New optimization techniques to integrate storage and renewable energy in the power network

自20世纪中叶以来，由于工业发展和人口增长，全球能源需求持续上升。发展可再生能源已成为满足日益增长的需求和限制温室气体排放的优先事项。然而，可再生能源的广泛采用受到其缺点的限制，即发电的不连续性可能导致供需不平衡和电网拥挤。克服这些问题的关键策略是实施智能管理系统并在配电网中引入灵活性。最近在许多技术领域的发展已经朝着实现智能电网迈出了重要的一步。这些包括a)先进的监测、通信和控制基础设施；B)储能资源；c)车辆到电网系统，这代表了电力灵活性的一个有前途的来源。该研究项目将通过开发数学优化模型和算法来解决后两项问题，从而将固定和移动储能整合到能源网络中，从而提高电网的可靠性并减少相关的不确定性。*** ***这项研究在几个重要的推力领域推进了现有的工作。开发的优化模型将考虑到问题的时间性质，由于潜在的技术和物理过程引起的非线性，负载和能源供应的不确定性，以及一些决策的离散性。这些数学公式将导致难以求解的非线性优化模型，这将需要新的求解方法。所提出的整体集成模型将允许优化基础设施设计以及储能系统的优化充放电操作。*** ***上述工作将在理论上和实践上为若干业务研究领域以及为解决可持续能源问题开发新模式和解决方法作出贡献。研究结果将用于提供可扩展的间歇性能源集成计划，使用先进的信息管理和分析来增加电力系统的灵活性，并平衡传统和可再生能源资源。通过拟议的研究项目，学生将获得在运筹学、数据分析和能源网络等学科领域的全面知识和培训，以及在数据科学家、解决方案开发人员或运营经理等角色中必不可少的专业知识，以改善加拿大的能源网络，并帮助加拿大在长期内增加可再生资源产生的能源的吸收。

Global energy demand has continued to rise since the mid-20th century as a result of industrial development and population growth. The development of renewable energy sources has become a matter of priority to keep up with increasing demand and to limit greenhouse gas emissions. However, the widespread adoption of renewable energy has been limited by its drawbacks, namely, the discontinuity of generation which can lead to demand-supply imbalances and grid congestions. A key strategy to overcome such issues is implementing intelligent management systems and introducing flexibility in the distribution network. Recent developments in a number of technological areas have made important strides toward the realization of a smarter grid. These include a) advanced monitoring, communication and control infrastructure; b) energy storage resources; and c) vehicle-to-grid systems, which represent a promising source of electrical flexibility. This research program will address the last two items by developing mathematical optimization models and algorithms to allow the integration of stationary and mobile energy storage into the energy network, thus enhancing grid reliability and reducing associated uncertainties.*** ***This research advances existing work in several important thrust areas. The developed optimization models will take into account the temporal nature of the problems, the non-linearities due to underlying technical and physical processes, the uncertainties in the load and supply of energy, and the discrete nature of some of the decisions. These mathematical formulations will result in non-linear optimization models that are difficult to solve, and that will require novel solution methods. The proposed holistic integrated models will allow for optimal infrastructure design as well as optimal charging and discharging operations of energy storage systems.*** ***The work described above will contribute both theoretically and practically to several areas of operations research and the development of new models and solution methods for solving sustainable energy problems. The results will be used to provide a plan for scalable integration of intermittent energy using advanced information management and analytics to increase power system flexibility, and balance conventional and renewable energy resources. Through the proposed research program, students will gain comprehensive knowledge and training in the subject areas such as operations research, data analytics, and energy networks, and expertise that will be essential in roles such as data scientist, solution developer, or operations manager to improve Canada's energy network and help Canada increase its uptake of energy generated by renewable resources in the long-term.