Enabling a Flexible, Non-disruptive Demand Control to Improve Grid Security & Performance

随着智能可控负荷在配电系统中的普及率不断提高，公用事业公司和ISO对使用这些设备来改善电网整体性能的兴趣也越来越大。恒温可控负荷(TCL)(供暖、通风和空调(HVAC))很少用于直接处理与稳定性和安全相关的问题。在这个项目中，我们系统地利用暖通空调系统的蓄热来提高电网在不同时间尺度(5秒-2小时)的安全性。虽然我们将专注于增强系统稳定性，但建议的灾难恢复聚合和控制方法也可用于其他服务(应急储备、旋转储备、负载整形等)。以确保客户的舒适性不被破坏的方式。研究成果将在相关会议上传播。此外，我们还计划在研究的基础上开发课程模块和短期课程。具有设定点控制的需求响应(DR)聚合器的独特之处在于，它降低了频繁切换带来的DR的控制成本，降低了服务非自愿中断的可能性，使更灵活、更积极和更顺畅的需求控制适用于广泛的应用。利用时变扩展马尔可夫模型(EMM)在线控制TCL的温度设定值，同时考虑到用户的不适感。EMM的补充是时变模型预测控制(MPC)方案，以确保TCL的有效控制以跟踪参考功率。建议的方案有效地扩大了可用工具和资源的集合，以在严重事件发生时拯救系统。该方法实时监测电压稳定裕度，预测非平稳运行环境下的电压稳定裕度，在通过需求侧控制保证电压稳定的同时，确保用户的不满是有限的。利用相量测量单元(PMU)和微型PMU在2-5秒内检测到暖通空调失速装置的位置和比例，确保了相关装置被选择用于DR控制，从而提高了系统的短期稳定性。此外，为了确保重新连接不会导致TCL的同步，使用EMMS的MPC公式被解决以确定有效的重新连接方案。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As the penetration of smart controllable loads in the distribution system has been increasing, there has been a rising interest by utilities and ISOs to use these devices to improve the overall grid performance. Thermostatic controllable loads (TCLs) (heating, ventilation and air-conditioners (HVAC)) are rarely used for directly handling stability and security related issues. In this project, we systematically utilize the thermal storage of the HVAC systems to improve the security of the grid over various time scales (5 sec - 2 hrs). While we will concentrate on enhancing system stability, the proposed DR aggregation and control methodology can be utilized for other services (emergency reserves, spinning reserves, load shaping, etc.) in a manner that ensures the customer comfort is not disrupted. The research results will be disseminated at relevant conferences. Also, we plan to develop curriculum modules and short courses based on the research.The uniqueness of the Demand Response(DR) aggregator with set-point control is that it reduces the control cost of DR imposed by frequent switching and reduces the likelihood of involuntary service interruption, enabling a more flexible, aggressive, and smooth demand control for a broad range of applications. A time-varying Extended Markov Model (EMM) is used in an online manner to control temperature set points of TCLs by taking into account the customer discomfort. The EMM is complimented by a time varying model predictive control (MPC) scheme to ensure effective control of TCLs to follow the reference power. The proposed scheme effectively broadens the set of available tools and resources to save the system at the onset of a severe event. The proposed approach monitors real-time voltage stability margin (VSM) and predicts VSM in a nonstationary operating environment and ensures that the customer dissatisfaction is limited while ensuring voltage stability via demand side control. The novel detection of the location and proportion of HVAC stalling devices using phasor measurement units (PMUs) & micro-PMUs within 2-5 seconds ensures that relevant devices are selected for DR control, leading to an improvement in the short-term stability of the system. Also, to ensure that the reconnection does not induce synchronization of TCLs, an MPC formulation using EMMs is solved to determine the effective reconnection scheme.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.