CN105184465B - Photovoltaic power station output decomposition method based on clearance model - Google Patents
Photovoltaic power station output decomposition method based on clearance model Download PDFInfo
- Publication number
- CN105184465B CN105184465B CN201510527839.5A CN201510527839A CN105184465B CN 105184465 B CN105184465 B CN 105184465B CN 201510527839 A CN201510527839 A CN 201510527839A CN 105184465 B CN105184465 B CN 105184465B
- Authority
- CN
- China
- Prior art keywords
- output
- photovoltaic
- data
- headroom
- photovoltaic 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
- 238000000034 method Methods 0.000 title claims abstract description 17
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 10
- 238000010248 power generation Methods 0.000 claims abstract description 13
- 238000004891 communication Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 5
- 230000007774 longterm Effects 0.000 abstract description 4
- 230000003203 everyday effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000000418 atomic force spectrum Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000008080 stochastic effect Effects 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Photovoltaic Devices (AREA)
Abstract
本发明提供一种基于净空模型的光伏电站出力分解方法，所述方法包括，收集历史光伏出力数据，并对收集的历史光伏出力数据进行分析整理；建立净空模型，获取光伏电站的净空理论出力PDCI(i,t)和相对出力PN(i,t)，完成光伏出力的分解。该方法可广泛应用于电力系统规划与运行分析，为光伏发电中长期时间序列建模提供了重要技术支撑。
The present invention provides a method for decomposing output of a photovoltaic power station based on a headroom model. The method includes: collecting historical photovoltaic output data, analyzing and arranging the collected historical photovoltaic output data; establishing a headroom model to obtain the theoretical headroom output P of the photovoltaic power station DCI (i,t) and relative output P N (i,t) complete the decomposition of photovoltaic output. This method can be widely used in power system planning and operation analysis, and provides important technical support for mid- and long-term time series modeling of photovoltaic power generation.
Description
Technical Field
The invention relates to a photovoltaic power station output characteristic analysis method, in particular to a photovoltaic power station output decomposition method based on a clearance model.
Background
In recent years, photovoltaic power generation technology is continuously and rapidly developed, the installed capacity of a photovoltaic power generation grid-connected grid is rapidly increased, 10.6GW is additionally added in China by the end of 2014, and the accumulated installed capacity reaches 28.7 GW. However, the photovoltaic power generation has randomness and volatility characteristics, and the uncertainty of power generation of a power system is increased due to large-scale photovoltaic power generation grid connection, so that certain influence is caused on peak load regulation and frequency modulation of a power grid.
The model of photovoltaic output is the basis for planning and operation research of the related power system. The photovoltaic output is influenced by many factors, including natural factors such as the position of the sun, various attenuations of the clean atmosphere, clouds in the air and other sunlight shelters, and photovoltaic equipment factors such as the arrangement of the photovoltaic array and the characteristics of the photovoltaic cells. Under the combined action of the factors, the photovoltaic output has certain regularity and strong randomness. And currently there is less analysis on the medium and long term stochastic properties of photovoltaic power generation.
Disclosure of Invention
In order to make up for the technical defects, the invention provides the photovoltaic power station output decomposition method based on the clearance model, which can be widely applied to power system planning and operation analysis and provides important technical support for long-term time sequence modeling in photovoltaic power generation.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
a headroom model-based photovoltaic power plant output decomposition method, comprising the steps of:
(1) collecting historical photovoltaic output data, and analyzing and sorting the collected historical photovoltaic output data;
(2) establishing a clearance model to obtain the clearance theoretical output P of the photovoltaic power stationDCI(i, t) and relative force PN(i, t), completing the resolution of the photovoltaic output.
Preferably, the historical photovoltaic output data collected in step (1) is the photovoltaic output P (i, t) of the photovoltaic power station at a time scale of 15 minutes for 1 year or more than 1 year.
Preferably, the analyzing and sorting the collected historical photovoltaic output data in the step (1) includes deleting the error data and completing the missing data by replacing the missing data with adjacent data.
Further, the error data comprises data exceeding the installed capacity of the photovoltaic power station and being negative; the missing data is data which cannot be collected at a specified time point due to communication faults and is judged according to the time corresponding to the data.
Preferably, the step (2) specifically includes converting the photovoltaic output P (i, t) at each time of day, and decomposing the converted photovoltaic output P (i, t) into a product of a headroom theoretical output and a relative output, where the expression is:
P(i,t)＝PDCI(i,t)·PN(i,t) (1)
in the formula (1), PDCI(i, t) and PN(i, t) respectively representing clearance theoretical force and relative force based on the clearance model at the ith day t.
Further, P is addedN(i, t) converting the power into a power reference value, and accumulating the uncertain output caused by different cloud layers and weather states, wherein the expression is as follows:
PN(i,t)＝PS(i)+ΔPN(i,t) (2)
in the formula (2), Δ PN(i, t) isFluctuation value, P, caused by different cloud layer states and weather states at the moment of i days tS(i) And the power reference value of the ith day reflects the photovoltaic output degree of the current day.
Further, the expression of the power reference value is:
in the formula (3), n is the output time period of photovoltaic power generation.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of decomposing photovoltaic output into a deterministic part and a stochastic part, and respectively establishing models of the deterministic part and the stochastic part according to the deterministic part and the stochastic part of the photovoltaic output based on a headroom model. A complete model description of the photovoltaic power generation output is thus formed.
The method can be widely applied to planning and operation analysis of the power system, and provides a new idea and an important technical support for long-term time series modeling in photovoltaic power generation.
The established photovoltaic power station clearance model with good universality and universality does not need the fitting of historical data, and is suitable for newly-built photovoltaic power stations.
Drawings
FIG. 1 is a flow chart of a headroom model-based photovoltaic power plant output decomposition method;
FIG. 2 is a graph illustrating a comparison between an actual force and a clearance theoretical force;
fig. 3 is a diagram illustrating a relative force curve based on a headroom model.
Detailed Description
The following detailed description of embodiments of the invention will be made with reference to the accompanying drawings.
As shown in fig. 1, a headroom model-based photovoltaic power plant output decomposition method includes the following steps:
(1) collecting historical photovoltaic output data, and analyzing and sorting the collected historical photovoltaic output data;
in the step (1), the collected historical photovoltaic output data is photovoltaic output P (i, t) of the photovoltaic power station with the time scale of 15 minutes for 1 year or more than 1 year. And analyzing and sorting the collected historical photovoltaic output data, namely deleting the error data and completing missing data by using a mode of replacing adjacent data. Wherein. The error data comprises data which exceeds the installed capacity of the photovoltaic power station and is a negative value; the missing data is data which cannot be collected at a specified time point due to communication faults and is judged according to the time corresponding to the data.
(2) Establishing a clearance model to obtain the clearance theoretical output P of the photovoltaic power stationDCI(i, t) and relative force PN(i, t), completing the resolution of the photovoltaic output. Decomposing the photovoltaic output into a deterministic portion and a stochastic portion based on a headroom model; the product of the headroom theoretical output and the relative output;
the specific method for decomposition comprises the following steps: converting the photovoltaic output P (i, t) at each moment every day, and decomposing the photovoltaic output P (i, t) into the product of the clearance theoretical output and the relative output, wherein the expression is as follows:
P(i,t)＝PDCI(i,t)·PN(i,t) (1)
in the formula (1), PDCI(i, t) and PN(i, t) respectively representing clearance theoretical force and relative force based on the clearance model at the ith day t.
Comparing the theoretical headroom output with the actual output, and obtaining a curve chart as shown in fig. 2; the actual output is the photovoltaic output P (i, t) at each moment every day.
The relative contribution from the headroom model shown in fig. 3 can also be decomposed as a power reference value plus an uncertainty contribution due to different clouds and weather conditions. The method specifically comprises the following steps:
will PN(i, t) converting the power into a power reference value, and accumulating the uncertain output caused by different cloud layers and weather states, wherein the expression is as follows:
PN(i,t)＝PS(i)+ΔPN(i,t) (2)
in the formula (2), Δ PN(i, t) is the fluctuation value caused by different cloud layer states and weather states at the time t of the ith day, PS(i) And the power reference value of the ith day reflects the photovoltaic output degree of the current day.
The expression of the power reference value is as follows:
in the formula (3), n is the output time period of photovoltaic power generation.
Finally, it should be noted that: the above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person of ordinary skill in the art can make modifications or equivalents to the specific embodiments of the present invention with reference to the above embodiments, and such modifications or equivalents without departing from the spirit and scope of the present invention are within the scope of the claims of the present invention as set forth in the claims.
Claims (2)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510527839.5A CN105184465B (en) | 2015-08-25 | 2015-08-25 | Photovoltaic power station output decomposition method based on clearance model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510527839.5A CN105184465B (en) | 2015-08-25 | 2015-08-25 | Photovoltaic power station output decomposition method based on clearance model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105184465A CN105184465A (en) | 2015-12-23 |
| CN105184465B true CN105184465B (en) | 2021-10-12 |
Family
ID=54906528
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510527839.5A Active CN105184465B (en) | 2015-08-25 | 2015-08-25 | Photovoltaic power station output decomposition method based on clearance model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105184465B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105760895B (en) * | 2016-03-18 | 2021-04-30 | 中国电力科学研究院 | Method for dividing photovoltaic output fluctuation types |
| CN106126934A (en) * | 2016-06-24 | 2016-11-16 | 中国电力科学研究院 | A kind of photovoltaic generation correlation metric acquisition methods |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102799948B (en) * | 2012-06-21 | 2016-12-21 | 华北电力大学 | A kind of parallel networking type photovoltaic power station power generation system output power Forecasting Methodology |
| CN103530819A (en) * | 2013-10-18 | 2014-01-22 | 国家电网公司 | Method and equipment for determining output power of grid-connected photovoltaic power station power generation system |
| WO2015066348A2 (en) * | 2013-10-30 | 2015-05-07 | Invensense, Inc. | Method and system for estimating multiple modes of motion |
| CN103580046B (en) * | 2013-11-27 | 2016-02-03 | 上海空间电源研究所 | A kind of composite energy storage matching method for active distribution network |
| CN103678889A (en) * | 2013-12-02 | 2014-03-26 | 国家电网公司 | Photovoltaic power generation accommodating capacity calculation method taking static voltage stability into consideration |
| CN104701880B (en) * | 2015-02-17 | 2017-01-18 | 国家电网公司 | Method for calculating maximum photovoltaic capacity accepted by power grid based on peak regulation constraint |
-
2015
- 2015-08-25 CN CN201510527839.5A patent/CN105184465B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 太阳能光伏发电的中长期随机特性分析;张曦等;《电力系统自动化》;20140325;第38卷(第6期);第6-13页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105184465A (en) | 2015-12-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103927695B (en) | Ultrashort-term wind power prediction method based on self study complex data source | |
| CN110705727B (en) | Photovoltaic power station shadow shielding diagnosis method and system based on random forest algorithm | |
| CN113496311A (en) | Photovoltaic power station generated power prediction method and system | |
| CN110443405A (en) | A kind of built photovoltaic power station power generation amount forecasting system and method | |
| CN104021427A (en) | Method for predicting daily generating capacity of grid-connected photovoltaic power station based on factor analysis | |
| Yang et al. | A novel ARX-based multi-scale spatio-temporal solar power forecast model | |
| CN103218673A (en) | Method for predicating short-period output power of photovoltaic power generation based on BP (Back Propagation) neural network | |
| CN105426956A (en) | Ultra-short-period photovoltaic prediction method | |
| CN107093007A (en) | It is a kind of to consider the distribution network reliability evaluation method that light storage continues load capacity | |
| CN107064165B (en) | Photovoltaic module surface area gray scale online detection device and cleaning method | |
| CN109636054A (en) | Solar energy power generating amount prediction technique based on classification and error combination prediction | |
| CN104318314A (en) | Power generation capacity prediction method based on photovoltaic power generation efficiency | |
| CN104217077A (en) | Method for establishing wind-driven generator power output random model capable of reflecting wind speed variation characteristics | |
| CN108074038A (en) | A kind of power generation analogy method for considering regenerative resource and load multi-space distribution character | |
| CN109960885A (en) | A power prediction method for distributed photovoltaic power generation | |
| CN105373970A (en) | Method of overall performance evaluation of photovoltaic power station | |
| CN111931331A (en) | Distribution robust optimization method suitable for power distribution network unit combination | |
| CN105184465B (en) | Photovoltaic power station output decomposition method based on clearance model | |
| CN104346659A (en) | Short-term power generation prediction method applied to high-concentration-ratio photovoltaic power generation system | |
| TWI671996B (en) | Method for judging the orientation of a solar power module | |
| CN106126934A (en) | A kind of photovoltaic generation correlation metric acquisition methods | |
| CN105068590A (en) | Photovoltaic power generation power control method and system | |
| CN113780643A (en) | A short-term output prediction method of photovoltaic power station based on case reasoning | |
| CN116800172B (en) | Photoelectric conversion method and energy storage system | |
| CN108011586B (en) | Nominal operating temperature measuring system and nominal operating temperature measuring method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |