Electricity production, capacity factor, and plant efficiency index at the Mutriku wave farm (2014–2016)

Ibarra-Berastegi, Gabriel; Sáenz, Jon; Ulazia, Alain; Serras, Paula; Esnaola, Ganix; García-Soto, Carlos

doi:10.1016/j.oceaneng.2017.10.018

Cited by 95 publications

(59 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the parameters discussed above, CUF is of paramount importance as it exemplifies the average load of the SPV plant and specifies efficacy in power equipment usage . Therefore, trend assessment of CUF versus month is performed by using six mathematical models (linear, logarithmic, exponential, quadratic, quartic, and hexic).…”

Section: Resultsmentioning

confidence: 99%

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Mukherji

Mathur

Bhati

et al. 2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

Currently, India is espousing nonconventional energy sources at an express rate owing to concerns about climate change, emission of harmful greenhouse gases (GHG), and exhausting conventional energy sources. The solar photovoltaic (SPV) plants are, hence, expected to play a noteworthy role to meet energy security and sustainability goals. Contemporary studies reveal substantial ecological concerns associated with installing the ground mounted SPV plants in urban locations. Further observations depict that aforementioned plants need additional land, transmission, and distribution infrastructures. Consequently, rooftop SPV plants are the best solution to produce energy in urban locations, owing to the availability of a large number of empty rooftop spaces with least capacity expenditure. The present article discusses the techno-economic and ecological aspects of a 50 kWp rooftop SPV plant installed at ICFAI University, Jaipur. The plant has produced around 64.149 MWh in the year 2017. The technical assessment focuses on the effects of the meteorological parameters upon energy generation factors. The annual average final yield, reference yield, capacity utilization factor, and performance ratio found to be 106.9 kWh/kWp/month, 149.7 kWh/kWp/month, 14.64 and 70%, respectively. The economic parameters like net present value, internal rate of return, profitability index, and payback period are computed with 0-50% subsidy rates, which supports the financial viability of the plant. The plant contributed significantly to reducing GHG emissions by mitigating 102 t CO 2 , 128 kg SO 2 , 268 kg NO x , and 7,033 kg ash in the year 2017 as evident from the ecological investigation.Statement of industrial relevance: Manufacturing industries are extremely power intensive, as they utilize a substantial amount of energy during the production process. The utilization of sustainable energy sources can considerably curtail the major overhead cost, that is, power, and at the same time minimize pollution. Solar energy utilization is a viable option as large spaces are available not only on rooftops but also allied spaces like parking lots of the industries for installation of rooftop SPV plants.The industries by virtue of location receive extended periods of unimpeded sunlight, as these setups are positioned in separate industrial areas away from residential

show abstract

Section: Resultsmentioning

confidence: 99%

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Mukherji

Mathur

Bhati

et al. 2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

show abstract

“…The design of this study follows the reasoning presented in many references already discussed in the introduction to this paper [8,12,26,27,31,33]. This has also been the authors' experience in previous papers [52,76]. It is a common result in the literature that the use of a statistical model, which is fed with data provided by a numerical weather forecast model, yields better results than the use of a statistical model alone or a numerical model alone.…”

Section: Discussionmentioning

confidence: 90%

Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

et al. 2019

Self Cite

View full text Add to dashboard Cite

This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1-24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R 2 ) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1-4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4-24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2-5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical-statistical methods can be used to improve short-term wind forecasts.Atmosphere 2020, 11, 45 2 of 22Wind predictions are issued based on meteorological numerical weather forecast (NWF) systems, in which data assimilation (DA) plays an important role in order to properly correct errors in the forecasting model by the use of observations in the data assimilation stages. In these NWF systems, the data assimilation methods optimally estimate the state of the atmosphere [19,20] and a numerical weather forecast model is used to predict the future states of the atmosphere. Two examples of these advanced NWP systems are the ERA-Interim [21] global atmospheric reanalysis model, from 1979 until 2019 [21], and NCEP re-analyses [22]. They are not real-time operational NWF systems, since the DA and the forecasting models are frozen in time, but they are re-run for the whole period using all the available observations commonly used in numerical weather forecasts in order to avoid, as much as possible, errors in the record due to changes in the structure (architecture, resolution, kind of observations processed) of the archives derived from operational NWF systems. In the case of the ERA-Interim re-analyses, besides analyses, forecasts are also provided starting at the analysis times. These forecasts are the ones used in this paper as a surrogate of an operational model. However, re-analyses are quite coarse-resolution models when compared with operational models of the same generation. In the particular case of the ERA-Interim, the spatial resolution is 0.75 • , too coarse for local wind forecasts.To fill this resolution gap, numerical and statistical downscaling techniques are customarily used. Numerical downscaling increases the resolution by nesting a higher-resolution model, such ...

show abstract

“…Several studies suggest that the capacity factor of wave power is ca. 20-40% at the most [55][56][57][58]. Being so, the number of WECs (20 kW) required for a daily supply of freshwater to 5000 inhabitants would increase or decrease in function of the capacity factor.…”

Section: Wave Powered Desalinationmentioning

confidence: 99%

Wave Power as Solution for Off-Grid Water Desalination Systems: Resource Characterization for Kilifi-Kenya

et al. 2018

View full text Add to dashboard Cite

Abstract:Freshwater scarcity is one of humanity's reoccurring problems that hamper socio-economic development in many regions across the globe. In coastal areas, seawater can be desalinated through reverse osmosis (RO) and transformed into freshwater for human use. Desalination requires large amounts of energy, mostly in the form of a reliable electricity supply, which in many cases is supplied by diesel generators. The objective of this work is to analyze the wave power resource availability in Kilifi-Kenya and evaluate the possible use of wave power converter (WEC) to power desalination plants. A particular focus is given use of WECs developed by Uppsala University (UU-WEC). The results here presented were achieved using reanalysis-wave data revealed that the local wave climate has an approximate annual mean of 7 kW/m and mode of 5 kW/m. Significant wave height and wave mean period are within 0.8-2 m and 7-8 s respectively, with a predominant wave mean direction from southeast. The seasonal cycle appeared to be the most relevant for energy conversion, having the highest difference of 6 kW/m, in which April is the lowest (3.8 kW/m) and August is the peak (10.5 kW/m). In such mild wave climates, the UU-WEC and similar devices can be suitable for ocean energy harvesting for water desalination systems. Technically, with a capacity factor of 30% and energy consumption of 3 kWh/m 3 , a coastal community of about five thousand inhabitants can be provided of freshwater by only ten WECs with installed capacity of 20 kW.

show abstract

Electricity production, capacity factor, and plant efficiency index at the Mutriku wave farm (2014–2016)

Cited by 95 publications

References 27 publications

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Assessment of 50 kWp rooftop solar photovoltaic plant at The ICFAI University, Jaipur: A case study

Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

Wave Power as Solution for Off-Grid Water Desalination Systems: Resource Characterization for Kilifi-Kenya

Contact Info

Product

Resources

About