Abstract
The characterization of the mapping relationship (MR) between outflow of the upstream reservoir (OUR) and inflow of the downstream reservoir (IDR) in the short-term generation dispatching of cascade reservoirs (SGDCR) greatly impacts the safe and economic operation of hydropower plants. If this MR is not characterized properly, the operation process of hydropower stations will deviate from the planning dispatching schemes. Especially when the upstream reservoir (UR) undertakes peak load regulation tasks frequently and the downstream reservoir (DR) owns weak re-regulation capacity, the safety of cascade reservoirs will be seriously threatened. In this extreme system, the commonest characterizing models in SGDCR, such as the lag time (LT) model and the Muskingum model, may cause a huge deviation when simulating or forecasting the IDR. Given this dilemma, the Gaussian process regression (GPR) model, which is a representative for Bayesian regression methods, is firstly introduced to handle this MR and compared with the LT and the Muskingum model in this paper. The Three Gorges-Gezhouba cascade reservoirs (TGGCR) in China are selected as a typical case study. The performance indicators of four categories are adopted to evaluate the simulated IDR in a single period and a set of pivotal indicators are proposed to estimate the simulation dispatching in multiple periods. The results show that (1) The GPR model reduces the mean absolute deviation (MAD) about inflow of Gezhouba by 197m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /s and 263m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /s than the LT and the Muskingum model respectively; (2) The distribution characteristics of inflow deviation produced by the GPR model are most competitive. Meanwhile, the GPR model has the strongest ability when conducting the multi-period simulation dispatching and owns best applicability on the whole range of streamflow. (3) The Muskingum model is not recommended to characterize the hour-scale hydraulic connection in the extreme system of SGDCR.
Highlights
Large reservoirs with large installed capacity are usually equipped with immediate downstream reservoirs to buffer the sharp fluctuation of outflow caused by the peak load regulation in a short period [1], [2]
The Gaussian process regression (GPR) which is a representative for Bayesian regression methods is firstly introduced to characterize the hour-scale hydraulic connection in short-term generation dispatching of cascade reservoirs (SGDCR)
The peak load regulation amplitude of the Three Gorges reservoir (TGR) can reach 7000MW. It is very common for the outflow variation amplitude of TGR to reach 5000m3/s or even more within 2 hours. (Since the streamflow data of the Three Gorges-Gezhouba cascade reservoirs (TGGCR) is done once every 2 hours in the actual dispatching, the period length is determined as 2 hours in this paper [1])
Summary
Large reservoirs with large installed capacity are usually equipped with immediate downstream reservoirs to buffer the sharp fluctuation of outflow caused by the peak load regulation in a short period [1], [2]. The hydraulic connection will be characterized roughly because of the aim of simplifying generation dispatching models and the difficulty in handling the aftereffect of streamflow in most studies on SGDCR [1]–[3], [5], [6] In this extreme system, the effective characterization on the hydraulic connection between the outflow of TGR and the inflow of Gezhouba becomes important, which can avoid frequent adjustments on the developed power generation scheme and ensure the safety of cascade reservoirs [7], [8]. The main outcomes of this study are as follows: 1) The GPR model is for the first time introduced to characterize the hour-scale hydraulic connection of cascade reservoirs for short-term generation dispatching.
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