CN110210777A - A kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station - Google Patents
A kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station Download PDFInfo
- Publication number
- CN110210777A CN110210777A CN201910502082.2A CN201910502082A CN110210777A CN 110210777 A CN110210777 A CN 110210777A CN 201910502082 A CN201910502082 A CN 201910502082A CN 110210777 A CN110210777 A CN 110210777A
- Authority
- CN
- China
- Prior art keywords
- power
- micro
- capacitance sensor
- charging station
- distribution network
- 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.)
- Granted
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 claims abstract description 10
- 238000000342 Monte Carlo simulation Methods 0.000 claims abstract description 6
- 230000005611 electricity Effects 0.000 claims description 40
- 238000004146 energy storage Methods 0.000 claims description 18
- 238000004088 simulation Methods 0.000 claims description 16
- 238000011144 upstream manufacturing Methods 0.000 claims description 13
- 238000002955 isolation Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000009467 reduction Effects 0.000 claims description 4
- 230000004888 barrier function Effects 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 claims description 3
- 230000008439 repair process Effects 0.000 claims description 2
- 230000008901 benefit Effects 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 description 8
- 238000007726 management method Methods 0.000 description 4
- 239000013589 supplement Substances 0.000 description 4
- 239000000203 mixture Substances 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 244000131316 Panax pseudoginseng Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 238000013070 change management Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/30—Constructional details of charging stations
- B60L53/31—Charging columns specially adapted for electric vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/388—Islanding, i.e. disconnection of local power supply from the network
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Mechanical Engineering (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Transportation (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The present invention relates to a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station, 1) variation for considering power distribution network electrical structure and the method for operation after micro-capacitance sensor and electric automobile charging station access, establishes the distribution network structure structure of meter and micro-capacitance sensor and electric automobile charging station access;2) operation conditions of power distribution network is obtained according to the charge-discharge characteristic of the different operation conditions of grid-connected micro-capacitance sensor and electric automobile charging station;3) according to the different operating statuses of power distribution network, the distribution network reliability after being accessed to meter and electric automobile charging station with grid type micro-capacitance sensor using sequential Monte Carlo simulation is assessed.Compared with prior art, the present invention has many advantages, such as to consider that comprehensive, assessment is accurate.
Description
Technical field
The present invention relates to distribution network planning fields, more particularly, to a kind of distribution containing micro-capacitance sensor and electric automobile charging station
Net reliability estimation method.
Background technique
With the continuous maturation that micro-capacitance sensor technology develops, grid type micro-capacitance sensor has become the organic composition of power distribution network now
Part.It is also bigger by the technical advantage that grid type micro-capacitance sensor is embodied in terms of solving distributed generation resource access power distribution network
Promote its development.Simultaneously as the rapid development of current era ev industry, and the concentration to realize electric car
Change management, electric automobile charging station also has become the important composition element of micro-capacitance sensor and power distribution network, the structure and fortune of power distribution network
Row also becomes to become increasingly complex.The raising of requirement along with modern day user to power quality connects consideration various new element
The Reliability Evaluation of power distribution network after entering also seems more important.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to contain micro-capacitance sensor and electricity
The distribution network reliability evaluation method of electrical automobile charging station.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station, comprising the following steps:
1) variation for considering power distribution network electrical structure and the method for operation after micro-capacitance sensor and electric automobile charging station access, is established
Meter and the distribution network structure structure of micro-capacitance sensor and electric automobile charging station access;
2) distribution is obtained according to the charge-discharge characteristic of the different operation conditions of grid-connected micro-capacitance sensor and electric automobile charging station
The operation conditions of net;
3) according to the different operating statuses of power distribution network, using sequential Monte Carlo simulation to meter and electric automobile charging station
It is assessed with the distribution network reliability after the access of grid type micro-capacitance sensor.
The step 1) specifically includes the following steps:
11) according to the type of grid type micro-capacitance sensor, geographical location and internal electric source factor, the operation shape of micro-capacitance sensor is determined
Condition;
12) the operation shape of electric automobile charging station is determined according to the type of vehicle of electric automobile charging station and construction area
Condition;
13) the distribution net work structure schematic diagram of building meter and micro-capacitance sensor and electric automobile charging station access.
In the step 2), the operation conditions of power distribution network includes that failure-free operation situation and different zones break down
Operation conditions interacts with the power between power distribution network including grid type micro-capacitance sensor under failure-free operation situation and independent operation is electric
Electrical automobile charging station interacts two seed situations with the power between power distribution network, includes under the operation conditions that different zones break down
Two seed situations that access point upstream region breaks down and access point downstream area breaks down.
Under the mutual mover situation of power between grid type micro-capacitance sensor and power distribution network, except power distribution network be in peak load period it
Outside, in other times section, power distribution network can be used as the backup power source of micro-capacitance sensor, when electricity shortage in micro-capacitance sensor, micro-capacitance sensor
Electricity is bought to meet the power demand of microgrid internal loading user from external power grid, then is had:
When bulk power system load is in peak value, power calculation model is interacted between micro-capacitance sensor and power distribution network are as follows:
Wherein, Δ PM→W(t) power that period micro-capacitance sensor is conveyed to power distribution network thus, PDGIt (t) is distributed electrical in microgrid
The power output in source, PLIt (t) is the load power in micro-capacitance sensor, PEV·chIt (t) is the charge requirement power of EV charging station in microgrid;
When bulk power system load is in average operation level, micro-grid connection is run at this time, and power distribution network is to micro-capacitance sensor
The power calculation model of conveying are as follows:
PW→M(t)=PL(t)+PEV·ch(t)-PESS·dis(t)-PDG(t)
Wherein, PW→M(t) power that period power distribution network is conveyed to micro-capacitance sensor thus, PESS·disIt (t) is the electric discharge of energy storage device
Power works as PW→M(t) when > 0, micro-capacitance sensor interior power power output is less than workload demand at this time, and otherwise, micro-capacitance sensor will be conveyed to external power grid
Power, watt level be | PW→M(t)|;
When bulk power system load is at a low ebb, micro-grid connection is run at this time, the function that power distribution network is conveyed to micro-capacitance sensor
Rate computation model are as follows:
PW→M(t)=PL(t)+PEV·ch(t)+PESS·ch(t)-PDG(t)
Wherein, PBat·ch(t) thus in period micro-capacitance sensor energy storage device charge power, work as PW→M(t) micro- at this time when > 0
Power grid interior power power output is less than workload demand, and otherwise, micro-capacitance sensor will be to external power grid transmission power, watt level | PW→M(t)
|。
Under the mutual mover situation of power between independent operation electric automobile charging station and power distribution network, then have:
When bulk power grid load is in peak value, charging station is without charging, then the power that charging station is conveyed to power grid are as follows:
Wherein, PEV→W(t) power conveyed for the period electric automobile charging station to power grid, N1For in charging station this when
Section may participate in the electric car quantity of electric discharge,Discharge power for i-th electric car in t moment, N2For the period
It may participate in the quantity of the standby battery of electric discharge in charging station,For jth platform battery t moment discharge power;
When bulk power grid load is in level values and ebb period, charging station no longer feeds electric energy to power grid, is only used as power grid
Charging load, the charge power of charging station at this time are as follows:
Wherein, PEV·chIt (t) is electric automobile charging station in the charge power of t moment, n1 is that charging is participated in charging station
Electric car quantity,For the charge power of a electric car, n2 is the standby battery that charging is participated in charging station
Quantity,For the charge power of a platform standby battery.
Trip region is broken down under sub- situation on an access point, according to the organizational structure in region and accesses power conditions then
Have:
When in the isolated island region after Fault Isolation containing the power distribution network power load outside micro-capacitance sensor and micro-capacitance sensor, in region
Power-balance situation are as follows:
ΔP1(t)=PDG(t)+PESS·dis(t)+PEv·dis(t)-Pwl(t)-Pl(t)
Wherein, Δ P1It (t) is the balance power in fault-free region at this time, PDGIt (t) is distributed generation resource power output in region,
PESS·dis(t)、PEv·dis(t) be respectively energy storage and electric automobile charging station in micro-capacitance sensor discharge power, PwlIt (t) is micro-capacitance sensor
Interior conventional load power, PlIt (t) is the distribution network load in fault-free region, as Δ P1(t) when < 0, in region electricity shortage into
Row load reduction operation;
Power-balance situation when containing multiple micro-capacitance sensors in the fault-free isolated island region of formation, in region are as follows:
Wherein, Δ P2It (t) is the balance power in fault-free region at this time,For the richness of k-th of micro-capacitance sensor in region
Complementary work rate, as Δ P2(t) >=0 when, region interior power, which is contributed, is able to satisfy the demand of load, all load normal electricity consumptions, as Δ P2
(t) 0 <, andWhen, then part distribution network load is cut down, whenWhen, each micro- electricity
Net switchs to isolated operation, and all distribution network loads in region have a power failure;
When charging station and micro-capacitance sensor exist simultaneously, if there is rich power in micro-capacitance sensor, rich power is conveyed to and is matched
Power grid, if micro-capacitance sensor will turn into isolated operation without rich power in micro-capacitance sensor, at this point, the discharge power of charging station calculates mould
Type are as follows:
PEV·dismax(t)=PEV→W(t)
Wherein, PEV·disIt (t) is the discharge power of charging station, m is the micro-capacitance sensor number in region,It is τ
The rich power of micro-capacitance sensor, PlIt (t) is the power demand of distribution network load in region, PEV·dismax(t) stage charging station thus
Maximum can discharge power, PEV→W(t) power conveyed for charging station to power distribution network, when the balance power in fault-free regionWhen, load will be cut down by load priority;
When being free of micro-capacitance sensor in isolated island region, and when electric automobile charging station and distribution network load containing independent operation,
Charging station carries out concentrating electric discharge being that other power loads in isolated island region are powered at this time, when the discharge power of charging station is small
When distribution network load demand in region, then load will according to priority be cut down, when the balance power in fault-free region
ΔP4(t)=Pl(t)-PEV·dismax(t) when > 0, distribution network load in region will be cut down by load priority.
It breaks down under sub- situation in access point downstream area, Fault Isolation, faulty section is carried out by block switch at this time
The power off time of domain internal loading continues to fault restoration, the grid type micro-capacitance sensor and independent operation for fault point upstream it is electronic
Vehicle charging station is not influenced by failure and is incorporated into the power networks, and operation conditions is identical as by power distribution network fault-free situation, when failure is sent out
Raw at the inside of grid type micro-capacitance sensor, then micro-capacitance sensor will turn into isolated operation, when micro-capacitance sensor or electric automobile charging station access
When electric power main line where point breaks down, micro-capacitance sensor will turn into isolated operation, and electric automobile charging station is also disconnected with power grid
Electrically operated without charge and discharge, the distribution network load on faulty line all has a power failure at this time, until fault restoration.
The step 3) specifically includes the following steps:
31) each element initial data of power distribution network is obtained, determines emulation time limit Tlim, and initialize the simulation time T=of system
0;
32) when obtaining the time between failures sequence TTF of all elements in distribution network system, and choosing no-failure operation
Between the smallest element be fault element, i.e. TTFi=min [TTF];
33) in T → T+TTFiIn period, system failure-free operation is emulated in this period according to failure-free operation situation,
Statistics in network load peak period, in micro-capacitance sensor as electricity shortage and caused by the number of users cut down of load and cut down power,
And accumulative simulation time T=T+TTFi;
34) judge whether simulation time T reaches the emulation time limit, i.e. whether T is greater than Tlim, if so, then follow the steps 38), if
It is no, it thens follow the steps 35);
35) it is enumerating fault element and then is generating a random number, obtaining the repair time TTR of fault elementi;
36) in T → T+TTRiIn period, element failure in system first determines whether fault element region, if therefore
The isolated island region formed in the upstream region for the access point that barrier point is located at micro-capacitance sensor and electric automobile charging station or after Fault Isolation
Inside there are micro-capacitance sensor or electric automobile charging station access, is then emulated according to the sub- state of access point upstream region failure, if
Fault point is located in the downstream area of the access point of micro-capacitance sensor and electric automobile charging station, then the load in the region all stops
Electricity, power off time are fault correction time TTRiIf malfunctioning node is located inside micro-capacitance sensor, which switchs to isolated island fortune
Row adds up simulation time T=T+TTRiWith the power off time of load user;
37) judge whether simulation time T reaches the emulation time limit, i.e. whether T is greater than Tlim, if so, then follow the steps 38), if
It is no, then return step 32);
38) according to the frequency of power cut and power off time of each load point, the related reliability index of each node and system is obtained,
And reliability assessment is completed according to related reliability index.
In the step 38), the related reliability evaluation index include system annual power failure frequency SAIFI,
System annual power off time SAIDI, user annual interruption duration CAIDI and the availability ASAI that averagely powers.
Compared with prior art, the invention has the following advantages that
The present invention, will in order to accurately simulate the structure of the power distribution network containing micro-capacitance sensor and electric automobile charging station and run turntable
It is interacted in the grid type micro-capacitance sensor under failure-free operation situation with the power between power distribution network and independent operation electric car charges
The power between power distribution network of standing interacts two seed situations, the access point upstream under the operation conditions that different zones break down
The correspondence situation for two seed situations that domain is broken down and access point downstream area breaks down all is taken into account, and considers complete
Meter and electric automobile charging station and grid type micro-capacitance sensor are finally accessed using sequential Monte Carlo simulation using common in face
Distribution network reliability afterwards is assessed, and assessment is accurate, and the control for subsequent distribution network planning and each unit provides ginseng
Examine foundation.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention.
Fig. 2 is the distribution network structure structure chart of meter and micro-capacitance sensor and electric automobile charging station access.
Fig. 3 is the power interaction mode under unfaulty conditions between power distribution network and micro-capacitance sensor.
Fig. 4 is independent operation charging station charge and discharge strategy.
Fig. 5 is reliability assessment flow chart.
Fig. 6 is distribution network reliability computer sim- ulation figure in embodiment.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
As shown in Figure 1, the present invention provides a kind of evaluating reliability of distribution network side containing micro-capacitance sensor and electric automobile charging station
Method, comprising the following steps:
1) consider the variation of power distribution network electrical structure and the method for operation after micro-capacitance sensor and electric automobile charging station largely access,
Establish the distribution network structure structure of meter and micro-capacitance sensor and electric automobile charging station access;
2) charge-discharge characteristic of the different operation conditions of grid-connected micro-capacitance sensor and electric automobile charging station is comprehensively considered to matching
The operation conditions of power grid is analyzed
3) based on the operation reserve to new distribution net under different working condition, using sequential Monte Carlo method to meter and
Distribution network reliability after electric automobile charging station is accessed with grid type micro-capacitance sensor is assessed.
Power distribution network electrical structure and the method for operation after consideration micro-capacitance sensor and electric automobile charging station largely access in step 1)
Variation, establish meter and micro-capacitance sensor and electric automobile charging station access distribution network structure structure, specific steps are as follows:
Step 11: according to the difference of micro-capacitance sensor application scenarios, grid type micro-capacitance sensor have residential quarters type, industry park zone type,
The multiple types such as commercial office zone type.And the micro-capacitance sensor on remote districts or island is substantially based on isolated operation.Meanwhile it is all kinds of
The type micro-capacitance sensor geographical location different according to its, internal electric source component type and the factors such as capacity and corresponding device configuration
Difference, respective operation conditions is not also identical, has their own characteristics each, according to the type of grid type micro-capacitance sensor and its geographical position
It sets, the factors such as internal electric source, determines the operation conditions of micro-capacitance sensor;
Step 12: also according to the difference of its charge inside type of vehicle, the region of construction also respectively has electric automobile charging station
Difference.Wherein, electric bus charging station is generally uniformly built by public transport company due to its independent operation management system
If geographical location is generally the first and last website of public bus network with management, and only carries out management of charging and discharging to electric bus.And it is private
Cell and industry and commerce Administrative Area where the charging place of its habit of family's electric vehicle is generally house, therefore its charging station generally may be used
It is managed collectively by the micro-capacitance sensor of charging station region, i.e. the charging station internal portions that belong to micro-capacitance sensor.Therefore,
The mode of different types of electric automobile charging station access power distribution network can be divided into: pass through two kinds of sides of micro-capacitance sensor access and independent access
Formula.Its operation conditions is determined according to the type of vehicle of electric automobile charging station, construction area etc.;
Step 13: the distribution net work structure schematic diagram of meter and micro-capacitance sensor and electric automobile charging station access is determined, such as Fig. 2 institute
Show.
The charge and discharge that the different operation conditions of grid-connected micro-capacitance sensor and electric automobile charging station are comprehensively considered in step 2) are special
Property analyzes the operation conditions of power distribution network, specific steps are as follows:
Step 21: to operating analysis is carried out under power distribution network unfaulty conditions, including between grid type micro-capacitance sensor and power distribution network
Power interaction (as shown in Figure 3), independent operation electric automobile charging station interact two kinds of situation (such as Fig. 4 with the power between power distribution network
It is shown);
(1) grid type micro-capacitance sensor is interacted with the power between power distribution network
At this point, the power between micro-capacitance sensor and power distribution network interacts the operating status that situation then depends on bulk power system.
1) when bulk power system load is in peak value: the electricity price of this period is relatively high, should reduce micro-capacitance sensor as far as possible
The electricity bought from power distribution network, reduces micro-capacitance sensor operation cost.Therefore, in meter and microgrid electric car charging load basis
On, micro-capacitance sensor generated output has affluence, and micro-capacitance sensor alleviates bulk power grid operation to power distribution network output power to a certain extent at this time
Pressure;If electricity shortage in micro-capacitance sensor at this time, for the burden for avoiding bulk power grid load " on peak plus peak ", micro-capacitance sensor will at this time
It is disconnected and isolated operation with bulk power grid, energy storage and electric automobile charging station participate in discharge operation in microgrid, to meet micro- electricity
The power demand of conventional load, grid-connected after distribution network load crosses peak in netting, energy storage at this time and electric car charged into
Row electricity supplement.
At this point, interacting power calculation model between micro-capacitance sensor and power distribution network are as follows:
In formula: Δ PM→W(t) power that period micro-capacitance sensor is conveyed to power distribution network thus;PDGIt (t) is distributed electrical in microgrid
The power output in source;PLIt (t) is the load power in micro-capacitance sensor;PEV·chIt (t) is the charge requirement function of electric automobile charging station in microgrid
Rate.
2) when bulk power system load is in average operation level, micro-grid connection is run at this time.Electricity in micro-capacitance sensor
It is charging load in this period of electrical automobile charging station, when the distributed generation resource power output in micro-capacitance sensor can satisfy all in microgrid bear
When lotus demand, electricity more than needed will charge to energy storage, be transported to external power grid if having affluence again;If distribution in micro-capacitance sensor at this time
When formula power supply is unable to satisfy the power demand of its internal load (comprising electric car charging load), energy storage device will in micro-capacitance sensor
Joint external power supply, which will owe electricity collectively as the supplement power supply of micro-grid load, to be supplemented by external power grid.
At this point, the power calculation model that power distribution network is conveyed to micro-capacitance sensor are as follows:
PW→M(t)=PL(t)+PEV·ch(t)-PESS·dis(t)-PDG(t)
In formula: PW→M(t) power conveyed for the period power distribution network to micro-capacitance sensor;PESS·disIt (t) is the electric discharge function of energy storage
Rate.If PW→M(t) 0 > then illustrates that micro-capacitance sensor interior power power output is less than workload demand at this time;Otherwise, micro-capacitance sensor will be to external power grid
Transmission power, watt level be | PW→M(t)|。
3) when bulk power system load period at a low ebb, micro-grid connection is run at this time, and electricity price is lower at this time, therefore micro-
Energy storage device and electric car are in charged state, the strong supplement that external power grid is powered as micro-grid system in netting.
At this point, the power calculation model that power distribution network is conveyed to micro-capacitance sensor are as follows:
PW→M(t)=PL(t)+PEV·ch(t)+PESS·ch(t)-PDG(t)
In formula: PBat·chIt (t) is the charge power of energy storage device in the period micro-capacitance sensor;If PW→M(t) 0 > then illustrates
Micro-capacitance sensor interior power power output is less than workload demand at this time;Otherwise, micro-capacitance sensor will be to external power grid transmission power, watt level | PW→M
(t)|。
As the above analysis, when power distribution network and micro-capacitance sensor when no fault occurs, in addition to power distribution network is in peak load
Except period, other times section, power distribution network can be used as the backup power source of micro-capacitance sensor.When electricity shortage in micro-capacitance sensor, micro- electricity
Net buys electricity from external power grid to meet the power demand of microgrid internal loading user.To reduce in micro-capacitance sensor due to electricity shortage
Caused by customer interrupted number and power off time per family, improve the power supply reliability of power distribution network on the whole.
(2) independent operation electric automobile charging station is interacted with the power between power distribution network
1) operational application
Station is changed as filled in Fig. 2 by the electric car that transformer T2 accesses power distribution network, charge and discharge strategy then equally relies on
In the operating status of power grid.Because the electric automobile charging station of independent operation is generally the Electric Transit of public transport company's management and construction
Power battery, which fills, changes station, and the charge and discharge time is influenced by vehicle running characteristics.By analysis it is found that bus concentrates the charging time
The generally operation of power networks load time at a low ebb is also only used as the charging load of power grid at this time, according to stand in electric car and
The charge requirement of battery, reasonable arrangement charging.
In network load peak value, it can choose and change the mode of battery and carry out electricity supplement.For the battery changed according to
Its remaining capacity number, guarantee the minimum state-of-charge limit value of battery requirement under, reasonable arrangement its participate in discharge operation, to
Power grid feeding electric power carrys out the safe operation of auxiliary power grid, arranges to charge again when network load is lower.Can preferably it recognize
For charging station in network load peak value without charging, load value is suitably charged when being in level values, and low ebb period carries out
Charging is concentrated, so that it is guaranteed that the safe operation of power grid.
2) electric automobile charging station charge-discharge electric power computation model
1. in bulk power grid load peak period, the power that charging station is conveyed to power grid are as follows:
In formula: PEV→W(t) power conveyed for the period electric automobile charging station to power grid;N1For in charging station this when
Section may participate in the electric car quantity of electric discharge,For i-th electric car t moment discharge power;N2For the period
It may participate in the quantity of the standby battery of electric discharge in charging station,For jth platform battery t moment discharge power;Its
Middle i=1,2,3N1, j=1,2,3N2。
2. charging station no longer feeds electric energy to power grid when bulk power grid load is in level values and ebb period, it is only used as electricity
The charging load of net.The charge power of charging station at this time are as follows:
In formula: PEV·chIt (t) is charge power of the electric automobile charging station in t moment;N1 is that charging is participated in charging station
Electric car quantity,For the charge power of a electric car;N2 is the standby battery that charging is participated in charging station
Quantity,For the charge power of a platform standby battery;Wherein, a=1,2,3n1, b=1,2,
3····n2。
Step 22: the analysis power distribution network operation reserve in different zones failure, including the failure of access point upstream region,
Access point downstream area two kinds of situations of failure.
(1) access point upstream region breaks down
For the operation conditions in the trouble-free isolated island region formed after Fault Isolation, can according to the organizational structure in region and
Access power conditions are analyzed:
If 1) when in the isolated island region after Fault Isolation containing the power distribution network power load outside micro-capacitance sensor and micro-capacitance sensor, such as scheme
Downstream area when transformer T2 failure in 2 after Fault Isolation, the distributed generation resource in micro-capacitance sensor have also taken on isolated island simultaneously
Power distribution network power load outside the micro-capacitance sensor of region.Meanwhile energy storage device will combine the electric automobile charging station in microgrid, according to
The power-balance situation in isolated island region participates in discharge operation.When the power supply gross capability in isolated island region is less than total capacity requirement
When, for guarantee microgrid in important load reliable electricity consumption, by first to micro-capacitance sensor outside distribution network load cut down.If micro-
When load electricity consumption in power grid can not also be fully met, then microgrid internal loading will be cut down step by step according to priority.At this point,
Power-balance status analysis in region calculates are as follows:
ΔP1(t)=PDG(t)+PESS·dis(t)+PEv·dis(t)-Pwl(t)-Pl(t)
In formula: PDGIt (t) is distributed generation resource power output in region;PESS·dis(t)、PEv·disIt (t) is respectively storage in micro-capacitance sensor
It can be with the discharge power of electric automobile charging station;PwlIt (t) is conventional load power in micro-capacitance sensor, Pl(t) in fault-free region
Distribution network load.As Δ P1(t) when < 0, electricity shortage in region will carry out load reduction operation.
If 2) contain multiple micro-capacitance sensors in the fault-free isolated island region formed, each micro-capacitance sensor similar can be equivalent to different
Power supply.When there is rich power in micro-capacitance sensor, can be powered to the load except micro-capacitance sensor itself.It is supplied inside micro-capacitance sensor
When electric insufficient, the electric energy for the micro-capacitance sensor output for having electricity more than needed from other can be absorbed.I.e. each micro-capacitance sensor not only can be isolated island
Distribution network load is powered in region, can also carry out the shared of the energy each other.When the general supply power output in fault-free region
When less than total workload demand, then load reduction will be carried out according to load priority.Wherein, the load priority in micro-capacitance sensor is high
In the distribution network load outside micro-capacitance sensor.If all micro-capacitance sensors without rich electricity when, each micro-capacitance sensor will turn into independent isolated operation
State, the distribution network load in fault-free region will be forced whole power failures at this time, the then foundation of the load electric power thus supplied in micro-capacitance sensor
In the respective isolated operation situation of micro-capacitance sensor.The power-balance state analysis in region calculates in such cases are as follows:
In formula:For the power margin of k-th of micro-capacitance sensor in region;As Δ P2(t) >=0 when, region interior power goes out
Power is able to satisfy the demand of load, all load normal electricity consumptions;As Δ P2(t) 0 <, andWhen, it will be to part
Distribution network load is cut down;WhenWhen, each micro-capacitance sensor switchs to isolated operation, all power distribution networks in region
Load has a power failure.
3) when charging station and micro-capacitance sensor exist simultaneously: if there is rich power in micro-capacitance sensor, rich power being conveyed to
Power distribution network;If micro-capacitance sensor will turn into isolated operation without rich power in micro-capacitance sensor.Micro-capacitance sensor is equivalent in region at this time
Power supply point.And at this point, electric automobile charging station can see the energy storage device in the region as fault-free isolated island region,
Difference of its discharge power size dependent on distribution network load demand power in the power output size of each equivalent source point and region, with
And it is limited to the maximum discharge power in this stage of charging station.That is, the discharge power computation model of charging station in such cases are as follows:
Wherein, peak power output of the charging station in the period are as follows:
PEV·dismax(t)=PEV→W(t)
In formula: m is the micro-capacitance sensor number in region,For the rich power of the τ micro-capacitance sensor;PlIt (t) is region
The power demand of interior distribution network load;PEV·dismax(t) maximum of stage charging station can discharge power thus.
WhenWhen, load will be cut down by load priority.
If 4) be free of micro-capacitance sensor in isolated island region, and electric automobile charging station and distribution network load containing independent operation
When.Charging station is by according to the state-of-charge of access vehicle in station at this time, and in the case where guaranteeing subsequent traveling demand, rationally
Vehicle and battery are arranged, concentration discharge operation is carried out, is powered for other power loads in isolated island region.Work as charging station
Discharge power be less than region in distribution network load demand when, then load will according to priority be cut down.As Δ P4(t)=
Pl(t)-PEV·dismax(t) when > 0, distribution network load in region will be cut down by load priority.
(2) access point downstream area breaks down
When the distribution network downstream node in micro-capacitance sensor access point occurs for failure, as event occurs for the customer charge 3 in Fig. 2
Barrier, will carry out Fault Isolation by block switch at this time.Due to the not access of distributed generation resource, failure in the area of isolation of downstream
The power off time of region internal loading will continue to fault restoration.And for the grid type micro-capacitance sensor and independent operation of fault point upstream
Electric automobile charging station, will not be influenced and be incorporated into the power networks by failure, operation conditions can be carried out by power distribution network non-failure conditions
Analysis.When failure occurs at the inside of grid type micro-capacitance sensor, then micro-capacitance sensor will turn into isolated operation.At failure in micro-capacitance sensor
Reason method and general micro-capacitance sensor failure separation method and operation reserve are identical.
When the electric power main line where micro-capacitance sensor or electric automobile charging station access point breaks down, such as the P1 in Fig. 2
Or P2 point breaks down, micro-capacitance sensor will turn into isolated operation at this time, and electric automobile charging station is also disconnected with power grid without charge and discharge
It is electrically operated.At this point, distribution network load on faulty line is by whole power failures, until fault restoration.
Based on the operation reserve to new distribution net under different working condition in step 3), using sequential Monte Carlo method
Distribution network reliability after accessing to meter and electric automobile charging station with grid type micro-capacitance sensor is assessed, and specific steps are such as
Shown in Fig. 5:
Step 31: reading each element initial data of power distribution network, confirmation emulation time limit, and initialize the simulation time T of system
=0;
Step 32: according to formula
The time between failures sequence TTF of all elements in computing system, and it is the smallest to choose time between failures
Element is fault element, i.e. TTFi=min [TTF];
Step 33: in T → T+TTFiIn period, system failure-free operation.It can be according to being run in step S31 in this period
Strategy analyzes the operation conditions of power distribution network, statistics in network load peak period, micro-capacitance sensor as electricity shortage and caused by
The number of users and cut down power that load is cut down.Accumulative simulation time T=T+TTFi;
Step 34: judging whether simulation time T reaches emulation time limit, i.e. T >=TlimIf so, thening follow the steps S8;If it is not,
Then perform the next step;
Step 35: enumerating fault element i and then generating a random number, when calculating the reparation of fault element
Between TTRi。
Step 36: in T → T+TTRiIn period, element i breaks down in system.Fault element location should be judged first
Domain, if fault point is located in the upstream region of the access point of micro-capacitance sensor and electric automobile charging station or is formed after Fault Isolation
There are micro-capacitance sensor or electric automobile charging station access in isolated island region, then the region is analyzed;If fault point is located at micro-capacitance sensor
In the downstream area of the access point of electric automobile charging station, that is, not including in the isolated island region that is formed has other kinds of distribution
Formula power supply, then the load in the region all has a power failure, and power off time is fault correction time TTRi;If it is micro- that malfunctioning node is located at certain
Inside power grid, then the micro-capacitance sensor switchs to isolated operation, and operation reserve is the same as isolated island micro-capacitance sensor operation reserve.Accumulative simulation time T
=T+TTRiWith the power off time of load user.
Step 37: judging whether simulation time T reaches emulation time limit, i.e. T >=TlimIf so, thening follow the steps S8;
If it is not, then jumping to S2, emulation is continued to execute;
Step 38: according to the frequency of power cut and power off time of each load point, calculating the related reliability of each node and system
Index.
Embodiment:
This example is improved on the basis of the F4 feeder line of IEEE RBTS BUS6, as shown in fig. 6, in former network
On the basis of joined the Electric Transit charging station (Electric Bus Station) an of independent operation, set independence in this example
There are 12 electric bus in the Electric Transit charging station of operation, the power battery capacity of each electric car is 87kWh, fastly
The specified charge-discharge electric power filled under mode is 60kW, and the charge-discharge electric power under trickle charge mode is 12kW.One piece is loaded in each car
On the basis of power battery, separately equipped with 12 pieces of stand-by power battery for battery altering in charging station.Meanwhile in improved system
By access distributed generation resource and energy storage device in system, two micro-capacitance sensors W1 and W2 are formd.Wherein, W1 is the micro- electricity in residential area
Net, internal photovoltaic installed power are 800kW, are equipped with 50 vehicles in cell underground parking in electric automobile charging station EV1
The identical private savings electric car of model, the power battery capacity of each vehicle are 45kWh, the charge and discharge electric work under trickle charge charging modes
Rate is 3.5kW;W2 is then garden micro-capacitance sensor, and internal photovoltaic installed capacity is 1MW, Wind turbines by 2 0.8MW blower
Composition, the capacity of energy-storage system ESS are set as 500kWh.Wherein, the incision wind speed of blower is 4m/s, and rated wind speed is
12.5m/s, cut-out wind speed 25m/s.Electric car quantity is equally set as 50 in EV2 charging station in W2, the power of vehicle
Battery capacity and charge-discharge electric power are identical as in W1.The power equipments element such as transformer, route in analogue system it is reliable
Property parameter.And the peak period of distribution network system internal loading level is 7:00-11:00 and 18:00-22:00, usually section is 11:
00-18:00, low-valley interval are 22:00- next day 7:00.
For this Electric Transit charging station, this example is set as starting station that the station is 2 public bus network uplinks, every line
It is chronologically runed equipped with 6 buses on road.Wherein the time of departure and idle time of each shift vehicle are as shown in table 1.The operation phase
Between bus can charging station carry out once change electricity, to meet the operation demand of all day.Electric car in micro-capacitance sensor is accessed
Time response it is as shown in table 2.
The time of departure and idle time of each shift vehicle of table 1
The Line 1 departure time | Line 1 idle time | No. 2 line departure times | No. 2 line idle times |
5:30 | 20:50 | 5:40 | 20:50 |
5:50 | 21:10 | 6:00 | 21:10 |
6:10 | 21:30 | 6:20 | 21:30 |
6:30 | 21:50 | 6:40 | 21:50 |
6:50 | 22:10 | 7:00 | 22:10 |
7:10 | 22:30 | 7:20 | 22:30 |
The time response that electric car accesses in 2 micro-capacitance sensor of table
According to the running time characteristic of different type electric car, it is known that the access in different time nodes, each charging station
Electric car quantity, as shown in table 3.Electric automobile charging station participates in the power dispatching of power distribution network or micro-capacitance sensor, can basis
The state-of-charge of the electric car quantity of access in different time points station and each vehicle determines the total charge and discharge of charging station
The size of power.Meanwhile consider the operation of electric car and the traveling demand of car owner, electric bus when accessing power grid,
When vehicle-mounted and stand-by power battery charge state is lower than 0.65, discharge operation will not participate in.Private savings electric car state-of-charge exists
When lower than 0.5, it is equally not involved in discharge operation.
The quantity of the electric car accessed in each charging station of table 3
Because this analogue system on the basis of original system by improving, wherein in the area load bus LP18-LP21
Domain adds distributed generation resource and energy storage and constitutes micro-capacitance sensor W1 system, forms micro-capacitance sensor W2 system in the region node LP36-LP40, and
Electric automobile charging station is accessed on the branch line where LP32.Therefore, not according to each region power supply architecture and operation reserve
Together, also not identical to the effect of the electricity consumption reliability raising of the load bus in each region.Table 4 then lists ratio out of each region
The annual power off time of more typical load bus before the system reform with improved comparison in difference, Lai Fanying different zones
The effect that internal loading node reliably sexually revises.
4 comparison in difference of table
Based on this section under distribution network system difference operation conditions, power interactive tactics between each section, to formation
The power supply reliability of New Kind of Simulation System carried out the calculating analysis of corresponding evaluation index, and the reliability index with original system
It has carried out to mark, calculated result is as shown in table 5.
5 reliability index of table is to mark
Claims (9)
1. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station, which is characterized in that including following
Step:
1) consider micro-capacitance sensor and electric automobile charging station access after power distribution network electrical structure and the method for operation variation, establish meter and
The distribution network structure structure of micro-capacitance sensor and electric automobile charging station access;
2) power distribution network is obtained according to the charge-discharge characteristic of the different operation conditions of grid-connected micro-capacitance sensor and electric automobile charging station
Operation conditions;
3) according to the different operating statuses of power distribution network, using sequential Monte Carlo simulation to meter and electric automobile charging station and simultaneously
Distribution network reliability after the access of net type micro-capacitance sensor is assessed.
2. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station according to claim 1,
It is characterized in that, the step 1) specifically includes the following steps:
11) according to the type of grid type micro-capacitance sensor, geographical location and internal electric source factor, the operation conditions of micro-capacitance sensor is determined;
12) operation conditions of electric automobile charging station is determined according to the type of vehicle of electric automobile charging station and construction area;
13) the distribution net work structure schematic diagram of building meter and micro-capacitance sensor and electric automobile charging station access.
3. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station according to claim 1,
It is characterized in that, the operation conditions of power distribution network includes that failure-free operation situation and different zones event occur in the step 2)
The operation conditions of barrier includes that grid type micro-capacitance sensor is interacted with the power between power distribution network and independently transported under failure-free operation situation
Battalion's electric automobile charging station interacts two seed situations with the power between power distribution network, under the operation conditions that different zones break down
Including the failure of access point upstream region and access point downstream area two seed situations of failure.
4. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station according to claim 3,
It is characterized in that, under the mutual mover situation of power between grid type micro-capacitance sensor and power distribution network, when being in peak load except power distribution network
Except phase, in other times section, power distribution network can be used as the backup power source of micro-capacitance sensor, micro- when electricity shortage in micro-capacitance sensor
Power grid buys electricity from external power grid to meet the power demand of microgrid internal loading user, then has:
When bulk power system load is in peak value, power calculation model is interacted between micro-capacitance sensor and power distribution network are as follows:
Wherein, Δ PM→W(t) power that period micro-capacitance sensor is conveyed to power distribution network thus, PDG(t) go out for distributed generation resource in microgrid
Power, PLIt (t) is the load power in micro-capacitance sensor, PEV·chIt (t) is the charge requirement power of EV charging station in microgrid;
When bulk power system load is in average operation level, micro-grid connection is run at this time, and power distribution network is conveyed to micro-capacitance sensor
Power calculation model are as follows:
PW→M(t)=PL(t)+PEV·ch(t)-PESS·dis(t)-PDG(t)
Wherein, PW→M(t) power that period power distribution network is conveyed to micro-capacitance sensor thus, PESS·disIt (t) is the electric discharge function of energy storage device
Rate works as PW→M(t) when > 0, micro-capacitance sensor interior power power output is less than workload demand at this time, and otherwise, micro-capacitance sensor will convey function to external power grid
Rate, watt level be | PW→M(t)|;
When bulk power system load is at a low ebb, micro-grid connection is run at this time, the power meter that power distribution network is conveyed to micro-capacitance sensor
Calculate model are as follows:
PW→M(t)=PL(t)+PEV·ch(t)+PESS·ch(t)-PDG(t)
Wherein, PBat·ch(t) thus in period micro-capacitance sensor energy storage device charge power, work as PW→M(t) when > 0, micro-capacitance sensor at this time
Interior power power output is less than workload demand, and otherwise, micro-capacitance sensor will be to external power grid transmission power, watt level | PW→M(t)|。
5. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station according to claim 3,
It is characterized in that, then having under the mutual mover situation of power between independent operation electric automobile charging station and power distribution network:
When bulk power grid load is in peak value, charging station is without charging, then the power that charging station is conveyed to power grid are as follows:
Wherein, PEV→W(t) power conveyed for the period electric automobile charging station to power grid, N1For can in the period in charging station
The electric car quantity of electric discharge is participated in,Discharge power for i-th electric car in t moment, N2For period charging
It may participate in the quantity of the standby battery of electric discharge in standing,For jth platform battery t moment discharge power;
When bulk power grid load is in level values and ebb period, charging station no longer feeds electric energy to power grid, is only used as filling for power grid
Electric load, the at this time charge power of charging station are as follows:
Wherein, PEV·chIt (t) is electric automobile charging station in the charge power of t moment, n1 is that the electronic of charging is participated in charging station
Automobile quantity,For the charge power of a electric car, n2 is the number that the standby battery of charging is participated in charging station
Amount,For the charge power of a platform standby battery.
6. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station according to claim 3,
It is characterized in that, trip region is broken down under sub- situation on an access point, according to the organizational structure in region and access power conditions
Then have:
Function when in the isolated island region after Fault Isolation containing the power distribution network power load outside micro-capacitance sensor and micro-capacitance sensor, in region
Rate equilibrium condition are as follows:
ΔP1(t)=PDG(t)+PESS·dis(t)+PEv·dis(t)-Pwl(t)-Pl(t)
Wherein, Δ P1It (t) is the balance power in fault-free region at this time, PDGIt (t) is distributed generation resource power output, P in regionESS·dis
(t)、PEv·dis(t) be respectively energy storage and electric automobile charging station in micro-capacitance sensor discharge power, PwlIt (t) is conventional in micro-capacitance sensor
Load power, PlIt (t) is the distribution network load in fault-free region, as Δ P1(t) when < 0, electricity shortage carries out load in region
Reduction operation;
Power-balance situation when containing multiple micro-capacitance sensors in the fault-free isolated island region of formation, in region are as follows:
Wherein, Δ P2It (t) is the balance power in fault-free region at this time,For the function more than needed of k-th of micro-capacitance sensor in region
Rate, as Δ P2(t) >=0 when, region interior power, which is contributed, is able to satisfy the demand of load, all load normal electricity consumptions, as Δ P2(t) <
0, andWhen, then part distribution network load is cut down, whenWhen, each micro-capacitance sensor turns
For isolated operation, all distribution network loads in region have a power failure;
When charging station and micro-capacitance sensor exist simultaneously, if there is rich power in micro-capacitance sensor, rich power is conveyed to power distribution network,
If micro-capacitance sensor will turn into isolated operation, at this point, the discharge power computation model of charging station without rich power in micro-capacitance sensor are as follows:
PEV·dismax(t)=PEV→W(t)
Wherein, PEV·disIt (t) is the discharge power of charging station, m is the micro-capacitance sensor number in region,For τ micro- electricity
The rich power of net, PlIt (t) is the power demand of distribution network load in region, PEV·dismax(t) maximum of stage charging station thus
Can discharge power, PEV→W(t) power conveyed for charging station to power distribution network, when the balance power in fault-free regionWhen, load will be cut down by load priority;
When being free of micro-capacitance sensor in isolated island region, and when electric automobile charging station and distribution network load containing independent operation, at this time
Charging station carries out concentrating electric discharge being that other power loads in isolated island region are powered, when the discharge power of charging station is less than area
When distribution network load demand in domain, then load will according to priority be cut down, as the balance power Δ P in fault-free region4
(t)=Pl(t)-PEV·dismax(t) when > 0, distribution network load in region will be cut down by load priority.
7. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station according to claim 3,
It is characterized in that, breaking down under sub- situation in access point downstream area, Fault Isolation, failure are carried out by block switch at this time
The power off time of region internal loading continues to fault restoration, for the grid type micro-capacitance sensor of fault point upstream and the electricity of independent operation
Electrical automobile charging station, is not influenced by failure and is incorporated into the power networks, and operation conditions is identical as by power distribution network fault-free situation, works as failure
Occur at the inside of grid type micro-capacitance sensor, then micro-capacitance sensor will turn into isolated operation, when micro-capacitance sensor or electric automobile charging station connect
When electric power main line where access point breaks down, micro-capacitance sensor will turn into isolated operation, and electric automobile charging station is also disconnected with power grid
Open, at this time distribution network load faulty line on all power failure, until fault restoration electrically operated without charge and discharge.
8. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station according to claim 1,
It is characterized in that, the step 3) specifically includes the following steps:
31) each element initial data of power distribution network is obtained, determines emulation time limit Tlim, and initialize the simulation time T=0 of system;
32) the time between failures sequence TTF of all elements in distribution network system is obtained, and chooses time between failures most
Small element is fault element, i.e. TTFi=min [TTF];
33) in T → T+TTFiIn period, system failure-free operation is emulated in this period according to failure-free operation situation, statistics
In network load peak period, in micro-capacitance sensor as electricity shortage and caused by the number of users cut down of load and cut down power, and tire out
Count simulation time T=T+TTFi;
34) judge whether simulation time T reaches the emulation time limit, i.e. whether T is greater than Tlim, if so, then follow the steps 38), if it is not,
It thens follow the steps 35);
35) it is enumerating fault element and then is generating a random number, obtaining the repair time TTR of fault elementi;
36) in T → T+TTRiIn period, element failure in system first determines whether fault element region, if fault point
Have in the isolated island region formed in the upstream region of micro-capacitance sensor and the access point of electric automobile charging station or after Fault Isolation
Micro-capacitance sensor or electric automobile charging station access are then emulated according to the sub- state of access point upstream region failure, if failure
Point is located in the downstream area of the access point of micro-capacitance sensor and electric automobile charging station, then the load in the region all has a power failure, and stops
The electric time is fault correction time TTRiIf malfunctioning node is located inside micro-capacitance sensor, which switchs to isolated operation, adds up
Simulation time T=T+TTRiWith the power off time of load user;
37) judge whether simulation time T reaches the emulation time limit, i.e. whether T is greater than Tlim, if so, then follow the steps 38), if it is not,
Then return step 32);
38) according to the frequency of power cut and power off time of each load point, the related reliability index of each node and system, and root are obtained
Reliability assessment is completed according to related reliability index.
9. a kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station according to claim 8,
It is characterized in that, the related reliability evaluation index includes system annual power failure frequency in the step 38)
SAIFI, system annual power off time SAIDI, user annual interruption duration CAIDI and availability of averagely powering
ASAI。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910502082.2A CN110210777B (en) | 2019-06-11 | 2019-06-11 | Power distribution network reliability assessment method containing micro-grid and electric vehicle charging station |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910502082.2A CN110210777B (en) | 2019-06-11 | 2019-06-11 | Power distribution network reliability assessment method containing micro-grid and electric vehicle charging station |
Publications (2)
Publication Number | Publication Date |
---|---|
CN110210777A true CN110210777A (en) | 2019-09-06 |
CN110210777B CN110210777B (en) | 2021-05-04 |
Family
ID=67791926
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910502082.2A Active CN110210777B (en) | 2019-06-11 | 2019-06-11 | Power distribution network reliability assessment method containing micro-grid and electric vehicle charging station |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110210777B (en) |
Cited By (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110571855A (en) * | 2019-09-16 | 2019-12-13 | 国网河北省电力有限公司电力科学研究院 | Park micro-grid combined power response control method with energy storage device and EV charging station |
CN111027842A (en) * | 2019-12-04 | 2020-04-17 | 清华大学 | Electric vehicle charging and new energy power generation cooperative scheduling method and device |
CN111736573A (en) * | 2020-06-24 | 2020-10-02 | 清科优能(深圳)技术有限公司 | Simulation system suitable for microgrid central controller closed-loop test |
CN111768311A (en) * | 2020-06-19 | 2020-10-13 | 浙江大学 | Micro-grid energy management system based on two-stage optimal charging strategy |
CN111884219A (en) * | 2020-07-31 | 2020-11-03 | 国网重庆市电力公司电力科学研究院 | Method and device for evaluating reliability of power distribution network accessed by electric automobile |
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 |
CN112428834A (en) * | 2020-11-17 | 2021-03-02 | 宁波工程学院 | Monte Carlo method-based intelligent electric vehicle charging optimization method and system |
CN113313403A (en) * | 2021-06-15 | 2021-08-27 | 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 |
CN113675867A (en) * | 2021-07-16 | 2021-11-19 | 国网上海市电力公司 | Method and device for recovering toughness of power distribution network of electric bus |
CN115663867A (en) * | 2022-11-01 | 2023-01-31 | 广东天枢新能源科技有限公司 | Electric vehicle charging scheduling method based on intelligent charging network system |
EP4166379A1 (en) * | 2021-10-15 | 2023-04-19 | NW Joules | Device for quick recharging of a motor vehicle |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102022129728A1 (en) * | 2022-11-10 | 2024-05-16 | Chargex Gmbh | System and method for transferring a charging system for electrically charging several electric vehicles into at least or exactly two physically separate networks |
Citations (6)
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 |
CN104734171A (en) * | 2015-04-16 | 2015-06-24 | 合肥工业大学 | Electric vehicle charging station modeling method for reliability assessment of power distribution network and application of electric vehicle charging station modeling method |
CN104836334A (en) * | 2014-02-08 | 2015-08-12 | 中国农业大学 | Low voltage microgrid group independent coordination control system |
CN105305424A (en) * | 2015-10-19 | 2016-02-03 | 重庆大学 | Distribution network reliability assessment method considering electric vehicle access |
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 |
CN109713674A (en) * | 2019-02-25 | 2019-05-03 | 重庆大学 | Meter and the off-network type micro-capacitance sensor reliability estimation method of the orderly charge and discharge of electric car |
-
2019
- 2019-06-11 CN CN201910502082.2A patent/CN110210777B/en active Active
Patent Citations (6)
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 |
CN104836334A (en) * | 2014-02-08 | 2015-08-12 | 中国农业大学 | Low voltage microgrid group independent coordination control system |
CN104734171A (en) * | 2015-04-16 | 2015-06-24 | 合肥工业大学 | Electric vehicle charging station modeling method for reliability assessment of power distribution network and application of electric vehicle charging station modeling method |
CN105305424A (en) * | 2015-10-19 | 2016-02-03 | 重庆大学 | Distribution network reliability assessment method considering electric vehicle access |
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 |
CN109713674A (en) * | 2019-02-25 | 2019-05-03 | 重庆大学 | Meter and the off-network type micro-capacitance sensor reliability estimation method of the orderly charge and discharge of electric car |
Cited By (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110571855A (en) * | 2019-09-16 | 2019-12-13 | 国网河北省电力有限公司电力科学研究院 | Park micro-grid combined power response control method with energy storage device and EV charging station |
CN111027842A (en) * | 2019-12-04 | 2020-04-17 | 清华大学 | Electric vehicle charging and new energy power generation cooperative scheduling method and device |
CN111027842B (en) * | 2019-12-04 | 2022-04-12 | 清华大学 | Electric vehicle charging and new energy power generation cooperative scheduling method and device |
CN111768311B (en) * | 2020-06-19 | 2023-11-03 | 浙江大学 | Micro-grid energy management system based on two-stage optimal charging strategy |
CN111768311A (en) * | 2020-06-19 | 2020-10-13 | 浙江大学 | Micro-grid energy management system based on two-stage optimal charging strategy |
CN111736573A (en) * | 2020-06-24 | 2020-10-02 | 清科优能(深圳)技术有限公司 | Simulation system suitable for microgrid central controller closed-loop test |
CN111736573B (en) * | 2020-06-24 | 2022-03-29 | 清科优能(深圳)技术有限公司 | Simulation system suitable for microgrid central controller closed-loop test |
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 |
CN111884219A (en) * | 2020-07-31 | 2020-11-03 | 国网重庆市电力公司电力科学研究院 | Method and device for evaluating reliability of power distribution network accessed by electric automobile |
CN112428834A (en) * | 2020-11-17 | 2021-03-02 | 宁波工程学院 | Monte Carlo method-based intelligent electric vehicle charging optimization method and system |
CN113313403A (en) * | 2021-06-15 | 2021-08-27 | 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 |
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 |
CN113675867A (en) * | 2021-07-16 | 2021-11-19 | 国网上海市电力公司 | Method and device for recovering toughness of power distribution network of electric bus |
EP4166379A1 (en) * | 2021-10-15 | 2023-04-19 | NW Joules | Device for quick recharging of a motor vehicle |
FR3128167A1 (en) * | 2021-10-15 | 2023-04-21 | Nw Joules | QUICK CHARGING DEVICE FOR A MOTOR VEHICLE |
CN115663867B (en) * | 2022-11-01 | 2023-09-26 | 广东天枢新能源科技有限公司 | Electric automobile charging scheduling method based on intelligent charging network system |
CN115663867A (en) * | 2022-11-01 | 2023-01-31 | 广东天枢新能源科技有限公司 | Electric vehicle charging scheduling method based on intelligent charging network system |
Also Published As
Publication number | Publication date |
---|---|
CN110210777B (en) | 2021-05-04 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN110210777A (en) | A kind of distribution network reliability evaluation method containing micro-capacitance sensor and electric automobile charging station | |
Aliasghari et al. | Optimal scheduling of plug-in electric vehicles and renewable micro-grid in energy and reserve markets considering demand response program | |
Das et al. | Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality | |
Xie et al. | Autonomous optimized economic dispatch of active distribution system with multi-microgrids | |
Tabatabaee et al. | Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources | |
Newbery | Shifting demand and supply over time and space to manage intermittent generation: The economics of electrical storage | |
Ghasemi et al. | Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms | |
Mírez | A modeling and simulation of optimized interconnection between DC microgrids with novel strategies of voltage, power and control | |
CN107086668A (en) | A kind of distributed energy storage networking operation platform and electric energy optimizing concocting method | |
Rautiainen | Aspects of electric vehicles and demand response in electricity grids | |
CN109066659A (en) | Micro-capacitance sensor isolated operation reliability estimation method and terminal device | |
CN110429596A (en) | The distribution network reliability evaluation method of meter and electric car spatial and temporal distributions | |
Parast et al. | Resilience improvement of distribution networks using a two-stage stochastic multi-objective programming via microgrids optimal performance | |
Erdinç et al. | Decision-making framework for power system with RES including responsive demand, ESSs, EV aggregator and dynamic line rating as multiple flexibility resources | |
Aluisio et al. | Planning and reliability of DC microgrid configurations for Electric Vehicle Supply Infrastructure | |
CN110245858A (en) | A kind of micro-capacitance sensor reliability estimation method containing electric automobile charging station | |
Gundogdu et al. | Battery SOC management strategy for enhanced frequency response and day-ahead energy scheduling of BESS for energy arbitrage | |
Zhang et al. | The operating schedule for battery energy storage companies in electricity market | |
Wu et al. | Enhancing resilient restoration of distribution systems utilizing electric vehicles and supporting incentive mechanism | |
Zhou et al. | Bi-level framework for microgrid capacity planning under dynamic wireless charging of electric vehicles | |
Saber et al. | Transactive charging management of electric vehicles in office buildings: A distributionally robust chance-constrained approach | |
Thomas et al. | A scheduling optimization model for minimizing the energy demand of a building using electric vehicles and a micro-turbine | |
CN112531788B (en) | Transparent micro-grid group planning method considering multiple uncertainties and self-optimization-approaching operation | |
Secchi et al. | Peer-to-peer electricity sharing: Maximising PV self-consumption through BESS control strategies | |
Marra | Electric vehicles integration in the electric power system with intermittent energy sources-the charge/discharge infrastructure |
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 |