CN109325879B - Space-time polymerization method for calculating variable comprehensive output coefficient of long-term scheduling in hydropower station - Google Patents
Space-time polymerization method for calculating variable comprehensive output coefficient of long-term scheduling in hydropower station Download PDFInfo
- Publication number
- CN109325879B CN109325879B CN201811076152.4A CN201811076152A CN109325879B CN 109325879 B CN109325879 B CN 109325879B CN 201811076152 A CN201811076152 A CN 201811076152A CN 109325879 B CN109325879 B CN 109325879B
- Authority
- CN
- China
- Prior art keywords
- output
- hydropower station
- value
- station
- units
- 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
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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a space-time polymerization method for calculating a variable comprehensive output coefficient of long-term scheduling in a hydropower station, which comprises the following steps:s1, acquiring the actual measurement data of the hydropower station, counting the output range of the hydropower station, and calculating the comprehensive output coefficient k of each unit when a given output value and a generating head are given according to the n-h-q characteristic curves of the units of different types0A value; s2, dividing the output range of the hydropower station into a plurality of sections, and respectively counting the running time of all the units in each section as time weight; in each interval, further counting the output conditions of different units as spatial weight; s3, establishing a space-time polymerization model to obtain a changed comprehensive output coefficient k value of the whole hydropower station; and S4, establishing a corresponding relation between the comprehensive output coefficient of the whole hydropower station and the corresponding generating head of the hydropower station to form a k-h curve chart for interpolation. The method can reduce the calculation error in the medium and long-term scheduling of the hydropower station and improve the calculation precision.
Description
Technical Field
The invention relates to the technical field of reservoir scheduling, in particular to a space-time polymerization method for calculating a variable comprehensive output coefficient of long-term scheduling in a hydropower station.
Background
Hydroelectric power has dominated renewable energy sources. Reservoir dispatching is a key link in hydroelectric power generation. In order to further improve the energy supply efficiency, there are many studies for improving the efficiency of hydroelectric generation. Most of the researches focus on the improvement of reservoir operation dispatching rules and the improvement of reservoir optimal dispatching methods. In fact, conventional dispatching and optimized dispatching simulation in a hydropower station are the basis for evaluating the generating efficiency of the reservoir. Therefore, ensuring the accuracy of reservoir dispatching simulation calculation is a part of research.
In the medium and long term scheduling of the hydropower station, the power generation benefit can be measured by the output N of the hydropower station. A common calculation formula for the output N of a hydropower station is as follows:
N＝k×q×h
wherein q is the power generation flow, h is the power generation water head, and k is the comprehensive output coefficient. The coefficient k is equal to the gravity constant g multiplied by the efficiency of the hydroelectric generating set and is a key parameter in hydroelectric generation simulation. To simplify the calculation, the value of k is usually considered as a fixed constant. In fact, the k value is related to the type of the unit and the actual operation condition, and is a coefficient which changes continuously. Therefore, the change of the k value influences the output simulation calculation of the power station scheduling, and the simplified calculation influences the simulation precision of the conventional scheduling and the optimized scheduling.
In order to estimate the change k value, a common method is to directly estimate based on measured data and search for a rule between the k value and a water head or other variables, but the method is greatly influenced by the measured data and can only be used for a single type of unit. The other method is to continuously update the n-h-q characteristic curves of different types of units in the power station, and calculate the output by using the modified curves, but the method can only be used for short-term dispatching of the reservoir and cannot be used for medium-term and long-term dispatching. How to calculate the comprehensive output coefficient k value aiming at the change of the medium-term scheduling of the whole power station is the key for improving the conventional scheduling and optimizing the scheduling simulation calculation precision of the power station.
Disclosure of Invention
The invention aims to solve the technical problem of providing a space-time polymerization method for calculating a variable comprehensive output coefficient of long-term scheduling in a hydropower station aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides a space-time polymerization method for calculating a variable comprehensive output coefficient of long-term scheduling in a hydropower station, which comprises the following steps:
s1, acquiring the actual measurement data of the hydropower station, counting the output range of the hydropower station, calculating the comprehensive output coefficient k of each unit when a given output value and a given power generation head are given according to the n-h-q characteristic curves of different types of units, wherein n represents the output value, h represents the power generation head, and q represents the power generation flow0A value;
s2, dividing the output range of the hydropower station into a plurality of sections, and respectively counting the running time of all the units in each section as time weight; in each interval, further counting the output conditions of different units as spatial weight;
s3, establishing a space-time polymerization model, and giving out force values and comprehensive output coefficients k of generating heads for different types of unitsijValue, with time weight and nullMultiplying and accumulating the inter-weight to obtain a changed comprehensive output coefficient k value of the whole hydropower station; wherein k isijRepresenting the change k value of the jth unit in the ith interval;
and S4, establishing a corresponding relation between the comprehensive output coefficient k value of the whole hydropower station and the corresponding generating head h of the hydropower station to form a k-h curve chart, and performing interpolation for use during medium-term and long-term dispatching control of the hydropower station.
Further, the specific method of step S1 of the present invention is:
s11, acquiring actual measurement data of the hydropower station operation, and counting the output range of the hydropower station;
s12, according to the n-h-q characteristic curves of different types of units, using a formula
n represents a force value, h represents a power generation water head, q represents a power generation flow, and a comprehensive output coefficient k when each unit gives a given force value and a given power generation water head is calculated0The value is obtained.
Further, the specific method of step S2 of the present invention is:
dividing the output range of the power station into M grades to form M-1 output intervals, and respectively counting the running time a of the unit of the whole power station in each interval according to the actually measured output data of the power stationiWhen i is 1, 2, … …, M, the time weights of the plant in different intervals can be found as follows:
wherein, aiRepresenting the output frequency of the power station in the ith interval, A representing the total output frequency of the power station in the normal operation range, AiThe time weight of the power station in the ith interval in normal operation is obtained; further counting the actual running time b of different types of units of the power station aiming at the ith intervaljIf j is 1, 2, … …, P, the space allocation of the power station units in a certain section can be determined as follows:
wherein, bjRepresenting the output frequency of the j-th unit in the i-th interval, B representing the output frequency of all the units in the power station in the i-th interval, BjNamely the space weight of the operation of the power station unit in a certain operation interval.
Further, the specific method of step S3 of the present invention is:
giving out force value and comprehensive output coefficient k of generating head for different types of unitsijThe values, multiplied by the temporal and spatial weights and accumulated, solve a series of varying k values for the entire hydropower station as follows:
k＝∑kij×Ai×Bj
wherein k isijRepresenting the variation k value, A, of the j-th unit in the i-th intervaliAnd BjThe temporal weight and the spatial weight are represented, respectively.
Further, the specific method of step S4 of the present invention is:
and establishing a corresponding relation between the comprehensive output coefficient k value of the whole hydropower station and the corresponding generating head h thereof to form a k-h curve graph for difference values, and interpolating to obtain corresponding k values according to the established curve graph and aiming at different h values during long-term dispatching control of the water supply power station.
The invention has the following beneficial effects: the time-space polymerization method for calculating the comprehensive output coefficient of the change of the medium-and-long-term scheduling of the hydropower station can reduce the output simulation calculation error caused by taking the actual change k value as the fixed value in the medium-and-long-term scheduling of the hydropower station for simplifying the calculation; the characteristic that the comprehensive output coefficient is changed constantly is fully considered, a reliable and simple calculation method for the k value of the changed comprehensive output coefficient suitable for the whole power station in medium-long term scheduling can be provided according to the combination of a theoretical n-h-q unit characteristic curve and unit actual operation data, and the power station output simulation calculation accuracy is improved.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a spatiotemporal aggregation method of deriving a varying integrated output coefficient for long term dispatch in a hydroelectric power plant in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the space-time aggregation method for varying the integrated output coefficient of the hydropower station in the medium-and-long-term scheduling provided by this embodiment includes the following steps:
1. and counting the output range of the operation of the power station according to the measured data, and calculating the k value of the comprehensive output coefficient which changes in the operation range of each unit of the power station based on the characteristic curves of the different types of n-h-q units of the power station.
Firstly, counting the output range of the power station operation according to the measured data.
Secondly, according to the characteristic curves of the different types of the units n-h-q of the power station, a formula is utilized
Calculating the comprehensive output coefficient k when each unit of the power station gives a given output value and water head0The value is obtained.
2. Dividing the power station output range into a plurality of sections, and respectively counting the running time of all the units in each section as time weight; and in each interval, further counting the output conditions of different units as space weight.
Firstly, according to measured data, counting the output range of the power station operation, and dividing the output range into 1, 2 and … … M grades, thereby dividing the power station operation range into M-1 intervals.
Secondly, respectively counting the running time a of the unit of the whole power station in each interval according to the actually measured output data of the power stationi(i 1, 2, … …, M), the power station can be determined for different intervalsThe temporal weights are as follows:
in the formula aiRepresenting the output frequency of the power station in the ith interval, A representing the total output frequency of the power station in the normal operation range, AiI.e. the time weight of the operation of the power station in the ith interval.
Thirdly, further counting the actual running time b of different types of units of the power station aiming at the ith intervalj(j ═ 1, 2, … …, P), the output of the power plant unit in a certain interval can be determined as follows:
in the formula bjRepresenting the output frequency of the j-th unit in the i-th interval, B representing the output frequency of all the units in the power station in the i-th interval, BjNamely the space weight of the operation of the power station unit in a certain operation interval.
3. Establishing a space-time polymerization model, and giving out a comprehensive output coefficient k of a force value and a water head to each unit of the power stationijThe values are multiplied by the time weight and the space weight and accumulated, and a series of changed k values corresponding to the whole power station are solved.
k＝∑kij×Ai×Bj
In the formula, kijRepresenting the variation k value, A, of the j-th unit in the i-th intervaliAnd BjThe temporal weight and the spatial weight are represented, respectively.
4. Based on data analysis, a relation between the k value and the corresponding water head h is established, and a k-h curve graph of the changed k value is formed for interpolation solution.
To facilitate the resolution of the k value, it is desirable to establish the relationship of the k value to other variables, thus establishing a k-h relationship, and provide a k-h interpolation graph, which after h is determined, finds the corresponding change k value.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (4)
1. A space-time polymerization method for calculating a variable comprehensive output coefficient of long-term scheduling in a hydropower station is characterized by comprising the following steps:
s1, acquiring the actual measurement data of the hydropower station, counting the output range of the hydropower station, calculating the comprehensive output coefficient k of each unit when a given output value and a given power generation head are given according to the n-h-q characteristic curves of different types of units, wherein n represents the output value, h represents the power generation head, and q represents the power generation flow0A value;
s2, dividing the output range of the hydropower station into a plurality of sections, and respectively counting the running time of all the units in each section as time weight; in each interval, further counting the output conditions of different units as spatial weight;
the specific method of step S2 is:
dividing the output range of the power station into M grades to form M-1 output intervals, and respectively counting the running time a of the unit of the whole power station in each interval according to the actually measured output data of the power stationiWhen i is 1, 2, … …, M, the time weights of the plant in different intervals can be found as follows:
wherein, aiRepresenting the output frequency of the power station in the ith interval, A representing the total output frequency of the power station in the normal operation range, AiThe time weight of the power station in the ith interval in normal operation is obtained; further counting the actual running time b of different types of units of the power station aiming at the ith intervaljIf j is 1, 2, … …, P, the space allocation of the power station units in a certain section can be determined as follows:
wherein, bjRepresenting the output frequency of the j-th unit in the i-th interval, B representing the output frequency of all the units in the power station in the i-th interval, BjThe space weight of the operation of the power station unit in a certain operation interval is obtained;
s3, establishing a space-time polymerization model, and giving out force values and comprehensive output coefficients k of generating heads for different types of unitsijMultiplying and accumulating the value by the time weight and the space weight to obtain a changed comprehensive output coefficient k value of the whole hydropower station; wherein k isijRepresenting the change k value of the jth unit in the ith interval;
and S4, establishing a corresponding relation between the comprehensive output coefficient k value of the whole hydropower station and the corresponding generating head h of the hydropower station to form a k-h curve chart, and performing interpolation for use during medium-term and long-term dispatching control of the hydropower station.
2. The spatiotemporal aggregation method for deriving a varying integrated output coefficient of long-term scheduling in a hydroelectric power plant according to claim 1, wherein the specific method of step S1 is:
s11, acquiring actual measurement data of the hydropower station operation, and counting the output range of the hydropower station;
s12, according to the n-h-q characteristic curves of different types of units, using a formula
n represents a force value, h represents a power generation water head, q represents a power generation flow, and a comprehensive output coefficient k when each unit gives a given force value and a given power generation water head is calculated0The value is obtained.
3. The spatiotemporal aggregation method for deriving a varying integrated output coefficient of long-term scheduling in a hydroelectric power plant according to claim 1, wherein the specific method of step S3 is:
giving out force value and comprehensive output coefficient k of generating head for different types of unitsijThe values, multiplied by the temporal and spatial weights and accumulated, solve a series of varying k values for the entire hydropower station as follows:
k＝∑kij×Ai×Bj
wherein k isijRepresenting the variation k value, A, of the j-th unit in the i-th intervaliAnd BjThe temporal weight and the spatial weight are represented, respectively.
4. The spatiotemporal aggregation method for deriving a varying integrated output coefficient of long-term scheduling in a hydroelectric power plant according to claim 1, wherein the specific method of step S4 is:
and establishing a corresponding relation between the comprehensive output coefficient k value of the whole hydropower station and the corresponding generating head h thereof to form a k-h curve graph for interpolation, and interpolating to obtain the corresponding k value aiming at different h values according to the established curve graph when the long-term dispatching control is performed in the water supply power station.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811076152.4A CN109325879B (en) | 2018-09-14 | 2018-09-14 | Space-time polymerization method for calculating variable comprehensive output coefficient of long-term scheduling in hydropower station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811076152.4A CN109325879B (en) | 2018-09-14 | 2018-09-14 | Space-time polymerization method for calculating variable comprehensive output coefficient of long-term scheduling in hydropower station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109325879A CN109325879A (en) | 2019-02-12 |
| CN109325879B true CN109325879B (en) | 2022-04-12 |
Family
ID=65265532
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811076152.4A Active CN109325879B (en) | 2018-09-14 | 2018-09-14 | Space-time polymerization method for calculating variable comprehensive output coefficient of long-term scheduling in hydropower station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109325879B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103593508A (en) * | 2013-10-23 | 2014-02-19 | 广东电网公司电力科学研究院 | Universal simulation platform for large-sized pumped storage power stations |
| CN105678046A (en) * | 2014-11-18 | 2016-06-15 | 日本电气株式会社 | Missing data repairing method and device in time-space sequence data |
| CN107059761A (en) * | 2017-06-19 | 2017-08-18 | 武汉大学 | Multi-reservoir storage capacity space-time distribution design method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6614914B1 (en) * | 1995-05-08 | 2003-09-02 | Digimarc Corporation | Watermark embedder and reader |
| US10125740B2 (en) * | 2016-01-18 | 2018-11-13 | Horacio L. Velasco | Method for operation of hydropower reservoir with a 2-parameter elevation rule curve |
-
2018
- 2018-09-14 CN CN201811076152.4A patent/CN109325879B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103593508A (en) * | 2013-10-23 | 2014-02-19 | 广东电网公司电力科学研究院 | Universal simulation platform for large-sized pumped storage power stations |
| CN105678046A (en) * | 2014-11-18 | 2016-06-15 | 日本电气株式会社 | Missing data repairing method and device in time-space sequence data |
| CN107059761A (en) * | 2017-06-19 | 2017-08-18 | 武汉大学 | Multi-reservoir storage capacity space-time distribution design method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109325879A (en) | 2019-02-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108846517B (en) | Integration method for predicating quantile probabilistic short-term power load | |
| CN110474339B (en) | Power grid reactive power control method based on deep power generation load prediction | |
| CN107301472B (en) | Distributed photovoltaic planning method based on scene analysis method and voltage regulation strategy | |
| CN109524993B (en) | Wind power photovoltaic typical cycle output scene generation method for medium-long term optimization scheduling | |
| CN102663513B (en) | Utilize the wind power combined prediction modeling method of grey relational grade analysis | |
| CN105654207A (en) | Wind power prediction method based on wind speed information and wind direction information | |
| CN111144663B (en) | Ultra-short-term wind power prediction method for offshore wind farm considering output fluctuation process | |
| CN107528350B (en) | A kind of wind power output typical scene generation method adapting to long -- term generation expansion planning | |
| CN111027775A (en) | Step hydropower station generating capacity prediction method based on long-term and short-term memory network | |
| CN106875033A (en) | A kind of wind-powered electricity generation cluster power forecasting method based on dynamic self-adapting | |
| CN103996079B (en) | Wind power weighting predication method based on conditional probability | |
| CN116128167B (en) | Distributed photovoltaic power generation analysis method based on cloud computing real-time monitoring | |
| CN109274131B (en) | Probability statistics theory-based non-time sequence quantitative evaluation method for new energy consumption capability | |
| CN107403015A (en) | A kind of short-term luminous power Forecasting Methodology based on Time Series Similarity | |
| CN112508246A (en) | Photovoltaic power generation power prediction method based on similar days | |
| CN106372440B (en) | A kind of adaptive robust state estimation method of the power distribution network of parallel computation and device | |
| CN111104750A (en) | Reservoir dispatching diagram drawing method of coupled ensemble prediction information | |
| CN106503828B (en) | Ultra-short term chaotic prediction method for photovoltaic output power | |
| CN109325879B (en) | Space-time polymerization method for calculating variable comprehensive output coefficient of long-term scheduling in hydropower station | |
| CN112966221A (en) | Method for converting total water consumption of assessment year in southern rich water region | |
| CN110458344B (en) | Ultrashort-term wind power prediction method adaptive to time resolution | |
| CN116454928A (en) | Multi-type energy storage cooperative scheduling method considering multiple time scales | |
| CN109617131A (en) | A kind of electric system production cost measuring method and system | |
| CN112803403B (en) | Offshore wind power clustering effect multi-level evaluation method based on time-frequency characteristics | |
| CN110322063B (en) | Power consumption simulation prediction method and storage medium |
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 |