit prediction, and specifically includes the following steps:
S1.1, electric automobile cluster access charging pile rule analysis;
S1.2, predicting the upper limit and the lower limit of the generating power of the virtual machine set.
Further, the steps S1.1 and S1.2 include the following specific contents:
S1.1, electric automobile cluster access charging pile rule analysis:
The process that electric vehicles sequentially reach the charging pile is expressed by poisson distribution, namely, for any t >0, s >0, the increment N t+s－Ns of the arrival quantity of the electric vehicles obeys poisson distribution, and k=N t+s-Ns, so that the probability that k electric vehicles arrive at the charging pile in the time interval of (t, s+t) is as follows:
wherein: lambda is the number of the electric vehicles reaching the charging piles in unit time;
According to a 2-stage poisson distribution mode, poisson distribution numbers of vehicles reaching industrial and commercial areas in each stage are respectively generated as follows:
Wherein: n C1、NC2 is a poisson distribution array of the arrival of the vehicle in the 1 st stage and the 2 nd stage respectively; n 1i、n2i is the number of vehicles reached by a single time segment (i is more than or equal to 1 and less than or equal to M 1,M2);λ1、λ2 is poisson distribution parameters of the 1 st stage and the 2 nd stage respectively, M 1、M2 is the total number of the time segments of the 1 st stage and the 2 nd stage, T 1、T2 is the time length of the 1 st stage and the 2 nd stage respectively, and Δt is the time length of the time segment unit;
Similarly, the poisson distribution number of vehicles reaching the residential area at each stage is respectively generated as follows:
Wherein: n C3、NC4 is a poisson distribution array of the arrival of the vehicles in the 3 rd stage and the 4 th stage respectively; n 3i、n4i is the number of vehicles reached by a single time segment (i is more than or equal to 1 and less than or equal to M 1,M2);λ1、λ2 is poisson distribution parameters of the 3 rd stage and the 4 th stage respectively, M 3、M4 is the total number of the time segments of the 3 rd stage and the 4 th stage, and T 3、T4 is the time length of the 3 rd stage and the 4 th stage respectively;
According to the characteristics of the poisson process, an array of the number of electric vehicles reaching the charging station per time segment can be generated;
S1.2, predicting upper and lower limits of the generating power of the virtual machine set:
Based on the analysis result in S1.1, the electric vehicles in the area can be aggregated into virtual units, the upper limit and the lower limit of the generating power of the virtual units are mainly related to the regulation time period and the number of vehicles connected with the charging piles, the virtual units of the electric vehicles have the dual characteristics of source load, the generating power is positive to indicate the electric vehicle cluster to discharge, the generating power is negative to indicate the electric vehicle cluster to charge, one day is divided into N t = 24 x 60/Δt control time periods according to the time slot length delta t (unit is min), the artificial neural network can be trained to obtain a prediction model based on the historical data of the table for any regulation time period N (N = 1,2, ··, N t),
In the table: n n,j (j=1, 2, ·, m) accessing the number of the charging pile vehicles into the j-th group of historical data of the period N; p CH,n,j is the lower limit of the generating power of the virtual machine set in the j-th set of historical data of the period n; p DH,n,j is the upper limit of the generating power of the virtual machine set in the j-th set of historical data of the period n;
Based on the artificial neural network prediction model and the number of electric vehicles connected with the charging piles in each period obtained through previous analysis, the upper limit and the lower limit P' DH,n,P′CH,n of the power generation power of the virtual power plant in each period in one day can be predicted.
Further, the step S2 of carrying out virtual machine set planning generation power optimization solving is carried out according to the following steps:
And under the new energy collaborative scheduling scene, carrying out daily scheduling on the virtual machine set with the aim of stabilizing the new energy output fluctuation. The sum of the new energy output, the electric automobile basic load and the virtual machine set output is as follows:
Pn＝PG,n+PW,n-Eb,n+Ev,n (6)
wherein P G,n is the predicted force of the photovoltaic power generation system in the period n system; p W,n is the predicted force of the wind power generation system in the period n system; e b,n is the charging pile load corresponding to the time period n non-controllable electric automobile cluster; e v,n is the controllable electric automobile cluster adjustable power of period n, namely the virtual machine set power generation power.
The scheduling objective function is to minimize the variance of the comprehensive load curve of 1 to N t time periods, and the power generation power of the virtual power plant in each time periodFor decision variables, the objective function is as follows:
According to step S1, E v,n needs to satisfy the following constraints:
P′CH,n≤Ev,n≤P′DH,n (9)
the optimal power of the virtual machine set in each period can be obtained by adopting genetic algorithm to carry out optimal solution of the output of the virtual machine set
Further, the step S3 is performed for real-time grouping regulation and control of the electric automobile clusters in the virtual machine set, and the steps are as follows:
Obtaining the optimal power of the virtual machine set in a period N (n=1, 2, & gtN t) through solving an optimization model Then, in the actual operation stage, the virtual machine set control center distributes the planned power to the charging piles connected with the controllable vehicle group to realize the control of the charging and discharging power of each EV in the controllable vehicle group;
S3.1, analyzing actual regulation and control capability of the electric automobile group;
s3.2, grouping electric automobile clusters;
and S3.3, correcting the optimal power generation power of the virtual machine set.
Further, the steps S3.1 and S3.3 include the following specific contents:
s3.1, actual regulation and control capability analysis of the electric automobile group:
In order to meet the travel demands of users, according to the initial state of charge SOC 0,i of the electric automobile i, the required charging time length of the electric automobile i can be obtained as follows:
Wherein: SOC E,i is the expected state of charge of electric vehicle i; w i is the battery capacity; p i is the charge and discharge power of the battery;
During the process that the electric automobile is connected into the charging pile, whether the electric automobile participates in orderly scheduling or not is determined according to the charging requirement and the residence time of the electric automobile, and the predicted residence time of a user i is as follows:
Tstay,i＝(tleave,i-tstart,i)Δt (11)
When n=1 represents period 1, the remaining residence time of the electric vehicle i in period n is as follows:
Tstay,i,n＝Tstay,i-(n-tstart,i)Δt (12)
wherein: t leave,i is a departure period set by the user; t start,i is a period when the electric vehicle reaches the charging pile;
According to the state of charge SOC n,i of the electric vehicle i in the period n, the required charging time of the electric vehicle i in the period n can be obtained as follows:
When T stay,i,n≤Tneed,i,n is reached, the electric automobile must be in a charging state in the network period to meet the charging requirement of the electric automobile, and the electric automobile does not have the capability of being scheduled at the moment;
When T stay,i,n>Tneed,i,n is carried out, the virtual machine set control center has enough time for charging the electric automobile i in the period n and meets the electric quantity requirement of a user, and a certain time margin is reserved, and under the condition, the virtual machine set can realize the purpose of power grid dispatching by changing the charging load of the electric automobile;
s3.2, grouping electric automobile clusters:
According to the conditions, whether the electric automobile i has a schedulable space or not can be judged, and for the electric automobiles which can be scheduled in order, the charging piles can divide the electric automobile clusters into charging groups and discharging groups according to the charge states of the electric automobiles in a period n, and the electric automobiles are grouped according to the charge states SOC n,i at the current moment and expected electric quantity SOC E,i preset by an automobile owner;
When the SOC n,i≤SOCE,i is used for charging the electric automobile i, whether the electric automobile i is charged or not in a period n can ensure that the expected charge quantity is met when the electric automobile i travels, the electric automobile i is considered to have charging controllability in the period, and the electric automobiles which meet the formula (14) and are in the SOC n,i≤SOCE,i are listed in a charging group;
If the SOC n,i>SOCE,i is the same as the SOC n,i－SOCE,i, the electric vehicle has a discharge margin, and when the discharge capacity of the vehicle group is evaluated, it is required to ensure that the electric vehicle i meets the expected charge amount when traveling. Therefore, the electric vehicles satisfying the formula (15) and the SOC n,i>SOCE,i are listed in a discharge group to take charge of discharge;
s3.3, correcting the optimal power of the virtual machine set:
The electric automobile set phi and the electric automobile set psi of discharging are charged in a certain area by a network:
Φ＝{CHi|i∈NCH} (16)
Ψ＝{DHj|j∈NDH} (17)
Wherein: CH i represents the number of the ith charging electric automobile N CH in the area as the charging group electric automobile; DH j represents the j-th discharging electric car in the region; n DH is the number of electric vehicles in the discharge group.
The maximum chargeable load of the charging group car group at period N DH is:
pi＝ps (19)
the maximum dischargeable load of the discharge group in period n is:
pj＝-ps (21)
For the lower limit of the generating power of the virtual machine set in the period n,/> And generating an upper limit of power for the period n virtual machine set. Considering that the day-ahead optimization in step S2 is performed based on the power constraint P' CH,n、P′DH,n of each period of the predicted vehicle group charging and discharging power, the real regulation and control process needs to be performed on/>The correction is performed as follows:
the virtual machine set control center adjusts the power according to the corrected optimal adjustable power And determining the charge and discharge states of the controllable vehicle group, adjusting the charge and discharge power of each EV in the controllable vehicle group, and issuing a charge and discharge instruction to a charge pile connected with the regulation and control vehicle group.
The invention has the beneficial effects that:
1. Aiming at the characteristics of huge quantity and wide distribution of the electric vehicle body, the method provided by the invention realizes the rule analysis of the arrival charge piles of the vehicle and the upper and lower limit prediction of the generating power of the virtual machine set, and realizes the plan generating power optimization of the virtual machine set based on a genetic algorithm;
2. The invention realizes the real-time grouping regulation and control of the electric automobile clusters, provides technical support for stabilizing the fluctuation of the new energy output, and has great significance for realizing the safe and stable grid connection of the new energy.
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The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of an electric vehicle virtual machine set regulation strategy suitable for stabilizing new energy output fluctuation.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to be suitable for the electric automobile virtual machine set regulation and control method for stabilizing the new energy output fluctuation, as shown in figure 1, the method specifically comprises the following steps:
and S1, carrying out electric vehicle cluster load characteristic analysis and virtual machine set power generation upper and lower limit prediction.
S1.1 electric automobile cluster access charging pile rule analysis
In practice, the process of the electric vehicles reaching the charging piles in sequence can be expressed by poisson distribution, namely, for any t >0 and s >0, the increment N t+s－Ns of the reaching quantity of the electric vehicles obeys poisson distribution. If k=n t+s-Ns, then in the time interval of (t, s+t), the probability that k electric vehicles reach the charging pile is:
Wherein: lambda is the number of electric vehicles reaching the charging piles in unit time.
As can be seen from the travel habit survey of domestic ordinary residents, the time for the electric automobile to reach the industrial and commercial area is concentrated at 7:00-11:00, the time to reach the residential area is concentrated at 18:00-24:00.7:00-9:00 is the peak time of arrival, 9:00-11:00 is the low peak period reached by the vehicle, assumed at 11:00 vehicles all arrive; when an electric vehicle returns to a residential area from a commercial area, most vehicles are represented by 18:00-20:00 returns, remaining vehicles at 20:00-24:00 returns, assume 24: the 00 vehicle is all returned. The unit electric automobile arrival increment in each period obeys poisson distribution.
According to the analysis, according to a 2-stage poisson distribution mode, the poisson distribution number list of vehicles reaching the industrial and commercial areas in each stage is respectively generated as follows:
Wherein: n C1、NC2 is a poisson distribution array of the arrival of the vehicle in the 1 st stage and the 2 nd stage respectively; n 1i、n2i is the number of vehicles reaching a single time segment (i is greater than or equal to 1 and less than or equal to M 1,M2);λ1、λ2 is poisson distribution parameters of the 1 st stage and the 2 nd stage respectively, M 1、M2 is the total number of the time segments of the 1 st stage and the 2 nd stage, T 1、T2 is the time length of the 1 st stage and the 2 nd stage respectively, and Δt is the time length of the time segment unit.
Similarly, the poisson distribution number of vehicles reaching the residential area at each stage is respectively generated as follows:
Wherein: n C3、NC4 is a poisson distribution array of the arrival of the vehicles in the 3 rd stage and the 4 th stage respectively; n 3i、n4i is the number of vehicles reached by a single time segment (i is more than or equal to 1 and less than or equal to M 3,M4);λ3、λ4 is poisson distribution parameters of the 3 rd stage and the 4 th stage respectively, M 3、M4 is the total number of time segments of the 3 rd stage and the 4 th stage, and T 3、T4 is the time length of the 3 rd stage and the 4 th stage respectively.
According to the characteristics of the poisson process, an array of the number of electric vehicles arriving at the charging station per time segment can be generated.
S1.2 prediction of upper and lower limits of generating power of virtual machine set
Based on the analysis result in the S1, the electric vehicles in the area can be aggregated into a virtual machine set, so that unified regulation and control of the electric vehicles in the later stage through a virtual machine set control center are facilitated. The upper and lower limits of the generating power of the virtual machine set are mainly related to the regulation and control time period and the number of automobiles connected with the charging pile. The electric automobile virtual machine set has the dual characteristics of source and load, the generated power is positive and indicates that the electric automobile cluster is discharged, and the generated power is negative and indicates that the electric automobile cluster is charged. A day is divided into N t =24×60/Δt control periods by a slot length Δt (in min), and a time interval between the arbitrary control periods N (n=1, 2, N t) an artificial neuron network may be trained to obtain a predictive model based on historical data as shown in Table 1.
TABLE 1 period n virtual machine set historical regulatory data
In the table: : n n,j (j=1, 2, ·, m) accessing the number of the charging pile vehicles into the j-th group of historical data of the period N; p CH,n,j is the lower limit of the generating power of the virtual machine set in the j-th set of historical data of the period n; p DH,n,j is the upper limit of the generating power of the virtual machine set in the j-th set of historical data of the period n.
Based on the artificial neural network prediction model and the number of electric vehicles connected with the charging piles in each period obtained through previous analysis, the upper limit and the lower limit P' DH,n,P′CH,n of the power generation power of the virtual power plant in each period in one day can be predicted.
And S2, carrying out virtual machine set planning generation power optimization solution.
And under the new energy collaborative scheduling scene, carrying out daily scheduling on the virtual machine set with the aim of stabilizing the new energy output fluctuation. The sum of the new energy output, the electric automobile basic load and the virtual machine set output is as follows:
Pn＝PG,n+PW,n-Eb,n+Ev,n (6)
wherein P G,n is the predicted force of the photovoltaic power generation system in the period n system; p W,n is the predicted force of the wind power generation system in the period n system; e b,n is the charging pile load corresponding to the time period n non-controllable electric automobile cluster; e v,n is the controllable electric automobile cluster adjustable power of period n, namely the virtual machine set power generation power.
The scheduling objective function is to minimize the variance of the comprehensive load curve of 1 to N t time periods, and the power generation power of the virtual power plant in each time periodFor decision variables, the objective function is as follows:
According to step (1), E v,n needs to satisfy the following constraints:
P′CH,n≤Ev,n≤P′DH,n (9)
the optimal power of the virtual machine set in each period can be obtained by adopting genetic algorithm to carry out optimal solution of the output of the virtual machine set
And S3, carrying out real-time grouping regulation and control on the electric automobile clusters in the virtual machine set.
Obtaining the optimal power of the virtual machine set in a period N (n=1, 2, & gtN t) through solving an optimization modelAnd then, in the actual operation stage, the virtual machine set control center is required to distribute the planned power to the charging piles connected with the controllable vehicle group so as to realize the control of the charging and discharging power of each EV in the controllable vehicle group.
S3.1 actual regulation and control capability analysis of electric automobile group
In order to meet the travel demands of the user, according to the initial state of charge SOC 0,i of the electric automobile i, the required charging time length of the electric automobile i can be obtained as follows:
Wherein: SOC E,i is the expected state of charge of electric vehicle i; w i is the battery capacity; p i is the charge-discharge power.
And during the process that the electric automobile is connected into the charging pile, determining whether the electric automobile participates in orderly scheduling according to the charging requirement and the residence time of the electric automobile. The expected residence time for user i is as follows:
Tstay,i＝(tleave,i-tstart,i)Δt (11)
if n=1 represents period 1, the remaining residence time of the electric vehicle i in period n is as follows:
Tstay,i,n＝Tstay,i-(n-tstart,i)Δt (12)
wherein: t leave,i is a departure period set by the user; t start,i is a period when the electric vehicle reaches the charging pile.
According to the state of charge SOC n,i of the electric vehicle i in the period n, the required charging time of the electric vehicle i in the period n can be obtained as follows:
When T stay,i,n≤Tneed,i,n is reached, the electric vehicle must be in a charging state in the network period to meet the charging requirement of the electric vehicle (or the time is too short to meet the requirement of the vehicle owner), and the electric vehicle does not have the capability of being scheduled.
When T stay,i,n>Tneed,i,n, the virtual machine set control center has enough time to charge the electric automobile i in the period n and meet the electric quantity requirement of the user, and a certain time margin is reserved. Under the condition, the virtual machine set can achieve the purpose of power grid dispatching by changing the charging load of the electric automobile.
S3.2 electric automobile cluster grouping
According to the conditions, whether the electric automobile i has a schedulable space can be judged. For the electric vehicles which can be orderly scheduled, the charging pile can divide the electric vehicle clusters into charging groups and discharging groups according to the charge states of the electric vehicles in the period n. Grouping is carried out according to the state of charge SOC n,i at the current moment and the expected electric quantity SOC E,i preset by the vehicle owner.
If SOC n,i≤SOCE,i is set, electric vehicle i needs to be charged. If the expected charge quantity can be met when the electric automobile i travels no matter whether the electric automobile i is charged or not in the period n, the electric automobile i is considered to have charging controllability in the period. Electric vehicles satisfying the formula (14) and the SOC n,i≤SOCE,i are put into a charging group.
If the SOC n,i>SOCE,i is the same as the SOC n,i－SOCE,i, the electric vehicle has a discharge margin, and when the discharge capacity of the vehicle group is evaluated, it is required to ensure that the electric vehicle i meets the expected charge amount when traveling. Accordingly, electric vehicles that satisfy equation (15) and SOC n,i>SOCE,i are classified into a discharge group and are responsible for discharge.
S3.3 correction of optimal power generation power of virtual machine set
Assume that the network charging electric automobile set phi and the discharging electric automobile set ψ in a certain area are as follows:
Φ＝{CHi|i∈NCH} (16)
Ψ＝{DHj|j∈NDH} (17)
Wherein: CH i represents the ith charged electric vehicle in the area; n CH is the number of electric vehicles in the charging group; DH j represents the j-th discharging electric car in the region; n DH is the number of electric vehicles in the discharge group.
The maximum chargeable load of the charging group vehicle group in the period n is:
pi＝ps (19)
the maximum dischargeable load of the discharge group in period n is:
pj＝-ps (21)
For the lower limit of the generating power of the virtual machine set in the period n,/> And generating an upper limit of power for the period n virtual machine set. Considering that the day-ahead optimization in step S2 is performed based on the power constraint P' CH,n、P′DH,n of each period of the predicted vehicle group charging and discharging power, the real regulation and control process needs to be performed on/>The correction is performed as follows:
the virtual machine set control center adjusts the power according to the corrected optimal adjustable power And determining the charge and discharge states of the controllable vehicle group, adjusting the charge and discharge power of each EV in the controllable vehicle group, and issuing a charge and discharge instruction to a charge pile connected with the regulation and control vehicle group.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (3)
1. The electric vehicle virtual machine set regulation and control method suitable for stabilizing the fluctuation of new energy output is characterized by comprising the following steps of: the method comprises the following steps:
S1, carrying out electric vehicle cluster load characteristic analysis and virtual machine set power generation upper and lower limit prediction;
S2, carrying out virtual machine set plan generation power optimization solution;
s3, carrying out real-time grouping regulation and control on the electric automobile clusters in the virtual machine set;
The prediction of the upper limit and the lower limit of the generating power of the virtual machine set specifically comprises the following steps:
The method comprises the steps that electric vehicles in an area are aggregated into virtual units, the upper limit and the lower limit of the generating power of the virtual units are related to regulation and control time periods and the number of vehicles connected with a charging pile, the virtual units of the electric vehicles have dual characteristics of source load, the generating power is positive to represent the electric vehicle cluster to discharge, the generating power is negative to represent the electric vehicle cluster to charge, one day is divided into N t = 24 x 60/deltat control time periods according to the time slot length deltat, the unit is min, and a prediction model is obtained by training an artificial neuron network based on historical data for any regulation and control time period N (N = 1,2, ··, N t), wherein: n n,j (j=1, 2, ·, m) accessing the number of the charging pile vehicles into the j-th group of historical data of the period N; p CH,n,j is the lower limit of the generating power of the virtual machine set in the j-th set of historical data of the period n; p DH,n,j is the upper limit of the power generation power of the virtual machine set in the j-th set of historical data of the period n
Based on an artificial neural network prediction model and the number of electric vehicles connected with the charging piles in each period obtained through previous analysis, predicting the upper limit P 'DH,n,P′CH,n and the lower limit P' DH,n,P′CH,n of the power generation power of the virtual power plant in each period in one day;
the step S2 is performed for optimizing and solving the planned generating power of the virtual machine set, and the method comprises the following steps of:
under the new energy collaborative scheduling scene, the virtual machine set is scheduled in the future with the aim of stabilizing the new energy output fluctuation, and the sum of the new energy output, the electric automobile basic load and the virtual machine set output is as follows:
Pn＝PG,n+PW,n-Eb,n+Ev,n (6)
Wherein P G,n is the predicted force of the photovoltaic power generation system in the period n system; p W,n is the predicted force of the wind power generation system in the period n system; e b,n is the charging pile load corresponding to the time period n non-controllable electric automobile cluster; e v,n is the controllable electric automobile cluster adjustable power in the period n, namely the generating power of the virtual machine set;
The scheduling objective function is to minimize the variance of the comprehensive load curve of 1 to N t time periods, and the power generation power of the virtual power plant in each time period For decision variables, the objective function is as follows:
According to step S1, E v,n needs to satisfy the following constraints:
P′CH,n≤Ev,n≤P′DH,n (9)
the genetic algorithm is adopted to carry out the optimal solution of the output of the virtual machine set, and the optimal power of the virtual machine set in each period is obtained
The step S3 is performed for real-time grouping regulation and control of the electric automobile clusters in the virtual machine set, and the steps are as follows:
Obtaining the optimal power of the virtual machine set in a period N (n=1, 2, …, N t) through solving an optimization model Then, in the actual operation stage, the virtual machine set control center distributes the planned power to the charging piles connected with the controllable vehicle group to realize the control of the charging and discharging power of each EV in the controllable vehicle group;
S3.1, analyzing actual regulation and control capability of the electric automobile group;
s3.2, grouping electric automobile clusters;
S3.3, correcting the optimal power of the virtual machine set;
The steps S3.1 and S3.3 comprise the following specific contents:
s3.1, actual regulation and control capability analysis of the electric automobile group:
In order to meet the travel demands of users, according to the initial state of charge SOC 0,i of the electric automobile i, the required charging time length of the electric automobile i is obtained as follows:
Wherein: SOC E,i is the expected state of charge of electric vehicle i; w i is the battery capacity; p i is the charge and discharge power of the battery;
During the process that the electric automobile is connected into the charging pile, whether the electric automobile participates in orderly scheduling or not is determined according to the charging requirement and the residence time of the electric automobile, and the predicted residence time of a user i is as follows:
Tstay,i＝(tleave,i-tstart,i)Δt (11)
When n=1 represents period 1, the remaining residence time of the electric vehicle i in period n is as follows:
Tstay,i,n＝Tstay,i-(n-tstart,i)Δt (12)
wherein: t leave,i is a departure period set by the user; t start,i is a period when the electric vehicle reaches the charging pile;
According to the state of charge SOC n,i of the electric automobile i in the period n, the required charging time length of the electric automobile i in the period n is obtained as follows:
When T stay,i,n≤Tneed,i,n is reached, the electric automobile must be in a charging state in the network period to meet the charging requirement of the electric automobile, and the electric automobile does not have the capability of being scheduled at the moment;
When T stay,i,n>Tneed,i,n is reached, the virtual machine set control center has enough time for charging the electric automobile i in the period n and meets the electric quantity requirement of a user, and a certain time margin is reserved, and under the condition, the virtual machine set realizes the purpose of power grid dispatching by changing the charging load of the electric automobile;
s3.2, grouping electric automobile clusters:
Judging whether the electric automobile i has a schedulable space or not according to the conditions, for the electric automobiles which can be scheduled in order, dividing the electric automobile cluster into a charging group and a discharging group by a charging pile according to the charge state of the electric automobile in a period n, and grouping according to the charge state SOC n,i at the current moment and the expected electric quantity SOC E,i preset by an automobile owner;
When the SOC n,i≤SOCE,i is used for charging the electric automobile i, whether the electric automobile i is charged or not in a period n can ensure that the expected charge quantity is met when the electric automobile i travels, the electric automobile i is considered to have charging controllability in the period, and the electric automobiles which meet the formula (14) and are in the SOC n,i≤SOCE,i are listed in a charging group;
If the SOC n,i>SOCE,i is the SOC n,i－SOCE,i with the discharging margin, when the discharging capacity of the vehicle group is evaluated, the electric vehicle i needs to be ensured to meet the expected charge quantity when traveling; therefore, the electric vehicles satisfying the formula (15) and the SOC n,i>SOCE,i are listed in a discharge group to take charge of discharge;
s3.3, correcting the optimal power of the virtual machine set:
The electric automobile set phi and the electric automobile set psi of discharging are charged in a certain area by a network:
Φ＝{CHi|i∈NCH} (16)
Ψ＝{DHj|j∈NDH} (17)
wherein: CH i represents the number of the ith charging electric automobile N CH in the area as the charging group electric automobile; DH j represents the j-th discharging electric car in the region; n DH is the number of electric vehicles in the discharge group;
the maximum chargeable load of the charging group car group at period N DH is:
pi＝ps (19)
the maximum dischargeable load of the discharge group in period n is:
pj＝-ps (21)
For the lower limit of the generating power of the virtual machine set in the period n,/> Generating power upper limit for the period n virtual machine set; considering that the day-ahead optimization in step S2 is performed based on the power constraint P' CH,n、P′DH,n of each period of the predicted vehicle group charging and discharging power, the real regulation and control process needs to be performed on/>The correction is performed as follows:
the virtual machine set control center adjusts the power according to the corrected optimal adjustable power And determining the charge and discharge states of the controllable vehicle group, adjusting the charge and discharge power of each EV in the controllable vehicle group, and issuing a charge and discharge instruction to a charge pile connected with the regulation and control vehicle group.
2. The method for regulating and controlling the virtual machine set of the electric vehicle, which is suitable for stabilizing the fluctuation of the output of new energy, according to the claim 1, is characterized in that the step S1 is used for analyzing the load characteristics of the electric vehicle clusters and predicting the upper limit and the lower limit of the generating power of the virtual machine set, and specifically comprises the following steps:
S1.1, electric automobile cluster access charging pile rule analysis;
S1.2, predicting the upper limit and the lower limit of the generating power of the virtual machine set.
3. The method for regulating and controlling the virtual machine set of the electric automobile, which is suitable for stabilizing the fluctuation of the output of the new energy, according to claim 2, is characterized in that the step S1.1 comprises the following specific contents:
S1.1, electric automobile cluster access charging pile rule analysis:
The process that electric vehicles sequentially reach the charging pile is expressed by poisson distribution, namely, for any t >0, s >0, the increment N t+s－Ns of the arrival quantity of the electric vehicles obeys poisson distribution, and k=N t+s-Ns, so that the probability that k electric vehicles arrive at the charging pile in the time interval of (t, s+t) is as follows:
wherein: lambda is the number of the electric vehicles reaching the charging piles in unit time;
According to a 2-stage poisson distribution mode, poisson distribution numbers of vehicles reaching industrial and commercial areas in each stage are respectively generated as follows:
Wherein: n C1、NC2 is a poisson distribution array of the arrival of the vehicle in the 1 st stage and the 2 nd stage respectively; n 1i、n2i is the number of vehicles reached by a single time segment, i is more than or equal to 1 and less than or equal to M 1,M2;λ1、λ2, and poisson distribution parameters of the 1 st stage and the 2 nd stage are respectively; m 1、M2 is the total number of time slices in the 1 st and 2 nd phases; t 1、T2 is the time length of the 1 st and 2 nd phases respectively; Δt is the time duration of the segment per unit time;
Similarly, the poisson distribution number of vehicles reaching the residential area at each stage is respectively generated as follows:
Wherein: n C3、NC4 is a poisson distribution array of the arrival of the vehicles in the 3 rd stage and the 4 th stage respectively; n 3i、n4i is the number of vehicles reached by a single time segment, i is more than or equal to 1 and less than or equal to M 1,M2;λ1、λ2, and poisson distribution parameters of the 3 rd stage and the 4 th stage are respectively; m 3、M4 is the total number of time slices in the 3 rd and 4 th phases; t 3、T4 is the time length of the 3 rd and 4 th phases respectively;
an array of the number of electric vehicles arriving at the charging station per time segment is generated according to the characteristics of the poisson process.
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