A processing method for orderly regulating and controlling charging of an electric vehicle

Abstract

The embodiment of the application relates to a processing method for orderly regulating and controlling electric vehicle charging, the method comprising: step 1, predicting a charging load curve of a power distribution area based on a Monte Carlo method, and obtaining a resident load curve, a photovoltaic power generation curve and a storage charging and discharging curve; step 2, setting a power grid power supply curve based on the charging load, the resident load, the photovoltaic power generation and the storage charging and discharging curve, setting an optimization objective function, and optimizing a starting charging time probability distribution for minimizing the optimization objective function in a delayed starting charging time mode; and simulating a new charging load curve based on the new probability distribution by using the Monte Carlo method; step 3, identifying whether the new charging load curve meets the orderly regulating and controlling requirement, returning to step 2 for continuous optimization if the new charging load curve does not meet the orderly regulating and controlling requirement, and sending the latest starting charging time probability distribution to each charging pile of the power distribution area if the new charging load curve meets the orderly regulating and controlling requirement. The application can reduce a charging load peak and balance a charging period.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a processing method for orderly regulating the charging of electric vehicles. Background Art

[0002] If electric vehicle (EV) charging piles are connected to the residential power distribution area, the power load composition 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 cause insufficient power supply for residents' daily electricity use, especially during the peak period of EV charging. The construction of distributed integrated photovoltaic storage and charging power stations (PV charging stations for short) will help improve this power load competition problem. However, with the rapid growth in the number of electric vehicles, even with the supplementary power supply of PV charging stations, there will still be disorderly charging loads that are too large and the civil and charging loads cannot be balanced during the peak charging period. To solve this problem, it is necessary to further provide an orderly charging control rule under the condition of supplementary power supply of PV charging stations.

[0003] We know that the power grid management side can collect and analyze big data to collect statistics on the probability distribution of daily mileage of electric vehicles and the probability distribution of starting charging time in each residential power distribution area. Both probability distributions satisfy the normal distribution, among which the probability distribution of starting charging time is closely related to orderly charging regulation. Before regulation, the curve state of the probability distribution of starting charging time is: there is an obvious peak period, and the width of the continuous charging period is narrow. If the curve state of the probability distribution of starting charging time can be optimized to: the peak period is not obvious, and the width of the continuous charging period is larger and more balanced; and the optimized probability of starting charging time is sent to each charging pile in the area as a guide for charging regulation, then the purpose of reducing load peaks and balancing charging periods can be achieved. This is also the technical problem that the present invention needs to solve. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for orderly regulating the charging of electric vehicles in response to the defects of the prior art. The present invention first records the probability distribution of daily mileage of electric vehicles and the probability distribution of the starting charging time corresponding to the distribution area as the first and second probability distributions, and uses the Monte Carlo method to predict the charging load curve of the distribution area according to the first and second probability distributions to obtain the corresponding charging load curve, and uses the daily average curve of the residential electricity load in the distribution area as the resident load curve, and sets the photovoltaic power generation curve and energy storage charging and discharging curve of the photovoltaic storage charging station in the area; then sets the grid power supply curve based on the charging load curve, the resident load curve, the photovoltaic power generation curve and the energy storage charging and discharging curve, and sets the corresponding optimization objective function based on the grid power supply curve in order to minimize the power supply on the grid side and minimize the power supply fluctuation on the grid side. The method uses the first probability distribution and the new second probability distribution to predict the charging load curve of the distribution area based on the first probability distribution and the new second probability distribution. The method then identifies whether the new charging load curve meets the requirements of orderly regulation. If the identification result is satisfied, the latest second probability distribution is sent as the final orderly regulation probability distribution to each charging pile in the area as the latest orderly charging guidance for the charging pile. If the identification result is not satisfied, the second probability distribution is continuously optimized until the latest charging load curve meets the requirements of orderly regulation. The present invention can help reduce the peak charging load and balance the charging period in the residential distribution area.

[0005] To achieve the above objectives, an embodiment of the present invention provides a method for orderly regulating the charging of an electric vehicle, the method comprising:

[0006] Step 1: record any residential distribution area equipped with a photovoltaic storage charging station as the corresponding first distribution area; record the photovoltaic storage charging station in the first distribution area as the corresponding first power station; record the probability distribution of daily mileage of electric vehicles and the probability distribution of starting charging time corresponding to the first distribution area as the corresponding first and second probability distributions; and use the Monte Carlo method to predict the charging load curve of the first distribution area according to the first and second probability distributions to obtain the corresponding charging load curve P ev ; And the residential electricity load daily average curve of the first distribution area is used as the corresponding residential load curve P basic ; and set the photovoltaic power generation curve P of the first power station pv And the energy storage charge and discharge curve P bat ;

[0007] Step 2: Based on the charging load curve P ev , the resident load curve Pbasic , the photovoltaic power generation curve P pv and the energy storage charge and discharge curve P bat Set the corresponding grid power supply curve P net ; And to minimize the power supply on the grid side and minimize the fluctuation of the power supply on the grid side as the optimization direction, based on the grid power supply curve P net Set the corresponding optimization objective function O; and optimize the mean and standard deviation of the second probability distribution that minimizes the optimization objective function O by delaying the start charging time to obtain a new second probability distribution; and use the Monte Carlo method to predict the charging load curve of the first distribution area again based on the first probability distribution and the new second probability distribution to obtain a new charging load curve

[0008] Step 3: the new charging load curve Whether the preset orderly control requirements are met is identified to obtain a corresponding first recognition result; the first recognition result includes satisfaction and non-satisfaction;

[0009] Step 4: Identify whether the first identification result is satisfied; if not, update the latest charging load curve As the new charging load curve P ev And return to step 2; if so, go to step 5;

[0010] Step 5: Use the latest second probability distribution as the final orderly control probability distribution and send it to each charging pile in the first power distribution area.

[0011] Preferably, the first probability distribution obeys a lognormal distribution, specifically:

[0012]

[0013] Where l is the mileage length, f1() is the probability distribution function of the mileage length l, μ1 and σ1 are the preset mean and standard deviation parameters;

[0014] The second probability distribution obeys the normal distribution, specifically:

[0015]

[0016] Among them, t start is the starting charging time, f2() is the starting charging time t start The probability distribution function of , μ2 and σ2 are the preset mean and standard deviation parameters.

[0017] Preferably, the charging load curve P ev Set as:

[0018]

[0019] Wherein, t is the sampling time of the curve, the charging load curve P ev The amplitude of the curve corresponding to time t is recorded as P ev (t), N ev The total number of charging vehicles in the first power distribution area;

[0020] is the charging load curve of the i-th charging vehicle, the charging load curve The amplitude of the curve corresponding to time t is recorded as Specifically:

[0021] like but:

[0022]

[0023] and

[0024] like but:

[0025]

[0026] and

[0027]

[0028] Among them, p ev The charging power for the preset electric vehicle; is the mileage of the i-th charging vehicle, extracted from the first probability distribution based on the Monte Carlo extraction method; α is the preset power consumption per unit mileage; is the charging time of the i-th charging vehicle; is the starting charging time of the i-th charging vehicle, extracted from the second probability distribution based on the Monte Carlo extraction method; is the end charging time of the i-th charging vehicle; t earliest The earliest departure time is the preset time. earliest A specified time within the preset early morning period; latest is the preset latest charging time, the latest charging time t latest is a specified time in the preset night time period, t earliest <t latest .

[0029] Preferably, the photovoltaic power generation curve P pv Set as:

[0030]

[0031] Among them, t is the sampling time of the curve, λ pv is the photoelectric conversion efficiency, S is the area of ​​the solar panels of the first power station, I is the light intensity curve of the area where the first power distribution area is located, and the photovoltaic power generation curve P pv The amplitude of the curve corresponding to time t is recorded as P pv (t), the curve amplitude corresponding to time t on the light intensity curve I is recorded as I(t); in the early morning period of each day, the amplitude of the light intensity curve I is 0, and the corresponding photovoltaic power generation curve P pv The amplitude is also 0; during the sunshine period of each day, the photovoltaic power generation curve P pv The amplitude of is positively correlated with the amplitude of the light intensity curve I; during the night period of each day, the amplitude of the light intensity curve I is 0, and the corresponding photovoltaic power generation curve P pv The amplitude is also 0; the early morning period is set to [00:00, 06:00] by default, the daylight period is set to [06:00, 19:00] by default, and the night period is set to [19:00, 24:00] by default.

[0032] Preferably, when the energy storage battery pack of the first power station is in a charging state, the energy storage charge and discharge curve P bat Set as:

[0033]

[0034] When the energy storage battery pack of the first power station is in a discharging state, the energy storage charge and discharge curve P bat Set as:

[0035]

[0036] 100%-D bat ≤SOC bat (t)≤100%;

[0037] Among them, t and t-1 are the sampling moments of two adjacent curves, △t is the time interval between sampling moments t and t-1, and SOC bat is the state of charge curve of the energy storage battery pack, E bat is the overall energy storage capacity of the energy storage battery pack, η bat is the charge-discharge conversion efficiency of the energy storage battery pack, D bat is the maximum discharge depth of the energy storage battery pack; the energy storage charge and discharge curve P bat The amplitude of the curve corresponding to time t is recorded as P bat (t), the state of charge curve SOC batThe curve amplitude corresponding to time t and t-1 is recorded as SOC bat (t), SOC bat (t-1); if the energy storage battery pack is charging Then the charging stops and the system automatically switches to the discharging state. Then the discharge stops and the battery automatically switches to charging state. is the maximum charging power of the energy storage battery pack.

[0038] Preferably, the grid power supply curve P net Set as:

[0039] P net (t) = P ev (t)+P basic (t)+P bat (t)-P pv (t),

[0040] Wherein, t is the sampling time of the curve, the grid power supply curve P net The amplitude of the curve corresponding to time t is recorded as P net (t), the resident load curve P basic The amplitude of the curve corresponding to time t is recorded as P basic (t).

[0041] Preferably, the optimization objective function O is set to:

[0042] argmin{ε1[∑ t P net (t)]+δ2[max(P net (t))-min(P net (t))]},

[0043] δ1+δ2=1;

[0044] Among them, t is the sampling time of the curve, and ε1 and ε2 are two preset weighting parameters.

[0045] Preferably, optimizing the mean and standard deviation of the second probability distribution that minimizes the optimization objective function O by delaying the start charging time to obtain a new second probability distribution specifically includes:

[0046] Using the preset optimization calculation tool, according to the optimization objective function O and the constraints of all curves, the mean and standard deviation μ2, σ2 of the second probability distribution that makes the optimization objective function O reach the minimum value are optimized by delaying the start charging time to obtain the optimal mean and standard deviation. And the mean and standard deviation The probability distribution function of the second probability distribution is brought in to obtain a new second probability distribution; the optimization calculation tool includes a CPLEX tool.

[0047] The method further comprises:

[0048] Each of the charging piles in the first power distribution area stores the latest second probability distribution after receiving it;

[0049] Each time a new charging vehicle is connected, the remaining battery capacity of the current vehicle is identified as the corresponding current remaining capacity; and the charging target capacity and expected latest end time input by the current vehicle owner are used as the corresponding current target capacity and current expected end time;

[0050] and calculating the corresponding current charging duration based on the current remaining capacity, the current target capacity, and the charging power of the charging pile; and taking the time obtained by adding the current time and the current charging duration as the corresponding latest completion time;

[0051] and identifying whether the latest completion time is no later than the current expected end time; if so, setting the corresponding first recognition state to the first state; otherwise, setting the corresponding first recognition state to the second state;

[0052] and extracting a charging start time from the second probability distribution based on the Monte Carlo extraction method as the corresponding current planned start time; and extracting a new charging start time from the second probability distribution based on the Monte Carlo extraction method as the new current planned start time when the current planned start time is later than the current time until the latest current planned start time is later than the current time; and using the time obtained by adding the latest current planned start time to the current charging duration as the corresponding current planned end time;

[0053] Identify whether the current planned end time is later than the current expected end time; if the current planned end time is later than the current expected end time, identify whether the first identification state is the first state; if so, use the time obtained by subtracting the current charging duration from the current expected end time as the corresponding current charging start time; if not, use the current time as the corresponding current charging start time; if the current planned end time is not later than the current expected end time, use the latest current planned start time as the corresponding current charging start time;

[0054] After obtaining the current charging start time, monitoring is performed to determine whether the current time has reached the current charging start time; and when it is confirmed that the current time has reached the current charging start time, the current time monitoring is stopped and the charging operation for the current vehicle is started.

[0055] The embodiment of the present invention provides a processing method for orderly regulating the charging of electric vehicles. As can be seen from the above content, the embodiment of the present invention first records the probability distribution of daily mileage of electric vehicles and the probability distribution of starting charging time corresponding to the distribution area as the first and second probability distributions, and uses the Monte Carlo method to predict the charging load curve of the distribution area according to the first and second probability distributions to obtain the corresponding charging load curve, and uses the daily average curve of residential electricity load in the distribution area as the residential load curve, and sets the photovoltaic power generation curve and energy storage charging and discharging curve of the photovoltaic storage charging station in the area; then sets the grid power supply curve based on the charging load curve, the residential load curve, the photovoltaic power generation curve and the energy storage charging and discharging curve, and sets the corresponding grid power supply curve based on the grid power supply curve in the optimization direction so as to minimize the grid side power supply and minimize the grid side power supply fluctuation. Optimize the objective function, and optimize the mean and standard deviation of the second probability distribution that minimizes the optimization objective function by delaying the start charging time to obtain a new second probability distribution, and use the Monte Carlo method to predict the charging load curve of the distribution area again based on the first probability distribution and the new second probability distribution to obtain a new charging load curve; then identify whether the new charging load curve meets the orderly regulation requirements. If the identification result is satisfied, the latest second probability distribution is sent as the final orderly regulation probability distribution to each charging pile in the area as the latest orderly charging guidance for the charging piles. If the identification result is not satisfied, continue to optimize the second probability distribution until the latest charging load curve meets the orderly regulation requirements. The embodiment of the present invention not only reduces the charging load peak in the residential distribution area, but also plays a balancing role in the battery charging period in the residential distribution area. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of a method for orderly regulating the charging of an electric vehicle provided by an embodiment of the present invention;

[0057] Figure 2 A schematic diagram of the probability distribution of daily mileage and the probability distribution of the starting charging time of an electric vehicle provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only a part but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of the present application.

[0059] The embodiment of the present application provides a processing method for orderly regulating and controlling electric vehicle charging for a smart power terminal or a smart power system of a resident power distribution area side (such as a power distribution network node, a power distribution area, etc.) or a smart power system of a power grid management side, which is as follows Figure 1 A processing method for orderly regulating and controlling electric vehicle charging provided by the embodiment of the present application is shown in a schematic diagram, and mainly includes the following steps:

[0060] Step 1, any one resident power distribution area provided with a light storage charging station is recorded as a corresponding first power distribution area; and the light storage charging station of the first power distribution area is recorded as a corresponding first power station; and the daily driving mileage probability distribution and the starting charging time probability distribution of the electric vehicle corresponding to the first power distribution area are recorded as corresponding first and second probability distributions; and the charging load curve of the first power distribution area is predicted by using a Monte Carlo method according to the first and second probability distributions to obtain a corresponding charging load curve P ev ; and the daily average curve of the resident power load of the first power distribution area is taken as a corresponding resident load curve P basic ; and the photovoltaic power generation curve P pv and the energy storage charging and discharging curve P bat of the first power station are set.

[0061] Specifically, step 11, any one resident power distribution area provided with a light storage charging station is recorded as a corresponding first power distribution area; and the light storage charging station of the first power distribution area is recorded as a corresponding first power station.

[0062] Step 12, and the daily driving mileage probability distribution and the starting charging time probability distribution of the electric vehicle corresponding to the first power distribution area are recorded as corresponding first and second probability distributions.

[0063] Here, the first and second probability distributions of the embodiment of the present application are the latest daily driving mileage probability distribution and starting charging time probability distribution of the electric vehicle of the first power distribution area obtained from the power grid management side, as shown in Figure 2 The daily driving mileage probability distribution and the starting charging time probability distribution provided by the embodiment of the present application are shown in a schematic diagram; specifically:

[0064] 1) The first probability distribution is subject to a lognormal distribution, specifically:

[0065]

[0066] Where l is the mileage length, f1() is the probability distribution function of the mileage length l, μ1 and σ1 are the preset mean and standard deviation parameters;

[0067] 2) The second probability distribution obeys the normal distribution, specifically:

[0068]

[0069] Among them, t start is the starting charging time, f2() is the starting charging time t start The probability distribution function of , μ2 and σ2 are the preset mean and standard deviation parameters;

[0070] Step 13, and use the Monte Carlo method to predict the charging load curve of the first distribution area according to the first and second probability distributions to obtain the corresponding charging load curve P ev ;

[0071] Here, it is a public conventional technical means to predict the charging load curve based on the Monte Carlo method under the premise of knowing the probability distribution of the daily mileage of the electric vehicle and the probability distribution of the starting charging time. Therefore, the implementation steps of the Monte Carlo method will not be described here in detail, and only the charging load curve will be described. Specifically, the charging load curve P in the embodiment of the present invention ev Set as:

[0072]

[0073] Among them, t is the sampling time of the curve, and the charging load curve P ev The amplitude of the curve corresponding to time t is recorded as P ev (t), N ev The total number of charging vehicles in the first distribution area;

[0074] is the charging load curve of the i-th charging vehicle, the charging load curve The amplitude of the curve corresponding to time t is recorded as Specifically:

[0075] 1) If but:

[0076]

[0077] and

[0078] 2) If but:

[0079]

[0080] and

[0081] in addition,

[0082] Among them, p ev The charging power for the preset electric vehicle; is the mileage of the i-th charging vehicle, which is extracted from the first probability distribution based on the Monte Carlo extraction method; α is the preset power consumption per unit mileage; is the charging time of the i-th charging vehicle; is the starting charging time of the i-th charging vehicle, which is extracted from the second probability distribution based on the Monte Carlo extraction method; is the end charging time of the i-th charging vehicle; t earliest The earliest departure time is t earliest A specified time within the preset early morning period; latest The latest charging time is the preset latest charging time, the latest charging time t latest is a specified time in the preset night time period, t earliest <t latest ;

[0083] Step 14, and use the daily average curve of residential electricity load in the first distribution area as the corresponding residential load curve P basic ;

[0084] Here, the resident load curve P of the embodiment of the present invention is basic is set to a known curve and cannot be optimized further;

[0085] Step 15, and set the photovoltaic power generation curve P of the first power station pv And the energy storage charge and discharge curve P bat .

[0086] Here, the photovoltaic power generation curve P of the embodiment of the present invention pv Set as:

[0087]

[0088] Among them, t is the sampling time of the curve, λ pv is the photoelectric conversion efficiency, S is the area of ​​solar panels of the first power station, I is the light intensity curve of the first distribution area, and the photovoltaic power generation curve P pv The amplitude of the curve corresponding to time t is recorded as P pv(t), the amplitude of the curve I corresponding to the time t on the light intensity curve I is denoted as I(t); in the early morning period of each day, the amplitude of the light intensity curve I is 0, and the amplitude of the photovoltaic power generation curve P pv corresponding to the light intensity curve I is also 0; in the sunshine period of each day, the amplitude of the photovoltaic power generation curve P pv corresponding to the light intensity curve I is positively correlated; in the night period of each day, the amplitude of the light intensity curve I is 0, and the amplitude of the photovoltaic power generation curve P pv corresponding to the light intensity curve I is also 0; the early morning period is set as [00:00, 06:00] by default, the sunshine period is set as [06:00, 19:00] by default, and the night period is set as [19:00, 24:00] by default.

[0089] The energy storage charge-discharge curve P bat of the embodiment of the application has two branches:

[0090] 1) When the energy storage battery group of the first power station is in a charging state, the energy storage charge-discharge curve P bat is set as:

[0091]

[0092] At this time, SOC bat (t) > SOC bat (t-1);

[0093] 2) When the energy storage battery group of the first power station is in a discharging state, the energy storage charge-discharge curve P bat is set as:

[0094]

[0095] At this time, SOC bat (t) < SOC bat (t-1);

[0096] In addition, 100% - D bat ≤ SOC bat (t) ≤ 100%;

[0097] Wherein, t, t-1 are two adjacent curve sampling time points, △t is the time interval of the sampling time points t, t-1, SOC bat is the state of charge curve of the energy storage battery group, E bat is the overall energy storage capacity of the energy storage battery group, η bat is the charge-discharge conversion efficiency of the energy storage battery group, and D bat is the maximum discharge depth of the energy storage battery group; the curve amplitude corresponding to the time t on the energy storage charge-discharge curve P bat is denoted as P bat (t), the state of charge curve SOC batThe curve amplitude corresponding to time t and t-1 is recorded as SOC bat (t), SOC bat (t-1); if the energy storage battery pack is charging Then the charging stops and the system automatically switches to the discharging state. Then the discharge stops and the battery automatically switches to charging state. The maximum charging power of the energy storage battery pack.

[0098] Step 2: Based on the charging load curve P ev , Residential load curve P basic , Photovoltaic power generation curve P pv And the energy storage charge and discharge curve P bat Set the corresponding grid power supply curve P net ; And to minimize the power supply on the grid side and minimize the fluctuation of the power supply on the grid side as the optimization direction, based on the power supply curve P net Set the corresponding optimization objective function O; and optimize the mean and standard deviation of the second probability distribution that minimizes the optimization objective function O by delaying the start charging time to obtain a new second probability distribution; and use the Monte Carlo method to predict the charging load curve of the first distribution area again based on the first probability distribution and the new second probability distribution to obtain a new charging load curve

[0099] Specifically including: Step 21, based on the charging load curve P ev , Residential load curve P basic , Photovoltaic power generation curve P pv And the energy storage charge and discharge curve P bat Set the corresponding grid power supply curve P net ;

[0100] Here, the grid power supply curve P of the embodiment of the present invention net Set as:

[0101] P net (t) = P ev (t)+P basic (t)+P bat (t)-P pv (t),

[0102] Among them, t is the sampling time of the curve, and the power supply curve P net The amplitude of the curve corresponding to time t is recorded as P net (t), resident load curve P basic The amplitude of the curve corresponding to time t is recorded as P basic (t);

[0103] Step 22, and taking the power supply on the grid side to the minimum and the power supply fluctuation on the grid side to the minimum as the optimization direction, based on the grid power supply curve P net Set the corresponding optimization objective function O;

[0104] Here, the optimization objective function O of the embodiment of the present invention is set to:

[0105] argmin{ε1[∑ t P net (t)]+ε2[max(P net (t))-min(P net (t))]},

[0106] ε1+ε2=1;

[0107] Among them, t is the sampling time of the curve, ε1 and ε2 are two preset weighting parameters;

[0108] Step 23, optimizing the mean and standard deviation of the second probability distribution that minimizes the optimization objective function O by delaying the start charging time to obtain a new second probability distribution;

[0109] Specifically, the preset optimization calculation tool is used to optimize the mean and standard deviation μ2, σ2 of the second probability distribution that minimizes the optimization objective function O according to the constraints of the optimization objective function O and all curves by delaying the start charging time to obtain the optimal mean and standard deviation. And the mean and standard deviation Substitute the probability distribution function of the second probability distribution into the new second probability distribution;

[0110] Here, the optimization calculation tool used in the embodiment of the present invention includes at least the CPLEX tool;

[0111] Step 24, and use the Monte Carlo method to predict the charging load curve of the first distribution area again according to the first probability distribution and the new second probability distribution to obtain a new charging load curve

[0112] Step 3: New charging load curve Whether the preset orderly control requirements are met to obtain a corresponding first recognition result;

[0113] The first recognition result includes satisfied and unsatisfied.

[0114] Here, the orderly control requirement is a pre-set curve inspection rule, which can be customized based on application requirements. Under normal circumstances, a peak-valley difference threshold can be set in this requirement, and a corresponding mid-segment time width threshold or mid-segment time width percentage threshold can be set based on a pre-specified mid-segment amplitude interval. In principle, if the charging load curve The smaller the peak-valley difference and the larger the middle time width, the better the orderly regulation effect. If the current orderly regulation requirement consists of the peak-valley difference threshold and the middle time width threshold, then in the charging load curve When the maximum peak-to-valley difference does not exceed the peak-to-valley difference threshold in the requirement and the time width corresponding to the middle amplitude interval is not less than the middle time width threshold in the requirement, it is set to meet the current orderly regulation requirement and the first recognition result is set to meet; otherwise, it is set to not meet the current orderly regulation requirement and the first recognition result is set to not meet.

[0115] Step 4: Identify whether the first identification result is satisfied; if not, the latest charging load curve As the new charging load curve P ev And return to step 2; if so, go to step 5.

[0116] Step 5: Use the latest second probability distribution as the final orderly control probability distribution and send it to each charging pile in the first power distribution area.

[0117] Here, in this embodiment of the present invention, when the smart power terminal or smart power system on the residential distribution area side or the smart power system on the grid management side sends the latest second probability distribution as the final orderly control probability distribution to each charging pile in the first distribution area, it also sends the latest second probability distribution to the corresponding interface on the grid management side, which replaces the start charging time probability distribution stored on the management side with the latest second probability distribution. The smart power system on the grid management side can further optimize the charging and discharging time of the photovoltaic storage charging station in the first distribution area, i.e., the first power station, based on the latest start charging time probability distribution.

[0118] It should also be noted that, in the embodiment of the present invention, the processing steps of each charging pile in the first power distribution area after receiving the latest second probability distribution are as follows:

[0119] Step 101: Store the second probability distribution received this time.

[0120] Step 102: Each time a new charging vehicle is connected, the remaining battery capacity of the current vehicle is identified as the corresponding current remaining capacity; and the charging target capacity and the expected latest end time input by the owner of the current vehicle are used as the corresponding current target capacity and the current expected end time.

[0121] Step 103 , calculate the corresponding current charging duration based on the current remaining capacity, the current target capacity and the charging power of the charging pile; and add the current time and the current charging duration to obtain the time as the corresponding latest completion time.

[0122] Step 104 , identifying whether the latest completion time is not later than the current expected end time; if so, setting the corresponding first recognition state to the first state; otherwise, setting the corresponding first recognition state to the second state.

[0123] The first identification state includes a first state and a second state.

[0124] Step 105: Extract a charging start time from the second probability distribution based on the Monte Carlo extraction method as the corresponding current plan start time; and when the current plan start time is later than the current time, extract a new charging start time from the second probability distribution again based on the Monte Carlo extraction method as the new current plan start time until the latest current plan start time is later than the current time; and use the time obtained by adding the latest current plan start time and the current charging duration as the corresponding current plan end time.

[0125] Step 106, identify whether the current planned end time is later than the current expected end time; if the current planned end time is later than the current expected end time, identify whether the first identification state is the first state, if so, use the time obtained by subtracting the current charging duration from the current expected end time as the corresponding start time of this charging, otherwise use the current time as the corresponding start time of this charging; if the current planned end time is not later than the current expected end time, use the latest current planned start time as the corresponding start time of this charging.

[0126] Step 107 , after obtaining the current charging start time, monitoring whether the current time has reached the current charging start time is performed; and when it is confirmed that the current time has reached the current charging start time, the current time monitoring is stopped and the charging operation for the current vehicle is started.

[0127] In summary, the embodiment of the present invention provides a processing method for orderly regulating the charging of electric vehicles. As can be seen from the above content, the embodiment of the present invention first records the probability distribution of daily mileage of electric vehicles and the probability distribution of starting charging time corresponding to the distribution area as the first and second probability distributions, and uses the Monte Carlo method to predict the charging load curve of the distribution area according to the first and second probability distributions to obtain the corresponding charging load curve, and uses the daily average curve of residential electricity load in the distribution area as the resident load curve, and sets the photovoltaic power generation curve and energy storage charging and discharging curve of the photovoltaic storage charging station in the area; then sets the grid power supply curve based on the charging load curve, the resident load curve, the photovoltaic power generation curve and the energy storage charging and discharging curve, and sets the corresponding grid power supply curve based on the grid power supply curve in the optimization direction so as to minimize the power supply on the grid side and minimize the power supply fluctuation on the grid side. Optimize the objective function, and optimize the mean and standard deviation of the second probability distribution that minimizes the optimization objective function by delaying the start charging time to obtain a new second probability distribution, and use the Monte Carlo method to predict the charging load curve of the distribution area again based on the first probability distribution and the new second probability distribution to obtain a new charging load curve; then identify whether the new charging load curve meets the orderly regulation requirements. If the identification result is satisfied, the latest second probability distribution is sent as the final orderly regulation probability distribution to each charging pile in the area as the latest orderly charging guidance for the charging piles. If the identification result is not satisfied, continue to optimize the second probability distribution until the latest charging load curve meets the orderly regulation requirements. The embodiment of the present invention not only reduces the charging load peak in the residential distribution area, but also plays a balancing role in the battery charging period in the residential distribution area.

[0128] 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.

[0129] 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.

[0130] 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 orderly regulating the charging of electric vehicles, characterized in that: The method comprises: Step 1: record any residential distribution area equipped with a photovoltaic storage charging station as the corresponding first distribution area; record the photovoltaic storage charging station in the first distribution area as the corresponding first power station; record the probability distribution of electric vehicle daily mileage and the probability distribution of starting charging time corresponding to the first distribution area as the corresponding first and second probability distributions; and use the Monte Carlo method to predict the charging load curve of the first distribution area based on the first and second probability distributions to obtain the corresponding charging load curve P ev ; And the residential electricity load daily average curve of the first distribution area is used as the corresponding residential load curve P basic ; and set the photovoltaic power generation curve P of the first power station pv And the energy storage charge and discharge curve P bat ; Step 2: Based on the charging load curve P ev , the resident load curve P basic , the photovoltaic power generation curve P pv and the energy storage charge and discharge curve P bat Set the corresponding grid power supply curve P net ; And to minimize the power supply on the grid side and minimize the fluctuation of the power supply on the grid side as the optimization direction, based on the grid power supply curve P net Set the corresponding optimization objective function O; and optimize the mean and standard deviation of the second probability distribution that minimizes the optimization objective function O by delaying the start charging time to obtain a new second probability distribution; and use the Monte Carlo method to predict the charging load curve of the first distribution area again based on the first probability distribution and the new second probability distribution to obtain a new charging load curve Step 3: the new charging load curve Whether the preset orderly control requirements are met is identified to obtain a corresponding first recognition result; the first recognition result includes satisfaction and non-satisfaction; Step 4: Identify whether the first identification result is satisfied; if not, update the latest charging load curve As the new charging load curve P ev And return to step 2; if so, go to step 5; Step 5: Use the latest second probability distribution as the final orderly control probability distribution and send it to each charging pile in the first power distribution area.

2. The method for orderly regulating the charging of electric vehicles according to claim 1, characterized in that: The first probability distribution obeys the lognormal distribution, specifically: Where l is the mileage length, f1() is the probability distribution function of the mileage length l, μ1 and σ1 are the preset mean and standard deviation parameters; The second probability distribution obeys the normal distribution, specifically: Among them, t start is the starting charging time, f2() is the starting charging time t start The probability distribution function of , μ2 and σ2 are the preset mean and standard deviation parameters.

3. The method for orderly regulating the charging of electric vehicles according to claim 2, characterized in that: The charging load curve P ev Set as: Wherein, t is the sampling time of the curve, the charging load curve P ev The amplitude of the curve corresponding to time t is recorded as P ev (t), N ev The total number of charging vehicles in the first power distribution area; is the charging load curve of the i-th charging vehicle, the charging load curve The amplitude of the curve corresponding to time t is recorded as Specifically: like but: and like but: and Among them, p ev The charging power for the preset electric vehicle; is the mileage of the i-th charging vehicle, extracted from the first probability distribution based on the Monte Carlo extraction method; α is the preset power consumption per unit mileage; is the charging time of the i-th charging vehicle; is the starting charging time of the i-th charging vehicle, extracted from the second probability distribution based on the Monte Carlo extraction method; is the charging end time of the i-th charging vehicle; t earliest The earliest departure time is the preset time. earliest A specified time within the preset early morning period; latest is the preset latest charging time, the latest charging time t latest is a specified time in the preset night time period, t earliest <t latest .

4. The method for orderly regulating the charging of electric vehicles according to claim 3, characterized in that: The photovoltaic power generation curve P pv Set as: Among them, t is the sampling time of the curve, λ pv is the photoelectric conversion efficiency, S is the area of ​​the solar panels of the first power station, I is the light intensity curve of the area where the first power distribution area is located, and the photovoltaic power generation curve P pv The amplitude of the curve corresponding to time t is recorded as P pv (t), the curve amplitude corresponding to time t on the light intensity curve I is recorded as I(t); in the early morning period of each day, the amplitude of the light intensity curve I is 0, and the corresponding photovoltaic power generation curve P pv The amplitude is also 0; during the sunshine period of each day, the photovoltaic power generation curve P pv The amplitude of is positively correlated with the amplitude of the light intensity curve I; during the night period of each day, the amplitude of the light intensity curve I is 0, and the corresponding photovoltaic power generation curve P pv The amplitude is also 0; the early morning period is set to [00:00, 06:00] by default, the daylight period is set to [06:00, 19:00] by default, and the night period is set to [19:00, 24:00] by default.

5. The method for orderly regulating the charging of electric vehicles according to claim 4, characterized in that: When the energy storage battery pack of the first power station is in a charging state, the energy storage charge and discharge curve P bat Set as: SOC bat (t)>SOC bat (t-1); When the energy storage battery pack of the first power station is in a discharging state, the energy storage charge and discharge curve P bat Set as: SOC bat (t)<SOC bat (t-1); 100%-D bat ≤SOC bat (t)≤100%; Among them, t and t-1 are the sampling moments of two adjacent curves, △t is the time interval between sampling moments t and t-1, and SOC bat is the state of charge curve of the energy storage battery pack, E bat is the overall energy storage capacity of the energy storage battery pack, η bat is the charge-discharge conversion efficiency of the energy storage battery pack, D bat is the maximum discharge depth of the energy storage battery pack; the energy storage charge and discharge curve P bat The amplitude of the curve corresponding to time t is recorded as P bat (t), the state of charge curve SOC bat The curve amplitude corresponding to time t and t-1 is recorded as SOC bat (t), SOC bat (t-1); if the energy storage battery pack is charging Then the charging stops and the system automatically switches to the discharging state. Then the discharge stops and the battery automatically switches to charging state. is the maximum charging power of the energy storage battery pack.

6. The method for orderly regulating the charging of electric vehicles according to claim 5, characterized in that: The grid power supply curve P net Set as: P net (t)=P ev (t)+P basic (t)+P bat (t)-P pv (t), Wherein, t is the sampling time of the curve, the grid power supply curve P net The amplitude of the curve corresponding to time t is recorded as P net (t), the resident load curve P basic The amplitude of the curve corresponding to time t is recorded as P basic (t).

7. The method for orderly regulating the charging of electric vehicles according to claim 6, characterized in that: The optimization objective function O is set as: argmin{ε1[∑ t P net (t)]+ε2[max(P net (t))-min(P net (t))]}, δ1+δ2=1; Among them, t is the sampling time of the curve, and ε1 and ε2 are two preset weighting parameters.

8. The method for orderly regulating the charging of electric vehicles according to claim 7, characterized in that: The step of optimizing the mean and standard deviation of the second probability distribution that minimizes the optimization objective function O by delaying the start charging time to obtain a new second probability distribution specifically includes: Using the preset optimization calculation tool, according to the optimization objective function O and the constraints of all curves, the mean and standard deviation μ2, σ2 of the second probability distribution that makes the optimization objective function O reach the minimum value are optimized by delaying the start charging time to obtain the optimal mean and standard deviation. And the mean and standard deviation The probability distribution function of the second probability distribution is brought in to obtain a new second probability distribution; the optimization calculation tool includes a CPLEX tool.

9. The method for orderly regulating the charging of electric vehicles according to claim 1, characterized in that: The method further comprises: Each of the charging piles in the first power distribution area stores the latest second probability distribution after receiving it; Each time a new charging vehicle is connected, the remaining battery capacity of the current vehicle is identified as the corresponding current remaining capacity; and the charging target capacity and expected latest end time input by the current vehicle owner are used as the corresponding current target capacity and current expected end time; and calculating the corresponding current charging duration based on the current remaining capacity, the current target capacity, and the charging power of the charging pile; and taking the time obtained by adding the current time and the current charging duration as the corresponding latest completion time; and identifying whether the latest completion time is no later than the current expected end time; if so, setting the corresponding first recognition state to the first state; otherwise, setting the corresponding first recognition state to the second state; and extracting a charging start time from the second probability distribution based on the Monte Carlo extraction method as the corresponding current planned start time; and extracting a new charging start time from the second probability distribution based on the Monte Carlo extraction method as the new current planned start time when the current planned start time is later than the current time until the latest current planned start time is later than the current time; and using the time obtained by adding the latest current planned start time to the current charging duration as the corresponding current planned end time; Identify whether the current planned end time is later than the current expected end time; if the current planned end time is later than the current expected end time, identify whether the first identification state is the first state; if so, use the time obtained by subtracting the current charging duration from the current expected end time as the corresponding current charging start time; if not, use the current time as the corresponding current charging start time; if the current planned end time is not later than the current expected end time, use the latest current planned start time as the corresponding current charging start time; After obtaining the current charging start time, monitoring is performed to determine whether the current time has reached the current charging start time; and when it is confirmed that the current time has reached the current charging start time, the current time monitoring is stopped and the charging operation for the current vehicle is started.

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