CN111832936A - Distribution network power supply reliability assessment method containing distributed power supply - Google Patents
Distribution network power supply reliability assessment method containing distributed power supply Download PDFInfo
- Publication number
- CN111832936A CN111832936A CN202010664571.0A CN202010664571A CN111832936A CN 111832936 A CN111832936 A CN 111832936A CN 202010664571 A CN202010664571 A CN 202010664571A CN 111832936 A CN111832936 A CN 111832936A
- Authority
- CN
- China
- Prior art keywords
- sequence
- power supply
- data
- load
- 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
- 238000009826 distribution Methods 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000003860 storage Methods 0.000 claims abstract description 39
- 238000005315 distribution function Methods 0.000 claims abstract description 24
- 238000005070 sampling Methods 0.000 claims abstract description 19
- 238000004590 computer program Methods 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 9
- 238000007599 discharging Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000005520 cutting process Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 6
- 230000007547 defect Effects 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004088 simulation Methods 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- 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
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Physics (AREA)
- Databases & Information Systems (AREA)
- Power Engineering (AREA)
- Fuzzy Systems (AREA)
- Game Theory and Decision Science (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Quality & Reliability (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (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 invention belongs to the technical field of power grid operation safety, and particularly relates to a method for evaluating power supply reliability of a distribution network with a distributed power supply. The method comprises the following steps: collecting historical wind speed data of an area where a power distribution network is located; setting data of wind speed of a fan; calculating the output of the fan according to the data of the wind speed to form a fan output sequence; collecting historical load data of the power distribution network; setting a normal distribution function of the load, and randomly generating a load sequence by the distribution function; calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan; and calculating the reliability index of the sampling period according to the fan output sequence, the load sequence and the storage battery output sequence. The method and the device describe the operation characteristics of the distribution network based on historical data and random data, improve the accuracy of power supply reliability, contribute to reasonably quantizing the reliability benefits of distributed power supply access, and are suitable for being widely popularized and applied in the power grid operation industry.
Description
Technical Field
The invention belongs to the technical field of power grid operation safety, and particularly relates to a method for evaluating power supply reliability of a distribution network with a distributed power supply.
Background
In consideration of environmental benefits and sustainable development of energy, the distributed power supply is rapidly developed, the trend supply relation of the power distribution network is changed, the reliability of the power distribution network is influenced, and economic benefits are improved. Meanwhile, the cost is increased by the distributed power supply access, when a planning department formulates a distributed power supply access scheme, the reliability improvement benefit and the access cost of the distributed access power distribution network need to be comprehensively considered, and the reasonable capacity of the distributed power supply needs to be determined, so that the reliability evaluation of the power distribution network containing the distributed power supply needs to be accurately evaluated.
At present, the reliability evaluation of the distributed power supply mainly adopts a Monte Carlo method, and the output of the distributed power supply is sampled by adopting a random simulation method, so that the defects of long time consumption and incapability of truly reflecting the processing characteristics of a specific area exist.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for evaluating the power supply reliability of a distribution network with a distributed power supply. The method aims to describe the operation characteristics of the distribution network based on historical data and random data, improve the accuracy of power supply reliability and contribute to reasonably quantizing the reliability benefits of distributed power supply access.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a method for evaluating the power supply reliability of a distribution network with a distributed power supply comprises the following steps:
step 1, collecting historical wind speed data of an area where a power distribution network is located;
step 2, setting wind speed data of a fan;
step 3, calculating the output of the fan according to the data of the wind speed to form a fan output sequence;
step 4, collecting historical load data of the power distribution network;
step 5, setting a normal distribution function of the load, and randomly generating a load sequence by the distribution function;
step 6, calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan;
and 7, calculating the reliability index of the sampling period according to the fan processing sequence, the load sequence and the storage battery output sequence.
The method comprises the steps of collecting historical wind speed data of an area where a power distribution network is located, wherein the sampling interval is delta T, the sampling duration is T, and forming a wind speed data sequence H [ v (T) … v (T + n delta T) … v (T) ], wherein H represents that the data sequence is composed of historical data, and v represents wind speed.
The data for setting the wind speed of the fan is a Weibull distribution function f (v) for setting the wind speed of the fan, and a wind speed sequence S [ v (t) … v (t + n delta t) … v (T) is randomly generated according to the distribution function, wherein S represents that the data sequence is composed of random data, and n represents the number of samples.
And calculating the output of the fan according to the data of the wind speed to form a fan output sequence:
H[Pw(t)…Pw(t+nΔt)…Pw(T)]and S [ P ]w(t)…Pw(t+nΔt)…Pw(T)]The calculation formula is as follows:
in the formula: h denotes that the data sequence is composed of historical data, S denotes that the data sequence is composed of random data, Pw(t) wind power output power at time t; psIs the rated power of the fan; v. ofciIs the cut-in wind speed; v. ofrIs the rated wind speed; v. ofoCutting off the wind speed; a, B, C are constants.
The collecting of the historical load data of the power distribution network comprises the following steps: the sampling interval is delta T, the sampling duration is T, and a load data sequence H [ L (T) … L (T + n delta T) … L (T) ]isformed, wherein L (T) is the load demand at the time T.
The setting of the normal distribution function of the load and the random generation of the load sequence by the distribution function means that the setting of the normal distribution function f (v) of the load, the random generation of the load sequence by the random distribution function:
S[L(t)…L(t+nΔt)…L(T)]
where σ represents the standard deviation, m represents the mean, and L represents the load.
And calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan:
S[Pb(t)…Pb(t+nΔt)…Pb(T)]and H [ P ]b(t)…Pb(t+nΔt)…Pb(T)]
In the formula Pb(t) is the battery output at time t.
The method for calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan comprises the following steps of:
step (1), calculating a storage battery output data sequence:
Pb(t)≤Eb(t+Δt)-Eb(t)
in the formula Pb(t) the output of the storage battery at the moment t, wherein positive numbers represent charging and negative numbers represent discharging; pdmRepresents the maximum discharge power; pcmDenotes the maximum charging power, PwRepresenting wind power output, EbRepresenting the battery stored energy;
step (2), the stored energy data sequence of the storage battery:
in the formula Eb(t) the accumulator energy at time t, Eb(t + Δ t) is the storage energy of the storage battery at the time of t + Δ t; emaxRepresents the maximum capacity; eminDenotes the minimum capacity, Pb(t) is the battery output at time t.
And calculating the reliability index of the sampling period according to the fan processing sequence, the load sequence and the storage battery output sequence:
in the formula, H (LOL) and S (LOL) respectively represent the loss load reliability index of the power distribution network based on historical data and random data, LOL represents the loss load reliability index of the power distribution network, and P (LOL) represents the loss load reliability index of the power distribution networkb(t) the output of the accumulator at time t, PwAnd (t) represents the wind power output power at the time t, and pr represents a probability function.
A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for evaluating reliability of power supply of a distribution network including a distributed power source.
The invention has the following beneficial effects and advantages:
the method and the device describe the operation characteristics of the distribution network based on historical data and random data, improve the accuracy of power supply reliability, contribute to reasonably quantizing the reliability benefits of distributed power supply access, and are suitable for being widely popularized and applied in the power grid operation industry.
The invention considers energy storage elements and can accurately analyze the charging and discharging conditions of energy storage and the influence of power supply on the reliability of a power distribution network.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
The solution of some embodiments of the invention is described below with reference to fig. 1.
Example 1
The invention discloses a method for evaluating the power supply reliability of a distribution network with a distributed power supply, which comprises the following steps as shown in figure 1:
step 1, collecting historical wind speed data of an area where a power distribution network is located;
step 2, setting wind speed data of a fan;
step 3, calculating the output of the fan according to the data of the wind speed to form a fan output sequence;
step 4, collecting historical load data of the power distribution network;
step 5, setting a normal distribution function of the load, and randomly generating a load sequence by the distribution function;
step 6, calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan;
6.1, calculating a storage battery output data sequence;
6.2, calculating a stored energy data sequence of the storage battery;
and 7, calculating the reliability index of the sampling period according to the fan output sequence, the load sequence and the storage battery output sequence. Namely the power distribution network load loss reliability index.
Example 2
The invention discloses a method for evaluating the power supply reliability of a distribution network with a distributed power supply, which comprises the following steps as shown in figure 1:
step 1, collecting historical wind speed data of an area where a power distribution network is located, wherein the sampling interval is delta T, the sampling time length is T, and forming a wind speed data sequence H [ v (T) … v (T + n delta T) … v (T) ], wherein H represents that the data sequence is composed of historical data, and v represents wind speed.
And 2, setting a Weibull distribution function f (v) of the wind speed of the fan, and randomly generating a wind speed sequence S [ v (t) … v (t + n delta t) … v (T) according to the distribution function, wherein S represents that the data sequence consists of random data, and n represents the number of samples.
Step 3, calculating the output of the fan according to the data of the wind speed to form the fanOutput sequence H [ P ]w(t)…Pw(t+nΔt)…Pw(T)]And S [ P ]w(t)…Pw(t+nΔt)…Pw(T)]The calculation formula is as follows:
in the formula: h denotes that the data sequence is composed of historical data, S denotes that the data sequence is composed of random data, Pw(t) wind power output power at time t; psIs the rated power of the fan; v. ofciIs the cut-in wind speed; v. ofrIs the rated wind speed; v. ofoCutting off the wind speed; a, B, C are constants
Step 4, collecting historical load data of the power distribution network, wherein the sampling interval is delta T, the sampling duration is T, and forming a load data sequence H [ L (T) … L (T + n delta T) … L (T) ], wherein L (T) is the load demand at the moment T
Step 5, setting a normal distribution function f (v) of the load, and randomly generating a load sequence by a random distribution function
S[L(t)…L(t+nΔt)…L(T)]
Where σ represents the standard deviation, m represents the mean, and L represents the load.
Step 6, calculating the output sequence S [ P ] of the storage battery according to the output sequence and the load sequence of the fanb(t)…Pb(t+nΔt)…Pb(T)]And H [ P ]b(t)…Pb(t+nΔt)…Pb(T)]
In the formula Pb(t) is the battery output at time t.
Step 6.1 of calculating the output data sequence of the storage battery
Pb(t)≤Eb(t+Δt)-Eb(t)
In the formula Pb(t) is time tOutput of the accumulator, positive number for charging, negative number for discharging, PdmDenotes the maximum discharge power, PcmRepresents the maximum charging power formula, PwRepresenting wind power output, EbIndicating the battery stored energy.
Step 6.2 stored energy data sequence of Battery
In the formula Eb(t) the accumulator energy at time t, Eb(t + Δ t) is the storage energy of the storage battery at the time of t + Δ t; emaxRepresents the maximum capacity; eminDenotes the minimum capacity, Pb(t) the output of the battery at time t, Eb(t) represents the battery stored energy at time t.
And 7, calculating the reliability index of the sampling period according to the fan output sequence, the load sequence and the storage battery output sequence.
In the formula, H (LOL) and S (LOL) respectively represent the loss load reliability index of the power distribution network based on historical data and random data, LOL represents the loss load reliability index of the power distribution network, and P (LOL) represents the loss load reliability index of the power distribution networkb(t) the output of the accumulator at time t, PwAnd (t) represents the wind power output power at the time t, and pr represents a probability function.
Example 3
Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored on the computer storage medium, and when the computer program is executed by a processor, the method for evaluating the power supply reliability of the distribution network with the distributed power source according to embodiment 1 or 2 is implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for evaluating the power supply reliability of a distribution network with a distributed power supply is characterized by comprising the following steps: the method comprises the following steps:
step 1, collecting historical wind speed data of an area where a power distribution network is located;
step 2, setting wind speed data of a fan;
step 3, calculating the output of the fan according to the data of the wind speed to form a fan output sequence;
step 4, collecting historical load data of the power distribution network;
step 5, setting a normal distribution function of the load, and randomly generating a load sequence by the distribution function;
step 6, calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan;
and 7, calculating the reliability index of the sampling period according to the fan processing sequence, the load sequence and the storage battery output sequence.
2. The method for evaluating the power supply reliability of the distribution network with the distributed power supply as claimed in claim 1, wherein the method comprises the following steps: the method comprises the steps of collecting historical wind speed data of an area where a power distribution network is located, wherein the sampling interval is delta T, the sampling duration is T, and forming a wind speed data sequence H [ v (T) … v (T + n delta T) … v (T) ], wherein H represents that the data sequence is composed of historical data, and v represents wind speed.
3. The method for evaluating the power supply reliability of the distribution network with the distributed power supply as claimed in claim 1, wherein the method comprises the following steps: the data for setting the wind speed of the fan is a Weibull distribution function f (v) for setting the wind speed of the fan, and a wind speed sequence S [ v (t) … v (t + n delta t) … v (T) is randomly generated according to the distribution function, wherein S represents that the data sequence is composed of random data, and n represents the number of samples.
4. The method for evaluating the power supply reliability of the distribution network with the distributed power supply as claimed in claim 1, wherein the method comprises the following steps: and calculating the output of the fan according to the data of the wind speed to form a fan output sequence:
H[Pw(t)…Pw(t+nΔt)…Pw(T)]and S [ P ]w(t)…Pw(t+nΔt)…Pw(T)]The calculation formula is as follows:
in the formula: h denotes that the data sequence is composed of historical data, S denotes that the data sequence is composed of random data, Pw(t) wind power output power at time t; psIs the rated power of the fan; v. ofciIs the cut-in wind speed; v. ofrIs the rated wind speed; v. ofoCutting off the wind speed; a, B, C are constants.
5. The method for evaluating the power supply reliability of the distribution network with the distributed power supply as claimed in claim 1, wherein the method comprises the following steps: the collecting of the historical load data of the power distribution network comprises the following steps: the sampling interval is delta T, the sampling duration is T, and a load data sequence H [ L (T) … L (T + n delta T) … L (T) ]isformed, wherein L (T) is the load demand at the time T.
6. The method for evaluating the power supply reliability of the distribution network with the distributed power supply as claimed in claim 1, wherein the method comprises the following steps: the setting of the normal distribution function of the load and the random generation of the load sequence by the distribution function means that the setting of the normal distribution function f (v) of the load, the random generation of the load sequence by the random distribution function:
S[L(t)…L(t+nΔt)…L(T)]
where σ represents the standard deviation, m represents the mean, and L represents the load.
7. The method for evaluating the power supply reliability of the distribution network with the distributed power supply as claimed in claim 1, wherein the method comprises the following steps: and calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan:
S[Pb(t)…Pb(t+nΔt)…Pb(T)]and H [ P ]b(t)…Pb(t+nΔt)…Pb(T)]
In the formula Pb(t) is the battery output at time t.
8. The method for evaluating the power supply reliability of the distribution network with the distributed power supply as claimed in claim 2, wherein the method comprises the following steps: the method for calculating the output sequence of the storage battery according to the output sequence and the load sequence of the fan comprises the following steps of:
step (1), calculating a storage battery output data sequence:
Pb(t)≤Eb(t+Δt)-Eb(t)
in the formula Pb(t) the output of the storage battery at the moment t, wherein positive numbers represent charging and negative numbers represent discharging; pdmRepresents the maximum discharge power; pcmDenotes the maximum charging power, PwRepresenting wind power output, EbRepresenting the battery stored energy;
step (2), the stored energy data sequence of the storage battery:
in the formula Eb(t) the battery stores energy at time t,Eb(t + Δ t) is the storage energy of the storage battery at the time of t + Δ t; emaxRepresents the maximum capacity; eminDenotes the minimum capacity, Pb(t) is the battery output at time t.
9. The method for evaluating the power supply reliability of the distribution network with the distributed power supply as claimed in claim 2, wherein the method comprises the following steps: and calculating the reliability index of the sampling period according to the fan processing sequence, the load sequence and the storage battery output sequence:
in the formula, H (LOL) and S (LOL) respectively represent the loss load reliability index of the power distribution network based on historical data and random data, LOL represents the loss load reliability index of the power distribution network, and P (LOL) represents the loss load reliability index of the power distribution networkb(t) the output of the accumulator at time t, PwAnd (t) represents the wind power output power at the time t, and pr represents a probability function.
10. A computer storage medium, characterized by: the computer storage medium has a computer program stored thereon, and the computer program when executed by a processor implements the steps of the method for evaluating reliability of power supply of the distribution network including the distributed power source according to claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010664571.0A CN111832936B (en) | 2020-07-10 | 2020-07-10 | Distribution network power supply reliability assessment method containing distributed power supply |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010664571.0A CN111832936B (en) | 2020-07-10 | 2020-07-10 | Distribution network power supply reliability assessment method containing distributed power supply |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111832936A true CN111832936A (en) | 2020-10-27 |
| CN111832936B CN111832936B (en) | 2023-12-19 |
Family
ID=72900501
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010664571.0A Active CN111832936B (en) | 2020-07-10 | 2020-07-10 | Distribution network power supply reliability assessment method containing distributed power supply |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111832936B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113964866A (en) * | 2021-10-27 | 2022-01-21 | 黄河勘测规划设计研究院有限公司 | Unit type multi-energy complementary clean energy base construction method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102593828A (en) * | 2012-02-28 | 2012-07-18 | 重庆大学 | Reliability assessment method for electric distribution network with WTG (wind turbine generator) |
| CN105226650A (en) * | 2015-10-19 | 2016-01-06 | 重庆大学 | Based on the micro-capacitance sensor reliability calculation method of miniature combustion engine-energy storage cooperation strategy |
| CN109149555A (en) * | 2017-06-15 | 2019-01-04 | 华北电力大学 | Consider the credible capacity evaluation method of power distribution network broad sense power supply power transformation of powering mode |
| CN109245155A (en) * | 2018-09-30 | 2019-01-18 | 国网河北省电力有限公司经济技术研究院 | The credible capacity evaluating method of power distribution network broad sense power supply power transformation based on uncertain theory |
| CN109583635A (en) * | 2018-11-16 | 2019-04-05 | 贵州电网有限责任公司 | A kind of short-term load forecasting modeling method towards operational reliability |
| CN111211556A (en) * | 2019-12-29 | 2020-05-29 | 国网辽宁省电力有限公司电力科学研究院 | Distribution network power supply reliability assessment method considering wind power |
-
2020
- 2020-07-10 CN CN202010664571.0A patent/CN111832936B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102593828A (en) * | 2012-02-28 | 2012-07-18 | 重庆大学 | Reliability assessment method for electric distribution network with WTG (wind turbine generator) |
| CN105226650A (en) * | 2015-10-19 | 2016-01-06 | 重庆大学 | Based on the micro-capacitance sensor reliability calculation method of miniature combustion engine-energy storage cooperation strategy |
| CN109149555A (en) * | 2017-06-15 | 2019-01-04 | 华北电力大学 | Consider the credible capacity evaluation method of power distribution network broad sense power supply power transformation of powering mode |
| CN109245155A (en) * | 2018-09-30 | 2019-01-18 | 国网河北省电力有限公司经济技术研究院 | The credible capacity evaluating method of power distribution network broad sense power supply power transformation based on uncertain theory |
| CN109583635A (en) * | 2018-11-16 | 2019-04-05 | 贵州电网有限责任公司 | A kind of short-term load forecasting modeling method towards operational reliability |
| CN111211556A (en) * | 2019-12-29 | 2020-05-29 | 国网辽宁省电力有限公司电力科学研究院 | Distribution network power supply reliability assessment method considering wind power |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113964866A (en) * | 2021-10-27 | 2022-01-21 | 黄河勘测规划设计研究院有限公司 | Unit type multi-energy complementary clean energy base construction method |
| CN113964866B (en) * | 2021-10-27 | 2024-02-20 | 黄河勘测规划设计研究院有限公司 | Construction method of unit type multifunctional complementary clean energy base |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111832936B (en) | 2023-12-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110533222B (en) | Electric vehicle charging load prediction method and device based on peak-to-valley electricity price | |
| CN105226650A (en) | Based on the micro-capacitance sensor reliability calculation method of miniature combustion engine-energy storage cooperation strategy | |
| CN111832936B (en) | Distribution network power supply reliability assessment method containing distributed power supply | |
| CN111915079A (en) | Hybrid KNN wind power prediction method and system | |
| CN114358378A (en) | User side energy storage optimal configuration system and method for considering demand management | |
| CN112564160B (en) | Wind power uncertainty-based random configuration method for energy storage system, terminal and storage medium | |
| EP4311074A1 (en) | Charging method and apparatus for energy storage power source, and device and medium | |
| CN104915559B (en) | A kind of electrical equipment determines method and system | |
| CN116826859A (en) | Power supply carbon-electricity collaborative planning method, device, equipment and storage medium | |
| CN109214624B (en) | Energy storage capacity optimization method, device and system based on Monte Carlo method | |
| CN114254946A (en) | New energy power generation equivalent annual cost comparison method, system, equipment and storage medium | |
| CN111009927B (en) | Wind power and compressed air energy storage capacity optimization method and system | |
| CN110061493B (en) | Method and system for determining energy storage capacity of energy storage system | |
| CN114400660A (en) | Method and system for determining proportion of wind power photovoltaic hydrogen production system | |
| CN111178593A (en) | Photovoltaic system output power prediction method and device | |
| US9183327B2 (en) | Use of second battery life to reduce CO2 emissions | |
| CN113157203A (en) | Storage method of network computer | |
| CN110707744A (en) | Method and device for monitoring power generation state of wind power plant cluster and storage medium | |
| CN114189017B (en) | Battery temperature control method and device, electronic equipment and storage medium | |
| CN116703249B (en) | Reliability analysis method based on CKL wind power capacity prediction | |
| CN111082457B (en) | Wind power consumption capacity analysis method | |
| CN111181200B (en) | Practical analysis method, device and system for power generation feasible region | |
| CN112803524B (en) | Battery charging control method, device and terminal | |
| CN116227924A (en) | Method and related device for evaluating transient voltage instability risk of receiving-end power grid | |
| CN116093943A (en) | Wind power system probability optimal power flow determining method, device and medium based on quasi-spectrum point distribution method |
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 |