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

将存储和可再生能源整合到电网中的新优化技术

• 批准号: RGPIN-2017-04185
• 负责人: Ghaddar, Bissan
• 金额: $ 1.6万
• 依托单位: University of Western Ontario
• 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=678814
• 关键词: New; optimization; techniques; integrate; storage

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.

自世纪中期以来，由于工业发展和人口增长，全球能源需求持续上升。开发可再生能源已成为一个优先事项，以满足日益增长的需求并限制温室气体排放。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，这可能导致供需失衡和电网瘫痪。克服这些问题的一个关键策略是实施智能管理系统，并在配电网络中引入灵活性。许多技术领域的最新发展在实现智能电网方面取得了重要进展。其中包括a）先进的监测、通信和控制基础设施; B）储能资源;以及c）车辆到电网系统，这是电力灵活性的一个有前途的来源。该研究计划将通过开发数学优化模型和算法来解决最后两个项目，以允许将固定和移动的储能集成到能源网络中，从而提高电网可靠性并减少相关的不确定性。* 这项研究推动了几个重要重点领域的现有工作。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的非线性，在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，并且这将需要新颖的求解方法。拟议的整体集成模型将允许最佳的基础设施设计以及储能系统的最佳充电和放电操作。*** 上述工作将在理论上和实践上对业务研究的若干领域以及为解决可持续能源问题开发新的模型和解决方法作出贡献。研究结果将用于提供一个计划，利用先进的信息管理和分析技术对间歇性能源进行可扩展的整合，以提高电力系统的灵活性，并平衡传统能源和可再生能源。通过拟议的研究计划，学生将获得在学科领域的全面知识和培训，如运筹学，数据分析和能源网络，以及专业知识，这将是必不可少的角色，如数据科学家，解决方案开发人员，或运营经理，以改善加拿大的能源网络，并帮助加拿大增加其可再生资源产生的能源的长期吸收。