Developing novel optimization models and algorithms for energy systems with renewable energy and storage capacities

为具有可再生能源和存储能力的能源系统开发新颖的优化模型和算法

• 批准号: RGPIN-2019-06699
• 负责人: Pirnia, Mehrdad
• 金额: $ 1.89万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681439
• 关键词: Developing; novel; optimization; models; algorithms

Electric networks continue to increase in complexity because of the deployment of market mechanisms, the integration of renewable generation and distributed energy sources (e.g., wind and solar), and the introduction of dynamic demand from sources such as electric vehicles and price sensitive electricity loads. These revolutionary changes and their associated uncertainties can be controlled by implementing intelligent data-driven management systems, and adding flexibility into the distribution network. Although advances in power electronic devices enable the collection of real-time data related to consumers' demand and generators' supply, we lack proper approaches to describe electricity consumption and production patterns, that can accurately predict future demand and electricity supply, and to prescribe efficient and reliable policy mechanisms to build resilient and affordable power grids. ***The long-term objective of this research program is to provide a framework for an environmentally clean, economically efficient, and strongly resilient power grid. To achieve this goal, models and algorithms that allow the integration of renewable energy and battery technologies in both short-term operational and long-term planning models will be developed. The program also investigates pricing models, providing both consumers and power generators with satisfactory electricity market prices to encourage them to participate in the electricity market. In addition, the research program will investigate robust, accurate and efficient solution methodologies for nonlinear and integer stochastic power system operational and planning models. The research program seeks to find answers to the following questions:*** Which mathematical approaches and heuristics should be used to solve optimization problems in power systems? *** How should the short-term operational models be formulated to optimize the power system variables, while maximizing affordability, reliability and resiliency of the system?*** How do we collect and analyze data related to consumers' behaviour and generation capacities to forecast future trends and prescribe efficient policy mechanisms to promote clean and affordable energy?*** What policy instruments should be deployed to encourage economical and efficient business models in the long run, while considering environmental obligations and short-term operational constraints?***Through the program, HQP will gain comprehensive knowledge in the areas of operations research, data analytics, and energy networks. They will be poised to hold academic and industrial positions as data scientists, solution developers, energy market specialists, and operations managers to improve Canada's energy future by promoting sustainable solutions.**

由于市场机制的部署，可再生能源和分布式能源(如风能和太阳能)的整合，以及电动汽车和对价格敏感的电力负荷等来源的动态需求的引入，电力网络的复杂性继续增加。这些革命性的变化及其相关的不确定性可以通过实施智能数据驱动的管理系统并为配电网络增加灵活性来控制。虽然电力电子设备的进步使人们能够收集与消费者需求和发电机供应有关的实时数据，但我们缺乏适当的方法来描述电力消费和生产模式，能够准确预测未来的需求和电力供应，并制定有效和可靠的政策机制来建设有弹性和负担得起的电网。*该研究计划的长期目标是为环境清洁、经济高效和具有很强弹性的电网提供一个框架。为了实现这一目标，将开发允许在短期运营和长期规划模型中整合可再生能源和电池技术的模型和算法。该计划还研究定价模式，为消费者和发电商提供令人满意的电力市场价格，以鼓励他们参与电力市场。此外，该研究计划还将研究非线性和整数随机电力系统运行和规划模型的稳健、准确和有效的求解方法。该研究计划试图找到以下问题的答案：*应该使用哪些数学方法和启发式方法来解决电力系统中的优化问题？*应该如何制定短期运行模型来优化电力系统变量，同时最大化系统的可负担性、可靠性和弹性？*我们如何收集和分析与消费者行为和发电能力有关的数据，以预测未来的趋势，并制定有效的政策机制，以促进清洁和负担得起的能源？*从长远来看，应该采用哪些政策工具来鼓励经济和高效的商业模式，在考虑环境义务和短期运营限制的同时？*通过该计划，HQP将获得运筹学、数据分析和能源网络领域的全面知识。他们将担任数据科学家、解决方案开发人员、能源市场专家和运营经理等学术和行业职位，通过推广可持续解决方案来改善加拿大的能源未来。**