CN108074035B - Multi-scene distributed photovoltaic access power distribution network operation risk assessment method - Google Patents
Multi-scene distributed photovoltaic access power distribution network operation risk assessment method Download PDFInfo
- Publication number
- CN108074035B CN108074035B CN201711206448.9A CN201711206448A CN108074035B CN 108074035 B CN108074035 B CN 108074035B CN 201711206448 A CN201711206448 A CN 201711206448A CN 108074035 B CN108074035 B CN 108074035B
- Authority
- CN
- China
- Prior art keywords
- scene
- distribution network
- photovoltaic
- power distribution
- power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims description 27
- 238000012502 risk assessment Methods 0.000 title claims description 9
- 238000011156 evaluation Methods 0.000 claims abstract description 9
- 238000005070 sampling Methods 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 15
- 238000005315 distribution function Methods 0.000 claims description 6
- 238000000342 Monte Carlo simulation Methods 0.000 description 11
- 238000004088 simulation Methods 0.000 description 8
- 238000010248 power generation Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 238000009827 uniform distribution Methods 0.000 description 2
- 238000012614 Monte-Carlo sampling Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000000053 physical method Methods 0.000 description 1
- 238000000528 statistical test Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The evaluation method provided by the invention mainly describes power loss caused by the influence of node voltage deviation rate and harmonic distortion rate brought by the power distribution network after distributed photovoltaic access, simultaneously considers the characteristics of photovoltaic output, and comprehensively evaluates the operation risk of the distributed photovoltaic access system under multiple scenes.
Description
Technical Field
The invention relates to the technical field of operation risk assessment of a distributed photovoltaic access power system, in particular to an operation risk assessment method of a multi-scene distributed photovoltaic access power distribution network.
Background
At present, a smart power grid taking distribution network intelligence as an important sign is widely researched, and safely and reliably receiving distributed power supplies such as wind power and photovoltaic power generation is one of important functions of a future intelligent distribution network.
The access of distributed photovoltaic will change and join in marriage net network structure and trend, and traditional radial distribution network becomes many power systems, will bring certain risk for the operation of distribution network, control. Due to the characteristics of the distributed photovoltaic, the reliability and risk evaluation of the distributed photovoltaic cannot be completely equal to that of a traditional standby power supply, the existing power distribution network reliability and risk evaluation method is difficult to continue to be applicable, and it is necessary to establish a power distribution network risk evaluation index containing the distributed photovoltaic and research the power distribution network risk evaluation method of the distributed photovoltaic.
On the other hand, the photovoltaic is an intermittent renewable energy source, and the power generation of the photovoltaic is fluctuant and random along with the influence of external weather factors, so that the traditional deterministic research method is not applicable any more.
Disclosure of Invention
There is a need to provide a multi-scenario distributed photovoltaic access power distribution network operation risk assessment method.
A multi-scene distributed photovoltaic access power distribution network operation risk assessment method comprises the following steps:
firstly, determining photovoltaic output scenes and the probability of occurrence of each scene, and numbering the scenes (i is 1, … S);
secondly, calculating the probability distribution parameters of the photovoltaic output curve of the scene i by adopting a parameter identification method so as to obtain the photovoltaic output probability distribution function f of the scene ii；
Thirdly, sampling according to the photovoltaic output probability distribution function of the scene i to obtain photovoltaic output, and setting the photovoltaic output obtained by sampling for the jth time as Pj；
Fourthly, photovoltaic output P obtained by sampling for the j timejPhotovoltaic output P obtained according to j samplingjRespectively carrying out load flow calculation and harmonic load flow calculation on the power distribution network to obtain a node voltage deviation ratio and a voltage distortion ratio of the power distribution network, calculating node voltage power loss and voltage distortion ratio power loss caused by voltage deviation ratio out-of-limit and voltage distortion ratio out-of-limit, and further calculating to obtain an operation risk index value r of the j-th photovoltaic access power distribution networkj；
Step five, judging whether the sampling frequency reaches the maximum value, if so, carrying out the next step, if not, changing j to j +1, and returning to the step three;
the sixth step, judging whether the scene number reaches the maximum value, if so, the next step, if not, i is i +1, and returning to the second step;
seventhly, calculating to obtain a distributed photovoltaic access power distribution network operation risk index r considering multiple scenes under each scene according to the following formula;
in the formula, r is a comprehensive evaluation index of the power grid operation risk considering the voltage quality under multiple scenes, i is the ith scene, and piThe scene occurrence probability corresponding to the ith scene is obtained by local meteorological department big data statistics for a certain specific area, and is constant, delta UiIs the node voltage deviation ratio, THD, of the PCC pointiNode voltage distortion rate, P (Δ U), for PCC pointi) And P (THD)i) The node voltage power loss caused by node voltage deviation rate out-of-limit and the power loss caused by voltage distortion rate out-of-limit are respectively.
The method mainly describes the power loss caused by the influence of the node voltage deviation rate and the harmonic distortion rate brought by the power distribution network after the distributed photovoltaic access, and simultaneously, comprehensively evaluates the operation risk of the distributed photovoltaic access system under multiple scenes by considering the characteristic of photovoltaic output. The risk assessment method considers a probability model of photovoltaic output, obtains the photovoltaic output through Monte Carlo sampling according to typical photovoltaic output models of different types, further performs comprehensive calculation on the operation risk of the power distribution network in different scenes, and achieves comprehensive assessment on the operation risk of the distributed photovoltaic access power distribution network in multiple scenes.
Drawings
Fig. 1 is a schematic diagram of a power distribution network including distributed power sources in a region according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a distributed photovoltaic access distribution network operation risk calculation process considering multiple scenes in the present invention.
Fig. 3 is a single line diagram of a power distribution network employed by the test system of the present invention.
Fig. 4 is a graph of a typical sunrise force for a first typical scenario for a region in an embodiment of the invention.
Fig. 5 is a graph of a typical sunrise force for a second exemplary scenario for a region in an embodiment of the present invention.
Fig. 6 is a graph of an exemplary sunrise force for a third exemplary scenario for a region in an embodiment of the present invention.
Fig. 7 is a graph of an exemplary sunrise force for a fourth exemplary scenario for a region in an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will further describe the embodiments, and those skilled in the art can also obtain other drawings according to these drawings without creative efforts.
The invention is based on the fact that on one hand, the most obvious influence of the distributed photovoltaic access power distribution network on the power distribution network is the influence on the voltage quality, and mainly comprises two aspects of node voltage deviation rate and voltage distortion rate, and on the other hand, because the photovoltaic power generation is intermittent energy, the output of the photovoltaic power generation has random fluctuation, has important relations with weather conditions, seasons, moments and the like, and has various output scenes, and therefore, the comprehensive consideration and analysis are carried out after the operation conditions under different scenes need to be considered.
(1) Node voltage deviation ratio of system
Most of the power distribution networks in China are designed to operate in an open loop mode in a closed loop mode, so that the power distribution networks in normal operation can be equivalent to a single-power-supply radiation network, as shown in figure 1. Neglecting the admittance of the feeder line, the load adopts a three-phase symmetrical load model with constant power. The distributed power supply has the characteristics of economy and environmental protection, and active power is output as much as possible. Meanwhile, the distributed power supply connected into the power distribution network has small single machine capacity and generally does not have power regulation capacity, and the distributed fan and the photovoltaic generally work in a constant power control mode, so that the distributed power supply is used as a PQ node with a constant power factor to be analyzed.
In the equivalent single-power-supply radiation power distribution network shown in fig. 1, node 0 is a common connection point (i.e., PCC point) between the power distribution network and a higher-level transmission network, and nodes 1 to n are nodes in the power distribution network. Each node of the model shown in the figure is connected with a distributed power supply and a load, and if the power of the distributed power supply and the load of a certain node is set to be 0, the node is not connected with the distributed power supply and the load.
The distributed power supply is directly connected to the load node, and the direction of the distributed power supply is opposite to the load flow direction, so that the distributed power supply has a certain effect of offsetting the load, the distributed power supply can be regarded as a load with a negative value, and the voltage deviation ratio at the node k after the distributed power supply is connected can be represented as shown in the following formula:
in the formula: r and X represent the resistance and reactance of the feed line, PDG+jQDGIs the output power of the distributed power supply, PL+jQLIs the power of the load, Pj+jQjThe feeder ij is a feeder between two nodes in the power grid for the power flowing through the feeder ij.
The limit value of the power supply voltage deviation rate in the national standard GB/T12325-2008 'Power quality-Power supply Voltage deviation' is specified as follows:
1) the sum of the absolute values of the positive and negative deviations of the supply voltages of 35kV and above does not exceed 10% of the nominal voltage. (for example, when the upper and lower deviation of the power supply voltage is in the same sign (positive or negative), the absolute value of the larger deviation is used as the measurement basis);
2) the deviation ratio of the three-phase power supply voltage of 20kV or below is +/-7% of the nominal voltage;
3) the deviation rate of the 220V single-phase power supply voltage is + 7% and-10% of the nominal voltage;
the users with small short circuit capacity of the power supply point, long power supply distance and special requirements on the deviation rate of the power supply voltage are determined by the agreement of the power supply party and the power utilization party.
Therefore, in a distribution network system accessed by the distributed photovoltaic system, the node voltage should meet the following constraint condition,
UN(1-ε1)≤Ui≤UN(1+ε2) (2)
in the formula: u shapeNIs the nominal voltage of the system; epsilon1、ε2The allowable deviation ratio specified for the national standard.
(2) Voltage distortion rate due to harmonics
A photovoltaic power supply can be regarded as a nonlinear load for injecting harmonic waves into a feeder line of a power distribution network, the photovoltaic power connected into the power distribution network through a power converter can generate harmonic current, and if the harmonic current generated by the photovoltaic power supply at a certain moment is large enough, the voltage distortion of the power distribution network can exceed a distortion limit value specified by a standard.
After the distributed power supplies except the synchronous machine are connected into the power grid, the harmonic voltage of the public connection point should meet the regulation of GB/T14549 & 1993 'harmonic wave of power quality public power grid', as shown in Table 1.
Table 1:
by combining the system node voltage deviation rate and the voltage distortion rate index, the comprehensive evaluation index of the operation risk of the distributed photovoltaic access power distribution network under the multi-scene consideration and the calculation formula thereof are provided as follows:
in the formula, r is a comprehensive evaluation index of the power grid operation risk considering the voltage quality under multiple scenes, i is the ith scene, and piThe scene occurrence probability corresponding to the ith scene is obtained by local meteorological department big data statistics for a certain specific area, and is constant, delta UiIs the node voltage deviation ratio, THD, of the PCC pointiNode voltage distortion rate, P (Δ U), for PCC pointi) And P (THD)i) The node voltage power loss caused by node voltage deviation rate out-of-limit and the power loss caused by voltage distortion rate out-of-limit are respectively.
The operation risk of the multi-scene distributed photovoltaic access power distribution network can be evaluated based on a Monte Carlo method, and the method comprises the following specific steps:
firstly, determining photovoltaic output scenes and the probability of occurrence of each scene, and numbering the scenes (i is 1, … S);
secondly, calculating the probability distribution parameters of the photovoltaic output curve of the scene i by adopting a parameter identification method so as to obtain a photovoltaic output probability distribution function fi of the scene i;
thirdly, sampling according to the photovoltaic output probability distribution function of the scene i to obtain photovoltaic output, and setting the photovoltaic output obtained by sampling for the jth time as Pj；
Fourthly, photovoltaic output P obtained by sampling for the j timejPhotovoltaic output P obtained according to j samplingjRespectively carrying out load flow calculation and harmonic load flow calculation on the power distribution network to obtain a node voltage deviation ratio and a voltage distortion ratio of the power distribution network, calculating node voltage power loss and voltage distortion ratio power loss caused by voltage deviation ratio out-of-limit and voltage distortion ratio out-of-limit, and further calculating to obtain an operation risk index value r of the j-th photovoltaic access power distribution networkj；
Step five, judging whether the sampling frequency reaches the maximum value, if so, carrying out the next step, if not, changing j to j +1, and returning to the step three;
the sixth step, judging whether the scene number reaches the maximum value, if so, the next step, if not, i is i +1, and returning to the second step;
and seventhly, calculating to obtain a distributed photovoltaic access power distribution network operation risk index r considering multiple scenes under each scene according to the following formula.
The typical scene of photovoltaic output can be obtained by analyzing through a clustering method, and the output curve under the typical weather condition can also be obtained by converting the output characteristic of a light irradiance curve under the typical weather condition provided by a meteorological department. A typical scenario of known photovoltaic contribution is a prerequisite and basic condition for the analysis herein.
Under a certain photovoltaic output typical scene, the photovoltaic output correspondingly obeys a certain determined probability distribution, and the research of more scholars at home and abroad proves that the photovoltaic output is distributed from Beta within a few hours. Probability description of photovoltaic output is also used herein with Beta distributions. According to the specific probability distribution, a Monte Carlo simulation method can be adopted to perform load flow calculation and harmonic load flow calculation on the power distribution network accessed by the photovoltaic in the scene, so that a voltage deviation rate and a voltage distortion rate are obtained, and the operation risk index of the power distribution network in the scene is obtained.
The basic principle and steps of the Monte Carlo simulation method are mainly as follows:
the monte carlo method is based on the probability-quantity theory, and the measurement of data forms the most intuitive concept of probability theory, namely, the occurrence of an event is possibly accompanied by a plurality of possible results, so that the probability of the event can be defined as the ratio of the occurrence quantity of the event to all possible results. The monte carlo method has the same principle as this method, but is the reverse of this principle, i.e. the number of times an event occurs is measured and is taken as the probability of the event occurring. To measure the proportion of a certain block area in the whole space, only points need to be randomly drawn in the space arbitrarily, and the proportion of the number of the points falling into the block area in all the points is calculated. Such as the values calculated by the monte carlo method, the results of the simulation calculations have been infinitely close. The reliability of the monte carlo method is based on two very important theorems in probability theory, namely the law of large numbers and the central limit theorem. The majority theorem ensures that the estimated value calculated by the Monte Carlo method can be converged to the true value along with the increase of the simulation times; the central limit theorem gives the range of simulation errors after a finite number of simulation experiments.
The convergence rate of the Monte Carlo method is
Convergence speed is relatively slow, which means that the number of simulations needs to be increased by four times if the standard deviation is to be reduced to half; increasing the accuracy by one decimal point requires that the number of experiments be increased by 100 times. The method mainly comprises the following steps:
(1) constructing or describing probabilistic processes
For problems which are inherently random, such as particle transport problems, the probability process is mainly described and modeled correctly, and for deterministic problems which are not random in nature, such as calculating a definite integral, an artificial probability process must be constructed in advance, and certain parameters of the artificial probability process are exactly the solution of the required problem. I.e. to convert the problem of not having random properties into the problem of random properties.
(2) Enabling sampling from a known probability distribution
After the probability model is constructed, since each probability model can be regarded as being composed of various probability distributions, generating random variables (or random vectors) of known probability distributions becomes a basic means for realizing a simulation experiment of the monte carlo method, which is also called random sampling. One of the simplest, most basic, and most important probability distributions is a uniform distribution (or rectangular distribution) over (0, 1). Random numbers are random variables with such a uniform distribution. A random number sequence is a simple sub-instance of a population having such a distribution, i.e. a sequence of mutually independent random variables having such a distribution. The problem of generating random numbers is the sampling problem from this distribution. On a computer, random numbers can be generated by a physical method, but the random numbers are expensive, cannot be repeated and are inconvenient to use. Another method is to generate with a mathematical recurrence formula. The sequence thus generated is different from a truly random number sequence and is therefore called a pseudo-random number, or pseudo-random number sequence. However, it has been shown by various statistical tests to have properties similar to a true random number, or sequence of random numbers, and can therefore be used as a true random number. From the known distributed random sampling there are various methods, which, unlike the uniformly distributed sampling from (0,1), are realized by means of random sequences, that is to say, on the premise of generating random numbers. It follows that random numbers are the basic tool we implement monte carlo simulations.
(3) Establishing various estimators
Generally, after a probabilistic model is constructed and sampled, i.e. after a simulation experiment is implemented, a random variable is determined as a solution to the required problem, which is called unbiased estimation. Establishing various estimators is equivalent to investigating and registering the results of the simulation experiment to obtain a solution to the problem.
Taking a rural power distribution network in a certain area as an example, the operation risk assessment index r of the distributed photovoltaic access power distribution network is calculated by combining multiple sunrise scenes in the local area.
The system branch parameters and bus load data of the regional power grid system are shown in table 2,
table 2:
the permeability of distributed photovoltaic is set to be 150%, the distributed photovoltaic is connected to the tail end of a power distribution network, and the numbers of access nodes are 18, 19, 22 and 23.
And analyzing the annual photovoltaic output curve of the region to obtain four types of typical photovoltaic output power curves, as shown in fig. 4-7. Given that the photovoltaic output obeys Beta probability distribution of two parameters, the least square method is adopted to analyze and obtain alpha and Beta parameters under four typical weather scenes, the result is shown in table 3, and table 3 is a photovoltaic output probability distribution model parameter under the typical weather scenes:
table 3:
the calculation results obtained by the operation risk indicators and the method provided by the invention are shown in table 4:
table 4:
specifically, the distributed photovoltaic operation risk in the local area under the multi-weather scene is calculated as follows:
the first step, selecting a scene as a typical scene 1, the probability of occurrence is 0.25, and sampling is carried out by a Monte Carlo methodObtaining a photovoltaic output value, and obtaining power loss P (delta U) caused by voltage deviation rate out-of-limit through load flow calculation and harmonic load flow calculationi) Power loss P (Δ THD) due to voltage distortion rate caused by harmonics at 1.4408MWi) 2.256MW, then the risk indicator in typical scenario 1 is
r＝pi·(P(ΔUi)+P(THDi))＝0.25×(1.4408+2.256)＝0.9242MW
And similarly, calculating the risk indexes under other scenes.
Finally, the distributed photovoltaic operation risk in a multi-weather scene is
The modules or units in the device of the embodiment of the invention can be combined, divided and deleted according to actual needs.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (1)
1. A multi-scene distributed photovoltaic access power distribution network operation risk assessment method is characterized by comprising the following steps:
firstly, determining photovoltaic output scenes and the probability of occurrence of each scene, and numbering the scenes (i is 1, … S);
secondly, calculating the probability distribution parameters of the photovoltaic output curve of the scene i by adopting a parameter identification method so as to obtain the photovoltaic output probability distribution function f of the scene ii；
Thirdly, sampling according to the photovoltaic output probability distribution function of the scene i to obtain photovoltaic output, and setting the photovoltaic output obtained by sampling for the jth time as Pj；
The fourth step, photovoltaic output obtained by the jth samplingPjPhotovoltaic output P obtained according to j samplingjRespectively carrying out load flow calculation and harmonic load flow calculation on the power distribution network to obtain a node voltage deviation ratio and a voltage distortion ratio of the power distribution network, calculating node voltage power loss and voltage distortion ratio power loss caused by voltage deviation ratio out-of-limit and voltage distortion ratio out-of-limit, and further calculating to obtain an operation risk index value r of the j-th photovoltaic access power distribution networkj；
Step five, judging whether the sampling frequency reaches the maximum value, if so, carrying out the next step, if not, changing j to j +1, and returning to the step three;
the sixth step, judging whether the scene number reaches the maximum value, if so, the next step, if not, i is i +1, and returning to the second step;
seventhly, calculating to obtain a distributed photovoltaic access power distribution network operation risk index r considering multiple scenes under each scene according to the following formula;
in the formula, r is a comprehensive evaluation index of the power grid operation risk considering the voltage quality under multiple scenes, i is the ith scene, and piThe scene occurrence probability corresponding to the ith scene is obtained by local meteorological department big data statistics for a certain specific area, and is constant, delta UiIs the node voltage deviation ratio, THD, of the PCC pointiNode voltage distortion rate, P (Δ U), for PCC pointi) And P (THD)i) The node voltage power loss caused by node voltage deviation rate out-of-limit and the power loss caused by voltage distortion rate out-of-limit are respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711206448.9A CN108074035B (en) | 2017-11-27 | 2017-11-27 | Multi-scene distributed photovoltaic access power distribution network operation risk assessment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711206448.9A CN108074035B (en) | 2017-11-27 | 2017-11-27 | Multi-scene distributed photovoltaic access power distribution network operation risk assessment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108074035A CN108074035A (en) | 2018-05-25 |
| CN108074035B true CN108074035B (en) | 2021-09-14 |
Family
ID=62157631
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711206448.9A Active CN108074035B (en) | 2017-11-27 | 2017-11-27 | Multi-scene distributed photovoltaic access power distribution network operation risk assessment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108074035B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108805745B (en) * | 2018-06-06 | 2021-10-29 | 浙江大学 | Flexibility evaluation method for power distribution network |
| CN109165846A (en) * | 2018-08-23 | 2019-01-08 | 国网上海市电力公司 | A kind of power distribution network methods of risk assessment containing distributed photovoltaic power |
| CN111130098B (en) * | 2019-12-30 | 2023-03-24 | 国网辽宁省电力有限公司电力科学研究院 | Risk assessment method for power distribution network system with distributed power supplies |
| CN112380490B (en) * | 2020-09-09 | 2023-08-29 | 浙江华云信息科技有限公司 | Voltage fluctuation evaluation method for access of transformer area to distributed power supply |
| CN112290602B (en) * | 2020-11-10 | 2024-03-26 | 上海电气工程设计有限公司 | Micro-grid comprehensive operation method and system with heavy oil generator set and photovoltaic |
| CN113629778B (en) * | 2021-08-06 | 2023-07-21 | 国网安徽省电力有限公司经济技术研究院 | Design method of distributed photovoltaic power distribution network optical storage system |
| CN115102168B (en) * | 2022-08-24 | 2023-04-11 | 山东大学 | Time-varying current evaluation method and system considering distributed photovoltaic harmonic coupling |
| CN117638878B (en) * | 2023-11-16 | 2024-07-12 | 国网安徽省电力有限公司铜陵供电公司 | Relay protection analysis method for distributed photovoltaic access power distribution network based on probability model |
| CN117350549B (en) * | 2023-12-04 | 2024-02-23 | 国网江苏省电力有限公司经济技术研究院 | Distribution network voltage risk identification method, device and equipment considering output correlation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102594216A (en) * | 2012-03-21 | 2012-07-18 | 江西省电力科学研究院 | Probability assessment method of effects of distributed photovoltaic power supply access |
| CN105186514A (en) * | 2015-10-20 | 2015-12-23 | 南京南瑞集团公司 | Distribution network safety evaluating and early warning method after large scale distributed photovoltaic grid connected |
| US9507367B2 (en) * | 2012-04-09 | 2016-11-29 | Clemson University | Method and system for dynamic stochastic optimal electric power flow control |
| CN107039995A (en) * | 2016-11-28 | 2017-08-11 | 国网湖北省电力公司宜昌供电公司 | A kind of distributed photovoltaic power access planing method for considering power quality problem |
-
2017
- 2017-11-27 CN CN201711206448.9A patent/CN108074035B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102594216A (en) * | 2012-03-21 | 2012-07-18 | 江西省电力科学研究院 | Probability assessment method of effects of distributed photovoltaic power supply access |
| US9507367B2 (en) * | 2012-04-09 | 2016-11-29 | Clemson University | Method and system for dynamic stochastic optimal electric power flow control |
| CN105186514A (en) * | 2015-10-20 | 2015-12-23 | 南京南瑞集团公司 | Distribution network safety evaluating and early warning method after large scale distributed photovoltaic grid connected |
| CN107039995A (en) * | 2016-11-28 | 2017-08-11 | 国网湖北省电力公司宜昌供电公司 | A kind of distributed photovoltaic power access planing method for considering power quality problem |
Non-Patent Citations (2)
| Title |
|---|
| 《光伏发电功率持续时间特性的概率分布定量分析》;黎嘉明等;《电力系统自动化》;20170325;第41卷(第6期);第30-36页 * |
| 《多接入点分布式光伏发电系统与配电网交互影响研究》;崔红芬等;《电力系统保护与控制》;20150516;第43卷(第10期);第91-97页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108074035A (en) | 2018-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108074035B (en) | Multi-scene distributed photovoltaic access power distribution network operation risk assessment method | |
| Jannat et al. | Optimal capacitor placement in distribution networks regarding uncertainty in active power load and distributed generation units production | |
| Fatemi et al. | Introducing a novel DC power flow method with reactive power considerations | |
| Dall’Anese et al. | Optimal dispatch of photovoltaic inverters in residential distribution systems | |
| WO2016026355A1 (en) | Voltage sag simulation and evaluation method of active power distribution grid | |
| Zhang et al. | Three-stage hierarchically-coordinated voltage/var control based on PV inverters considering distribution network voltage stability | |
| CN106532778B (en) | Method for calculating maximum access capacity of distributed photovoltaic grid connection | |
| Liu et al. | Global MPPT algorithm with coordinated control of PSO and INC for rooftop PV array | |
| CN108306303A (en) | Voltage stability evaluation method considering load increase and new energy output randomness | |
| Ke et al. | Coordinative real‐time sub‐transmission volt–var control for reactive power regulation between transmission and distribution systems | |
| Rashidi et al. | Lyapunov exponent‐based optimal PMU placement approach with application to transient stability assessment | |
| CN108336739A (en) | A kind of Probabilistic Load Flow on-line calculation method based on RBF neural | |
| Li et al. | Distribution system voltage control under uncertainties using tractable chance constraints | |
| Wu et al. | Reliability evaluation of the solar power system based on the Markov chain method | |
| Liu et al. | Two‐step method for identifying photovoltaic grid‐connected inverter controller parameters based on the adaptive differential evolution algorithm | |
| CN114336628A (en) | Photovoltaic power supply access distribution network safety evaluation method based on random power flow | |
| Rahman et al. | Distributed generation’s integration planning involving growth load models by means of genetic algorithm | |
| Fang et al. | Active distribution system state estimation incorporating photovoltaic generation system model | |
| Li et al. | Decentralised conic optimisation of reactive power considering uncertainty of renewable energy sources | |
| CN113609699A (en) | Calculation method and system for alternating current power flow model of radial power distribution network | |
| Ali et al. | Simple moving voltage average incremental conductance MPPT technique with direct control method under nonuniform solar irradiance conditions | |
| Subramanian et al. | Stochastic backward/forward sweep power flow analysis for islanded microgrids | |
| Du et al. | Hosting capacity assessment in distribution networks considering wind–photovoltaic–load temporal characteristics | |
| Zhao et al. | Electrical principles of verification of the topological structure of a low‐voltage distribution network based on voltage from an Advanced Metering Infrastructure | |
| CN108233373B (en) | Probability harmonic analysis method considering weather scene for distributed photovoltaic access power distribution network |
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 |