CN111216586B - Residential community electric vehicle ordered charging control method considering wind power consumption - Google Patents
Residential community electric vehicle ordered charging control method considering wind power consumption Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/63—Monitoring or controlling charging stations in response to network capacity
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/16—Information or communication technologies improving the operation of electric vehicles
- Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
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Abstract
The invention provides a residential community electric vehicle ordered charging control method considering wind power consumption, and belongs to the technical field of electric vehicle charging and discharging. The method comprises the following steps: determining the time granularity of electric vehicle charging power control, and determining a current day wind power output prediction curve, a current day cell electric vehicle charging load prediction curve, a current day cell non-electric vehicle power load prediction curve and a power planning constraint condition; calculating a charging load reference curve of the electric automobile in the community with ideal wind power consumption according to the prediction curve; calculating and optimizing by using a differential evolution algorithm to obtain a charging power pre-distribution curve of the electric automobile in the community, wherein the optimization target is to improve wind power consumption and charging timeliness of the electric automobile; and distributing the charging power of each electric automobile in real time by combining a charging pile with a pre-distribution curve of the charging power of the electric automobiles in the community and the charged time of the target electric automobile. The invention can improve wind power consumption and relieve the pressure of a power grid.
Description
Technical Field
The invention relates to the technical field of electric automobile charging and discharging, and particularly provides a residential community electric automobile ordered charging control method considering wind power consumption.
Background
Wind power as clean energy and renewable energy occupies an important position in an energy system, in recent years, the wind power in China develops rapidly, and the cumulative grid-connected wind power installed capacity in China reaches 1.98 hundred million kilowatts by 9 months in 2019. Wind power is naturally uncertain on a time scale, wind power directly generated by a fan is difficult to meet the load requirement of a common user side under the common condition, and the wind power has certain inverse peak regulation characteristics for residential electricity loads.
The electric automobile is used as a novel green environment-friendly vehicle and is rapidly popularized in China on a large scale, and the holding capacity of the pure electric automobile in China reaches 281 ten thousands by 6 months in 2019. With the expansion of the holding scale of the electric automobile, the charging process of the electric automobile has greater and greater influence on an energy system. Generally, the disordered charging of the electric automobile causes great impact on a power grid, a phenomenon of peak load on a power load peak is caused, and more attention is paid to how to reasonably control the charging process so as to improve a load curve on a demand side.
At present, the existing orderly charging method for the electric automobile mainly reduces the charging electricity price and balances the load fluctuation, and a proper method matched with local wind power output is not provided; in addition, the existing method utilizes the discharge of the battery of the electric automobile to feed back to the power grid under the normal condition, and a control scheme which enables the owner of the electric automobile to easily accept and only changes the charging time is not provided; meanwhile, the application places of the orderly charging method for many electric automobiles are not clear. Therefore, aiming at the characteristics that the number of vehicles in the residential area is relatively fixed and the daily electric power information is easy to obtain, the invention needs to provide the method for orderly charging the electric automobile in the residential area, which can enhance the local wind power consumption and does not use the battery for discharging. However, there is no charging method that can satisfy the above requirements in the prior art.
Disclosure of Invention
In order to solve the problems, the invention provides the orderly charging control method of the electric automobile in the residential community considering the wind power consumption, which can improve the wind power consumption and relieve the pressure of a power grid.
In order to achieve the purpose, the invention provides the following technical scheme:
a residential community electric vehicle ordered charging control method considering wind power consumption comprises the following steps:
step 1: determining the time granularity of electric vehicle charging power control, and determining a current day wind power output prediction curve, a current day community electric vehicle charging load prediction curve, a current day community non-electric vehicle electric load power prediction curve and a power planning constraint condition which correspond to the time granularity;
step 2: calculating a charging load reference curve of the electric vehicle in the community with ideal wind power consumption according to the wind power output prediction curve, the charging power demand prediction curve of the electric vehicle in the community and the power prediction curve of the electric load of the non-electric vehicle in the community;
and step 3: according to a charging load reference curve of a residential electric vehicle with ideal wind power consumption, a residential electric vehicle charging power demand prediction curve and power planning constraint conditions, calculating and optimizing by using a differential evolution algorithm to obtain a residential electric vehicle charging power pre-distribution curve with the aim of improving wind power consumption and electric vehicle charging timeliness as an optimization target;
and 4, step 4: and distributing the charging power of each electric automobile in real time by combining a charging pile with a pre-distribution curve of the charging power of the electric automobiles in the community and the charged time of the target electric automobile.
Further, in step 1, the process of obtaining the charging load prediction curve of the electric vehicle in the community is as follows:
determining that the charging access time of the electric vehicles in the community obeys normal distribution, the charging quantity demand of the electric vehicles in the community obeys log-normal distribution, the charging power is a fixed value, simulating the charging behavior of the electric vehicles by using a Monte Carlo method, and accumulating the charging quantity of the electric vehicles under each time granularity, thereby calculating to obtain a charging load prediction curve of the electric vehicles in the community.
Preferably, the time granularity of the electric vehicle charging power control in the step 1 is hours.
Preferably, the power planning constraint in step 1 includes a maximum value of power of the cell power load.
Further, the step 2 specifically includes the following steps:
according to the charging load prediction curve of the electric automobile in the community and the power load power prediction curve of the non-electric automobile in the community, calculating the total power load prediction curve of the community on the current day as follows:
Lall(t)＝Lvehicle(t)+Lothers(t)
wherein L isall(t) represents the power of the cell on the day at time tTotal load prediction value, Lvehicle(t) represents a predicted value of the charging power demand of the electric automobile in the community at the moment t, Lothers(t) representing the predicted value of the power of the electric load of the non-electric automobile at the time t;
and calculating to obtain a wind power scaling factor by combining the total power load prediction curve and the wind power output prediction curve of the cell on the same day by using the following formula:
where k denotes the wind scaling factor, Lall(t) represents a predicted value of the total daily electric power load of the cell at time t, Wraw(t) representing a predicted value of wind power output at the moment t;
according to the wind power output prediction curve, the wind power scaling factor and the power load power prediction curve for the non-electric automobile in the community, calculating a charging load reference curve for the electric automobile in the community with ideal wind power consumption by using the following formula:
wherein R iswind(t) represents a charging load reference curve of the residential electric vehicle with ideal wind power consumption, k represents a wind power scaling factor, Wraw(t) represents the predicted value of wind power output at t moment, LothersAnd (t) represents a predicted value of the electric load power for the non-electric automobile in the cell at the time t.
Further, in step 3, the objective function of the differential evolution algorithm is as follows:
wherein V (t) represents a pre-distribution curve of charging power of electric vehicles in a community, cwindRepresenting wind power consumption weight, Rwind(t) reference curve of charging load of electric vehicle in residential area for ideal wind power consumption, Lvehicle(t) shows the electric steam of the district at the time tPredicted value of vehicle charging power demand, cshiftRepresents a charging aging characteristic weight, FshiftRepresenting a sequence offset distance function;
the constraint conditions of the differential evolution algorithm comprise that the pre-distribution curve of the charging power of the electric automobile in the community is not negative, the accumulated sum of the pre-distribution curve of the charging power of the electric automobile in the community is equal to the accumulated sum of the predicted values of the charging power demand of the electric automobile in the community, and the power planning constraint conditions determined in the step 1.
Further, the sequence offset distance function Fshift(f), (t), g (t)) is calculated by the following method:
obtaining the difference d (t) between the finite sequence f (t) and g (t) by the following formula:
d(t)＝g(t)-f(t)，t∈[0，T-1]
wherein T is the length of the finite sequence;
the following steps are carried out:
s1, initializing index to be 0, dist to be 0, and executing S2;
s2, if d (index) is greater than 0, let pre be 1, execute S3; otherwise, go to S6;
s3, if d ((index-pre + T)% T) < 0, executing S4; otherwise, go to S5;
s4, if-d ((index-pre + T)% T) > d (index), dist is increased by d (index) pre, d ((index-pre + T)% T) is increased by d (index), d (index) is set to zero, execute S6; otherwise, increasing dist by-d ((index-pre + T)% pre, increasing d (index) by d ((index-pre + T)% T), setting d ((index-pre + T)% T) to zero, and executing S5;
s5, if pre < T-1, pre is increased by 1, executing S3; otherwise, go to S6;
s6, if index is less than T-1, the index is increased by 1, and S2 is executed; otherwise, go to S7;
s7, obtaining Fshift(f(t)，g(t))＝dist。
Further, in step 4, the real-time distribution strategy of the charging power of the electric vehicle is as follows:
if the current time period Nv(t)*PvmaxV (t) is less than or equal to V, all the electric automobiles are charged with the maximum power; wherein N isv(t) represents the actual situationElectric vehicle access quantity curve, V (t) represents a pre-distribution curve of charging power of electric vehicles in a community, PvmaxRepresenting the maximum charging power of the electric automobile;
otherwise, charging the electric vehicles according to the charging power distribution limit of each electric vehicle
Wherein, Pv(i, t) is the charging power limit distributed by the ith electric automobile at the moment t, I (i) is the time length of the ith electric automobile connected into the charging pile, PstepFor the charging duration compensation function, the calculation method is
Where M is a charging time threshold for starting compensation.
Preferably, M is taken to be 4 hours.
Compared with the prior art, the invention has the following advantages and beneficial effects:
according to the method, the electric automobile charging pre-allocation amount is obtained by utilizing the wind power prediction information, and a residential community power load curve is improved, so that the wind power consumption capacity of a power grid is improved; the method only controls the charging power of the electric automobile and does not relate to the discharging of the electric automobile, so that the method has a good protection effect on the service life of the battery of the electric automobile.
Drawings
Fig. 1 is a flow chart of the orderly charging control method for the electric vehicle in the residential area considering wind power consumption provided by the invention.
Fig. 2 is a schematic diagram of a wind power output prediction curve provided by the embodiment of the invention.
Fig. 3 is a schematic diagram of electrical loads of a cell according to an embodiment of the present invention.
Fig. 4 is a schematic view of a charging load of a cell electric vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions provided by the present invention will be described in detail below with reference to specific examples, and it should be understood that the following specific embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention.
Example 1
The embodiment is an electric automobile charging system in a certain residential quarter, the maximum allowable electric power of the residential quarter is 5500kW, and the residential quarter has 2000 households and 1000 electric automobiles. The ordered charging control method is executed according to the flow shown in the attached figure 1.
And 2, calculating a total power load prediction curve of the community on the day according to the charging load prediction curve of the electric automobile in the community and the power load prediction curve of the non-electric automobile in the community.
Lall(t)＝Lvehicle(t)+Lothers(t)
Wherein L isall(t) represents a predicted value of the total daily electric power load of the cell at time t, Lvehicle(t) represents the charging power requirement of the electric automobile in the community at the moment tTo find a predicted value, LothersAnd (t) represents the predicted value of the electric load power of the non-electric automobile at the time t.
And then, combining the prediction curve of the total power load of the current day of the cell and the prediction curve of the wind power output, and calculating by using the following formula to obtain a wind power scaling factor k of 19.2.
Where k denotes the wind scaling factor, Lall(t) represents a predicted value of the total daily electric power load of the cell at time t, WrawAnd (t) represents a predicted value of wind power output at the moment t.
And calculating a charging load reference curve of the electric automobile in the community with ideal wind power consumption by using the following formula according to the wind power output prediction curve, the wind power scaling factor and the power prediction curve of the electric load of the non-electric automobile in the community.
Wherein R iswind(t) represents a charging load reference curve of the residential electric vehicle with ideal wind power consumption, k represents a wind power scaling factor, Wraw(t) represents the predicted value of wind power output at t moment, LothersAnd (t) represents a predicted value of the electric load power of the non-electric automobile in the cell at the time t.
Wherein V (t) represents a pre-distribution curve of charging power of electric vehicles in a community, cwindRepresenting wind power consumption weight, Rwind(t) reference curve of charging load of electric vehicle in residential area for ideal wind power consumption, Lvehicle(t) represents a predicted value of the charging power demand of the electric automobile in the community at the moment t, cshiftRepresents a charging aging characteristic weight, FshiftRepresenting a sequence offset distance function. In this example, the wind power absorption weight is taken to be 0.85, and the charging timeliness weight is taken to be 0.15.
Sequence offset distance function Fshift(f), (t), g (t)) is calculated by the following method:
obtaining the difference d (t) between the finite sequence f (t) and g (t)
d(t)＝g(t)-f(t)，t∈[0，T-1]
Where T is the length of the finite sequence.
The following steps are carried out
S1, initializing index to 0, dist to 0, and executing S2.
S2, if d (index) is greater than 0, let pre be 1, execute S3; otherwise, S6 is executed.
S3, if d ((index-pre + T)% T) < 0, executing S4; otherwise, S5 is executed.
S4, if-d ((index-pre + T)% T) > d (index), dist is increased by d (index) pre, d ((index-pre + T)% T) is increased by d (index), d (index) is set to zero, execute S6; otherwise, dist is increased by-d ((index-pre + T)% pre, d (index) is increased by d ((index-pre + T)% T), d ((index-pre + T)% T) is set to zero, and S5 is performed.
S5, if pre < T-1, increasing pre by 1, executing S3; otherwise, S6 is executed.
S6, if index is less than T-1, index is increased by 1, executing S2; otherwise, S7 is executed.
S7, obtaining Fshift(f(t)，g(t))＝dist。
The constraint conditions of the differential evolution algorithm comprise that a charging power pre-distribution curve of the electric automobile in the community is not negative, the accumulated sum of the charging power pre-distribution curve of the electric automobile in the community is equal to the accumulated sum of the predicted values of the charging power demand of the electric automobile in the community, and the power planning constraint conditions determined in the step 1, namely the maximum value of the power load power of the community is not more than 5500 kW.
The calculated pre-distribution curve of the charging power of the electric automobile in the community is shown in the attached figure 4.
And 4, regenerating electric vehicle data (used for simulation) actually arriving at the cell for charging according to the probability distribution information of the same electric vehicle access and charging requirements in the step 1, and distributing the charging power of each electric vehicle in real time by combining a cell electric vehicle charging power pre-distribution curve and the charged time of the target electric vehicle. And the community charging pile controller controls each charging pile to charge the electric automobile according to the execution result of the real-time distribution strategy of the charging power of the electric automobile.
The real-time distribution strategy of the charging power of the electric automobile is as follows:
if the current time period N isv(t)*PvmaxAnd V (t) or less, charging all the electric automobiles at the maximum power. Wherein N isv(t) represents an electric vehicle access quantity curve under actual conditions, y (t) represents a residential electric vehicle charging power pre-distribution curve, PvmaxAnd represents the maximum charging power of the electric automobile.
Otherwise, charging the electric automobile according to the charging power distribution limit of each electric automobile, wherein the charging power limit distributed by the electric automobile is calculated according to the following formula:
wherein, Pv(i, t) is the charging power limit distributed by the ith electric automobile at the moment t, I (i) is the time length of the ith electric automobile connected into the charging pile, PstepFor the charging duration compensation function, the calculation method is
Where M is a charging time threshold for starting compensation.
In this example, the charging time threshold M for starting compensation is taken as 4 hours, and the charging load curve of the ordered charging is obtained as shown in fig. 4.
Therefore, after the method provided by the invention is implemented, the power load of the community during the day wind power output valley period is reduced, the utilization of the wind power at night is increased, and the charging load of the electric automobile of the community is improved. Therefore, the implementation result fully verifies the feasibility and the effectiveness of the method provided by the invention.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and such improvements and modifications are also considered to be within the scope of the present invention.
Claims (8)
1. A residential community electric vehicle ordered charging control method considering wind power consumption is characterized by comprising the following steps:
step 1: determining the time granularity of electric vehicle charging power control, and determining a current day wind power output prediction curve, a current day community electric vehicle charging load prediction curve, a current day community non-electric vehicle electric load power prediction curve and a power planning constraint condition which correspond to the time granularity;
step 2: calculating a charging load reference curve of the electric vehicle in the community with ideal wind power consumption according to the wind power output prediction curve, the charging load prediction curve of the electric vehicle in the community and the power prediction curve of the electric load of the non-electric vehicle in the community;
the step 2 specifically comprises the following steps:
according to the charging load prediction curve of the electric automobile in the community and the power load power prediction curve of the non-electric automobile in the community, calculating the total power load prediction curve of the community on the current day as follows:
Lall(t)＝Lvehicle(t)+Lothers(t)
wherein L isall(t) represents a predicted value of the total daily electric power load of the cell at time t, Lvehicle(t) represents a predicted value of charging load of the electric automobile in the community at the time t, Lothers(t) representing the predicted value of the power of the electric load of the non-electric automobile at the time t;
and calculating to obtain a wind power scaling factor by combining the total power load prediction curve and the wind power output prediction curve of the cell on the same day by using the following formula:
where k denotes the wind scaling factor, Lall(t) the predicted value of the total daily electric power load of the cell at time t, Wraw(t) representing a predicted value of wind power output at the moment t;
according to the wind power output prediction curve, the wind power scaling factor and the power load power prediction curve for the non-electric automobile in the community, calculating a charging load reference curve for the electric automobile in the community with ideal wind power consumption by using the following formula:
wherein R iswind(t) represents a charging load reference curve of the residential electric vehicle with ideal wind power consumption, k represents a wind power scaling factor, Wraw(t) represents the predicted value of wind power output at t moment, Lothers(t) representing a predicted value of the electric load power of the non-electric automobile in the cell at the time t;
and step 3: according to a charging load reference curve of a residential area electric vehicle with ideal wind power consumption, a charging load prediction curve of the residential area electric vehicle and a power planning constraint condition, calculating and optimizing by using a differential evolution algorithm to obtain a charging power pre-distribution curve of the residential area electric vehicle with the aim of improving wind power consumption and charging timeliness of the electric vehicle as an optimization target;
and 4, step 4: and distributing the charging power of each electric automobile in real time by combining a charging pile with a pre-distribution curve of the charging power of the electric automobiles in the community and the charged time of the target electric automobile.
2. The orderly charging control method for residential electric vehicles considering wind power consumption according to claim 1, wherein in step 1, the process of acquiring the charging load prediction curve of residential electric vehicles is as follows:
determining that the charging access time of the electric vehicles in the community obeys normal distribution, the charging quantity demand of the electric vehicles in the community obeys log-normal distribution, the charging power is a fixed value, simulating the charging behavior of the electric vehicles by using a Monte Carlo method, and accumulating the charging quantity of the electric vehicles under each time granularity, thereby calculating to obtain a charging load prediction curve of the electric vehicles in the community.
3. The orderly charging control method of electric vehicles in residential communities with consideration of wind power consumption as claimed in claim 1, characterized in that: and the time granularity of the electric vehicle charging power control in the step 1 is hour.
4. The method for controlling orderly charging of electric vehicles in residential areas in consideration of wind power consumption as claimed in claim 1, wherein the power planning constraint conditions in step 1 include the maximum value of the electric load power of the residential area.
5. The method for controlling orderly charging of electric vehicles in residential areas considering wind power consumption as claimed in claim 1, wherein in said step 3, the objective function of the differential evolution algorithm is:
wherein V (t) represents a pre-distribution curve of charging power of electric vehicles in a community, cwindRepresenting wind power consumption weight, Rwind(t) reference curve of charging load of electric vehicle in residential area for ideal wind power consumption, Lvehicle(t) represents a predicted value of charging load of the electric vehicle in the cell at the time t, cshiftRepresents a charging aging characteristic weight, FshiftRepresenting a sequence offset distance function;
the constraint conditions of the differential evolution algorithm comprise that the pre-distribution curve of the charging power of the electric automobile in the community is not negative, the accumulated sum of the pre-distribution curve of the charging power of the electric automobile in the community is equal to the accumulated sum of the predicted values of the charging load of the electric automobile in the community, and the power planning constraint conditions determined in the step 1.
6. The method according to claim 5, wherein the sequence offset distance function F is a function of the sequential charging of electric vehicles in a residential area with consideration of wind power consumptionshift(f), (t), g (t)) is calculated by the following method:
obtaining the difference d (t) between the finite sequence f (t) and g (t) by the following formula:
d(t)＝g(t)-f(t)，t∈[0，T-1]
wherein T is the length of the finite sequence;
the following steps are carried out:
s1, initializing index to be 0, dist to be 0, and executing S2;
s2, if d (index) is greater than 0, let pre be 1, execute S3; otherwise, go to S6;
s3, if d ((index-pre + T)% T) < 0, executing S4; otherwise, go to S5;
s4, if-d ((index-pre + T)% T) > d (index), dist is increased by d (index) pre, d ((index-pre + T)% T) is increased by d (index), d (index) is set to zero, execute S6; otherwise, increasing dist by-d ((index-pre + T)% pre, increasing d (index) by d ((index-pre + T)% T), setting d ((index-pre + T)% T) to zero, and executing S5;
s5, if pre < T-1, pre is increased by 1, executing S3; otherwise, go to S6;
s6, if index is less than T-1, the index is increased by 1, and S2 is executed; otherwise, go to S7;
s7, obtaining Fshift(f(t)，g(t))＝dist。
7. The orderly charging control method for electric vehicles in residential communities considering wind power consumption according to claim 1, characterized in that in the step 4, the real-time distribution strategy for electric vehicle charging power is as follows:
if the current time period N isv(t)*PvmaxV (t) is less than or equal to V, all the electric automobiles are charged with the maximum power; wherein N isv(t) represents an electric vehicle access quantity curve under actual conditions, V (t) represents a residential electric vehicle charging power pre-distribution curve, PvmaxIndicating the best of the electric automobileA large charging power;
otherwise, charging the electric vehicles according to the charging power distribution limit of each electric vehicle
Wherein, Pv(i, t) is the charging power limit distributed by the ith electric automobile at the moment t, I (i) is the time length of the ith electric automobile connected into the charging pile, PstepFor the charging duration compensation function, the calculation method is
Where M is a charging time threshold for starting compensation.
8. The method for controlling orderly charging of electric vehicles for residential areas in consideration of wind power consumption as claimed in claim 7, wherein M is 4 hours.
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