Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka

Abstract

Impact of climate change or climate variability on water resources is an exceedingly concerned issue. Hydropower development is one of the most affected industries due to the climatic variability. Therefore, this paper presents the promising results from a study of the impact of climate variability on hydropower generation of Erathna run-of-the-river (ROR) hydropower plant located in Rathnapura district, Sri Lanka. This study was based on surrounded rain gauges outside the catchment as Erathna catchment area is an un-gauged catchment. 30-year rainfall trend analysis from 1988 to 2017 was done using Mann-Kendall and Sen's slope estimator tests to predict the available trends. Pearson's correlation coefficient was used to investigate the relationship between rainfall and Erathna power generation. Results show negative trends for annual rainfalls in several rain gauges, while seasonal trend analyses support that observation. July is the most critical month for most of the rain gauges around the catchment. The results also show a good correlation between the rainfalls and power generation. Therefore, the results conclude the importance of rainfall trend analysis in un-gauged catchments and its forecasting capacity of water resources usage in hydropower development.

Application of improved fuzzy best worst analytic hierarchy process on renewable energy

“…The results indicate that "Hydraulic head" is the most significant indicator observed by our proposed method. Some existing work also supports our result which is found by our proposed hybrid method (Perera & Rathnayake, 2019;Majumder, Majumder & Saha, 2018). Here comparative study scenario analysis and sensitivity analysis are done which also support our result.…”

Relationships between Hydropower Generation and Rainfall-Gauged and Ungauged Catchments from Sri Lanka

“…erefore, it can be concluded, herein, that the gauged catchment's rainfall supports the prediction of hydropower production. Perera and Rathnayake [28] have suggested to cluster the rain gauges around the Erathna catchment into two major groups, one covering to the Sri Pada mountain range and the other is the western side of the catchment. e analyses were carried out to a similar approach here; however, there is no significant correlation between the rainfall to the hydropower generation in the Erathna catchment.…”

“…Similar to the abovementioned modeling techniques, the Erathna minihydropower plant was modeled using neural networks. However, as Perera and Rathnayake [28] suggested, two sets of rain gauges were selected to observe the relationship between rainfall and the power generation. Set 1 includes Galaboda Estate, Keragala, Pussella S.P., Maliboda, and Anhetigama Estate rain gauges, whereas the set 2 includes Laxapana, Maskeliya, Maussakele, and Hapugastenna Estate rain gauges (please refer Figure 1).…”

“…Nevertheless, Bayesian regularization and Scaled Conjugate Gradient algorithms are widely used in the ANN architecture [31,32]. Detailed descriptions of these algorithms LM, BR, and SCG can be found in the work of Perera and Rathnayake [28]. Table 1 exhibits the coefficient of correlation for the predicted and actually generated hydropower by the ANN for Denawaka Ganga minihydropower station power generation.…”

“…Several studies have been carried out in Sri Lanka to investigate the impact of climate variability on the power generation in minihydropower plants. Perera [28] have studied on the impact of expected climate change on the hydropower generation using the Erathna run-of-the-river hydropower plant. Furthermore, Khaniya [29] studied on the impact of climate variability on hydropower generation using the Denawaka Ganga minihydropower plant.…”