A method for estimating the number of electric vehicles in a power distribution area

Abstract

The embodiment of the present invention relates to a processing method for estimating the number of electric vehicles in a power distribution area, the method comprising: obtaining a probability distribution of electric vehicle daily mileage / start charging time in a residential power distribution area as a first probability distribution and a second probability distribution, obtaining a regional transformer capacity range as a regional load range, obtaining a single-day load curve S b , the actual number of electric vehicles is recorded as the first actual quantity, and the vehicle quantity growth rate is recorded as the first growth rate; and based on the first and second probability distributions, the single-day load curve S b The maximum value of the number of electric vehicles estimated by the regional load range is recorded as a first estimated value; and based on the comparison relationship between the first real value and the first estimated value, a regional power supply control requirement is set as a need for control and sent to the power grid management interface. The present invention can improve the stability and security of power grid operation.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a processing method for estimating the number of electric vehicles in a power distribution area. Background Art

[0002] If electric vehicle (EV) charging stations are connected to a residential power distribution area, the power load component of the current area will change from the original daily power load to the superposition of daily power load and charging load. This superposition effect may lead to a significant increase in power load, especially during peak charging hours for EVs. If the theoretical value of the number of EVs in a region (EV population) can be estimated, the transmission network can pre-optimize the power supply plan for the region based on the difference between the actual value and the theoretical value of the EV population. How to estimate the theoretical value of the EV population is the technical problem to be solved by this invention. Summary of the Invention

[0003] The purpose of the present invention is to address the shortcomings of the existing technology and provide a method for estimating the number of electric vehicles in a distribution area. The present invention records any residential distribution area as the first distribution area, and records the probability distribution of the daily mileage of electric vehicles in this area and the probability distribution of the starting charging time as the corresponding first and second probability distributions, and records the transformer capacity range corresponding to the main power grid access point in this area as the corresponding regional load range, and uses the daily average value curve of the daily electricity load in this area within the most recently specified time period as the corresponding daily load curve S b , and record the actual number of electric vehicles in this area and the growth rate of the number as the first actual quantity and the first growth rate; then, based on the first probability distribution, the second probability distribution, and the single-day load curve S b The maximum number of electric vehicles in the first distribution area is estimated based on the regional load range to obtain a corresponding first estimated value; and when the first actual value is greater than or equal to the first estimated value, the regional power supply control requirement is set to require control; and when the first actual value is less than the first estimated value, the length of time the actual number of electric vehicles exceeds the first estimated value is first estimated based on the first growth rate to obtain a first time length, and whether the first time length is lower than a preset time length threshold is identified. If so, the regional power supply control requirement is set to require control; otherwise, the regional power supply control requirement is set to not require control; finally, the obtained regional power supply control requirement is sent to a preset power grid management interface. Through the present invention, the power grid management side can optimize the power supply planning of the residential distribution area before the charging load conflicts with the daily power load.

[0004] To achieve the above objectives, an embodiment of the present invention provides a method for estimating the number of electric vehicles in a power distribution area, the method comprising:

[0005] Any residential power distribution area is recorded as the corresponding first power distribution area; the probability distribution of daily mileage of electric vehicles and the probability distribution of starting charging time corresponding to the first power distribution area are recorded as the corresponding first and second probability distributions; the transformer capacity range corresponding to the main power grid access point of the first power distribution area is recorded as the corresponding regional load range; and the daily average value curve of the daily power load of the first power distribution area in the most recent specified time period is recorded as the corresponding single-day load curve S b ; and the actual number of electric vehicles in the first distribution area is counted to obtain the corresponding first actual quantity; and the growth rate of the actual number of electric vehicles in the first distribution area is calculated to obtain the corresponding first growth rate; the load range of the area includes the minimum load s min and maximum load s max The recently specified time period includes at least the most recent week, the most recent month, the most recent quarter, and the most recent year;

[0006] Based on the first probability distribution, the second probability distribution, the single-day load curve S b estimating the maximum number of electric vehicles in the first power distribution area based on the regional load range to obtain a corresponding first estimated value;

[0007] If the first true value is greater than or equal to the first estimated value, setting the corresponding regional power supply control requirement to require control;

[0008] If the first actual quantity is less than the first estimated quantity, then estimating the length of time that the actual number of electric vehicles exceeds the first estimated quantity based on the first growth rate to obtain a corresponding first length of time; and identifying whether the first length of time is lower than a preset time length threshold; if so, setting the corresponding regional power supply regulation requirement to require regulation; if not, setting the corresponding regional power supply regulation requirement to not require regulation;

[0009] The obtained regional power supply control requirement is sent to a preset power grid management interface.

[0010] Preferably, the first probability distribution obeys a lognormal distribution, specifically:

[0011]

[0012] Where l is the mileage length, f1(l) is the probability corresponding to the mileage length l, μ1 and σ1 are the preset mean and standard deviation parameters;

[0013] The second probability distribution obeys the normal distribution, specifically:

[0014]

[0015] Among them, t s is the starting time of charging, f2(t s ) is the starting charging time t s The corresponding probability, μ2 and σ2 are the preset mean and standard deviation parameters.

[0016] Preferably, the single-day load curve S b The time length is 24 hours; the single-day load curve S b It is an average curve of all daily power load curves of the first power distribution area on all single days within the most recent specified time period.

[0017] Preferably, the first probability distribution, the second probability distribution, the single-day load curve S b The method estimates the maximum number of electric vehicles in the first power distribution area based on the regional load range to obtain a corresponding first estimated value, specifically including:

[0018] Step 41, initializing the total number of vehicles C based on a preset initial total number;

[0019] Wherein, the initial total is a preset positive integer;

[0020] Step 42, initializing the first counter to 1;

[0021] Step 43: Randomly draw the mileage based on the first probability distribution to obtain the corresponding random mileage l rnd ;

[0022] Step 44: randomly extract the starting charging time based on the second probability distribution to obtain the corresponding random starting time t rnd ;

[0023] Step 45, based on the random mileage l rnd Estimate the total charging power to obtain the corresponding total charging power s all ;

[0024] Among them, s all =l rnd ×α, α is the preset power consumption per unit mileage;

[0025] Step 46: Based on the total charging power s all , the random starting time t rnd and performing a charging load curve estimation based on a preset charging power p to obtain a corresponding first charging load curve;

[0026] The time length of the first charging load curve is 24 hours; the time length from time 0 to the random starting time t rnd The charging load between is 0, starting from the random starting time t rnd At the end of charging time t end The charging load between is p, from the charging end time t end The charging load between the time when the curve ends is 0; the charging end time t end With the random starting time t rnd The time between the charging time and the charging time is t ch =s all / p;

[0027] Step 47, add 1 to the first counter; and identify whether the first counter after adding 1 is greater than the total number of vehicles C; if so, superimpose the obtained C first charging load curves and use the superimposed curve as the corresponding total charging load curve S ev , and go to step 48; if not, return to step 43;

[0028] Step 48: the total charging load curve S ev With the single-day load curve S b Superimpose and use the superimposed curve as the corresponding regional load total curve S A ; and the total load curve S of the area A It is confirmed whether there is a curve segment that exceeds the regional load range; if it is confirmed that there is no curve segment, the total number of vehicles C is increased by 1 and the process returns to step 42; if it is confirmed that there is a curve segment, the total number of vehicles C is reduced by 1 as the corresponding first estimated amount.

[0029] Furthermore, the mileage is randomly drawn based on the first probability distribution to obtain the corresponding random mileage l rnd , specifically including:

[0030] A random number r1 is drawn from a normal distribution with a mean of μ1 and a standard deviation of σ1; and the random number r1 is subjected to an exponential transformation to obtain the corresponding random mileage l rnd , l rnd =exp(r1).

[0031] Furthermore, the random sampling of the starting charging time based on the second probability distribution obtains the corresponding random starting time t rnd , specifically including:

[0032] Step 61, draw a random number r2 from a standard normal distribution;

[0033] Wherein, the value range of the random number r2 is [0,1];

[0034] Step 62: Calculate the corresponding random starting time t based on the random number r2, the standard deviation σ2 and the mean μ2. rnd ;

[0035] Among them, t rnd =mod((μ2+σ2*r1),24),

[0036] mod() is the modulo function, and mod((μ2+σ2*r1),24) is the modulus of 24 of (μ2+σ2*r1).

[0037] The embodiment of the present invention provides a processing method for estimating the number of electric vehicles in a distribution area. As can be seen from the above content, the embodiment of the present invention pre-records any residential distribution area as the first distribution area, and records the probability distribution of the daily mileage of electric vehicles in this area and the probability distribution of the starting charging time as the corresponding first and second probability distributions, and records the transformer capacity range corresponding to the main power grid access point in this area as the corresponding regional load range, and uses the daily average value curve of the daily electricity load in this area within the most recently specified time period as the corresponding single-day load curve S b , and record the actual number of electric vehicles in this area and the growth rate of the number as the first actual quantity and the first growth rate; then, based on the first probability distribution, the second probability distribution, and the single-day load curve S b The maximum number of electric vehicles in the first distribution area is estimated based on the regional load range to obtain a corresponding first estimated amount; and when the first real amount is greater than or equal to the first estimated amount, the regional power supply control requirement is set to need to be regulated; and when the first real amount is less than the first estimated amount, the time length during which the number of real electric vehicles exceeds the first estimated amount is estimated based on the first growth rate to obtain a first time length, and whether the first time length is lower than a preset time length threshold is identified, if so, the regional power supply control requirement is set to need to be regulated, otherwise the regional power supply control requirement is set to not need to be regulated; finally, the obtained regional power supply control requirement is sent to the preset power grid management interface. Through the embodiment of the present invention, the power grid management side can optimize the power supply plan of the residential distribution area before the charging load conflicts with the daily power load, which not only improves the stable operation guarantee of the power grid, but also improves the timeliness and effectiveness of power expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a processing method for estimating the number of electric vehicles in a power distribution area provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the embodiments described herein are merely some, rather than all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0040] The embodiment of the present invention provides a method for estimating the number of electric vehicles in a distribution area for a smart power terminal or smart power system on the residential distribution area side (such as a distribution network node, a distribution station area, etc.). Figure 1 A schematic diagram of a processing method for estimating the number of electric vehicles in a power distribution area provided by an embodiment of the present invention is shown, which mainly includes the following steps:

[0041] Step 1: record any residential power distribution area as the corresponding first power distribution area; record the probability distribution of electric vehicle daily mileage and the probability distribution of starting charging time corresponding to the first power distribution area as the corresponding first and second probability distributions; record the transformer capacity range corresponding to the main power grid access point of the first power distribution area as the corresponding regional load range; and record the daily average value curve of the daily power load of the first power distribution area in the most recent specified time period as the corresponding single-day load curve S b ; and the actual number of electric vehicles in the first distribution area is counted to obtain the corresponding first real quantity; and the growth rate of the actual number of electric vehicles in the first distribution area is calculated to obtain the corresponding first growth rate.

[0042] Here, the probability distribution of electric vehicle daily mileage and the probability distribution of starting charging time in the embodiment of the present invention are both big data statistical data obtained from the power grid management side; the power grid management side refers to the management system or platform of the power management agency to which the current residential power distribution area belongs;

[0043] The probability distribution of electric vehicle daily mileage, i.e. the first probability distribution, follows the log-normal distribution, specifically:

[0044]

[0045] l is the mileage length, f1(l) is the probability corresponding to the mileage length l, μ1 and σ1 are the preset mean and standard deviation parameters;

[0046] The probability distribution of the starting charging time, i.e., the second probability distribution, obeys the normal distribution, specifically:

[0047]

[0048] t s is the starting time of charging, f2(t s) is the starting charging time t s The corresponding probability, μ2 and σ2 are the preset mean and standard deviation parameters.

[0049] The regional load range of the embodiment of the present invention includes the minimum load s min and maximum load s max .

[0050] The most recent specified time period in the embodiment of the present invention includes at least the most recent week, the most recent month, the most recent quarter, and the most recent year.

[0051] Single-day load curve S of the embodiment of the present invention b The time length is 24 hours, and the single-day load curve S b It is the average curve of the daily power load curves of all single days in the first distribution area within the most recent specified time period.

[0052] The first growth rate in the embodiment of the present invention is calculated as follows: first growth rate = total number of new vehicles added in the most recent month / actual number of electric vehicles one month ago.

[0053] Step 2: Based on the first probability distribution, the second probability distribution, and the daily load curve S b and the regional load range to estimate the maximum value of electric vehicles in the first distribution area to obtain a corresponding first estimated value;

[0054] Specifically comprising: step 21, initializing the total number of vehicles C based on a preset initial total number;

[0055] Here, the initial total is a preset positive integer;

[0056] Step 22, initialize the first counter to 1;

[0057] Step 23: Randomly draw the mileage based on the first probability distribution to obtain the corresponding random mileage l rnd ;

[0058] Specifically: draw a random number r1 from a normal distribution with a mean of μ1 and a standard deviation of σ1; and perform an exponential transformation on the random number r1 to obtain the corresponding random mileage l rnd , l rnd =exp(r1);

[0059] Step 24: randomly extract the starting charging time based on the second probability distribution to obtain the corresponding random starting time t rnd ;

[0060] Specifically comprising: step 241, extracting a random number r2 from a standard normal distribution;

[0061] Here, the value range of random number r2 is [0,1];

[0062] Step 242: Calculate the corresponding random starting time t based on the random number r2, standard deviation σ2 and mean μ2. rnd ;

[0063] Here, t rnd =mod((μ2+σ2*r1),24);

[0064] Among them, mod() is the modulo function, mod((μ2+σ2*r1),24) means taking the modulus of 24 for (μ2+σ2*r1);

[0065] Step 25, based on random mileage l rnd Estimate the total charging power to obtain the corresponding total charging power s all ;

[0066] Here, s all =l rnd ×α, α is the preset power consumption per unit mileage;

[0067] Step 26: Based on the total charge capacity s all , random starting time t rnd and performing a charging load curve estimation based on a preset charging power p to obtain a corresponding first charging load curve;

[0068] Here, the time length of the first charging load curve of the embodiment of the present invention is 24 hours; the time length from time 0 to the random starting time t rnd The charging load between is 0, starting from the random starting time t rnd At the end of charging time t end The charging load between is p, and the charging end time is t end The charging load between the time when the curve ends is 0; the charging end time t end With random starting time t rnd The time between the charging time and the charging time is t ch =s all / p;

[0069] Step 27, add 1 to the first counter; and identify whether the first counter after adding 1 is greater than the total number of vehicles C; if so, superimpose the obtained C first charging load curves and use the superimposed curve as the corresponding total charging load curve S ev , and go to step 28; if not, return to step 23;

[0070] Step 28: The total charging load curve S ev Compared with the single-day load curve S bSuperimpose and use the superimposed curve as the corresponding regional load total curve S A ; and the regional load total curve S A Check whether there is a curve segment that exceeds the regional load range; if it is confirmed that there is no curve segment, add 1 to the total number of vehicles C and return to step 22; if it is confirmed that there is a curve segment, subtract 1 from the total number of vehicles C as the corresponding first estimated amount.

[0071] Step 3: If the first real value is greater than or equal to the first estimated value, then set the corresponding regional power supply control requirement to require control.

[0072] Step 4: If the first actual quantity is less than the first estimated quantity, the time length that the actual number of electric vehicles exceeds the first estimated quantity is estimated based on the first growth rate to obtain the corresponding first time length; and whether the first time length is lower than the preset time length threshold is identified; if so, the corresponding regional power supply regulation requirement is set to require regulation; if not, the corresponding regional power supply regulation requirement is set to not require regulation.

[0073] Here, the first time length=(first estimated value−first true value) / first growth rate; the time length threshold is a preset threshold parameter.

[0074] Step 5: Send the obtained regional power supply control requirements to the preset power grid management interface.

[0075] Here, the grid management interface is a data upload interface opened by the grid management side to the smart power terminals or smart power systems on the side of each residential power distribution area.

[0076] In summary, the embodiment of the present invention provides a method for estimating the number of electric vehicles in a distribution area. As can be seen from the above, the embodiment of the present invention pre-records any residential distribution area as the first distribution area, and records the probability distribution of the daily mileage of electric vehicles in this area and the probability distribution of the starting charging time as the corresponding first and second probability distributions, and records the transformer capacity range corresponding to the main power grid access point in this area as the corresponding regional load range, and uses the daily average value curve of the daily electricity load in the recent specified time period in this area as the corresponding single-day load curve S b , and record the actual number of electric vehicles in this area and the growth rate of the number as the first actual quantity and the first growth rate; then, based on the first probability distribution, the second probability distribution, and the single-day load curve S bThe maximum number of electric vehicles in the first distribution area is estimated based on the regional load range to obtain a corresponding first estimated amount; and when the first real amount is greater than or equal to the first estimated amount, the regional power supply control requirement is set to need to be regulated; and when the first real amount is less than the first estimated amount, the time length during which the number of real electric vehicles exceeds the first estimated amount is estimated based on the first growth rate to obtain a first time length, and whether the first time length is lower than a preset time length threshold is identified, if so, the regional power supply control requirement is set to need to be regulated, otherwise the regional power supply control requirement is set to not need to be regulated; finally, the obtained regional power supply control requirement is sent to the preset power grid management interface. Through the embodiment of the present invention, the power grid management side can optimize the power supply plan of the residential distribution area before the charging load conflicts with the daily power load, which not only improves the stable operation guarantee of the power grid, but also improves the timeliness and effectiveness of power expansion.

[0077] Professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0078] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0079] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for estimating the number of electric vehicles in a power distribution area, characterized in that: The method comprises: Any residential power distribution area is recorded as the corresponding first power distribution area; the probability distribution of daily mileage of electric vehicles and the probability distribution of starting charging time corresponding to the first power distribution area are recorded as the corresponding first and second probability distributions; the transformer capacity range corresponding to the main power grid access point of the first power distribution area is recorded as the corresponding regional load range; and the daily average value curve of the daily power load of the first power distribution area in the most recent specified time period is recorded as the corresponding single-day load curve S b ; and the actual number of electric vehicles in the first distribution area is counted to obtain the corresponding first actual quantity; and the growth rate of the actual number of electric vehicles in the first distribution area is calculated to obtain the corresponding first growth rate; the load range of the area includes the minimum load s min and maximum load s max The recently specified time period includes at least the most recent week, the most recent month, the most recent quarter, and the most recent year; Based on the first probability distribution, the second probability distribution, the single-day load curve S b estimating the maximum number of electric vehicles in the first power distribution area based on the regional load range to obtain a corresponding first estimated value; If the first true value is greater than or equal to the first estimated value, setting the corresponding regional power supply control requirement to require control; If the first actual quantity is less than the first estimated quantity, then estimating the length of time that the actual number of electric vehicles exceeds the first estimated quantity based on the first growth rate to obtain a corresponding first length of time; and identifying whether the first length of time is lower than a preset time length threshold; if so, setting the corresponding regional power supply regulation requirement to require regulation; if not, setting the corresponding regional power supply regulation requirement to not require regulation; The obtained regional power supply control requirement is sent to a preset power grid management interface.

2. The method for estimating the number of electric vehicles in a power distribution area according to claim 1, wherein: The first probability distribution obeys the lognormal distribution, specifically: , Where l is the mileage length, f1(l) is the probability corresponding to the mileage length l, µ1 and σ1 are the preset mean and standard deviation; The second probability distribution obeys the normal distribution, specifically: , Among them, t s is the starting time of charging, f2(t s ) is the starting charging time t s The corresponding probability, µ2 and σ2 are the preset mean and standard deviation.

3. The method for estimating the number of electric vehicles in a power distribution area according to claim 1, wherein: The single-day load curve S b The time length is 24 hours; the single-day load curve S b It is an average curve of the daily power load curves of all single days in the first power distribution area within the most recent specified time period.

4. The method for estimating the number of electric vehicles in a power distribution area according to claim 2, wherein: The method based on the first probability distribution, the second probability distribution, the single-day load curve S b The method estimates the maximum number of electric vehicles in the first power distribution area based on the regional load range to obtain a corresponding first estimated value, specifically including: Step 41, initializing the total number of vehicles C based on a preset initial total number; Wherein, the initial total is a preset positive integer; Step 42, initializing the first counter to 1; Step 43: Randomly draw the mileage based on the first probability distribution to obtain the corresponding random mileage l rnd ; Step 44: randomly extract the starting charging time based on the second probability distribution to obtain the corresponding random starting time t rnd ; Step 45, based on the random mileage l rnd Estimate the total charging power to obtain the corresponding total charging power s all ; in, , α is the preset power consumption per unit mileage; Step 46: Based on the total charging power s all , the random starting time t rnd and performing a charging load curve estimation based on a preset charging power p to obtain a corresponding first charging load curve; The time length of the first charging load curve is 24 hours; the time length from time 0 to the random starting time t rnd The charging load between is 0, starting from the random starting time t rnd At the end of charging time t end The charging load between is p, from the charging end time t end The charging load between the time when the curve ends is 0; the charging end time t end With the random starting time t rnd The time between the charging time and the charging time is t ch =s all / p; Step 47, add 1 to the first counter; and identify whether the first counter after adding 1 is greater than the total number of vehicles C; if so, superimpose the obtained C first charging load curves and use the superimposed curve as the corresponding total charging load curve S ev , and go to step 48; if not, return to step 43; Step 48: the total charging load curve S ev With the single-day load curve S b Superimpose and use the superimposed curve as the corresponding regional load total curve S A ; and the total load curve S of the area A It is confirmed whether there is a curve segment that exceeds the regional load range; if it is confirmed that there is no curve segment, the total number of vehicles C is increased by 1 and the process returns to step 42; if it is confirmed that there is a curve segment, the total number of vehicles C is reduced by 1 as the corresponding first estimated amount.

5. The method for estimating the number of electric vehicles in a power distribution area according to claim 4, characterized in that: The mileage is randomly drawn based on the first probability distribution to obtain the corresponding random mileage l rnd , specifically including: A random number r1 is drawn from a normal distribution with a mean of µ1 and a standard deviation of σ1; and the random number r1 is subjected to an exponential transformation to obtain the corresponding random mileage l rnd , l rnd =exp(r1).

6. The method for estimating the number of electric vehicles in a power distribution area according to claim 4, characterized in that: The random starting time t is obtained by randomly extracting the starting charging time based on the second probability distribution. rnd , specifically including: Step 61, draw a random number r2 from a standard normal distribution; Wherein, the value range of the random number r2 is [0,1]; Step 62: Calculate the corresponding random starting time t based on the random number r2, the standard deviation σ2 and the mean µ2. rnd ; in, , mod() is the modulo function, For Take the modulus of 24.

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