태양광발전시스템 고정식과 고정가변식의 운전효율분석

원종운 한국기계기술학회 2017 한국기계기술학회지 Vol.19 No.4

This study introduces the comparison of efficiency levels of photovoltaics systems by analyzing various installation systems of photovoltaics systems and optimization techniques and proposes a system using techniques In this study, the generation time and power generation of two types of photovoltaic power generation system were measured and compared. Comparing the monthly power generation time with the power generation amount, it is found that there are many fixed variable photovoltaic power generation systems with a large average daily power generation time of 0.8h and an average power generation capacity of 2,871kw from November to December. Total Fixed Variable Total Daily Power Generation Time 2.4h The power generation amount is 23,184kw, showing a large amount of electric power generation.

Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea

Hong, Sungjun,Chung, Yanghon,Woo, Chungwon Elsevier 2015 ENERGY Vol.79 No.-

<P><B>Abstract</B></P> <P>South Korea, as the 9th largest energy consuming in 2013 and the 7th largest greenhouse gas emitting country in 2011, established ‘Low Carbon Green Growth’ as the national vision in 2008, and is announcing various active energy policies that are set to gain the attention of the world. In this paper, we estimated the decrease of photovoltaic power generation cost in Korea based on the learning curve theory. Photovoltaic energy is one of the leading renewable energy sources, and countries all over the world are currently expanding R&D, demonstration and deployment of photovoltaic technology. In order to estimate the learning rate of photovoltaic energy in Korea, both conventional 1FLC (one-factor learning curve), which considers only the cumulative power generation, and 2FLC, which also considers R&D investment were applied. The 1FLC analysis showed that the cost of power generation decreased by 3.1% as the cumulative power generation doubled. The 2FCL analysis presented that the cost decreases by 2.33% every time the cumulative photovoltaic power generation is doubled and by 5.13% every time R&D investment is doubled. Moreover, the effect of R&D investment on photovoltaic technology took after around 3 years, and the depreciation rate of R&D investment was around 20%.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We analyze the learning effects of photovoltaic energy technology in Korea. </LI> <LI> In order to calculate the learning rate, we use 1FLC (one-factor learning curve) and 2FLC methods, respectively. </LI> <LI> 1FLC method considers only the cumulative power generation. </LI> <LI> 2FLC method considers both cumulative power generation and knowledge stock. </LI> <LI> We analyze a variety of scenarios by time lag and depreciation rate of R&D investment. </LI> </UL> </P>

상업용 건물을 대상으로 한 태양광열-지열 이용 Tri-generation 시스템의 성능분석

배상무,남유진 대한설비공학회 2022 설비공학 논문집 Vol.34 No.5

본 연구에서는 오피스 건물에 Tri-generation 시스템을 적용하여 건물부하와 연계한 시스템의 연간 열 및 전기적 성능을 정량적으로 분석하고 기존의 GSHP 시스템과 비교를 통해 Tri-generation 시스템의 적용가능성을 평가하였다. 결과는 다음과 같다. (1) 난방기(12~3월)의 Tri-generation 시스템과 GSHP 시스템의 시스템 성능을 비교한 결과, 태양광열 모듈을 이용한 Tri-generation 시스템이 GSHP 시스템보다 시스템 평균 COP가 16% 높은 것으로 확인되었다. 이는, 히트펌프가 지중열원보다 높은 온도의 태양열원을 공급받아 성능이 향상된 것으로 사료된다. (2) 냉방기(5~9월)의 Tri-generation 시스템과 GSHP 시스템의 시스템 성능을 비교한 결과, 두 시스템의 시스템 COP의 차이는 거의 없는 것으로 확인되었다. 그러나, 태양광열 모듈에 의해 생산된 전력을 시스템 운전에 자체적으로 이용할 경우 Tri-generation 시스템이 GSHP 시스템보다 시스템 평균 COP가 50% 향상되었다. (3) 향후, 건물이나 지역에 따라 달라지는 부하패턴을 고려하여 Tri-generation 시스템 각 유닛의 적정용량 산정하기 위한 최적화 기반 시뮬레이션을 실시할 예정이다. It is necessary to introduce renewable energy systems for zero energy building realization. Recently, many individual renewable energy systems are introducing the commercial buildings. However, the individual renewable energy systems have many disadvantages such as imbalance between energy generation and use, and intermittent energy production dependent on weather conditions. To resolve these disadvantages, a Tri-generation system combined with a photovoltaic-thermal (PVT) and ground source heat pump (GSHP) is proposed. The Tri-generation system can overcome the disadvantages of the individual system and stably provide thermal and electrical energy to buildings. Many researchers are conducting a feasibility assessment of the introduction of the Tri-generation system for energy independence in residential buildings, but there are few studies on performance analysis of the Tri-generation system for commercial buildings. In this study, to promote the supply of the Tri-generation system for commercial buildings, performance analysis of the system was conducted through an energy simulation model. The annual thermal and electrical production of the Tri-generation system was analyzed and the coefficient of performance (COP) was compared between the Tri-generation system and the conventional GSHP system. As for the performance analysis results, the COP of the Tri-generation system was up by 50% compared to the conventional GSHP system.

영농형 태양광 발전의 진단을 위한 지능형 예측 시스템

정설령(Seol-Ryung Jung),박경욱(Kyoung-Wook Park),이성근(Sung-Keun Lee) 한국전자통신학회 2021 한국전자통신학회 논문지 Vol.16 No.5

영농형 태양광 발전은 농지 상부에 태양광 발전 설비를 설치하는 방식으로 농작물과 전기를 동시에 생산함으로써 농가 소득을 증대시키는 새로운 모델이다. 최근 영농형 태양광 발전을 활용하는 다양한 시도들이 이루어지고 있다. 영농형 태양광 발전은 기존의 태양광 발전과는 달리 비교적 높은 구조물 상부에 설치하게 되므로 유지 보수가 상대적으로 어렵다는 단점이 있다. 이러한 문제를 해결하기 위해 지능적이고 효율적인 운용 및 진단 기능이 요구된다. 본 논문에서는 영농형 태양광 발전 설비의 전력 생산량을 수집, 저장하여 지능적인 예측 모델을 구현하기 위한 예측 및 진단 시스템의 설계 및 구현에 대해 논한다. 제안된 시스템은 태양광 발전량과 환경 센서 데이터를 기반으로 발전량을 예측하여 설비의 이상 유무를 판별하며 설비의 노화 정도를 산출하여 사용자에게 제공한다. Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

일기 예보와 예측 일사 및 일조를 이용한 태양광 발전 예측

신동하 ( Dong-ha Shin ),박준호 ( Jun-ho Park ),김창복 ( Chang-bok Kim ) 한국항행학회 2017 한국항행학회논문지 Vol.21 No.6

Photovoltaic generation which has unlimited energy sources are very intermittent because they depend on the weather. Therefore, it is necessary to get accurate generation prediction with reducing the uncertainty of photovoltaic generation and improvement of the economics. The Meteorological Agency predicts weather factors for three days, but doesn’t predict the sunshine and solar radiation that are most correlated with the prediction of photovoltaic generation. In this study, we predict sunshine and solar radiation using weather, precipitation, wind direction, wind speed, humidity, and cloudiness which is forecasted for three days at Meteorological Agency. The photovoltaic generation forecasting model is proposed by using predicted solar radiation and sunshine. As a result, the proposed model showed better results in the error rate indexes such as MAE, RMSE, and MAPE than the model that predicts photovoltaic generation without radiation and sunshine. In addition, DNN showed a lower error rate index than using SVM, which is a type of machine learning.

장단기 시간 패턴 학습을 통한 그래프 신경망 기반의 태양광 발전량 예측 기법

이재승,박성우,문재욱,황인준 한국정보과학회 2024 정보과학회논문지 Vol.51 No.8

최근 태양광 에너지의 활용이 크게 보편화되면서, 태양광 에너지의 효율 향상을 위한 태양광 발전량 예측 연구가 활발히 진행되고 있다. 이와 관련하여, 기존의 심층 학습 모델을 넘어 그래프 신경망 기반의 태양광 발전량 예측 모델들이 제시되었다. 이 모델들은 특정 지역의 태양광 발전량이 인접 지역의 기후 조건에 영향을 받는 공간적 상호작용과 태양광 발전량의 시간 패턴을 함께 고려하는 지역 간 상관관계를 학습함으로써 예측 정확도를 개선한다. 하지만, 기존 모델들은 주로 고정된 형태의 그래프 구조에 의존하여, 시간적 및 공간적 상호작용을 반영하기 어려운 한계가 있다. 이에, 본 논문은 지역별 태양광 발전량 데이터의 장기 및 단기적 시간 패턴을 고려하고, 이를 지역 간 상관관계의 학습에 반영하는 그래프 신경망 기반의 태양광 발전량 예측 기법을 제안한다. 제안 기법은 타 그래프 신경망 기반 예측 모델과 비교하여 RRSE 기준 최대 7.49%의 성능 개선을 달성하여 그 우수성을 입증하였다. As the use of solar energy has become increasingly common in recent years, there has been active research in predicting the amount of photovoltaic power generation to improve the efficiency of solar energy. In this context, photovoltaic power forecasting models based on graph neural networks have been presented, going beyond existing deep learning models. These models enhance prediction accuracy by learning the interactions between regions. Specifically, they consider how the amount of photovoltaic power in a specific region is affected by the climate conditions of adjacent regions and the time pattern of photovoltaic power generation. However, existing models mainly rely on a fixed graph structure, making it difficult to capture temporal and spatial interactions. In this paper, we propose a graph neural networks-based photovoltaic power forecasting scheme that takes into account both long-term and short-term time patterns of regional photovoltaic power generation data. We then incorporate these patterns into the learning process to establish correlations between regions. Compared to other graph neural networks-based prediction models, our proposed scheme achieved a performance improvement of up to 7.49% based on the RRSE, demonstrating its superiority.

태양광발전시스템의 장기운전에 의한 성능특성 분석

김의환(Kim, Eui-Hwan),강승원(Kang, Seng-Won),김재언(Kim, Jae-Eon) 한국신재생에너지학회 2011 신재생에너지 Vol.7 No.2

Recently, photovoltaic systems have been devolved into much larger systems up to MW-scale. Photovoltaic industry participants give their focus on power generation capability of photovoltaic modules because their benefits can be decided from the amount of generation. The information on long-term performance change of photovoltaic modules helps to estimate the amount of power generation and evaluate the economic cost-benefits. Long-term performance of a PV system has been analyzed with operation data for 12 years from 1999 to 2010. In the first year, the amount of yearly power generation was 57.7 MWh with 13.2% capacity factor. In 2007, the amount of yearly generation was 44.3 MWh with 10.14% capacity factor, and in 2010, the amount was decreased down to 38.1 MWh with 8.7% capacity factor. The result means that long-term capacity factor has been 4.5% decreased for 12 years and that the amount of generation has been decreased 34.0% for 12 years which is 2.8 % per year. The latter capacity factor has been decreased faster than 0.20%, the average rate for 10 years. The performance decrease of the PV system is meant to be accelerated. The decrease of performance and utilization is due to aged deterioration of photovoltaic modules and lowering conversion efficiency of PCS.

기계학습을 이용한 태양광 발전량 예측 및 결함 검출 시스템 개발

이승민 ( Seungmin Lee ),이우진 ( Woo Jin Lee ) 한국정보처리학회 2016 정보처리학회논문지. 컴퓨터 및 통신시스템 Vol.5 No.10

Recently, solar photovoltaic(PV) power generation which generates electrical power from solar panels composed of multiple solar cells, showed the most prominent growth in the renewable energy sector worldwide. However, in spite of increased demand and need for a photovoltaic power generation, it is difficult to early detect defects of solar panels and equipments due to wide and irregular distribution of power generation. In this paper, we choose an optimal machine learning algorithm for estimating the generation amount of solar power by considering several panel information and climate information and develop a defect detection system by using the chosen algorithm generation. Also we apply the algorithm to a domestic solar photovoltaic power plant as a case study.

Examination of scintillator-photovoltaic cell-based spent fuel radiation energy conversion for electricity generation

Lee, Haneol,Yim, Man-Sung Elsevier 2017 Progress in nuclear energy Vol.94 No.-

<P><B>Abstract</B></P> <P>Using computational models, this research examined electricity generation from spent nuclear fuel and its possible uses. The proposed approach was based on converting gamma radiation energy into electricity using scintillators and photovoltaic cells. The work includes performing gamma radiation environment analysis around spent fuel, scintillated photon analysis, and photovoltaic cell analysis for electricity generation. The OrigenArp code was used for gamma radiation environment analysis and the MCNPX 2.7.0 code was used for analyzing scintillation process. For the scintillated photon analysis and photovoltaic cell analysis, a new simulation model was developed and validated based on comparison with experimental data. The effect of self-absorption and radiation damage within the scintillator was described by using experimental data. Based on using 14 energy conversion system units in a spent fuel storage pool in a PWR with CdWO<SUB>4</SUB> as scintillator and SiO2 as photovoltaic cell, generation of electric energy was estimated to range between a few hundred watts and a few watts depending on the cooling time. The estimated amount of electric power generation from spent fuel energy conversion was not enough for large scale applications. But the converted electric power could be utilized as emergency power source in an operating nuclear power plant for various detection and monitoring purposes and for the support of spent fuel pool cooling pump operations.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The feasibility on radiation conversion into electricity using scintillator photovoltaic cell was demonstrated. </LI> <LI> The generated electricity inside a spent fuel storage pool was calculated using computational models. </LI> <LI> The computational model used considered photon self-absorption and radiation damage in a scintillator. </LI> <LI> The computational model used was validated using a lab-scale experiment and was found to be reasonably accurate. </LI> <LI> The generated electricity could be utilized for small-scale applications such as for spent fuel safeguards. </LI> </UL> </P>

단독주택용 PV설비의 유지관리에 따른 발전량 분석

강석화,노상태,김재엽 청운대학교 건설환경연구소 2014 청운대학교 건설환경연구소 논문집 Vol.9 No.2

본 연구에서는 단독주택에 설치되고 있는 태양광발전설비의 발전량을 분석하였다. 발전량은 유지관리의 조건에 따라 월평균 발전량을 산출하였다. 유지관리의 조건으로는 모듈 청소, 발전량 검토, 모듈청결 상태, 태양광 발전 조건으로 구분하였다. 발전량을 분석한 결과, 주기적인 모듈청소와 발전량 검토, 모듈이 청결하고 발전 조건이 좋은 가구가 발전량이 더 높게 나타났다. 또한 PV설비의 수명인 20년 동안 유지관리 조건에 따라 발전효율의 차이가 크게 나타나는 것으로 분석되었다. 발전효율을 제고하기 위해서는 주기적으로 모듈을 청소하고 발전량을 검토하며, 모듈의 청결을 유지하고 PV설비의 일사조건이 좋은 곳으로 선택해야 하는 것으로 분석되었다. This study investigated power generation of photovoltaic equipment at detached houses. Thestudy estimated monthly mean generation according to maintenance conditions that included modulecleaning, inspection into generation, cleaning of module and photovoltaic generation. At analysisupon generation, households with periodical module cleaning and inspection into generation, cleanmodule and good generation conditions had high generation. 20-years of PV equipment life variedmuch depending upon maintenance conditions. Users should keep module clean and inspectgeneration regularly and put PV equipment at the place with good solar radiation