CN111055718A - Electric automobile ordered charging method and system based on dual-target layered control - Google Patents

Electric automobile ordered charging method and system based on dual-target layered control Download PDF

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CN111055718A
CN111055718A CN201911091756.0A CN201911091756A CN111055718A CN 111055718 A CN111055718 A CN 111055718A CN 201911091756 A CN201911091756 A CN 201911091756A CN 111055718 A CN111055718 A CN 111055718A
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charging
load
power
user
time
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沈国辉
陈�光
赵宇
刘群
张世坤
梁强
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Beijing Kedong Electric Power Control System Co Ltd
State Grid Electric Power Research Institute
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Beijing Kedong Electric Power Control System Co Ltd
State Grid Electric Power Research Institute
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F15/00—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity
    • G07F15/003—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity for electricity
    • G07F15/005—Coin-freed apparatus with meter-controlled dispensing of liquid, gas or electricity for electricity dispensed for the electrical charging of vehicles
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • H01M10/446—Initial charging measures
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
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  • Mechanical Engineering (AREA)
  • Manufacturing & Machinery (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention discloses an electric automobile ordered charging method and system based on dual-target layered control. In the method, the upper-layer optimization strategy is applied to optimize the user charging cost, and the lower-layer optimization strategy is applied to stabilize the power load fluctuation of the platform area, so that the double-layer optimization of the user charging cost and the power grid load fluctuation is realized, the defect of single optimization target in the prior art is overcome, and the method has the advantages of strong operability, easiness in implementation, capability of meeting various requirements of a user side and a power grid side to the maximum extent and the like.

Description

Electric automobile ordered charging method and system based on dual-target layered control
Technical Field
The invention relates to an orderly charging method for an electric automobile, in particular to a large-scale orderly charging method for the electric automobile based on dual-target layered control, and also relates to an electric automobile charging system adopting the method, belonging to the technical field of electric automobile charging.
Background
At present, the large-scale popularization of electric vehicles has become an irreversible era trend. Correspondingly, the wide access of the charging load of the electric automobile has a great influence on the safe operation of the power distribution network. In practice, the charging period of the electric automobile is basically concentrated in the peak time of going to work and getting off duty in the city, and the large-scale electric automobile is connected to the power distribution network in a non-sequential manner at this time, so that short-term load of the power distribution network is overlarge, and serious impact is caused on safe and economic operation of the power distribution network. In addition, factors such as the number of charging piles and user behavior habits also limit the large-scale access capability of the electric automobile to a certain extent, so that a set of reasonable and effective orderly charging method for the electric automobile is very necessary.
In chinese patent application publication No. CN109484240A, a method for optimizing real-time charging of electric vehicle clusters based on cluster control is disclosed. The method comprises the following steps: step 1, based on a distributed hierarchical control framework, performing queue division on an electric vehicle cluster by taking a charging end time as a discrimination amount according to the charging characteristics of the electric vehicle cluster; step 2, solving a real-time optimization model of the electric automobile cluster charging and discharging power, which aims at minimizing daily load fluctuation, optimizing the electric automobile cluster charging and discharging power in real time, and carrying out safety constraint verification on a power distribution network; and 3, on the basis of the dispatching result of the electric automobile cluster, considering the charge and discharge cost of the electric automobile owner, and solving the power distribution model in the electric automobile cluster to obtain the optimal power distribution of the charge and discharge power of a single electric automobile in the electric automobile cluster, so that the total output of the electric automobile cluster is as close to the dispatching result as possible.
However, although the existing electric vehicle charging method provides a plurality of mathematical models for electric vehicle charging optimization, the existing electric vehicle charging method generally has the defects that the optimization target is too single, and the optimization of the user charging cost and the power grid load fluctuation does not form an organic whole. In addition, the existing electric vehicle charging method does not fully consider load prediction, particularly the influence of the load prediction of a small-range platform zone level on large-scale electric vehicle charging energy scheduling and charging strategy implementation.
Disclosure of Invention
The invention aims to provide a large-scale electric vehicle ordered charging method based on dual-target layered control.
The invention also provides an electric vehicle charging system adopting the electric vehicle orderly charging method.
In order to achieve the purpose, the invention adopts the following technical scheme:
according to a first aspect of the embodiments of the present invention, a method for orderly charging a large-scale electric vehicle based on dual-target hierarchical control is provided, which includes the following steps:
1) a master station acquires load information of a transformer area;
2) judging whether the current load of the platform area exceeds the limit, if so, executing a safety control strategy of charge power reduction and even stop;
3) judging whether the charging power of the current charging pile can meet the charging requirement of a user, and if the charging requirement of the user cannot be met, adjusting a charging plan of the electric automobile by the master station;
4) executing a first-level optimization strategy for optimizing user cost, and solving the start-stop charging time and charging power of a user;
5) executing a second-stage optimization strategy for stabilizing the power grid load fluctuation under the constraint of the first-stage optimization strategy, and solving the starting and stopping charging time and charging power of a user;
6) and the master station issues a final charging plan comprising the start-stop charging time and the charging power of the electric automobile.
Preferably, the safety control strategy is as follows:
when the load of the transformer area is out of limit, the charging power of the charging pile is preferentially reduced; and when the load of the transformer area is out of limit, further suspending or stopping charging of the charging pile.
Preferably, the charging power of the charging piles is reduced according to the charging proportion of the user at the moment of exceeding the limit until the platform area load is not exceeded, if the charging power is not met, the charging power is reduced to the lowest charging power of the charging piles, and if the platform area load is still exceeded under the condition that all the charging piles are reduced to the lowest power and are still exceeded, the charging of the charging piles is suspended or stopped in sequence until the platform area load is not exceeded.
Preferably, when the load of the transformer area is out of limit, the power reduction and restoration of the charging pile are performed according to the following sequence:
s31: sequentially reducing/stopping the charging power of the orderly charging users from low to high according to the charging requirement degree of the users; for users with the same charging demand degree, sequentially reducing/stopping the charging sequence of the users by adopting a principle of reducing after charging and first charging according to the sequence of the charging time of the users;
s32: sequentially reducing/stopping the orders of the normal charging users from low to high according to the charging demand degree of the users; for users with the same charging demand degree, sequentially reducing/stopping the charging sequence of the users by adopting a principle of reducing after charging and first charging according to the sequence of the charging time of the users;
s33: and when the load of the transformer area is adjusted to a safe operation interval, the charging piles are started in a reverse sequence mode in sequence, the power of the charging piles is adjusted upwards, and the operation sequence is opposite to the reduction/stop sequence.
Wherein preferably said first level optimization strategy consists of an objective function F1Represents:
Figure BDA0002267046880000031
wherein, T0Charging start time for user, Pt nFor charging power of a certain vehicle at time T, Δ T is a predetermined time unit, Δ T1And charging time for the user meeting the requirements under the first-level optimization strategy.
Preferably, a sliding recursion algorithm is adopted, and a charging power limit equation of the electric automobile is combined to solve the starting and stopping charging time controlled by the charging cost of the user and the charging power of each time unit of the user; and on the basis of a time-of-use electricity price model, summing all charging time intervals meeting the requirements.
Wherein preferably said second level optimization strategy consists of an objective function F2Represents:
Figure BDA0002267046880000032
wherein the content of the first and second substances,
Figure BDA0002267046880000033
charging period delta T under optimization strategy for the second level2Average value of total electric load in inner platform area.
Preferably, on the premise of meeting the first-stage optimization strategy, the charging time window and the charging power solved by the first-stage optimization strategy are used as input limiting conditions of the second-stage optimization strategy.
Preferably, meteorological factors are considered, and a multivariate linear regression method is adopted to predict the power load in the normal working day; and (4) forecasting the power load of the non-working day transformer area by adopting a multiple ratio smoothing algorithm in consideration of the day type factor.
According to a second aspect of the embodiment of the invention, an electric vehicle charging system adopting the electric vehicle ordered charging method is provided, which comprises an energy control system master station, an energy controller, an energy router and a charging pile;
the charging pile is controlled by the energy router and is used for providing charging service for the electric automobile;
the energy controller is connected with the energy router on one hand, issues a charging plan to the energy router and obtains the running state data of the charging pile from the charging plan, and is connected with the energy control system main station on the other hand, obtains the charging plan issued by the energy control system main station and uploads a local order.
Compared with the prior art, the load prediction method considering meteorological and holiday influence factors is provided, the defects that in the prior art, the load prediction of the transformer area is neglected, the correlation between real influence factors such as meteorological and the like and the transformer area load is not emphasized are overcome, and a basis is provided for the optimal implementation of the charging energy scheduling of the electric vehicle in the transformer area range. Secondly, the charging cost of the user is optimized by utilizing an upper-layer strategy, and the power load fluctuation of the platform area is stabilized by utilizing a lower-layer strategy, so that the double-layer optimization of the charging cost of the user and the power grid load fluctuation is realized, the defect of single optimization target in the prior art is overcome, and the method has the advantages of strong operability, easiness in implementation, capability of meeting various requirements of the user side and the power grid side to the maximum extent and the like.
Drawings
FIG. 1 is a logic architecture diagram of an orderly charging method for an electric vehicle according to the present invention;
FIG. 2 is a schematic diagram of an electric vehicle charging system for implementing the orderly charging method for an electric vehicle;
FIG. 3 is a flow chart of a safety control strategy in an orderly charging method for an electric vehicle
FIG. 4 is a flow chart of an orderly charging method for an electric vehicle according to the present invention;
FIG. 5 is a diagram illustrating power load prediction during a business day of a distribution room, according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating power load prediction in a holiday area of a transformer district according to an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating an actual measurement effect of an electric vehicle implementing the ordered charging method;
FIG. 8 is a load simulation diagram of a large-scale electric vehicle performing a chaotic charging;
fig. 9 is a load simulation diagram of orderly charging of a large-scale electric vehicle.
Detailed Description
The technical contents of the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the method for orderly charging an electric vehicle according to the embodiment of the present invention is a process of completing energy scheduling of a charging behavior of an electric vehicle in a predetermined area range by integrating constraint conditions, influence factors, and the like on the basis of intelligent power utilization. In this process, on the one hand, the charging costs for the user are controlled and, on the other hand, fluctuations in the load within the predetermined range are smoothed out. The constraint conditions include, but are not limited to, safe operation conditions of a power grid, charging requirements of users and the like, the influence factors include, but are not limited to, meteorological factors, holiday factors and the like, and the predetermined area range is preferably a range of residential areas, unit parks and other districts (namely, a power supply range or an area of a single transformer).
In one embodiment of the invention, the orderly charging method for the electric automobile mainly comprises the following steps:
step 1: forecasting the power load of the normal working day by adopting a multivariate linear regression method in consideration of meteorological factors; taking the day type factor into consideration, and adopting a multiple proportion smoothing algorithm to predict the power load of the non-working day transformer area;
step 2: establishing a double-layer double-target platform power load optimization strategy based on the margin of the electric automobile charging power in the platform area range; the charging cost of a user is optimized by applying an upper-layer optimization strategy, and the power load fluctuation of a platform area is stabilized by applying a lower-layer optimization strategy;
and step 3: and under the constraint condition of meeting the safe and stable operation of the power grid and the charging requirement of a user, establishing an ordered charging optimization strategy.
The following describes in detail an implementation process of the above-mentioned orderly electric vehicle charging method with reference to the electric vehicle charging system shown in fig. 2. The electric vehicle charging system comprises an energy control system main station (hereinafter referred to as a main station), an energy controller, an energy router, a charging pile and the like; wherein, fill electric pile by energy router control for provide charging service to electric automobile. The energy controller is connected with the energy router on one hand, issues the charging plan to the energy router, and obtains the running state data of the charging pile from the charging plan, and is connected with the main station on the other hand, so that the charging plan issued by the main station is obtained, and the local order is uploaded in time. In addition, the user may connect with the master station and the energy router respectively through application software (APP) installed in the mobile terminal in order to submit the charging demand and apply for feedback of the technician.
The electric vehicle charging system shown in fig. 2 is hierarchically and cooperatively controlled by a master station and an energy controller, and the participating parties are a mobile terminal of a user, an intelligent device (an energy router, an energy controller) and a master station control system. The user initiates a charging request through the APP, and the request content includes selection of a charging type (normal charging, ordered charging), a charging electric quantity, a charging start time, a charging end time and the like. The method comprises the steps that a master station and intelligent equipment receive a charging request, a corresponding charging plan is formulated by combining a basic load curve, a platform area load limiting curve and platform area working conditions, and the energy router executes the charging plan to start charging; the charging plan sent by the master station to the energy controller rolls every 15 minutes, and comprises charging power, charging starting time, charging ending time and the like.
In one embodiment of the present invention, the electricity prices in the region are divided into four time periods of tip, peak, flat and valley, and the time-of-use electricity price model is generally of the following form:
Figure BDA0002267046880000061
in the above formula, l (t) is the corresponding electricity price in the period of t, ltop、lpeak、lflat、lvalleyRespectively the tip, peak, flat and valley electricity prices corresponding to a t period (taking 15 minutes as a time unit) in 96 periods in a day, wherein t is t1,t2,...,t96。
On the basis of the time-of-use electricity price model, a double-layer double-target platform power load optimization strategy is realized according to the following steps. The concrete description is as follows:
step S21: determining charging power for an electric vehicle when not out-of-limit
When a user initiates a charging request, a charging service mode (normal charging or ordered charging), an order starting time T ', a user car lifting time T', and a user charging demand electric quantity Q are inputnThe charging power arranged by the current nth vehicle master station in the t period is Pt nThe rated charging power planned by the t period of the electric automobile is P under the condition of sufficient charging powermaxLimiting load P in time t of station areat AAnd the basic load P of residents in the station area t periodt BScheduled charging capacity P of electric vehicle scheduled at time interval t of platform areat KThen, the currently scheduled charging power of the electric vehicle is determined as limited by the following formula:
Figure BDA0002267046880000062
step S22: optimizing charging costs for a user
The electric automobile ordered charging method gives priority to the charging cost of the user, and takes the charging cost of the user as a first-level optimization target. The user charging cost optimization strategy needs to meet the user's requirements of the time of the lift and the charging electric quantity, and the optimization objective function F of the user charging cost1Can be determined by the following formula, wherein T0Charging start time for user, Pt nFor charging power of a certain vehicle at time T, Δ T is a time unit of 15 minutes, Δ T1Charging time length of the users meeting the requirements under the first-level user charging cost optimization strategy is as follows:
Figure BDA0002267046880000063
in one embodiment of the invention, a sliding recursive algorithm is adopted, and the starting and stopping charging time controlled by the user charging cost and the charging power of the user every 15 minutes are solved by combining with the electric vehicle charging power limit equation shown in the formula (2); based on the time-of-use price model, the charging start-stop time which actually meets the requirement can beMultiple segment (T)a1,Tb1)，(Ta2,Tb2)，…，(Tan,Tbn) And the integration of all charging time intervals meeting the requirements is solved, so that a complete available charging time interval meeting the control requirement of the charging cost of the user can be obtained:
(Ta,Tb)＝(Ta1,Tb1)∪(Ta2,Tb2)...∪(Tan,Tbn) (4)
step S23: smoothing fluctuations in the load of an electrical network
On the premise of meeting the requirement of a first-level user charging cost optimization strategy, a charging time window and charging power solved by the first-level optimization strategy are used as input limiting conditions of a next-level optimization strategy. Therefore, in the orderly charging method for the electric vehicle provided by the embodiment of the invention, the basic load of residents in the control console area and the total load fluctuation level of electric vehicle charging are used as a second-stage optimization target, and a control equation for stabilizing the load fluctuation of the power grid is established, wherein the control equation is used for
Figure BDA0002267046880000071
Charging period delta T under optimization strategy for the second level2Average value of total electrical loads in inner zone area, F2In order to stabilize the objective function of the power grid load fluctuation:
Figure BDA0002267046880000072
Figure BDA0002267046880000073
Figure BDA0002267046880000074
in one embodiment of the invention, a Particle Swarm algorithm (Particle Swarm Optimization) is adopted to solve the solution of the second-level Optimization strategy stabilizing load fluctuation objective function, including the charging start-stop time (t) of each usera,tb) Scheduled charging of each time segmentElectric power Pt nThe above steps S21 to S23 are repeated, and the charge schedule is corrected by scrolling every 15 minutes.
In step S23, when the platform area overload overrun condition occurs, the electric vehicle charging system implements a safety control policy according to the degree of user demand, so as to ensure safe and economic operation of the platform area, which is specifically described as follows:
referring to fig. 3, first, the charging demand β (t) of the user is quantitatively described according to the charging ratio of the user at the out-of-limit time:
β＝(β1,β2,...,βn) (8)
the following formula is a quantitative determination method of the degree of charge demand β (T) of the user, where Tc nCharging a certain user, t' is the off-limit time of the load of the platform area, t is the charging time of the user, QnThe required amount of electricity is charged for a certain user,
Figure BDA0002267046880000081
for a certain moment the user has charged:
Figure BDA0002267046880000082
when the load of the transformer area is out of limit, the charging power of the charging pile is preferentially reduced; and when the load of the transformer area is out of limit, further suspending or stopping charging of the charging pile. And under the condition that all the charging piles are reduced to the lowest power and are still out of limit, if the platform area load is still out of limit, the charging of the charging piles is suspended or stopped in sequence until the platform area load is not out of limit.
When the platform district load is more limited, fill electric pile power and cut down and resume and go on according to following order:
s31: sequentially reducing/stopping the charging power of the orderly charging users from low to high according to the charging requirement degree of the users; for users with the same charging demand degree, sequentially reducing/stopping the charging sequence of the users by adopting a principle of reducing after charging and first charging according to the sequence of the charging time of the users;
s32: sequentially reducing/stopping the orders of the normal charging users from low to high according to the charging demand degree of the users; for users with the same charging demand degree, sequentially reducing/stopping the charging sequence of the users by adopting a principle of reducing after charging and first charging according to the sequence of the charging time of the users;
s33: and when the load of the transformer area is adjusted to a safe operation interval, starting the charging piles in a reverse sequence mode in sequence and increasing the power of the charging piles, wherein the specific sequence is opposite to the reduction/stop sequence.
Referring to fig. 4, considering the above-mentioned ordered charging optimization strategy and the safety control strategy for platform load out-of-limit adjustment comprehensively, the ordered charging method for the electric vehicle provided in the embodiment of the present invention includes the following steps:
1) the method comprises the steps that a main station acquires transformer area load information which comprises transformer area resident predicted basic loads, arranged electric automobile charging loads in all time periods and a transformer area load threshold value; the user provides the charging service type, the charging required electric quantity and the time information of the user-reserved lift car;
2) judging whether the current load of the platform area exceeds the limit, if so, executing a safety control strategy of charge power reduction and even stop;
3) judging whether the charging power of the current charging pile can meet the charging requirement of a user, and if the charging requirement of the user cannot be met, adjusting a charging plan of the electric automobile by the master station;
4) executing a first-level optimization strategy for optimizing user cost, and solving the start-stop charging time and charging power of a user;
5) executing a second-stage optimization strategy for stabilizing the power grid load fluctuation under the constraint of the first-stage optimization strategy, and solving the starting and stopping charging time and charging power of a user;
6) and the master station issues a final charging plan comprising the start-stop charging time and the charging power of the electric automobile.
The following further describes the implementation steps and effects of the ordered charging method for an electric vehicle through different embodiments.
The first embodiment:
in the first embodiment, the load decomposition-based multiple linear regression prediction considering meteorological factors and the prediction model considering day type factors represented by holidays are combined and applied to the platform district No. 1 of the three distribution transformers in the south of some places in Henan. The prediction of the basic load of the residents from two points in the afternoon of 16 th in 4 th and 4 th in 2019 to two points in the afternoon of 17 th in 4 th and 4 th in 2019 is completed by using the historical load data and the meteorological data of the last two weeks. In a short-term range of two weeks, it can be approximated that the basic load P of a resident is linearly related to the temperature T and the humidity H, and the daily load P can be decomposed into a weather-factor-independent medium-and long-term load component P' and a weather-factor-dependent load component P ″:
P＝P′+P″ (10)
the daily load P and the medium-and long-term daily load component P' may be loads P of 24 time periods corresponding to each 1 hour time unit in a dayi、P′iAnd (4) forming. Considering that the meteorological conditions are generally substantially constant in two weeks, the medium and long-term daily load P '═ P'1,P′2,...,P′24) It can be considered that the historical sample load P (P) is (t is taken as a historical sample of 14 weeks) corresponding to t time periods1,P2,...,P24) Wherein i is 1,2,. 24:
P′i＝(Pi_1+Pi_2+...+Pi_t)/t (11)
on the basis, an analysis model of the load component P' linearly related to the meteorological factors of the temperature T and the humidity H is established. Wherein, P ″)t＝(P″t_1,P″t_2,...,P″t_24) The load influenced by meteorological factors in 24 time periods in a day is corresponding to the tth sample day, Tt＝(Tt_1,Tt_2,...,Tt_24) Historical temperature data for the corresponding time period of the tth sample day, Ht＝(Ht_1,Ht_2,...,Ht_24) Historical humidity data for the corresponding time period of the tth sample day, W1＝(W1_1,W1_2,…W1_24)TFor a matrix of temperature coefficients to be solved, W2＝(W2_1,W2_2,...,W2_24)TFor the humidity coefficient matrix to be determined, E ═ E1,E2,...,E24)TIf epsilon is a random error coefficient matrix, then:
P″t＝W1Tt+W2Ht+E+ε (12)
adopting an undetermined coefficient method to obtain a corresponding coefficient matrix W, if X is meteorological data of the acquired historical temperature T and historical humidity H, and Y is the acquired corresponding historical load, the general form of solving the coefficient matrix is as follows:
W＝(XTX)-1XTY (13)
for the medium and long term load item P of which the day to be measured is irrelevant to meteorological factorsD' forecasting is carried out by combining an exponential smoothing method with a cluster analysis method considering day types, and weather forecast can obtain weather data of each period of 24 hours a day in forecasting days:
temperature: t isD＝(TD_1,TD_2,...,TD_24)；
Humidity: hD＝(HD_1,HD_2,...,HD_24)；
In this embodiment, day 16 is a sunny day, the temperature is 15 ℃ to 28 ℃, the humidity is 15% to 34%, day 17 is a sunny day, the temperature is 18 ℃ to 31 ℃, and the humidity is 18% to 32%.
Then the daily load curve of the predicted day
Figure BDA0002267046880000101
The prediction equation of (a) is:
Figure BDA0002267046880000102
the predicted load curve and the actual load curve for non-holidays are shown in fig. 5, and the error is shown in table 1.
TABLE 1 error statistics for non-holiday resident basic load prediction
Figure BDA0002267046880000103
In addition, according to the embodiment of the invention, the prediction of the basic load of the residents during the 2019 labor festival holiday is completed by utilizing the historical load data and the meteorological data of the last two weeks and the historical load data and the meteorological data during the last five labor festival holiday. For example, local cloudy in 1 month 5 in 2019, with a temperature of 13 ℃ to 25 ℃ and a humidity of 63% to 75%, local cloudy in 2 months 5 in 2019, with a temperature of 13 ℃ to 27 ℃ and a humidity of 57% to 64%. And adopting a point-to-point multiple ratio smoothing method for load prediction of holidays. Taking a primary smooth value of a related time period n days before a holiday festival of the year
Figure BDA0002267046880000111
The first smooth value of the time period corresponding to n days (n is 5) before the false day of the same type of the previous year
Figure BDA0002267046880000112
α is a smoothing factor (α may be 0.618 in an embodiment of the invention), P1t、P2tThe historical load values of the time period of the current year and the last year respectively,
Figure BDA0002267046880000113
the smoothing values calculated for the previous cycle, respectively, where t is 1, 2.
Figure BDA0002267046880000114
Figure BDA0002267046880000115
If it is
Figure BDA0002267046880000116
The load predicted value of the corresponding time of the holiday of the year,
Figure BDA0002267046880000117
historical load values for the time corresponding to the holiday of the previous year, then
Figure BDA0002267046880000118
The prediction equation is:
Figure BDA0002267046880000119
the predicted load curve and the actual load curve of the holiday are shown in fig. 6, and the error is shown in table 2.
TABLE 2 error statistics for residential base load predictions during labor savings
Figure BDA00022670468800001110
Next, taking the ordered charging test point developed in the platform area No. 1 of the third distribution transformer in the south of the certain square as an example, the implementation effect of the ordered charging method for the electric vehicle provided by the embodiment of the invention is subjected to example verification analysis.
The electricity price model of the peak flat valley in a place in Henan province is shown in the following table:
TABLE 3 TIME-OF-TIME ELECTRICITY VALUE MODEL FOR TESTING CHARGING LOCAL IN ORDER
Figure BDA00022670468800001111
The size of the No. 1 platform area of the south three-distribution transformer of a certain square is small, and the capacity threshold is 500 kW. And 5 electric automobiles participate in the implementation effect verification of the electric automobile ordered charging method during the period from 16 days in 4 months in 2019 to 14:00 days in 17 months in 4 months in 2019. The charging demand electric quantity of each electric automobile and the time of connecting into the charging pile are shown in the following table:
TABLE 4 vehicle basic parameters for ordered Charge testing
Figure BDA0002267046880000121
Taking the example that a certain electric vehicle user participates in the ordered charging, the specific application of the ordered charging method of the electric vehicle is shown. The charging vehicle of the user is ChereQ 1, the charging demand electric quantity input by the user from APP is 26.73 degrees, and the vehicle lifting time input by the user is 2019, 4 months, 17 morning and 8 morning: 00, the time of the electric automobile to be connected into the charging pile is 17 points in 16 days in 4 months, and the starting and stopping time of the orderly charging is 45 minutes from 2 points in 2 morning to 5 points in 17 days in 4 months. The starting time and the ending time of the orderly charging of the 5 electric automobiles are both in the low valley period of the electricity price, and the cost is saved by 63.98% compared with the average cost of each user of the unordered charging. In addition, from the case of the station load fluctuation, 16 days 18 to 23 are peak periods of the total load of the station, the next morning of 0:00 to 8:00 is the valley period of the total load of the platform area, compared with the disordered charging, after the electric automobile participates in the ordered charging, the load in the peak period is reduced to a certain degree, and the load at the valley time is improved to a certain degree, so that the goal of peak clipping and valley filling to a certain degree is realized. The off-limit value of the load of the transformer area is 500kW, and the off-limit condition of the load does not occur, so that the safety control strategy of off-limit and recovery of the load of the transformer area is not executed.
In the first embodiment, the implementation effect of the ordered charging method for the electric vehicle is shown in fig. 7 and table 5:
TABLE 5 implementation Effect of the electric vehicle ordered charging method in a certain place
Figure BDA0002267046880000122
Second embodiment:
in view of the fact that the capacity of the transformer area participating in the test point is small in the first embodiment, and the number of electric vehicles capable of participating in the implementation effect verification of the ordered charging method of the electric vehicle is limited, in order to further explain the effect of the ordered charging method of the large-scale electric vehicle, a simpler MATLAB is adopted to perform a simulation experiment in the second embodiment. The simulation uses the load of residents in 24 hours as the basic load, the out-of-limit value is 9MW, the Monte Carlo method is adopted to simulate the unordered charging load of the electric automobile, the quantity of the electric automobile which participates in orderly charging is set to 250, the unordered charging time of the electric automobile respectively meets normal distribution N (8, 0.8) and N (17, 1.2) from 0 point to 12 points and from 12 points to 24 points, the charging electric quantity of the electric automobile meets the normal distribution N (32.94, 5), the unordered charging power is calculated according to 7kW/h, and the simulation of the unordered charging load of the electric automobile is shown in figure 8.
Next, a simulation experiment of the ordered charging method of the electric vehicle is performed, and a test point electricity price model of a certain place in the south of the river is still adopted, so that the situation that the platform load exceeds the limit of 9MW set by simulation at about 21 points can be found if the disordered charging is adopted. After the electric automobile ordered charging method is executed, the charging load of the electric automobile is transferred to the valley period of the power grid load and the electricity price model of 0: 00-8: 00, the off-line load threshold value of a platform area is effectively avoided, and multiple targets of stabilizing the load fluctuation and saving the charging cost of electric automobile users are achieved. The effect of the method for orderly charging an electric vehicle provided by the embodiment of the invention in the simulation experiment is shown in fig. 9 and table 6:
TABLE 6 simulation effect of electric vehicle ordered charging method
Figure BDA0002267046880000131
In the simulation experiment, the safety control strategy of the ordered charging method for the electric automobile is as follows:
1) sequentially reducing/stopping the charging power of the orderly charging users from low to high according to the charging requirement degree of the users; for users with the same charging demand degree, sequentially reducing/stopping the charging sequence of the users by adopting a principle of reducing after charging and first charging according to the sequence of the charging time of the users;
2) sequentially reducing/stopping the orders of the normal charging users from low to high according to the charging demand degree of the users; for users with the same charging demand degree, sequentially reducing/stopping the charging sequence of the users by adopting a principle of reducing after charging and first charging according to the sequence of the charging time of the users;
3) and when the load of the transformer area is adjusted to a safe operation interval, sequentially starting the charging piles in a reverse order by adopting a recovery strategy and up-regulating the power of the charging piles, wherein the specific order is opposite to the out-of-limit control strategy, namely, the charging piles are recovered in a reverse order mode.
Compared with the prior art, the load prediction method considering meteorological and holiday influence factors is provided, the defects that in the prior art, the load prediction of the transformer area is neglected, the correlation between real influence factors such as meteorological and the like and the transformer area load is not emphasized are overcome, and a basis is provided for the optimal implementation of the charging energy scheduling of the electric vehicle in the transformer area range. Secondly, the charging cost of the user is optimized by utilizing an upper-layer strategy, and the power load fluctuation of the platform area is stabilized by utilizing a lower-layer strategy, so that the double-layer optimization of the charging cost of the user and the power grid load fluctuation is realized, the defect of single optimization target in the prior art is overcome, and the method has the advantages of strong operability, easiness in implementation, capability of meeting various requirements of the user side and the power grid side to the maximum extent and the like.
The electric vehicle ordered charging method based on the dual-target hierarchical control and the system thereof provided by the invention are explained in detail above. It will be apparent to those skilled in the art that any obvious modifications thereof can be made without departing from the spirit of the invention, which infringes the patent right of the invention and bears the corresponding legal responsibility.

Claims (10)

1. A double-target hierarchical control-based orderly charging method for an electric automobile is characterized by comprising the following steps:
1) a master station acquires load information of a transformer area;
2) judging whether the current load of the platform area exceeds the limit, if so, executing a safety control strategy of charge power reduction and even stop;
3) judging whether the charging power of the current charging pile can meet the charging requirement of a user, and if the charging requirement of the user cannot be met, adjusting a charging plan of the electric automobile by the master station;
4) executing a first-level optimization strategy for optimizing user cost, and solving the start-stop charging time and charging power of a user;
5) executing a second-stage optimization strategy for stabilizing the power grid load fluctuation under the constraint of the first-stage optimization strategy, and solving the starting and stopping charging time and charging power of a user;
6) and the master station issues a final charging plan comprising the start-stop charging time and the charging power of the electric automobile.
2. The ordered charging method for electric vehicles according to claim 1, wherein the safety control strategy is:
when the load of the transformer area is out of limit, the charging power of the charging pile is preferentially reduced; and when the load of the transformer area is out of limit, further suspending or stopping charging of the charging pile.
3. The ordered charging method for the electric vehicle according to claim 2, characterized in that:
and reducing the charging power of the charging pile according to the charging proportion of the user at the moment of exceeding the limit until the platform area load is not exceeded, reducing the charging power to the lowest charging power of the charging pile if the platform area load is still exceeded under the condition that all the charging piles are reduced to the lowest power and are still exceeded, and sequentially pausing or stopping the charging of the charging pile until the platform area load is not exceeded.
4. The orderly charging method of electric vehicles according to claim 3, characterized in that when the platform load is out of limit, the power reduction and restoration of the charging pile are carried out according to the following sequence:
s31: sequentially reducing/stopping the charging power of the orderly charging users from low to high according to the charging requirement degree of the users; for users with the same charging demand degree, sequentially reducing/stopping the charging sequence of the users by adopting a principle of reducing after charging and first charging according to the sequence of the charging time of the users;
s32: sequentially reducing/stopping the orders of the normal charging users from low to high according to the charging demand degree of the users; for users with the same charging demand degree, sequentially reducing/stopping the charging sequence of the users by adopting a principle of reducing after charging and first charging according to the sequence of the charging time of the users;
s33: and when the load of the transformer area is adjusted to a safe operation interval, the charging piles are started in a reverse sequence mode in sequence, the power of the charging piles is adjusted upwards, and the operation sequence is opposite to the reduction/stop sequence.
5. The method of claim 1, wherein the first-level optimization strategy is defined by an objective function F1Represents:
Figure FDA0002267046870000021
wherein, T0Charging start time for user, Pt nFor charging power of a certain vehicle at time T, Δ T is a predetermined time unit, Δ T1And charging time for the user meeting the requirements under the first-level optimization strategy.
6. The ordered charging method for the electric vehicle according to claim 5, characterized in that:
a sliding recursion algorithm is adopted, and the starting and stopping charging time controlled by the charging cost of a user and the charging power of each time unit of the user are solved by combining an electric vehicle charging power limiting equation; and on the basis of a time-of-use electricity price model, summing all charging time intervals meeting the requirements.
7. The method of claim 1, wherein the second-stage optimization strategy is defined by an objective function F2Represents:
Figure FDA0002267046870000022
wherein the content of the first and second substances,
Figure FDA0002267046870000023
charging period delta T under optimization strategy for the second level2Average value of total electric load in inner platform area.
8. The ordered charging method for electric vehicles according to claim 7, characterized in that:
and on the premise of meeting the first-stage optimization strategy, taking the charging time window and the charging power solved by the first-stage optimization strategy as input limiting conditions of the second-stage optimization strategy.
9. The ordered charging method for electric vehicles according to claim 1, further comprising the steps of:
forecasting the power load of the normal working day by adopting a multivariate linear regression method in consideration of meteorological factors; and (4) forecasting the power load of the non-working day transformer area by adopting a multiple ratio smoothing algorithm in consideration of the day type factor.
10. An electric vehicle charging system adopting the orderly electric vehicle charging method according to any one of claims 1 to 9, which is characterized by comprising an energy control system master station, an energy controller, an energy router and a charging pile;
the charging pile is controlled by the energy router and is used for providing charging service for the electric automobile;
the energy controller is connected with the energy router on one hand, issues a charging plan to the energy router and obtains the running state data of the charging pile from the charging plan, and is connected with the energy control system main station on the other hand, obtains the charging plan issued by the energy control system main station and uploads a local order.
CN201911091756.0A 2019-11-10 2019-11-10 Electric automobile ordered charging method and system based on dual-target layered control Pending CN111055718A (en)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111660861A (en) * 2020-06-03 2020-09-15 国网重庆市电力公司营销服务中心 Charging control method for electric vehicle in charging station
CN111845453A (en) * 2020-07-10 2020-10-30 国网天津市电力公司 Electric vehicle charging station double-layer optimization charging and discharging strategy considering flexible control
CN111873841A (en) * 2020-07-31 2020-11-03 武汉瑞莱保能源技术有限公司 Intelligent charging station management system
CN114274821A (en) * 2021-11-30 2022-04-05 南瑞集团有限公司 Intelligent alternating-current charging and discharging pile
CN115360804A (en) * 2022-10-17 2022-11-18 国网浙江慈溪市供电有限公司 Ordered charging system and ordered charging method

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111660861A (en) * 2020-06-03 2020-09-15 国网重庆市电力公司营销服务中心 Charging control method for electric vehicle in charging station
CN111845453A (en) * 2020-07-10 2020-10-30 国网天津市电力公司 Electric vehicle charging station double-layer optimization charging and discharging strategy considering flexible control
CN111845453B (en) * 2020-07-10 2024-01-30 国网天津市电力公司 Electric vehicle charging station double-layer optimized charging and discharging strategy considering flexible control
CN111873841A (en) * 2020-07-31 2020-11-03 武汉瑞莱保能源技术有限公司 Intelligent charging station management system
CN114274821A (en) * 2021-11-30 2022-04-05 南瑞集团有限公司 Intelligent alternating-current charging and discharging pile
CN115360804A (en) * 2022-10-17 2022-11-18 国网浙江慈溪市供电有限公司 Ordered charging system and ordered charging method

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