Distributed Management Systems and Performance Analysis Methods for Smart Microgrids

微电网由分布式发电机（如风能和光伏）和配电级的本地负荷组成，并有可能连接到主电网。使用微电网是一个很有前途的解决方案，可以提高脆弱电网的能源效率、可持续性、灵活性、可靠性和弹性。部署具有智能管理系统的微电网是目前加拿大减少温室气体排放和环境影响的研究重点之一。传统上，微电网采用三级分级控制方案，包括局部控制器、集中监控和能量管理。在本项目下一代智能微电网的开发中，管理单元集成在本地控制器中，增加了通过聚合器管理分布式资源的灵活性。这种架构为微电网提供了更有弹性的配置，因为它可以在网络故障和/或网络攻击的情况下继续运行。该平台将以人工智能（AI）为基础，实现对新兴智能微电网的节能和可靠控制。本研究的第二部分开发了计算效率高的数学算法，用于微电网及其电源变流器的性能分析。将为风能/太阳能公园开发适当的模型，使大规模模块化可再生能源的模拟准确而快速，同时为改进微电网的设计和性能提供关键组件。该项目的成果将为下一代微电网配备分布式管理架构。这有助于使用人工智能以更可靠和更有弹性的方式管理和控制微电网，并将有助于在学术界的这一研究领域和加拿大工业部门的新兴绿色能源经济中创造机会。

Microgrids consist of distributed power generators such as wind and photovoltaic and local loads at the distribution level, with a possibility to be connected to the main grid. Using a microgrid is a promising solution to improve the energy efficiency, sustainability, flexibility, reliability and resiliency of vulnerable power grids. Deployment of microgrids with smart management systems are among current research priorities to reduce greenhouse gas (GHG) emissions and environmental impacts in Canada.Conventionally, a three-level hierarchical control scheme including local controllers, and centralized supervisory control and energy management, is used for microgrids. In the development of the next-generation of smart microgrids in this project, management units are integrated in local controllers adding flexibility to manage distributed resources by an aggregator. This architecture provides a more resilient configuration for a microgrid as it can continue operating in case of network failures and/or cyber attacks. The proposed platform will be based on artificial intelligence (AI) that enables energy-efficient and reliable control of emerging smart microgrids. The second part of this research develops computationally efficient mathematical algorithms for performance analysis of microgrids including its power converters. Appropriate models for wind/solar parks will be developed which allow accurate but fast simulation of large-scale modular renewables while providing a key components for improving the design and performance of microgrids.The result of this project will equip the next-generation of microgrids with distributed management architecture. This facilitate the use of artificial intelligence for managing and controlling microgrids in a more reliable and resilient manner and will contribute to create opportunities in this research area in academia and in the emerging green energy economy in industry sectors in Canada.