JP2018019555A - Method for estimating photovoltaic power generation output with consideration of influence of shade - Google Patents
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Abstract
Description
この発明は、太陽光発電所における発電出力推定方法において、太陽光パネルにかかる影の影響を考慮した発電出力の推定方法に関するものである。 The present invention relates to a power generation output estimation method in a solar power plant, and relates to a power generation output estimation method in consideration of the influence of shadows on a solar panel.
大規模太陽光発電所の運用において、安全性を確保し、事業収益を向上させるためには、発電所のモニタリングによって運転状況を把握することが必要であるが、太陽光発電所の発電量は日射や外気温といった周囲環境条件、及び発電所の広さや構成といった固有の条件に大きく左右されるため、単にモニタリングを行うだけでは運転状況を適切に把握することは困難である。 In order to ensure safety and improve business profits in the operation of large-scale solar power plants, it is necessary to grasp the operating status through monitoring of the power plant. Since it is greatly affected by ambient conditions such as solar radiation and outside air temperature, and specific conditions such as the size and structure of the power plant, it is difficult to properly grasp the driving situation simply by performing monitoring.
そこで、日射量や外気温といった周囲環境データ、及び発電所の設備情報から発電量を推定する手法が多数考案されているが、十分な精度が得られない。あるいは精度を得るためにできるだけ多くの計測設備を設けて細かく計測することや専門家による分析が必要である、といった問題点があった。 Thus, many methods have been devised for estimating the amount of power generation from ambient environment data such as the amount of solar radiation and outside temperature, and facility information of the power plant, but sufficient accuracy cannot be obtained. Or, in order to obtain accuracy, there are problems such as providing as many measuring facilities as possible and performing detailed measurement and analysis by experts.
特許文献１の「発電量予測装置およびその方法」では、太陽光発電システムのそれぞれについて、システム係数と発電実験データから前記太陽光発電システムの設置されたサイトにおける気象状況を推定することにより、推定気象状況値を得る気象状況推定部と、前記推定気象状況値を、各前記太陽光発電システムの設置位置に応じて補正処理することにより補正気象状況値を得る気象状況空間補正部と、前記太陽光発電システム毎に、前記補正気象状況値と前記発電実績データに基づいて、前記システム係数を更新するパラメータ学習部と、前記太陽光発電システム毎に、前記参照気象データと前記システム係数に基づき、発電量予測を行う発電予測部とを備えるものである。 In the “power generation amount prediction device and method” of Patent Document 1, estimation is performed by estimating the weather condition at the site where the solar power generation system is installed from the system coefficient and the power generation experiment data for each of the solar power generation systems. A weather condition estimation unit for obtaining a weather condition value, a weather condition space correction unit for obtaining a corrected weather condition value by correcting the estimated weather condition value according to an installation position of each of the solar power generation systems, and the sun For each photovoltaic power generation system, based on the corrected weather condition value and the actual power generation data, a parameter learning unit that updates the system coefficient, and for each photovoltaic power generation system, based on the reference weather data and the system coefficient, A power generation prediction unit that performs power generation amount prediction.
特許文献１のものでは、推定を行おうとする発電所の過去の実績データ（日射・外気温・発電量等）から所定の発電量推定モデルのパラメータを学習し、そのパラメータを用いて以降の発電量を推定することにより自律的に推定精度の向上を図る方法が開示されており、これにより、気象データ及び設備情報から高い精度で発電量を推定することが可能である。 In Patent Document 1, the parameters of a predetermined power generation amount estimation model are learned from past performance data (sunlight, outside temperature, power generation amount, etc.) of a power plant to be estimated, and subsequent power generation is performed using the parameters. A method for autonomously improving the estimation accuracy by estimating the amount is disclosed, whereby the power generation amount can be estimated with high accuracy from the weather data and the facility information.
図８は、評価する太陽光発電所のアレイ容量やパワーコンディショナー（ＰＣＳ）の容量を予め入力しておき、日射強度や外気温を測定し、さらにＰＣＳ発電量や連系点電力を測定し、所定の発電量推定モデルのパラメータを学習し、そのパラメータを用いて以降の発電量を推定するシステムを示す。 FIG. 8 shows the solar power plant array capacity to be evaluated and the power conditioner (PCS) capacity inputted in advance, the solar radiation intensity and the outside air temperature are measured, the PCS power generation amount and the interconnection power are further measured, A system for learning parameters of a predetermined power generation amount estimation model and estimating the subsequent power generation amount using the parameters is shown.
当該手法を用いて気象データから推定された発電量は、原則的には太陽光パネルの日射を遮る障害物がない状態での推定量である。 The amount of power generation estimated from meteorological data using this method is, in principle, an estimated amount with no obstacles blocking the solar panel's solar radiation.
また、前記特許文献１のものは、複数の太陽光発電システムにおいて一部のシステムの日射計に影がかかっている場合、当該影のかかっている影響を排除するものである。そのため、複数の太陽光発電システムから、影の影響を受けている発電量を補正（排除）して１つの発電量の推定パラメータを算出するものである。従って、影のかかっている状態での発電量の推定は得られない。 Moreover, the thing of the said patent document 1 eliminates the influence which the said shadow has, when the solar radiation meter of some systems has a shadow in a some solar power generation system. Therefore, an estimation parameter of one power generation amount is calculated by correcting (excluding) the power generation amount affected by the shadow from a plurality of solar power generation systems. Therefore, the estimation of the power generation amount in the shadowed state cannot be obtained.
しかしながら、現実には図９に示すように、発電所周辺の樹木や構造物、前方アレイ（太陽光パネル）などの影が太陽光パネルにかかっている時がある。この様な影の影響で図１０に示すように、発電出力が低下した場合の出力を正確に推定することは難しい。なお、図１０は１日を通した発電出力値を表したもので、実線は部分影なしの場合、点線で表したのが部分影有りの場合の各発電出力値を示す。 However, in reality, as shown in FIG. 9, shadows of trees and structures around the power plant, the front array (solar panel), etc. are sometimes applied to the solar panel. As shown in FIG. 10 due to the influence of such a shadow, it is difficult to accurately estimate the output when the power generation output decreases. In addition, FIG. 10 represents the power generation output value through one day, and the solid line indicates each power generation output value when there is no partial shadow, and the dotted line indicates each power generation output value when there is a partial shadow.
影による太陽光発電出力の低下の度合いは、日射・気温といった気象条件に加え、影のかかる位置とその範囲、太陽電池モジュールの特性や構造によって様々に異なるため、出力低下の度合いを推定するモデル（式）を作成することは困難である。 The degree of decrease in solar power output due to shadows varies depending on the location and range of the shadows, as well as the weather conditions such as solar radiation and temperature, and the characteristics and structure of the solar cell module. It is difficult to create (formula).
また、周辺障害物が作る影は、太陽の高度と方位によってその長さや方向を刻々と変えている。また、仮に太陽の高度と方位が定まっていたとしても、晴天時かそうでないか（全日射に占める直射日光の割合）によって影が発生するかどうかは異なる。そのため、ある時刻において太陽光パネルに影がかかっているかどうかを判定するのは非常に困難である。 The shadows created by the surrounding obstacles change their length and direction according to the altitude and direction of the sun. Even if the altitude and direction of the sun are determined, whether or not a shadow is generated depends on whether it is sunny or not (ratio of direct sunlight in the total solar radiation). Therefore, it is very difficult to determine whether the solar panel is shaded at a certain time.
そこで、この発明は、過去の実績データを用い、太陽の位置及び日射強度によって条件分けを行った上で個別に推定パラメータを学習させることで、気象条件に加えて影による出力低下の影響も加味した形で正確に発電量の推定を行う方法を提供することを目的としたものである。 Therefore, the present invention uses past performance data, classifies the conditions according to the position of the sun and the solar radiation intensity, and then learns the estimated parameters individually, thereby taking into account the effect of the output drop due to shadows in addition to the weather conditions. It is an object of the present invention to provide a method for accurately estimating the amount of power generation.
請求項１の発明は、太陽光発電所の設備情報を予め入力しておき、日射強度や外気温を測定し、また、当該太陽光発電所の実際の発電量を測定して、所定の発電量推定モデルのパラメータを算出し、これを一定期間繰り返して発電量推定モデルのパラメータを学習し、そのパラメータを用いて以降の発電量を推定する方法において、予め太陽高度、太陽方位角による条件分け、及び日射強度による条件分けにより複数に区分けし、測定時刻ごとに太陽高度、太陽方位角、日射強度、外気温及び測定時の発電量により発電量推定モデルの発電量推定パラメータを算出して前記複数の各区分に振り分け、これを一定期間繰り返して発電量推定パラメータを学習し、前記複数の区分ごとの発電量推定パラメータを作成しておき、測定時刻、測定した日射強度及び外気温に基づいて、前記複数に区分された特定の太陽高度、太陽角度、日射強度の発電量推定パラメータを用いて推定発電量を算出する、影の影響を考慮した太陽光発電出力推定方法とした。 The invention of claim 1 inputs the facility information of the solar power plant in advance, measures the solar radiation intensity and the outside air temperature, measures the actual power generation amount of the solar power plant, and determines the predetermined power generation. In this method, the parameters of the energy estimation model are calculated, and this is repeated for a certain period to learn the parameters of the power generation estimation model and the subsequent power generation is estimated using the parameters. And by dividing into conditions by solar radiation intensity, and calculating the power generation amount estimation parameter of the power generation estimation model from the solar altitude, solar azimuth angle, solar radiation intensity, ambient temperature and power generation amount at the time of measurement for each measurement time Allocate to each of a plurality of categories, repeat this for a certain period of time to learn the power generation amount estimation parameter, create a power generation amount estimation parameter for each of the plurality of categories, and measure the measurement time and Based on the radiant intensity and the outside air temperature, the estimated power generation amount is calculated using the power generation amount estimation parameters of the specific solar altitude, solar angle, and solar radiation intensity divided into the above, and the photovoltaic power generation output considering the influence of shadows The estimation method was used.
また、請求項２の発明は、前記太陽高度は９０個、太陽方位角は３６０個及び日射強度は０．１ｋＷ／ｍ２刻みに１０個以上として区分し、前記一定期間は１年以上とした、請求項１に記載の影の影響を考慮した太陽光発電出力推定方法とした。 In the invention of claim 2, the solar altitude is classified as 90, the solar azimuth is 360, and the solar radiation intensity is classified as 10 or more in increments of 0.1 kW / m 2 , and the predetermined period is set to 1 year or more. The solar power generation output estimation method considering the influence of the shadow according to claim 1.
請求項１の発明によれば、過去の実績データを用い、太陽の位置及び日射強度の条件によって区分けし、これらの区分けされた個々の発電量推定パラメータを学習させることで、気象条件に加えて影による出力低下の影響も加味した形で正確に発電量の推定を行うことができる。 According to the invention of claim 1, by using past performance data, it is classified according to the conditions of the sun position and solar radiation intensity, and by learning these divided individual power generation amount estimation parameters, in addition to the weather conditions It is possible to accurately estimate the amount of power generation in consideration of the effect of output drop due to shadows.
また、請求項２の発明によれば、前記区分けをきめ細かくし、さらに１年以上の個々の発電量推定パラメータを多数回学習することにより、極めて精度の高い発電量が推定可能である。 In addition, according to the invention of claim 2, it is possible to estimate the power generation amount with extremely high accuracy by finely dividing the classification and further learning each power generation amount estimation parameter for one year or more.
（実施の形態例１）
以下、この発明の実施の形態例１の影の影響を考慮した太陽光発電出力推定方法（システム）を図に基づいて説明する。
(Embodiment 1)
Hereinafter, a photovoltaic power generation output estimation method (system) in consideration of the influence of shadows according to Embodiment 1 of the present invention will be described with reference to the drawings.
図５に太陽高度及び方位角による条件分けのイメージを示す。図示のように、周辺障害物が作る影の長さと位置（方向）は「障害物の大きさと高さ」及び「太陽の高度と方位」によって決まる。このうちの前者は基本的に変化することがないため、太陽の高度と方位が同じであれば、障害物による影の長さと位置（方向）も同じであり、従って、晴天時であれば太陽光発電出力に対して影が与える影響も同等であると考えられる。 FIG. 5 shows an image of condition classification based on solar altitude and azimuth. As shown in the figure, the length and position (direction) of the shadow created by the surrounding obstacle are determined by “the size and height of the obstacle” and “the altitude and direction of the sun”. Of these, the former basically does not change, so if the altitude and direction of the sun are the same, the length and position (direction) of the shadow caused by the obstacle is the same. The effect of shadows on photovoltaic power output is considered to be equivalent.
そこで、学習に用いる過去の計測データより、タイムスタンプから当該時刻における太陽高度と方位角を計算し、太陽が特定範囲の高度と方位角（例：１度刻み）に存在する時のみのデータを抽出して個別に推定パラメータの学習を行うこととした。 Therefore, from the past measurement data used for learning, the solar altitude and azimuth at that time are calculated from the time stamp, and the data only when the sun is at a specific range of altitude and azimuth (eg 1 degree increments) Extraction and learning of estimation parameters were performed individually.
このようにして、太陽が特定範囲に位置する時のデータのみを用いて学習することで、太陽光パネルに影がかかっている時とかかっていない時の区別（部分影の有無の判定）、及びそれぞれの状態における発電出力の推定方式の算出、乃至は影の影響の定量的な評価を同時に行うことが可能となる。 In this way, by learning using only data when the sun is in a specific range, distinction between when the solar panel is shaded and when it is not shaded (determination of the presence or absence of partial shadows), In addition, it is possible to simultaneously calculate the estimation method of the power generation output in each state or to quantitatively evaluate the influence of the shadow.
しかしながら、影が発生するか否かは太陽の位置のみならず、太陽に対して雲がかかっているかどうかによっても変わってくるため、太陽の位置による条件分けのみでは影の影響を正しく評価できるとは限らない。 However, whether or not a shadow occurs depends not only on the position of the sun but also on whether or not the sun is clouded. Is not limited.
ただし、影がかかるか否か、引いては影がかかったことによる影響があるかどうかは、基本的には「晴れているかそうでないか、又は太陽に雲がかかっているかそうでないか」の二択であるため、図６に示すように、日射強度が一定未満の時はほとんど影響が発生せず、一定以上になると影響が出てくるという傾向がある。そこで、上記太陽の位置による条件分けに加え、日射強度（例：０．１ｋＷ／ｍ２刻み）でも条件分けを行い、条件ごとに発電量の推定パラメータの学習を行うこととした。 However, whether or not there is a shadow and whether or not there is an influence due to the shadow is basically whether it is sunny or not, or whether the sun is clouded or not Since there are two choices, as shown in FIG. 6, there is a tendency that almost no influence occurs when the solar radiation intensity is less than a certain value, and there is a tendency that the influence appears when the intensity exceeds a certain value. Therefore, in addition to the above-described condition classification based on the position of the sun, the condition classification is also performed based on the solar radiation intensity (for example, in increments of 0.1 kW / m 2 ), and the estimation parameter of the power generation amount is learned for each condition.
また、気象データから発電量を推定する際に、該当時刻の太陽高度及び方位角を算出し、計測された日射強度データと組み合わせて、該当するあるいは近似の太陽高度・方位角・日射強度において学習された推定パラメータを用いて発電量の推定を行う。これにより、図７に示すように、影の有無に関わらず発電量を精度よく推定することが可能である。 In addition, when estimating the amount of power generation from meteorological data, the solar altitude and azimuth angle at the relevant time are calculated and combined with the measured solar intensity data to learn at the relevant or approximate solar altitude, azimuth angle, and solar intensity. The power generation amount is estimated using the estimated parameters. As a result, as shown in FIG. 7, the power generation amount can be accurately estimated regardless of the presence or absence of a shadow.
なお、図７において、外側の薄い輪郭線は影の影響を考慮しない推定（発電量）値Ａ、内側の濃い輪郭線は影の影響を考慮した推定発電量Ｂを示し、実測発電量Ｃは前記影の影響を考慮した推定発電量Ｂ内の薄く塗りつぶした領域で表示している。当該推定発電量Ｂと実測発電量Ｃとは、１５時以降やや離れているのを除き、ほぼ同じである。 In FIG. 7, the thin outline on the outside indicates an estimated (power generation) value A that does not consider the influence of the shadow, the dark outline on the inside indicates the estimated power generation B that takes into consideration the influence of the shadow, and the actually measured power generation C is It is displayed in a lightly painted area in the estimated power generation amount B considering the influence of the shadow. The estimated power generation amount B and the actually measured power generation amount C are substantially the same except that they are slightly separated after 15:00.
次に、実際の発電出力推定方法を図１〜４に基づいて説明する。 Next, an actual power generation output estimation method will be described with reference to FIGS.
図１〜３に示すように、データ測定時刻１、日射強度２、外気温３及び発電量４を測定する。また、例えば、太陽高度（０度〜９０°）９０個、太陽方位角（０〜３６０°）３６０個及び日射強度（例えば、０．１ｋＷ／ｍ２刻み）１５個を予め区分分けしておく。従って、この場合、４８．６万個に区分けすることとなる。 As shown in FIGS. 1-3, the data measurement time 1, the solar radiation intensity 2, the external temperature 3, and the electric power generation amount 4 are measured. Further, for example, 90 solar altitudes (0 degrees to 90 degrees), 360 solar azimuth angles (0 to 360 degrees) 360 pieces, and solar radiation intensity (for example, 0.1 kW / m 2 increments) 15 pieces are divided in advance. . Therefore, in this case, it is divided into 48.6 million.
そして、推定パラメータ学習部５において、前記測定したデータ測定時刻１により太陽高度・方位角を算出する。さらに、測定した日射強度２により、前記の区分けの１つを特定し、前記測定した発電量４を当該区分けに入れる。 Then, the estimated parameter learning unit 5 calculates the solar altitude / azimuth angle from the measured data measurement time 1. Further, one of the above classifications is specified based on the measured solar radiation intensity 2, and the measured power generation amount 4 is put into the classification.
この様にして、多数区分の発電量４の推定パラメータを作成し、これを一定期間、例えば、１年間繰り返し、当該推定パラメータの学習をする。これにより、多種類の太陽高度・方位角及び日射強度別の発電量推定パラメータ６を作成する。 In this way, the estimation parameter of the power generation amount 4 in a large number of sections is created, and this is repeated for a certain period, for example, one year to learn the estimation parameter. Thereby, the power generation amount estimation parameter 6 for each of various types of solar altitude / azimuth angle and solar radiation intensity is created.
そして、発電量を推定するに際し、発電量推定部７において、データ測定時刻８にもとづいて、太陽高度・方位角を算出し、その際測定した日射強度９と合わせて、前記多数の区分から特定の区分を選定し、これに測定した外気温１０を勘案して推定発電量１１を算出する。 Then, when estimating the power generation amount, the power generation amount estimation unit 7 calculates the solar altitude and azimuth based on the data measurement time 8, and together with the solar radiation intensity 9 measured at that time, specifies from the above-mentioned many categories. And the estimated power generation amount 11 is calculated in consideration of the measured outside air temperature 10.
この様にして算出した推定発電量１１は、影の影響を考慮した極めて精度の高いものとなる。 The estimated power generation amount 11 calculated in this way is extremely accurate considering the influence of shadows.
上記の各ステップは計測手段、当該各計測値をデータとして記憶する記憶手段、パラメータ学習手段、推定パラメータ決定手段、推定発電量算出手段とから成るコンピュータシステムを使用して実現できる。そして、これらの各構成手段による上記ステップ作用は、たとえばコンピュータプログラムモジュールとして実現することができ、各プログラムモジュールを含むプログラムをコンピュータシステムにおいて各機能を実現することができる。 Each of the above steps can be realized using a computer system comprising measuring means, storage means for storing each measured value as data, parameter learning means, estimated parameter determining means, and estimated power generation amount calculating means. And the said step effect | action by each of these structure means can be implement | achieved as a computer program module, for example, and each function can be implement | achieved in a computer system by the program containing each program module.
このコンピュータシステムには、図４に示すように、プログラム命令を実行するＣＰＵ２１、メモリ等の主記憶装置２２、ハードディスク、磁気ディスク装置又は光磁気ディスク装置等の外部記憶装置２３、データ入力装置２４、表示装置２５及びこれらを相互に接続するバス２６を具備している。プログラムは外部記憶装置２３に保存されており、ＣＰＵ２１がこのプログラムを主記憶装置２２に展開し、展開したプログラムを逐次読み出し実行する。 As shown in FIG. 4, the computer system includes a CPU 21 for executing program instructions, a main storage device 22 such as a memory, an external storage device 23 such as a hard disk, a magnetic disk device or a magneto-optical disk device, a data input device 24, A display device 25 and a bus 26 for connecting them to each other are provided. The program is stored in the external storage device 23, and the CPU 21 expands the program in the main storage device 22, and sequentially reads and executes the expanded program.
この様にして得た推定発電量により、当該太陽光発電所のある時の発電量を予測でき、将来の消費電力に対する対応を予め考慮することができる。また、当該発電所の太陽光発電に異常をきたしているかどうかの診断も可能である。 Based on the estimated power generation amount obtained in this way, the power generation amount at a certain time of the solar power plant can be predicted, and the correspondence to the future power consumption can be considered in advance. It is also possible to diagnose whether or not there is an abnormality in the photovoltaic power generation at the power plant.
なお、上記実施の形態例１では、太陽高度９０個、太陽方位角３６０個及び日射強度１５個の合計４８．６万個に区分けしているが、区分けの数はこれらに限定されるものではない。また、推定パラメータ学習部では、データを１年分蓄積しているが、データの蓄積期間は長ければ長いほど推定発電量の精度は上がる。また、この発明での日射強度の測定は、日射計に影がかからない場所での測定である。 In the first embodiment, the solar altitude is 90, the solar azimuth is 360, and the solar radiation intensity is 15 in total, which is 48.6 million. However, the number of classification is not limited to these. Absent. The estimated parameter learning unit accumulates data for one year. The longer the data accumulation period, the higher the accuracy of the estimated power generation amount. In addition, the measurement of the solar radiation intensity in the present invention is a measurement at a place where the solar radiation meter is not shaded.
１ データ測定時刻 ２ 日射強度
３ 外気温 ４ 発電量
５ 推定パラメータ学習部 ６ 発電量推定パラメータ
７ 発電量推定部 ８ データ測定時刻
９ 日射強度 １０ 外気温
１１ 推定発電量
２１ ＣＰＵ ２２ 主記憶装置
２３ 外部記憶装置 ２４ 入力装置
２５ 表示装置 ２６ バス
1 Data measurement time 2 Solar radiation intensity 3 Ambient temperature 4 Power generation 5 Estimated parameter learning unit 6 Power generation estimation parameter
7 Power generation amount estimation unit 8 Data measurement time 9 Solar radiation intensity 10 Outside air temperature 11 Estimated power generation amount 21 CPU 22 Main storage device 23 External storage device 24 Input device 25 Display device 26 Bus
Claims (2)
予め太陽高度、太陽方位角による条件分け、及び日射強度による条件分けにより複数に区分けし、測定時刻ごとに太陽高度、太陽方位角、日射強度、外気温及び測定時の発電量により発電量推定モデルの発電量推定パラメータを算出して前記複数の各区分に振り分け、これを一定期間繰り返して発電量推定パラメータを学習し、前記複数の区分けごとの発電量推定パラメータを作成しておき、
測定時刻、測定した日射強度及び外気温に基づいて、前記複数に区分された特定の太陽高度、太陽角度、日射強度の発電量推定パラメータを用いて推定発電量を算出することを特徴とする、影の影響を考慮した太陽光発電出力推定方法。 Input the facility information of the solar power plant in advance, measure the solar radiation intensity and the outside air temperature, measure the actual power generation amount of the solar power plant, and calculate the parameters of the predetermined power generation estimation model In a method of learning the parameters of the power generation amount estimation model by repeating this for a certain period, and estimating the subsequent power generation amount using the parameters,
Divided into multiple categories according to the conditions of solar altitude, solar azimuth, and solar intensity, and the power generation estimation model based on solar altitude, solar azimuth, solar intensity, ambient temperature, and power generation during measurement at each measurement time The power generation amount estimation parameter is calculated and distributed to each of the plurality of categories, this is repeated for a certain period to learn the power generation amount estimation parameter, and the power generation amount estimation parameter for each of the plurality of sections is created,
Based on the measurement time, the measured solar radiation intensity and the outside temperature, the estimated power generation amount is calculated by using the plurality of specific solar altitudes, solar angles, and solar power generation estimation parameters. Photovoltaic power output estimation method considering the influence of shadows.
The solar altitude is 90 pieces, the solar azimuth is 360 pieces, and the solar radiation intensity is divided into 10 pieces or more in increments of 0.1 kW / m 2 , and the predetermined period is set to one year or more. Solar power generation output estimation method considering the influence of the shadow described in 1.
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