Benefit and Risk Balance Optimization for Stochastic Hydropower Scheduling

Abstract

Limited by inflow forecasting methods, the forecasting results are so unreliable that we have to take their uncertainty and risk into account when incorporating stochastic inflow into reservoir operation. Especially in the electricity market, punishment often happens when the hydropower station does not perform as planned. Therefore, focusing on the risk of power generation, a benefit and risk balance optimization model (BRM) which takes stochastic inflow as the major risk factor is proposed for stochastic hydropower scheduling. The meanvariance theory is firstly introduced into the optimal dispatching of hydropower station, and a variational risk coefficient is employed to give service to managers' subjective preferences. Then, the multi-period stochastic inflow is simulated by multi-layer scenario tree. Moreover, a specific scenario reduction and reconstruction method is put forward to reduce branches and computing time accordingly. Finally, the proposed model is applied to the Three Gorges Reservoir (TGR) in China for constructing a weekly generation scheduling in falling stage. Compared to deterministic dynamic programming (DDP) and stochastic dynamic programming (SDP), BRM achieves more satisfactory performance. Moreover, the tradeoffs for riskaverse decision makers are discussed, and an efficient curve about benefit and risk is formed to help make decision.Water Resour Manage

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

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Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach

“…Also, the fact that larger differences have a larger effect on RMSE, given that it is based on the square root of the average of squared errors, the high streamflow values generated in the unbiased system when using the higher spread factor penalize this system, leading to RMSE scores very close to the scores of the biased UnD and UnE systems, where high streamflow forecast values tend to occur less often. Finally, we note that the range of NRMSE values of the OvE system comprises the range of normalized RMSE values found by Zalachori et al (2012) (NRMSE ranging from 1.7 to 2.4) when analyzing the raw (without bias correction) operational forecasts over a similar dataset of catchments.…”

“…It produces 7 d ahead streamflow forecasts daily. The quality of these operational forecasts, for a similar catchment dataset and evaluation period, is discussed in Zalachori et al (2012).…”

Optimization dispatch rule of the Three Gorges Reservoir considering ecological and power generation benefit

“…In recent years, many researchers have carried out a series of studies around the issue of deterministic multiobjective optimization [1,2], uncertain scheduling for power generation benefit [3,4] and suitable ecological flow [5,6]. None of these studies involved the joint operation of ecological and power generation under uncertain water conditions.…”