Cascade hydroelectric scheme: River flow estimation for reservoir regulation improvement and flood-risk mitigation

Abstract

Due to present and imminent extreme weather patterns, regulation of hydroelectric reservoirs and flood risk management requires better measurement of river flows. To improve reservoir regulation at our hydroelectric scheme in northern part of Malaysia, a budget river flow measurement system have been installed to estimate flows of rivers that fed four of our hydroelectric reservoirs. The main goal of this measurement system is to estimate water flows into four cascade hydroelectric reservoirs using fore-bay and tail race water levels alone as sensors. With limited number of level sensors installed, mathematical computations have been used to estimate incoming cascade flows. The computation is based on mass balance that volumes of incoming flows should be equal to water stored in the reservoirs over a period plus volumes of water discharge from these hydroelectric plants. The volumes are obtained by integration of flows over selected intervals. However, computation of water volume stored in reservoirs can yield a temporary drift result due to resolution error associated with fore-bay water level measurement that has smallest measured change of O.Olm. These volume computations can causes significant errors as the level variations will be amplified by large surface areas of the reservoirs. Although this measurement system has been able to perform as anticipated, further refinements are needed including to estimate water flow travel times between different reservoir systems. Also, as the lowest level of cascaded hydroelectric stations has limited capacity (due to historical background and construction cost) that spillage is often occur, this proposed system can be used in the future to improve its energy output, in addition to reduce risks of flooding downstream.

Methods for Hydropower Discharge Prediction: A Review

“…The development of hydropower can be challenging due to changing climate conditions. For example, the hydropower system may not work optimally, resulting in high spillage and loss of electricity generation [23]. High spillage results to a high risk of flooding in the lower stream, typically in the case of cascade hydropower systems.…”

Water flow Forecasting Methods for Optimal Water Resource Operation and Management: A Review

“…Moreover, paper [31] present a Nonlinear Auto Regressive model with eXogenous inputs (NARX) model for daily reservoir inflow forecasting for 365-day lead time. Meanwhile, a budget river flow measurement system has been built to estimate the inflows that fed into the four hydroelectric in the northern part of Malaysia in order to improve reservoir regulation [32]. A modified SVM-based forecasting model is proposed in paper [33] to improve the predictability of the inflow at the Shihmen Reservoir using previous-period climate data.…”

Flood Risk Prediction for a Hydropower System using Artificial Neural Network

“…For instance, hydropower system has high spillage due to the extreme raining season. Consequently, high flooding occur at the low-lying area [3]. In order to mitigate the potential risk of flooding at the downstream, streamflow forecasting is required to estimate the hydropower discharge.…”