CN111682550B - Reliability evaluation method for power distribution network connected with high-permeability power supply and electric automobile - Google Patents

Reliability evaluation method for power distribution network connected with high-permeability power supply and electric automobile Download PDF

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CN111682550B
CN111682550B CN202010562338.1A CN202010562338A CN111682550B CN 111682550 B CN111682550 B CN 111682550B CN 202010562338 A CN202010562338 A CN 202010562338A CN 111682550 B CN111682550 B CN 111682550B
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power
load
state
electric automobile
distribution network
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CN111682550A (en
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刘朝章
燕树民
封国栋
张焕云
袁清柳
葛杨
苏冰
高文浩
韩立群
栗君
李冰
李晓博
陈新华
张宝宇
周通
米有为
陶灿
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Dezhou Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Dezhou Power Supply Co of State Grid Shandong Electric Power Co Ltd
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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
    • H02J3/12—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
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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

Abstract

The invention discloses a method for evaluating reliability of a power distribution network accessed to a high-permeability power supply and an electric automobile, which comprises the following steps: according to the travel behavior data of the electric automobile, fitting the daily travel distance and the daily travel ending time of the electric automobile to obtain a probability distribution function of the travel distance and the arrival time of the electric automobile to each destination; setting a dispatching grade of the electric automobile based on the battery charge state of the electric automobile, and enabling the electric automobile to discharge in order according to the dispatching grade according to a probability distribution function; and after discharging, respectively carrying out corresponding reliability evaluation according to different states of the power distribution network. And the electric automobile is enabled to discharge in order according to the dispatching level according to the probability distribution function, the dimensionality number of the particle swarm is reduced, and the operation time and the convergence of the particle swarm algorithm are improved.

Description

Reliability evaluation method for power distribution network connected with high-permeability power supply and electric automobile
Technical Field
The invention belongs to the technical field of operation and control of a power distribution network, and particularly relates to a reliability evaluation method for a power distribution network with a high-permeability power supply and multiple loads.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
A large number of distributed power sources are connected into the power distribution network, so that the power supply capacity of the power distribution network is improved to a certain extent, but the uncertainty of the distributed power sources and the multi-load also brings a severe challenge to the power supply reliability of the power distribution network. The reliability of the novel power distribution network containing the distributed power supply and the multi-element load can be accurately evaluated, certain theoretical support can be provided for the future extension and planning of the power distribution network, and the popularization of renewable energy sources and the multi-element load of the power distribution network in the future can be guided.
According to the knowledge of the inventor, firstly, research is carried out in a relatively deep mode on the basis of charging load modeling and ordered charging and discharging scheduling of a cluster type electric automobile, but the electric automobile electricity utilization characteristics mainly depend on travel behaviors, individual differences cannot be ignored, and therefore the ordered charging and discharging scheduling for the electric automobile is accurate to scheduling of a single vehicle. Secondly, reliability evaluation research of low-permeability distributed power supplies accessed to the power distribution network is deep, but after high-permeability distributed power supplies are accessed to the power distribution network, the assumption of traditional power distribution network reliability evaluation is not applicable.
Firstly, the assumption of infinite capacity of the upper-level system is no longer true, the high-permeability distributed power supply can bear 30% or more of system load power supply in the system, and if the distributed power supply fails and quits operation, load shedding can also occur in a system area. Secondly, even if a non-power supply element in the system does not break down or quit operation, the system cannot match the load demand due to the fluctuation of the distributed power supply output and the uncertainty of the multi-load demands of the electric automobile and the like, and the load in the island is cut due to the failure of the distribution network area.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the reliability assessment method for the power distribution network accessed with the high-permeability power supply and the electric automobile, which can be used for assessing the reliability of the power distribution network containing the high-permeability distributed power supply and the electric automobile and assisting operation and maintenance scheduling personnel to make accurate and effective judgment.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
the reliability evaluation method for the power distribution network connected to the high-permeability power supply and the electric automobile comprises the following steps:
according to the travel behavior data of the electric automobile, fitting the daily travel distance and the daily travel ending time of the electric automobile to obtain a probability distribution function of the travel distance and the arrival time of the electric automobile to each destination;
setting a dispatching grade of the electric automobile based on the battery charge state of the electric automobile, and enabling the electric automobile to discharge in order according to the dispatching grade according to the original load of a power grid and the output difference value of a distributed power supply in a fault period;
dividing the power distribution network into different running states through sampling, and respectively carrying out corresponding reliability assessment: if the power distribution network is in a large power grid state, calling an electric automobile ordered charging and discharging strategy to obtain the matching condition of the distributed power supply output and load in the power distribution network at the moment, counting the load losing time and the load losing amount of the system within the state duration time, and calculating to obtain a system reliability index;
if the power distribution network is in the micro-grid operation state, obtaining the independent operation state in each micro-grid, calling the electric vehicle ordered charging and discharging strategy, then carrying out load reduction, counting the number of times of losing load, the time of losing load and the load loss of each load point of the system in the state duration time, and calculating to obtain the reliability index of the system.
The above one or more technical solutions have the following beneficial effects:
the method aims at the situation that after a high-permeability distributed power source and a multi-load are connected into a power distribution network, the hypothesis of the reliability evaluation of the classical power distribution network is not applicable, the quantitative reliability evaluation can be carried out on the high-permeability distributed power source and the power distribution network connected with an electric automobile on the technical scheme, the dispatching grade of the electric automobile is set based on the battery charge state of the electric automobile, the electric automobile is enabled to discharge orderly according to the dispatching grade according to a probability distribution function, the dimensionality number of particle swarms is reduced, and the operation time and the convergence of the particle swarm algorithm are improved.
Compared with the traditional orderly charging and discharging scheduling method of the electric automobile taking the cluster as the unit, the method can perform charging and discharging scheduling on the electric automobile accurate to a single electric automobile, and the calculation speed cannot be reduced due to the increase of the quantity when the large-scale electric automobile is scheduled.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an ordered charging and discharging load curve of an electric vehicle obtained by simulation analysis according to an embodiment of the present disclosure;
fig. 3(a) -3 (b) are schematic diagrams comparing the convergence rate of the present disclosure and the conventional method.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment discloses a method for evaluating reliability of a power distribution network connected to a high-permeability power supply and an electric automobile, which is shown in the attached drawing 1 and comprises the following steps:
(1) establishing an electric vehicle trip behavior probability distribution function according to trip behavior characteristics of a traditional fuel vehicle, and establishing an electric vehicle natural state charge-discharge model based on internal properties of energy storage of the electric vehicle;
(2) setting a scheduling grade according to the electric automobile time sequence capacity, and establishing a large-scale electric automobile ordered charging and discharging model by using a particle swarm algorithm;
(3) and (3) carrying out reliability evaluation on the power distribution network containing the high-permeability distributed power supply and the multi-load based on the Monte Carlo method of system state sampling transfer.
Specifically, the following expressions (1), (2), and (3) are natural state charge-discharge models. The ordered charging and discharging model is realized based on a particle swarm algorithm and comprises the steps of a fitness function, scheduling level setting and the particle swarm algorithm.
In the step (1), a sufficient amount of user travel behavior data is obtained according to the survey result of the automobile travel behaviors of the United states department of transportation NHTS2009, and the daily travel distance and the daily travel end time are fitted to obtain the travel distance D of the electric automobile to each destinationiAnd initial charging time TsnIs determined.
Randomly extracting the driving distance D of the Nth journey destination of the electric automobile by using a Monte Carlo methodiAnd initial charging time TsnThe probability distribution function of (2) calculates the SOC (State of Charge) of the electric vehicle to be charged at the beginning according to the equation (1), if the SOC satisfies the constraint of the equation (2), the electric vehicle is not charged at the destination, and if the SOC does not satisfy the constraint of the equation (2), the electric vehicle is charged according to the power of the charging pile at the destination. The charging period is calculated according to equation (3) to obtain the starting SOC of the next trip. And repeating the process until the simulation is finished to obtain the daily SOC distribution and the daily charging load curve of the single electric vehicle.
Figure BDA0002545040590000041
In the formula (I), the compound is shown in the specification,
Figure BDA0002545040590000042
indicating the state of charge of the electric vehicle at the beginning of the ith stroke,
Figure BDA0002545040590000043
indicating the state of charge of the electric vehicle at the end of the ith journey, DiAnd R is a constant coefficient of the electric automobile, and is generally 40 km.
SOCj·C-ωlj+1≤0.4C (2)
In the formula, SOCjRepresenting the state of charge of the electric vehicle when the electric vehicle runs to a parking point j; c is the battery capacity of the electric automobile, and the unit is kW.h; ω represents the unit mileage traveled to the stopping point jPower consumption; l represents the distance from the electric vehicle to the parking point j; j +1 represents the next trip of the trip chain.
Figure BDA0002545040590000051
In the formula (I), the compound is shown in the specification,
Figure BDA0002545040590000052
represents the i +1 th stroke initial state of charge;
Figure BDA0002545040590000053
represents the state of charge at the end of the ith stroke;
Figure BDA0002545040590000054
the charging and discharging power of the electric automobile at the stopping point of the trip chain j is represented, the charging is represented when the value is positive, and the discharging is represented when the value is negative; Δ t represents a charge-discharge time interval and may also represent a time period available for control; and C represents the upper limit of the capacity of the electric vehicle.
In the step (2), because the electric vehicles are large in scale and quantity in practice, each electric vehicle is a dimension of a particle swarm, and the direct application of the particle swarm algorithm not only consumes time and labor, but also has the problem of non-convergence. Therefore, the scheduling level of the electric vehicle is set in the embodiment, so that the electric vehicle discharges in order according to the scheduling level, the dimensionality number of the particle swarm is reduced, and the operation time and the convergence of the particle swarm algorithm are improved.
And setting the difference value between the original load in the power grid and the output of the distributed power supply as a target value of a particle group algorithm, and coordinating that each electric automobile individual discharges outwards as far as possible when the constraint of the formula (2) is met. The fitness function of the particle swarm algorithm is set as shown in the formula (4). The scheduling level of the electric vehicle can divide the dischargeable quantity at the time t according to the daily SOC distribution of the single electric vehicle in the step (1), as shown in the formula (5). When the power distribution network fails but can continuously run through the active island, electric vehicles in the same island can form a cluster to transmit power to the island, and the power calculation of external discharge is based on a particle swarm algorithm to coordinate external discharge according to a scheduling level.
Figure BDA0002545040590000055
In the formula (I), the compound is shown in the specification,
Figure BDA0002545040590000056
indicates a target value of the particle group at the i-th time,
Figure BDA0002545040590000057
for the original total load in the island during a fault period,
Figure BDA0002545040590000058
for the determined total load of the electric automobile which does not participate in charging and discharging in the island during the fault period,
Figure BDA0002545040590000059
and representing the total output of the renewable energy in the island during the fault period.
Figure BDA00025450405900000510
In the formula, Cres_k(t) represents the dischargeable capacity min C of the kth electric vehicle in the t periodres(t) represents the minimum amount of out-discharge in the cluster during the t-th period.
Figure BDA0002545040590000061
The larger the value is, the higher the SOC state of the battery of the electric vehicle is, the larger the magnitude of the external discharge is allowed, and relatively more flexible control is possible, and the external discharge should be preferentially performed. When in use
Figure BDA0002545040590000062
The smaller the value, the closer it is to the SOC critical point at that time, the more it should be charged preferentially. The difference in discharge index was constructed as follows:
Figure BDA0002545040590000063
therefore, the electric vehicle group dispatching level can be divided according to the following formula:
Figure BDA0002545040590000064
the electric vehicle ordered charging and discharging based on the particle swarm algorithm comprises the following steps:
2.1 Alev1charging the vehicles in the grade according to the maximum charging power, and calculating the system load
Figure BDA0002545040590000065
Wherein
Figure BDA0002545040590000066
Is Alev1The grade of the total charging power of the electric automobile;
2.2 Alev5grade electric automobile according to maximum discharge power
Figure BDA0002545040590000067
Discharge is carried out if
Figure BDA0002545040590000068
Then calling the particle swarm algorithm to calculate all Alev5Discharge power of class electric vehicle, Alev4、Alev3The level does not carry out charging and discharging operations, if the distributed power supply output is matched with the load requirement at the moment, the island can run in the period, and the process is ended; otherwise go to 2.3;
2.3 Alev4grade electric automobile according to maximum discharge power
Figure BDA0002545040590000069
Discharge is carried out if
Figure BDA00025450405900000610
Then calling the particle swarm algorithm to calculate all Alev4Discharge power of class electric vehicle, Alev3、Alev2Grade No charging and discharging operation, Alev5Supplying power to the outside according to the maximum discharge power, if the distributed power supply output is matched with the load requirement at the moment, the island can operate in the period, and the process is ended; otherwise go to 2.4;
2.4 Alev3grade electric automobile according to maximum discharge power
Figure BDA00025450405900000611
Discharge is carried out if
Figure BDA0002545040590000071
Then calling the particle swarm algorithm to calculate all Alev3Discharge power of class electric vehicle, Alev2Grade No charging and discharging operation, Alev5、Alev4Supplying power to the outside according to the maximum discharge power, if the distributed power supply output is matched with the load requirement at the moment, the island can operate in the period, and the process is ended; otherwise, turning to 2.5;
2.5 Alev2grade electric automobile according to maximum discharge power
Figure BDA0002545040590000072
Discharge if
Figure BDA0002545040590000073
Then, calling the particle swarm algorithm to calculate all Alev2Discharge power of class electric vehicle, Alev5、Alev4、Alev3Supplying power to the outside according to the maximum discharge power, if the distributed power supply output is matched with the load requirement at the moment, the island can operate in the period, and the process is ended; otherwise, the power shortage exists in the island at the moment, and the load cutting is carried out to ensure the continuous power supply of important loads.
Fig. 2 is a schematic diagram of an ordered charging and discharging load curve of an electric vehicle obtained through simulation analysis of an embodiment of the disclosure.
Using monte based system sample transferAnd evaluating the reliability of the power distribution network by using a Carlo method. First, a [0,1 ] is randomly generated]Random number R in between1Calculating the duration T of the current system state by using the formula (10)n。
Figure BDA0002545040590000074
Wherein, TnIs the total duration of the system in that state; r1Random numbers between (0,1) obtained for sampling; lambda [ alpha ]nIs the state transition rate of element n; and N is the total number of elements in the system.
If all non-output elements are in a normal operation state at the moment, the power distribution network is judged to be in a large power network state at the moment, the matching situation of the output and the load of the distributed power source in the power distribution network at the moment is obtained through simulation, and the state duration time T is calculated through statisticsnOff-load time of internal system LLDnAnd loss of load ENSn。
The non-output elements are all elements except for the distributed power supply, and comprise lines, transformers, switches and the like.
If one and only one non-output element in the system fails at the moment, the power distribution network is judged to be in the micro-grid operation state at the moment, the independent operation state in each micro-grid is simulated, load reduction is carried out, and the state duration time T is obtained through statistical calculationnNumber of times of load loss of each load point of internal system
Figure BDA0002545040590000081
Time of loss of load
Figure BDA0002545040590000082
And loss of load
Figure BDA0002545040590000083
And (3) judging the states of the system, namely the operation state of the large power grid and the operation state of the micro power grid according to the result of the sampling and transferring of the system. State space aggregation after system sampling transferInto two subsets S1And S2. Wherein in the subset S1In the middle, all non-output elements in the system are in a normal operation state; in subset S2And only one non-contributing element within the system fails. As can be seen from the properties of the subsets, the subset S1Corresponding to the operating state of the large power grid, subset S2Corresponding to the micro-grid operation state. The reliability evaluation flow of the two operating states is as follows:
3.1 evaluating the reliability of the operation state of the large power grid.
3.1.1 initialize the system parameters, power shortage time LLD is 0, power shortage amount ENS is 0, and system analog clock t is 0. After sampling is performed, the time sequence running states of all distributed power supplies and energy storage in the current state and the duration of the system in the state can be obtained;
3.1.2 if t<TnIf the simulation process is in the duration of the nth state, continuing to perform simulation jump to 3.1.3; otherwise, turning to 3.1.7 to calculate the reliability index;
3.1.3 sampling to obtain the running-stopping states of all the distributed power supplies, and superposing the running-stopping states with the predicted time sequence output curve of the distributed power supplies to obtain a predicted time sequence state curve of the distributed power supplies;
3.1.4 applying the electric automobile optimization model of the particle swarm optimization to calculate and obtain the total output P of the distributed power supply at the time tt DGTotal load P of optimized systemt L_OP(including original load of system and total charging load of electric automobile) and total discharging power P of electric automobilet EVdch_OPAnd external system output Pt E. Net switched power P in systemt ex1＝Pt L_OP-Pt EVdch_OP-Pt DG。
3.1.5, advancing the clock t to t + Δ t, updating the SOC of the electric vehicle at the same time, and going to step 3.1.2;
3.1.6 calculate the power down time and power down amount of the system for the entire duration of this state.
3.2 reliability evaluation process of micro-grid operation state.
3.2.1 obtaining the system fault state duration t and the area division state of the power distribution network at the moment according to the fault occurrence position;
3.2.2 at Fault duration [ ts,tend]The total load amount of each region at that time is calculated within the initial time.
All loads are not reduced at this time;
3.2.3 calling the reliability simulation flow of the operation state of the large power grid in a subarea mode along with clock advance;
3.2.4 if at fault duration ts,tend]When the formula (8) is satisfied at a certain moment, reducing the load point with the minimum required electric quantity in the regional load according to the formula (9), and turning to 3.2.3;
3.2.5 when the fault duration time is over, recording the power failure times, power failure time and power shortage amount of each load point.
Figure BDA0002545040590000091
Figure BDA0002545040590000092
In the formula (I), the compound is shown in the specification,
Figure BDA0002545040590000093
the load demand amount at the nth load point at time t is shown, and x (n) shows the reduction state at point n. X (n) 0 indicates that the load point needs to be reduced, and x (n) 1 indicates that the load point remains.
3.3 after the simulation is finished, counting the statistical data of all the load points, including the LLD in the running state of the large power gridnAnd ENSnAnd the number of times of load loss in the operating state of the microgrid
Figure BDA0002545040590000094
Time of loss of load
Figure BDA0002545040590000095
And loss of load
Figure BDA0002545040590000096
And calculating according to a national grid reliability calculation standard formula to obtain a system reliability index.
Fig. 3(a) -3 (b) are schematic diagrams comparing the convergence rate of the present disclosure with that of the conventional method.
Example two
The present embodiment is directed to a computing device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the specific steps of the first embodiment.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of the first embodiment.
Example four
The purpose of this embodiment is to provide a distribution network reliability evaluation system who contains high permeability distributed power source and many loads, includes:
the parameter acquisition unit is configured to acquire parameters and a topological structure of the regional power distribution network, annual output data of the distributed power supply and annual load data of each load point in the topological structure;
the model building unit is configured to obtain each electric vehicle daily trip chain through calculation according to regional electric vehicle parameters, and build an ordered charging and discharging model of each electric vehicle by applying a particle swarm algorithm.
And the reliability evaluation unit is configured to perform reliability evaluation on the power distribution network containing the high-permeability distributed power supply and the multi-load by using a system state sampling transfer method.
The method can solve the problem that the existing electric automobile can not carry out ordered charging and discharging scheduling on a single electric automobile, can carry out ordered charging and discharging scheduling on large-scale cluster electric automobiles, can carry out reliability assessment on a power distribution network containing a high-permeability distributed power supply and the electric automobile, assists operation and maintenance scheduling personnel to make accurate and effective judgment, and provides certain theoretical support for extension and planning of the future power distribution network.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (9)

1. The reliability assessment method for the power distribution network connected with the high-permeability power supply and the electric automobile is characterized by comprising the following steps of:
according to the travel behavior data of the electric automobile, fitting the daily travel distance and the daily travel ending time of the electric automobile to obtain a probability distribution function of the travel distance and the arrival time of the electric automobile to each destination;
randomly sampling the probability distribution function to obtain the battery charge state of the electric automobile, setting the dispatching grade of the electric automobile based on the battery charge state of the electric automobile, and enabling the electric automobile to discharge in order according to the dispatching grade according to the difference value of the original load of a power grid and the output of a distributed power supply in a failure period;
after discharging, dividing the power distribution network into different operation states, namely a large power grid operation state and a micro power grid operation state, and respectively carrying out corresponding reliability evaluation; the reliability evaluation flow of the two operating states is as follows:
3.1 reliability evaluation process of operation state of the large power grid:
3.1.1 initializing system parameters, wherein the power shortage time LLD is 0, the power shortage amount ENS is 0, and the system analog clock t is 0, and after sampling is performed, obtaining the time sequence running states of all distributed power supplies and energy storage in the current state and the duration of the system in the state;
3.1.2 if T < TnIf the simulation process is in the duration of the nth state, continuing to perform simulation jump to 3.1.3; otherwise, turning to 3.1.7 to calculate the reliability index;
3.1.3 sampling to obtain the running-stopping states of all the distributed power supplies, and superposing the running-stopping states with the predicted time sequence output curve of the distributed power supplies to obtain a predicted time sequence state curve of the distributed power supplies;
3.1.4 applying the electric automobile optimization model of the particle swarm optimization to calculate and obtain the total output P of the distributed power supply at the time tt DGTotal load P of optimized systemt L_OPIncluding the original load of the system, the total charging load of the electric automobile and the total discharging power P of the electric automobilet EVdch_OPAnd external system output Pt E(ii) a Net switched power P in systemt ex1＝Pt L_OP-Pt EVdch_OP-Pt DG；
3.1.5, advancing the clock t to t + Δ t, updating the SOC of the electric vehicle at the same time, and going to step 3.1.2;
3.1.6 calculating the power shortage time and power shortage amount of the system in the state in the whole duration;
3.2 reliability evaluation process of micro-grid operation state:
3.2.1 obtaining the system fault state duration t and the area division state of the power distribution network at the moment according to the fault occurrence position;
3.2.2 at Fault duration [ ts,tend]The total load of each region at the moment is calculated in the initial moment, and all the loads are not reduced at the moment;
3.2.3 calling the reliability simulation flow of the operation state of the large power grid in a subarea mode along with clock advancing;
3.2.4 if at fault duration ts,tend]When the formula (8) is satisfied at a certain moment, reducing the load point with the minimum required electric quantity in the regional load according to the formula (9), and turning to 3.2.3;
3.2.5 when the fault duration is over, recording the power failure times, power failure time and power shortage amount of each load point,
Figure FDA0003510727720000021
Figure FDA0003510727720000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003510727720000023
denotes the load demand at the nth load point at time t, x (n) denotes the reduction state at point n, x (n) 0 denotes that the load point needs to be reduced, x (n) 1 denotes that the load point remains, and Pt DGRepresents the total output of the distributed power supply at the moment t, Pt EVdch_OPRepresenting the total discharge power of the electric automobile;
3.3 after the simulation is finished, counting the statistical data of all the load points, including the LLD in the running state of the large power gridnAnd ENSnAnd the number of times of load loss in the operating state of the microgrid
Figure FDA0003510727720000024
Time of loss of load
Figure FDA0003510727720000025
And loss of load
Figure FDA0003510727720000026
And calculating according to a national grid reliability calculation standard formula to obtain a system reliability index.
2. The method for evaluating the reliability of the power distribution network accessed to the high-permeability power supply and the electric vehicle as claimed in claim 1, wherein the power distribution network is divided into different operation states, and if the power distribution network is in a large power grid state, after the electric vehicle is called for the ordered charging and discharging strategy, when the reliability evaluation is performed: and obtaining the matching condition of the distributed power supply output and the load in the power distribution network at the moment, counting the load losing time and the load losing amount of the system in the state duration time, and calculating to obtain the reliability index of the system.
3. The method according to claim 1, wherein before the scheduling level of the electric vehicle is set based on the battery state of charge of the electric vehicle, the driving distance and the initial charging time of the nth trip destination of the electric vehicle are randomly extracted, the initial charging electric vehicle state of charge is calculated, if the state of charge satisfies the set constraint, the electric vehicle is not charged at the destination, and if the state of charge does not satisfy the set constraint, the electric vehicle is charged according to the power of the charging pile.
4. The method for evaluating the reliability of the power distribution network accessed to the high-permeability power supply and the electric vehicle according to claim 1, wherein the dispatching level of the electric vehicle is set based on the battery charge state of the electric vehicle, and the electric vehicle is enabled to discharge in order according to the dispatching level according to the difference between the original load of the power grid and the output of the distributed power supply during the fault period, and the method comprises the following specific steps:
setting a difference value between an original load in a power grid and the output of a distributed power supply to a target value of a particle swarm algorithm, setting a fitness function of the particle swarm algorithm, forming a cluster by electric vehicles in the same island to transmit power to the island when the power distribution network fails but continuously operates through the active island, and coordinating external discharge based on the particle swarm algorithm according to a scheduling level in power calculation of external discharge so that each electric vehicle individual discharges externally when the constraint is met.
5. The method for evaluating the reliability of the power distribution network accessed to the high-permeability power supply and the electric vehicle as claimed in claim 1, wherein the reliability evaluation is performed respectively for different states of the power distribution network:
specifically, a Monte Carlo method based on system sampling transfer is used for evaluating the reliability of the power distribution network.
6. The method for evaluating the reliability of the power distribution network connected to the high-permeability power supply and the electric vehicle as claimed in claim 1, wherein the judgment modes of different states of the power distribution network are as follows: if all the non-output elements are in the normal operation state, the power distribution network is judged to be in the large power grid state at the moment; if one or only one non-output element in the system fails, the power distribution network is judged to be in the micro-grid operation state at the moment, and system reliability indexes can be obtained after different states are respectively simulated.
7. Insert high permeability power and electric automobile's distribution network reliability evaluation system, characterized by includes:
a parameter acquisition unit: the method comprises the steps that parameters of a regional power distribution network, a topological structure, annual output data of a distributed power supply and annual load data of each load point in the topological structure are obtained, and according to travel behavior data of the electric automobile, a daily travel distance and a daily travel ending time of the electric automobile are fitted to obtain a probability distribution function of the travel distance and the arrival time of the electric automobile to each destination;
the model building unit is configured to set a scheduling level of the electric automobile based on the battery charge state of the electric automobile, and enable the electric automobile to discharge in order according to the scheduling level according to a probability distribution function to build an ordered charging and discharging model of the electric automobile;
the reliability evaluation unit is configured to respectively perform corresponding reliability evaluation on different states of the power distribution network, namely a large power grid operation state and a micro power grid operation state, after the ordered charge-discharge model discharges; the reliability evaluation flow of the two operating states is as follows:
3.1 reliability evaluation process of operation state of the large power grid:
3.1.1 initializing system parameters, wherein the power shortage time LLD is 0, the power shortage amount ENS is 0, and the system analog clock t is 0, and after sampling is performed, obtaining the time sequence running states of all distributed power supplies and energy storage in the current state and the duration of the system in the state;
3.1.2 if T < TnIf the simulation process is in the duration of the nth state, continuing to perform simulation jump to 3.1.3; otherwise, turning to 3.1.7 to calculate the reliability index;
3.1.3 sampling to obtain the running-stopping states of all the distributed power supplies, and superposing the running-stopping states with the predicted time sequence output curve of the distributed power supplies to obtain a predicted time sequence state curve of the distributed power supplies;
3.1.4 applying the electric automobile optimization model of the particle swarm optimization to calculate and obtain the total output P of the distributed power supply at the time tt DGTotal load P of optimized systemt L_OPIncluding the original load of the system, the total charging load of the electric automobile and the total discharging power P of the electric automobilet EVdch_OPAnd external system output Pt E(ii) a Net switched power P in systemt ex1＝Pt L_OP-Pt EVdch_OP-Pt DG；
3.1.5, advancing the clock t to t + Δ t, updating the SOC of the electric vehicle at the same time, and going to step 3.1.2;
3.1.6 calculating the power shortage time and power shortage amount of the system in the state in the whole duration;
3.2 reliability evaluation process of micro-grid operation state:
3.2.1 obtaining the system fault state duration t and the area division state of the power distribution network at the moment according to the fault occurrence position;
3.2.2 at Fault duration [ ts,tend]The total load of each region at the moment is calculated in the initial moment, and all the loads are not reduced at the moment;
3.2.3 calling the reliability simulation flow of the operation state of the large power grid in a subarea mode along with clock advancing;
3.2.4 if at fault duration ts,tend]When the formula (8) is satisfied at a certain moment, reducing the load point with the minimum required electric quantity in the regional load according to the formula (9), and turning to 3.2.3;
3.2.5 when the fault duration is over, recording the power failure times, power failure time and power shortage amount of each load point,
Figure FDA0003510727720000051
Figure FDA0003510727720000061
in the formula (I), the compound is shown in the specification,
Figure FDA0003510727720000062
denotes the load demand at the nth load point at time t, x (n) denotes the reduction state at point n, x (n) 0 denotes that the load point needs to be reduced, x (n) 1 denotes that the load point remains, and Pt DGRepresents the total output of the distributed power supply at the moment t, Pt EVdch_OPRepresenting the total discharge power of the electric automobile;
3.3 after the simulation is finished, counting the statistical data of all the load points, including the LLD in the running state of the large power gridnAnd ENSnAnd microgrid operationNumber of times of loss of load in state
Figure FDA0003510727720000063
Time of loss of load
Figure FDA0003510727720000064
And loss of load
Figure FDA0003510727720000065
And calculating according to a national grid reliability calculation standard formula to obtain a system reliability index.
8. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the steps of any of the methods of assessing reliability of a power distribution network coupled to a high permeability power source and an electric vehicle of any of claims 1-6.
9. A computer-readable storage medium, on which a computer program is stored, which when executed by a processor performs the specific steps of implementing the method for reliability assessment of a power distribution network accessing high permeability power sources and electric vehicles as claimed in any one of claims 1 to 6.
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