Applicability of ABCD model for the identification of suitable locations for run-of-the-river hydropower generation in upper Kelani river basin in Sri Lanka

Abstract

The recent crisis in the energy sector has raised the need of exploration for additional renewable energy sources. Run-of-the-River (RoR) hydropower systems that harvest energy from flowing water to generate electricity in the absence of a large dam and reservoir required in conventional impoundment hydroelectric facilities are gaining interest due to their minimum impact to the environment. Identifying of suitable locations with significant potential RoR hydropower capacity by using conventional methods is hindered in remote hilly inaccessible areas. The GIS tools and ABCD hydrologic model are used in the present study to remotely identify and estimate potential RoR capacity based on the favourable river hydro-morphological and site-specific geographical features in the project area in Kelani Basin. The Upper Kelani Basin was selected as the overall project study area and two uppermost sub-catchments, namely Norwood and Holombuwa, were selected to optimize the ABCD model parameters for simulating stream flows with the selected rain gauge stations in each watershed. The ABCD daily hydrological model was calibrated using 5 years data from 2008~2013 and validated based on 4 years data from 2013~2017. The Shuttle Radar Topography Mission (SRTM) 90 m and 30 m Digital Elevation Model (DEM) data verified against 1:50,000 topographical map terrain was used in catchment delineation and available hydraulic head calculation along river channel. The ABCD model parameters established based on the two sub catchments were progressively transferred to the downstream sub catchments estimate the flow rates at locations where the feasible heads were available as identified based on an auto-algorithm to establish the overall potential RoR hydropower potential in the basin. The identified a, b, c and d hydrologic parameters for Norwood and Holombuwa sub watersheds were (0.986, 248.0, 0.012 and 0.001) and (0.902, 289.0, 0.241 and 0.180), respectively. The Pearson correlation (r) and coefficient of determination (R2) were used as objective functions which found to be in acceptable range for both calibration and validation model runs. The algorithm developed with GIS tools in ArcGIS (v 10.3) platform to detect feasible sites based on river gradient coupled with flow estimates from the ABCD hydrologic model were found to be capable of remotely identifying potential locations for RoR hydropower generation. The study shows that the proposed approach has a vast advantage over the slow, cumbersome, uneconomical, conventional survey-based methods used for identification of potential RoR sites and further studies are recommended to recognize the sensitivity to terrain variations, sub catchment size and location in the basin and to incorporate alternatives for overall system optimization. Keywords: Automated algorithm, Hydrological modelling, Sensitivity and optimization 1. Introduction The sustainable development and utilization of remaining global hydroelectric potential is crucial to contribute optimally to the ever-growing future demand for electrical power. Today, hydropower is well established as a source of energy having the greatest capability to supply the clean renewable energy in most parts of the world. When implemented as multipurpose water resources projects, a hydro schemes can offer a number of side benefits which no other source can possibly