Grant: Discovery Grants Program - Individual

Canada has 78,359 megawatts of installed capacity of hydropower generated energy, and the potential is more than double the actual capacity. In this era of climate change, countries are focusing on clean energy generation and hydropower is one of them. It is important to efficiently manage the hydropower systems actually in place to maximize the energy production with the available water. The present research proposal aims at developing new strategies to efficiently manage hydropower plants on an operational basis. Traditionally, mid-term optimization models are used to determine reservoir volumes or total expected energy production throughout a week. Short-term optimization models are used on a daily basis to determine the exact dispatch of water between the turbines and power plants of the hydropower system. The optimization problems are usually stochastic, since inflows in the reservoirs, demand and/or energy prices are unknown at the time of making a decision. The long term objectives of this research program is the formulation of mathematical models and development of optimization methods to solve the mid- and short-term hydropower problems as precisely as possible and new developments in the numerical implementations of these models. Furthermore, the models developed are to be tested on different hydropower systems across the globe, to further improve the proposed models and solution methods. On a shorter time scale, the present proposal is divided into three objectives and aims at training two doctoral students and two master's students. Recent trends such as machine learning are promising avenues for hydropower management since the hydropower producers have access to large amounts of data. Therefore, the first objective of this proposal is to derive tools from machine learning, such as neural networks to generate inflow scenarios used as input to the stochastic optimization problems. The second objective focuses on bidding concepts to enhance formulations of the short-term hydropower problem. Most of the hydropower producers evolve in a deregulated market, competing to sell energy at high prices and to buy at lower prices. Novel formulations of the problem and solution methods will be explored to improve the current optimization models. Modeling approaches that use combinations of turbines are interesting as they reduce the number of decision variables. Finally, the third objective is concerned with computational developments to solve hydropower problems, to reduce computing time, but also the required infrastructure. Hydropower producers update their inflow forecasts daily and decisions to manage their systems efficiently are required multiple times a day. Solutions need to be available quickly and this objective addresses different parallelization techniques on computer clusters as well as the use of graphical processing units to perform calculations.