Abstract

Small hydropower (SHP) plants are advantageous as they have a short construction period and can be easily maintained. They also have a higher energy density than other alternative energy sources as environmentally-friendly energy sources. In general, hydropower potential is estimated based on the discharge in the river basin, and the discharge can be obtained from the stage station in the gaged basin. However, if there is no station (i.e., ungaged basin) or no sufficient discharge data, the discharge should be estimated based on rainfall data. The flow duration characteristic model is the most widely used method for the estimation of mean annual discharge because of its simplicity and it consists of rainfall, basin area, and runoff coefficient. Due to the characteristics of hydroelectric power depending on the discharge, there is a limit to guaranteeing the accuracy of estimating the generated power with only one method of the flow duration characteristic model. Therefore, this study assumes the gaged basins of the three hydropower plants of Deoksong, Hanseok, and Socheon in Korea exist as ungaged basins and the river discharges were simulated using the Kajiyama formula, modified-TPM(Two-Parameter Monthly) model, and Tank model for a comparison with the flow duration characteristics model. Furthermore, to minimize the uncertainty of the simulated discharge, four blending techniques of simple average method, MMSE(Multi-Model Super Ensemble), SMA(Simple Model Average), and MSE(Mean Square Error) were applied. As for the results, the obtained discharges from the four models were compared with the observed discharge and we noted that the discharges by the Kajiyama formula and modified-TPM model were better fitted with the observations than the discharge by the flow duration characteristics model. However, the result by the Tank model was not well fitted with the observation. Additionally, when we investigated the four blending techniques, we concluded that the MSE technique was the most appropriate for the discharge simulation of the ungaged basin. This study proposed a methodology to estimate power generation potential more accurately by applying discharge simulation models that have not been previously applied to the estimation of SHP potential and blending techniques were also used to minimize the uncertainty of the simulated discharge. The methodology proposed in this study is expected to be applicable for the estimation of SHP potential in ungaged basins.

Highlights

  • Hydropower is a clean regenerative energy source that is fueled by water and sustainable even in future climate change scenarios [1]

  • Existing studies on hydropower generation potential have calculated the discharge data using the flow-duration characteristics model proposed by Park and Lee (2008) with precipitation data, runoff coefficient, and basin area used as input [18]

  • Where (QMMSE )t is the multiple model prediction obtained through the Multi-Model Super Ensemble (MMSE) Equation at time t; (Qs im )i, t is the runoff value of the ith model at time t; (Qs im )i is the mean of the ith model discharge values over the entire period; Qobs is the mean of the measured values; and xi is the regression coefficient of each of the N models, which can be determined through regression analysis

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Summary

Introduction

Hydropower is a clean regenerative energy source that is fueled by water and sustainable even in future climate change scenarios [1]. Yu et al (2017) calculated the annual mean discharge, plant capacity, and annual SHP generation by unit head to analyze the potential resources [9]. The flowduration characteristics model of Park and Lee (2008) is typically applied using rainfall data This method estimates the hydropower potential of a site by calculating the annual mean discharge using annual precipitation data, basin area, and runoff coefficient [13,18]. To improve the accuracy of the power generation potential, the reliability of the models should be verified by comparing the results of various methods with the potential estimated using conventional methods For this purpose, monthly data should be derived using runoff models to calculate the parameters such as seasonal discharge variations, plant capacity, and the efficiency of power plants for the estimation of power generation potential.

Flow-Duration Characteristics Model
Kajiyama Formula
Tank Model
Blending Techniques
Target Basin
Collection
Monthly Runoff Simulation
Monthly Runoff Simulation Using the Flow-Duration Characteristics Model
Monthly Runoff Simulation Using the Kajiyama Formula
Monthly Runoff Simulation Using Modified TPM
Monthly Runoff Simulation Using the Tank Model
Comparison and Analysis of the Monthly Runoff Simulation Results
The discharge using the modified a small error of th mean and the smallest
Application of the Blending Technique
Calculation of SHP Potential
Conclusions
Full Text
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