CN108400585A - Distributed real-time power distribution method of the more electric vehicles in micro-grid system - Google Patents
Distributed real-time power distribution method of the more electric vehicles in micro-grid system Download PDFInfo
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Classifications
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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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/64—Optimising energy costs, e.g. responding to electricity rates
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
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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
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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
- H02J3/28—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
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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
- 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]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
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- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The present invention relates to a kind of distributed real-time power distribution method of more electric vehicles in micro-grid system, include the following steps：According to the mobility of electric vehicle, the Spot Price planning system cost optimization problem of the battery loss of electric vehicle, micro-capacitance sensor, the system cost optimization problem is to minimize system cost as goal programming object function；Problem after planning is converted, the form that can be solved by Liapunov optimization method is made；The problem after conversion is handled using Liapunov optimization method, obtains Optimal Control Strategy.The present invention, which does not need any statistical data in system, can reduce system cost.
Description
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 respectivelyAndwhen the electric vehicle is in the system, Si,t+1＝Si,t+xi,tWherein1i,tThe moving state of the electric automobile i at the time t, after the electric automobile leaves the system for the m time,wherein,indicating that the electric vehicle i leaves the system for the mth time,indicating that the electric vehicle i arrives at the system m, when the electric vehicle returns to the system m times,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. byWherein, 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, and the supply and demand balance in the system is kept at any moment, so that the required electricity of users and renewable energy and cogeneration at any momentThe power generation quantity of the device, the charging and discharging quantity of the electric automobile and the power purchasing quantity of the system from an external power grid are balanced with each other, namelyWherein R istFor renewable energy power generation at time t, ηcaFor 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,tFor the heat demand of the system user at time t, ηcwFor the efficiency of conversion of natural gas to heat in cogeneration units, ηbwFor the efficiency of the conversion of natural gas into heat in water heaters, Pb,tFor the natural gas consumption of the water heater at time t, ηbwPb,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 tThe 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: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: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
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 respectivelyAndwhen the electric vehicle is in the system, Si,t+1＝Si,t+xi,tWherein1i,tThe moving state of the electric automobile i at the time t, after the electric automobile leaves the system for the m time,wherein,indicating that the electric vehicle i leaves the system for the mth time,to representThe electric vehicle reaches the system m, when the electric vehicle returns to the system m times,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. byWherein, 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 ofWherein R istFor renewable energy power generation at time t, ηcaFor 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 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,tFor the heat demand of the system user at time t, ηcwFor the efficiency of conversion of natural gas to heat in cogeneration units, ηbwFor the efficiency of the conversion of natural gas into heat in water heaters, Pb,tFor the natural gas consumption of the water heater at time t, ηbwPb,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 tThe 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: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: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: introducing 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
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:
step 3.3: minimizing the right part of the inequality in step 3.2.2, the following optimization problem is obtained:
the constraint conditions are as follows:
calculating the above problems to obtain an optimized control strategy
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 whenWhen 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 strategyAnd 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 (3)
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;
(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 of a plurality of electric vehicles in a microgrid system according to claim 1, characterized in that the step (1) comprises the following sub-steps:
(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 respectivelyAndwhen the electric vehicle is in the system, Si,t+1＝Si,t+xi,tWherein1i,tThe moving state of the electric automobile i at the time t, after the electric automobile leaves the system for the m time, wherein,indicating that the electric vehicle i leaves the system for the mth time,indicating that the electric vehicle i arrives at the system m, when the electric vehicle returns to the system m times,wherein Δ Si,mIs an electric automobileThe difference between the state of charge when m +1 times arrives at the system and the state of charge when it left the system 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. byWherein, 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 ofWherein R istFor renewable energy power generation at time t, ηcaFor 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,tFor the heat demand of the system user at time t, ηcwFor the efficiency of conversion of natural gas to heat in cogeneration units, ηbwFor the efficiency of the conversion of natural gas into heat in water heaters, Pb,tIs natural of the water heater at the time of tGas consumption, ηbwPb,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 tThe 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: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:wherein, atTo optimize the strategy, gt＝g1t+g2tAnd the equality and inequality in the steps (11) to (15) are constraint conditions.
3. 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.
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