Data-driven Methods for Integration of Distributed Energy Resources

数据驱动的分布式能源整合方法

• 批准号: RGPIN-2017-05866
• 负责人: Musilek, Petr
• 金额: $ 2.4万
• 依托单位: University of Alberta
• 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=684621
• 关键词: Data; driven; Methods; Integration; Distributed

The balance between demand and supply is crucial to the safe, reliable and efficient operation of electric power systems. With the advent of distributed energy resources (such as photovoltaic panels and battery storage systems), maintaining this balance becomes more and more challenging. This is caused by the unprecedented complexity of modern power grids, and by the limited control over the energy produced by intermittent energy sources, such as wind and solar. This research program will address these challenges by exploiting energy system data (including data on generation and loads, weather forecasts and energy market conditions) for the design, monitoring and control of electric power grids.*** This research will leverage and expand the successful results developed during the previous discovery funding cycle, and under related collaborative R&D projects with industry. It will develop new solutions for managing energy in a variety of contexts, from residential buildings, through community energy storage systems, to grids powering extensive urban and rural areas with a high penetration of dispersed generation and renewable energy sources (such as roof-top solar panels or small wind turbines combined with battery energy storage).*** The use of a data-driven approach is a distinguishing aspect of this proposal. It means that the safe, reliable and efficient operation of modern electric power systems will be driven by local measurements of how the system is behaving now and has behaved in the past. The data-based predictive energy management methods and agent-based balancing protocols will effectively integrate smart grid components with forecasting, analytic and control functions that will also be developed in this research. This will lead to a new type of distributed, reconfigurable systems capable of local optimization while providing global efficiency and reliability through coordination. In practice, this will mean lower power bills for consumers and improved profitability for energy companies.*** The developed technology will be essential in realizing the expected environmental, reliability and economic benefits of modern electrical grids. This is crucial as the implementation of renewable generation technologies is expected to double by 2020, also doubling the current Canadian investment in renewables of C$10B per year. In addition to ensuring return on this investment, the research outcomes will also bring more direct economic benefits through commercialization, technology transfer and spin-offs. *** Over the course of five years, this research program will also train a number of highly qualified professionals (HQPs) including up to 4 PhD and 6 MSc graduates and 10 BSc students. They will be equipped with the expertise to design, plan and operate the future smart power systems, and conduct related advanced research in an academic or industry setting.

电力供需平衡是电力系统安全、可靠、高效运行的关键。随着分布式能源（如光伏板和电池存储系统）的出现，保持这种平衡变得越来越具有挑战性。这是由现代电网前所未有的复杂性以及对风能和太阳能等间歇性能源产生的能量的有限控制造成的。该研究计划将通过利用能源系统数据（包括发电和负荷数据，天气预报和能源市场条件）来解决这些挑战，用于电网的设计，监测和控制。 这项研究将利用和扩大在上一个发现资助周期中开发的成功成果，以及与行业合作的相关研发项目。它将为各种环境下的能源管理开发新的解决方案，从住宅建筑，通过社区能源存储系统，到为广泛的城市和农村地区供电的电网，分散发电和可再生能源（如屋顶太阳能电池板或小型风力涡轮机与电池储能相结合）。 使用数据驱动的方法是本建议的一个突出方面。这意味着现代电力系统的安全、可靠和高效运行将由系统现在和过去的行为方式的本地测量来驱动。基于数据的预测性能源管理方法和基于代理的平衡协议将有效地将智能电网组件与预测，分析和控制功能集成在一起，这些功能也将在本研究中开发。这将导致一种新型的分布式、可重构系统，能够进行局部优化，同时通过协调提供全局效率和可靠性。实际上，这将意味着消费者的电费更低，能源公司的盈利能力也会提高。 所开发的技术对于实现现代电网的预期环境、可靠性和经济效益至关重要。这一点至关重要，因为可再生能源发电技术的实施预计到2020年将翻一番，也将使加拿大目前每年100亿加元的可再生能源投资翻一番。除了确保这项投资的回报外，研究成果还将通过商业化、技术转让和衍生产品带来更直接的经济效益。*** 在五年的时间里，该研究计划还将培养一些高素质的专业人员（HQP），包括多达4名博士和6名硕士毕业生和10名理科学生。他们将具备设计，规划和运营未来智能电力系统的专业知识，并在学术或行业环境中进行相关的高级研究。