CN112396223A - Electric vehicle charging station energy management method under interactive energy mechanism - Google Patents

Electric vehicle charging station energy management method under interactive energy mechanism Download PDF

Info

Publication number
CN112396223A
CN112396223A CN202011251341.8A CN202011251341A CN112396223A CN 112396223 A CN112396223 A CN 112396223A CN 202011251341 A CN202011251341 A CN 202011251341A CN 112396223 A CN112396223 A CN 112396223A
Authority
CN
China
Prior art keywords
charging station
charging
electric vehicle
power
platform
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.)
Pending
Application number
CN202011251341.8A
Other languages
Chinese (zh)
Inventor
黄啟茹
胡俊杰
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.)
North China Electric Power University
Original Assignee
North China Electric Power 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 North China Electric Power University filed Critical North China Electric Power University
Priority to CN202011251341.8A priority Critical patent/CN112396223A/en
Publication of CN112396223A publication Critical patent/CN112396223A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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
    • H02J2310/62—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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • 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
    • 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
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Development Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Game Theory and Decision Science (AREA)
  • Tourism & Hospitality (AREA)
  • Health & Medical Sciences (AREA)
  • Power Engineering (AREA)
  • Data Mining & Analysis (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Educational Administration (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention belongs to the field of optimized dispatching of power systems, and particularly relates to an energy management method of an electric vehicle charging station under an interactive energy mechanism. With the increase of the proportion of distributed renewable energy sources and the large-scale increase of the holding capacity of electric vehicles, the combination of renewable energy sources and electric vehicle charging stations becomes a new trend for future development. In order to better exert the flexibility of the charging station for providing services for the electric automobile, the charging station is mobilized to participate in the electric power market, the on-site consumption of distributed renewable energy sources is realized, and the economy of the electric automobile charging coordination control is improved on the premise of ensuring that the regional transformer is not overloaded. The distributed transaction framework and the model of the electric vehicle charging station with the distributed photovoltaic are constructed based on an interactive energy mechanism, the charging station is taken as a research object, and a day-ahead market transaction model and a management strategy with the maximum charging station profit target are established.

Description

Electric vehicle charging station energy management method under interactive energy mechanism
Technical Field
The invention belongs to the field of optimal scheduling of power systems, and particularly relates to an energy optimal management method for an electric vehicle charging station based on an interactive energy mechanism.
Background
With the difficulty of succession of fossil energy and the increasingly prominent problem of environmental pollution, China advocates the adoption of a green travel mode for the national people. An Electric Vehicle (EV) is used as a green vehicle and has the characteristics of energy conservation and environmental protection; the development of electric vehicles is one of effective ways to reduce the dependence on fossil fuels and environmental pollution, and has incomparable advantages compared with the traditional electric vehicles. In China, the 'ten-in-ten-vehicle' plan is implemented by leading the department of science and technology, and the large-scale popularization of electric automobiles in public transport means is promoted. With the large-scale access of future electric vehicles (PEVs) to the power grid, which will bring huge charging demands, new challenges will be brought to the operation planning of the power system, including: (1) the load of a power grid during the peak period of power utilization is increased; (2) more power plants need to be built to provide additional power, which is costly and presents environmental problems; (3) the transformer can be overloaded, and the safe and stable operation of the power grid is influenced. Due to the limited capacity of the storage battery of the electric automobile, the owner of the electric automobile still suffers from the problems of short driving mileage and charging of the electric automobile. It should be noted that charging of an electric vehicle is only meaningful when power of renewable energy is combined, and if the electric vehicle is charged by electric energy obtained in a conventional fossil energy power generation mode, the electric vehicle becomes an indirect carbon dioxide emission person. Nowadays, the development technology of renewable energy sources is relatively mature, and charging stations combined with renewable energy sources will play a key role in the use of large-scale electric vehicles in the future and even in the connection to the power grid.
At present, domestic and foreign scholars study the charging problem of electric vehicles mainly based on the problems of ordered charging control, site selection of electric vehicle charging stations and charging electric quantity distribution. At present, researches propose a micro-grid electric vehicle ordered charging strategy, a fuzzy control algorithm is adopted to optimize and arrange an electric vehicle charging plan, the charging requirement of the electric vehicle is met, and peak clipping and valley filling of a power grid are realized. The development of renewable energy and the mass use of electric vehicles are the development directions in the future, and the charging station is an effective way for realizing the fusion of the renewable energy and the electric vehicles. A charging station model of renewable energy sources, electric vehicles, a power grid system and storage batteries is researched and constructed, and a non-cooperative game charging scheduling method is provided. There is a study of the owner of the charging stations based on the energy management distribution rules of each charging station, establishing a mixed integer linear model, and combining the power consumption and production of the grid and renewable energy sources and modeling of the maximized revenue. The large-scale increase of the number of electric vehicles inevitably brings the increase of the number of electric vehicle charging stations, China currently becomes the country with the largest number of charging stations and charging piles established and operated in the world, and if measures for upgrading and expanding the capacity of a power grid by adopting a large amount of investment are adopted, the measures are difficult to realize in a short time and simultaneously the economical efficiency is low. The combination of renewable energy sources and charging stations becomes a new trend of future development, which delays the urgency of large-scale investment of power grids, avoids long-distance power transmission and reduces the electric energy loss in the power transmission process. The randomness and uncertainty of the charging behavior are difficult to apply to the unified coordination control of the electric automobile in the research, the renewable energy has the characteristic of distributed type, the interactive energy mechanism has the advantages of distributed type scheduling and control, participation main body economic benefits and the like compared with centralized control, and the interactive energy mechanism is more suitable for the transaction between charging stations of the electric automobile combined with the renewable energy.
The conventional electric vehicle charging station mainly comprises three charging modes of quick charging, slow charging and battery replacement, only performs a single function of providing charging service, better exerts the flexibility of providing service for the electric vehicle by the charging station, moves the charging station to participate in the electric power market, realizes the offset of distributed renewable energy sources, promotes the low-carbon development of the electric vehicle, and is a problem worthy of research.
Disclosure of Invention
According to the method, renewable energy is combined with the charging stations, a distributed transaction mechanism is applied, the charging stations are taken as a unit, the transaction of charging power is carried out between the charging stations, a distributed transaction mechanism model is established with the maximum profit of the charging stations as a target, the solution is carried out by utilizing the Lagrange decomposition principle and the secondary gradient method, the requirements between the charging stations are effectively coordinated, the charging willingness of electric vehicles of all the charging stations in the region is considered as much as possible on the premise that the total transformer capacity is not overloaded and the safe and stable operation of a power distribution network is ensured, and the individual charging willingness of users is better considered compared with the traditional scheduling method.
1. An energy optimization management method for an electric vehicle charging station under an interactive energy mechanism mainly comprises the following steps:
and A, establishing an energy management model of the electric vehicle charging station based on an interactive energy mechanism.
And B, establishing an electric vehicle charging station energy transaction model based on an interactive energy mechanism.
And C, formulating an energy trading strategy among the electric vehicle charging stations, independently carrying out optimization solution on each station, reporting the result after optimization solution to a trading platform, and checking whether power balance is met.
And D, if the power balance is not satisfied, updating and issuing the trading price by the platform, and then re-optimizing, solving and verifying again: if power balance is satisfied, the transaction is complete.
The transaction framework is shown in figure 1.
The machine learning technology-based family producer and consumer power management model in the step A specifically comprises the following steps:
2. the energy management model of the electric vehicle charging station based on the interactive energy mechanism in the step A is specifically as follows:
modeling the power generating unit and the power consuming unit in the charging station, expressed as:
modeling a power generation unit: if the cluster of the electric vehicle charging station is NevFor one of the charging stations n, the PV model is as follows:
Figure RE-GDA0002877161280000021
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0002877161280000022
the real output of the S scene PV in the t period is realized;
Figure RE-GDA0002877161280000023
the predicted maximum output of PV for the t period. Modeling the power utilization unit: the daily mileage S (km) of the electric vehicle follows a log-normal distribution, the daily mileage S (km) of the electric vehicle andthe charge time is generated using a monte carlo method simulation. The relation between the charging power and the state of charge SOC of the electric vehicle is expressed as
Figure RE-GDA0002877161280000031
Figure RE-GDA0002877161280000032
Figure RE-GDA0002877161280000033
Figure RE-GDA0002877161280000034
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0002877161280000035
the SOC is an average charging power of an electric vehicle, and represents a battery state of charge of the electric vehicle, wherein,
Figure RE-GDA0002877161280000036
and representing the upper and lower limit constraints of the state of charge of the ith electric vehicle.
Charging station energy management model:
the electricity purchasing power of the charging station n in the time period t is set as
Figure RE-GDA0002877161280000037
Selling electricity at a power of
Figure RE-GDA0002877161280000038
Wherein:
Figure RE-GDA0002877161280000039
in the formula
Figure RE-GDA00028771612800000310
The power purchased from the grid during time t for charging station n,
Figure RE-GDA00028771612800000311
the power purchased from the trading platform for the charging station n during the time period t.
Figure RE-GDA00028771612800000312
Figure RE-GDA00028771612800000313
Figure RE-GDA00028771612800000314
Figure RE-GDA00028771612800000315
Figure RE-GDA00028771612800000316
In the formula (I), the compound is shown in the specification,
Figure RE-GDA00028771612800000317
for the upper limit of power being purchased from the platform by charging station n during time t,
Figure RE-GDA00028771612800000318
the upper limit of the power selling of the charging station n at the power selling platform in the time period t,
Figure RE-GDA00028771612800000319
the upper limit for buying electricity from the grid for charging station n during time t. Equation (11) is the transformer constraint. The power balance equation for charging station n can be written as:
Figure RE-GDA00028771612800000320
3. the energy transaction model of the electric vehicle charging station based on the interactive energy mechanism in the step B is specifically as follows:
the electricity price model for purchasing electricity from the power grid is set as follows:
yt＝ct+βtpt (13)
in the formula: c. CtThe day-ahead initial electricity price at the time t is predicted. Beta is atAnd the coefficient represents the sensitivity coefficient of the power demand to the node electricity price, and the value can be analyzed by historical data to obtain an evaluation value. p is a radical oftPower purchased from the grid for the charging station during the time period t.
Figure RE-GDA0002877161280000041
In the random optimization problem of formula (14), nsFor the probability of occurrence of the scene s, the first two items are the cost of purchasing electricity from the power grid by the electric vehicle charging station, and the fourth item is the income brought to the charging station by charging the electric vehicle. Wherein g ishThe network loss parameter is the network loss parameter during the electric energy interaction. The corresponding common constraints are:
Figure RE-GDA0002877161280000042
4. the energy transaction strategy between the electric vehicle charging stations in step C, D is specifically as follows:
the balance of the whole trading system is realized through a multi-time information interaction mode, namely a mode of exchanging power and price for multiple times. The specific process is as follows:
(1) and initializing data, namely predicting the transaction price of the power market in the day before by the electric vehicle charging station through analyzing the past data, configuring initial charging resources for each charging station, and reporting the initial transaction price to a transaction platform. Each charging station provides charging service for the electric automobile in the station, the charging power of the electric automobile at each moment is used as the load of the charging station, the charging station reports the electricity purchasing and selling condition of the station to the transaction platform, and the electricity is purchased to the power grid when the slave platform cannot meet the requirement.
(2) On the premise of meeting all constraints, the transaction platform issues the updated price to the charging station, and the charging station optimizes and solves the updated price to obtain a new power plan and a new transaction price.
(3) And the trading platform checks whether the power is balanced on the spot or not on the system level, if not, the charging station is updated to purchase the electricity price of the electricity sold from the platform, and the power plan is regenerated until the platform dispatches the electric energy of the system to be balanced through the price factor.
The transaction flow diagram is shown in fig. 2.
5. Different from a centralized control method for charging electric vehicles, the method is based on an interactive energy mechanism, and performs distributed control on the electric vehicle charging station combined with the renewable photovoltaic, so that compared with the traditional method, the method saves the operation cost of a control center and improves the economical efficiency. Meanwhile, a large amount of information interaction is avoided.
7. The method has generality in a modeling mode, does not have any special application condition, has a wide application range, and is easy to popularize to be applied to the electric energy optimization scheduling process of various electric vehicle charging stations.
Drawings
Fig. 1 is a diagram of an electric vehicle charging station electric energy transaction framework.
Fig. 2 is an energy transaction process of an electric vehicle charging station.
FIG. 3 is a predicted day-ahead transaction price.
Detailed Description
Public constraint introduction Lagrange multiplier eta of electric vehicle charging station energy transaction modeltThe lagrangian function can be obtained as equation (18).
Figure RE-GDA0002877161280000051
According to the Lagrange dual decomposition principle, the objective function can be decoupled, and therefore the optimal solution is obtained by solving the subproblems of a plurality of participating charging stations.
Figure RE-GDA0002877161280000052
For solving such a sub-problem, we can adopt a distributed algorithm based on a secondary gradient method. The lagrangian multiplier may be updated using equation (20).
Figure RE-GDA0002877161280000053
Where k is the number of iterations of the Lagrangian multiplier, αhThe step size coefficient, which is constant in the iterative process, is generally taken as a positive number so that the iteration can converge. The convergence criterion is as follows (21):
|ηt[k+1]-ηt[k]|≤εh (21)
in the formula, epsilonhIs an iterative convergence criterion parameter.
The method assumes 30min as a scheduling cycle, performs optimized scheduling on the time from 00:00 to 24:00 today in one day, namely, dividing a 24h scheduling time period into 48 time periods to perform day-ahead work, performs example analysis by taking three public charging stations in the same region as an example, and analyzes the charging behavior of the electric vehicle under the condition of considering the fluctuation of distributed photovoltaic power generation. And respectively taking a fast charging station and two slow charging stations as examples, and simulating the transaction model of the electric vehicle charging station.

Claims (5)

1. An energy management method of an electric vehicle charging station under an interactive energy mechanism comprises the following steps:
a, initializing data, namely, carrying out initial charging resource configuration on each charging station by the electric vehicle charging station through analyzing the past data, and reporting an initial transaction price to a transaction platform;
b, each charging station provides charging service for the electric automobile in the station, the charging power of the electric automobile at each moment is used as the load of the charging station, the charging station reports the electricity purchasing and selling condition of the station to the transaction platform, and the slave platform purchases electricity from the power grid when the slave platform cannot meet the requirement;
step C, on the premise of meeting all constraints, the trading platform issues updated prices to the charging stations, and the charging stations respectively optimize and solve to obtain new power plans and new trading prices;
and D, the trading platform checks whether the power is balanced on the spot on the system level, if the power is not balanced, the charging station is updated to purchase the electricity price from the platform, and the power plan is regenerated until the platform dispatches the electric energy of the system to be balanced through the price factor.
2. The method according to claim 1, wherein in step a, the electric vehicle charging stations perform statistical analysis on historical data, predict the transaction price of the power market in the future, perform initial resource allocation on each electric vehicle charging station, and report the initial transaction price to the transaction platform.
3. The method according to claim 2, wherein in step B, each electric vehicle charging station in step a provides a charging service for an electric vehicle in the station, the electric vehicle serves as a load of the charging station, at each moment, for the charging station with distributed photovoltaic, the charging demand of the electric vehicle is preferentially met by the distributed photovoltaic power generation amount, and when the charging demand cannot be met by the photovoltaic power generation in the station, a power purchase request can be submitted to the transaction platform; similarly, at the same time, the other charging stations have electric energy exceeding the charging requirement and can submit the electricity selling request to the transaction platform; and when the slave platform cannot meet the requirements, electricity is purchased to the power grid.
4. The method according to claim 3, wherein in step C, the purchase/sale demand is submitted to the trading platform according to each electric vehicle charging station in step B, the trading platform issues the updated price to the charging station, and each charging station is optimized and solved to obtain a new power plan and a new trading price.
5. In the step D, the trading platform checks whether the power is balanced on site or not on the system level according to the power plan and the trading price obtained by the optimal solution of each electric vehicle charging station in the step C, if the power is not balanced, the charging stations are updated to buy/sell the electricity price from the trading platform, and the power plan is regenerated until the electric energy of the system is balanced under the guidance of the price.
CN202011251341.8A 2020-11-10 2020-11-10 Electric vehicle charging station energy management method under interactive energy mechanism Pending CN112396223A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011251341.8A CN112396223A (en) 2020-11-10 2020-11-10 Electric vehicle charging station energy management method under interactive energy mechanism

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011251341.8A CN112396223A (en) 2020-11-10 2020-11-10 Electric vehicle charging station energy management method under interactive energy mechanism

Publications (1)

Publication Number Publication Date
CN112396223A true CN112396223A (en) 2021-02-23

Family

ID=74599425

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011251341.8A Pending CN112396223A (en) 2020-11-10 2020-11-10 Electric vehicle charging station energy management method under interactive energy mechanism

Country Status (1)

Country Link
CN (1) CN112396223A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113627791A (en) * 2021-08-11 2021-11-09 上海交通大学 Integrated electric automobile management method
TWI767868B (en) * 2021-11-08 2022-06-11 國立清華大學 Method and apparatus for planning energy usage of charging station based on reinforcement learning

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113627791A (en) * 2021-08-11 2021-11-09 上海交通大学 Integrated electric automobile management method
TWI767868B (en) * 2021-11-08 2022-06-11 國立清華大學 Method and apparatus for planning energy usage of charging station based on reinforcement learning

Similar Documents

Publication Publication Date Title
Yang et al. A comprehensive review on electric vehicles integrated in virtual power plants
CN111310966A (en) Micro-grid site selection and optimal configuration method containing electric vehicle charging station
Yi et al. Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle
Yan et al. Many-objective charging optimization for electric vehicles considering demand response and multi-uncertainties based on Markov chain and information gap decision theory
CN113326467B (en) Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties
CN112396223A (en) Electric vehicle charging station energy management method under interactive energy mechanism
Fu et al. Research on the stackelberg game method of building micro-grid with electric vehicles
Zeng et al. Optimal configuration planning of vehicle sharing station-based electro-hydrogen micro-energy systems for transportation decarbonization
Li et al. Multi-objective optimal dispatching of electric vehicle cluster considering user demand response
Jia On supply demand coordination in vehicle-to-grid—A brief literature review
Yin et al. Optimal scheduling of electric vehicle integrated energy station using a novel many-objective stochastic competitive optimization algorithm
Yang et al. Demand responsive market decision-makings and electricity pricing scheme design in low-carbon energy system environment
Das et al. Game theoretical energy management of EV fast charging station with V2G capability
Yuan et al. Electric vehicle smart charging network under the energy internet framework
Sraidi et al. Study of electric vehicle charging impact
Shan et al. Summary of Micro-grid Economic Optimization Operation
Wang et al. Study on orderly charging and discharging strategy of EVs with customer satisfaction
Xiaoxin et al. Optimal scheduling of integrated energy systems including hydrogen electrolyzers, HFCVs, and electric vehicles
Yong et al. Research on charging and discharging dispatching strategy for Electric Vehicles
Ren et al. Decision-making approach in charging mode for electric vehicle based on cumulative prospect theory
Hui et al. LOW CARBON DISPATCH OF THE PARK INTEGRATED ENERGY SYSTEM BASED ON THE ELECTRIC VEHICLES FLEXIBLE LOAD STORAGE CHARACTERISTICS.
Li et al. Orderly charging model of private electric vehicle based on double sequence valley period
CN112270432B (en) Energy management method of comprehensive energy system considering multi-subject benefit balance
Cao et al. A design model of hierarchical dispatching for electric vehicles with photovoltaic power
Xiao et al. Coordinated Optimization of Multi-station Integration Systems Considering Uncertainties

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20210223

WD01 Invention patent application deemed withdrawn after publication