CN111723993B - Double-layer cooperative scheduling method, device, terminal and storage medium for power distribution network - Google Patents

Double-layer cooperative scheduling method, device, terminal and storage medium for power distribution network Download PDF

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Publication number
CN111723993B
CN111723993B CN202010587457.2A CN202010587457A CN111723993B CN 111723993 B CN111723993 B CN 111723993B CN 202010587457 A CN202010587457 A CN 202010587457A CN 111723993 B CN111723993 B CN 111723993B
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China
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charging
scheduling
power
distribution network
charging station
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CN111723993A (en
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钱斌
林晓明
肖勇
罗欣儿
田杰
陈思琳
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CSG Electric Power Research Institute
Shenzhen Power Supply Bureau Co Ltd
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CSG Electric Power Research Institute
Shenzhen Power Supply Bureau Co Ltd
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    • 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"
    • 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
    • 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
    • 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
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • 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
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • 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
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • 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
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    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging
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    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/14—Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

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Abstract

The application discloses a double-layer cooperative scheduling method, a device, a terminal and a storage medium of a power distribution network, wherein the application is based on a distributed cooperative scheduling architecture of a charging station and a scheduling center, a scheduling task of a vehicle charging scheme is configured at the charging station, a lower layer scheduling model is used for carrying out optimal solution of the vehicle charging scheme scheduling, and an upper layer scheduling model configured at the power distribution network scheduling terminal is used for carrying out optimal solution of the scheduling result of the vehicle charging scheme based on the lower layer scheduling model, so that the total power scheduling result of the charging station is obtained.

Description

Double-layer cooperative scheduling method, device, terminal and storage medium for power distribution network
Technical Field
The application relates to the technical field of power distribution networks, in particular to a power distribution network double-layer cooperative scheduling method, a device, a terminal and a storage medium.
Background
In recent years, with popularization of electric vehicles, the number of charging devices of the electric vehicles also shows explosive growth, when large-scale electric vehicles are connected into a power distribution network, excessive power distribution network operation load is extremely easy to generate negative effects such as overload operation of a circuit and a transformer, increase of load peak-valley difference, increase of network loss and the like, and in order to reduce the influence of the negative factors on the power distribution network, a conventional processing mode schedules corresponding power for a charging station in a corresponding period of time through a power distribution network load scheduling mode so as to avoid the occurrence of a phenomenon that actual power exceeds a set load.
However, in the prior art, a centralized optimal scheduling mode is adopted, a unified scheduling model of the power distribution network and the electric automobile is generally established, and when the electric automobile is connected in a large scale, the technical problems of low calculation speed and high solving difficulty exist.
Disclosure of Invention
The application provides a double-layer cooperative scheduling method, a device, a terminal and a storage medium for a power distribution network, which are used for solving the technical problems of low calculation speed and high solving difficulty when the electric vehicle is connected in a large scale in the prior art by adopting a centralized optimal scheduling mode and generally establishing a unified scheduling model of the power distribution network and the electric vehicle.
The first aspect of the application provides a double-layer collaborative scheduling method of a power distribution network, which is applied to a power distribution network scheduling terminal and comprises the following steps:
Receiving the total charging power, wherein the total charging power is obtained by optimally scheduling according to the time-interval electricity price and charging station charging records through an objective function and constraint conditions of a lower scheduling model configured at a charging station scheduling terminal;
inputting the charging total power to an upper layer scheduling model, and performing optimal scheduling through an objective function and constraint conditions of the upper layer scheduling model to obtain a charging total power scheduling result and a charging total power relaxation difference value corresponding to the power distribution network scheduling optimization result;
and outputting the charging total power dispatching result when the charging total power relaxation difference value is smaller than a preset precision threshold value.
Preferably, the objective function of the upper layer scheduling model is:
Wherein: t is a scheduling period, r ij is the resistance of a branch between a node i and a node j in the power distribution network, h ij,t is the square of the magnitude of the branch current between the node i and the node j in the period of T, deltaP l,t is the charging total power relaxation difference value of the first charging station, and M is the penalty coefficient of the charging total power relaxation difference value;
the constraint conditions of the upper layer scheduling model comprise: node power constraints, node voltage magnitude constraints, line power constraints, node voltage phase angle constraints, and genset output constraints.
The second aspect of the application provides a double-layer collaborative scheduling method of a power distribution network, which is applied to a charging station scheduling terminal and comprises the following steps:
acquiring a charging record of a charging station;
According to the time period electricity price and the charging record of the charging station, optimizing and dispatching are carried out through an objective function and constraint conditions of a lower layer dispatching model configured at a charging station dispatching terminal, and a vehicle charging scheme dispatching result and a charging total power corresponding to the vehicle charging scheme dispatching result are obtained;
and sending the total charging power to a power distribution network dispatching terminal, so that the power distribution network dispatching terminal obtains a total charging power dispatching result according to the total charging power.
Preferably, the objective function of the lower layer scheduling model specifically includes:
Wherein: f l is the charge cost of the first charging station, N l is the number of electric vehicles connected to the first charging station, c t is the charge price of the t period, To characterize the 0-1 variable of the charging state of the nth electric vehicle of the first charging station,/>The charging power of the nth electric automobile of the first charging station is delta t which is an optimized time interval;
The constraint conditions of the lower layer scheduling model comprise: a charging demand constraint, a charging time constraint, and a total charging power constraint.
The third aspect of the present application provides a power distribution network upper layer scheduling device, configured at a power distribution network scheduling terminal, comprising:
The receiving unit is used for receiving the total charging power, wherein the total charging power is obtained by optimizing and dispatching according to the time period electricity price and the charging record of the charging station through an objective function and constraint conditions of a lower layer dispatching model configured at the dispatching terminal of the charging station;
The upper layer scheduling optimization unit is used for inputting the charging total power into an upper layer scheduling model, and performing optimal scheduling through an objective function and constraint conditions of the upper layer scheduling model to obtain a charging total power scheduling result and a charging total power relaxation difference value corresponding to the power distribution network scheduling optimization result;
and the result output unit is used for outputting the charging total power dispatching result when the charging total power relaxation difference value is smaller than a preset precision threshold value.
A fourth aspect of the present application provides a lower layer scheduling device of a power distribution network, configured to a charging station scheduling terminal, including:
the acquisition unit is used for acquiring a charging record of the charging station;
The lower-layer dispatching optimization unit is used for carrying out optimized dispatching through an objective function and constraint conditions of a lower-layer dispatching model configured at a charging station dispatching terminal according to the time-of-day electricity price and the charging record of the charging station to obtain a vehicle charging scheme dispatching result and a charging total power corresponding to the vehicle charging scheme dispatching result;
and the sending unit is used for sending the total charging power to a power distribution network dispatching terminal, so that the power distribution network dispatching terminal obtains a total charging power dispatching result according to the total charging power.
The fifth aspect of the application provides a power distribution network scheduling terminal, which comprises a memory and a processor;
the memory is used for storing program codes corresponding to the power distribution network double-layer cooperative scheduling method according to the first aspect of the application;
The processor is configured to execute the program code.
The sixth aspect of the application provides a charging station dispatch terminal, comprising a memory and a processor;
The memory is used for storing program codes corresponding to the power distribution network double-layer cooperative scheduling method according to the second aspect of the application;
The processor is configured to execute the program code.
A seventh aspect of the present application provides a storage medium, where program codes corresponding to the power distribution network dual-layer cooperative scheduling method according to the first aspect of the present application are stored in the storage medium.
An eighth aspect of the present application provides a storage medium, where program codes corresponding to the power distribution network dual-layer cooperative scheduling method according to the second aspect of the present application are stored in the storage medium.
From the above technical solutions, the embodiment of the present application has the following advantages:
The application provides a double-layer collaborative scheduling method of a power distribution network, which is applied to a power distribution network scheduling terminal and comprises the following steps: receiving the total charging power, wherein the total charging power is obtained by optimally scheduling according to the time-interval electricity price and charging station charging records through an objective function and constraint conditions of a lower scheduling model configured at a charging station scheduling terminal; inputting the charging total power to an upper layer scheduling model, and performing optimal scheduling through an objective function and constraint conditions of the upper layer scheduling model to obtain a charging total power scheduling result and a charging total power relaxation difference value corresponding to the power distribution network scheduling optimization result; and outputting the charging total power dispatching result when the charging total power relaxation difference value is smaller than a preset precision threshold value.
According to the application, based on a distributed collaborative scheduling architecture of a charging station and a scheduling center, scheduling tasks of a vehicle charging scheme are configured at the charging station, a lower-layer scheduling model is used for carrying out vehicle charging scheme scheduling optimization solution, and an upper-layer scheduling model configured at a power distribution network scheduling terminal is used for carrying out scheduling optimization solution based on a vehicle charging scheme scheduling result obtained by the lower-layer scheduling model, so that a charging total power scheduling result of the charging station is obtained, and the technical problems of low calculation speed and high solution difficulty when a unified scheduling model of a power distribution network and an electric vehicle is required to be established in the prior art, but the electric vehicle access scale is large, are solved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic flow chart of an embodiment of a power distribution network double-layer cooperative scheduling method applied to a power distribution network scheduling terminal;
fig. 2 is a schematic flow chart of an embodiment of a power distribution network double-layer cooperative scheduling method applied to a charging station scheduling terminal;
fig. 3 is a schematic structural diagram of an embodiment of an upper layer scheduling device on a power distribution network according to the present application;
fig. 4 is a schematic structural diagram of an embodiment of a lower layer scheduling device of a power distribution network according to the present application.
Detailed Description
The embodiment of the application provides a double-layer collaborative scheduling method, a device, a terminal and a storage medium for a power distribution network, which are used for solving the technical problems of low calculation speed and high solving difficulty when the electric vehicle is connected in a large scale in a centralized optimal scheduling mode adopted in the prior art, and a unified scheduling model of the power distribution network and the electric vehicle is generally established.
In order to make the objects, features and advantages of the present application more comprehensible, the technical solutions in the embodiments of the present application are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present application, but not all embodiments of the present application. 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.
Referring to fig. 1, a first embodiment of the present application provides a dual-layer cooperative scheduling method for a power distribution network, which is applied to a scheduling terminal of the power distribution network, and includes:
Step 101, receiving the total charging power, wherein the total charging power is obtained by optimally scheduling according to the time-interval electricity price and charging record of the charging station through an objective function and constraint conditions of a lower scheduling model configured at a charging station scheduling terminal;
Step 102, inputting the total charging power into an upper layer scheduling model, and performing optimal scheduling through an objective function and constraint conditions of the upper layer scheduling model to obtain a total charging power scheduling result and a total charging power relaxation difference value corresponding to a power distribution network scheduling optimization result;
It can be understood that the total power charging schedule of the power distribution network to the charging station belongs to a typical second order cone planning problem, and Mosek solvers are superior to most other solvers in the aspect of comprehensive performance of solving cone optimization, so that the effect of efficient and stable solving can be achieved by combining Mosek optimization solving mode based on the configured upper layer scheduling model.
And 103, outputting a charging total power dispatching result when the charging total power relaxation difference value is smaller than a preset precision threshold value.
It should be noted that, first, the complete process of the distributed dual-layer cooperative scheduling in this embodiment includes:
1) Optimizing and dispatching the lower dispatching model, and obtaining the charging total power corresponding to an optimized electric automobile charging scheme output by the lower dispatching model;
2) Based on the charging total power obtained in the last step, carrying out optimized dispatching through an upper dispatching model, and obtaining a charging station total power relaxation difference value corresponding to the scheme based on a charging total power dispatching result output by the upper dispatching model;
3) Judging whether the precision requirement is met, namely |DeltaP l,t | is less than or equal to epsilon, if yes, exiting the solution to obtain an optimal scheduling scheme, if not, adding the following constraint to the lower layer scheduling model, and returning to the first step to continue to optimally schedule the lower layer model:
Wherein: The charging total power of a first charging station in a t period in the lower layer scheduling model is iterated for the g th time; And (3) the total charging power of the first charging station in the t period in the lower layer scheduling model is iterated for the g+1th time.
Based on the above complete flow, the following embodiment will specifically specify the above upper layer scheduling model as follows: the objective function of the upper scheduling model is that the sum of the power distribution network loss and the total power relaxation difference penalty term of the charging station is minimized;
Wherein: t is a scheduling period, r ij is the resistance of a branch between a node i and a node j in the power distribution network, h ij,t is the square of the magnitude of the branch current between the node i and the node j in the period of T, deltaP l,t is the charging total power relaxation difference value of the first charging station, and M is the penalty coefficient of the charging total power relaxation difference value. The penalty factor is typically a very large constant, taken as 10 5.
More specifically, the charging total power relaxation difference value of this embodiment is specifically:
Wherein: Δp l,t is the total power charged relaxation difference.
Constraint conditions of the upper layer scheduling model of the power distribution network optimization scheduling comprise node power constraint, node voltage amplitude constraint, line power constraint, node voltage phase angle constraint and generator set output constraint;
The node power constraint is:
Wherein: And/> The active output and the reactive output of the generator set are respectively node j; p jk,t and Q jk,t are the active power and reactive power, respectively, flowing from node j to node k during period t; p ij,t and Q ij,t are the active power and reactive power, respectively, flowing from node i to node j during period t; a j is an end node set of a branch having a node j as an end node, a node k belongs to the end node set of the branch having the node j as an end node, and a node i belongs to the Bj; g j and b j are the conductance to ground and susceptance, respectively, of node j; x ij is the reactance of the branch between node i and node j, respectively; /(I)And/>Active load and reactive load of the node j in the t period are respectively; v j,t is the voltage amplitude of the node j in the t period; /(I)The node j is connected with the power generation output of the photovoltaic for the period t; the node voltage magnitude constraint of (a) is:
Wherein: v i,t is the voltage amplitude of the node j in the t period;
the line power constraints of (a) are:
The node voltage amplitude phase angle constraint of (a) is:
wherein: θ i,t and θ j,t are the voltage phase angles of the node i and the node j, respectively, of the period t; And/> The typical values of the voltage amplitude values of the node i and the node j are respectively taken as a per unit value of 1.0;
The output constraint of the generator set is as follows:
Wherein: And/> The upper limit and the lower limit of the active power of the node j generator set are respectively set; /(I)And/>The upper limit and the lower limit of the reactive power of the generator set of the node j are respectively set.
The charging station charging total power can be obtained by solving both the upper layer scheduling model and the lower layer scheduling model, but the two are not equal, and the physical two are actually equal, so the iterative solving method is adopted. In the solving process, the lower layer scheduling model is solved first to obtainAnd then transferred to an upper layer, and the upper layer scheduling model utilizes the relation/>Optimizing ΔP l,t is equivalent to pair/>And optimizing, and judging whether iteration termination is achieved by judging whether the delta P l,t meets the precision requirement.
The above is a detailed description of an embodiment of a dual-layer cooperative scheduling method for a power distribution network, which is applied to a power distribution network scheduling terminal, and the following is a dual-layer cooperative scheduling method for a power distribution network, which is applied to a charging station scheduling terminal.
Referring to fig. 2, a second embodiment of the present application provides a dual-layer collaborative scheduling method for a power distribution network, which is applied to a charging station scheduling terminal, and includes:
step 201, acquiring a charging record of a charging station;
it should be noted that, the charging record acquiring manner of the charging station of this embodiment may refer to the following manner:
1) Randomly extracting charging data of each electric automobile by adopting a Monte Carlo method, wherein the charging data comprise charging start time, charging end time and charging start SoC;
Specifically, the electric automobile charging start time satisfies a normal distribution function:
wherein: t s is the charging start time of the electric automobile; And/> Respectively the mean value and standard deviation of the normal distribution function of the charging start time of the electric automobile, wherein/>
The electric automobile charging end time satisfies a lognormal distribution function:
Wherein: t e is the charging end time of the electric automobile; And/> The mean value and standard deviation of the normal distribution function of the charging end time of the electric automobile are respectively shown, wherein mu s＝2.23,σs = 0.30;
The electric automobile charging start SoC satisfies a normal distribution function:
Wherein: s is the charging start SoC of the electric automobile; mu s and sigma s are the mean and standard deviation, respectively, of the normal distribution function of the SoC of the electric vehicle charge initiation, where mu s＝0.3,σs = 0.1.
2) Judging whether the extracted electric vehicle charging data meet the expected SoC requirement of the charging end time, if so, entering the next step, and if not, re-extracting the electric vehicle charging data;
3) And judging whether the sampling times reach the number of the electric vehicles, if so, ending the sampling to obtain a charging record of the charging station required by executing the lower-layer dispatching optimization, and if not, returning to the first step, and continuing to sample the charging data of the electric vehicles.
Step 202, optimizing and dispatching according to the time period electricity price and charging station charging records through an objective function and constraint conditions of a lower layer dispatching model configured at a charging station dispatching terminal to obtain a vehicle charging scheme dispatching result and charging total power corresponding to the vehicle charging scheme dispatching result;
It can be understood that the charging scheme scheduling of the charging station for the charging vehicle belongs to a typical mixed integer linear programming problem, and Cplex solvers are superior to most other solvers in the aspect of comprehensive performance of the solution cone optimization, so that the efficient and stable solving effect can be achieved by combining the Cplex optimization solving mode based on the configured lower layer scheduling model.
And 203, transmitting the total charging power to a power distribution network dispatching terminal, so that the power distribution network dispatching terminal obtains a total charging power dispatching result according to the total charging power.
It may be appreciated that the content mentioned in the first embodiment may be referred to for the power distribution network scheduling terminal to obtain the total power charging scheduling result according to the total power charging, which is not described herein.
More specifically, the charging station electric vehicle optimized dispatching lower dispatching model comprises an objective function and constraint conditions; is to minimize the charge cost:
Wherein: f l is the charge cost of the first charging station, N l is the number of electric vehicles connected to the first charging station, c t is the charge price of the t period, To characterize the 0-1 variable of the charge state of the nth electric vehicle of the first charging station, u A,i,t =1,/>, when in the charge stateAnd the charging power of the nth electric automobile of the first charging station is delta t which is the optimized time interval.
The constraint conditions of (1) comprise a charging demand constraint, a charging time constraint and a charging total power constraint;
The charging demand constraints of (a) are:
Wherein: And/> The method comprises the steps of respectively starting a charging start SoC and ending an expected charging SoC of an nth electric automobile of an ith charging station; e l,n and eta l,n are the battery capacity and the charging efficiency of the nth electric vehicle of the first charging station respectively;
the charging time constraints of (2) are:
Wherein: And/> The charging start time and the charging end time of the nth electric automobile of the first charging station are respectively;
The total power charged constraint of (a) is:
Pl,min≤Pl,t≤Pl,max
Wherein: p l,max and P l,min are the upper and lower limits, respectively, of the total power charged by the first charging station.
According to the embodiment of the application, based on a distributed collaborative scheduling architecture of a charging station and a scheduling center, scheduling tasks of a vehicle charging scheme are configured in the charging station, a lower-layer scheduling model is used for carrying out vehicle charging scheme scheduling optimization solution, and an upper-layer scheduling model configured at a power distribution network scheduling terminal is used for carrying out scheduling optimization solution based on a vehicle charging scheme scheduling result obtained by the lower-layer scheduling model, so that a charging total power scheduling result of the charging station is obtained, and the technical problems of low calculation speed and high solution difficulty when a unified scheduling model of a power distribution network and an electric vehicle is required to be established in the prior art, but the electric vehicle access scale is large, are solved.
The above is a detailed description of an embodiment of a dual-layer cooperative scheduling method for a power distribution network, which is applied to a charging station scheduling terminal, and the following is a detailed description of an embodiment of an upper layer scheduling device for a power distribution network, which is provided by the application.
Referring to fig. 3, a third embodiment of the present application provides an upper layer scheduling apparatus for a power distribution network, including:
the receiving unit 301 is configured to receive a total charging power, where the total charging power is obtained by performing optimal scheduling according to a time-of-day electricity price and a charging record of the charging station through an objective function and constraint conditions of a lower layer scheduling model configured at a charging station scheduling terminal;
the upper layer scheduling optimization unit 302 is configured to input the total charging power to an upper layer scheduling model, perform optimal scheduling according to an objective function and constraint conditions of the upper layer scheduling model, and obtain a total charging power scheduling result and a total charging power relaxation difference value corresponding to the power distribution network scheduling optimization result;
And the result output unit 303 is configured to output the charging total power scheduling result when the charging total power relaxation difference is smaller than the preset precision threshold.
The above is a detailed description of an embodiment of power distribution network load upper layer scheduling provided by the present application, and the following is a detailed description of an embodiment of power distribution network load lower layer scheduling provided by the present application.
Referring to fig. 4, a fourth embodiment of the present application provides a lower layer scheduling apparatus of a power distribution network, including:
An acquisition unit 401 for acquiring a charging station charging record;
The lower-layer dispatching optimization unit 402 is configured to perform optimized dispatching according to the time-interval electricity price and the charging station charging record through an objective function and constraint conditions of a lower-layer dispatching model configured at the charging station dispatching terminal, so as to obtain a vehicle charging scheme dispatching result and a charging total power corresponding to the vehicle charging scheme dispatching result;
And the sending unit 403 is configured to send the total charging power to the power distribution network scheduling terminal, so that the power distribution network scheduling terminal obtains a total charging power scheduling result according to the total charging power.
The foregoing details of an embodiment of a lower layer scheduling device for a power distribution network provided by the present application, and the following details of embodiments of a power distribution network scheduling terminal, a charging station scheduling terminal and a related storage medium provided by the present application.
The fifth embodiment of the application provides a power distribution network scheduling terminal, which comprises a memory and a processor;
the memory is used for storing program codes corresponding to the power distribution network double-layer cooperative scheduling method according to the first aspect of the application;
The processor is configured to execute the program code.
A sixth embodiment of the present application provides a charging station dispatch terminal, including a memory and a processor;
The memory is used for storing program codes corresponding to the power distribution network double-layer cooperative scheduling method according to the second aspect of the application;
The processor is configured to execute the program code.
A seventh embodiment of the present application provides a storage medium, where program codes corresponding to the power distribution network dual-layer cooperative scheduling method according to the first aspect of the present application are stored in the storage medium.
An eighth embodiment of the present application provides a storage medium, where program codes corresponding to the power distribution network dual-layer cooperative scheduling method according to the second aspect of the present application are stored in the storage medium.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims (8)

1. The double-layer cooperative scheduling method for the power distribution network is applied to a scheduling terminal of the power distribution network and is characterized by comprising the following steps:
Receiving the total charging power, wherein the total charging power is obtained by optimally scheduling according to the time-interval electricity price and charging station charging records through an objective function and constraint conditions of a lower scheduling model configured at a charging station scheduling terminal;
the objective function of the lower layer scheduling model specifically comprises:
Wherein: f l is the charge cost of the first charging station, N l is the number of electric vehicles connected to the first charging station, c t is the charge price of the t period, To characterize the 0-1 variable of the charging state of the nth electric vehicle of the first charging station,/>The charging power of the nth electric automobile of the first charging station is delta t which is an optimized time interval;
The constraint conditions of the lower layer scheduling model comprise: a charging demand constraint, a charging time constraint and a charging total power constraint;
inputting the charging total power to an upper layer scheduling model, and performing optimal scheduling through an objective function and constraint conditions of the upper layer scheduling model to obtain a charging total power scheduling result and a charging total power relaxation difference value corresponding to the power distribution network scheduling optimization result;
outputting the charging total power dispatching result when the charging total power relaxation difference value is smaller than a preset precision threshold value;
The objective function of the upper layer scheduling model is as follows:
Wherein: t is a scheduling period, r ij is the resistance of a branch between a node i and a node j in the power distribution network, h ij,t is the square of the magnitude of the branch current between the node i and the node j in the period of T, deltaP l,t is the charging total power relaxation difference value of the first charging station, and M is the penalty coefficient of the charging total power relaxation difference value;
the constraint conditions of the upper layer scheduling model comprise: node power constraints, node voltage magnitude constraints, line power constraints, node voltage phase angle constraints, and genset output constraints.
2. The double-layer cooperative scheduling method for the power distribution network is applied to a charging station scheduling terminal and is characterized by comprising the following steps of:
acquiring a charging record of a charging station;
According to the time period electricity price and the charging record of the charging station, optimizing and dispatching are carried out through an objective function and constraint conditions of a lower layer dispatching model configured at a charging station dispatching terminal, and a vehicle charging scheme dispatching result and a charging total power corresponding to the vehicle charging scheme dispatching result are obtained;
The charging total power is sent to a power distribution network dispatching terminal, so that the power distribution network dispatching terminal obtains a charging total power dispatching result according to the charging total power;
the objective function of the lower layer scheduling model specifically comprises:
Wherein: f l is the charge cost of the first charging station, N l is the number of electric vehicles connected to the first charging station, c t is the charge price of the t period, To characterize the 0-1 variable of the charging state of the nth electric vehicle of the first charging station,/>The charging power of the nth electric automobile of the first charging station is delta t which is an optimized time interval;
The constraint conditions of the lower layer scheduling model comprise: a charging demand constraint, a charging time constraint, and a total charging power constraint.
3. An upper layer scheduling device of a power distribution network is configured at a power distribution network scheduling terminal and is characterized by comprising:
The receiving unit is used for receiving the total charging power, wherein the total charging power is obtained by optimizing and dispatching according to the time period electricity price and the charging record of the charging station through an objective function and constraint conditions of a lower layer dispatching model configured at the dispatching terminal of the charging station;
the objective function of the lower layer scheduling model specifically comprises:
Wherein: f l is the charge cost of the first charging station, N l is the number of electric vehicles connected to the first charging station, c t is the charge price of the t period, To characterize the 0-1 variable of the charging state of the nth electric vehicle of the first charging station,/>The charging power of the nth electric automobile of the first charging station is delta t which is an optimized time interval;
The constraint conditions of the lower layer scheduling model comprise: a charging demand constraint, a charging time constraint and a charging total power constraint;
The upper layer scheduling optimization unit is used for inputting the charging total power into an upper layer scheduling model, and performing optimal scheduling through an objective function and constraint conditions of the upper layer scheduling model to obtain a charging total power scheduling result and a charging total power relaxation difference value corresponding to the power distribution network scheduling optimization result;
The result output unit is used for outputting the charging total power dispatching result when the charging total power relaxation difference value is smaller than a preset precision threshold value;
The objective function of the upper layer scheduling model is as follows:
Wherein: t is a scheduling period, r ij is the resistance of a branch between a node i and a node j in the power distribution network, h ij,t is the square of the magnitude of the branch current between the node i and the node j in the period of T, deltaP l,t is the charging total power relaxation difference value of the first charging station, and M is the penalty coefficient of the charging total power relaxation difference value;
the constraint conditions of the upper layer scheduling model comprise: node power constraints, node voltage magnitude constraints, line power constraints, node voltage phase angle constraints, and genset output constraints.
4. A power distribution network lower layer scheduling device configured at a charging station scheduling terminal, comprising:
the acquisition unit is used for acquiring a charging record of the charging station;
The lower-layer dispatching optimization unit is used for carrying out optimized dispatching through an objective function and constraint conditions of a lower-layer dispatching model configured at a charging station dispatching terminal according to the time-of-day electricity price and the charging record of the charging station to obtain a vehicle charging scheme dispatching result and a charging total power corresponding to the vehicle charging scheme dispatching result;
The sending unit is used for sending the total charging power to a power distribution network dispatching terminal, so that the power distribution network dispatching terminal obtains a total charging power dispatching result according to the total charging power;
the objective function of the lower layer scheduling model specifically comprises:
Wherein: f l is the charge cost of the first charging station, N l is the number of electric vehicles connected to the first charging station, c t is the charge price of the t period, To characterize the 0-1 variable of the charging state of the nth electric vehicle of the first charging station,/>The charging power of the nth electric automobile of the first charging station is delta t which is an optimized time interval;
The constraint conditions of the lower layer scheduling model comprise: a charging demand constraint, a charging time constraint, and a total charging power constraint.
5. The power distribution network dispatching terminal is characterized by comprising a memory and a processor;
the memory is used for storing program codes corresponding to the power distribution network double-layer cooperative scheduling method of claim 1;
The processor is configured to execute the program code.
6. A charging station dispatch terminal comprising a memory and a processor;
The memory is used for storing program codes corresponding to the power distribution network double-layer cooperative scheduling method of claim 2;
The processor is configured to execute the program code.
7. A storage medium, wherein program codes corresponding to the power distribution network double-layer cooperative scheduling method of claim 1 are stored in the storage medium.
8. A storage medium, wherein program codes corresponding to the power distribution network double-layer cooperative scheduling method of claim 2 are stored in the storage medium.
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Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103280822A (en) * 2013-05-27 2013-09-04 东南大学 Intelligent distribution network scheduling management system for charging behavior of electric automobile
CN107133415A (en) * 2017-05-22 2017-09-05 河海大学 A kind of electric automobile charge and discharge Electric optimization for considering user's satisfaction and distribution safety
CN108667018A (en) * 2018-06-08 2018-10-16 河海大学 It is a kind of meter and electric vehicle and heat pump power distribution network node electricity price computational methods
CN109103878A (en) * 2018-09-14 2018-12-28 国网冀北电力有限公司张家口供电公司 The orderly charging method of electric car group and power distribution network Electric optimization
CN109559035A (en) * 2018-11-29 2019-04-02 国网(苏州)城市能源研究院有限责任公司 A kind of urban power distribution network bi-level programming method considering flexibility
CN109713732A (en) * 2019-01-17 2019-05-03 南京邮电大学 A kind of method of more micro-capacitance sensors and power distribution network coordination optimization scheduling
CN110443415A (en) * 2019-07-24 2019-11-12 三峡大学 It is a kind of meter and dynamic electricity price strategy electric automobile charging station Multiobjective Optimal Operation method
CN110648013A (en) * 2019-08-26 2020-01-03 广东工业大学 Electric vehicle charging load prediction method based on dual-mode maximum entropy
CN110688743A (en) * 2019-09-12 2020-01-14 南京蓝人电力科技有限公司 Economic charging method for electric bus charging station
CN110739690A (en) * 2019-10-31 2020-01-31 山东大学 Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility
CN110733370A (en) * 2019-11-20 2020-01-31 国网江苏省电力有限公司南通供电分公司 electric vehicle charging station ordered charging method based on double-layer optimization algorithm
CN110826880A (en) * 2019-10-24 2020-02-21 成都信息工程大学 Active power distribution network optimal scheduling method for large-scale electric vehicle access
CN110880755A (en) * 2019-11-06 2020-03-13 国网浙江省电力有限公司经济技术研究院 Collaborative planning method for charging network and power distribution network considering guide type charging control
CN111260237A (en) * 2020-01-21 2020-06-09 福州大学 Multi-interest-subject coordinated game scheduling method considering EV (electric vehicle) owner intention

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103280822A (en) * 2013-05-27 2013-09-04 东南大学 Intelligent distribution network scheduling management system for charging behavior of electric automobile
CN107133415A (en) * 2017-05-22 2017-09-05 河海大学 A kind of electric automobile charge and discharge Electric optimization for considering user's satisfaction and distribution safety
CN108667018A (en) * 2018-06-08 2018-10-16 河海大学 It is a kind of meter and electric vehicle and heat pump power distribution network node electricity price computational methods
CN109103878A (en) * 2018-09-14 2018-12-28 国网冀北电力有限公司张家口供电公司 The orderly charging method of electric car group and power distribution network Electric optimization
CN109559035A (en) * 2018-11-29 2019-04-02 国网(苏州)城市能源研究院有限责任公司 A kind of urban power distribution network bi-level programming method considering flexibility
CN109713732A (en) * 2019-01-17 2019-05-03 南京邮电大学 A kind of method of more micro-capacitance sensors and power distribution network coordination optimization scheduling
CN110443415A (en) * 2019-07-24 2019-11-12 三峡大学 It is a kind of meter and dynamic electricity price strategy electric automobile charging station Multiobjective Optimal Operation method
CN110648013A (en) * 2019-08-26 2020-01-03 广东工业大学 Electric vehicle charging load prediction method based on dual-mode maximum entropy
CN110688743A (en) * 2019-09-12 2020-01-14 南京蓝人电力科技有限公司 Economic charging method for electric bus charging station
CN110826880A (en) * 2019-10-24 2020-02-21 成都信息工程大学 Active power distribution network optimal scheduling method for large-scale electric vehicle access
CN110739690A (en) * 2019-10-31 2020-01-31 山东大学 Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility
CN110880755A (en) * 2019-11-06 2020-03-13 国网浙江省电力有限公司经济技术研究院 Collaborative planning method for charging network and power distribution network considering guide type charging control
CN110733370A (en) * 2019-11-20 2020-01-31 国网江苏省电力有限公司南通供电分公司 electric vehicle charging station ordered charging method based on double-layer optimization algorithm
CN111260237A (en) * 2020-01-21 2020-06-09 福州大学 Multi-interest-subject coordinated game scheduling method considering EV (electric vehicle) owner intention

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