CN113541140A - Virtual power plant peak regulation control method and system for distributed charging pile - Google Patents

Virtual power plant peak regulation control method and system for distributed charging pile Download PDF

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Publication number
CN113541140A
CN113541140A CN202111065776.8A CN202111065776A CN113541140A CN 113541140 A CN113541140 A CN 113541140A CN 202111065776 A CN202111065776 A CN 202111065776A CN 113541140 A CN113541140 A CN 113541140A
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charging pile
power plant
peak regulation
virtual power
peak
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CN113541140B (en
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余俊杰
阮志杰
方嵩
白一鸣
杨涌新
何湛兴
叶建国
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Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Zhongshan Power Supply Bureau of Guangdong Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • B60L53/30—Constructional details of charging stations
    • B60L53/31—Charging columns specially adapted for electric vehicles
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The utility model discloses a virtual power plant peak regulation control method and a system of a distributed charging pile, which utilizes the virtual power plant technology to integrate the charging pile into a whole, and the charging pile is subjected to power control through the virtual power plant, thereby more accurately matching the load demand of a power grid, meanwhile, a peak regulation demand instruction is generated by acquiring the peak regulation demand of the charging pile in the next preset time period and the regulation margin of the charging pile in the virtual power plant, so as to meet the peak regulation demand and the regulation margin, the power regulation quantity of the charging pile is determined by the regulation margin and the peak regulation demand instruction, and a linear planning model of the virtual power plant is established with the aim of minimum power cost, thereby enhancing the economy of the distributed charging pile participating in peak regulation, meeting the economic operation requirement of the power distribution network, improving the power supply reliability of the charging pile, and ensuring the safety of the power grid, the need for spinning reserve on the power generation side is reduced, minimizing the impact on the load side.

Description

Virtual power plant peak regulation control method and system for distributed charging pile
Technical Field
The application relates to the technical field of virtual power plant scheduling, in particular to a virtual power plant peak regulation control method and system for distributed charging piles.
Background
At present, with the rapid development of modern urban power distribution networks, network structures tend to be complicated. Along with the continuous rising of urban power load, the continuous increase of distribution network load demand needs to supply the vacancy of load through the rotatory reserve of electricity generation side, but, the mode of supplying the vacancy of load through the rotatory reserve of electricity generation side is not only with high costs, moreover, is difficult to satisfy the economic operation requirement of distribution network. And the load requirement is reduced through the traditional load side, which can seriously affect the power supply reliability.
Meanwhile, as the number of charging piles is gradually increased, the load of the charging piles always influences the power supply reliability. Due to the distributed layout characteristics of the charging piles, the distributed charging piles are difficult to control without a centralized control means, so that the power supply reliability of the charging piles is poor, and the economic operation requirement of a power distribution network is difficult to meet.
Disclosure of Invention
The application provides a virtual power plant peak regulation control method and system for a distributed charging pile, which are used for solving the technical problems that the power supply reliability of the charging pile is poor and the economic operation requirement of a power distribution network is difficult to meet.
In view of this, the first aspect of the present application provides a virtual power plant peak regulation control method for a distributed charging pile, including the following steps:
acquiring peak regulation demand of each charging pile in a target area in the next preset time period;
acquiring the adjustment margin of each virtual power plant containing the charging pile in the next preset time period;
comparing the adjustment margin of each virtual power plant with the peak regulation demand, generating a peak regulation demand instruction according to a comparison result, and distributing the peak regulation demand instruction to the corresponding virtual power plants;
determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, establishing a linear programming model of a virtual power plant by taking the minimum power cost as a target, solving the linear programming model, and outputting the optimal power adjustment quantity of the charging pile;
the optimal power adjustment quantity of the charging pile and the current power of the charging pile are determined through a virtual power plant, the optimal peak regulation demand instruction of the charging pile is distributed to the charging pile correspondingly, and therefore the current power of the charging pile is adjusted.
Preferably, the step of obtaining peak shaving demand of each charging pile in the target area in the next preset time period specifically includes:
assuming that there are m virtual power plants containing charging piles, setting a variable i to represent the ith virtual power plant,
Figure 264505DEST_PATH_IMAGE001
if the number of charging pile devices included in the ith virtual power plant is equal to
Figure 348130DEST_PATH_IMAGE002
Setting variables
Figure 959239DEST_PATH_IMAGE004
A jth charging post representing an ith virtual power plant,
Figure 732767DEST_PATH_IMAGE005
；
obtaining an intra-day load forecast based on an ultra-short term load forecast system, and recording the intra-day load forecast as
Figure 937483DEST_PATH_IMAGE006
Acquiring a daily interconnection plan of the next preset time period based on a scheduling plan system, and recording the daily interconnection plan as the daily interconnection plan
Figure 74067DEST_PATH_IMAGE007
And recording the upper rotation standby rate of the next preset time period as
Figure 536753DEST_PATH_IMAGE008
The formula is calculated by the following formula,
Figure 608746DEST_PATH_IMAGE009
in the formula (I), the compound is shown in the specification,
Figure 487709DEST_PATH_IMAGE010
represents the total power generation capacity;
assuming that the minimum reserved fixed value of the upper rotation reserve rate is
Figure 162404DEST_PATH_IMAGE011
Then according to the upper rotation standby rate of the next preset time period
Figure 371275DEST_PATH_IMAGE008
And minimum reserved fixed value of the upper rotation reserve rate
Figure 738802DEST_PATH_IMAGE011
Determining peak regulation demand of each charging pile in the target area in the next preset time period according to the comparison result, and recording the peak regulation demand as the peak regulation demand
Figure 901799DEST_PATH_IMAGE012
Specifically, the method comprises the following steps of,
upper rotation standby rate at next preset time period
Figure 380185DEST_PATH_IMAGE008
A minimum reserved fixed value higher than or equal to the upper rotational reserve rate
Figure 836705DEST_PATH_IMAGE011
Then peak shaving demand
Figure 795041DEST_PATH_IMAGE013
；
Upper rotation standby rate at next preset time period
Figure 274695DEST_PATH_IMAGE008
A minimum reserved fixed value lower than the upper rotation reserve rate
Figure 478143DEST_PATH_IMAGE011
Then, the peak shaving demand is calculated by the following formula
Figure 599289DEST_PATH_IMAGE012
：
Figure 918406DEST_PATH_IMAGE014
；
If peak shaving demand
Figure 757793DEST_PATH_IMAGE012
If the current time is not equal to 0, the step of obtaining the adjustment margin of each virtual power plant containing the charging pile in the next preset time period is carried out; if peak shaving demand
Figure 827249DEST_PATH_IMAGE012
And if the current time is equal to 0, when the next scheduling period comes, re-executing the step of obtaining the peak shaving demand of each charging pile in the target area in the next preset time period.
Preferably, the method further comprises:
the symbol of the assumed peak-shaving status is recorded as
Figure 117416DEST_PATH_IMAGE015
Wherein, in the step (A),
Figure 686063DEST_PATH_IMAGE016
indicating that a peak shaver condition is being achieved,
Figure 310948DEST_PATH_IMAGE017
indicating the end of peak regulation and the initial state of peak regulationThe state is marked as
Figure 669248DEST_PATH_IMAGE018
Then, there are the following cases:
upper rotation standby rate at next preset time period
Figure 850741DEST_PATH_IMAGE008
A minimum reserved fixed value higher than or equal to the upper rotational reserve rate
Figure 636295DEST_PATH_IMAGE019
Then, the peak regulation status flag is converted into
Figure 748476DEST_PATH_IMAGE020
The limit on the peak regulation instruction of the charging pile is cancelled, and the charging power is determined according to the load under the condition that the rated power of the charging pile is not exceeded;
upper rotation standby rate at next preset time period
Figure 598883DEST_PATH_IMAGE008
A minimum reserved fixed value lower than the upper rotation reserve rate
Figure 611445DEST_PATH_IMAGE011
And peak shaving demand
Figure 833479DEST_PATH_IMAGE021
And then, when waiting for the coming of the next scheduling period, re-executing the step of obtaining the peak shaving demand of each charging pile in the target area in the next preset time period.
Preferably, the step of obtaining the adjustment margin of each virtual power plant including the charging pile in the next preset time period includes:
traversing all virtual power plants containing charging piles, and recording the current power of each virtual power plant as
Figure 557590DEST_PATH_IMAGE022
Then the ith virtual power plant is at the next placeAdjusting margin to charging pile in preset time period
Figure 67812DEST_PATH_IMAGE023
In order to realize the purpose,
Figure 436346DEST_PATH_IMAGE024
；
calculating the adjustment margin of the virtual power plant of the whole network in the preset time period through the following formula
Figure 829281DEST_PATH_IMAGE025
：
Figure 417519DEST_PATH_IMAGE026
。
Preferably, the step of comparing the adjustment margin of each virtual power plant with the peak shaving demand, generating a peak shaving demand instruction according to the comparison result, and allocating the peak shaving demand instruction to the corresponding virtual power plant specifically includes:
determining an adjustment margin
Figure 45947DEST_PATH_IMAGE027
Whether or not it is greater than peak shaving demand
Figure 705205DEST_PATH_IMAGE012
If, if
Figure 144407DEST_PATH_IMAGE028
Then the peak regulation demand instruction of the ith virtual power plant
Figure 656160DEST_PATH_IMAGE029
，
Figure 321234DEST_PATH_IMAGE030
Entering the step of establishing a linear programming model of a virtual power plant based on the adjustment margin and the peak regulation demand instruction and aiming at the minimum power cost so as to output the optimal power adjustment quantity of the charging pile(ii) a If it is
Figure 602043DEST_PATH_IMAGE031
Then, the expected load rate of the virtual power plant is calculated by the following formula
Figure 25196DEST_PATH_IMAGE032
：
Figure 758666DEST_PATH_IMAGE033
Peak shaving demand order of ith virtual power plant
Figure 617644DEST_PATH_IMAGE034
Comprises the following steps:
Figure 566009DEST_PATH_IMAGE035
。
preferably, the step of determining the power adjustment amount of the charging pile based on the adjustment margin and the peak shaving demand instruction, establishing a linear programming model of a virtual power plant with a target of minimum power cost, solving the linear programming model, and outputting the optimal power adjustment amount of the charging pile specifically includes:
determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, and establishing a linear programming model of the virtual power plant by taking the minimum power cost as a target:
Figure 720915DEST_PATH_IMAGE036
in the above formula, the first and second carbon atoms are,
Figure 443146DEST_PATH_IMAGE037
the minimum value of the power cost is indicated,
Figure 826854DEST_PATH_IMAGE038
in order to achieve the cost of the charging,
Figure 82255DEST_PATH_IMAGE039
adjusting the power of the charging pile;
transforming the linear programming model into a matrix form such that,
Figure 906598DEST_PATH_IMAGE040
in the formula (I), the compound is shown in the specification,
Figure 427709DEST_PATH_IMAGE041
an array of expected adjustments representing a virtual plant,
Figure 864376DEST_PATH_IMAGE042
；
Figure 787332DEST_PATH_IMAGE043
representing the electricity price transposition of each charging pile in the preset time period,
Figure 785506DEST_PATH_IMAGE044
；
Figure 793914DEST_PATH_IMAGE045
representing each charging pile adjustment amount array corresponding to the ith virtual power plant,
Figure 34271DEST_PATH_IMAGE046
；
Figure 811734DEST_PATH_IMAGE047
an equality constraint coefficient array is represented,
Figure 251500DEST_PATH_IMAGE048
；
Figure 294674DEST_PATH_IMAGE049
represents the upper bound constraint matrix of the ith virtual plant,
Figure 276405DEST_PATH_IMAGE050
；
the min problem is changed into a max problem, and a negative value coefficient is added to obtain the problem,
Figure 656178DEST_PATH_IMAGE051
and introducing a relaxation variable s for the constraint condition of inequality in the max problem, updating the max problem into a max problem updating model,
Figure 307739DEST_PATH_IMAGE052
in the formula (I), the compound is shown in the specification,
Figure 290738DEST_PATH_IMAGE053
；
introducing artificial variable y and artificial variable coefficient into the max problem updating model
Figure 872898DEST_PATH_IMAGE054
Converting the max problem update model into a linear programming standard model,
Figure 359374DEST_PATH_IMAGE055
solving the linear programming standard model to obtain the power adjustment quantity of the charging pile
Figure 447416DEST_PATH_IMAGE056
And the optimal power adjustment quantity is used as the optimal power adjustment quantity of the charging pile.
Preferably, the preset time period is 15 minutes.
In a second aspect, the present invention further provides a virtual power plant peak regulation control system for a distributed charging pile, including:
the peak regulation demand acquisition module is used for acquiring peak regulation demand of each charging pile in a target area in the next preset time period;
the adjusting margin obtaining module is used for obtaining the adjusting margin of each virtual power plant containing the charging pile in the next preset time period;
the peak regulation instruction module is used for comparing the regulation margin of each virtual power plant with the peak regulation demand, generating a peak regulation demand instruction according to the comparison result, and distributing the peak regulation demand instruction to the corresponding virtual power plant;
the power adjustment output module is used for determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, establishing a linear programming model of a virtual power plant by taking the minimum power cost as a target, solving the linear programming model and outputting the optimal power adjustment quantity of the charging pile;
and the charging pile power adjustment module is used for determining the optimal power adjustment quantity of the charging pile and the current power of the charging pile through a virtual power plant, and distributing the optimal peak regulation demand instruction to the corresponding charging pile so as to adjust the current power of the charging pile.
According to the technical scheme, the invention has the following advantages:
the invention utilizes the virtual power plant technology to integrate the charging piles into a whole, performs power control on the charging piles through the virtual power plant, thereby more accurately matching the load demand of the power grid, simultaneously generating a peak regulation demand instruction by acquiring the peak regulation demand of the charging pile in the next preset time period and the regulation margin of the charging pile in the virtual power plant, so as to meet the peak shaving demand and the regulation margin, and determine the power regulation of the charging pile according to the regulation margin and the peak shaving demand instruction, and with the aim of minimizing power cost, a linear programming model of a virtual power plant is established, the economy of the distributed charging piles participating in peak shaving is enhanced, the economic operation requirement of a power distribution network is met, the power supply reliability of the charging piles is improved, therefore, on the premise of guaranteeing the safety of a power grid, the rotating standby requirement on the power generation side is reduced, and the influence on the load side is minimized.
Drawings
Fig. 1 is a flowchart of a virtual power plant peak shaving control method for a distributed charging pile according to an embodiment of the present application;
fig. 2 is a system block diagram of a virtual power plant peak shaving control system of a distributed charging pile provided in an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
For convenience of understanding, please refer to fig. 1, the method for controlling peak shaving of a virtual power plant of a distributed charging pile provided by the present invention includes the following steps:
s1, acquiring peak shaving demand of each charging pile in the target area in the next preset time period;
s2, obtaining the adjustment margin of each virtual power plant containing the charging pile in the next preset time period;
s3, comparing the adjustment margin of each virtual power plant with the peak regulation demand, generating a peak regulation demand instruction according to the comparison result, and distributing the peak regulation demand instruction to the corresponding virtual power plants;
s4, determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, establishing a linear programming model of the virtual power plant by taking the minimum power cost as a target, solving the linear programming model, and outputting the optimal power adjustment quantity of the charging pile;
s5, determining the optimal peak regulation demand instruction of the charging pile through the optimal power adjustment amount of the charging pile and the current power of the charging pile in the virtual power plant, and distributing the optimal peak regulation demand instruction to the corresponding charging pile so as to adjust the current power of the charging pile.
It should be noted that the invention utilizes the virtual power plant technology to integrate the charging piles into a whole, and performs power control on the charging piles through the virtual power plant, thereby more accurately matching the load demand of the power grid, simultaneously generating a peak regulation demand instruction by acquiring the peak regulation demand of the charging pile in the next preset time period and the regulation margin of the charging pile in the virtual power plant, so as to meet the peak shaving demand and the regulation margin, and determine the power regulation of the charging pile according to the regulation margin and the peak shaving demand instruction, and with the aim of minimizing power cost, a linear programming model of a virtual power plant is established, the economy of the distributed charging piles participating in peak shaving is enhanced, the economic operation requirement of a power distribution network is met, the power supply reliability of the charging piles is improved, therefore, on the premise of guaranteeing the safety of a power grid, the rotating standby requirement on the power generation side is reduced, and the influence on the load side is minimized.
The invention provides a virtual power plant peak regulation control method of a distributed charging pile, which comprises the following steps:
s100, acquiring peak shaving demand of each charging pile in a target area in the next preset time period;
in the present embodiment, the preset time period is 15 minutes.
Step S100 specifically includes:
s101, assuming that m virtual power plants containing charging piles exist, setting a variable i to represent the ith virtual power plant,
Figure 100002_DEST_PATH_IMAGE057
if the number of charging pile devices included in the ith virtual power plant is equal to
Figure 402865DEST_PATH_IMAGE002
Setting variables
Figure 273869DEST_PATH_IMAGE003
A jth charging post representing an ith virtual power plant,
Figure 129698DEST_PATH_IMAGE058
；
s102, acquiring a load forecast in the day based on the ultra-short-term load forecasting system, and recording the load forecast in the day
Figure 100002_DEST_PATH_IMAGE059
Acquiring a daily interconnection plan of the next preset time period based on the scheduling plan system, and recording the daily interconnection plan as the daily interconnection plan
Figure 326325DEST_PATH_IMAGE060
The upper rotation reserve rate of the next preset time period is recorded as
Figure 500561DEST_PATH_IMAGE008
The formula is calculated by the following formula,
Figure DEST_PATH_IMAGE061
in the formula (I), the compound is shown in the specification,
Figure 440835DEST_PATH_IMAGE062
represents the total power generation capacity;
s103, assuming the minimum reserved fixed value of the upper rotation standby rate to be
Figure 151171DEST_PATH_IMAGE011
Then according to the upper rotation standby rate of the next preset time period
Figure 315436DEST_PATH_IMAGE008
And minimum reserved fixed value of upper spin reserve rate
Figure 494744DEST_PATH_IMAGE011
Determining peak regulation demand of each charging pile in the target area in the next preset time period according to the comparison result, and recording the peak regulation demand as the peak regulation demand
Figure 723863DEST_PATH_IMAGE012
Specifically, the method comprises the following steps of,
upper spin reserve rate for next predetermined time period
Figure 101754DEST_PATH_IMAGE008
Minimum reserved fixed value higher than or equal to upper rotation reserve rate
Figure 640183DEST_PATH_IMAGE011
Then peak shaving demand
Figure 290476DEST_PATH_IMAGE013
；
Upper spin reserve rate for next predetermined time period
Figure 634870DEST_PATH_IMAGE008
Minimum reserved fixed value lower than upper rotation reserve rate
Figure 70530DEST_PATH_IMAGE011
Then, the peak shaving demand is calculated by the following formula
Figure 262084DEST_PATH_IMAGE012
：
Figure 478302DEST_PATH_IMAGE063
；
If peak shaving demand
Figure 298490DEST_PATH_IMAGE012
If the difference is not equal to 0, the step of obtaining the adjustment margin of each virtual power plant containing the charging pile in the next preset time period is carried out; if peak shaving demand
Figure 837925DEST_PATH_IMAGE012
If it is equal to 0, the step S100 is executed again when the next scheduling period comes.
It should be noted that the minimum reserved fixed value of the upper rotation reserve rate
Figure 514894DEST_PATH_IMAGE011
The setting is carried out according to the actual conditions of the power grids in different regions, if the conditions are correct
Figure 890511DEST_PATH_IMAGE008
Higher than or equal to
Figure 265123DEST_PATH_IMAGE011
When the current time is up, the rotating standby requirement is considered to be sufficient; when in use
Figure 472114DEST_PATH_IMAGE008
Is lower than
Figure 523246DEST_PATH_IMAGE011
In the meantime, the spinning standby is considered to be tense, and the peak shaving process needs to be started.
In a specific embodiment, step S100 further includes:
the symbol of the assumed peak-shaving status is recorded as
Figure 635428DEST_PATH_IMAGE015
Wherein, in the step (A),
Figure DEST_PATH_IMAGE064
indicating that a peak shaver condition is being achieved,
Figure 797419DEST_PATH_IMAGE065
indicating that the peak regulation is finished, the initial state of peak regulation is marked as
Figure 809981DEST_PATH_IMAGE018
Then, there are the following cases:
upper spin reserve rate for next predetermined time period
Figure 94331DEST_PATH_IMAGE008
Minimum reserved fixed value higher than or equal to upper rotation reserve rate
Figure 178962DEST_PATH_IMAGE066
Then, the peak regulation status flag is converted into
Figure DEST_PATH_IMAGE067
To cancel the limitation on the peak regulation instruction of the charging pile and to carry out the regulation according to the load under the condition that the rated power of the charging pile is not exceededDetermining charging power according to the load size;
upper spin reserve rate for next predetermined time period
Figure 393912DEST_PATH_IMAGE008
Minimum reserved fixed value lower than upper rotation reserve rate
Figure 201593DEST_PATH_IMAGE068
And peak shaving demand
Figure 656845DEST_PATH_IMAGE013
If so, the step S100 is executed again when waiting for the next scheduling period, otherwise, the step S200 is executed.
S200, obtaining the adjustment margin of each virtual power plant containing the charging pile in the next preset time period;
in this embodiment, step S200 specifically includes:
s201, traversing all virtual power plants containing charging piles, and recording the current power of each virtual power plant as
Figure 494351DEST_PATH_IMAGE022
And adjusting margin of the ith virtual power plant to the charging pile in the next preset time period
Figure 185095DEST_PATH_IMAGE069
In order to realize the purpose,
Figure DEST_PATH_IMAGE070
；
s202, calculating the adjustment margin of the virtual power plant of the whole network in the next preset time period according to the following formula
Figure 844353DEST_PATH_IMAGE071
：
Figure 595141DEST_PATH_IMAGE026
。
S300, comparing the adjustment margin of each virtual power plant with the peak regulation demand, generating a peak regulation demand instruction according to a comparison result, and distributing the peak regulation demand instruction to the corresponding virtual power plants;
in this embodiment, step S300 specifically includes:
determining an adjustment margin
Figure 342779DEST_PATH_IMAGE027
Whether or not it is greater than peak shaving demand
Figure 899531DEST_PATH_IMAGE012
If, if
Figure DEST_PATH_IMAGE072
Then the peak regulation demand instruction of the ith virtual power plant
Figure 616558DEST_PATH_IMAGE029
，
Figure 600563DEST_PATH_IMAGE073
Establishing a linear programming model of a virtual power plant based on an adjustment margin and a peak regulation demand instruction and aiming at the minimum power cost, thereby outputting the optimal power adjustment quantity of the charging pile; if it is
Figure DEST_PATH_IMAGE074
Then, the expected load rate of the virtual power plant is calculated by the following formula
Figure 835498DEST_PATH_IMAGE032
：
Figure 681094DEST_PATH_IMAGE075
Peak shaving demand order of ith virtual power plant
Figure DEST_PATH_IMAGE076
Comprises the following steps:
Figure 501895DEST_PATH_IMAGE077
。
s400, determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, establishing a linear programming model of a virtual power plant by taking the minimum power cost as a target, solving the linear programming model, and outputting the optimal power adjustment quantity of the charging pile;
in this embodiment, step S400 specifically includes:
s401, determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, and establishing a linear programming model of the virtual power plant by taking the minimum power cost as a target:
Figure DEST_PATH_IMAGE078
in the above formula, the first and second carbon atoms are,
Figure 220584DEST_PATH_IMAGE079
the minimum value of the power cost is indicated,
Figure 441349DEST_PATH_IMAGE038
in order to achieve the cost of the charging,
Figure DEST_PATH_IMAGE080
adjusting the power of the charging pile;
it should be noted that charging costs are classified into three categories, the first category is owner of self-use charging pile (assigned to own parking space), and charging is charged according to resident meter-combining users (non-step electricity price users). The second type is an operational social utility charging facility, which adds a service fee to the electricity price. The third category is charging facilities in police, enterprises and institutions, and social public parking lots.
S402, transforming the linear programming model into a matrix form,
Figure 737243DEST_PATH_IMAGE081
in the formula (I), the compound is shown in the specification,
Figure 618742DEST_PATH_IMAGE082
an array of expected adjustments representing a virtual plant,
Figure DEST_PATH_IMAGE083
；
Figure 567719DEST_PATH_IMAGE043
representing the electricity price transposition of each charging pile in the preset time period,
Figure 823251DEST_PATH_IMAGE084
；
Figure 525497DEST_PATH_IMAGE045
representing each charging pile adjustment amount array corresponding to the ith virtual power plant,
Figure 448454DEST_PATH_IMAGE085
；
Figure 758212DEST_PATH_IMAGE047
an equality constraint coefficient array is represented,
Figure 251773DEST_PATH_IMAGE086
；
Figure 242862DEST_PATH_IMAGE087
represents the upper bound constraint matrix of the ith virtual plant,
Figure 269593DEST_PATH_IMAGE088
；
s403, the min problem is changed into a max problem, and a negative value coefficient is added to obtain the min problem,
Figure 687936DEST_PATH_IMAGE089
s404, introducing a relaxation variable S for the constraint condition of inequality in the max problem, updating the max problem into a max problem updating model,
Figure 931442DEST_PATH_IMAGE090
in the formula (I), the compound is shown in the specification,
Figure 460644DEST_PATH_IMAGE091
；
s405, introducing artificial variable y and artificial variable coefficient into max problem updating model
Figure 607460DEST_PATH_IMAGE054
Converting the max problem update model into a linear programming standard model,
Figure 196705DEST_PATH_IMAGE092
solving the linear planning standard model to obtain the power adjustment quantity of the charging pile
Figure 602540DEST_PATH_IMAGE093
And the optimal power adjustment quantity is used as the optimal power adjustment quantity of the charging pile.
It should be noted that the condition in the max problem update model includes equality constraint, so that introducing artificial variable y and artificial variable coefficient can be introduced
Figure 184700DEST_PATH_IMAGE054
。
Meanwhile, the linear programming standard model can be solved through MATLAB.
S500, determining the optimal peak regulation demand instruction of the charging pile through the optimal power adjustment quantity of the charging pile and the current power of the charging pile by the virtual power plant, and distributing the optimal peak regulation demand instruction to the corresponding charging pile so as to adjust the current power of the charging pile.
In this embodiment, the optimal peak shaving demand instruction of the charging pile
Figure 936756DEST_PATH_IMAGE094
In the formula (I), wherein,
Figure 444704DEST_PATH_IMAGE095
and the optimal power adjustment amount of the charging pile is shown.
S600, when waiting for the arrival of the next scheduling period, re-executing the steps S100-S500.
The above is a detailed description of an embodiment of the virtual power plant peak shaving control method for the distributed charging pile, and the following is a detailed description of an embodiment of the virtual power plant peak shaving control system for the distributed charging pile.
For convenience of understanding, please refer to fig. 2, the virtual power plant peak shaving control system for a distributed charging pile provided by the present invention includes:
the peak regulation demand acquisition module 100 is configured to acquire peak regulation demand of each charging pile in a target area in a next preset time period;
an adjustment margin obtaining module 200, configured to obtain an adjustment margin of each virtual power plant including the charging pile in a next preset time period;
the peak regulation instruction module 300 is configured to compare the regulation margin of each virtual power plant with the peak regulation demand, generate a peak regulation demand instruction according to the comparison result, and distribute the peak regulation demand instruction to the corresponding virtual power plant;
the power adjustment output module 400 is used for determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, establishing a linear programming model of a virtual power plant by taking the minimum power cost as a target, solving the linear programming model and outputting the optimal power adjustment quantity of the charging pile;
and the charging pile power adjustment module 500 is used for determining an optimal peak regulation demand instruction of the charging pile through the optimal power adjustment amount of the charging pile and the current power of the charging pile in the virtual power plant, and distributing the optimal peak regulation demand instruction to the corresponding charging pile so as to adjust the current power of the charging pile.
It should be noted that the working process of the virtual power plant peak shaving control system of the distributed charging pile provided by this embodiment is consistent with the flow of the virtual power plant peak shaving control method of the distributed charging pile provided by the above embodiment, and is not described herein again.
The system integrates the charging piles into a whole by utilizing the virtual power plant technology, performs power control on the charging piles through the virtual power plant, thereby more accurately matching the load demand of the power grid, simultaneously generating a peak regulation demand instruction by acquiring the peak regulation demand of the charging pile in the next preset time period and the regulation margin of the charging pile in the virtual power plant, so as to meet the peak shaving demand and the regulation margin, and determine the power regulation of the charging pile according to the regulation margin and the peak shaving demand instruction, and with the aim of minimizing power cost, a linear programming model of a virtual power plant is established, the economy of the distributed charging piles participating in peak shaving is enhanced, the economic operation requirement of a power distribution network is met, the power supply reliability of the charging piles is improved, therefore, on the premise of guaranteeing the safety of a power grid, the rotating standby requirement on the power generation side is reduced, and the influence on the load side is minimized.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (8)

1. A virtual power plant peak regulation control method of a distributed charging pile is characterized by comprising the following steps:
acquiring peak regulation demand of each charging pile in a target area in the next preset time period;
acquiring the adjustment margin of each virtual power plant containing the charging pile in the next preset time period;
comparing the adjustment margin of each virtual power plant with the peak regulation demand, generating a peak regulation demand instruction according to a comparison result, and distributing the peak regulation demand instruction to the corresponding virtual power plants;
determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, establishing a linear programming model of a virtual power plant by taking the minimum power cost as a target, solving the linear programming model, and outputting the optimal power adjustment quantity of the charging pile;
the optimal power adjustment quantity of the charging pile and the current power of the charging pile are determined through a virtual power plant, the optimal peak regulation demand instruction of the charging pile is distributed to the charging pile correspondingly, and therefore the current power of the charging pile is adjusted.
2. The virtual power plant peak regulation control method for the distributed charging piles according to claim 1, wherein the step of obtaining the peak regulation demand of each charging pile in the target area in the next preset time period specifically comprises:
assuming that there are m virtual power plants containing charging piles, setting a variable i to represent the ith virtual power plant,
Figure 538387DEST_PATH_IMAGE001
if the number of charging pile devices included in the ith virtual power plant is equal to
Figure 143943DEST_PATH_IMAGE002
Setting variables
Figure 861364DEST_PATH_IMAGE003
A jth charging post representing an ith virtual power plant,
Figure 443524DEST_PATH_IMAGE004
；
obtaining an intra-day load forecast based on an ultra-short term load forecast system, and recording the intra-day load forecast as
Figure 195579DEST_PATH_IMAGE005
Acquiring a daily interconnection plan of the next preset time period based on a scheduling plan system, and recording the daily interconnection plan as the daily interconnection plan
Figure 703527DEST_PATH_IMAGE006
And recording the upper rotation standby rate of the next preset time period as
Figure 970561DEST_PATH_IMAGE007
The formula is calculated by the following formula,
Figure 107144DEST_PATH_IMAGE008
in the formula (I), the compound is shown in the specification,
Figure 962973DEST_PATH_IMAGE009
represents the total power generation capacity;
assuming that the minimum reserved fixed value of the upper rotation reserve rate is
Figure 894020DEST_PATH_IMAGE010
Then according to the upper rotation standby rate of the next preset time period
Figure 648350DEST_PATH_IMAGE007
And minimum reserved fixed value of the upper rotation reserve rate
Figure 73777DEST_PATH_IMAGE011
Determining peak regulation demand of each charging pile in the target area in the next preset time period according to the comparison result, and recording the peak regulation demand as the peak regulation demand
Figure 534845DEST_PATH_IMAGE012
Specifically, the method comprises the following steps of,
upper rotation standby rate at next preset time period
Figure 151640DEST_PATH_IMAGE007
A minimum reserved fixed value higher than or equal to the upper rotational reserve rate
Figure 393266DEST_PATH_IMAGE013
Then peak shaving demand
Figure 606072DEST_PATH_IMAGE014
；
Upper rotation standby rate at next preset time period
Figure 935029DEST_PATH_IMAGE007
A minimum reserved fixed value lower than the upper rotation reserve rate
Figure 270196DEST_PATH_IMAGE013
Then, the peak shaving demand is calculated by the following formula
Figure 936800DEST_PATH_IMAGE012
：
Figure 202565DEST_PATH_IMAGE015
；
If peak shaving demand
Figure 700543DEST_PATH_IMAGE012
If the current time is not equal to 0, the step of obtaining the adjustment margin of each virtual power plant containing the charging pile in the next preset time period is carried out; if peak shaving demand
Figure 409873DEST_PATH_IMAGE012
And if the current time is equal to 0, when the next scheduling period comes, re-executing the step of obtaining the peak shaving demand of each charging pile in the target area in the next preset time period.
3. The virtual power plant peak shaving control method for the distributed charging pile according to claim 2, further comprising:
the symbol of the assumed peak-shaving status is recorded as
Figure 48927DEST_PATH_IMAGE016
Wherein, in the step (A),
Figure 869115DEST_PATH_IMAGE017
indicating that a peak shaver condition is being achieved,
Figure 408550DEST_PATH_IMAGE018
indicating the peak regulation is finished and the peak regulation is at the beginningThe initial state is marked as
Figure 351098DEST_PATH_IMAGE019
Then, there are the following cases:
upper rotation standby rate at next preset time period
Figure 726716DEST_PATH_IMAGE007
A minimum reserved fixed value higher than or equal to the upper rotational reserve rate
Figure 832819DEST_PATH_IMAGE020
Then, the peak regulation status flag is converted into
Figure 243071DEST_PATH_IMAGE021
The limit on the peak regulation instruction of the charging pile is cancelled, and the charging power is determined according to the load under the condition that the rated power of the charging pile is not exceeded;
upper rotation standby rate at next preset time period
Figure 356521DEST_PATH_IMAGE007
A minimum reserved fixed value lower than the upper rotation reserve rate
Figure 468702DEST_PATH_IMAGE022
And peak shaving demand
Figure 630693DEST_PATH_IMAGE023
And then, when waiting for the coming of the next scheduling period, re-executing the step of obtaining the peak shaving demand of each charging pile in the target area in the next preset time period.
4. The method for controlling peak shaving in a virtual power plant of a distributed charging pile according to claim 2, wherein the step of obtaining the adjustment margin of each virtual power plant containing the charging pile in the next preset time period comprises:
traverse allVirtual power plants with charging piles record the current power of each virtual power plant as
Figure 692190DEST_PATH_IMAGE024
And the adjustment margin of the ith virtual power plant to the charging pile in the next preset time period
Figure 664957DEST_PATH_IMAGE025
In order to realize the purpose,
Figure 15167DEST_PATH_IMAGE026
；
calculating the adjustment margin of the virtual power plant of the whole network in the preset time period through the following formula
Figure 230116DEST_PATH_IMAGE027
：
Figure 411699DEST_PATH_IMAGE028
。
5. The virtual power plant peak regulation control method for the distributed charging pile according to claim 4, wherein the step of comparing the regulation margin of each virtual power plant with the peak regulation demand, generating a peak regulation demand instruction according to the comparison result, and distributing the peak regulation demand instruction to the corresponding virtual power plant specifically comprises:
determining an adjustment margin
Figure 539055DEST_PATH_IMAGE029
Whether or not it is greater than peak shaving demand
Figure 147801DEST_PATH_IMAGE030
If, if
Figure 651595DEST_PATH_IMAGE031
Then the ith virtualPeak shaving demand instructions for power plants
Figure 953263DEST_PATH_IMAGE032
，
Figure 500788DEST_PATH_IMAGE033
Entering the step of establishing a linear programming model of a virtual power plant based on the adjustment margin and the peak regulation demand instruction and aiming at the minimum power cost so as to output the optimal power adjustment quantity of the charging pile; if it is
Figure 825590DEST_PATH_IMAGE034
Then, the expected load rate of the virtual power plant is calculated by the following formula
Figure 195391DEST_PATH_IMAGE035
：
Figure 774403DEST_PATH_IMAGE036
Then of the ith virtual plant
Figure 509140DEST_PATH_IMAGE037
Figure 117976DEST_PATH_IMAGE038
Comprises the following steps:
Figure 478419DEST_PATH_IMAGE039
。
6. the method for controlling peak shaving in a virtual power plant of a distributed charging pile according to claim 5, wherein the step of determining the power adjustment amount of the charging pile based on the adjustment margin and the peak shaving demand instruction, establishing a linear programming model of the virtual power plant with the goal of minimizing power cost, solving the linear programming model, and outputting the optimal power adjustment amount of the charging pile specifically comprises:
determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, and establishing a linear programming model of the virtual power plant by taking the minimum power cost as a target:
Figure 426784DEST_PATH_IMAGE040
in the above formula, the first and second carbon atoms are,
Figure 80226DEST_PATH_IMAGE041
the minimum value of the power cost is indicated,
Figure 176358DEST_PATH_IMAGE042
in order to achieve the cost of the charging,
Figure 825645DEST_PATH_IMAGE043
adjusting the power of the charging pile;
transforming the linear programming model into a matrix form such that,
Figure 877783DEST_PATH_IMAGE044
in the formula (I), the compound is shown in the specification,
Figure 16641DEST_PATH_IMAGE045
an array of expected adjustments representing a virtual plant,
Figure 537752DEST_PATH_IMAGE046
；
Figure 475883DEST_PATH_IMAGE047
representing the electricity price transposition of each charging pile in the preset time period,
Figure 398840DEST_PATH_IMAGE048
；
Figure 708598DEST_PATH_IMAGE049
representing each charging pile adjustment amount array corresponding to the ith virtual power plant,
Figure 966273DEST_PATH_IMAGE050
；
Figure 957363DEST_PATH_IMAGE051
an equality constraint coefficient array is represented,
Figure 482629DEST_PATH_IMAGE052
；
Figure 963289DEST_PATH_IMAGE053
represents the upper bound constraint matrix of the ith virtual plant,
Figure DEST_PATH_IMAGE054
；
the min problem is changed into a max problem, and a negative value coefficient is added to obtain the problem,
Figure 380364DEST_PATH_IMAGE055
and introducing a relaxation variable s for the constraint condition of inequality in the max problem, updating the max problem into a max problem updating model,
Figure 237461DEST_PATH_IMAGE056
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE057
；
introducing artificial variable y and artificial variable coefficient into the max problem updating model
Figure 557846DEST_PATH_IMAGE058
Converting the max problem update model into a linear programming standard model,
Figure DEST_PATH_IMAGE059
solving the linear programming standard model to obtain the power adjustment quantity of the charging pile
Figure 147091DEST_PATH_IMAGE060
And the optimal power adjustment quantity is used as the optimal power adjustment quantity of the charging pile.
7. The virtual power plant peak shaving control method for the distributed charging pile according to claim 1, wherein the preset time period is 15 minutes.
8. The utility model provides a virtual power plant peak regulation control system of distributed charging stake which characterized in that includes:
the peak regulation demand acquisition module is used for acquiring peak regulation demand of each charging pile in a target area in the next preset time period;
the adjusting margin obtaining module is used for obtaining the adjusting margin of each virtual power plant containing the charging pile in the next preset time period;
the peak regulation instruction module is used for comparing the regulation margin of each virtual power plant with the peak regulation demand, generating a peak regulation demand instruction according to the comparison result, and distributing the peak regulation demand instruction to the corresponding virtual power plant;
the power adjustment output module is used for determining the power adjustment quantity of the charging pile based on the adjustment margin and the peak regulation demand instruction, establishing a linear programming model of a virtual power plant by taking the minimum power cost as a target, solving the linear programming model and outputting the optimal power adjustment quantity of the charging pile;
and the charging pile power adjustment module is used for determining the optimal power adjustment quantity of the charging pile and the current power of the charging pile through a virtual power plant, and distributing the optimal peak regulation demand instruction to the corresponding charging pile so as to adjust the current power of the charging pile.
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