CN113313301A - Photovoltaic power generation power ultra-short term prediction result combination optimization method and system - Google Patents

Photovoltaic power generation power ultra-short term prediction result combination optimization method and system Download PDF

Info

Publication number
CN113313301A
CN113313301A CN202110572661.1A CN202110572661A CN113313301A CN 113313301 A CN113313301 A CN 113313301A CN 202110572661 A CN202110572661 A CN 202110572661A CN 113313301 A CN113313301 A CN 113313301A
Authority
CN
China
Prior art keywords
moment
generated power
prediction
power
ultra
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.)
Pending
Application number
CN202110572661.1A
Other languages
Chinese (zh)
Inventor
王铮
王勃
王伟胜
冯双磊
刘纯
赵艳青
姜文玲
裴岩
车建峰
张菲
汪步惟
王钊
靳双龙
胡菊
宋宗朋
王姝
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Shanghai Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Shanghai Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, China Electric Power Research Institute Co Ltd CEPRI, State Grid Shanghai Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202110572661.1A priority Critical patent/CN113313301A/en
Publication of CN113313301A publication Critical patent/CN113313301A/en
Pending legal-status Critical Current

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
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Water Supply & Treatment (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides a combined optimization method and a combined optimization system for a photovoltaic power generation power ultra-short term prediction result, which comprise the following steps: collecting the generated power of the photovoltaic power station at the current moment, the generated power ultra-short term prediction result at the prediction moment and the actual generated power at the same prediction moment in the previous preset period; optimizing the generated power ultra-short-term prediction result at the prediction moment by utilizing a pre-established combined optimization model based on the acquired generated power of the photovoltaic power station at the current moment and the actual generated power at the same prediction moment in the previous preset period; the method can effectively improve the precision of the photovoltaic ultra-short term prediction result, and support real-time adjustment within a power generation plan day, so that the new energy consumption capability is improved on the premise of ensuring the system safety.

Description

Photovoltaic power generation power ultra-short term prediction result combination optimization method and system
Technical Field
The invention belongs to the technical field of new energy grid-connected operation, and particularly relates to a photovoltaic power generation power ultra-short term prediction result combination optimization method and system.
Background
The photovoltaic ultra-short-term power prediction is one of the bases of photovoltaic regulation and control operation, and has an important supporting effect on the real-time adjustment of a power generation plan in a day. With the advance of the electric power market reformation process, the function of high-precision prediction of photovoltaic ultra-short-term power is further highlighted. Due to the periodic change of the irradiation intensity and the random interference of the cloud layer, the output power of the inertia-free photovoltaic power generation system fluctuates violently, the introduction of real-time cloud observation data is one of effective means for improving the ultra-short-term prediction precision of the photovoltaic output power, but the real-time cloud observation data is influenced by the evaluation deviation of the moving track of the cloud layer, the living elimination and the deformation of the cloud layer and the like, the problem that the error is large still exists in the photovoltaic ultra-short-term prediction result only depending on a physical model, and the new energy absorption space can not be excavated to the maximum extent in the regulation and control operation of a power grid.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a combined optimization method for a photovoltaic power generation power ultra-short term prediction result, which comprises the following steps:
collecting the generated power of the photovoltaic power station at the current moment, the generated power ultra-short term prediction result at the prediction moment and the actual generated power at the same prediction moment in the previous preset period;
optimizing the generated power ultra-short-term prediction result at the prediction moment by utilizing a pre-established combined optimization model based on the acquired generated power of the photovoltaic power station at the current moment and the actual generated power at the same prediction moment in the previous preset period;
the combined optimization model comprises the steps of respectively optimizing the generated power ultra-short term prediction results according to the generated power of the photovoltaic power station at the current moment and the actual generated power of the photovoltaic power station at the same prediction moment in the previous preset period, and then combining the two optimization results.
Preferably, the combinatorial optimization model includes: the method comprises the following steps of combining results of a continuous algorithm model and a periodic optimization model;
the continuous algorithm model comprises a continuous coefficient under the current prediction time scale obtained through continuous calculation as a weight, and the generated power of the photovoltaic power station at the current moment and the generated power ultra-short term prediction result at the prediction moment are fitted to obtain a continuous algorithm power ultra-short term prediction result;
the period optimization model comprises the step of fitting the actual generated power at the same prediction moment of a preset period of the photovoltaic power station and the generated power ultra-short term prediction result at the prediction moment by taking the fusion coefficient under the current prediction time scale obtained through continuous calculation as a weight to obtain a period optimization power ultra-short term prediction result.
Preferably, the continuous algorithm model comprises the following calculation formula:
Figure BDA0003083294240000021
in the formula, k is the time scale of the prediction moment; t represents the current time;
Figure BDA0003083294240000022
the power ultra-short term prediction result of the continuous algorithm at the t + k moment is obtained; p is a radical ofSCADA,tThe real-time generated power at the moment t;
Figure BDA0003083294240000023
the generated power ultra-short term prediction result at the t + k moment is obtained; alpha is alphakThe persistence coefficient at the time scale is predicted for the kth.
Preferably, the persistence coefficient α at the k-th prediction time scalekIs calculated as follows:
Figure BDA0003083294240000024
wherein ε represents an adjustment coefficient.
Preferably, the period optimization model comprises the following calculation formula:
Figure BDA0003083294240000025
in the formula, k is the time scale of the prediction moment; t tableShowing the current time;
Figure BDA0003083294240000026
optimizing a power ultra-short term prediction result for the period at the t + k moment; beta is a fusion coefficient; p is a radical ofday-ahead,t+kThe actual generated power at the t + k moment of the last preset period is obtained;
Figure BDA0003083294240000027
and the prediction result is the generated power ultra-short term prediction result at the t + k moment.
Preferably, the fusion coefficient is obtained by optimizing with the goal of minimizing an error between an actual value of the historical generated power at the historical prediction time and a cycle optimization power ultra-short term prediction result at the prediction time.
Preferably, the results of the continuous algorithm model and the periodic optimization model are combined according to the following formula:
Figure BDA0003083294240000028
wherein k is the time scale of the prediction moment; t represents the current time;
Figure BDA0003083294240000029
combining and optimizing results at the t + k th moment; p is a radical ofSCADA,tActual power at the t moment;
Figure BDA00030832942400000210
the power ultra-short term prediction result of the continuous algorithm at the t + k moment is obtained;
Figure BDA00030832942400000211
optimizing a power ultra-short term prediction result for the period at the t + k moment;
Figure BDA00030832942400000212
generating power at the current moment predicted by the last combined optimization result; and gamma is a combination coefficient.
Preferably, the combination coefficient is obtained by optimizing with a target of minimizing an error between an actual value of the historical generated power at the historical prediction time and a combined optimization result at the prediction time.
Preferably, the continuous algorithm model includes that a continuous coefficient under a current prediction time scale obtained by continuous calculation is used as a weight, after fitting the generated power of the photovoltaic power station at the current moment and the generated power ultra-short term prediction result at the prediction moment to obtain a continuous algorithm power ultra-short term prediction result, and before the cycle optimization model includes that a fusion coefficient under the current prediction time scale obtained by continuous calculation is used as a weight, fitting the actual generated power of the photovoltaic power station at the same prediction moment and the generated power ultra-short term prediction result at the prediction moment in a preset cycle to obtain a cycle optimization power ultra-short term prediction result, the method further includes:
calculating clearance irradiation intensity by adopting a clearance model in preset time periods of sunrise and sunset;
and converting the clearance irradiation intensity into the output power of the photovoltaic power station, and taking the output power as a power ultra-short term prediction result of a continuous algorithm at the prediction moment when the prediction moment is in the preset time periods of sunrise and sunset.
Based on the same inventive concept, the invention also provides a photovoltaic power generation power ultra-short term prediction result combination optimization system, which comprises: the system comprises an acquisition module and an optimization module;
the acquisition module is used for acquiring the generated power of the photovoltaic power station at the current moment, the generated power ultra-short term prediction result at the prediction moment and the actual generated power at the same prediction moment in the previous preset period;
the optimization module is used for optimizing the ultra-short-term prediction result of the generated power at the prediction moment by utilizing a pre-established combined optimization model based on the collected generated power of the photovoltaic power station at the current moment and the actual generated power at the same prediction moment in the previous preset period;
the combined optimization model comprises the steps of respectively optimizing the generated power ultra-short term prediction results according to the generated power of the photovoltaic power station at the current moment and the actual generated power of the photovoltaic power station at the same prediction moment in the previous preset period, and then combining the two optimization results.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention discloses a method and a system for optimizing the combination of ultra-short term prediction results of photovoltaic power generation power, which comprises the following steps: collecting the generated power of the photovoltaic power station at the current moment, the generated power ultra-short term prediction result at the prediction moment and the actual generated power at the same prediction moment in the previous preset period; optimizing the generated power ultra-short-term prediction result at the prediction moment by utilizing a pre-established combined optimization model based on the acquired generated power of the photovoltaic power station at the current moment and the actual generated power at the same prediction moment in the previous preset period; the combined optimization model comprises the steps of respectively optimizing the generated power ultra-short term prediction results according to the generated power of the photovoltaic power station at the current moment and the actual generated power of the photovoltaic power station at the same prediction moment in the previous preset period, and then combining the two optimization results; the method can effectively improve the precision of the photovoltaic ultra-short term prediction result, and support real-time adjustment within a power generation plan day, so that the new energy consumption capability is improved on the premise of ensuring the system safety.
Drawings
FIG. 1 is a schematic flow chart of a method for optimizing the combination of ultra-short term prediction results of photovoltaic power generation power provided by the invention;
fig. 2 is a schematic structural diagram of a combined optimization system for a super-short term prediction result of photovoltaic power generation provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following 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.
Example 1:
the application of the principles of the present invention will be further described with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for optimizing a combination of ultra-short term prediction results of photovoltaic power generation power according to an embodiment of the present invention, as shown in fig. 1, includes:
step 1: collecting the generated power of the photovoltaic power station at the current moment, the generated power ultra-short term prediction result at the prediction moment and the actual generated power at the same prediction moment in the previous preset period;
step 2: optimizing the generated power ultra-short-term prediction result at the prediction moment by utilizing a pre-established combined optimization model based on the acquired generated power of the photovoltaic power station at the current moment and the actual generated power at the same prediction moment in the previous preset period;
the combined optimization model comprises the steps of respectively optimizing the generated power ultra-short term prediction results according to the generated power of the photovoltaic power station at the current moment and the actual generated power of the photovoltaic power station at the same prediction moment in the previous preset period, and then combining the two optimization results.
Wherein, step 2 specifically includes:
(1) combined optimization model
The method comprises the following steps of taking output results of a continuous algorithm model and a periodic optimization model as input, and correcting a combination result of the continuous algorithm model and the periodic optimization model through a real-time adjustment coefficient, wherein the specific method comprises the following steps:
Figure BDA0003083294240000041
in the formula,
Figure BDA0003083294240000042
combining and optimizing results at the t + k th moment; p is a radical ofSCADA,tActual power at the t moment;
Figure BDA0003083294240000043
the power ultra-short term prediction result of the continuous algorithm at the t + k moment is obtained;
Figure BDA0003083294240000044
optimizing a power ultra-short term prediction result for the period at the t + k moment;
Figure BDA0003083294240000045
a first value of the optimization result for the last combination; gamma is a combination coefficient, and is obtained by optimizing (namely, the error between the historical actual value of the historical prediction time and the corrected prediction result is minimum) by taking the error optimization of the combined optimization result as a target.
The output result of the continuous algorithm model is calculated as follows:
(2) continuous algorithm model
(2-1) optimizing the ultra-short term power prediction result of the photovoltaic power station by using the real-time power generation power of the photovoltaic power station as input and adopting a continuous algorithm, wherein the method comprises the following steps:
Figure BDA0003083294240000046
wherein,
Figure BDA0003083294240000051
in the formula,
Figure BDA0003083294240000052
the corrected ultra-short-term prediction result (the continuous algorithm power ultra-short-term prediction result at the t + k moment) at the t + k moment is obtained; p is a radical ofSCADA,tThe actual generated power at the moment t;
Figure BDA0003083294240000053
the super-short term prediction result before the optimization at the t + k moment (the super-short term prediction result of the generated power at the t + k moment) is obtained; t represents the current time; alpha is alphakPredicting a persistence coefficient at a time scale for the kth; epsilon is an adjustment coefficient, the value range is basically between 0.1 and 3, and the optimal value can be obtained by a random optimization method; k is the time scale of the prediction time, integers are taken, according to relevant industrial standards, the current ultra-short term prediction time scale in China is 4 hours, the data resolution is 15 minutes, and therefore the value of k is between 1 and 16.
(2-2) in the model, errors are introduced into sunrise and sunset periods, so that correction is performed by adopting a headroom model, and under a headroom condition, the irradiation intensity is as follows:
Figure BDA0003083294240000054
wherein,
Figure BDA0003083294240000055
Figure BDA0003083294240000056
in the formula IclearFor the clearance of the irradiation intensity, w/m2；ISCIs the sun constant, ISC＝1367W/m2(ii) a q is a space quality coefficient, and is generally 0.7; Γ is the solar angle of day N; alpha is alphasThe solar altitude is a fixed calculation method, and the description is not repeated here.
The method adopts a physical model of the photovoltaic power generation system to convert the clearance irradiation intensity into output power pclear：
pclear＝f(Iclear) (6)
And correcting the abnormal optimization result by using the clearance output within 4 hours after sunrise and the clearance output within 4 hours before sunset.
The output result of the period optimization model is calculated as follows:
(3) periodic optimization model
The actual generated power at the corresponding moment in the previous day is used as input, and the ultra-short term power prediction result of the photovoltaic power station is corrected by adopting a continuous algorithm (calculation in a rolling mode), wherein the method comprises the following steps:
Figure BDA0003083294240000057
in the formula，
Figure BDA0003083294240000061
The ultra-short term prediction result after the modification at the t + k moment (the period optimization power ultra-short term prediction result at the t + k moment) is obtained; k is the time scale of the prediction moment; t represents the current time; beta is a fusion coefficient, can obtain the minimum error after correction as a target through optimization (namely, the minimum error between a historical actual value of a historical prediction time and a corrected prediction result is obtained); p is a radical ofday-ahead,t+kActual generated power at the t + k time of the previous day (last preset period);
Figure BDA0003083294240000062
and the prediction result is the ultra-short term prediction result before the optimization at the t + k moment. It should be noted that the previous preset period may be a period length such as a previous day, a previous week, a previous month, etc., and the previous day is used in the present embodiment to represent the previous preset period.
The method can improve the accuracy of the ultra-short term prediction result of the generated power of different types of photovoltaic power stations by 1-2 percentage points.
Example 2:
based on the same inventive concept, the invention also provides a combined optimization system for the ultra-short term prediction result of the photovoltaic power generation power.
The system, as shown in fig. 2, includes: the system comprises an acquisition module and an optimization module;
the acquisition module is used for acquiring the generated power of the photovoltaic power station at the current moment, the generated power ultra-short term prediction result at the prediction moment and the actual generated power at the same prediction moment in the previous preset period;
the optimization module is used for optimizing the ultra-short-term prediction result of the generated power at the prediction moment by utilizing a pre-established combined optimization model based on the collected generated power of the photovoltaic power station at the current moment and the actual generated power at the same prediction moment in the previous preset period;
the combined optimization model comprises the steps of respectively optimizing the generated power ultra-short term prediction results according to the generated power of the photovoltaic power station at the current moment and the actual generated power of the photovoltaic power station at the same prediction moment in the previous preset period, and then combining the two optimization results.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 scope of protection thereof, 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: after reading this disclosure, those skilled in the art will be able to make various changes, modifications and equivalents to the embodiments of the invention, which fall within the scope of the appended claims.

Claims (10)

1. A combined optimization method for a photovoltaic power generation power ultra-short term prediction result is characterized by comprising the following steps:
collecting the generated power of the photovoltaic power station at the current moment, the generated power ultra-short term prediction result at the prediction moment and the actual generated power at the same prediction moment in the previous preset period;
optimizing the generated power ultra-short-term prediction result at the prediction moment by utilizing a pre-established combined optimization model based on the acquired generated power of the photovoltaic power station at the current moment and the actual generated power at the same prediction moment in the previous preset period;
the combined optimization model comprises the steps of respectively optimizing the generated power ultra-short term prediction results according to the generated power of the photovoltaic power station at the current moment and the actual generated power of the photovoltaic power station at the same prediction moment in the previous preset period, and then combining the two optimization results.
2. The method of claim 1, wherein the combinatorial optimization model comprises: the method comprises the following steps of combining results of a continuous algorithm model and a periodic optimization model;
the continuous algorithm model comprises a continuous coefficient under the current prediction time scale obtained through continuous calculation as a weight, and the generated power of the photovoltaic power station at the current moment and the generated power ultra-short term prediction result at the prediction moment are fitted to obtain a continuous algorithm power ultra-short term prediction result;
the period optimization model comprises the step of fitting the actual generated power at the same prediction moment of a preset period of the photovoltaic power station and the generated power ultra-short term prediction result at the prediction moment by taking the fusion coefficient under the current prediction time scale obtained through continuous calculation as a weight to obtain a period optimization power ultra-short term prediction result.
3. The method of claim 2, wherein the continuous algorithm model comprises the following equation:
Figure FDA0003083294230000011
in the formula, k is the time scale of the prediction moment; t represents the current time;
Figure FDA0003083294230000012
the power ultra-short term prediction result of the continuous algorithm at the t + k moment is obtained; p is a radical ofSCADA,tThe real-time generated power at the moment t;
Figure FDA0003083294230000013
the generated power ultra-short term prediction result at the t + k moment is obtained; alpha is alphakThe persistence coefficient at the time scale is predicted for the kth.
4. The method according to claim 3, wherein the persistence coefficient α at the kth prediction time scalekIs calculated as follows:
Figure FDA0003083294230000014
wherein ε represents an adjustment coefficient.
5. The method of claim 2, wherein the period optimization model comprises the following calculation:
Figure FDA0003083294230000015
in the formula, k is the time scale of the prediction moment; t represents the current time;
Figure FDA0003083294230000021
optimizing a power ultra-short term prediction result for the period at the t + k moment; beta is a fusion coefficient; p is a radical ofday-ahead,t+kThe actual generated power at the t + k moment of the last preset period is obtained;
Figure FDA0003083294230000022
and the prediction result is the generated power ultra-short term prediction result at the t + k moment.
6. The method according to claim 2 or 5, wherein the fusion coefficient is obtained by optimizing with the aim of minimizing the error between the actual value of the historical generated power at the historical prediction time and the cycle optimization power ultra-short term prediction result at the prediction time.
7. The method of claim 2, wherein the results of the continuous algorithm model, the periodic optimization model are combined as follows:
Figure FDA0003083294230000023
wherein k is the time scale of the prediction moment; t represents the current time;
Figure FDA0003083294230000024
combining and optimizing results at the t + k th moment; p is a radical ofSCADA,tActual power at the t moment;
Figure FDA0003083294230000025
the power ultra-short term prediction result of the continuous algorithm at the t + k moment is obtained;
Figure FDA0003083294230000026
optimizing a power ultra-short term prediction result for the period at the t + k moment;
Figure FDA0003083294230000027
generating power at the current moment predicted by the last combined optimization result; and gamma is a combination coefficient.
8. The method according to claim 7, wherein the combination coefficient is optimized with a view to minimizing an error between an actual value of the historical generated power at the historical prediction time and a result of the combined optimization at the prediction time.
9. The method of claim 2, wherein the continuous algorithm model comprises a continuous coefficient under a current prediction time scale obtained by continuous calculation as a weight, after fitting the generated power at the current time of the photovoltaic power station and the generated power ultra-short term prediction result at the prediction time to obtain a continuous algorithm power ultra-short term prediction result, and the cycle optimization model comprises a fusion coefficient under the current prediction time scale obtained by continuous calculation as a weight, before fitting the actual generated power at the same prediction time of a preset cycle on the photovoltaic power station and the generated power ultra-short term prediction result at the prediction time to obtain a cycle optimization power ultra-short term prediction result, further comprising:
calculating clearance irradiation intensity by adopting a clearance model in preset time periods of sunrise and sunset;
and converting the clearance irradiation intensity into the output power of the photovoltaic power station, and taking the output power as a power ultra-short term prediction result of a continuous algorithm at the prediction moment when the prediction moment is in the preset time periods of sunrise and sunset.
10. A photovoltaic power generation power ultra-short term prediction result combination optimization system is characterized by comprising: the system comprises an acquisition module and an optimization module;
the acquisition module is used for acquiring the generated power of the photovoltaic power station at the current moment, the generated power ultra-short term prediction result at the prediction moment and the actual generated power at the same prediction moment in the previous preset period;
the optimization module is used for optimizing the ultra-short-term prediction result of the generated power at the prediction moment by utilizing a pre-established combined optimization model based on the collected generated power of the photovoltaic power station at the current moment and the actual generated power at the same prediction moment in the previous preset period;
the combined optimization model comprises the steps of respectively optimizing the generated power ultra-short term prediction results according to the generated power of the photovoltaic power station at the current moment and the actual generated power of the photovoltaic power station at the same prediction moment in the previous preset period, and then combining the two optimization results.
CN202110572661.1A 2021-05-25 2021-05-25 Photovoltaic power generation power ultra-short term prediction result combination optimization method and system Pending CN113313301A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110572661.1A CN113313301A (en) 2021-05-25 2021-05-25 Photovoltaic power generation power ultra-short term prediction result combination optimization method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110572661.1A CN113313301A (en) 2021-05-25 2021-05-25 Photovoltaic power generation power ultra-short term prediction result combination optimization method and system

Publications (1)

Publication Number Publication Date
CN113313301A true CN113313301A (en) 2021-08-27

Family

ID=77374709

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110572661.1A Pending CN113313301A (en) 2021-05-25 2021-05-25 Photovoltaic power generation power ultra-short term prediction result combination optimization method and system

Country Status (1)

Country Link
CN (1) CN113313301A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113922734A (en) * 2021-09-18 2022-01-11 南京南瑞继保电气有限公司 Light storage controller and light storage control method for reshaping external characteristics of photovoltaic power station
CN114256843A (en) * 2022-03-01 2022-03-29 中国电力科学研究院有限公司 Distributed photovoltaic radiation data correction method and device

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113922734A (en) * 2021-09-18 2022-01-11 南京南瑞继保电气有限公司 Light storage controller and light storage control method for reshaping external characteristics of photovoltaic power station
CN113922734B (en) * 2021-09-18 2023-12-29 南京南瑞继保电气有限公司 Light storage controller for remolding external characteristics of photovoltaic power station and light storage control method
CN114256843A (en) * 2022-03-01 2022-03-29 中国电力科学研究院有限公司 Distributed photovoltaic radiation data correction method and device
CN114256843B (en) * 2022-03-01 2022-07-08 中国电力科学研究院有限公司 Distributed photovoltaic radiation data correction method and device

Similar Documents

Publication Publication Date Title
CN103020487B (en) A kind of photovoltaic plant irradiance predicted value modification method
CN113313301A (en) Photovoltaic power generation power ultra-short term prediction result combination optimization method and system
EP3026774B1 (en) Method for the control of power ramp-rates minimizing energy storage requirements in intermittent power generation plants
CN105162141B (en) Consider wind-powered electricity generation uncertainty and the reactive power optimization method of voltage stabilization sexual factor
AU2017420806B2 (en) Method for controlling power ramps with prediction in intermittent power generation plants
KR101277762B1 (en) Method for Maximum Power Point Tracking in Mismatched Solar Cell
CN105207272B (en) The random economic load dispatching method and device of Electrical Power System Dynamic based on general distribution
CN109960885A (en) A kind of distributed photovoltaic power generation power forecasting method
CN109345046A (en) The prediction technique and device of photovoltaic power
CN106887858A (en) A kind of energy-storage system tracking plan for accessing generation of electricity by new energy is exerted oneself method and device
CN106959707A (en) A kind of solar radiation quantity for photovoltaic generation monitors method of adjustment
CN104333008B (en) A kind of reactive voltage control method improving wind energy turbine set rate of qualified voltage
CN105356451A (en) Probability trend calculating method considering large-scale photovoltaic grid-connected power generation system
CN115441447B (en) New energy power generation power prediction method
CN116562459A (en) Carbon emission analysis algorithm combining air temperature, humidity, wind speed, solar cloud cover environment factors
CN107732962B (en) Abandoned wind reduction method based on ultra-short term abandoned wind curve prediction
CN116594432B (en) Sensorless control method and sensorless control equipment for photovoltaic power generation light tracking system
CN110555220B (en) Calibration method and system of photoelectric conversion model
ZA202308530B (en) Source-grid-load-storage coordinated control method and system
CN107478335A (en) A kind of method of microdefect solar module hot spot temperature computation
CN116679761A (en) Method for tracking quality of full life cycle of photovoltaic module
CN110190622B (en) Power reduction operation control method for photovoltaic power generation system
CN112561234A (en) Quantification method and system for on-site limited electric quantity and off-site limited electric quantity
CN109636041B (en) Unit maintenance simulation method and system suitable for large-scale complex power grid
CN105956685A (en) Photovoltaic power factor table prediction 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