Optimization models, methods and algorithms applied to hydropower operations planning

加拿大拥有78，359兆瓦的水电装机容量，潜力是实际容量的两倍多。在这个气候变化的时代，各国都在关注清洁能源发电，水电就是其中之一。重要的是要有效地管理实际到位的水电系统，以最大限度地利用现有的水资源生产能源。目前的研究建议旨在制定新的战略，有效地管理水电厂的业务基础上。传统上，中期优化模型用于确定一周内的储层体积或总预期能量生产。每天使用短期优化模型，以确定水力发电系统的涡轮机和发电厂之间的准确水分配。优化问题通常是随机的，因为水库的流入量，需求和/或能源价格在决策时是未知的。* 本研究计划的长期目标是制定数学模型和开发优化方法，以尽可能精确地解决中期和短期水电问题，以及这些模型数值实现的新发展。此外，所开发的模型将在地球仪的不同水电系统上进行测试，以进一步改进所提出的模型和求解方法。* 在较短的时间范围内，本提案分为三个目标，旨在培养两名博士生和两名硕士生。最近的趋势，如机器学习是水电管理的有前途的途径，因为水电生产商可以获得大量的数据。因此，该提案的第一个目标是从机器学习中获得工具，例如神经网络，以生成用作随机优化问题输入的流入场景。 第二个目标的重点是投标的概念，以加强制定短期水电问题。大多数水电生产商在一个放松管制的市场中发展，竞相以高价出售能源，以低价购买。将探索新的配方的问题和解决方法，以改善目前的优化模型。使用涡轮机组合的建模方法很有趣，因为它们减少了决策变量的数量。最后，第三个目标涉及解决水电问题的计算开发，以减少计算时间，但也需要基础设施。水电生产商每天更新其流入预测，每天需要多次做出有效管理其系统的决策。解决方案需要快速可用，这一目标涉及计算机集群上的不同并行化技术以及使用图形处理单元执行计算。

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.**************