Wide-area information based control and optimization of stressed and smarter power systems

基于广域信息的压力和智能电力系统控制和优化

• 批准号: 106451-2010
• 负责人: Kamwa, Innocent
• 金额: $ 3.13万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569141
• 关键词: Wide; area; information; based; control

In a market-driven environment with unanticipated load flow patterns over wider geographic areas, network operators cannot rely on the old central planning approach with simplified model and static deterministic performance criteria. Instead, they turn to a wide-area information based approach, for assessing and managing the dynamic performance of an increasingly complex system without undue conservativeness. Optimizing the dynamic performance of bulk power systems is a demanding task, which requires (1) identification of accurate stability models of the system components; (2) performance assessment by predictive simulation and real-time monitoring of security boundaries; and (3) performance control using a coordinated hierarchy of local devices and overreaching special protection systems. Our research touches on all these areas with the aim of contributing to the emergence of smarter power grids operated closer to their limits to satisfy our reliability-sensitive digital society, while integrating new forms of sustainable energy (such as wind farms) without expansive non-ecological investments in new transmission facilities. The three proposed research paths are tied together by the broad goal of improving system performance using analytical concepts together with actual wide-area measurements: 1)On-line identification/monitoring techniques based on enhanced Phasor Measurement Units (PMU) incorporating generator dynamic state (rotor speed/angle); 2)Wide-area control schemes including, global stabilizing control using remote sensing and actuation, intelligent systems based stability controls able to adaptively adjust emergency protection to more accurate values, wide-area and agent-based defense plans intercommunicating with the energy management system to safely achieve a closer to limits operation. 3)On-line Dynamic Security and Venerability Assessment (DSVA) frameworks based on intelligent systems and wide-area measurements, that can respond effectively to fast changing conditions and stressing operations induced by market activity and intermittent generation.

在市场驱动的环境中，在更广泛的地理区域上具有不可预测的负荷流模式，网络运营商不能依赖于具有简化模型和静态确定性性能标准的旧的中央规划方法。相反，他们转向一个广域信息为基础的方法，评估和管理一个日益复杂的系统的动态性能，而没有过度的保守性。优化大容量电力系统的动态性能是一项要求很高的任务，需要（1）识别系统组件的精确稳定模型;（2）通过预测模拟和实时监控安全边界进行性能评估;以及（3）使用本地设备和超范围特殊保护系统的协调层次结构进行性能控制。我们的研究涉及所有这些领域，旨在促进更智能电网的出现，使其接近其极限，以满足我们对可靠性敏感的数字社会，同时整合新形式的可持续能源（如风电场），而无需对新的输电设施进行广泛的非生态投资。这三条研究路径的共同目标是利用分析概念和实际广域测量来提高系统性能：1）基于增强型相量测量单元（PMU）的发电机动态在线辨识/监测技术（转子速度/角度）; 2）广域控制方案，包括使用遥感和致动的全局稳定控制，基于智能系统的稳定性控制能够自适应地将紧急保护调整到更准确的值，广域和基于代理的防御计划与能量管理系统相互通信，以安全地实现更接近极限的操作。3）基于智能系统和广域测量的在线动态安全和可信性评估（DSVA）框架，可以有效地应对快速变化的条件以及市场活动和间歇性发电引起的压力操作。