CN116544920B - Residential area electric automobile night charging optimal control method, equipment and storage medium - Google Patents
Residential area electric automobile night charging optimal control method, equipment and storage medium Download PDFInfo
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- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/40—The network being an on-board power network, i.e. within a vehicle
- H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
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- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
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- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/62—The condition being non-electrical, e.g. temperature
- H02J2310/64—The condition being economic, e.g. tariff based load management
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Abstract
The invention discloses a night charging optimization control method, equipment and a storage medium for an electric automobile in a residential area, and the load margin of the residential area is calculated; calculating the day-ahead incentive price of the hour-level electric automobile; obtaining updated night load of the residential area in the day without considering the load of the electric automobile; solving a residential area electric vehicle night charging optimization control model based on a daily excitation price model by considering the influence of a daily excitation price constraint condition and an electric vehicle charging constraint condition and the updated daily residential area night load which does not consider electric vehicle load, and obtaining a daily hour-level charging state of each electric vehicle in a night period; and the charging state of each electric automobile in the daily hour stage in the night period is sent to the electric automobile charging controller in advance. The night charging optimization control method, the night charging optimization control equipment and the storage medium for the electric vehicles in the residential areas, provided by the invention, guide the night optimized power utilization of the electric vehicles in the residential areas, and effectively relieve the night heavy load problem of the residential areas.
Description
Technical Field
The invention relates to a night charging optimal control method, device and storage medium for an electric automobile in a residential area, and belongs to the technical field of electric automobile optimal control.
Background
Electric vehicles are a necessary trend of future travel, but the electric vehicles are rapidly increased and can inevitably cause larger impact and influence on a power grid. On one hand, the existing public power distribution network and user side power distribution facilities do not consider the charging requirement of the electric automobile in the construction of the current year, and the short-time centralized charging behavior of the electric automobile enables the local power distribution network in a part of areas to generate capacity-increasing and reconstruction requirements; on the other hand, the electric automobile charging facilities are high-power nonlinear load equipment, the layout is distributed, very high harmonic current and surge voltage can be generated, the problems of wire drawing and flying wire charging of users exist, and the like, so that a great challenge is brought to management of the distribution side of a power grid company.
By 2030, peak load increases by 1.53 kw in the national grid company operating area, and the aggregate charging of electric vehicles can lead to localized area load stresses, the most obvious effect being the increase in peak night load. When people get home after going off duty, users choose to plug in a power supply for charging, however, 7-11 points at night are often residents' peak electricity consumption, and the superposition of charging time of electric vehicles or charging behavior of load peak periods further burden a power distribution network.
How to optimally control the night charging behavior of the resident electric automobile through an effective excitation decision method is a technical problem which needs to be solved by the technicians in the field.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a night charging optimization control method, equipment and storage medium for an electric automobile in a residential area.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
in a first aspect, a nighttime charging optimization control method for an electric automobile in a residential area includes the following steps:
step 1: and obtaining a predicted value of night load of the residential area which does not consider the load of the electric automobile in the future, and calculating the load margin of the residential area in the future according to the predicted value.
Step 2: and calculating the day-ahead incentive price of the hour-level electric automobile according to the load margin of the day-ahead district and a day-ahead incentive price model for promoting the electric automobile to charge at night.
Step 3: and obtaining the updated night load of the residential area in the day without considering the load of the electric automobile.
Step 4: and collecting the data of the electric vehicle charged at night in advance, and converting the data of the electric vehicle into the charging constraint conditions of the electric vehicle.
Step 5: converting the day-ahead incentive price of the hour-level electric automobile into a day-in incentive price constraint condition;
step 6: and solving a residential area electric vehicle night charging optimization control model based on the daily excitation price model by taking the minimum running cost of the residential area into consideration of the influence of the daily excitation price constraint condition and the electric vehicle charging constraint condition and the updated daily residential area night load which does not consider the electric vehicle load, so as to obtain the daily hour charging state of each electric vehicle in the night period.
Step 7: and the charging state of each electric automobile in the daily hour stage in the night period is sent to the electric automobile charging controller in advance.
Further, the daily foreground area load margin has a calculation formula as follows:
ΔP t pre ＝P max -P t pre,oth
wherein T stands for T, T stands for night time period; ΔP t pre Load margin before day for a station area in a period t; p (P) max The maximum load of the transformer in the transformer area is set; p (P) t pre,oth The load is predicted before the day for the region where the electric vehicle load is not considered for the period t.
Further, the day-ahead excitation price model for promoting the night charging of the electric automobile has a calculation formula as follows:
wherein: ρ t pre Incentive prices for time period t day ago; ρ base To motivate price base values.
Furthermore, the residential area electric automobile night charging optimization control model based on the daily excitation price model is min C.
The formula for min C is as follows:
wherein:
wherein:for the daily charge load (constant value) of the ith electric automobile in the period t, x i,t For the charging state of the ith electric automobile in the period t, x i,t At 1, charge is indicated, x i,t When 0, the battery is not charged; />For the incentive price in time t days, I is the total number of electric vehicles, and Deltat is the charging time.
Wherein: p (P) t real,oth The night load of a residential area in the day, which is updated in the time period t and does not consider the load of the electric automobile, is represented by a secondary coefficient and a primary coefficient of the overload cost of the transformer loss, c is the maximum value of the overload cost of the transformer loss, and z 0 Is the variance of each period of the transformer in an ideal state, z max And the maximum variance value of each period of the transformer is T, and the total night charging period of the ith electric automobile is T.
Further, the daily incentive price constraint condition specifically includes:
1) Constraint 1: the average value of the daily incentive price and the daily incentive price is the same.
Wherein:for period t day before incentive price mean, +.>And the average value of the excitation price in the period T days is T which is the total night charging period of the ith electric automobile.
2) Constraint 2: price maximum constraints are motivated.
Wherein:price is stimulated for time period t days.
Further, the electric automobile charging constraint condition specifically includes:
1) And off-grid electric quantity constraint.
Wherein: e (E) i,(t＝T) For the amount of electricity of the ith electric car when t=t,and the threshold value of off-grid electric quantity of the ith electric automobile.
2) And the constraint of the electricity storage quantity.
Wherein: e (E) i,t For the electricity storage capacity of the ith electric automobile in the period t, E i,t-1 The electricity storage capacity x of the ith electric automobile in the period t-1 i,t The charging state of the ith electric automobile in the period t is that 1 is charged and 0 is uncharged;charging power for the ith electric vehicle in period t +.>For the charging efficiency of the ith electric automobile in the period t, sigma EV The self-discharge rate of the electric automobile is achieved.
3) Continuous charging constraints. Make the network-connected electric quantity beIf the power is continuously charged to the off-grid power threshold value +.>The required time is:
wherein: t (T) i c The time required for charging the ith electric automobile,and (3) starting the charging time for the ith electric automobile, wherein T is the total night charging period of the ith electric automobile.
Further, T i c The calculation formula is as follows:
in the method, in the process of the invention,the power is the network access power of the ith electric automobile.
In a second aspect, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements a residential area electric vehicle night charge optimization control method as described in any one of the first aspects.
In a third aspect, a computer device comprises:
and the memory is used for storing the instructions.
And the processor is used for executing the instructions to enable the computer equipment to execute the operation of the night charging optimization control method for the electric automobile in the residential area according to any one of the first aspect.
The beneficial effects are that: compared with the prior art, the night charging optimization control method, the night charging optimization control equipment and the storage medium for the electric automobile in the residential area have the following beneficial effects:
1) For the market, an accurate decision system of the night charging incentive price of the electric automobile is constructed based on the concept of 'prediction before the day-correction in the day', so that the real-time supply and demand state matching of the incentive price and the platform area is realized, the real market relation is reflected, and the defect of the existing resident time-sharing electricity price policy is overcome.
2) For the power grid, the charging time and the power grid demand are synchronously considered in the excitation price, so that the night power grid overload problem of the residential area is effectively relieved by economic excitation, the aging acceleration problem caused by overload or unbalanced utilization of the transformer is avoided, the economic operation capability of the transformer is improved, and the method has good popularization and implementation.
Drawings
Fig. 1 is a flowchart of a nighttime charging optimization control method for an electric automobile in a residential area according to a first embodiment of the present invention.
Fig. 2 is a graph showing a predicted daily load of a bay without considering an electric vehicle load, and an actual daily load of a bay with considering an electric vehicle load.
FIG. 3 is a schematic diagram of the day-ahead incentive price and day-in incentive price optimization results.
Detailed Description
The following description of the embodiments of the present invention will be made more apparent and fully by reference to the accompanying drawings, in which embodiments of the invention are shown, and in which it is evident that the embodiments shown are only some, but not all embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention.
The invention will be further described with reference to specific examples.
An electric automobile night charging optimization control system for a residential area, comprising: residential electric automobile control center, district transformer and electric automobile charge controller.
The residential electric vehicle control center is responsible for receiving and processing transformer power data, electric vehicle state data, electric vehicle charging demand data and the like of the residential area, designs an electric vehicle day-ahead excitation price model taking the load margin of the residential area into consideration in a day-ahead period, and serves as a constraint boundary of the day-ahead excitation price model; after entering a day period, taking the minimum running cost of a district as a target, establishing a night charging control model of the electric vehicles in the residential district based on the day excitation price model, obtaining the day hour charging state of each electric vehicle in the night period through solving the model, issuing the state to an electric vehicle charging controller, and executing an instruction whether the electric vehicles are charged in the period.
The night charging optimization control method for the electric automobile in the residential area comprises the following steps:
step 1: entering a day-ahead dimension, a residential area electric automobile control center predicts night loads of residential areas which do not consider electric automobile loads in the day ahead, and at the moment, the influence of the electric automobile loads is not considered, so that a day-ahead area load margin is obtained.
Step 2: and constructing a day-ahead incentive price model for promoting the electric automobile to charge at night according to the load margin of the day-ahead district, and obtaining the day-ahead incentive price of the hour-level electric automobile.
Step 3: and entering into a daily dimension, and updating the night load of the residential area which does not consider the electric automobile load in the day by the electric automobile control center of the residential area, wherein the influence of the electric automobile load is not considered at the moment, so as to obtain the updated night load of the residential area which does not consider the electric automobile load in the day.
Step 4: and entering a night time period in the day, and collecting electric vehicle data charged at night by the electric vehicle control center of the residential area in advance of t minutes, so that the electric vehicle data are converted into electric vehicle charging constraint conditions. The electric vehicle data such as the number of vehicles to be charged, the network access electric quantity/network access time/expected network access electric quantity/expected network access time of each electric vehicle, and the like, preferably, t=5.
Step 5: converting the day-ahead incentive price of the hour-level electric automobile obtained in the step 2 into a day-ahead incentive price constraint condition;
step 6: and solving a residential area electric vehicle night charging optimization control model based on the daily excitation price model by taking the minimum running cost of the residential area into consideration of the influence of the daily excitation price constraint condition and the electric vehicle charging constraint condition and the updated daily residential area night load which does not consider the electric vehicle load, so as to obtain the daily hour charging state of each electric vehicle in the night period.
Step 7: and the electric automobile control center of the residential area sends the intra-day hour-level charging state of each electric automobile in the night period to the electric automobile charging controllers in advance, and each electric automobile charging controller executes charging.
Further, in the step 1, a daily foreground area load margin calculation formula is as follows:
ΔP t pre ＝P max -P t pre,oth (1)
wherein T stands for T, T stands for night time period; ΔP t pre Load margin before day for a time period t station area, kW; p (P) max Maximum load of the transformer in the transformer area is kW; p (P) t pre,oth The load is predicted before the day for the region where the electric vehicle load is not considered for the period t, kW.
Further, in the step 2, a calculation formula of a day-ahead excitation price model for promoting the night charging of the electric automobile is as follows:
wherein: ρ t pre Exciting a price for a period of time t days ago, in minutes/kilowatt-hour; ρ base To motivate price base values. In the formula (2), when the next day period t is the district load margin delta P t pre The larger the corresponding period t day before the incentive price ρ t pre The larger the price p is, the user is encouraged to use electricity in the period, and the price p is encouraged in the future t pre The smaller this is, the user is guided not to use electricity in the present period.
Further, in the step 5: converting the day-ahead incentive price of the small-and-medium-hour-level electric automobile in the step 2 into a day-ahead incentive price constraint condition, which specifically comprises the following steps:
1) Constraint 1: the average value of the daily incentive price and the daily incentive price is the same.
Wherein:for period t day before incentive price mean, +.>And the average value of the excitation price in the period T days is T which is the total night charging period of the ith electric automobile.
2) Constraint 2: price maximum constraints are motivated. Considering price fluctuation risk, the daily incentive price maximum value is required not to exceed the daily incentive price maximum value.
Further, in the step 6: and the residential area electric automobile night charging optimal control model based on the daily excitation price model is min C.
The operation cost of the station area comprises the charge incentive costAnd transformer loss overload cost->The calculation formula is as follows:
1) Charging excitation cost:
wherein:for the daily charge load (constant value) of the ith electric automobile in the period t, x i,t For the charging state of the ith electric automobile in the period t, x i,t At 1, charge is indicated, x i,t When 0, the battery is not charged; />For the incentive price in time t days, I is the total number of electric vehicles, and Deltat is the charging time.
2) Transformer loss overload cost:
let the variance of each period of the transformer in ideal state be z 0 Transformers each when actually operatingTime interval variance is greater than z 0 When this occurs, a transformer loss overload cost will be generated, i.e. the transformer loss overload cost is a function of the load variance z of the transformer for each period.
Wherein: p (P) t real,oth The night load of a residential area in the day, which is updated in the time period t and does not consider the load of the electric automobile, is represented by a secondary coefficient and a primary coefficient of the overload cost of the transformer loss, c is the maximum value of the overload cost of the transformer loss, and z 0 Is the variance of each period of the transformer in an ideal state, z max The maximum value of variance of each period of the transformer.
The constraint conditions include two major classes, one is the daily incentive price constraint, as shown in formulas (3) and (4).
The other category is electric automobile charging constraint, specifically comprising:
1) And off-grid electric quantity constraint. To ensure that the ith electric automobile finishes the charging expected target at the time t=T, setting an off-grid electric quantity threshold value of the ith electric automobile
Wherein: e (E) i,(t＝T) The electric quantity of the ith electric automobile at the time t=t.
2) And the constraint of the electricity storage quantity.
Wherein: e (E) i,t For the electricity storage capacity of the ith electric automobile in the period t, E i,t-1 The electricity storage capacity x of the ith electric automobile in the period t-1 i,t For the charge state of the ith electric vehicle in period t1 is charged, 0 is uncharged;charging power for the ith electric vehicle in period t +.>For the charging efficiency of the ith electric automobile in the period t, sigma EV The self-discharge rate of the electric automobile is achieved.
3) Continuous charging constraints. Make the network-connected electric quantity beIf the power is continuously charged to the off-grid power threshold value +.>The required time is:
in order to reduce the influence on the service life of the battery pack, the electric automobile needs to be continuously charged, and then the charging state x of the electric automobile is calculated i,t The following should be satisfied:
wherein:and (3) starting the charging time for the ith electric automobile, wherein T is the total night charging period of the ith electric automobile.
A second embodiment is a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a residential area electric vehicle night charge optimization control method as described in any one of the first embodiments.
A third embodiment is a computer device comprising:
and the memory is used for storing the instructions.
And a processor, configured to execute the instructions, so that the computer device executes the operation of the nighttime charging optimization control method for the electric automobile in the residential area according to any one of the first embodiment.
Examples:
in the embodiment, the invention is explained in the process of optimizing and controlling the night charging of the electric automobile in the residential area.
Step 1: let the load of the platform area be maximum value P max 800kW, night ΔP t pre The values were (123.2, 115.2, 80.8, 44, 8.8, 63.2, 260.8, 433.6, 452.8, 418.4, 410.4, 359.2) kW in this order, and are shown in Table 1.
Table 1 shows the predicted load before/within the day for a bay area not taking into account the load of an electric vehicle
Step 2: when the next day period t is the district load margin delta P t pre The larger the corresponding period t day before the incentive price ρ t pre The larger this is to encourage the user to consume electricity during this period. Within the period ρ pre (t) is (0.203, 0.176, 0.12, 0.086, 0.178, 0.186, 0.287, 0.376, 0.389, 0.402, 0.513, 0.549) yuan/kwh in this order, as shown in FIG. 3.
Step 3: entering into the daytime dimension, the residential area electric automobile control center updates the nighttime load (without considering electric automobiles) of the residential area in the daytime, and P t pre,oth →P t real,oth Specific data are shown in table 1.
Step 4: and entering a night time period in the day, and collecting data of the electric vehicles charged at night by the electric vehicle control center of the residential area in advance by 5 minutes, such as the number of vehicles to be charged, the network-access electric quantity/network-access time/expected network-release electric quantity/expected network-release time of each electric vehicle and the like. There are 20 electric cars, each with 64kWh of EV capacity, 7kw of EV charging power, 1% of EV self-discharge rate, and the on-grid SOC as shown in table 2, with an off-grid power requirement up to 64kWh.
Table 2 electric vehicle network entry power
Step 5: converting the day-ahead incentive price of the small-and-medium-sized electric automobile in the step 2 into a day-ahead incentive price constraint condition;
1) Constraint 1: the average value of the daily incentive price and the daily incentive price is the same.
Wherein:price is stimulated for period t day ago, +.>Price is stimulated for time period t days.
2) Constraint 2: price maximum constraints are motivated. Considering the price fluctuation risk, the maximum incentive price value in the carbon nano Hui Ri is required to be not more than the maximum incentive price value before the day, and the minimum incentive price value in the carbon nano Hui Ri is required to be larger than the minimum incentive price value before the day.
Wherein: ρ max To motivate a price threshold.
Step 6: and solving a residential electric vehicle night charging optimization control model of the residential area based on the excitation price by taking the minimum running cost of the area into consideration of the daily excitation price and the electric vehicle charging constraint influence to obtain the daily hour-level charging state of each electric vehicle in the night period, wherein 1 represents charging and 0 represents non-charging as shown in table 3. The excitation cost is 427.5 yuan, and the transformer loss overload cost is 876.2 yuan.
Table 3 electric vehicle state of charge optimization results
Step 7: and the electric automobile control center of the residential area sends the daily hour-level charging strategy and daily incentive price of each electric automobile in the night period to the electric automobile charging controllers in advance, and each controller executes charging according to the strategy.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (5)
1. A night charging optimization control method for an electric automobile in a residential area is characterized by comprising the following steps of: the method comprises the following steps:
step 1: obtaining a predicted value of night load of a residential area which does not consider the load of the electric automobile before the day, and calculating a load margin of the residential area before the day according to the predicted value;
step 2: calculating the day-ahead incentive price of the hour-level electric automobile according to the load margin of the day-ahead district and a day-ahead incentive price model for promoting the electric automobile to charge at night;
step 3: obtaining updated night load of the residential area in the day without considering the load of the electric automobile;
step 4: collecting data of the electric vehicle charged at night in advance, and converting the data of the electric vehicle into electric vehicle charging constraint conditions;
step 5: converting the day-ahead incentive price of the hour-level electric automobile into a day-in incentive price constraint condition;
step 6: taking the minimum running cost of the residential area as a target, solving a residential area electric vehicle night charging optimization control model based on a daily excitation price model by considering the influence of a daily excitation price constraint condition and an electric vehicle charging constraint condition and the updated daily residential area night load which does not consider the electric vehicle load, and obtaining the daily hour charging state of each electric vehicle in a night period;
step 7: the method comprises the steps that the charging state of each electric automobile in the daytime hour level in the night period is sent to an electric automobile charging controller in advance;
the day-ahead excitation price model for promoting the night charging of the electric automobile comprises the following calculation formula:
wherein: ρ t pre Incentive prices for time period t day ago; ρ base To motivate the price base, ΔP t pre Load margin before day for a station area in a period t; p (P) max The maximum load of the transformer in the transformer area is set;
the residential area electric automobile night charging optimal control model based on the daily excitation price model is min C;
the formula for min C is as follows:
wherein:
wherein:for charging incentive costs, < >>For the daily charging load, x of the ith electric automobile in the period t i,t For the charging state of the ith electric automobile in the period t, x i,t At 1, charge is indicated, x i,t When 0, the battery is not charged; />For the exciting price in the period t days, I is the total number of the electric vehicles, and delta t is the charging time;
wherein:for the loss and overload cost of the transformer, P t real,oth The night load of a residential block in the day, which is updated in the time period t and does not consider the load of the electric automobile, is represented by a secondary coefficient and a primary coefficient of the overload cost of the loss of the transformer, c is the maximum value of the overload cost of the loss of the transformer, z is the variance of each time period of the transformer, z 0 Is the variance of each period of the transformer in an ideal state, z max The variance maximum value of each period of the transformer is T is the total night charging period of the ith electric automobile;
the daily incentive price constraint conditions specifically comprise:
1) Constraint 1: the average value of the daily incentive price and the daily incentive price is the same;
wherein:for period t day before incentive price mean, +.>The average value of the excitation price in the period T days is the total night charging period of the ith electric automobile;
2) Constraint 2: exciting price maximum constraint;
wherein:incentive prices for time period t days;
the electric automobile charging constraint condition specifically comprises:
1) Off-grid electric quantity constraint;
wherein: e (E) i,(t＝T) For the amount of electricity of the ith electric car when t=t,the off-grid electric quantity threshold value of the ith electric automobile;
2) Constraint of electricity storage quantity;
wherein: e (E) i,t For the electricity storage capacity of the ith electric automobile in the period t, E i,t-1 The electricity storage capacity x of the ith electric automobile in the period t-1 i,t The charging state of the ith electric automobile in the period t is that 1 is charged and 0 is uncharged;charging power eta of ith electric automobile in period t i EV For the charging efficiency of the ith electric automobile in the period t, sigma EV The self-discharge rate of the electric automobile is achieved;
3) Continuous charging constraints;
wherein: t (T) i c The time required for charging the ith electric automobile,and (3) starting the charging time for the ith electric automobile, wherein T is the total night charging period of the ith electric automobile.
2. The residential area electric automobile night charging optimization control method according to claim 1, wherein the method comprises the following steps of: the load margin of the day front platform area is calculated according to the following formula:
ΔP t pre ＝P max -P t pre,oth
wherein T is T, and T represents the total night charging period of the ith electric automobile; ΔP t pre Load margin before day for a station area in a period t; p (P) max The maximum load of the transformer in the transformer area is set; p (P) t pre,oth The load is predicted before the day for the region where the electric vehicle load is not considered for the period t.
3. The residential area electric automobile night charging optimization control method according to claim 1, wherein the method comprises the following steps of: the T is i c The calculation formula is as follows:
in the method, in the process of the invention,the power is the network access power of the ith electric automobile.
4. A computer-readable storage medium, characterized by: a computer program stored thereon, which when executed by a processor, implements a night charging optimization control method for electric vehicles in residential areas as set forth in any one of claims 1-3.
5. A computer device, characterized by: comprising the following steps:
a memory for storing instructions;
a processor for executing the instructions to cause the computer device to execute the operations of a residential block electric vehicle night charging optimization control method according to any one of claims 1 to 3.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107565607A (en) * | 2017-10-24 | 2018-01-09 | 华北电力大学（保定） | A kind of micro-capacitance sensor Multiple Time Scales energy dispatching method based on Spot Price mechanism |
| CN107618392A (en) * | 2017-09-29 | 2018-01-23 | 重庆卓谦科技有限公司 | The charging electric vehicle load Stochastic accessing control system and method for charging pile self-decision |
| JP2018190249A (en) * | 2017-05-09 | 2018-11-29 | 三菱重工業株式会社 | Management method, service management device, service management system and program |
| CN110739690A (en) * | 2019-10-31 | 2020-01-31 | 山东大学 | Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility |
| CN110774929A (en) * | 2019-10-25 | 2020-02-11 | 上海电气集团股份有限公司 | Real-time control strategy and optimization method for orderly charging of electric automobile |
| CN111619393A (en) * | 2020-04-30 | 2020-09-04 | 国网天津市电力公司电力科学研究院 | User-oriented orderly charging control method for electric automobile in transformer area |
| CN112101689A (en) * | 2019-06-18 | 2020-12-18 | 华北电力大学（保定） | Day-ahead intra-day scheduling method considering multi-type demand response uncertainty |
| CN112865082A (en) * | 2021-01-18 | 2021-05-28 | 西安交通大学 | Virtual power plant day-ahead scheduling method for aggregating multiple types of electric vehicles |
| CN113807554A (en) * | 2020-06-11 | 2021-12-17 | 国网电力科学研究院有限公司 | Load aggregator energy optimization method and device based on spot mode |
| CN115018198A (en) * | 2022-06-30 | 2022-09-06 | 国网河南省电力公司经济技术研究院 | Residential user electricity utilization optimization strategy considering differentiated demand response scheme |
| CN115439138A (en) * | 2022-08-03 | 2022-12-06 | 广东奔流能源有限公司 | Electric vehicle charging and discharging power optimal distribution method and system |
-
2023
- 2023-05-09 CN CN202310513527.3A patent/CN116544920B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2018190249A (en) * | 2017-05-09 | 2018-11-29 | 三菱重工業株式会社 | Management method, service management device, service management system and program |
| CN107618392A (en) * | 2017-09-29 | 2018-01-23 | 重庆卓谦科技有限公司 | The charging electric vehicle load Stochastic accessing control system and method for charging pile self-decision |
| CN107565607A (en) * | 2017-10-24 | 2018-01-09 | 华北电力大学（保定） | A kind of micro-capacitance sensor Multiple Time Scales energy dispatching method based on Spot Price mechanism |
| CN112101689A (en) * | 2019-06-18 | 2020-12-18 | 华北电力大学（保定） | Day-ahead intra-day scheduling method considering multi-type demand response uncertainty |
| CN110774929A (en) * | 2019-10-25 | 2020-02-11 | 上海电气集团股份有限公司 | Real-time control strategy and optimization method for orderly charging of electric automobile |
| CN110739690A (en) * | 2019-10-31 | 2020-01-31 | 山东大学 | Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility |
| CN111619393A (en) * | 2020-04-30 | 2020-09-04 | 国网天津市电力公司电力科学研究院 | User-oriented orderly charging control method for electric automobile in transformer area |
| CN113807554A (en) * | 2020-06-11 | 2021-12-17 | 国网电力科学研究院有限公司 | Load aggregator energy optimization method and device based on spot mode |
| CN112865082A (en) * | 2021-01-18 | 2021-05-28 | 西安交通大学 | Virtual power plant day-ahead scheduling method for aggregating multiple types of electric vehicles |
| CN115018198A (en) * | 2022-06-30 | 2022-09-06 | 国网河南省电力公司经济技术研究院 | Residential user electricity utilization optimization strategy considering differentiated demand response scheme |
| CN115439138A (en) * | 2022-08-03 | 2022-12-06 | 广东奔流能源有限公司 | Electric vehicle charging and discharging power optimal distribution method and system |
Non-Patent Citations (1)
| Title |
|---|
| 计及广义负荷不确定性和激励...响应的电力现货市场竞价方法;魏聪颖等;《电力自动化设备》;第第42卷卷(第第7期期);第76-82页 * |
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