CN108400585B - Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system - Google Patents

Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system Download PDF

Info

Publication number
CN108400585B
CN108400585B CN201810127590.2A CN201810127590A CN108400585B CN 108400585 B CN108400585 B CN 108400585B CN 201810127590 A CN201810127590 A CN 201810127590A CN 108400585 B CN108400585 B CN 108400585B
Authority
CN
China
Prior art keywords
time
electric
electric automobile
electricity
cost
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810127590.2A
Other languages
Chinese (zh)
Other versions
CN108400585A (en
Inventor
张光林
吴长乐
曹永胜
张文倩
李德敏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Donghua University
Original Assignee
Donghua University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Donghua University filed Critical Donghua University
Priority to CN201810127590.2A priority Critical patent/CN108400585B/en
Publication of CN108400585A publication Critical patent/CN108400585A/en
Application granted granted Critical
Publication of CN108400585B publication Critical patent/CN108400585B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • 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
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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]
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention relates to a distributed real-time energy distribution method of a plurality of electric automobiles in a micro-grid system, which comprises the following steps: planning a system cost optimization problem according to the mobility of the electric automobile, the battery loss of the electric automobile and the real-time electricity price of the microgrid, wherein the system cost optimization problem takes the minimized system cost as a target planning objective function; transforming the planned problem into a form that can be solved by a Lyapunov optimization method; and (4) processing the converted problem by utilizing a Lyapunov optimization method to obtain an optimization control strategy. The present invention can reduce the system cost without requiring any statistical data in the system.

Description

Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system
Technical Field
The invention relates to the technical field of micro-grid energy distribution, in particular to a distributed real-time energy distribution method of a plurality of electric vehicles in a micro-grid system.
Background
The microgrid is an important component of a future smart grid, and the structure of the microgrid mainly comprises distributed energy sources such as various distributed power sources (such as wind energy, solar energy, fuel cells, cogeneration systems and the like) and distributed energy storage devices (such as flywheel energy storage, electric vehicles, chemical storage batteries and the like). The micro-grid is used in a large amount in the power grid, so that the reliability of the power distribution network can be enhanced, the permeability of renewable energy sources is improved, and the energy utilization rate is improved.
In a micro-grid, a distributed energy storage device is an important component, can smooth output power fluctuation, improves the utilization rate of renewable energy, and can reduce the cost of the micro-grid through a reasonable charging and discharging mode. There is a great deal of literature on reducing the overall cost of the system by studying how to control the charging and discharging behavior of distributed energy storage devices. On the other hand, the international energy agency indicates that the market share of electric vehicles will reach 20% by 2020, and the stock quantity thereof will reach 1.4 million vehicles. Electric vehicles have attracted increasing attention as a typical distributed energy storage device. When the electric automobile is connected to a power system, the distribution of the electric quantity has strong flexibility, and the energy storage service can be provided in a micro-grid instead of an expensive traditional chemical battery. This is because the conventional chemical battery in the microgrid is not only expensive, but also needs to consume a large amount of cost for maintenance, and the difference indicates that the energy storage system needs to consume one third of the cost of the microgrid
Besides the attention of people on microgrid technology and electric vehicles, a cogeneration system is also widely used as a common distributed power generation device. Because the cogeneration system can provide the heat generated in the power generation for the user while generating the power, the connection of the cogeneration system as a distributed power generation device in the microgrid can greatly improve the energy utilization rate and reduce a part of the heat demand cost. For example, in some literature, authors have proposed a real-time algorithm to minimize the cost of a microgrid system containing cogeneration by considering real-time electricity prices.
Disclosure of Invention
The invention aims to solve the technical problem of providing a distributed real-time energy distribution method of a plurality of electric vehicles in a micro-grid system, which can reduce the system cost without any statistical data in the system.
The technical scheme adopted by the invention for solving the technical problems is as follows: the distributed real-time energy distribution method of a plurality of electric automobiles in a micro-grid system is provided, and comprises the following steps:
(1) planning a system cost optimization problem according to the mobility of the electric automobile, the battery loss of the electric automobile and the real-time electricity price of the microgrid, wherein the system cost optimization problem takes the minimized system cost as a target planning objective function;
(2) transforming the planned problem into a form that can be solved by a Lyapunov optimization method;
(3) and (4) processing the converted problem by utilizing a Lyapunov optimization method to obtain an optimization control strategy.
The step (1) comprises the following substeps:
(11) analyzing the mobility of the electric automobile, and assuming that the electric quantity state of the electric automobile i at the moment t is Si,tThe charge and discharge amounts at time t are respectively
Figure BDA0001573970190000021
And
Figure BDA0001573970190000022
when the electric vehicle is in the system, Si,t+1＝Si,t+xi,tWherein
Figure BDA0001573970190000023
1i,tThe moving state of the electric automobile i at the time t, after the electric automobile leaves the system for the m time,
Figure BDA0001573970190000024
wherein,
Figure BDA0001573970190000025
indicating that the electric vehicle i leaves the system for the mth time,
Figure BDA0001573970190000026
indicating that the electric vehicle i arrives at the system m, when the electric vehicle returns to the system m times,
Figure BDA0001573970190000027
wherein Δ Si,mThe difference between the electric quantity state when the electric automobile reaches the system for the m +1 th time and the electric quantity state when the electric automobile leaves the system for the last time, and li,txi,min≤xi,t≤li,txi,max，Si,min≤Si,t≤Si,max；
(12) Analyzing the battery loss of the electric automobile, and setting a maximum value d of long-time average lossi,upI.e. by
Figure BDA0001573970190000028
Wherein, T is the total time of operation, D (x)i,t) Representing the battery loss function of the electric vehicle i, and E (-) is an average value function;
(13) the energy aggregator is an energy controller in the system to keep supply and demand balance in the system at any moment, so that the required electricity of a user at any moment is balanced with the electricity generation quantity of the renewable energy source and the cogeneration device, the charge and discharge quantity of the electric automobile and the electricity purchasing quantity of the system from an external power grid, namely the required electricity is balanced with the electricity generated by the renewable energy source and the cogeneration device, the charge and discharge quantity of the system, and the electricity purchasing quantity of
Figure BDA0001573970190000029
Wherein R istFor the electricity production of renewable energy at time t etacaFor the efficiency of the conversion of natural gas into electricity in cogeneration plants, Pc,tIs the natural gas consumption of the cogeneration system at time t, Gb,tG is more than or equal to 0 and is the electricity purchasing quantity of the system at the time tb,t≤Gb,max；
(14) The hot water tank absorbs hot water from the cogeneration system and the water heater and provides it to the user, so the heat state in the hot water tank is related to the cogeneration unit and the user's heat demand, and therefore the recurrence formula is: wt+1＝Wt-Lw,t+ηcwPc,t+ηbwPb,tWherein W istW is more than or equal to 0 and is the heat state of the hot water tank at the moment tt≤Wmax，Lw,tHeat demand, η, for the system user at time tcwEfficiency, eta, for the conversion of natural gas to heat in cogeneration unitsbwFor the efficiency of the conversion of natural gas into heat in water heaters, Pb,tIs at t timeNatural gas consumption of water heater etabwPb,t≥Lw,max；
(15) Analyzing the total cost of the system, and assuming that the unit electricity price of the electric automobile for charging in the system is Cc,tThe unit price of electricity discharged is Cd,tIf the electric vehicle needs to be aggregated and the manager pays the fee within the time t
Figure BDA0001573970190000031
The aggregation manager in the microgrid needs to pay for purchasing electricity and natural gas in addition to providing a part of the cost for the electric vehicle users, and the cost of the aggregation manager in the time t is as follows:
Figure BDA0001573970190000032
wherein, Ce,tIs the real-time electricity price of the external power grid at time t, CgIs the natural gas price;
(16) planning cost optimization problem:
Figure BDA0001573970190000033
wherein, atTo optimize the strategy, gt＝g1t+g2tAnd the equality and inequality in the steps (11) to (15) are constraint conditions.
The step (3) includes the substeps of:
(31) introducing three virtual queues Ki,t、Hi,tAnd Qt；
(32) Obtaining a Lyapunov transfer function, and adding the Lyapunov transfer function and a penalty function containing a threshold value V;
(33) calculating the optimization strategy of the system by utilizing the Lyapunov optimization method
Figure BDA0001573970190000034
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the invention can lead the electric automobile to absorb the electric quantity from the external power grid when the electricity price is low and release the electric quantity to the load when the electricity price is high, thereby reducing the electricity purchasing quantity of the system and increasing the electricity generating quantity of the cogeneration device to reduce the cost of the system. In addition, the method can also ensure that the electric automobile absorbs excessive electric quantity of the renewable energy for future use when the renewable energy generates more electric quantity, thereby obviously improving the energy utilization rate of the system and reducing energy waste. Most importantly, the method does not need any statistical information in the system, can obtain the optimization strategy as long as the current system state is known, and is simple and convenient and easy to implement.
Drawings
FIG. 1 is a diagram of a system model of the present invention;
FIG. 2 is a graph of the effect of threshold V on the average cost of the system;
FIG. 3 is a graph of the effect of electric vehicle number on the average cost of the system.
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 embodiment of the invention relates to a distributed real-time energy distribution method of a plurality of electric automobiles in a micro-grid system. As shown in fig. 1, the method is based on a microgrid system comprising electric vehicles, renewable energy sources, cogeneration and heat storage devices, and can optimize the total cost of the microgrid system. The system can be applied to office buildings or hotels containing electric car charging stations, where electric cars can be connected to the system according to a bidirectional charger. The main contents are as follows:
(1) the long-time average cost optimization problem of the system is planned by considering the randomness of the electric quantity demand and the heat demand in the micro-grid system, the dynamic property of the electric quantity level and the heat level of the hot water tank of the electric automobile, the uncertainty of the power price of the power grid, the mobility of the electric automobile, the battery loss of the electric automobile and other factors.
(2) A real-time energy distribution algorithm is provided by utilizing a Lyapunov optimization method to optimize the total cost of the system, and the algorithm is proved to be independent of any statistical information in the system and to be capable of enabling the obtained system cost to be approximate to the optimal cost.
(3) The algorithm is numerically simulated by using a MATLAB software platform, and the performance of the analysis is higher than that of other benchmark algorithms.
The method comprises the following specific steps:
step 1: the problems of planning system cost optimization such as mobility of the electric automobile, battery loss of the electric automobile, real-time electricity price of the micro-grid and the like are considered.
Step 2: the planned problem is transformed into a form that can be solved by the Lyapunov optimization method.
And step 3: and (4) processing the converted problem by utilizing a Lyapunov optimization method to obtain an optimization control strategy.
The step 1 comprises the following steps:
step 1.1: analyzing the mobility of the electric automobile, and assuming that the electric quantity state of the electric automobile i at the moment t is Si,tThe charge and discharge amounts at time t are respectively
Figure BDA0001573970190000041
And
Figure BDA0001573970190000042
when the electric vehicle is in the system, Si,t+1＝Si,t+xi,tWherein
Figure BDA0001573970190000043
1i,tThe moving state of the electric automobile i at the time t, after the electric automobile leaves the system for the m time,
Figure BDA0001573970190000044
wherein,
Figure BDA0001573970190000045
indicating that the electric vehicle i leaves the system for the mth time,
Figure BDA0001573970190000046
indicating that the electric vehicle i arrives at the system m, when the electric vehicle returns to the system m times,
Figure BDA0001573970190000047
wherein Δ Si,mThe difference between the electric quantity state when the electric automobile reaches the system for the m +1 th time and the electric quantity state when the electric automobile leaves the system for the last time, and li,txi,min≤xi,t≤li,txi,max，Si,min≤Si,t≤Si,max；
Step 1.2: analyzing the battery loss of the electric automobile, and setting a maximum value d of long-time average lossi,upI.e. by
Figure BDA0001573970190000051
Wherein, T is the total time of operation, D (x)i,t) Representing the battery loss function of the electric vehicle i, and E (-) is an average value function;
step 1.3: the energy aggregator is an energy controller in the system to keep supply and demand balance in the system at any moment, so that the required electricity of a user at any moment is balanced with the electricity generation quantity of the renewable energy source and the cogeneration device, the charge and discharge quantity of the electric automobile and the electricity purchasing quantity of the system from an external power grid, namely the required electricity is balanced with the electricity generated by the renewable energy source and the cogeneration device, the charge and discharge quantity of the system, and the electricity purchasing quantity of
Figure BDA0001573970190000052
Wherein R istFor the electricity production of renewable energy at time t etacaFor the efficiency of the conversion of natural gas into electricity in cogeneration plants, Pc,tIs the natural gas consumption of the cogeneration system at time t, Gb,tG is more than or equal to 0 and is the electricity purchasing quantity of the system at the time tb,t≤Gb,max；
Step 1.4: the hot water tank absorbs hot water from the cogeneration system and the water heater and provides it to the user, so that the thermal state in the hot water tank and the cogeneration unit and the userIs related to the heat demand, so its recurrence formula is: wt+1＝Wt-Lw,t+ηcwPc,t+ηbwPb,tWherein W istW is more than or equal to 0 and is the heat state of the hot water tank at the moment tt≤Wmax，Lw,tHeat demand, η, for the system user at time tcwEfficiency, eta, for the conversion of natural gas to heat in cogeneration unitsbwFor the efficiency of the conversion of natural gas into heat in water heaters, Pb,tIs the natural gas consumption of the water heater at the moment t, etabwPb,t≥Lw,max；
Step 1.5: analyzing the total cost of the system, and assuming that the unit electricity price of the electric automobile for charging in the system is Cc,tThe unit price of electricity discharged is Cd,tIf the electric vehicle needs to be aggregated and the manager pays the fee within the time t
Figure BDA0001573970190000053
The aggregation manager in the microgrid needs to pay for purchasing electricity and natural gas in addition to providing a part of the cost for the electric vehicle users, and the cost of the aggregation manager in the time t is as follows:
Figure BDA0001573970190000054
wherein, Ce,tIs the real-time electricity price of the external power grid at time t, CgIs the natural gas price;
step 1.6: planning cost optimization problem:
Figure BDA0001573970190000061
wherein, atTo optimize the strategy, gt＝g1t+g2tAnd the equality and inequality in the step 1.1 to the step 1.5 are constraint conditions.
Step 2 requires transformation of the problem in step 1.6 into a form that can be solved by the lyapunov optimization method.
In the step 3, the Lyapunov optimization specifically comprises the following steps:
step 3.1: guiding deviceInto three virtual queues Ki,t、Hi,tAnd Qt；
Step 3.2: obtaining a Lyapunov transfer function, and adding the Lyapunov transfer function and a penalty function containing a threshold value V;
step 3.2.1: according to the virtual queue proposed in step 3.1, a vector Θ is definedt＝[Kt,Ht,Qt]And is and
Figure BDA0001573970190000062
step 3.2.2: computing the Lyapunov transfer function L (Θ)t+1)-L(Θt) And added to a penalty function comprising a threshold V:
Figure BDA0001573970190000063
step 3.3: minimizing the right part of the inequality in step 3.2.2, the following optimization problem is obtained:
Figure BDA0001573970190000064
the constraint conditions are as follows:
Figure BDA0001573970190000065
calculating the above problems to obtain an optimized control strategy
Figure BDA0001573970190000066
The invention will be further verified by means of a specific example.
The method comprises the following steps: initializing a virtual queue Kt，HtAnd QtGet the hypothesis Ki,0，Hi,0And Q0The value of (c). And when
Figure BDA0001573970190000067
When it is time to renew Ki,t。
Step two: at the moment t, the aggregator collects system state information of the microgrid system to obtain a moving state of the electric vehicle i at the moment, the required electric quantity and heat of a user, the electric quantity generated by renewable energy sources, the real-time electricity price, the electric quantity states of all electric vehicles and the heat state of the hot water tank.
Step three: calculating the objective function in the step 3.3 by using the data in the step two to obtain an optimization control strategy
Figure BDA0001573970190000071
And updating the virtual queue K at the next moment according to the optimization strategyt，HtAnd QtThe value of (c).
The data used in the simulation experiment are real data, the real-time electricity price used in the embodiment is data from 7 months and 3 days to 5 days in 2017 in maine, usa, the mobility of the electric automobile adopts a two-state Markov process with a state transition probability of 0.90, and the loss function of the battery of the electric automobile is 0.1x2The required electric quantity and the heat quantity of the user are respectively in the range of [0,32 ]]kWh and [0, 200]A random value of L. Fig. 2 and 3 are graphs showing the results of simulation experiments, wherein fig. 2 reflects the influence of the threshold V on the average cost of the system, and fig. 3 is the influence of the number of electric vehicles on the average cost of the system.

Claims (2)

1. A distributed real-time energy distribution method of a plurality of electric vehicles in a micro-grid system is characterized by comprising the following steps:
(1) planning a system cost optimization problem according to the mobility of the electric automobile, the battery loss of the electric automobile and the real-time electricity price of the microgrid, wherein the system cost optimization problem takes the minimized system cost as a target planning objective function; the method specifically comprises the following substeps:
(11) analyzing the mobility of the electric automobile, and assuming that the electric quantity state of the electric automobile i at the moment t is Si,tThe charge and discharge amounts at time t are respectively
Figure FDA0002749831750000011
And
Figure FDA0002749831750000012
when the electric vehicle is in the system, Si,t+1＝Si,t+xi,tWherein
Figure FDA0002749831750000013
1i,tThe moving state of the electric automobile i at the time t, after the electric automobile leaves the system for the m time,
Figure FDA0002749831750000014
wherein,
Figure FDA0002749831750000015
indicating that the electric vehicle i leaves the system for the mth time,
Figure FDA0002749831750000016
indicating that the electric vehicle i arrives at the system the mth time, and when the electric vehicle returns to the system the mth time,
Figure FDA0002749831750000017
wherein Δ Si,mThe difference between the electric quantity state when the electric automobile reaches the system for the m +1 th time and the electric quantity state when the electric automobile leaves the system for the last time, and li,txi,min≤xi,t≤li, txi,max，Si,min≤Si,t≤Si,max；
(12) Analyzing the battery loss of the electric automobile, and setting a maximum value d of long-time average lossi,upI.e. by
Figure FDA0002749831750000018
Wherein, T is the total time of operation, D (x)i,t) Representing the battery loss function of the electric vehicle i, and E (-) is an average value function;
(13) energy polymerizationThe energy controller in the system keeps the supply and demand balance in the system at any moment, so that the required electric quantity of a user at each moment is balanced with the electric quantity produced by the renewable energy source and the cogeneration device, the charge and discharge quantity of the electric automobile and the electricity purchasing quantity of the system from an external power grid, namely
Figure FDA0002749831750000019
Wherein R istFor the electricity production of renewable energy at time t etacaFor the efficiency of the conversion of natural gas into electricity in cogeneration plants, Pc,tIs the natural gas consumption of the cogeneration system at time t, Gb,tG is more than or equal to 0 and is the electricity purchasing quantity of the system at the time tb,t≤Gb,max；
(14) The hot water tank absorbs hot water from the cogeneration system and the water heater and provides it to the user, so the heat state in the hot water tank is related to the cogeneration unit and the user's heat demand, and therefore the recurrence formula is: wt+1＝Wt-Lw,t+ηcwPc,t+ηbwPb,tWherein W istW is more than or equal to 0 and is the heat state of the hot water tank at the moment tt≤Wmax，Lw,tHeat demand, η, for the system user at time tcwEfficiency, eta, for the conversion of natural gas to heat in cogeneration unitsbwFor the efficiency of the conversion of natural gas into heat in water heaters, Pb,tIs the natural gas consumption of the water heater at the moment t, etabwPb,t≥Lw,max；
(15) Analyzing the total cost of the system, and assuming that the unit electricity price of the electric automobile for charging in the system is Cc,tThe unit price of electricity discharged is Cd,tIf the electric vehicle needs to be aggregated and the manager pays the fee within the time t
Figure FDA0002749831750000021
The aggregation manager in the microgrid needs to pay for purchasing electricity and natural gas in addition to providing a part of the cost for the electric vehicle users, and the cost of the aggregation manager in the time t is as follows:
Figure FDA0002749831750000022
wherein, Ce,tIs the real-time electricity price of the external power grid at time t, CgIs the natural gas price;
(16) planning cost optimization problem:
Figure FDA0002749831750000023
wherein, atTo optimize the strategy, gt＝g1t+g2tThe equality and inequality in the steps (11) to (15) are constraint conditions;
(2) transforming the planned problem into a form that can be solved by a Lyapunov optimization method;
(3) and (4) processing the converted problem by utilizing a Lyapunov optimization method to obtain an optimization control strategy.
2. The distributed real-time energy distribution method for a plurality of electric vehicles in a microgrid system according to claim 1, characterized in that the step (3) comprises the following sub-steps:
(31) introducing three virtual queues Ki,t、Hi,tAnd Qt；
(32) Obtaining a Lyapunov transfer function, and adding the Lyapunov transfer function and a penalty function containing a threshold value V;
(33) and calculating the optimization strategy of the system by using a Lyapunov optimization method.
CN201810127590.2A 2018-02-08 2018-02-08 Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system Active CN108400585B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810127590.2A CN108400585B (en) 2018-02-08 2018-02-08 Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810127590.2A CN108400585B (en) 2018-02-08 2018-02-08 Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system

Publications (2)

Publication Number Publication Date
CN108400585A CN108400585A (en) 2018-08-14
CN108400585B true CN108400585B (en) 2021-04-02

Family

ID=63095488

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810127590.2A Active CN108400585B (en) 2018-02-08 2018-02-08 Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system

Country Status (1)

Country Link
CN (1) CN108400585B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110826781B (en) * 2019-10-25 2023-10-03 东华大学 Multi-intelligent power grid resource collaborative management method based on service quality
CN112428834B (en) * 2020-11-17 2022-06-21 宁波工程学院 Monte Carlo method-based intelligent electric vehicle charging optimization method and system
CN114701394B (en) * 2022-04-01 2024-09-06 中国第一汽车股份有限公司 Cloud edge cooperative control method and device for battery replacement, terminal and storage medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105787605A (en) * 2016-03-24 2016-07-20 上海电力学院 Micro-grid economic and optimal operation and scheduling method based on improved quantum genetic algorithm
CN105811409A (en) * 2016-04-28 2016-07-27 上海电力学院 Multi-target run scheduling method for micro-grid containing hybrid energy storage system of electric vehicle
CN106026152A (en) * 2016-05-19 2016-10-12 合肥工业大学 Charging and discharging scheduling method for electric vehicles connected to micro-grid
CN107453407A (en) * 2017-07-03 2017-12-08 东华大学 A kind of intelligent micro-grid distributed energy dispatching method
CN107482638A (en) * 2017-07-21 2017-12-15 杭州电子科技大学 Supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105787605A (en) * 2016-03-24 2016-07-20 上海电力学院 Micro-grid economic and optimal operation and scheduling method based on improved quantum genetic algorithm
CN105811409A (en) * 2016-04-28 2016-07-27 上海电力学院 Multi-target run scheduling method for micro-grid containing hybrid energy storage system of electric vehicle
CN106026152A (en) * 2016-05-19 2016-10-12 合肥工业大学 Charging and discharging scheduling method for electric vehicles connected to micro-grid
CN107453407A (en) * 2017-07-03 2017-12-08 东华大学 A kind of intelligent micro-grid distributed energy dispatching method
CN107482638A (en) * 2017-07-21 2017-12-15 杭州电子科技大学 Supply of cooling, heating and electrical powers type micro-capacitance sensor multiobjective Dynamic Optimization dispatching method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Economic assessment on V2G frequency regulation regarding the battery degradation;Sekyung Han 等;《2012 IEEE PES Innovative Smart Grid Technologies (ISGT)》;20120403;1-6 *

Also Published As

Publication number Publication date
CN108400585A (en) 2018-08-14

Similar Documents

Publication Publication Date Title
CN109523052B (en) Virtual power plant optimal scheduling method considering demand response and carbon transaction
Zhao et al. Strategic bidding of hybrid AC/DC microgrid embedded energy hubs: A two-stage chance constrained stochastic programming approach
Surender Reddy et al. Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm
CN106228258B (en) It is a kind of meter and demand side management home energy source local area network energy optimal control method
CN111626527B (en) Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle
WO2022193794A1 (en) Multi-objective energy management method for smart community microgrid that takes into consideration decommissioned batteries
CN112290592B (en) Capacity optimization planning method and system for wind-solar-storage combined power generation system and readable storage medium
CN108400585B (en) Distributed real-time energy distribution method of multiple electric vehicles in micro-grid system
CN109710882B (en) Off-grid micro-grid electric vehicle ordered charge-discharge load modeling and solving method based on optimized operation
CN114156951B (en) Control optimization method and device of source network load storage system
Molina et al. Optimal EV charge-discharge schedule in smart residential buildings
CN110796373A (en) Wind power consumption-oriented multi-stage scene generation electric heating system optimal scheduling method
CN111832807B (en) Multi-micro-grid coordination optimization scheduling method considering load characteristics and demand response
CN110991764A (en) Day-ahead rolling optimization method for comprehensive energy system
Hafiz et al. Coordinated control of PEV and PV-based storage system under generation and load uncertainties
Yi et al. Optimal energy management strategy for smart home with electric vehicle
Wang et al. Admission and scheduling mechanism for electric vehicle charging with renewable energy
CN109787294A (en) A kind of power system optimal dispatch method
Zhang et al. Coordinated dispatch of the wind-thermal power system by optimizing electric vehicle charging
CN116415793A (en) Comprehensive energy system for thermoelectric energy storage park and low-carbon optimal scheduling method thereof
Sun et al. Energy storage capacity configuration of building integrated photovoltaic‐phase change material system considering demand response
CN116307437A (en) Virtual power plant optimal scheduling method and system based on carbon emission right exchange mechanism
Li et al. Multiobjective Optimization Model considering Demand Response and Uncertainty of Generation Side of Microgrid
Ishowo-Oloko et al. A storage pricing mechanism for learning agents in Masdar City smart grid
CN113852139A (en) Wind-storage combined heating system optimal scheduling strategy considering electric heating demand response

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant