CN202523101U - Power generation output power prediction system for photovoltaic power station - Google Patents

Power generation output power prediction system for photovoltaic power station Download PDF

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Publication number
CN202523101U
CN202523101U CN2011204622212U CN201120462221U CN202523101U CN 202523101 U CN202523101 U CN 202523101U CN 2011204622212 U CN2011204622212 U CN 2011204622212U CN 201120462221 U CN201120462221 U CN 201120462221U CN 202523101 U CN202523101 U CN 202523101U
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power
photovoltaic
server
output power
generation output
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王伟胜
刘纯
冯双磊
王勃
卢静
丁茂生
施佳锋
张菲
赵艳青
姜文玲
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NINGXIA ELECTRIC POWER Co
China Electric Power Research Institute Co Ltd CEPRI
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NINGXIA ELECTRIC POWER Co
China Electric Power Research Institute Co Ltd CEPRI
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    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • 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

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  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The utility model discloses a power generation output power prediction system for a photovoltaic power station, comprising a numerical weather prediction processing server, a first two-layer switchboard, a reverse isolation device, a second two-layer switchboard and a database server, wherein the numerical weather prediction processing server, the first two-layer switchboard, the reverse isolation device, the second two-layer switchboard and the database server are connected successively; and the database server is also respectively connected with a power prediction server and a user interface server. The power generation output power prediction system for a photovoltaic power station is easy and convenient to operate and has high security.

Description

Photovoltaic power station power generation output power prognoses system
Technical field
The utility model relates to the photovoltaic power generation technology field, specifically a kind of photovoltaic power station power generation output power prognoses system.
Background technology
Solar energy power generating is to utilize the photovoltaic effect of solar cell solar radiant energy directly to be converted into a kind of forms of electricity generation of electric energy.Present stage, applying of sun power presents world trends in the ascendant day by day, and solar energy industry becomes one of new forms of energy industry that is surging forward in the whole world.The sun power of development and use cleaning, safety, environmental protection becomes human society and alleviates the energy starved that increasingly sharpens and select and administer the effective strength of severe environmental pollution jointly.The stable operation of electrical network need keep certain balance between both sides of supply and demand, promptly change according to user's consumption, and preset the unlatching of genset such as thermoelectricity, water power and close down, thus the general power of adjustment supply correspondingly.Because photovoltaic generation receives the influence of weather bigger, and can not freely control as thermoelectricity and water power, so the output power of photovoltaic power station power generation has characteristics such as acute variation and intermittence.Thus, photovoltaic plant is connected to the grid and will the balance of electrical network be had an immense impact on.
1) peaking problem.Along with the variation of weather, the output power acute variation of photovoltaic plant has a strong impact on the peak regulation of electrical network;
2) stabilization of power grids problem.When big disturbance took place electrical network, photovoltaic plant was not owing to possess low voltage ride-through capability, thereby out of service easily electrical network brought secondary pulse, influenced the transient stability of electrical network;
So the photovoltaic plant output power is effectively monitored and is predicted, include the photovoltaic plant output power generation schedule establishment of electrical network in, and participate in Real-Time Scheduling, be one of important measures that guarantee stabilization of power grids economical operation.Automatically control thereby can implement generator operation, realize polynary power supply combined dispatching.
The research of solar energy power generating power prediction is started late.States such as Germany, Denmark, Japan, the U.S., France and Canada all carried out correlative study.Mainly be to set up the solar energy resources monitoring point in the whole country, collect the solar energy resources data, and set up the photovoltaic generation power prediction model, predict the spatial and temporal distributions that nationwide photovoltaic generation is exerted oneself.Though abroad carried out the solar energy resources correlative study of monitoring and photovoltaic plant power prediction system of layouting, but still belonged to the starting stage, also do not had the photovoltaic plant power prediction service system of maturation at present.
In view of this, the design people actively studies and founds, and with a kind of photovoltaic power station power generation output power of utility model prognoses system, realizes the accurate prediction to the photovoltaic plant output power.
The utility model content
In order to solve the problems referred to above that exist in the prior art, the utility model provides a kind of photovoltaic power station power generation output power prognoses system.The utility model system has characteristics simple in structure, safe.
In order to solve the problems of the technologies described above, the utility model has adopted following technical scheme:
Photovoltaic power station power generation output power prognoses system; Comprise: the numerical weather forecast processing server, first Layer 2 switch, reverse isolation device and second Layer 2 switch and the database server that connect successively, database server also is connected with the user interface service device with the power prediction server respectively.
Compared with prior art, the beneficial effect of the utility model is:
The photovoltaic power station power generation output power prognoses system of the utility model is according to the residing geographic position of photovoltaic plant; Having analyzed influences the various meteorologic factors that photovoltaic plant is exerted oneself; Utilize the historical numerical weather forecast and the output power of the photovoltaic plant of history to set up neural network model; Realization is to the prediction of following photovoltaic plant output power, and is simple in structure, is easy to realize.The utility model system has adopted the system of artificial intelligence, does not need each class feature of photovoltaic plant inner member, the error of having avoided the component parameters out of true to cause, and prediction effect is better, and accuracy is high.Be the cooperation of photovoltaic plant and conventional power supply, ensure providing the foundation property of the measures data of power network safety operation.
Description of drawings
Fig. 1 is the structural representation of the photovoltaic power station power generation output power prognoses system of the utility model.
Embodiment
Below in conjunction with accompanying drawing and specific embodiment the utility model is described in further detail, but not as the qualification to the utility model.
Fig. 1 is the structural representation of the photovoltaic power station power generation output power prognoses system of the utility model.As shown in Figure 1, photovoltaic power station power generation output power prognoses system comprises database server and the numerical weather forecast processing server, power prediction server and the user interface service device that are connected with database server.Wherein database service realizes that the storage of data reaches alternately.The numerical weather forecast processing server is from internet network download numerical weather forecast data, and going forward side by side generates the meteorological element data of required predicted time section, and deposits database server in.The power prediction server is transferred the meteorological element data, handles to generate the meteorological element data output power of place time period.The user interface service device, the result of transferring the power prediction server from database server realizes and user interactions.
System in the face of the utility model carries out detailed explanation and explanation down.
Database server carries out the storage and the data interaction of data.
The numerical weather forecast processing server to handling the meteorological element data that generate the on-site predicted time section of photovoltaic plant from the numerical weather forecast data of network download, and is sent the meteorological element data into database server.
The power prediction server; Transfer the meteorological element data from database server; And obtain the photovoltaic power station power generation output power with the corresponding predicted time section of these meteorological element data, and the result is sent into database server stores according to the meteorological element data and the relation of output power.
The user interface service device, the result of transferring the power prediction server from database server realizes and user interactions.
As preferably, the numerical weather forecast processing server is connected with database server with second Layer 2 switch through first Layer 2 switch, reverse isolation device successively.Thereby guaranteed the safe operation of the utility model system.
The power prediction server is obtained the photovoltaic power station power generation output power of this predicted time section by the meteorological element data of predicted time section through the BP neural network.The BP neural network in advance with the on-site historical meteorological element data of photovoltaic plant as the input data, be that output data is trained with the output power corresponding with historical meteorological element data.The meteorological element data comprise irradiation intensity, temperature and wind speed.The power prediction server converts the irradiation intensity of transferring into effective irradiation intensity earlier, is effective temperature with temperature transition.
The power prediction server converts irradiation intensity into effective irradiation intensity through following formula:
I t = I b cos θ i + I d ( 1 + cos β 2 ) + ρ I h ( 1 - cos β 2 )
In the formula, I tBe the effective irradiation intensity on photovoltaic panel surface, I bBe direct projection irradiation intensity, I dBe scattering irradiation intensity, I hBe the total irradiation intensity of surface level, β is the photovoltaic panel inclination angle, θ iBe solar incident angle, ρ is a reflection coefficient.
The power prediction server is modified to temperature through following formula the temperature on photovoltaic panel surface: T=T Air+ KS
In the formula, T is the temperature on photovoltaic panel surface, T AirBe environment temperature, S is an intensity of illumination, and K is a temperature coefficient.General value is 0.03 (℃ m 2/ w).
The input data of BP neural network also comprise time data, said time data comprises month, day and the time.Because between the output power of time data and photovoltaic plant bigger relation is arranged also, so with the input data of time data as the BP neural network, and be associated with the output power of photovoltaic plant, accuracy for predicting can be improved.In addition, the meteorological element data are not limited only to irradiation intensity, temperature and wind speed, also can comprise the meteorological element data that other are associated with generated energy.
Following table 1 is carried out the predicated error statistical form of power prediction for photovoltaic plant uses the utility model system.
Table 1
Figure DEST_PATH_GDA00001858019000042
Figure DEST_PATH_GDA00001858019000051
From table, can find out the accuracy height that predicts the outcome of the utility model system, satisfy the requirement that operation of power networks is used.
The error statistics explanation:
1, average absolute value error (nMAE)
Figure DEST_PATH_GDA00001858019000052
P MiBe i real power constantly, P PiBe i predicted power constantly,
Figure DEST_PATH_GDA00001858019000053
Be the mean value of all sample real powers, Cap is the photovoltaic plant total installation of generating capacity, and n is all number of samples.
This index reflects the error of the absolute value that predicts the outcome, and can reflect the situation of error to a certain extent.But the point that error is bigger is submerged when doing statistical average easily, can not reflect the king-sized extreme case of error.
2, root-mean-square error (nRMSE)
nRMSE = Σ i = 1 n ( P Mi - P Pi ) 2 Cap · n
P MiBe i real power constantly, P PiBe i predicted power constantly,
Figure DEST_PATH_GDA00001858019000055
Be the mean value of all sample real powers, Cap is the photovoltaic plant total installation of generating capacity, and n is all number of samples.
This index does not have the corresponding physical meaning.Because be that quadratic sum is opened radical sign, amplified the influence of the bigger point of error.In the timing statistics scope; Most of point prediction values depart from actual value about 10%; But the predicted value that a small amount of point is arranged departs from actual value more than 50% (such situation is bigger to the scheduling influence), adopts this error to embody the influence of these errors point bigger than normal 15%～20% even bigger.
3, error is no more than 20% the shared ratio (pre20) of point
pre 20 = n P n
n PSurpass the number of 20% point for error, n is all number of samples
Embodied the probability of deviation in tolerance interval of predicted value and actual value.Here 20% is the parameter that can change.
The advantage dispatcher of this index can weigh the risk of the margin capacity of preparing for photovoltaic generation power according to this index.Can make the probability level of a plurality of scopes according to actual needs, for example error is no more than the ratio of 10% point, and error is no more than ratio of 30% point etc.
Above embodiment is merely the exemplary embodiment of the utility model, is not used in restriction the utility model, and the protection domain of the utility model is defined by the claims.Those skilled in the art can make various modifications or be equal to replacement the utility model in the essence and protection domain of the utility model, this modification or be equal to replacement and also should be regarded as dropping in the protection domain of the utility model.

Claims (1)

1. photovoltaic power station power generation output power prognoses system is characterized in that, comprising:
The numerical weather forecast processing server, first Layer 2 switch, reverse isolation device and second Layer 2 switch and the database server that connect successively, database server also is connected with the user interface service device with the power prediction server respectively.
CN2011204622212U 2011-11-18 2011-11-18 Power generation output power prediction system for photovoltaic power station Expired - Lifetime CN202523101U (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102567809A (en) * 2011-11-18 2012-07-11 中国电力科学研究院 Power generation output power prediction system of photovoltaic power station
CN103258118A (en) * 2013-04-19 2013-08-21 国家电网公司 Method for predicting temperature of photovoltaic battery pack
CN104537450A (en) * 2015-01-28 2015-04-22 国家电网公司 Power prediction system of distributed photovoltaic power generation system
CN108428019A (en) * 2018-05-15 2018-08-21 阳光电源股份有限公司 The method for building up and photovoltaic power prediction technique of component battery temperature computation model

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102567809A (en) * 2011-11-18 2012-07-11 中国电力科学研究院 Power generation output power prediction system of photovoltaic power station
CN102567809B (en) * 2011-11-18 2015-12-16 中国电力科学研究院 Power generation output power prediction system of photovoltaic power station
CN103258118A (en) * 2013-04-19 2013-08-21 国家电网公司 Method for predicting temperature of photovoltaic battery pack
CN104537450A (en) * 2015-01-28 2015-04-22 国家电网公司 Power prediction system of distributed photovoltaic power generation system
CN108428019A (en) * 2018-05-15 2018-08-21 阳光电源股份有限公司 The method for building up and photovoltaic power prediction technique of component battery temperature computation model
CN108428019B (en) * 2018-05-15 2021-09-03 阳光电源股份有限公司 Method for establishing component battery temperature calculation model and photovoltaic power prediction method

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