CN110774929A - Real-time control strategy and optimization method for orderly charging of electric automobile - Google Patents
Real-time control strategy and optimization method for orderly charging of electric automobile Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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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
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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
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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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Abstract
The invention provides a real-time control strategy and an optimization method for orderly charging of electric automobiles, which are used for orderly managing the charging behaviors of the electric automobiles in residential districts. When the load prediction calculation of the cell is carried out, the influence of the electric vehicle load which is not subjected to the ordered management is ignored by a plurality of documents, and when the load prediction calculation of the cell is carried out, the influence of the electric vehicle load which is not subjected to the ordered management is fully considered by the invention, and the load which is possibly generated in the future period are taken as the total predicted load to be counted and analyzed together with the basic load to be used as the basic data of the ordered charging optimization calculation.
Description
Technical Field
The invention relates to the field of novel electric automobile charging ordered management, in particular to a residential community electric automobile charging ordered management method and a control method.
Background
At present, the electric automobile of ordinary residential quarter charges and is basically according to user's trip demand, charges on fixed supporting electric pile that fills. The charging pile supporting equipment which is put into use in the market at present basically adopts a plug-and-play charging mode, the charging pile does not regulate and control the charging behavior of the electric automobile, even if a few charging piles are configured with remote reservation and start-stop control based on a mobile internet technology, the charging pile is only based on the angle of vehicle utilization of individual users, and all charging loads of the whole platform area still have obvious random, disordered and fluctuation characteristics when viewed from an electric automobile group of the whole platform area, and the adverse effect on a power distribution platform area cannot be changed.
On the whole, the daily work and rest of the residents have fixed regularity, namely the residents go out to work in the daytime and go home to be charged at night. The group effect causes the superposition of charging loads in the community, so that the transformer of the transformer area which is originally in the late peak is superposed with the charging load, the peak value of the transformer area is improved, the distribution loss of the transformer area is increased, and a lot of pressure is brought to the distribution of the community. Even if the electric vehicle user selects to charge at the valley time period after work, the blindness is strong, the problems of huge peak-valley difference, severe fluctuation and the like of the power distribution load of a community still cannot be effectively solved, and the charging trouble of the user is increased.
The charging load of the electric automobile has certain randomness and dispersity, and shows certain group consistency characteristics after reaching certain group scale. Unreasonable charging time and charging mode, large-scale electric vehicle charging load can aggravate fluctuation and uncertainty of power grid load, aggravate the 'peak-to-peak' effect of power distribution station load, even cause insufficient capacity of power distribution equipment and have to be increased or expanded and modified, reduce utilization rate of configuration, and be unfavorable for economical efficiency and stability of a power distribution network.
In order to reduce adverse effects on a distribution network area caused by random unordered charging of a large-scale electric automobile group, improve the stability of the distribution network area and the capacity of receiving charging loads, it is necessary to manage the charging behaviors of electric automobiles in residential communities in order.
Disclosure of Invention
The purpose of the invention is: in order to reduce adverse effects on a distribution network area caused by random unordered charging of a large-scale electric automobile group, the stability of the distribution network area and the capacity of receiving charging loads are improved.
In order to achieve the aim, the technical scheme of the invention provides a real-time control strategy and an optimization method for orderly charging of an electric automobile, and a novel electric automobile charging orderly management system is adopted, wherein the system comprises a residential area transformer, a load prediction unit, an orderly charging management unit, a charging pile power supply control unit and a plurality of charging piles; the input end of the ordered charging management unit is connected with the residential area transformer and is used for acquiring the rated capacity of the residential area transformer and the real-time information of the residential electricity load; the load prediction unit is connected with the ordered charging management unit and is used for predicting the load condition of a future time period and providing necessary load information for the ordered charging management unit; the ordered charging management unit is also respectively connected with the plurality of charging piles and used for monitoring and controlling the starting and stopping states of the charging piles and adjusting the output power of the charging piles; the orderly charging management unit is also connected with a charging pile power supply control unit, the charging pile power supply control unit is respectively connected with a plurality of charging piles and used for controlling the switching of the alternating current input of the plurality of charging piles, and the method is characterized by comprising the following steps:
step 1, monitoring the condition that an electric vehicle is connected into a charging pile in real time, and if no electric vehicle is connected into the charging pile, continuing waiting by the system; otherwise, entering the step 2 when monitoring that the charging pile is newly connected into the electric vehicle;
step 2, after the electric vehicle is connected into the charging pile, the system acquires and stores charging demand information by using a relevant communication protocol according to the service condition of the electric vehicle;
step 3, reading basic load information of a transformer of a residential area, load information of a charging motor car and a planned charging load, and reading load distribution conditions of 96 time periods in one day and residual available charging load capacity of distribution in a transformer area, which are predicted by a load prediction unit;
step 4, whether the user accepts the ordered management or not;
if the newly accessed electric vehicle user selects not to accept ordered management, calculating the charging power distribution and the charging ending time information of the current electric vehicle in N time periods according to the remaining available charging capacity of the distribution transformer of the distribution area, and showing the information as an unordered charging scheme to the user;
if the newly accessed electric vehicle user chooses to receive ordered management, an ordered charging optimization model for electric vehicle charging is constructed according to the charging demand information and the load power of the distribution transformer area obtained in the step 2 by taking the minimum peak-valley difference of the distribution transformer area and the optimal charging cost as targets, an optimization algorithm is called to carry out solution calculation on the ordered charging optimization model, the obtained optimization result is converted into charging scheme information, and the charging scheme information is displayed to the user;
step 5, displaying the ordered charging scheme to a user, returning to the step 2 if the user does not accept the ordered charging scheme, and re-reading the charging demand information set by the user; if the user confirms the ordered charging scheme, entering step 6;
and 6, after the ordered charging scheme is confirmed, the ordered charging scheme is issued to the charging pile, the charging pile executes a charging task according to the ordered charging scheme, and meanwhile, relevant charging scheme information is fed back to the load prediction unit.
Preferably, in step 2, the charging requirement information includes a battery capacity and a remaining capacity of the current electric vehicle, and a charging completion time and whether to perform ordered charging desired by a user.
Preferably, in step 4, the optimization algorithm adopts a basic genetic algorithm or an improved particle swarm algorithm, a proper optimization algorithm is selected according to different scales of the ordered charging optimization model, and the improved particle swarm algorithm is called by a system to solve under a default condition.
Preferably, the improved particle swarm algorithm comprises the steps of:
a) determining an objective function and a constraint condition, wherein: object function packageIncluding an objective function f
1: peak minimum of the total load curve over the charging period:
in the formula, P
t BaseBase load, base load P, representing period t
t BaseThe system comprises a resident basic load, all in-charge motor car loads and all planned charge loads; p
t EVRepresenting the charging load of the current electric vehicle in a t period; t is a time interval, a 96-point daily load curve method is adopted, 96 time intervals are equally divided for 24 hours a day, and each time interval is 15 minutes;
objective function f
2: minimum charge cost for user
Wherein, C
tThe decision plan of the current electric vehicle in the time period t is represented, the value is 0 or 1, and the decision plan is used for determining whether the current electric vehicle has charging behavior in the current time period, wherein 0 represents no charging and 1 represents charging; mu.s
tRepresents the electricity rate for the t period; Δ t represents a flag for each time period;
using linear weighting, will contain the objective function f
1And an objective function f
2The multi-objective optimization problem is converted into a single objective function f optimization problem to be solved, wherein the converted single objective function f is as follows:
minf＝λ
1f
1+λ
2f
2
in the formula, λ
1And λ
2Respectively, is a representative objective function f
1And an objective function f
2The weight coefficients of the importance degree of (2) are positive numbers;
defining constraints, including constraint one): constraint condition of battery state of electric vehicle
SOC
min≤SOC
i≤SOC
max
In the formula, SOC
minAnd SOC
maxAre respectively electricityMaximum and minimum charge states of the motor train; SOC
iThe current state of charge of the electric vehicle;
constraint two): total load limit for a cell
In the formula (I), the compound is shown in the specification,
representing the total load of the power supply area, wherein the constraint condition represents that the total load of the power supply area cannot exceed the power limit of the transformer;
b) calling an optimization algorithm to solve the target, wherein the method comprises the following steps:
b01) acquiring related data, and defining a fitness value calculation function according to a target function and a constraint condition;
b02) initializing a population, including population scale sizepop, maximum iteration times maxgen and a constraint range bound, and randomly assigning values to the initial population;
b03) calculating the fitness value of the individual members of the population by using a defined fitness value function;
b04) entering an iteration stage, if an iteration cutoff condition is met, jumping to b06), and outputting a charging plan of the globally optimal individual member as a result; otherwise, for a given member of the population:
(1) storing the individual history optimization of the previous iteration period;
(2) redistributing the search tasks of the member individuals, updating the speed and position information of the member individuals, calculating a fitness value function, and updating the historical optimal fitness value information of the individuals;
(3) if the historical optimal value of the member individual is better than the historical optimal value of the previous iteration cycle, executing a global optimal updating strategy;
(4) executing a global optimal information disturbance strategy, and updating global optimal value information;
b05) iteration step number plus 1, return to b 04);
b06) and after iteration is finished, storing and outputting the global optimal individual information as an optimal charging plan.
The invention provides a real-time control strategy and an optimization method for orderly charging of electric automobiles, which are used for orderly managing the charging behaviors of the electric automobiles in residential districts. When the load prediction calculation of the cell is carried out, the influence of the electric vehicle load which is not subjected to the ordered management is ignored by a plurality of documents, and when the load prediction calculation of the cell is carried out, the influence of the electric vehicle load which is not subjected to the ordered management is fully considered by the invention, and the load which is possibly generated in the future period are taken as the total predicted load to be counted and analyzed together with the basic load to be used as the basic data of the ordered charging optimization calculation.
At present, the existing patents are made in an ordered scheme based on a model mode, namely, a load model of a power distribution area is established first, and then ordered management is carried out according to the access condition of an electric vehicle. The method is simple and convenient, but has the defect that the model cannot accurately follow the change of the load characteristics of the power distribution area. The method is based on the load prediction for orderly management, the load prediction updates the load prediction condition in the future time period in real time, the change of the load characteristics of the power distribution area can be more truly followed, and the effectiveness of making an orderly management strategy is guaranteed.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The invention provides a real-time control strategy and an optimization method for orderly charging of an electric automobile, and a novel orderly management system for charging of the electric automobile is adopted. This system includes residential quarter transformer, load prediction unit, the management unit that charges in order, fills electric pile power control unit and many electric piles, according to electric automobile's demand of charging, formulates reasonable orderly charging plan, reduces the peak valley difference of distribution station district load, stabilizes the charging load fluctuation, reduces the charge expense, improves the operation convenience that charges, promotes the distribution region and accepts electric automobile charging ability.
Based on the system, the real-time control strategy and optimization method for orderly charging of the electric automobile provided by the invention comprises the following steps:
specifically, the method takes reduction of daily load peak-valley difference and reduction of electric vehicle charging cost as optimization targets, carries out ordered management and optimized scheduling on the electric vehicle charging time period, adopts an improved particle swarm optimization algorithm to carry out optimized calculation, and obtains an optimal solution as an ordered charging scheme. As shown in fig. 1, the optimization calculation steps are specifically as follows:
step 1, monitoring the condition that an electric vehicle is connected into a charging pile in real time, and if no electric vehicle is connected into the charging pile, continuing waiting by the system; otherwise, entering the step 2 when monitoring that the charging pile is newly connected into the electric vehicle;
step 2, after the electric vehicle is connected into the charging pile, according to the service condition of the electric vehicle, the system acquires and stores the battery capacity and the residual electric quantity of the electric vehicle, charging requirement information such as the charging completion time expected by a user, whether the charging is orderly carried out and the like by using a relevant communication protocol;
step 3, reading basic load information of a transformer of a residential area, load information of a charging motor car and a planned charging load, and reading load distribution conditions of 96 time periods in one day and residual available charging load capacity of distribution in a transformer area, which are predicted by a load prediction unit;
the load prediction unit is connected with the ordered charging management unit and used for predicting the load condition of a future period and providing necessary load information for charging management. The implementation technical scheme of the load prediction unit is as follows:
301, reading power distribution information, charging and planned charging load power and other information of a power distribution area in real time; and (3) emergency handling: when a user of the electric automobile needs to stop charging according to personal requirements, a charging stopping option is input, the system stops charging according to operation, charging load statistical information and prediction results in a future time period are corrected in time, and related ordered management strategies are adjusted.
Step 302, predicting the load of a power distribution area by using a load prediction algorithm and a model, and evaluating the available power limit of a distribution transformer;
and 303, dynamically adjusting the load prediction result of the power distribution area, storing the load prediction result into a corresponding unit, and preparing for the charging management scheme service.
Step 4, whether the user accepts the ordered management or not;
if the newly accessed electric vehicle user selects not to accept ordered management, calculating the charging power distribution and the charging ending time information of the current electric vehicle in N time periods according to the remaining available charging capacity of the distribution transformer of the distribution area, and showing the information as an unordered charging scheme to the user;
if the newly accessed electric vehicle user chooses to receive ordered management, an ordered charging optimization model for electric vehicle charging is constructed according to the charging demand information and the load power of the distribution transformer area obtained in the step 2 by taking the minimum peak-valley difference of the distribution transformer area and the optimal charging cost as targets, an optimization algorithm is called to carry out solving calculation on the ordered charging optimization model, the obtained optimization result is converted into charging scheme information, and the charging scheme information is displayed to the user.
In the above steps, the optimization algorithm adopts a basic genetic algorithm or an improved particle swarm algorithm, a proper optimization algorithm is selected according to different scales of the ordered charging optimization model, and the improved particle swarm algorithm is called by a system to solve under a default condition. The improved particle swarm algorithm comprises the following steps:
step 401, determining an objective function and a constraint condition, wherein: the objective function includes an objective function f
1: peak minimum of the total load curve over the charging period:
in the formula, P
t BaseBase for representing t periodLoad, base load P
t BaseThe system comprises a resident basic load, all in-charge motor car loads and all planned charge loads; p
t EVRepresenting the charging load of the current electric vehicle in a t period; t is a time interval, a 96-point daily load curve method is adopted, 96 time intervals are equally divided for 24 hours a day, and each time interval is 15 minutes;
objective function f
2: minimum charge cost for user
Wherein, C
tThe decision plan of the current electric vehicle in the time period t is represented, the value is 0 or 1, and the decision plan is used for determining whether the current electric vehicle has charging behavior in the current time period, wherein 0 represents no charging and 1 represents charging; mu.s
tRepresents the electricity rate for the t period; Δ t represents a flag for each time period;
using linear weighting, will contain the objective function f
1And an objective function f
2The multi-objective optimization problem is converted into a single objective function f optimization problem to be solved, wherein the converted single objective function f is as follows:
minf＝λ
1f
1+λ
2f
2
in the formula, λ
1And λ
2Respectively, is a representative objective function f
1And an objective function f
2The weight coefficients of the importance degree of (2) are positive numbers;
defining constraints, including constraint one): constraint condition of battery state of electric vehicle
SOC
min≤SOC
i≤SOC
max
In the formula, SOC
minAnd SOC
maxThe maximum charge state and the minimum charge state of the electric vehicle are respectively; SOC
iThe current state of charge of the electric vehicle;
constraint two): total load limit for a cell
In the formula (I), the compound is shown in the specification,
representing the total load of the power supply area, wherein the constraint condition represents that the total load of the power supply area cannot exceed the power limit of the transformer;
and 402, carrying out ordered management and optimized scheduling on the charging time interval of the electric vehicle by taking reduction of daily load peak-valley difference and reduction of electric vehicle charging cost as optimization targets, and carrying out optimized calculation by adopting an improved particle swarm optimization to obtain an optimal solution as an ordered charging scheme. The optimization calculation steps are as follows:
4021, acquiring related data, and defining a fitness value calculation function according to a target function and a constraint condition;
step 4022, initializing a population, including population scale sizepop, maximum iteration times maxgen and a constraint range bound, and randomly assigning a value to the initial population;
step 4023, calculating fitness values of individual members of the population by using the defined fitness value function;
step 4024, entering an iteration stage, if an iteration cutoff condition is met, jumping to step 4026, and outputting a charging plan of the globally optimal individual member as a result; otherwise, for a given member of the population:
(1) storing the individual history optimization of the previous iteration period;
(2) redistributing the search tasks of the member individuals, updating the speed and position information of the member individuals, calculating a fitness value function, and updating the historical optimal fitness value information of the individuals;
(3) if the historical optimal value of the member individual is better than the historical optimal value of the previous iteration cycle, executing a global optimal updating strategy;
(4) executing a global optimal information disturbance strategy, and updating global optimal value information;
step 4025, adding 1 to the number of iteration steps, and returning to the step 4024;
and 4026, finishing iteration, and storing and outputting the global optimal individual information as an optimal charging plan.
Step 5, displaying the ordered charging scheme to a user, returning to the step 2 if the user does not accept the ordered charging scheme, and re-reading the charging demand information set by the user; if the user confirms the ordered charging scheme, entering step 6;
and 6, after the ordered charging scheme is confirmed, the ordered charging scheme is issued to the charging pile, the charging pile executes a charging task according to the ordered charging scheme, and meanwhile, relevant charging scheme information is fed back to the load prediction unit.
Claims (4)
1. A real-time control strategy and an optimization method for orderly charging of an electric automobile adopt a novel electric automobile charging orderly management system, wherein the system comprises a residential area transformer, a load prediction unit, an orderly charging management unit, a charging pile power supply control unit and a plurality of charging piles; the input end of the ordered charging management unit is connected with the residential area transformer and is used for acquiring the rated capacity of the residential area transformer and the real-time information of the residential electricity load; the load prediction unit is connected with the ordered charging management unit and is used for predicting the load condition of a future time period and providing necessary load information for the ordered charging management unit; the ordered charging management unit is also respectively connected with the plurality of charging piles and used for monitoring and controlling the starting and stopping states of the charging piles and adjusting the output power of the charging piles; the orderly charging management unit is also connected with a charging pile power supply control unit, the charging pile power supply control unit is respectively connected with a plurality of charging piles and used for controlling the switching of the alternating current input of the plurality of charging piles, and the method is characterized by comprising the following steps:
step 1, monitoring the condition that an electric vehicle is connected into a charging pile in real time, and if no electric vehicle is connected into the charging pile, continuing waiting by the system; otherwise, entering the step 2 when monitoring that the charging pile is newly connected into the electric vehicle;
step 2, after the electric vehicle is connected into the charging pile, the system acquires and stores charging demand information by using a relevant communication protocol according to the service condition of the electric vehicle;
step 3, reading basic load information of a transformer of a residential area, load information of a charging motor car and a planned charging load, and reading load distribution conditions of 96 time periods in one day and residual available charging load capacity of distribution in a transformer area, which are predicted by a load prediction unit;
step 4, whether the user accepts the ordered management or not;
if the newly accessed electric vehicle user selects not to accept ordered management, calculating the charging power distribution and the charging ending time information of the current electric vehicle in N time periods according to the remaining available charging capacity of the distribution transformer of the distribution area, and showing the information as an unordered charging scheme to the user;
if the newly accessed electric vehicle user chooses to receive ordered management, an ordered charging optimization model for electric vehicle charging is constructed according to the charging demand information and the load power of the distribution transformer area obtained in the step 2 by taking the minimum peak-valley difference of the distribution transformer area and the optimal charging cost as targets, an optimization algorithm is called to carry out solution calculation on the ordered charging optimization model, the obtained optimization result is converted into charging scheme information, and the charging scheme information is displayed to the user;
step 5, displaying the ordered charging scheme to a user, returning to the step 2 if the user does not accept the ordered charging scheme, and re-reading the charging demand information set by the user; if the user confirms the ordered charging scheme, entering step 6;
and 6, after the ordered charging scheme is confirmed, the ordered charging scheme is issued to the charging pile, the charging pile executes a charging task according to the ordered charging scheme, and meanwhile, relevant charging scheme information is fed back to the load prediction unit.
2. The real-time control strategy and optimization method for orderly charging of electric vehicles according to claim 1, wherein in step 2, the charging requirement information includes the battery capacity and the remaining capacity of the current electric vehicle, and the charging completion time and whether the charging is orderly or not, which are expected by the user.
3. The real-time control strategy and optimization method for orderly charging of electric vehicles according to claim 1, characterized in that in step 4, the optimization algorithm adopts a basic genetic algorithm or an improved particle swarm algorithm, a suitable optimization algorithm is selected according to different scales of the orderly charging optimization model, and the improved particle swarm algorithm is systematically called to solve under a default condition.
4. The real-time control strategy and optimization method for orderly charging of electric vehicles according to claim 3, characterized in that the improved particle swarm optimization comprises the following steps:
a) determining an objective function and a constraint condition, wherein: the objective function includes an objective function f
1: peak minimum of the total load curve over the charging period:
in the formula, P
t BaseBase load, base load P, representing period t
t BaseThe system comprises a resident basic load, all in-charge motor car loads and all planned charge loads; p
t EVRepresenting the charging load of the current electric vehicle in a t period; t is a time interval, a 96-point daily load curve method is adopted, 96 time intervals are equally divided for 24 hours a day, and each time interval is 15 minutes;
objective function f
2: minimum charge cost for user
Wherein, C
tThe decision plan of the current electric vehicle in the time period t is represented, the value is 0 or 1, and the decision plan is used for determining whether the current electric vehicle has charging behavior in the current time period, wherein 0 represents no charging and 1 represents charging; mu.s
tRepresents the electricity rate for the t period; Δ t represents a flag for each time period;
using linear weighting, will contain the objective function f
1And an objective function f
2The multi-objective optimization problem is converted into a single objective function f optimization problem to be solved, wherein the converted single objective function f is as follows:
min f＝λ
1f
1+λ
2f
2
in the formula, λ
1And λ
2Respectively, is a representative objective function f
1And an objective function f
2The weight coefficients of the importance degree of (2) are positive numbers;
defining constraints, including constraint one): constraint condition of battery state of electric vehicle
SOC
min≤SOC
i≤SOC
max
In the formula, SOC
minAnd SOC
maxThe maximum charge state and the minimum charge state of the electric vehicle are respectively; SOC
iThe current state of charge of the electric vehicle;
constraint two): total load limit for a cell
In the formula (I), the compound is shown in the specification,
representing the total load of the power supply area, wherein the constraint condition represents that the total load of the power supply area cannot exceed the power limit of the transformer;
b) calling an optimization algorithm to solve the target, wherein the method comprises the following steps:
b01) acquiring related data, and defining a fitness value calculation function according to a target function and a constraint condition;
b02) initializing a population, including population scale sizepop, maximum iteration times maxgen and a constraint range bound, and randomly assigning values to the initial population;
b03) calculating the fitness value of the individual members of the population by using a defined fitness value function;
b04) entering an iteration stage, if an iteration cutoff condition is met, jumping to b06), and outputting a charging plan of the globally optimal individual member as a result; otherwise, for a given member of the population:
(1) storing the individual history optimization of the previous iteration period;
(2) redistributing the search tasks of the member individuals, updating the speed and position information of the member individuals, calculating a fitness value function, and updating the historical optimal fitness value information of the individuals;
(3) if the historical optimal value of the member individual is better than the historical optimal value of the previous iteration cycle, executing a global optimal updating strategy;
(4) executing a global optimal information disturbance strategy, and updating global optimal value information;
b05) iteration step number plus 1, return to b 04);
b06) and after iteration is finished, storing and outputting the global optimal individual information as an optimal charging plan.
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