Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Abstract

Abstract. This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories.

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

“…Dobson et al (2019), Rani and Moreira (2010), Ahmad et al (2014), Celeste and Billib (2009) and Labadie (2004) carried out extensive reviews of the most common optimization methods. Three main classes of optimization algorithms that are efficient for optimizing reservoir management are (1) linear and nonlinear programming (Arsenault and Côté, 2019;Yoo, 2009;Barros et al, 2003), (2) dynamic programming (Bellman, 1957) and its variants, deterministic dynamic programming (DDP) (Haguma and Leconte, 2018;Ming et al, 2017;Yuan et al, 2016), stochastic dynamic programming (SDP) (Wu et al, 2018;Yuan et al, 2016;Celeste and Billib, 2009;Tejada-Guibert et al, 1995), sampling stochastic dynamic programming (SSDP) (Haguma and Leconte, 2018;Faber and Stedinger, 2001;Kelman et al, 1990) and stochastic dual dynamic programming (SDDP) Tilmant and Kelman, 2007;Tilmant et al, 2008Tilmant et al, , 2011Pereira and Pinto, 1991), and (3) heuristic programming Ahmed and Sarma, 2005). The choice among these algorithms depends on many factors, such as the stakes and objectives to address, as well as the configuration of the system and the data available to parametrize and run the model.…”

Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models

“…In Table 1, we present a summary of some relevant and recent studies on streamflow forecasting. Such investigations used databases from countries as China, Canada, Ecuador, Iraq, Mozambique, United States, Serbia, Norway, Turkey, Sri-Lanka and Brazil (Arsenault and Côté, 2019;Hailegeorgis and Alfredsen, 2017;Siqueira et al, 2018;Stojković et al, 2017;Wei et al, 2013;Yang et al, 2017;Zhou et al, 2018;Zhu et al, 2016). Some aspects of these studies can be highlighted.…”

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

“…Additionally, their operation leads to a smaller environmental impact than burning carboniferous fuel. Due to this, many pieces of research have presented investigations on such fields for countries such as China [14], Canada [15], Serbia [16], Norway [17], Malaysia [7], and Brazil [9].…”