CN114221385B - Flexible charging method and system considering travel demands of electric automobile users - Google Patents

Flexible charging method and system considering travel demands of electric automobile users Download PDF

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Publication number
CN114221385B
CN114221385B CN202111553096.0A CN202111553096A CN114221385B CN 114221385 B CN114221385 B CN 114221385B CN 202111553096 A CN202111553096 A CN 202111553096A CN 114221385 B CN114221385 B CN 114221385B
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charging
power
constraint
photovoltaic
electric automobile
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CN114221385A (en
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苏粟
韦存昊
李玉璟
王陆飞
董刚
张明浩
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Beijing Chaochong Technology Co ltd
Beijing Jiaotong University
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Beijing Chaochong Technology Co ltd
Beijing Jiaotong University
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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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • 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
    • 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
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic 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
    • 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
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems
    • 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

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Abstract

The application relates to a flexible charging method and a flexible charging system considering travel demands of users of electric vehicles, which can carry out linearization treatment on a charging and discharging model of the electric vehicles, and realize flexible regulation and control on charging and discharging power and time period of the electric vehicles. Through the opportunistic constraint planning method, the state of charge of the electric automobile when leaving reaches the SOC expected by a user, and the voltage range of the power grid is set to be soft constraint, so that certain confidence level is met, and the flexibility of the charging and discharging behaviors of the electric automobile is utilized to cope with the random fluctuation of the output of the renewable energy source. Under the condition of ensuring the travel requirement of the electric automobile user, the optimization of the objective function is realized. The application can treat uncertainty brought by renewable energy sources with output as random variables, promote the on-site consumption of the renewable energy sources, and simultaneously reduce the burden of a large number of electric vehicles on a power grid caused by centralized charging.

Description

Flexible charging method and system considering travel demands of electric automobile users
Technical Field
The application relates to the technical field of new energy and electric automobiles, in particular to a flexible charging method and system considering the travel demands of users of electric automobiles.
Background
Along with the consumption of fossil energy and the deterioration of environment, the world places more and more importance on the concept of energy conservation and environmental protection. Through many years of exploration, the development of renewable energy sources and Electric Vehicles (EV) has become one of the effective ways for humans to solve energy and environmental problems.
Among renewable energy sources, solar energy has great development value due to the characteristics of cleanness, environmental protection and large coverage, and has become a mature renewable energy power generation technology. Although the permeability of photovoltaic power generation is increased year by year, the randomness and fluctuation of the output of the photovoltaic power generation system still are important barriers for preventing the further development of the photovoltaic power generation technology, and if the photovoltaic power generation system is directly connected with a grid, the photovoltaic power generation system has an influence on the aspects of power quality and the like of a power distribution network. How to further increase the in-situ level of photovoltaic, and how to increase the acceptance of photovoltaic by the distribution network, will become a key issue in advancing the development of renewable energy sources. With the development of technology, it has become a very common phenomenon to install photovoltaic panels on the roof or outer wall of a building and generate electricity in situ to power loads in the building. Thus, dealing with uncertainty in the output of the distributed photovoltaic by means of opportunistic constraints and in-situ digestion of the distributed photovoltaic by scheduling loads inside or around the building becomes a viable strategy.
And EV is used as an environment-friendly transportation tool, has good energy-saving, environment-friendly and low-emission potential, and can convert the great consumption of fossil fuel in the transportation field into electric power consumption, thereby generating great environmental benefit. Is important for the development target of world energy conservation and emission reduction, accords with the sustainable development strategy, and is popularized in countries of the world.
With the massive access of these renewable energy sources and flexible loads such as EVs, traditional distribution networks have gradually become active distribution networks (Active Distribution Network, ADN) with a certain controllability. However, the charging time of the EV is random and aggregated, and large-scale access to the EV in the same time period further increases peak-to-valley difference of the load of the power grid, thereby increasing the load and the running cost of the power grid and adversely affecting the power grid. Therefore, it is necessary to conduct certain guidance and control on the charging behavior of the EV, fully mine the demand response potential thereof, and further achieve flexible operation of the ADN.
Disclosure of Invention
In order to overcome the defects of the prior art, the application aims to provide a flexible charging method and system considering the travel demands of users of electric automobiles.
In order to achieve the above object, the present application provides the following solutions:
a flexible charging method considering travel demands of electric automobile users comprises the following steps:
acquiring charging data of an electric automobile, charging expected data of a user, photovoltaic output data and a power distribution network objective function; the power distribution network objective function comprises a total network loss function and a photovoltaic tracking function of the active power distribution network system;
determining travel constraints of the electric automobile according to the charging data and the charging expected data;
determining grid side constraint according to preset grid data;
determining a photovoltaic distributed photovoltaic model according to the photovoltaic output data;
constructing a flexible charging optimization model according to the power distribution network objective function, the electric vehicle travel constraint, the power grid side constraint and the photovoltaic distributed photovoltaic model;
and solving the flexible charging optimization model to obtain an optimal charging and discharging strategy of each electric automobile.
Preferably, the formula of the distribution network objective function is:
wherein,for the total loss function of the active distribution network system, < >>-providing the photovoltaic tracking function; t is a scheduling period; p (P) loss,t The total system loss at the time t is the total system loss; p (P) load,t And P PV,t The total load and the distributed photovoltaic actual output at the moment t respectively comprise a conventional power load and an electric automobile cluster charge-discharge load; alpha and beta are the first and second term weight coefficients, respectively, in the objective function.
Preferably, the determining the travel constraint of the electric vehicle according to the charging data and the charging desire data includes:
constructing initial constraint conditions according to the charging data and the charging expected data; the charging data comprises a network-access charge state, upper and lower limits of charging power and discharging power, charging efficiency and discharging efficiency, network-access time and total electric quantity of a battery; the charging desired data includes a desired departure time and a desired departure state of charge;
and converting the initial constraint condition by using a linearization method to obtain the travel constraint of the electric automobile.
Preferably, the formula of the initial constraint condition is:
Pr(S soc,v,t ＝S soc,v,dep )≥1-ξ 1 ,t＝t v,dep ；
S soc,min ≤S soc,v,t ≤S soc,max ；
wherein S is soc,v,t The state of charge at time t for a v-th vehicle;and->Charging power and discharging power at the t moment of the v-th vehicle respectively; />And->Charging efficiency and discharging efficiency of the v-th vehicle respectively; t is t v,arr And t v,dep The network access time and the network departure time of the v-th vehicle are respectively; />And->Respectively charging and discharging decision variables at the moment of the v-th vehicle t; e (E) car,v The total electric quantity of the battery of the v-th vehicle; s is S soc,v,dep The state of charge, ζ, of departure for the user of the v-th vehicle 1 S is the preset probability of out-of-limit of SOC when the vehicle leaves the network soc,min And S is soc,max Respectively the minimum sum of charge state allowanceA maximum value; t is t v,dep The departure time for the v-th vehicle.
Preferably, the conversion formula of the linearization method is:
wherein,and->The upper limit of the charging power and the lower limit of the charging power of the electric automobile are respectively set; />And->The upper limit of the discharge power and the lower limit of the discharge power of the electric automobile are respectively set; said->And->The power is respectively the charge and discharge decision power of the v vehicle after linearization at the t moment;
the formula of the travel constraint of the nonlinear electric automobile is converted into:
preferably, the formula of the grid-side constraint includes:
P in,n ＝P PV -P baseload -P EV ；
Pr[(V m,min ) 2 ≤v m,t ≤(V m,max ) 2 ]≥1-ξ 2 ；
0≤i mn ≤(I mn,max ) 2 ；
wherein P is mn And Q mn Active power and reactive power flowing into branch mn from m node; z mn 、r mn And x mn The impedance, resistance and reactance of branch mn respectively; p (P) in,n And Q in,n The active power and reactive power are injected into the n nodes respectively. v m And v n The squares of the voltage amplitudes of the m and n nodes are respectively; i.e mn The square of the current amplitude of line mn; k is a child node of the n node; p (P) PV Distributing the actual output power of the photovoltaic for the node; p (P) baseload A conventional electrical load for the node; p (P) EV Is the net charging power of the node electric automobile cluster.
Preferably, the photovoltaic distributed photovoltaic model is:
P PV,t ＝P pre,t +e PV,t ·ε PV,t ；
wherein P is PV,t And P pre,t The actual output and the predicted output of the distributed photovoltaic at the moment t are respectively; e, e PV,t The per unit value error parameter is the random variable error at the time t; epsilon PV,t The preset per unit value coefficient is the random variable error.
Preferably, the constructing a flexible charging optimization model according to the distribution network objective function, the electric vehicle travel constraint, the grid side constraint and the photovoltaic distributed photovoltaic model includes:
converting the opportunity constraint conditions in the electric vehicle travel constraint and the power grid side constraint to determine constraint conditions, and obtaining converted electric vehicle travel constraint and converted power grid side constraint;
and constructing the flexible charging optimization model according to the power distribution network objective function, the converted electric vehicle travel constraint, the converted power grid side constraint and the photovoltaic distributed photovoltaic model.
A flexible charging system that considers travel demand of an electric vehicle user, comprising:
the acquisition module is used for acquiring charging data of the electric automobile, charging expected data of a user, photovoltaic output data and a power distribution network objective function; the power distribution network objective function comprises a total network loss function and a photovoltaic tracking function of the active power distribution network system;
the first constraint set determining module is used for determining travel constraints of the electric automobile according to the charging data and the charging expected data;
the second constraint set determining module is used for determining grid side constraints according to preset grid data;
the photovoltaic model determining module is used for determining a photovoltaic distributed photovoltaic model according to the photovoltaic output data;
the model building module is used for building a flexible charging optimization model according to the power distribution network objective function, the electric vehicle travel constraint, the power grid side constraint and the photovoltaic distributed photovoltaic model;
and the solving module is used for solving the flexible charging optimization model to obtain the optimal charging and discharging strategy of each electric automobile.
Preferably, the first constraint set determining module includes:
an initial condition determining unit for constructing an initial constraint condition according to the charging data and the charging expectation data; the charging data comprises a network-access charge state, upper and lower limits of charging power and discharging power, charging efficiency and discharging efficiency, network-access time and total electric quantity of a battery; the charging desired data includes a desired departure time and a desired departure state of charge;
and the conversion unit is used for converting the initial constraint condition by using a linearization method to obtain the travel constraint of the electric automobile.
According to the specific embodiment provided by the application, the application discloses the following technical effects:
the application provides a flexible charging method and a flexible charging system considering travel demands of users of electric vehicles, which can carry out linearization processing on a charging and discharging model of the electric vehicles, and realize flexible regulation and control on charging power, discharging power and time period of the electric vehicles. In a specific embodiment, by means of the opportunistic constraint planning method, the state of charge of the electric vehicle when the electric vehicle leaves is reached to the SOC expected by a user, and the voltage range of the power grid is set to be soft constraint, so that a certain confidence level is met, and the flexibility of the charging and discharging behaviors of the electric vehicle is utilized to cope with random fluctuation of the output of the renewable energy source. Under the condition of ensuring the travel requirement of the electric automobile user, the optimization of the objective function is realized. The application can treat uncertainty brought by renewable energy sources with output as random variables, promote the on-site consumption of the renewable energy sources, and simultaneously reduce the burden of a large number of electric vehicles on a power grid caused by centralized charging.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a method flow diagram of a flexible charging method in an embodiment provided by the application;
fig. 2 is a schematic diagram of a charging feasible region of an electric vehicle according to an embodiment of the present application;
fig. 3 is a block diagram of a flexible charging system according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and drawings are used for distinguishing between different objects and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, inclusion of a list of steps, processes, methods, etc. is not limited to the listed steps but may alternatively include steps not listed or may alternatively include other steps inherent to such processes, methods, products, or apparatus.
The application aims to provide a flexible charging method and a system considering travel demands of electric automobile users, which can treat uncertainty caused by renewable energy sources with output as random variables, promote the on-site consumption of the renewable energy sources, and simultaneously reduce the burden of a large number of electric automobiles for centralized charging on a power grid.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a flowchart of a method of flexible charging method in an embodiment of the present application, as shown in fig. 1, the present application provides a flexible charging method considering travel requirements of an electric automobile user, including:
step 100: acquiring charging data of an electric automobile, charging expected data of a user, photovoltaic output data and a power distribution network objective function; the power distribution network objective function comprises a total network loss function and a photovoltaic tracking function of the active power distribution network system;
step 200: determining travel constraints of the electric automobile according to the charging data and the charging expected data;
step 300: determining grid side constraint according to preset grid data;
step 400: determining a photovoltaic distributed photovoltaic model according to the photovoltaic output data;
step 500: constructing a flexible charging optimization model according to the power distribution network objective function, the electric vehicle travel constraint, the power grid side constraint and the photovoltaic distributed photovoltaic model;
step 600: and solving the flexible charging optimization model to obtain an optimal charging and discharging strategy of each electric automobile.
Specifically, when an electric automobile user accesses the charging pile to charge, the insertion induction function on the charging gun can intelligently identify the type of the connected electric automobile, and the upper limit and the lower limit of the charging power and the discharging power of the electric automobile are obtainedBattery capacity E car SOCS at access soc,arr And the like. If the connected charging pile does not have the intelligent recognition function, the charging pile can be automatically input by an electric automobile user at a charging intelligent terminal such as a mobile phone APP and the like, and meanwhile, the expected departure time t of the user is determined dep And desired leave SOCS soc,dep 。
Further, the charging control system may obtain a charging feasible region of each electric automobile according to the above data, as shown in fig. 2. And then, according to the charging feasible regions of all the electric vehicles and the running conditions of the power distribution network, the charging and discharging behaviors of all the electric vehicles are intelligently regulated and controlled. Wherein the CF segment is the boundary point leaving the acceptable range of the SOC, the EF segment is the forced charging boundary, B and C are the upper limit boundary points, and D and E are the lower limit boundary points.
Preferably, the formula of the distribution network objective function is:
wherein,for the total loss function of the active distribution network system, < >>-providing the photovoltaic tracking function; t is a scheduling period; p (P) loss,t The total system loss at the time t is the total system loss; p (P) load,t And P PV,t Total load and distribution at time t respectivelyThe photovoltaic actual output, the total load comprises a conventional electricity load and an electric automobile cluster charge-discharge load; alpha and beta are the first and second term weight coefficients, respectively, in the objective function.
Preferably, the determining the travel constraint of the electric vehicle according to the charging data and the charging desire data includes:
constructing initial constraint conditions according to the charging data and the charging expected data; the charging data comprises a network-access charge state, upper and lower limits of charging power and discharging power, charging efficiency and discharging efficiency, network-access time and total electric quantity of a battery; the charging desired data includes a desired departure time and a desired departure state of charge;
and converting the initial constraint condition by using a linearization method to obtain the travel constraint of the electric automobile.
Preferably, the formula of the initial constraint condition is:
Pr(S soc,v,t ＝S soc,v,dep )≥1-ξ 1 ,t＝t v,dep ；
S soc,min ≤S soc,v,t ≤S soc,max ；
wherein S is soc,v,t The state of charge at time t for a v-th vehicle;and->Charging power and discharging power at the t moment of the v-th vehicle respectively; />And->Charging efficiency and discharging efficiency of the v-th vehicle respectively; t is t v,arr And t v,dep The network access time and the network departure time of the v-th vehicle are respectively; />And->Respectively charging and discharging decision variables at the moment of the v-th vehicle t; e (E) car,v The total electric quantity of the battery of the v-th vehicle; s is S soc,v,dep The state of charge, ζ, of departure for the user of the v-th vehicle 1 S is the preset probability of out-of-limit of SOC when the vehicle leaves the network soc,min And S is soc,max Respectively a minimum value and a maximum value which are allowed to be reached by the charge state; t is t v,dep The departure time for the v-th vehicle.
Preferably, the conversion formula of the linearization method is:
wherein,and->The upper limit of the charging power and the lower limit of the charging power of the electric automobile are respectively set; />And->The upper limit of the discharge power and the lower limit of the discharge power of the electric automobile are respectively set; said->And->The power is respectively the charge and discharge decision power of the v vehicle after linearization at the t moment;
the formula of the travel constraint of the electric automobile comprises:
optionally, the electric vehicle restrains:
wherein S is soc,v,t A state of charge (StateOfCharge, SOC) at time t for a v-th vehicle;and->Charging power and discharging power at the t moment of the v-th vehicle respectively; />And->Charging efficiency and discharging efficiency of the v-th EV respectively; t is t v,arr And t v,dep The network access time and the network departure time of the v-th vehicle are respectively; />And->The charge and discharge decision 0-1 variables at the time t of the v-th vehicle are respectively; e (E) car,v The battery total charge of the v-th EV. Equation (1) describes the relationship of SOC variation due to charge and discharge. Equation (2) describes that EV is only on grid connectionAnd receiving charge and discharge scheduling. Equation (3) describes that the same EV cannot be charged and discharged simultaneously in the same period.
Because the formula (2) is nonlinear constraint and contains integer variables, the global optimal solution of the formula is difficult to ensure, and the formula is converted into a convex model by a linearization method and then solved.
Based on the linearization process from the formula (5) to the formula (10), after the formula (2) is processed, the original EV model is converted into a mixed integer linear programming mathematical model from the mixed integer nonlinear programming mathematical model, and can be rapidly solved based on an accurate solution algorithm. The final formula after conversion of formula (2) is shown below:
besides, the constraint conditions of the electric automobile also comprise travel requirements of users, and the feasible range of each variable:
Pr(S soc,v,t ＝S soc,v,dep )≥1-ξ 1 ,t＝t v,dep (12)
S soc,min ≤S soc,v,t ≤S soc,max (13)
S soc,v,dep desired SOC value, ζ for user of v-th EV 1 The confidence level of the opportunistic constraint equation (12) is characterized for the probability of out-of-limit SOC when the EV is off-grid. S is S soc,min And S is soc,max Minimum and maximum values achievable for SOC set for protection of battery life.
Preferably, the formula of the grid-side constraint includes:
P in,n ＝P PV -P baseload -P EV ；
Pr[(V m,min ) 2 ≤v m,t ≤(V m,max ) 2 ]≥1-ξ 2 ；
0≤i mn ≤(I mn,max ) 2 ；
wherein P is mn And Q mn Active power and reactive power flowing into branch mn from m node; z mn 、r mn And x mn The impedance, resistance and reactance of branch mn respectively; p (P) in,n And Q in,n The active power and reactive power are injected into the n nodes respectively. v m And v n The squares of the voltage amplitudes of the m and n nodes are respectively; i.e mn The square of the current amplitude of line mn; k is a child node of the n node; p (P) PV Distributing the actual output power of the photovoltaic for the node; p (P) baseload A conventional electrical load for the node; p (P) EV Is the net charging power of the node electric automobile cluster.
Specifically, the grid-side constraints include:
P in,n ＝P PV -P baseload -P EV (20)
P mn and Q mn Active power and reactive power flowing into branch mn from m node; z mn 、r mn And x mn The impedance, resistance and reactance of branch mn respectively; p (P) in,n And Q in,n Injection active power and injection reactive power of n nodes respectivelyThe rate. v m And v n The squares of the voltage amplitudes of the m and n nodes are respectively; i.e mn The square of the current amplitude of line mn; k is a child node of the n node; p (P) PV Distributing the actual output power of the photovoltaic for the node; p (P) baseload A conventional electrical load for the node; p (P) EV Is the net charging power of the node EV cluster.
Since equation (17) is a non-convex nonlinear constraint that is difficult to solve, it is difficult to guarantee its globally optimal solution, so it is necessary to translate this constraint with a specific method. By the SOCR method, the formula (17) can be converted as follows:
the grid-side constraints also include ranges of voltage, current:
Pr[(V m,min ) 2 ≤v m,t ≤(V m,max ) 2 ]≥1-ξ 2 (22)
0≤i mn ≤(I mn,max ) 2 (23)
in the formula, v m,t The square of the voltage amplitude at the t-th moment of the node m; v (V) m,min And V m,max Respectively the minimum value and the maximum value of the voltage allowed by the node m; zeta type toy 2 Representing the confidence level of the opportunity constraint formula (22) for the probability of node voltage out-of-limit; i mn,max The maximum value of the current allowed for branch mn.
Preferably, the photovoltaic distributed photovoltaic model is:
P PV,t ＝P pre,t +e PV,t ·ε PV,t ；
wherein P is PV,t And P pre,t The actual output and the predicted output of the distributed photovoltaic at the moment t are respectively; e, e PV,t The per unit value error parameter is the random variable error at the time t; epsilon PV,t The preset per unit value coefficient is the random variable error.
Optionally, the output of the distributed photovoltaic is a random variable, and the actual output of the photovoltaic is assumed to be determined by the predicted value and the predicted error of the output together, and a specific formula is as follows:
P PV,t ＝P pre,t +e PV,t ·ε PV,t (24)
wherein P is PV,t And P pre,t The actual output and the predicted output of the distributed photovoltaic at the moment t are respectively; e, e PV,t The per unit value error parameter is the random variable error at the time t; epsilon PV,t For the per unit value coefficient of the random variable error, epsilon is generally set PV,t Meet the limit of [ -1,1]Normal distribution with internal mean 0.
Preferably, the constructing a flexible charging optimization model according to the distribution network objective function, the electric vehicle travel constraint, the grid side constraint and the photovoltaic distributed photovoltaic model includes:
converting the opportunity constraint conditions in the electric vehicle travel constraint and the power grid side constraint to determine constraint conditions, and obtaining converted electric vehicle travel constraint and converted power grid side constraint;
and constructing the flexible charging optimization model according to the power distribution network objective function, the converted electric vehicle travel constraint, the converted power grid side constraint and the photovoltaic distributed photovoltaic model.
After the distributed photovoltaic model is obtained in this embodiment, the optimization problem is as follows:
converting the opportunity constraint into a deterministic constraint:
the optimization model comprises the opportunity constraint condition that the SOC at the EV off-grid moment reaches the SOC expected by a user and the node voltage is in a specified range and meets a certain confidence level. Therefore, in order to integrate the opportunity constraints into the optimization model, to achieve efficient solution to the integrated optimization model, the opportunity constraints may be converted into deterministic constraints by the following method.
The opportunity constraint equations (12) and (22) may be converted to the following deterministic constraints, respectively, within a scheduling period:
wherein N is EV Is the total number of EVs; s is S soc,bias The maximum out-of-limit amplitude allowed when the out-of-limit condition of the EV off-network SOC occurs is adopted; n (N) bus The total number of system nodes of the power distribution network;and->The upper and lower limits of the amplitude value which cannot be exceeded even after the voltage of the node m is out of limit are respectively set. a, a v And b m,t The decision variables are respectively 0-1 of whether the off-grid SOC of the v EV is out of limit and whether the voltage amplitude of the mth node at the t moment is out of limit, 1 represents that out-of-limit condition occurs, and 0 represents that out-of-limit condition does not occur. Equation (26) (27) shows that the probability of out-of-limit EV off-grid SOC is less than ζ in the whole scheduling period 1 The probability of out-of-limit node voltage amplitude is less than ζ 2 And the opportunity constrains once active, an out-of-limit condition occurs, whose out-of-limit amplitude will also be controlled within acceptable limits.
Specifically, the solution to the flexible charging optimization model is performed to obtain an optimal charging and discharging strategy of each electric automobile, which specifically includes:
after the opportunity constraint condition is converted into a deterministic condition, a mathematical programming optimizer (e.g. a Gurobi solver) is called to solve the optimization model, so that the optimal charge-discharge strategy of each EV can be obtained.
In the practical application process, the method intelligently regulates the charging and discharging behaviors of the electric automobile, and makes an optimization decision on the charging and discharging time and power of the electric automobile. Meanwhile, the off-grid SOC of the electric vehicle and the node voltage range of the power distribution network are set to meet the soft constraint of certain confidence level, so that the feasible range of the optimization problem is enlarged, the flexibility of the charging and discharging behaviors of the electric vehicle is better exerted, the uncertainty caused by random variables is better processed, and the re-optimization of the optimization target is realized.
Fig. 3 is a module connection diagram of a flexible charging system according to an embodiment of the present application, as shown in fig. 3, the present application further provides a flexible charging system considering travel requirements of users of electric vehicles, including:
the acquisition module is used for acquiring charging data of the electric automobile, charging expected data of a user, photovoltaic output data and a power distribution network objective function; the power distribution network objective function comprises a total network loss function and a photovoltaic tracking function of the active power distribution network system;
the first constraint set determining module is used for determining travel constraints of the electric automobile according to the charging data and the charging expected data;
the second constraint set determining module is used for determining grid side constraints according to preset grid data;
the photovoltaic model determining module is used for determining a photovoltaic distributed photovoltaic model according to the photovoltaic output data;
the model building module is used for building a flexible charging optimization model according to the power distribution network objective function, the electric vehicle travel constraint, the power grid side constraint and the photovoltaic distributed photovoltaic model;
and the solving module is used for solving the flexible charging optimization model to obtain the optimal charging and discharging strategy of each electric automobile.
Preferably, the first constraint set determining module includes:
an initial condition determining unit for constructing an initial constraint condition according to the charging data and the charging expectation data; the charging data comprises a network-access charge state, upper and lower limits of charging power and discharging power, charging efficiency and discharging efficiency, network-access time and total electric quantity of a battery; the charging desired data includes a desired departure time and a desired departure state of charge;
and the conversion unit is used for converting the initial constraint condition by using a linearization method to obtain the travel constraint of the electric automobile.
The beneficial effects of the application are as follows:
the application can treat uncertainty brought by renewable energy sources with output as random variables, promote the on-site consumption of the renewable energy sources, and simultaneously reduce the burden of a large number of electric vehicles on a power grid caused by centralized charging.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present application and the core ideas thereof; also, it is within the scope of the present application to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the application.

Claims (4)

1. The flexible charging method considering the travel demands of the electric automobile user is characterized by comprising the following steps of:
acquiring charging data of an electric automobile, charging expected data of a user, photovoltaic output data and a power distribution network objective function; the power distribution network objective function comprises a total network loss function and a photovoltaic tracking function of the active power distribution network system;
determining travel constraints of the electric automobile according to the charging data and the charging expected data;
determining grid side constraint according to preset grid data;
determining a photovoltaic distributed photovoltaic model according to the photovoltaic output data;
constructing a flexible charging optimization model according to the power distribution network objective function, the electric vehicle travel constraint, the power grid side constraint and the photovoltaic distributed photovoltaic model;
solving the flexible charging optimization model to obtain an optimal charging and discharging strategy of each electric automobile;
the formula of the power distribution network objective function is as follows:
wherein,for the total loss function of the active distribution network system, < >>-providing the photovoltaic tracking function; t is a scheduling period; p (P) loss,t The total system loss at the time t is the total system loss; p (P) load,t And P PV,t The total load and the distributed photovoltaic actual output at the moment t respectively comprise a conventional power load and an electric automobile cluster charge-discharge load; alpha and beta are respectively a first term weight coefficient and a second term weight coefficient in the objective function;
determining the travel constraint of the electric vehicle according to the charging data and the charging expectation data comprises:
constructing initial constraint conditions according to the charging data and the charging expected data; the charging data comprises a network-access charge state, upper and lower limits of charging power and discharging power, charging efficiency and discharging efficiency, network-access time, network-off time and total electric quantity of a battery; the charging desired data includes a desired departure time and a desired departure state of charge;
converting the initial constraint condition by using a linearization method to obtain the travel constraint of the electric automobile;
the formula of the initial constraint condition is as follows:
Pr(S soc,v,t ＝S soc,v,dep )≥1-ξ 1 ,t＝t v,dep ；
S soc,min ≤S soc,v,t ≤S soc,max ；
wherein S is soc,v,t The state of charge at time t for a v-th vehicle;and->Charging power and discharging power at the t moment of the v-th vehicle respectively; />And->Charging efficiency and discharging efficiency of the v-th vehicle respectively; t is t v,arr And t v,dep The network access time and the network departure time of the v-th vehicle are respectively; />And->Respectively charging and discharging decision variables at the moment of the v-th vehicle t; e (E) car,v The total electric quantity of the battery of the v-th vehicle; s is S soc,v,dep The state of charge, ζ, of departure for the user of the v-th vehicle 1 S is the preset probability of out-of-limit of SOC when the vehicle leaves the network soc,min And S is soc,max Respectively a minimum value and a maximum value which are allowed to be reached by the charge state; t is t v,dep The departure time for the v-th vehicle;
the conversion formula of the linearization method is as follows:
wherein,and->The upper limit of the charging power and the lower limit of the charging power of the electric automobile are respectively set; />And->The upper limit of the discharge power and the lower limit of the discharge power of the electric automobile are respectively set; said->And->The power is respectively the charge and discharge decision power of the v vehicle after linearization at the t moment;
the formula of the travel constraint of the electric automobile comprises:
the formula of the grid-side constraint comprises:
P in,n ＝P PV -P baseload -P EV ；
Pr[(V m,min ) 2 ≤v m,t ≤(V m,max ) 2 ]≥1-ξ 2 ；
0≤i mn ≤(I mn,max ) 2 ；
wherein P is mn And Q mn Active power and reactive power flowing into branch mn from m node; z mn 、r mn And x mn The impedance, resistance and reactance of branch mn respectively; p (P) in,n And Q in,n The active power and the reactive power are respectively injected into n nodes; v m And v n The squares of the voltage amplitudes of the m and n nodes are respectively; i.e mn The square of the current amplitude of line mn; k is a child node of the n node; p (P) PV Distributing the actual output power of the photovoltaic for the node; p (P) baseload A conventional electrical load for the node; p (P) EV The net charging power of the node electric automobile cluster;
according to the power distribution network objective function, the electric vehicle travel constraint, the power grid side constraint and the photovoltaic distributed photovoltaic model, a flexible charging optimization model is built, and the method comprises the following steps:
converting the opportunity constraint conditions in the electric vehicle travel constraint and the power grid side constraint to determine constraint conditions, and obtaining converted electric vehicle travel constraint and converted power grid side constraint;
and constructing the flexible charging optimization model according to the power distribution network objective function, the converted electric vehicle travel constraint, the converted power grid side constraint and the photovoltaic distributed photovoltaic model.
2. The flexible charging method considering travel demands of electric automobile users according to claim 1, wherein the photovoltaic distributed photovoltaic model is:
P PV,t ＝P pre,t +e PV,t ·ε PV,t ；
wherein P is PV,t And P pre,t The actual output and the predicted output of the distributed photovoltaic at the moment t are respectively; e, e PV,t The per unit value error parameter is the random variable error at the time t; epsilon PV,t The preset per unit value coefficient is the random variable error.
3. Consider flexible charging system of electric automobile user's travel demand, characterized in that includes:
the acquisition module is used for acquiring charging data of the electric automobile, charging expected data of a user, photovoltaic output data and a power distribution network objective function; the power distribution network objective function comprises a total network loss function and a photovoltaic tracking function of the active power distribution network system;
the first constraint set determining module is used for determining travel constraints of the electric automobile according to the charging data and the charging expected data;
the second constraint set determining module is used for determining grid side constraints according to preset grid data;
the photovoltaic model determining module is used for determining a photovoltaic distributed photovoltaic model according to the photovoltaic output data;
the model building module is used for building a flexible charging optimization model according to the power distribution network objective function, the electric vehicle travel constraint, the power grid side constraint and the photovoltaic distributed photovoltaic model;
the solving module is used for solving the flexible charging optimization model to obtain an optimal charging and discharging strategy of each electric automobile;
the formula of the power distribution network objective function is as follows:
wherein,for the total loss function of the active distribution network system, < >>-providing the photovoltaic tracking function; t is a scheduling period; p (P) loss,t The total system loss at the time t is the total system loss; p (P) load,t And P PV,t The total load and the distributed photovoltaic actual output at the moment t respectively comprise a conventional power load and an electric automobile cluster charge-discharge load; alpha and beta are respectively a first term weight coefficient and a second term weight coefficient in the objective function;
determining the travel constraint of the electric vehicle according to the charging data and the charging expectation data comprises:
constructing initial constraint conditions according to the charging data and the charging expected data; the charging data comprises a network-access charge state, upper and lower limits of charging power and discharging power, charging efficiency and discharging efficiency, network-access time, network-off time and total electric quantity of a battery; the charging desired data includes a desired departure time and a desired departure state of charge;
converting the initial constraint condition by using a linearization method to obtain the travel constraint of the electric automobile;
the formula of the initial constraint condition is as follows:
Pr(S soc,v,t ＝S soc,v,dep )≥1-ξ 1 ,t＝t v,dep ；
S soc,min ≤S soc,v,t ≤S soc,max ；
wherein S is soc,v,t The state of charge at time t for a v-th vehicle;and->Charging power and discharging power at the t moment of the v-th vehicle respectively; />And->Charging efficiency and discharging efficiency of the v-th vehicle respectively; t is t v,arr And t v,dep The network access time and the network departure time of the v-th vehicle are respectively; />And->Respectively charging and discharging decision variables at the moment of the v-th vehicle t; e (E) car,v Is the firstv total battery power of the vehicle; s is S soc,v,dep The state of charge, ζ, of departure for the user of the v-th vehicle 1 S is the preset probability of out-of-limit of SOC when the vehicle leaves the network soc,min And S is soc,max Respectively a minimum value and a maximum value which are allowed to be reached by the charge state; t is t v,dep The departure time for the v-th vehicle;
the conversion formula of the linearization method is as follows:
wherein,and->The upper limit of the charging power and the lower limit of the charging power of the electric automobile are respectively set; />And->The upper limit of the discharge power and the lower limit of the discharge power of the electric automobile are respectively set; said->And->The power is respectively the charge and discharge decision power of the v vehicle after linearization at the t moment;
the formula of the travel constraint of the electric automobile comprises:
the formula of the grid-side constraint comprises:
P in,n ＝P PV -P baseload -P EV ；
Pr[(V m,min ) 2 ≤v m,t ≤(V m,max ) 2 ]≥1-ξ 2 ；
0≤i mn ≤(I mn,max ) 2 ；
wherein P is mn And Q mn Active power and reactive power flowing into branch mn from m node; z mn 、r mn And x mn The impedance, resistance and reactance of branch mn respectively; p (P) in,n And Q in,n The active power and the reactive power are respectively injected into n nodes; v m And v n The squares of the voltage amplitudes of the m and n nodes are respectively; i.e mn The square of the current amplitude of line mn; k is a child node of the n node; p (P) PV Distributing the actual output power of the photovoltaic for the node; p (P) baseload A conventional electrical load for the node; p (P) EV The net charging power of the node electric automobile cluster;
according to the power distribution network objective function, the electric vehicle travel constraint, the power grid side constraint and the photovoltaic distributed photovoltaic model, a flexible charging optimization model is built, and the method comprises the following steps:
converting the opportunity constraint conditions in the electric vehicle travel constraint and the power grid side constraint to determine constraint conditions, and obtaining converted electric vehicle travel constraint and converted power grid side constraint;
and constructing the flexible charging optimization model according to the power distribution network objective function, the converted electric vehicle travel constraint, the converted power grid side constraint and the photovoltaic distributed photovoltaic model.
4. The flexible charging system of claim 3, wherein the first constraint set determining module comprises:
an initial condition determining unit for constructing an initial constraint condition according to the charging data and the charging expectation data; the charging data comprises a network-access charge state, upper and lower limits of charging power and discharging power, charging efficiency and discharging efficiency, network-access time and total electric quantity of a battery; the charging desired data includes a desired departure time and a desired departure state of charge;
and the conversion unit is used for converting the initial constraint condition by using a linearization method to obtain the travel constraint of the electric automobile.
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