Projection of Future Hydropower Generation in Samanalawewa Power Plant, Sri Lanka

Bhabishya Khaniya,Miyuru B Gunathilake,Upaka Rathnayake,Chamaka Karunanayake,Danial Armaghani

doi:10.1155/2020/8862067

Bhabishya Khaniya, Miyuru B Gunathilake + Show 3 more

Open Access

https://doi.org/10.1155/2020/8862067

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Abstract

The projection of future hydropower generation is extremely important for the sustainable development of any country, which utilizes hydropower as one of the major sources of energy to plan the country’s power management system. Hydropower generation, on the other hand, is mostly dependent on the weather and climate dynamics of the local area. In this paper, we aim to study the impact of climate change on the future performance of the Samanalawewa hydropower plant located in Sri Lanka using artificial neural networks (ANNs). ANNs are one of the most effective machine learning tools for examining nonlinear relationships between the variables to understand complex hydrological processes. Validated ANN model is used to project the future power generation from 2020 to 2050 using future projected rainfall data extracted from regional climate models. Results showcased that the forecasted hydropower would increase in significant percentages (7.29% and 10.22%) for the two tested climatic scenarios (RCP4.5 and RCP8.5). Therefore, this analysis showcases the capability of ANN in projecting nonstationary patterns of power generation from hydropower plants. The projected results are of utmost importance to stakeholders to manage reservoir operations while maximizing the productivity of the impounded water and thus, maximizing economic growth as well as social benefits.

Highlights

Hydropower spines almost 16% of the total electricity generation, which is far more than the contribution from any other renewable resources all over the world [1]
We examine the capability of artificial neural networks (ANNs) to forecast the future hydropower generation by using historical rainfall data for training as well as validating the forecasting model and future rainfall data extracted from the regional climate model (Coordinated Regional Downscaling ExperimentCORDEX) for forecasting the future power generation through a feed-forward with backpropagation algorithm
With R value greater than 0.6, BFG and Conjugate gradient with Powell/Beale Restarts (CGB) algorithms have produced proficient training results. ough R values of 0.5-0.6 obtained during training cannot be considered promising for prediction purposes in the normal condition, they are acceptable especially based on input characteristics used in training. e spatial variation of rain gauges along with the spatiotemporal variation of precipitation, which is the chief input in this ANN model, could have negatively impacted on R values

Summary

Introduction

Hydropower spines almost 16% of the total electricity generation, which is far more than the contribution from any other renewable resources all over the world [1]. Hydropower plants supply as much as 40% of the energy required to satisfy Sri Lanka’s power demand [2]. Harnessing electricity from hydropower is still considered one of the most sustainable methods of power generation around the world. Many developing countries are rapidly investing significant capital in developing hydropower plants as it is regarded to be a secure and affordable form of sustainable energy, limiting carbon emissions [4]. Even though the seasonal rainfall is expected to increase in the coastal areas, rainfall patterns in mountainous areas of Sri Lanka, where most of the hydropower plants are located, are very dynamic and rapidly fluctuating [6]

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