CN108054790B - Wind-solar power generation cluster active real-time optimization control method based on predicted output successive approximation - Google Patents
Wind-solar power generation cluster active real-time optimization control method based on predicted output successive approximation Download PDFInfo
- Publication number
- CN108054790B CN108054790B CN201711438184.XA CN201711438184A CN108054790B CN 108054790 B CN108054790 B CN 108054790B CN 201711438184 A CN201711438184 A CN 201711438184A CN 108054790 B CN108054790 B CN 108054790B
- Authority
- CN
- China
- Prior art keywords
- wind
- output
- power generation
- solar power
- power station
- 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
- 238000010248 power generation Methods 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000005457 optimization Methods 0.000 title claims abstract description 18
- 238000004364 calculation method Methods 0.000 claims description 24
- 230000005611 electricity Effects 0.000 claims description 7
- 238000005192 partition Methods 0.000 claims description 3
- 230000035945 sensitivity Effects 0.000 claims description 3
- 230000001960 triggered effect Effects 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- 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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
-
- H02J3/382—
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Wind Motors (AREA)
Abstract
The invention discloses a wind-solar power generation cluster active real-time optimization control method based on predicted output successive approximation, and belongs to the technical field of power systems and automation thereof. According to the variation trend of the actual output and the instruction difference value of the wind and light power station in the instruction execution time period, the output prediction of the wind and light power station is gradually corrected. Based on a linear programming algorithm, the corrected output predicted value is taken as a distribution basis, the prediction precision, the control performance index and the factors influencing the instruction distribution are considered, the minimum cost of wind abandoning and light abandoning is taken as a target, the safety and stability constraint, the total peak regulation constraint and the wind and light independent constraint are considered, and the optimized distribution of the wind and light power station power generation instruction based on the three-public principle is realized. The invention fully utilizes the power generation capacity of the wind-light power station and the acceptance capacity of the power grid, and solves the problems of poor prediction precision, and insufficient utilization of the wind-light power generation capacity and the acceptance capacity of the power grid commonly existing in the grid-connected control of the wind-light power generation cluster.
Description
Technical Field
The invention belongs to the technical field of power systems and automation thereof, and particularly relates to a wind-solar power generation cluster active real-time optimization control method based on prediction output successive approximation.
Background
The power generation characteristics of new energy power stations such as wind power and photovoltaic power stations have the characteristics of volatility, randomness and intermittence, and in order to fully absorb the power of a wind-solar power station on the premise of ensuring the safety and stability of a power grid, a power grid regulation and control center needs to master grid-connected power prediction information of the wind-solar power station and can control the grid-connected power of the wind-solar power station in real time. GB/T19963-. In case of an accident or emergency of the power system, the wind power plant should rapidly control the active power output by the wind power plant according to the instruction of the power system dispatching department. 2011 in the temporary method for managing and forecasting the power of the wind power plant issued by the national energy agency, it is clear that all the wind power plants in grid-connected operation have the capability of forecasting the wind power, and the forecasting and forecasting of the wind power are carried out according to requirements, and the evaluation index of the forecasting and forecasting of the power of the wind power plant is provided. The submission of national standard 'technical regulation of photovoltaic power station access to power system' in 2012 has been reviewed by experts, where regulations are also made on active power and power prediction of photovoltaic power station access to power system.
The method is characterized in that a document 'design of a large-scale cluster wind power active intelligent control system' (automation of a power system, No. 34 and No. 17 in 2010) proposes two wind power active power control modes, namely a maximum output control mode, determines the maximum output upper limit value of a wind power plant group accessed to the same channel according to the wind power receiving capacity of different power transmission channels of a power grid, and calculates the maximum output upper limit value of each wind power plant according to an offline set rule; and in the output tracking mode, the wind power plant tracks the power generation instruction in real time to control the active power.
According to the patent application, a regional power grid active scheduling framework which is mainly based on 'power generation instruction tracking' of wind power prediction and is assisted by 'direct participation in frequency modulation' of a wind turbine generator set is provided (application number: 201010555191.X), the total output limit value of the wind power plants belonging to the same power transmission channel is calculated based on the limit requirement of a stable section, and then the output of each wind power plant is shared according to comprehensive sharing factors for evaluating the prediction deviation, the adjustment precision, the adjustment speed and the like of the wind power plants.
In order to reduce the adjustment times of the wind turbine generators and reduce the power fluctuation of the wind turbine generators, the patent application 'dynamic clustering control method of active power of wind farm' (application number: 201210202621.9) provides a method for clustering the wind turbine generators according to an ultra-short-term power change mode and distributing power according to ultra-short-term wind power prediction.
However, the existing technical achievement does not correct the output predicted value according to the actual output of the grid-connected wind-solar power station and the instruction difference value in the instruction execution period, and does not comprehensively consider the power grid safety and stability constraint, the wind-solar independent constraint and the peak regulation constraint, so that the optimal distribution of the wind-solar power station active instruction with the aim of minimum wind and light abandonment is realized.
Disclosure of Invention
The purpose of the invention is: aiming at the defects of the prior art, the active real-time optimization control method of the wind and light power generation cluster based on the successive approximation of the predicted output is provided. The method takes the corrected output predicted value as a basic distribution basis, further considers the prediction precision and the adjustment performance index of the wind and light power station to influence the distribution proportion of active power instructions, comprehensively considers the safety and stability constraint of a power grid, the wind and light independent constraint and the overall peak regulation constraint, and achieves the optimal distribution and closed-loop control of the maximum output of the wind and light power station based on the three-common scheduling principle by taking the minimum wind and light abandonment as the target on the premise of meeting the constraints.
The purpose of the invention is realized by the following technical scheme, which comprises the following steps:
1) if the calculation is the primary calculation, directly entering the step 3); otherwise, correcting the output predicted value of each wind and light power station by adopting a successive approximation strategy according to the variation trend between the real-time output of each wind and light power station and the active instruction in the instruction execution time period;
2) detecting the calculation triggering conditions of the wind-solar power generation cluster active optimization control in real time, wherein the calculation triggering conditions comprise calculation period triggering, manual triggering and event triggering formed by violating grid-connected constraint and electricity limitation, and if any calculation triggering condition is met, entering step 3); otherwise, returning to the step 1);
3) taking the output predicted value of each wind and light power station as a distribution basis, considering the prediction precision and control performance index influencing instruction distribution, taking the minimum cost of wind abandoning and light abandoning as a target, taking the grid-connected constraint related to the wind and light power generation cluster into account, and establishing an active optimization control model meeting the constraint, wherein the grid-connected constraint related to the wind and light power generation cluster comprises a safety and stability constraint, a total peak regulation constraint, a wind and light independent constraint and a partition artificial instruction upper limit constraint;
4) and performing optimal distribution of the wind and light power generation cluster active instructions based on a linear programming algorithm, and giving the active instructions of the wind and light power stations in the current round.
Further, the process of correcting the output prediction value of each wind and light power station in the step 1) is as follows:
calculating the estimated value of the output predicted value of each wind and light power station through a formula (1) according to the real-time output, and calculating the corrected value of the output predicted value of each wind and light power station through a formula (2) according to the prediction precision:
in the formula (1), p'p.j、pr.jAnd pc.j.t-1Respectively an estimated value of an output predicted value of the jth wind-solar power station, a real-time output and an active instruction of the previous round, and k is an estimation coefficient for calculating the estimated value of the output predicted value;
pp.j＝Ap.jp′p.j (2)
in the formula (2), pp.jCorrected value of the predicted value of the power output of the jth wind-solar power station, Ap.jThe prediction accuracy of the jth wind-solar power station.
Further, for the event trigger of violating the grid-connected constraint and the power limit in step 2), the event trigger of violating the grid-connected constraint is determined by formula (3), and the event trigger of the power limit is determined by formula (4):
in the formula (3), M is the total number of grid-connected constraints related to the wind-solar power generation cluster, etaiThe value range of the margin of the grid-connected constraint related to the ith wind-solar power generation cluster is [ -1, 1 [)]，f(ηi) Is corresponding to ηiEta is the state value triggered by the event violating the grid-connection constraint, if any eta isiLess than or equal to 0, wherein eta is less than 1, which means that the grid-connected constraint is violated and the event triggering condition is satisfied;
in the formula (4), lpIn order to limit the proportion of electrical power,n is the total number of wind-light power stations in the wind-light power generation cluster, lnumFor the ratio of the limited power station in the wind-solar power generation cluster, epsilon is a set ratio threshold value (generally 10 percent), wherein lnumCalculated by equation (5):
in the formula (5), ljFor the power limit sign of the jth wind-solar power station, the power limit sign is determined by the formula (6):
if lpIf the wind-solar power generation cluster power limit is larger than a set threshold value (generally 30%), the wind-solar power generation cluster power limit is judged to occur, and an event triggering condition is met.
Further, in the step 3), an active optimization control model satisfying the constraint is established through formula (7):
in the formula (7), wjThe cost of abandoning the light for the abandoned wind of the jth wind-solar power station; p is a radical ofp.j.1For the output prediction value of the jth wind-solar power station, when the output prediction value is primary calculation, pp.j.1The initial value of the output predicted value of the jth wind-solar power station is p when the initial value is not calculated for the first timep.j.1Get pp.j；pc.jThe active instruction of the current round of the jth wind-solar power station is obtained; pp.sumFor the sum of predicted values of the output of the wind-solar power generation cluster, through
Calculation of pp.j.1/Pp.sumIs a distribution factor determined according to the predicted value of the wind power station output, Ac.j、Aother.jRespectively the control performance index and other distribution factors of the jth wind-solar power station; k is a radical of1、k2、k3Are respectively provided withThe weight coefficient occupied by the distribution factor, the control performance index and other distribution factors when distributing the instruction is obtained; xii.jAnd the active sensitivity of the safety margin of the jth wind-solar power station for the ith wind-solar power generation cluster-related grid-connected constraint is obtained.
Compared with the prior art, the invention has the beneficial effects that: aiming at the problem of low wind and light prediction precision at present, the method gradually corrects the predicted output of the wind and light power station by analyzing the variation trend of the actual output and the instruction difference value of the wind and light power station in the instruction execution time period. Based on a linear programming algorithm, the corrected output predicted value is taken as a distribution basis, factors influencing the prediction precision of instruction distribution, control performance indexes and the like are considered, the minimum cost of wind abandon and light abandon is taken as a target, grid-connected constraints such as safety and stability constraints, overall peak regulation constraints, wind-light independent constraints and the like are considered, the optimal distribution of the wind-light power station power generation instructions based on the three-public principle is realized, the power generation capacity of the wind-light power station and the admitting capacity of a power grid are fully utilized, and the problems of poor prediction precision, insufficient wind-light power generation capacity and insufficient power grid admitting capacity commonly existing in the grid-connected control of the wind-light power generation cluster are solved.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Example 1:
the embodiment of the invention discloses a wind-solar power generation cluster active real-time optimization control method based on prediction output successive approximation, and the implementation steps are shown in fig. 1.
Step 1 in fig. 1 illustrates that, if the calculation is the first calculation, step 3 is directly entered; and otherwise, correcting the output predicted value of each wind and light power station by adopting a successive approximation strategy according to the variation trend between the real-time output of each wind and light power station and the active instruction in the instruction execution time period. The process of correcting the output predicted value of each wind and light power station is as follows:
calculating the estimated value of the output predicted value of each wind and light power station through a formula (1) according to the real-time output, and calculating the corrected value of the output predicted value of each wind and light power station through a formula (2) according to the prediction precision:
in the formula (1), p'p.j、pr.jAnd pc.j.t-1Respectively an estimated value of an output predicted value of the jth wind-solar power station, a real-time output and an active instruction of the previous round, and k is an estimation coefficient for calculating the estimated value of the output predicted value;
pp.j＝Ap.jp′p.j (2)
in the formula (2), pp.jCorrected value of the predicted value of the power output of the jth wind-solar power station, Ap.jThe prediction accuracy of the jth wind-solar power station.
Step 2 in fig. 1 describes that the calculation trigger conditions of the wind-solar power generation cluster active optimization control are detected in real time, the calculation trigger conditions include that a calculation cycle trigger is reached, a manual trigger is reached, and an event trigger formed by violating grid-connection constraint and electricity limitation is reached, if any calculation trigger condition is met, the step 3 is entered; otherwise, returning to the step 1. For the event trigger formed by violating the grid-connected constraint and the electricity limitation, wherein the event trigger violating the grid-connected constraint is determined by a formula (3), and the event trigger for limiting the electricity is determined by a formula (4):
in the formula (3), M is the total number of grid-connected constraints related to the wind-solar power generation cluster, etaiThe value range of the margin of the grid-connected constraint related to the ith wind-solar power generation cluster is [ -1, 1 [)]，f(ηi) Is corresponding to ηiEta is the state value triggered by the event violating the grid-connection constraint, if any eta isiLess than or equal to 0, wherein eta is less than 1, which means that the grid-connected constraint is violated and the event triggering condition is satisfied;
in the formula (4), lpFor the ratio of the power limit, N is the total number of wind-light power stations in the wind-light power generation cluster, lnumFor the ratio of the limited power station in the wind-solar power generation cluster, epsilon is a set ratio threshold value (generally 10 percent), wherein lnumCalculated by equation (5):
in the formula (5), ljFor the power limit sign of the jth wind-solar power station, the power limit sign is determined by the formula (6):
if lpIf the wind-solar power generation cluster power limit is larger than a set threshold value (generally 30%), the wind-solar power generation cluster power limit is judged to occur, and an event triggering condition is met.
Step 3 in fig. 1 describes that an active optimization control model meeting constraints is established by taking the output predicted value of each wind and light power station as a distribution basis, considering the prediction precision and control performance index influencing instruction distribution and taking the minimum cost of wind and light abandonment as a target, taking the grid-connected constraints related to the wind and light power generation cluster into account, wherein the grid-connected constraints related to the wind and light power generation cluster comprise safety and stability constraints, total peak regulation constraints, wind and light independent constraints and partition artificial instruction upper limit constraints. Specifically, an active optimization control model satisfying constraints is established by formula (7):
in the formula (7), wjThe cost of abandoning the light for the abandoned wind of the jth wind-solar power station; p is a radical ofp.j.1For the output prediction value of the jth wind-solar power station, when the output prediction value is primary calculation, pp.j.1For the output pre-of the jth wind-solar power stationInitial value of measured value, when not initially calculated, pp.j.1Get pp.j；pc.jThe active instruction of the current round of the jth wind-solar power station is obtained; pp.sumFor the sum of predicted values of the output of the wind-solar power generation cluster, through
Calculation of pp.j.1/Pp.sumIs a distribution factor determined according to the predicted value of the wind power station output, Ac.j、Aother.jRespectively the control performance index and other distribution factors of the jth wind-solar power station; k is a radical of1、k2、k3Respectively are weight coefficients occupied by the distribution factors, the control performance indexes and other distribution factors when distributing instructions; xii.jAnd the active sensitivity of the safety margin of the jth wind-solar power station for the ith wind-solar power generation cluster-related grid-connected constraint is obtained.
Step 4 in fig. 1 describes that, based on a linear programming algorithm, optimal distribution of active power instructions of the wind and photovoltaic power generation clusters is performed, and the active power instructions of the current round of each wind and photovoltaic power station are given.
Although the present invention has been described in terms of the preferred embodiment, it is not intended that the invention be limited to the embodiment. Any equivalent changes or modifications made without departing from the spirit and scope of the present invention also belong to the protection scope of the present invention. The scope of the invention should therefore be determined with reference to the appended claims.
Claims (3)
1. The wind-solar power generation cluster active real-time optimization control method based on the successive approximation of predicted output is characterized by comprising the following steps of:
1) if the calculation is the primary calculation, directly entering the step 3); otherwise, correcting the output predicted value of each wind and light power station by adopting a successive approximation strategy according to the variation trend between the real-time output of each wind and light power station and the active instruction in the instruction execution time period, wherein the correction process is as follows:
calculating the estimated value of the output predicted value of each wind and light power station through a formula (1) according to the real-time output, and calculating the corrected value of the output predicted value of each wind and light power station through a formula (2) according to the prediction precision:
in the formula (1), p'p.j、pr.jAnd pc.j.t-1Respectively an estimated value of an output predicted value of the jth wind-solar power station, a real-time output and an active instruction of the previous round, and k is an estimation coefficient for calculating the estimated value of the output predicted value;
pp.j＝Ap.jp′p.j (2)
in the formula (2), pp.jCorrected value of the predicted value of the power output of the jth wind-solar power station, Ap.jThe prediction accuracy of the jth wind-solar power station is obtained;
2) detecting the calculation triggering conditions of the wind-solar power generation cluster active optimization control in real time, wherein the calculation triggering conditions comprise calculation period triggering, manual triggering and event triggering formed by violating grid-connected constraint and electricity limitation, and if any calculation triggering condition is met, entering step 3); otherwise, returning to the step 1);
3) taking the output predicted value of each wind and light power station as a distribution basis, considering the prediction precision and control performance index influencing instruction distribution, taking the minimum cost of wind abandoning and light abandoning as a target, taking the grid-connected constraint related to the wind and light power generation cluster into account, and establishing an active optimization control model meeting the constraint, wherein the grid-connected constraint related to the wind and light power generation cluster comprises a safety and stability constraint, a total peak regulation constraint, a wind and light independent constraint and a partition artificial instruction upper limit constraint;
4) and performing optimal distribution of the wind and light power generation cluster active instructions based on a linear programming algorithm, and giving the active instructions of the wind and light power stations in the current round.
2. The active real-time optimization control method for the wind and photovoltaic power generation cluster based on the successive approximation of the predicted output according to claim 1, wherein for the event trigger formed by violating the grid-connection constraint and the electricity limiting in the step 2), the event trigger violating the grid-connection constraint is determined by formula (3), and the event trigger for the electricity limiting is determined by formula (4):
in the formula (3), M is the total number of grid-connected constraints related to the wind-solar power generation cluster, etaiThe value range of the margin of the grid-connected constraint related to the ith wind-solar power generation cluster is [ -1, 1 [)]，f(ηi) Is corresponding to ηiEta is the state value triggered by the event violating the grid-connection constraint, if any eta isiLess than or equal to 0, wherein eta is less than 1, which means that the grid-connected constraint is violated and the event triggering condition is satisfied;
in the formula (4), lpFor the ratio of the power limit, N is the total number of wind-light power stations in the wind-light power generation cluster, lnumFor the ratio of the limited power station in the wind-solar power generation cluster, epsilon is a set ratio threshold value, wherein lnumCalculated by equation (5):
in the formula (5), ljFor the power limit sign of the jth wind-solar power station, the power limit sign is determined by the formula (6):
if lpIf the threshold value is larger than the set threshold value, the wind-solar power generation cluster power limitation is judged to occur, and the event triggering condition is met.
3. The active real-time optimization control method for the wind and photovoltaic power generation cluster based on the successive approximation of the predicted output according to claim 2, wherein in the step 3), an active optimization control model satisfying constraints is established through a formula (7):
in the formula (7), wjThe cost of abandoning the light for the abandoned wind of the jth wind-solar power station; p is a radical ofp.j.1For the output prediction value of the jth wind-solar power station, when the output prediction value is primary calculation, pp.j.1The initial value of the output predicted value of the jth wind-solar power station is p when the initial value is not calculated for the first timep.j.1Get pp.j；pc.jThe active instruction of the current round of the jth wind-solar power station is obtained; pp.sumFor the sum of predicted values of the output of the wind-solar power generation cluster, through
Calculation of pp.j.1/Pp.sumIs a distribution factor determined according to the predicted value of the wind power station output, Ac.j、Aother.jRespectively the control performance index and other distribution factors of the jth wind-solar power station; k is a radical of1、k2、k3Respectively are weight coefficients occupied by the distribution factors, the control performance indexes and other distribution factors when distributing instructions; xii.jAnd the active sensitivity of the safety margin of the jth wind-solar power station for the ith wind-solar power generation cluster-related grid-connected constraint is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711438184.XA CN108054790B (en) | 2017-12-27 | 2017-12-27 | Wind-solar power generation cluster active real-time optimization control method based on predicted output successive approximation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711438184.XA CN108054790B (en) | 2017-12-27 | 2017-12-27 | Wind-solar power generation cluster active real-time optimization control method based on predicted output successive approximation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108054790A CN108054790A (en) | 2018-05-18 |
| CN108054790B true CN108054790B (en) | 2021-03-16 |
Family
ID=62128151
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711438184.XA Active CN108054790B (en) | 2017-12-27 | 2017-12-27 | Wind-solar power generation cluster active real-time optimization control method based on predicted output successive approximation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108054790B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109378863B (en) * | 2018-10-31 | 2021-06-01 | 国电南瑞科技股份有限公司 | Power grid real-time power generation control optimization decision method based on weight and constraint correlation adjustment |
| CN109636228A (en) * | 2018-12-21 | 2019-04-16 | 云南电网有限责任公司电力科学研究院 | A kind of new energy station is rationed the power supply period active distribution method |
| CN111342499B (en) * | 2020-03-05 | 2023-09-08 | 宁夏嘉泽新能源股份有限公司 | Wind farm real-time scheduling method based on wind power prediction data |
| CN113054688B (en) * | 2021-04-06 | 2022-09-16 | 华中科技大学 | Renewable energy cluster output distributed coordination control method considering uncertainty |
| CN114070201B (en) * | 2021-12-06 | 2024-03-15 | 国网河北省电力有限公司经济技术研究院 | Distributed photovoltaic monitoring control system |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104410105A (en) * | 2014-11-26 | 2015-03-11 | 沈阳工业大学 | Intelligent wind power plant control method based on direct-current bus grid structure |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8600572B2 (en) * | 2010-05-27 | 2013-12-03 | International Business Machines Corporation | Smarter-grid: method to forecast electric energy production and utilization subject to uncertain environmental variables |
-
2017
- 2017-12-27 CN CN201711438184.XA patent/CN108054790B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104410105A (en) * | 2014-11-26 | 2015-03-11 | 沈阳工业大学 | Intelligent wind power plant control method based on direct-current bus grid structure |
Non-Patent Citations (2)
| Title |
|---|
| 大用户直购电模式下含大规模风光的电力系统优化调度;张倩文等;《电力建设》;20111031;第38卷(第10期);第24-32页 * |
| 实时平抑风电场功率波动的电池储能系统优化控制方法;洪海生，江全元，严玉婷;《电力系统自动化》;20130110;第37卷(第1期);第103-109页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108054790A (en) | 2018-05-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108054790B (en) | Wind-solar power generation cluster active real-time optimization control method based on predicted output successive approximation | |
| CN105846461B (en) | Control method and system for large-scale energy storage power station self-adaptive dynamic planning | |
| CN104779631B (en) | Energy storage tracking wind power output method of planning and its system based on the pre- power scale of wind-powered electricity generation | |
| CN109492861B (en) | Method for decomposing medium-term electricity quantity trading plan of cascade hydropower station group | |
| CN103683326B (en) | A kind of regional power grid wind-powered electricity generation multiple spot accesses the computational methods of best receiving ability | |
| CN108092324B (en) | AGC control system and control method for wind power participating in peak shaving frequency modulation | |
| CN110224444B (en) | Multi-time scale coordination control method for island microgrid | |
| CN105680474B (en) | Control method for restraining rapid power change of photovoltaic power station through energy storage | |
| US20220147670A1 (en) | Optimal allocation method for stored energy coordinating electric vehicles to participate in auxiliary service market | |
| CN109765787B (en) | Power distribution network source load rapid tracking method based on intraday-real-time rolling control | |
| CN103296701B (en) | Active power control method in wind power plant | |
| CN105162149A (en) | Fuzzy adaptive control based method for tracking output of power generation plan of light storage system | |
| CN104299173B (en) | It is a kind of to optimize dispatching method a few days ago suitable for the robust that various energy resources are accessed | |
| CN104951899A (en) | Multi-time-scale optimal scheduling method for power distribution company containing large-scale renewable energy sources | |
| CN102904249B (en) | Security constraint-based real-time generation planning method | |
| CN112510703B (en) | Multi-energy access power grid optimal scheduling method considering coal consumption curve correction | |
| CN104143839B (en) | Wind power plant cluster based on power prediction limits active power distribution method of exerting oneself | |
| CN110676849B (en) | Method for constructing islanding micro-grid group energy scheduling model | |
| CN110165693A (en) | A kind of wind power plant optimization energy storage configuration method | |
| CN113408962A (en) | Power grid multi-time scale and multi-target energy optimal scheduling method | |
| CN113346555A (en) | Intraday rolling scheduling method considering electric quantity coordination | |
| CN114336762B (en) | Wind-solar power generation and power grid load fluctuation day-ahead scheduling energy storage configuration optimization method | |
| CN112821412B (en) | Automatic voltage control method based on active trend judgment | |
| Hui et al. | Short-term wind speed forecasting simulation research based on ARIMA-LSSVM combination method | |
| CN105207255B (en) | A kind of power system peak regulation computational methods suitable for wind power output |
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 |