CN110245858B - Micro-grid reliability evaluation method with electric vehicle charging station - Google Patents

Micro-grid reliability evaluation method with electric vehicle charging station Download PDF

Info

Publication number
CN110245858B
CN110245858B CN201910501490.6A CN201910501490A CN110245858B CN 110245858 B CN110245858 B CN 110245858B CN 201910501490 A CN201910501490 A CN 201910501490A CN 110245858 B CN110245858 B CN 110245858B
Authority
CN
China
Prior art keywords
power
time
charging station
fault
microgrid
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910501490.6A
Other languages
Chinese (zh)
Other versions
CN110245858A (en
Inventor
范宏
陈龙超
袁宏道
郭翔
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai University of Electric Power
Original Assignee
Shanghai University of Electric Power
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai University of Electric Power filed Critical Shanghai University of Electric Power
Priority to CN201910501490.6A priority Critical patent/CN110245858B/en
Publication of CN110245858A publication Critical patent/CN110245858A/en
Application granted granted Critical
Publication of CN110245858B publication Critical patent/CN110245858B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0639Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06Electricity, gas or water supply
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/388Islanding, i.e. disconnection of local power supply from the network

Abstract

The invention relates to a micro-grid reliability evaluation method comprising an electric vehicle charging station, which comprises the following steps: 1) acquiring the charge state of the power battery of the electric automobile at each time point according to the running characteristics of the electric automobile in the micro-grid; 2) establishing an interactive response power calculation model according to a microgrid internal power interaction strategy; 3) according to the positions of the network switches and the on-off characteristics of the switches of different types, carrying out region division on the micro-grid; 4) according to the difference of the areas where the internal fault points are located when the micro-grid isolated island operates, different switching action modes are adopted to carry out fault isolation; 5) and evaluating the operation reliability of the micro-grid containing the electric vehicle charging station by adopting a sequential Monte Carlo simulation method. Compared with the prior art, the method provided by the invention is in accordance with the development trend of the electric vehicle charging station accessing to the power grid in the present stage and in the future, is comprehensive in consideration and is wide in application.

Description

Micro-grid reliability evaluation method with electric vehicle charging station
Technical Field
The invention relates to the field of power distribution network planning, in particular to a micro-grid reliability evaluation method with an electric vehicle charging station.
Background
With the rapid development of economic society, electric automobiles are vigorously developed in recent years by using electric power to replace petroleum as a main power energy source, and the characteristics of no carbon emission, environmental friendliness and the like are achieved. As a novel power load, if the electric vehicle is charged in an unordered manner on a large scale, great impact is certainly brought to the safety stability and the economical efficiency of a power distribution system. Particularly, charging is carried out in the peak period of the power grid load, so that the peak is added to the peak of the power load, and the power supply burden and the operation risk of the system are increased.
Therefore, there is a need to analyze and study the usage habits of different types of electric vehicles and different vehicle users. Through the analysis of the charging and discharging behavior characteristics of different types of electric automobiles, an electric automobile charging station is established to carry out centralized planning and management on the electric automobiles, so that the charging load of the electric automobiles is controllable. Meanwhile, along with the development and research of a power interaction technology between the electric automobile and a power grid, the electric automobile is connected to the power distribution network as a mobile energy storage, and can be used as a standby power supply to deliver electric energy to the power grid during the peak load period of the power grid through a certain power interaction response strategy.
After the electric automobile is connected to the power grid in a large scale, the structure and the operation mode of the power grid become more complex. There is no evaluation method for evaluating the operational reliability of a microgrid including an electric vehicle charging station.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a microgrid reliability evaluation method comprising an electric vehicle charging station.
The purpose of the invention can be realized by the following technical scheme:
a reliability evaluation method for a micro-grid with an electric vehicle charging station comprises the following steps:
1) acquiring the charge state of the power battery of the electric automobile at each time point according to the running characteristics of the electric automobile in the micro-grid;
2) establishing an interactive response power calculation model according to a microgrid internal power interaction strategy;
3) according to the positions of the network switches and the on-off characteristics of the switches of different types, carrying out region division on the micro-grid;
4) according to the difference of the areas where the internal fault points are located when the micro-grid isolated island operates, different switching action modes are adopted to carry out fault isolation;
5) and evaluating the operation reliability of the microgrid with the electric vehicle charging station by adopting a sequential Monte Carlo simulation method under the condition of considering the internal power interaction and fault isolation of the microgrid.
In the step 1), the state of charge of the power battery of the electric automobile at each time point meets the following constraints:
Figure BDA0002090388380000021
wherein, T0、T′0Respectively, the departure time points of regular bus and private electric automobile in the morning, and T0<T′0, T1And T2Time points of access and departure, T, respectively3For the point in time, T ', at which the coach stops at the charging station after returning to the park'3Is the time point of the private electric automobile arriving at home, SOCmin(T0/T′0)、SOCmin(T1)、SOCmin(T2)、SOCmin(T3/T′3) Are respectively at T0Or T'0、T1、T2、T3Or T'3State of charge, S, of an electric vehicle at a point in time1For regular bus at N1、N3Distance traveled in time intervals, S2For private electric automobile at n1、n3Time-interval driving course, N1Time period for the electric regular bus to start from the garden area in the morning and return to the garden area after the electric regular bus is connected with the staff at the fixed station, N2Time period for parking electric duty car in park to charge station, N3Time period for afternoon bus to transport employees to fixed sites and back to park, N4For time periods when regular bus is parked at park charging station, n1For the time period from home to park when the employee takes the private electric automobile in the morning, n2Time period for a private electric car to stop at a charging station in a park, n3Time period from park to home for employee to take private electric automobile, n4For the time when a private electric vehicle stops at the house of an employee, W is the power consumption per kilometer of the electric vehicle, WedRated electric quantity, SOC, of the power battery of the electric automobilesd·minThe minimum state of charge threshold is set for ensuring a certain service life of the power battery.
The step 2) specifically comprises the following steps:
21) determining an operating period of the microgrid comprising N1、N2、N3And N4A time period;
22) and performing source and load power balance calculation on the fault-free area, wherein the source and load power balance formula is as follows:
Pph(t)=PWT(t)+PPV(t)-PL(t)
wherein, Pph(t) is the area balance power, PL(t) is the real-time load power within the microgrid; pWT(t) is the total power generation power of the wind turbine generator set at the moment t, PPV(t) is the total power generation power of the photovoltaic generator set at the moment t;
23) when P is presentph(t) > 0 and at N1And N3During the time interval, no electric automobile is connected, the micro-grid only charges the energy storage device, and N is2And N4In time periods, in order to ensure that the energy storage equipment is required to maintain the stability of the system operation when no electric automobile is accessed, the state of charge of the electric automobile is at least SOCESS·sdThen, charging the electric automobile in the electric automobile charging station;
24) when P is presentph(t) is less than or equal to 0 and is in the range of N1And N3At time intervals, only the energy storage device is discharged, at N2And N4During the period of time, when the electric vehicle is discharging, if Pph(t)+PEV·dis(t) < 0, the energy storage device participates in the discharging operation at the same time, and if the combined output of the energy storage device, the electric vehicle charging station and the renewable energy cannot meet the load requirement, namely Pph(t)+PEV·dis(t)+PESS·dis(t) < 0, load shedding is performed according to the important level of the load in the network, and if the power supply is insufficient when the output power is reduced to zero, the load in the island region is further shed, wherein PEV·dis(t) discharge power of the charging station at time t, PESS·dis(t) is the discharge power of the energy storage device at the moment t;
25) based on the internal power interaction strategy of the microgrid in the steps 23) and 24), the calculation formula of the charging and discharging power of the energy storage device at different time intervals is as follows:
Figure BDA0002090388380000031
Figure BDA0002090388380000032
Figure BDA0002090388380000033
Figure BDA0002090388380000041
wherein, PESS·dis(t) discharge power of the energy storage device, PESS·ch(t) charging power of the energy storage device, PESS·dis·maxIs the maximum discharge power, P, of the energy storage deviceESS·ch·maxMaximum charging power for energy storage, SOCESS(t) State of Charge, SOC, of the energy storage deviceESS·sdThe energy is stored in order to maintain the charge state of a subsequent system to be stable when no electric vehicle is in an electric vehicle charging station;
26) in N1、N3The number of electric vehicles in the charging station for the time period is approximately zero, considering that in N2、N4The interactive power condition between the time-period electric vehicle charging station and the microgrid, namely the interactive response power calculation model is as follows:
when P is presentph(t) > 0, charging station charging power:
Figure BDA0002090388380000042
when P is presentph(t) < 0, charging station discharge power:
Figure BDA0002090388380000043
PEV·dis·max(t)=Ncar(t)·Pcar·dis+Nbus(t)·Pbus·dis
wherein, PEV·maxMaximum allowable current of main line connected with micro-grid for charging stationDynamic power, PEV·dis·max(t) is the maximum power that can be delivered by the charging station at time t.
In the step 3), the specific classification of the area division of the microgrid comprises:
primary area: the method comprises the following steps that a region without any type of switching device is arranged inside, once an element fault occurs in the region, the region is isolated integrally, and when the fault is enumerated, a primary region is used as a minimum enumeration unit, and the integral fault rate of the region is considered;
and (3) secondary area: the circuit breaker is taken as a boundary, a region without the circuit breaker in the region is the same branch region formed by combining a plurality of primary regions.
In the step 4), the fault isolation by adopting different switching action modes specifically comprises:
when a fault occurs in a primary region with an isolating switch as a boundary, all the circuit breakers or intelligent switches in the upstream direction of the region act firstly to cut off the power supply current of all power supplies, the isolating switch in the fault region is cut off to isolate the fault, the circuit breakers and the intelligent switches are superposed, and the fault-free equipment of the micro-grid recovers to normal operation;
when a fault occurs in a primary region with the intelligent switch as a boundary, only the corresponding intelligent switch is switched off;
when a line branch circuit has a fault, the on-off operation of the isolating switch is not needed after the current is blocked, and meanwhile, the circuit breaker and the intelligent switch are not closed any more.
The step 5) specifically comprises the following steps:
51) reading original data, setting an initial value T of an analog clock to be 0, and assuming that all elements of the microgrid are in a normal working state initially;
52) obtaining a TTF time sequence table and a TTR time sequence table according to the non-fault working time TTF and the fault repairing time TTR of each element in the micro-grid;
53) enumerating faults and selecting the minimum value TTF in the TTF time sequence listiThe corresponding element is a failed element;
54) get from T to T + TTFiIn the simulation time period of (3), the micro-gridOperating conditions in different time periods of whether the electric vehicle charging station is connected or not are added, and simulation time T is accumulated as T + TTFi
55) Judging the type and position of the fault and determining a fault influence area;
56) fault isolation is carried out on the micro-grid after the fault;
57) before the fault node returns to normal operation, whether load reduction is needed or not is determined according to the operation condition of the area after fault isolation, if yes, the power failure time of the reduced load and the reduced load power are accumulated, and the simulation time T is accumulated as T + TTRi
58) Acquiring the power failure time of the affected load nodes;
59) judging whether the time reaches the specified time limit, if not, returning to the step 52); if yes, continuing the next step;
510) and performing reliability evaluation according to reliability evaluation indexes of the microgrid system and the load nodes.
In the step 510), the reliability evaluation indexes include conventional reliability evaluation indexes and microgrid reliability evaluation indexes including electric vehicle charging stations.
The conventional reliability evaluation indexes comprise system annual average power failure frequency SAIFI, system annual average power failure time SAIDI, user annual average power failure duration CAIDI and average power supply availability ASAI.
The micro-grid reliability evaluation indexes comprising the electric automobile charging stations comprise average charging depth ACD, average discharging depth ADD, average load reduction depth ALRD of the charging stations, and load reduction force eta of electric automobile charging stations in the case of insufficient power supply in a fault-free area due to discharging reduction.
The calculation formula of the micro-grid reliability evaluation index containing the electric vehicle charging station comprises the following steps:
Figure BDA0002090388380000061
Figure BDA0002090388380000062
Figure BDA0002090388380000063
Figure BDA0002090388380000064
wherein the content of the first and second substances,
Figure BDA0002090388380000065
the power of ith charging and jth discharging of the electric vehicle charging station is respectively, CHT and DIST are respectively the total charging and discharging times of the electric vehicle charging station, PGiThe power that the renewable energy source can send out when charging for electric automobile charging station ith time, and the ALR is the total number of times of load reduction in the microgrid, PzThe load power for the z-th reduction.
Compared with the prior art, the invention has the following advantages:
according to the method, the power interaction strategy between the energy storage system and the microgrid system in the electric automobile charging station combined network is provided according to the working properties of electric automobile users in the park and the running characteristics of different types of electric automobiles when the park microgrid runs in an isolated island, various actual running conditions (charging, faults, reduction and the like) are considered, the microgrid running reliability containing the electric automobile charging station is evaluated by adopting a sequential Monte Carlo simulation method and combining the evaluation indexes provided by the invention, the influence on the microgrid running reliability after the electric automobiles participate in V2G response is effectively and visually reflected, and the reference value is provided for the subsequent actual microgrid running.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 shows the driving states of two types of electric vehicles in different periods of a working day.
Fig. 3 is a schematic diagram of an internal power interaction operation strategy of a microgrid.
Figure 4 is an example campus microgrid emulation feeder system.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, the present invention provides a method for evaluating operational reliability of a micro-grid including an electric vehicle charging station, comprising the following steps:
s1, calculating the charge state of the power battery of the electric automobile at each time point according to the running characteristics of the electric automobile in the micro-grid;
s2, establishing an interactive response power calculation model according to the internal power interaction strategy of the microgrid;
s3, area division is carried out on the micro-grid according to the positions of the network switches and the on-off characteristics of the switches of different types;
s4, according to the difference of the areas where the internal fault points are located when the micro-grid isolated island operates, different switching action strategies are adopted to carry out fault isolation;
s5, the sequential Monte Carlo simulation algorithm is adopted to evaluate the operation reliability of the micro-grid with the electric vehicle charging station.
In step S1, the state of charge of the electric vehicle power battery at each time point is calculated according to the operating characteristics of the electric vehicle in the microgrid, and the specific steps are as follows:
step S11: according to the working time characteristics of electric vehicle users in a park, the running characteristics of different types of electric vehicles are analyzed, and fig. 2 is a schematic diagram of the running states of the electric vehicles in different periods of a working day. Wherein N is1The time period that the electric duty car starts from the park in the early season and returns to the park after receiving the staff at the fixed station; n is1The time period from home to park for the employee to take the private electric automobile in the morning; n is a radical of hydrogen2(n2) The time period for the electric regular bus (private electric automobile) to stop in the charging station in the garden area; n is a radical of3For the time period of the afternoon shift bus taking the employee to the fixed station and back to the park, n3The time period from the park to the home for the employee to take the private electric automobile; n is a radical of4For time periods when regular bus is parked at park charging station, n4The private electric automobile is stopped at the house of the staff for a certain time;
step S12: in order to meet the vehicle using requirements of electric vehicle users, the minimum constraint that the state of charge of the power battery at each time point needs to be met is as follows:
Figure BDA0002090388380000081
in the formula: t is0、T′0Respectively, the departure time points of regular bus and private electric automobile in the morning, and T0<T′0; T1And T2Respectively the time points of access (on-duty) and departure (off-duty); t is3Stopping at a charging station after the regular bus returns to the park; t'3A time point of arrival of the private EV; SOCmin(T0/T′0)、SOCmin(T1)、 SOCmin(T2)、SOCmin(T3/T′3) Is at T0Or T'0、T1、T2、T3Or T'3A charge state of each electric vehicle at a time point; s1、S2Respectively as regular bus (private electric automobile) in N1(n1)、N3(n3) A time-of-day travel route (kM); w is the power consumption per kilometer of the electric automobile, WedThe rated electric quantity is the rated electric quantity of the power battery of the electric automobile; SOCsd·minAnd the minimum state of charge threshold is set for ensuring certain service life of the power battery.
In step S2, an interactive response power calculation model is established according to the microgrid internal power interaction strategy, and the specific steps are as follows:
step S21: determining microgrid operating period, i.e. N1,N2,N3,N4
Step S22: and performing source and load power balance calculation on the fault-free area, wherein the source and load power balance formula is as follows:
Pph(t)=PWT(t)+PPV(t)-PL(t)
in the formula: pL(t) is the real-time load power within the microgrid; pWT(t) is the total power generated by the wind turbine generator at the moment t, PPVAnd (t) is the total power generation power of the photovoltaic generator set at the moment t.
Step S23: if Pph(t) > 0. In N1And N3In time periods, no electric vehicle is connected, and the microgrid will only charge the energy storage battery. At N2And N4In time interval, in order to ensure that the energy storage equipment is required to maintain the stability of the system operation when no electric automobile is accessed, the state of charge of the energy storage equipment is kept to a certain limit SOCESS·sd. Secondly, charging the electric automobile in the electric automobile charging station;
step S24: if Pph(t) is less than or equal to 0. In N1And N3And in the time interval, only the energy storage battery is discharged. In N2And N4In the time period, the electric automobile is firstly arranged to discharge. If Pph(t)+PEV·dis(t) < 0, the energy storage battery also participates in the discharging operation. If the energy storage battery, the electric vehicle charging station and the renewable energy source cannot meet the load requirement, namely Pph(t)+PEV·dis(t)+PESS·disWhen (t) < 0, load reduction is performed according to the importance level of the load in the network. Wherein P isEV·dis(t) discharge power of the charging station at time t, PESS·disAnd (t) is the discharge power of the energy storage battery at the moment t. Meanwhile, the variation of the output power of the electric vehicle charging station should be considered. When the output power is reduced to zero, the power supply is still insufficient, and the load in the island region is further reduced.
Step S25: based on the power interaction strategy, the calculation model of the charging and discharging power of the energy storage device at different time intervals is as follows:
Figure BDA0002090388380000091
Figure BDA0002090388380000092
Figure BDA0002090388380000093
Figure BDA0002090388380000094
in the formula: pESS·dis(t) discharge power of the energy storage device, PESS·ch(t) charging power of the energy storage device, PESS·dis·maxIs the maximum discharge power, P, of the energy storage deviceESS·ch·maxMaximum charging power for energy storage, SOCESS(t) State of Charge, SOC, of the energy storage deviceESS·sdThe energy is stored in the state of charge which is required to be kept for maintaining the stability of a subsequent system when no electric vehicle is in the electric vehicle charging station.
Step S26: in N1、N3The number of electric vehicles in the charging station is substantially zero during the time period. Therefore, consider only N2、N4The interactive power condition between period electric vehicle charging station and little electric wire netting:
when P is presentph(t) > 0, charging station charging power:
Figure BDA0002090388380000101
when P is presentph(t) < 0, charging station discharge power:
Figure BDA0002090388380000102
PEV·dis·max(t)=Ncar(t)·Pcar·dis+Nbus(t)·Pbus·dis
in the formula: pEV·maxThe maximum allowable flow power of a main line connected with the micro-grid for the charging station is constrained by line parameters; pEV·dis·max(t) the maximum power that can be delivered by the charging station at time t, at which time the station can be operatedNumber N of various electric vehicles participating in dischargingcar(t)、Nbus(t) and discharge power P of single electric vehiclecar·dis、Pbus·disOf (3) is performed.
In step S3, according to the positions of the network switches and the on-off characteristics of the switches of different types, the microgrid is divided into regions, which are specifically classified as:
a primary area: there is no area inside any switching device of any kind. The area will be isolated as a whole regardless of which element fails. Therefore, when enumerating faults, the primary region is taken as the minimum enumeration unit, and the overall fault rate of the region is considered.
And (3) secondary area: the circuit breaker is taken as a boundary, and the region no longer contains the circuit breaker. Generally, the same branch region is formed by combining a plurality of primary regions.
In step S4, according to the difference of the areas where the internal fault points are located in the micro grid island operation, different switching action strategies are adopted to perform fault isolation, and the specific steps are as follows:
step S41: when a fault occurs in a primary region with an isolating switch as a boundary, the circuit breakers or intelligent switches in all upstream directions of the region act firstly to cut off the power supply current of all power supplies, the isolating switch in the fault region is cut off to isolate the fault, the circuit breakers and the intelligent switches are superposed, and the fault-free equipment of the micro-grid recovers to normal operation;
step S42: when a fault occurs in a primary region with the intelligent switch as a boundary, the corresponding intelligent switch is only needed to be disconnected;
step S43: when a line branch fails, the disconnecting switch is not required to be switched on or off after the current is blocked, and the circuit breaker and the intelligent switch can not be switched on again.
In step S5, a sequential monte carlo simulation algorithm is used to evaluate the reliability of the operation of the microgrid including the electric vehicle charging station, and the specific steps are as follows:
step S51: reading original data, setting an initial value T of an analog clock to be 0, and assuming that all elements are in a normal working state initially;
step S52: calculating the Time To Failure (TTF) and the Time To Repair (TTR) of each element in the network to obtain a time sequence table: TTF, TTR;
step S53: the faults are enumerated. Selecting the minimum TTF of TTFsiThe corresponding element is a fault element;
step S54: analyzing simulation time T to T + TTFiIn the time period, the running conditions of the system in different time periods of whether the electric vehicle charging station is connected or not are added, and the accumulated simulation time T is T + TTFi
Step S55: judging the type and position of the fault, and determining a fault influence area;
step S56: fault isolation is carried out on the micro-grid after the fault;
step S57: and before the fault node recovers to normal operation, analyzing the operation condition of the area after the fault is isolated. It is determined whether load shedding is required. If yes, accumulating the power failure time of the reduced load and the reduced load power. And accumulating the simulation time T as T + TTRi
Step S58: calculating the power failure time of the affected load nodes;
step S59: judging whether the simulation time reaches a specified simulation time limit, if not, returning to the step S62; if yes, continuing the next step;
step S510: and calculating the reliability evaluation indexes of the system and the load nodes.
The reliability evaluation index specifically includes:
the power supply reliability evaluation index in the isolated island state of the micro-grid can adopt the reliability evaluation index (the average annual power failure frequency SAIFI of the system, the average annual power failure time SAIDI of the system, the average annual power failure duration CAIDI of a user and the average power supply availability ASAI) of the traditional power distribution network. Meanwhile, considering the power interaction condition between the accessed EV charging station and the microgrid system, the invention also provides the following indexes to analyze the influence on the operation reliability of the microgrid after the access of the EV charging station on the basis of the traditional indexes:
(1) firstly, the intimacy between the EV charging station and the microgrid is analyzed by counting the charging and discharging times (CHT and DIST) of the EV charging station in a simulation period, and parameters such as Average Charging Depth (ACD) (kW/time), Average Discharging Depth (ADD) (kW/time) and the like of the charging station. Wherein:
Figure BDA0002090388380000121
Figure BDA0002090388380000122
in the formula:
Figure BDA0002090388380000123
power for ith charging and jth discharging of the EV charging station respectively; PG (Picture experts group)iAnd when the EV power station is charged for the ith time, the renewable energy can generate power.
(2) The method comprises the steps of analyzing a microgrid with an EV charging station and a microgrid without an EV charging station by calculating the times (times/year) of load reduction and the Average load reduction depth (kW/time) of load reduction in the microgrid, and further analyzing the influence on the power supply reliability of the microgrid after the EV charging station is accessed. Wherein:
Figure BDA0002090388380000124
in the formula: pzThe load power for the z-th reduction.
(3) When the power supply in the non-fault area in the network is insufficient, the EV charging station participates in discharging, the power supply shortage in the network is reduced, the reduced load power is reduced, and therefore the number of users with power failure is reduced. The effect can be represented by the following formula:
Figure BDA0002090388380000125
fig. 4 shows a network structure diagram of an embodiment, which is based on a network structure of an IEEE power distribution system reliability evaluation test system RTBS Bus 6, and an area where load nodes LP13-LP23 are located is used as a microgrid and is connected to a power distribution network through a PCC switch.
The micro-grid is partitioned, the partitioning conditions are shown in table 1, 11 load primary areas are totally formed, and the photovoltaic generator set and the wind generating set are used as two power supply primary areas and are respectively connected into the No. I secondary area and the No. III secondary area. The photovoltaic installed capacity is 1.5MW, and the wind turbine generator set is composed of 3 fans of 0.8 MW. Wherein the cut-in wind speed of the fan is 4m/s, the rated wind speed is 12.5m/s, and the cut-out wind speed is 25 m/s. The energy storage battery ESS is used as a single primary area and is accessed into the No. three secondary areas, and the capacity is 5000 kW.h. And distribution lines in the microgrid area all adopt cables. Table 2 shows the corresponding reliability parameters.
TABLE 1 microgrid zoning scenario
Figure BDA0002090388380000126
TABLE 2 reliability parameters
Figure BDA0002090388380000131
For the charging stations EV1 and EV2 in fig. 4, 50 private electric vehicle charging piles and 5 electric regular vehicle charging piles are respectively arranged, the private electric vehicles are assumed to be unified to be a biedi E5 vehicle type, the electric regular vehicles are an aerospace E8 vehicle type, and configuration parameters of the two types of electric vehicles are shown in table 3. Time node T0(T0‘)、T1、T2、T3(T3') were taken as 7:00(7:30), 9:00, 17:00, 19:00(18:30), respectively.
TABLE 3 configuration parameters of two types of electric vehicles
Figure BDA0002090388380000132
In the present embodiment, the calculation of the related reliability index is performed for two situations, i.e., the access state of the EV charging station and the non-EV charging station, respectively, and the results are shown in table 4 below. In the present example, the load nodes LP13, LP17, and LP19 are set to have a low importance level and can be reduced preferentially, and the subsequent reduction of the corresponding loads is determined by the electrical distance between the load node and the power source node, and the distances are reduced in order from the far side to the near side.
TABLE 4 associated reliability index
Reliability index EV-free charging station EV-contained charging station
SAIFI (time/family, year) 2.449 2.212
SAIDI (h/family-year) 14.298 12.103
ASAI 0.99634 0.9986
ACD (kW/times) -- 0.2062
ADD (kW/times) -- 0.1983
ALR (time/year) 1577 676
ALRD (kW/times) 0.3842 0.2763
η -- 0.4178
As can be seen from the calculation results in table 4, in the isolated island operation state of the microgrid, considering the power interaction situation between the electric vehicle charging station and the microgrid, the average power failure time of the microgrid system is reduced by 0.237 (times/household/year), the average power failure frequency is reduced by 15.35%, the average load shedding depth (ALRD) is reduced by about 28.1%, the load shedding frequency (ALR) is reduced by 57.1%, and the improvement of the power supply quality of the microgrid to the load in the isolated island operation state is effectively reflected. The charging depth index (ACD) of the electric automobile also reflects, the V2G technology of the electric automobile charging station is fully utilized, the charging requirement of the electric automobile is met, and the consumption capacity of the system to distributed energy is increased. When the generated power in the microgrid is abundant, the electromobile is charged, and the wind and light abandoning rate in the system can be effectively reduced.

Claims (1)

1. A reliability evaluation method for a micro-grid with an electric vehicle charging station is characterized by comprising the following steps:
1) the method comprises the following steps of obtaining the charge state of the power battery of the electric automobile at each time point according to the running characteristics of the electric automobile in the microgrid, wherein the charge state of the power battery of the electric automobile at each time point meets the following constraints:
Figure FDA0003437326360000011
wherein, T0、T′0Respectively, the departure time points of regular bus and private electric automobile in the morning, and T0<T′0,T1And T2Time points of access and departure, T, respectively3For the point in time, T ', at which the coach stops at the charging station after returning to the park'3Is the time point of the private electric automobile arriving at home, SOCmin(T0/T′0)、SOCmin(T1)、SOCmin(T2)、SOCmin(T3/T′3) Are respectively at T0Or T'0、T1、T2、T3Or T'3State of charge, S, of an electric vehicle at a point in time1For regular bus at N1、N3Distance traveled in time intervals, S2For private electric automobile at n1、n3Driving distance in time interval, N1Time period for the electric regular bus to start from the park in the morning and return to the park after the staff is connected to the fixed station, N2Time period for parking electric duty car in park to charge station, N3Time period for afternoon bus to transport employees to fixed sites and back to park, N4For time periods when regular bus is parked at park charging station, n1For the time period from home to park when the employee takes the private electric automobile in the morning, n2Time period for a private electric car to stop at a charging station in a park, n3Time period from park to home for employee to take private electric automobile, n4For the time when the private electric automobile stops at the house of the staff, W is the power consumption of the electric automobile per kilometer, WedRated electric quantity, SOC, of the power battery of the electric automobilesd·minA minimum state of charge threshold set to ensure a certain service life of the power battery;
2) establishing an interactive response power calculation model according to a microgrid internal power interaction strategy, which specifically comprises the following steps:
21) determining operation of a microgridTime period including N1、N2、N3And N4A time period;
22) and performing source and load power balance calculation on the fault-free area, wherein the source and load power balance formula is as follows:
Pph(t)=PWT(t)+PPV(t)-PL(t)
wherein, Pph(t) is the area balance power, PL(t) is the real-time load power within the microgrid; pWT(t) is the total power generation power of the wind turbine generator at the moment t, PPV(t) is the total power generation power of the photovoltaic generator set at the moment t;
23) when P is presentph(t) > 0 and at N1And N3During the time interval, no electric automobile is connected, the micro-grid only charges the energy storage device, and N is2And N4In time periods, in order to ensure that the energy storage equipment is required to maintain the stability of the system operation when no electric automobile is accessed, the state of charge of the electric automobile is at least SOCESS·sdThen, charging the electric automobile in the electric automobile charging station;
24) when P is presentph(t) is less than or equal to 0 and is in the range of N1And N3At time intervals, only the energy storage device is discharged, at N2And N4During the period of time, when the electric vehicle is discharging, if Pph(t)+PEV·dis(t) < 0, the energy storage device participates in the discharging operation at the same time, and if the combined output of the energy storage device, the electric vehicle charging station and the renewable energy cannot meet the load requirement, namely Pph(t)+PEV·dis(t)+PESS·dis(t) < 0, load shedding is performed according to the important level of the load in the network, and if the power supply is insufficient when the output power is reduced to zero, the load in the island region is further shed, wherein PEV·dis(t) discharge power of the charging station at time t, PESS·dis(t) is the discharge power of the energy storage device at the moment t;
25) based on the internal power interaction strategy of the microgrid in the steps 23) and 24), the calculation formula of the charging and discharging power of the energy storage device at different time intervals is as follows:
Figure FDA0003437326360000021
Figure FDA0003437326360000022
Figure FDA0003437326360000023
Figure FDA0003437326360000031
wherein, PESS·dis(t) discharge power of the energy storage device, PESS·ch(t) charging power, P, of the energy storage deviceESS·dis·maxIs the maximum discharge power, P, of the energy storage deviceESS·ch·maxMaximum charging power for energy storage, SOCESS(t) State of Charge, SOC, of the energy storage deviceESS·sdThe energy is stored to maintain the stable state of charge of a subsequent system when the electric vehicle charging station has no electric vehicle;
26) in N1、N3The number of electric vehicles in the charging station for the time period is approximately zero, considering that in N2、N4The interactive power condition between the electric vehicle charging station and the microgrid in the time period, namely the interactive response power calculation model, is as follows:
when P is presentph(t) > 0, charging station charging power:
Figure FDA0003437326360000032
when P is presentph(t) < 0, charging station discharge power:
Figure FDA0003437326360000033
PEV·dis·max(t)=Ncar(t)·Pcar·dis+Nbus(t)·Pbus·dis
wherein, PEV·maxMaximum allowable flow power, P, of a main line connecting a charging station to a microgridEV·dis·max(t) the maximum power which can be sent out by the charging station at the moment t;
3) according to the position of network switch and the characteristic of opening and shutting down of different grade type switch, carry out region division to the microgrid, carry out region division's concrete classification to the microgrid includes:
a primary area: the method comprises the following steps that a region without any type of switching device is arranged inside, once an element fault occurs in the region, the region is isolated integrally, and when the fault is enumerated, a primary region is used as a minimum enumeration unit, and the integral fault rate of the region is considered;
and (3) secondary area: the circuit breaker is taken as a boundary, a region without the circuit breaker is arranged in the region, and the same branch region is formed by combining a plurality of primary regions;
4) according to the difference in the internal fault point place area when little electric wire netting island moves, take different switch action modes to carry out fault isolation and specifically include:
when a fault occurs in a primary region with an isolating switch as a boundary, all the circuit breakers or intelligent switches in the upstream direction of the region act firstly to cut off the power supply current of all power supplies, the isolating switch in the fault region is cut off to isolate the fault, the circuit breakers and the intelligent switches are superposed, and the fault-free equipment of the microgrid recovers to normal operation;
when a fault occurs in a primary region with the intelligent switch as a boundary, only the corresponding intelligent switch is switched off;
when a line branch circuit has a fault, the on-off operation of the isolating switch is not needed after the current is blocked, and meanwhile, the circuit breaker and the intelligent switch are not closed any more;
5) the method comprises the following steps of adopting a sequential Monte Carlo simulation method to evaluate the operation reliability of a microgrid with an electric vehicle charging station under the condition of considering the internal power interaction and fault isolation of the microgrid, and specifically comprising the following steps:
51) reading original data, setting an initial value T of an analog clock to be 0, and assuming that all elements of the micro-grid are in a normal working state initially;
52) obtaining a TTF time sequence table and a TTR time sequence table according to the non-fault working time TTF and the fault repairing time TTR of each element in the micro-grid;
53) enumerating faults and selecting the minimum value TTF in the TTF time sequence listiThe corresponding element is a failed element;
54) get from T to T + TTFiIn the simulation time period, the operating conditions of the micro-grid in different time periods whether the micro-grid is connected to the electric vehicle charging station or not are added, and the simulation time T is accumulated as T + TTFi
55) Judging the type and position of the fault and determining a fault influence area;
56) fault isolation is carried out on the micro-grid after the fault;
57) before the fault node returns to normal operation, whether load reduction is needed or not is determined according to the operation condition of the area after fault isolation, if yes, the power failure time of the reduced load and the reduced load power are accumulated, and the simulation time T is accumulated as T + TTRi
58) Acquiring the power failure time of the affected load nodes;
59) judging whether the time reaches the specified time limit, if not, returning to the step 52); if yes, continuing the next step;
510) reliability evaluation is carried out according to reliability evaluation indexes of a microgrid system and load nodes, the reliability evaluation indexes comprise conventional reliability evaluation indexes and microgrid reliability evaluation indexes comprising electric vehicle charging stations, the conventional reliability evaluation indexes comprise system annual average power failure frequency SAIFI, system annual average power failure time SAIDI, user annual average power failure duration CAIDI and average power supply availability ASAI, the microgrid reliability evaluation indexes comprising electric vehicle charging stations comprise average charging depth ACD, average discharging depth ADD, average load reduction depth ALRD of the charging stations, and load reduction force eta of the electric vehicle charging stations in the case of insufficient power supply of a fault-free area, and the calculation formula of the microgrid reliability evaluation indexes comprising electric vehicle charging stations comprises the following steps:
Figure FDA0003437326360000051
Figure FDA0003437326360000052
Figure FDA0003437326360000053
Figure FDA0003437326360000054
wherein the content of the first and second substances,
Figure FDA0003437326360000055
the power of ith charging and jth discharging of the electric vehicle charging station is respectively, CHT and DIST are respectively the total charging and discharging times of the electric vehicle charging station, PGiThe power that the renewable energy source can send out when charging for electric automobile charging station ith, and the ALR is the total number of times of load reduction in the microgrid, PzThe load power for the z-th reduction.
CN201910501490.6A 2019-06-11 2019-06-11 Micro-grid reliability evaluation method with electric vehicle charging station Active CN110245858B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910501490.6A CN110245858B (en) 2019-06-11 2019-06-11 Micro-grid reliability evaluation method with electric vehicle charging station

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910501490.6A CN110245858B (en) 2019-06-11 2019-06-11 Micro-grid reliability evaluation method with electric vehicle charging station

Publications (2)

Publication Number Publication Date
CN110245858A CN110245858A (en) 2019-09-17
CN110245858B true CN110245858B (en) 2022-06-10

Family

ID=67886668

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910501490.6A Active CN110245858B (en) 2019-06-11 2019-06-11 Micro-grid reliability evaluation method with electric vehicle charging station

Country Status (1)

Country Link
CN (1) CN110245858B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111985777A (en) * 2020-07-20 2020-11-24 中国农业大学 Method and system for establishing electric vehicle load aggregate regulation and control capability assessment model
CN113313403B (en) * 2021-06-15 2022-09-16 国网安徽省电力有限公司经济技术研究院 Power distribution network comprehensive evaluation method, device and system based on large-scale high-power electric vehicle charging and discharging and storage medium
CN115115279B (en) * 2022-08-25 2022-11-04 山西北斗智能科技有限公司 Micro-partition carbon emission management method, system, medium and equipment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103746402A (en) * 2013-12-13 2014-04-23 国家电网公司 Method for assessing reliability of power distribution network accessed with wind/ storage energy complementation microgrid
EP3048698A1 (en) * 2013-11-28 2016-07-27 State Grid Corporation of China (SGCC) Method for multi-fault power restoration of power distribution network
CN109066659A (en) * 2018-08-24 2018-12-21 国网河北省电力有限公司电力科学研究院 Micro-capacitance sensor isolated operation reliability estimation method and terminal device

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3048698A1 (en) * 2013-11-28 2016-07-27 State Grid Corporation of China (SGCC) Method for multi-fault power restoration of power distribution network
CN103746402A (en) * 2013-12-13 2014-04-23 国家电网公司 Method for assessing reliability of power distribution network accessed with wind/ storage energy complementation microgrid
CN109066659A (en) * 2018-08-24 2018-12-21 国网河北省电力有限公司电力科学研究院 Micro-capacitance sensor isolated operation reliability estimation method and terminal device

Also Published As

Publication number Publication date
CN110245858A (en) 2019-09-17

Similar Documents

Publication Publication Date Title
Hashemi-Dezaki et al. Risk management of smart grids based on managed charging of PHEVs and vehicle-to-grid strategy using Monte Carlo simulation
Erol-Kantarci et al. Prediction-based charging of PHEVs from the smart grid with dynamic pricing
Nayak et al. Economical management of microgrid for optimal participation in electricity market
CN110245858B (en) Micro-grid reliability evaluation method with electric vehicle charging station
Hussein et al. Distributed battery micro-storage systems design and operation in a deregulated electricity market
Ghiani et al. Impact of renewable energy sources and energy storage technologies on the operation and planning of smart distribution networks
CN109948823B (en) Self-adaptive robust day-ahead optimization scheduling method for light storage charging tower
Khoucha et al. Integrated energy management of a plug-in electric vehicle in residential distribution systems with renewables
CN110429596B (en) Power distribution network reliability assessment method considering electric vehicle time-space distribution
CN106026076B (en) A kind of subscriber&#39;s side powered reliability estimation method of meter and electric vehicle enabling capabilities
CN111478334B (en) Intelligent power grid for comprehensively utilizing social energy storage system
CN114285034A (en) Day-ahead regulation and control optimization method and system considering power receiving and new energy fluctuation
Rodriguez et al. Sizing of a fuel cell–battery backup system for a university building based on the probability of the power outages length
Cetinbas et al. Energy management of a PV energy system and a plugged-in electric vehicle based micro-grid designed for residential applications
Aluisio et al. AC and DC solutions for electric vehicle microgrid: Sizing and reliability analysis
Zhao et al. Scenario-based evaluation on the impacts of electric vehicle on the municipal energy supply systems
Nakamura et al. Green base station using robust solar system and high performance lithium ion battery for next generation wireless network (5G) and against mega disaster
Xiong et al. Reliability based strategic integration of plug-in hybrid electric vehicles in power systems
CN105896533B (en) A kind of active distribution network Static security assessment method
Sitch et al. Integration of pulsed electric bus fleet charging profiles through coordinated control of hybrid microgrids
Dang et al. Energy optimization in an eco-district with electric vehicles smart charging
Kordkheili et al. Managing high penetration of renewable energy in MV grid by electric vehicle storage
Sonder et al. Integrating DC fast/rapid chargers in low voltage distribution networks
Castro et al. Operating Reserve Assessment in Systems with Energy Storage and Electric Vehicles
Thakre et al. Potentially affect of a vehicle to grid on the electricity system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant