CN108062633A - A kind of power distribution network methods of risk assessment under distributed generation resource Thief zone - Google Patents
A kind of power distribution network methods of risk assessment under distributed generation resource Thief zone Download PDFInfo
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Abstract
The invention discloses the power distribution network methods of risk assessment under a kind of distributed generation resource Thief zone, the characteristics of considering the access of large-scale distributed power supply, risk assessment, step are carried out to power distribution network：1) consider the fluctuation and intermittence that distributed generation resource is contributed, establish distributed generation resource output model；2）Line outage model is established according to circuit historical failure information；3) consider the probability and consequence of power distribution network operation risk under distributed generation resource Thief zone, establish and consider power distribution network operational reliability and abundance Risk Assessment Index System；4) the power distribution network risk evaluation model under distributed generation resource Thief zone is solved using Monte Carlo simulation population breadth first search integration algorithm, determines risk assessment desired value, complete the power distribution network risk assessment under distributed generation resource Thief zone.
Description
Technical field
The invention belongs to power distribution network risk assessment fields, and in particular to the power distribution network wind under a kind of distributed generation resource Thief zone
Dangerous appraisal procedure.
Background technology
In recent years, with increasingly serious, the sustainable growth of electricity needs of primary energy situation, countries in the world are positive
Develop replaceable new energy.The problem of distributed energy based on wind power generation and photovoltaic generation accesses power distribution network
It is increasingly taken seriously, it has also become research hotspot both domestic and external.The access of large-scale distributed power supply has energy-saving and emission-reduction, mitigates
Environmental pollution reduces the advantages that line loss, improvement power quality and raising power supply reliability, but its power output is bullied simultaneously
It is very big to wait environment influence, there is apparent intermittent, randomness and fluctuation, the normal operation of electric system can be influenced.
Risk assessment is carried out to the power distribution network containing distributed generation resource, the power distribution network fortune containing distributed generation resource can be effectively improved
Capable security and power supply reliability avoids that major safety problems occur.And at present both at home and abroad for matching somebody with somebody containing distributed energy
The risk assessment study of power grid is less, and there are certain difficulties.Firstly, since the distribution mesh element containing distributed energy is many
More, risk assessment is related to substantial amounts of component parameters and operation data, so network equivalence processing and calculation analysis work amount are larger.
In addition, the calculating of risk indicator system and distribution power flow state are closely bound up, thus whether electric network state can react operation spy
Sign has large effect to risk evaluation result.The power distribution network risk assessment study containing distributed energy is lacked at present and is considered
The uncertainty of distributed energy power generation and influence problem of the high permeability to power distribution network safe operation.In view of the above problems, this
Text proposes the power distribution network methods of risk assessment under a kind of distributed generation resource Thief zone.
The content of the invention
It is an object of the invention to consider that the uncertainty of distributed energy power generation and high permeability transport safely power distribution network
Capable influence carries out risk assessment to power distribution network.
In order to solve the above technical problem, the present invention provides the power distribution network risk assessment under a kind of distributed generation resource Thief zone
Method comprises the following steps：
Step 1: considering fluctuation and intermittence that distributed generation resource is contributed, distributed generation resource output model is established；
Step 2: line outage model is established according to circuit historical failure information；
Step 3: considering the probability and consequence of power distribution network operation risk under distributed generation resource Thief zone, establish and consider power distribution network fortune
Row reliability and abundance Risk Assessment Index System；
Step 4: using Monte Carlo simulation-population-breadth first search's integration algorithm to the distribution under distributed generation resource Thief zone
Net risk evaluation model is solved, and determines risk assessment desired value, completes the power distribution network risk under distributed generation resource Thief zone
Assessment.
Further, in step 1, distributed generation resource output model includes wind power generation output model and photovoltaic generation is contributed
Model.
Further, in step 1, Wind speed model and sunlight model are initially set up, is contributed further according to wind speed and wind turbine, sunshine
The relation contributed with photovoltaic generation, establishes wind power generation output model and photovoltaic generation output model.
Further, in step 1, Wind speed model uses Weibull distributed models, and sunlight model uses Beta distributed models,
The parameter of model is acquired by historical wind speed information and the calculating of history sunshine information.
Further, in step 2, line outage model uses two state models of element, and the element has operating status
And malfunction.
Further, in step 3, reliability index includes load point reliability index and Reliability Index, load point
Reliability index includes failure rate, System average interruption duration and annual interruption duration, Reliability Index
Including system System average interruption frequency, system System average interruption duration and availability of averagely powering, the abundance index includes
Load-loss probability severity, not enough power supply severity and the important load extent of damage.
Further, in step 4, the power distribution network risk evaluation model under distributed generation resource Thief zone is solved comprising following step
Suddenly：
（1）Master data inputs, and the basic data includes line parameter circuit value, power parameter, load parameter and distributed generation resource ginseng
Number etc.；
（2）Emulation total time is set, and it is 0 to initialize risk assessment index and simulation parameter；
（3）Each element is generated（0,1）Between random number, and calculate run time and the repair time of each element；
（4）It generates corresponding with distributed generation resource（0,1）Between random number, and calculation of wind speed and intensity of sunshine, and then calculate
Wind power generation output and photovoltaic generation are contributed；
（5）The element i of run time minimum is chosen as the fault element sampled every time, and by its running time T TF (i) and is repaiied
Multiple time TTR (i) is accumulate to simulation time t, i.e. t=TTF (i)+TTR (i)；The value of i is 0 natural number herein；
（6）Connectivity analysis is carried out to power distribution network with breadth first search method, obtains initial connective matrix A after failure；
（7）Fault branch is disconnected, isolated island division is carried out to distribution with particle cluster algorithm；
（8）Connectivity analysis is carried out to power distribution network with breadth first search method again, obtains connective matrix B after isolated island division；
（9）Comparison step（6）In A and step（8）In B, load point is classified, is connected in A, B with power supply, no
Have a power failure；It is connected in A, it is disconnected in B, have a power failure, power off time is element i repair time TTR（i）, calculated according to classification situation
Simulation parameter；The A is initial connective matrix A after failure, and B is connective matrix after isolated island division；
（10）Judge whether simulation time t is more than emulation total time, if simulation time t is more than or equal to emulation total time, be transferred to
Step（11）；If simulation time t is less than emulation total time, step is transferred to（3）；
（11）According to simulation parameter calculation risk evaluation index value.
Further, in step 4, it is as follows that breadth first search method carries out connectivity analysis step：
（L1）Beginning and end is inputted, and starting point is added in set Q；
（L2）From step（L1）Node Vn is taken out successively in the set Q, judges whether set Q is at this time empty, if set Q is
Sky then exports beginning and end and does not connect；If set Q is not sky, next step is transferred to（L3）；
（L3）Find out step（L2）In adjacent node Vw being not included in set Q all in the node Vn that takes out, judge phase
Whether neighbors Vw has terminal, if adjacent node Vw has terminal, exports beginning and end connection；If adjacent node Vw is without eventually
Adjacent node Vw is added in set Q, and is transferred to step by point（L2）.
Further, in step 4, particle cluster algorithm to distribution carry out isolated island division the step of it is as follows：
（G1）It is random to be initially generated particle, population number is initialized, while initializes iterations；
（G2）Connectivity analysis is carried out using breadth first search method；
（G3）According to step（G2）Connectivity analysis as a result, calculate power distribution network fitness value, if to be unsatisfactory for if there are isolated islands
Power constraint, fitness value are set to negative infinite；
（G4）Update group it is optimal and individual it is optimal after, the speed of more new particle and position；
（G5）Judge whether to reach step（G1）The iterations of middle setting, if so, output optimal solution, if it is not, being transferred to step
（G2）.
Compared with prior art, the present invention its remarkable advantage is：（1）The present invention considers the not true of distributed energy power generation
Qualitative and influence of the high permeability to power distribution network safe operation, assessment result more accurate and effective；（2）The present invention is searched with range
The connectivity analysis of distribution after rope algorithm progress failure, after isolated island division can quickly judge the connectedness of power distribution network, and then
Carry out the calculating of risk indicator；（3）The present invention carries out isolated island division using particle cluster algorithm, and fast convergence rate can realize orphan
The quick division on island.
Description of the drawings
The present invention is described in further detail below with reference to attached drawing:
Fig. 1 is the power distribution network methods of risk assessment flow chart under distributed generation resource Thief zone of the present invention；
Fig. 2 is the power distribution network risk evaluation model derivation algorithm flow chart under distributed generation resource Thief zone of the present invention；
Fig. 3 is the breadth first search method connectivity analysis algorithm flow chart in the present invention；
Fig. 4 is that the particle cluster algorithm in the present invention carries out distribution isolated island division flow chart.
Specific embodiment
The present invention provides the power distribution network methods of risk assessment under a kind of distributed generation resource Thief zone, method flow diagram such as attached drawing
Shown in 1, comprise the following steps：
Step 1: considering fluctuation and intermittence that distributed generation resource is contributed, distributed generation resource output model is established；
Step 2: line outage model is established according to circuit historical failure information；
Step 3: considering the probability and consequence of power distribution network operation risk under distributed generation resource Thief zone, establish and consider power distribution network fortune
Row reliability and abundance Risk Assessment Index System；
Step 4: divided using Monte Carlo simulation-population-breadth first search's integration algorithm being included under distributed generation resource Thief zone
The power distribution network risk evaluation model of cloth power supply output model and line outage model is solved, and determines risk assessment index
Value completes the power distribution network risk assessment under distributed generation resource Thief zone.
Further, in step 1, distributed generation resource output model includes wind power generation output model, photovoltaic generation output mould
Type.
Further, in step 1, Wind speed model and sunlight model are initially set up, further according to wind speed and wind power generation output,
The relation that sunshine is contributed with photovoltaic generation, establishes wind power generation output model, photovoltaic generation output model.
Further, in step 1, Wind speed model uses Weibull distributed models, and sunlight model uses Beta distributed models,
The parameter of model is acquired by historical wind speed information, the calculating of history sunshine information.
Weibull wind speed profile models are as follows：
（1）
In formula,WithThe form parameter and scale parameter in Weibull distributions are represented respectively,It is wind speed.
WithIt can be by historical wind speed statistical information mean wind speedWith wind speed sample standard deviationIt obtains, calculation formula is such as
Under：
（2）
（3）
（4）
（5）
In above formula：For the of historical wind speed statistical informationA sample value；For sample size.
And then wind power generation output model shown in following formula can be established according to following formula wind speed and wind power output relation：
（6）
In formula,Represent wind speed,Represent wind power generation output,,WithIncision wind speed is represented respectively, is cut out
Wind speed and rated wind speed,Represent the nominal output of wind-power electricity generation.
Beta sunshine distributed models are as follows：
（7）
In formula：For intensity of sunshine,For the maximum intensity of sunshine value in timing statistics section, unit W/m2；A and b
It, can be by the intensity of sunshine average value in certain period for the form parameter of Beta distributionsAnd varianceIt is calculated, counts
It is as follows to calculate formula：
（8）
（9）
And then photovoltaic generation output model shown in following formula can be established according to following formula sunshine and photovoltaic generation output relation：
（10）
In formula：R represents intensity of illumination,Represent that photovoltaic generation is contributed,,Light-receiving area and opto-electronic conversion are represented respectively
Efficiency.
Further, in step 2, line outage model use element two state models, i.e., element only have operating status with
Malfunction.
The time that element is in operating status is average continuous working period TTF, and the time that element is in malfunction is
Mean repair time TTR.The cumulative distribution function of average continuous working period TTF and mean repair time TTR is respectively：
（11）
（12）
In formula,For (0,1）Between random number,For the failure rate of element,For the repair rate of element.
It can be seen from the above, the average continuous working period TTF and mean repair time TTR of element can be calculated by following formula respectively
Gained thus can obtain operating status sequence of the element in simulation process, be line outage model.
(13)
(14)
Further, in step 3, reliability index includes load point reliability index and Reliability Index, and load point is reliable
Property evaluation index includes：
Failure rate
It represents certain load pointThe number of power failure was caused due to power distribution network element fault during 1 year.
Annual interruption duration
It represents the time sum that load point i has a power failure in 1 year.It calculates as follows：
(15)
In formula,Represent the duration that load point i jth time has a power failure,For the failure rate of load point i.
System average interruption duration
It represents the average value the time required to load point i has a power failure from starting to restoring electricity every time.It calculates as follows：
（16）
In formula,For load point i annual interruption durations,For the failure rate of load point i.
Reliability Index includes：
1. system System average interruption frequency index
It represents the number that average each user has a power failure in system at the appointed time.
（17）
In formula：For the number of users of load point j,For the failure rate of load point j.
System System average interruption duration index
It represents System average interruption duration of the user run in system in one-year age.
（18）
In formula,Represent the annual interruption duration of load point j,For the number of users of load point j.
It averagely powers Availability Index
It represents the hourage and total the ratio between hourage of powering of user's requirement that user is powered in 1 year.
（19）
In formula,Represent the annual interruption duration of load point j,For the number of users of load point j.
Abundance index includes：
Load-loss probability severity
（20）
In formula：M is the total frequency in sampling of system mode；For the mistake load number of system；For permissible mistake load
Probability limit.
Not enough power supply severity
（21）
In formula：For cause load loss electricity and,For permissible not enough power supply boundary；M is system shape
The total frequency in sampling of state.
The important load extent of damage
（22）
In formula：And, n is important load sum for important load loss,、For the load weight and load of important load
Capacity.
Further, in step 4, the Monte Carlo of the power distribution network risk evaluation model solution under distributed generation resource Thief zone
Simulation-population-breadth first search's integration algorithm flow chart such as attached drawing 2, comprises the steps of：
（1）Master data inputs
Line parameter circuit value：Parts number, the repair rate of each element, failure rate；
Load parameter：Load number, the average load of each load, number of users, important load sum n, important load
Load weightAnd load capacity；
Power parameter：Power supply number, each source nominal capacity；
Distributed electrical source dates：Wind-power electricity generation number, photovoltaic generation number, the parameter of each wind-power electricity generation is including by wind speed
Historical information is according to formula（2）-（5）Handle c, k for acquiring and rated capacity, incision wind speed, cut-out wind speed, rated wind speed, the parameter of each photovoltaic generation is according to formula by illumination historical information（8）、（9）Handle a, the b acquired
And rmax, photovoltaic generation light-receiving area and photoelectric conversion efficiency、；
Other data：Permissible load-loss probability boundary, permissible not enough power supply boundary;
（2）Emulation total time T is setmax, initializing index system includes failure rate, annual interruption duration, System average interruption duration, system System average interruption frequency index, system System average interruption duration index, Availability Index of averagely powering, load-loss probability severity, not enough power supply severity, the important load extent of damageFor 0, simulation parameter is set, total sampling time including simulation time T, system
The important load loss of number M, the number of stoppages m of load point, fault time t, system, load loss, lose load
Number J is 0;
（3）It generatesIt is a（0,1）Between random number, and correspond inA element, passes through step（1）Middle line parameter circuit value with
And formula（13）、（14）Shown line outage model calculates average operating time TTF and the mean repair time of each element
TTR；
（4）It generates+It is a（0,1）Between random number, and correspond inA wind-power electricity generation,A photovoltaic generation,
According to step（1）The distributed electrical source dates and formula of input（1）、（7）Ask for the air speed value of each wind-power electricity generation and each light
The intensity of sunshine value of power generation is lied prostrate, and then according to formula（6）、（10）Acquire the output of each wind-power electricity generationWith each photovoltaic generation
It contributes;
（5）The element i of average operating time TTF minimums is chosen as the fault element sampled every time, and by its running time T TF
(i) and repair time TTR (i) is accumulate to simulation time T, i.e. T=TTF (i)+TTR (i), while updates simulation times M=M+1；
（6）Connectivity analysis is carried out to power distribution network with breadth first search method, obtains initial connective matrix A after failure；
（7）Fault branch is disconnected, according to step（1）The load parameter and power parameter and step of middle input（4）That acquires is every
The output of a wind-power electricity generationWith the output of each photovoltaic generation, isolated island division is carried out to distribution with particle cluster algorithm；
（8）Connectivity analysis is carried out to power distribution network with breadth first search method again, obtains connective matrix B after isolated island division；
（9）Comparator matrix A and B, judge load point situation, for load point, if the load point is connected with power supply in A, B,
Do not have a power failure；It is connected in A, it is disconnected in B, have a power failure, the number of stoppages m=m+1, fault time t=t+TTR of the load point that adds up.It is negative
After lotus point judges, for system, calculating and cumulative load loss, important load loss, lose load number J;
（10）Judge whether simulation time T is more than emulation total time TmaxIf turn（11）, if it is not, turning（3）；
（11）According to formula（15）-（22）And step（9）Data calculate initialization index system failure rate, annual
Interruption duration, System average interruption duration, system System average interruption frequency index, system averagely has a power failure and holds
Continuous time index, Availability Index of averagely powering, load-loss probability severity, not enough power supply it is serious
Degree, the important load extent of damage。
Further, in step 4, breadth first search method carries out connectivity analysis flow chart such as attached drawing 3, and step is as follows：
（1）Beginning and end is inputted, and starting point is added in set Q；
（2）It takes out node Vn successively from set Q, judges whether Q is at this time empty, if so, output terminus does not connect, if
It is no, turn（3）；
（3）All adjacent node Vw being not included in Q of Vn are found out, judge whether Vw has terminal, if so, output terminus connects
It is logical, if it is not, Vw is added in Q, turn（2）.
Further, in step 4, particle cluster algorithm carries out distribution in isolated island division flow chart such as attached drawing 4, and step is as follows：
（1）Random generation primary, initialization population number is 50, while initializes iterations；
（2）Connectivity analysis is carried out using breadth first search method；
（3）According to connectivity analysis as a result, calculating the fitness value of power distribution network, if if there are isolated islands to be unsatisfactory for power constraint, fit
Angle value is answered to be set to negative infinite；
（4）It is optimal optimal with individual to update group, the speed of more new particle and position；
（5）Judge whether to reach step（1）The iterations of middle setting, if so, output optimal solution, if it is not, turning（2）.
Claims (9)
1. the power distribution network methods of risk assessment under a kind of distributed generation resource Thief zone, considers the access of large-scale distributed power supply
Feature carries out risk assessment, which is characterized in that specific steps to power distribution network：
Step 1: considering fluctuation and intermittence that distributed generation resource is contributed, distributed generation resource output model is established；
Step 2: line outage model is established according to circuit historical failure information；
Step 3: considering the probability and consequence of power distribution network operation risk under distributed generation resource Thief zone, establish and consider power distribution network fortune
Row reliability and abundance Risk Assessment Index System；
Step 4: using Monte Carlo simulation-population-breadth first search's integration algorithm to the distribution under distributed generation resource Thief zone
Net risk evaluation model is solved, and determines risk assessment desired value, completes the power distribution network risk under distributed generation resource Thief zone
Assessment.
2. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1, which is characterized in that step
In rapid one, the distributed generation resource output model includes wind power generation output model and photovoltaic generation output model.
3. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1 or 2, feature exist
In, Wind speed model and sunlight model are initially set up, is contributed further according to wind speed and wind turbine, the relation that sunshine and photovoltaic generation are contributed,
Establish wind power generation output model and photovoltaic generation output model.
4. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 3, which is characterized in that wind
Fast model uses Weibull distributed models, and sunlight model uses Beta distributed models, and the parameter of model passes through historical wind speed information
It is acquired with the calculating of history sunshine information.
5. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1, which is characterized in that step
In rapid two, the line outage model uses two state models of element, and the element has running status and fault status.
6. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1, which is characterized in that step
In rapid three, the reliability index includes load point reliability index and Reliability Index, load point reliability index bag
Failure rate, System average interruption duration and annual interruption duration, Reliability Index is included to be averaged including system
Power failure frequency, system System average interruption duration and availability of averagely powering, it is tight that the abundance index includes load-loss probability
Severe, not enough power supply severity and the important load extent of damage.
7. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1, which is characterized in that step
In rapid four, the power distribution network risk evaluation model solution under the distributed generation resource Thief zone comprises the steps of：
（1）Master data inputs, and the basic data includes line parameter circuit value, power parameter, load parameter and distributed generation resource ginseng
Number etc.；
（2）Emulation total time is set, and it is 0 to initialize risk assessment index and simulation parameter；
（3）Each element is generated（0,1）Between random number, and calculate run time and the repair time of each element；
（4）It generates corresponding with distributed generation resource（0,1）Between random number, and calculation of wind speed and intensity of sunshine, and then calculate
Wind power generation output and photovoltaic generation are contributed；
（5）The element i of run time minimum is chosen as the fault element sampled every time, and by its running time T TF (i) and is repaiied
Multiple time TTR (i) is accumulate to simulation time t, i.e. t=TTF (i)+TTR (i)；The value of i is 0 natural number herein；
（6）Connectivity analysis is carried out to power distribution network with breadth first search method, obtains initial connective matrix A after failure；
（7）Fault branch is disconnected, isolated island division is carried out to distribution with particle cluster algorithm；
（8）Connectivity analysis is carried out to power distribution network with breadth first search method again, obtains connective matrix B after isolated island division；
（9）Comparison step（6）In A and step（8）In B, load point is classified, is connected in A, B with power supply, no
Have a power failure；It is connected in A, it is disconnected in B, have a power failure, power off time is element i repair time TTR（i）, calculated according to classification situation
Simulation parameter；The A is initial connective matrix A after failure, and B is connective matrix after isolated island division；
（10）Judge whether simulation time t is more than emulation total time, if simulation time t is more than or equal to emulation total time, be transferred to
Step（11）；If simulation time t is less than emulation total time, step is transferred to（3）；
（11）According to simulation parameter calculation risk evaluation index value.
8. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 7, which is characterized in that adopt
The step of carrying out connectivity analysis with the breadth first search method is as follows：
（L1）Beginning and end is inputted, and starting point is added in set Q；
（L2）From step（L1）Node Vn is taken out successively in the set Q, judges whether set Q is at this time empty, if set Q is
Sky then exports beginning and end and does not connect；If set Q is not sky, next step is transferred to（L3）；
（L3）Find out step（L2）In adjacent node Vw being not included in set Q all in the node Vn that takes out, judge phase
Whether neighbors Vw has terminal, if adjacent node Vw has terminal, exports beginning and end connection；If adjacent node Vw is without eventually
Adjacent node Vw is added in set Q, and is transferred to step by point（L2）.
9. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 7, which is characterized in that adopt
The step of carrying out isolated island division to distribution with the particle cluster algorithm is as follows：
（G1）It is random to be initially generated particle, population number is initialized, while initializes iterations；
（G2）Connectivity analysis is carried out using breadth first search method；
（G3）According to step（G2）Connectivity analysis as a result, calculate power distribution network fitness value, if to be unsatisfactory for if there are isolated islands
Power constraint, fitness value are set to negative infinite；
（G4）Update group it is optimal and individual it is optimal after, the speed of more new particle and position；
（G5）Judge whether to reach step（G1）The iterations of middle setting, if so, output optimal solution, if it is not, being transferred to step
（G2）.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108830485A (en) * | 2018-06-19 | 2018-11-16 | 广州供电局有限公司 | A kind of electric-thermal integrated energy system method for evaluating reliability |
| CN109034653A (en) * | 2018-08-16 | 2018-12-18 | 广东电网有限责任公司 | A kind of power source planning schemes synthesis evaluation method |
| CN109657913A (en) * | 2018-11-16 | 2019-04-19 | 国网江苏省电力有限公司南京供电分公司 | A kind of transmission and distribution network joint methods of risk assessment considering distributed generation resource |
| CN110009207A (en) * | 2019-03-25 | 2019-07-12 | 深圳供电局有限公司 | Urban power grid risk assessment method and device with high power receiving ratio |
| CN111553075A (en) * | 2020-04-27 | 2020-08-18 | 广东电网有限责任公司 | Power distribution network reliability assessment method and device considering distributed power source network access |
| CN111682550A (en) * | 2020-06-18 | 2020-09-18 | 国网山东省电力公司德州供电公司 | Reliability evaluation method for power distribution network connected with high-permeability power supply and electric automobile |
| CN111767633A (en) * | 2020-05-19 | 2020-10-13 | 国网山东省电力公司烟台供电公司 | High-efficiency simulation method and related equipment for micro gas turbine of comprehensive energy system |
| CN111882228A (en) * | 2020-07-31 | 2020-11-03 | 国网重庆市电力公司电力科学研究院 | Reliability evaluation method for power distribution network containing distributed power supply |
| CN112508313A (en) * | 2019-08-28 | 2021-03-16 | 北京科东电力控制系统有限责任公司 | Method, device and system for evaluating operation sequence of recoverable equipment after fault |
| CN112671045A (en) * | 2019-12-24 | 2021-04-16 | 国网新疆电力有限公司伊犁供电公司 | Distributed power supply optimal configuration method based on improved genetic algorithm |
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-
2018
- 2018-01-05 CN CN201810010716.8A patent/CN108062633A/en active Pending
Non-Patent Citations (3)
| Title |
|---|
| 廖婧舒: "计及分布式电源的配电网风险评估研究", 《中国优秀硕士学位论文全文数据库（电子期刊） 工程科技Ⅱ辑》 * |
| 李缙: ""含光伏发电系统的配电网可靠性评估研究"", 《电器与能效管理技术》 * |
| 陆亚南等: ""基于改进粒子群算法的含分布式发电配网孤岛划分"", 《电工电能新技术》 * |
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| CN109034653A (en) * | 2018-08-16 | 2018-12-18 | 广东电网有限责任公司 | A kind of power source planning schemes synthesis evaluation method |
| CN109034653B (en) * | 2018-08-16 | 2022-02-15 | 广东电网有限责任公司 | Comprehensive evaluation method for power supply planning scheme |
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| CN112671045A (en) * | 2019-12-24 | 2021-04-16 | 国网新疆电力有限公司伊犁供电公司 | Distributed power supply optimal configuration method based on improved genetic algorithm |
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