CAREER: Learning and Control Algorithms for Electricity Demand Response with Humans-in-the-Loop

电力需求响应（DR）技术旨在向电力终端用户告知电网的运行状态，并使他们能够在电网运营中发挥积极作用。具体而言，DR计划可以激励价格敏感型客户在供应充足时消耗更多电能，而在供应稀缺时消耗更少电能，从而提高客户机会性地消耗风能和太阳能的能力，并缓解电网压力来源。然而，在这种情况下平衡电网中的需求和供应的两个主要挑战是可再生能源输出的不可预测性，以及需要知道客户的价格响应行为，即，在不同的电价下他们如何改变用电模式。电力零售商无法获得此类信息，也无法轻易向客户索取此类信息。该项目的目标是推进科学知识的DR机制，在可靠的发电预测和客户价格响应模型的情况下运行的设计。为了证明我们提出的算法对特定类型的价格响应电力需求的价值，我们将重点介绍我们的方法在公共停车场和快速充电站的电动汽车智能充电中的应用。拟议的职业生涯计划将研究与教学和培训活动相结合，为加州-圣巴巴拉大学的本科生和研究生提供电力系统工程方面令人兴奋的机会。我们的外展工作将向当地的初中和高中学生介绍智能电网的概念。这个项目的智力价值是建立在算法技术的基础上，用于在线学习和控制具有未知参数的随机系统，以便在存在高度不确定性的情况下生成用于需求响应和零售市场运营的新系统工具。由于人类直接参与DR控制回路和高水平的可再生集成，我们现在面临的主要挑战是如何在缺乏以下内容的情况下优化系统操作和调度资源：1）系统的代数模型，例如，由于客户的未知价格响应; 2）系统未来面临的不确定性的表征，例如，由于可再生能源。我们将开发实时调度和定价解决方案，在用户需求灵活性和电网条件不确定的情况下提供最优保证。我们提出的学习和优化技术将与电网可靠性和网络安全约束相结合。此外，为了研究这一重要应用提案的实际影响，我们研究了移动感知实时电动汽车（EV）需求管理问题的在线学习和调度方法，该问题受到上述两个挑战的影响。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Electricity Demand response (DR) technologies aim to inform electricity end-users about the operational state of the power grid and enable them to take an active role in grid operations. Specifically, DR programs can incentivize price-responsive customers to consume more electric energy when supply is abundant and less when supply is scarce, hence increasing the customers' ability to opportunistically consume wind and solar energy and relieve sources of grid stress. However, two main challenges of balancing demand and supply in the grid under such scenarios is the unpredictability of renewable energy outputs, as well as the need to know customers' price response behavior, i.e., how they change their electricity consumption patterns given different prices. Such information is not available to electricity retailers and cannot be easily solicited from the customers either. The goal of this project is to advance scientific knowledge on the design of DR mechanisms that operate in the absence of reliable generation forecasts and customer price response models. To demonstrate the value of our proposed algorithms for a specific type of price responsive electricity demand, we will highlight the application of our methods for electric vehicle smart charging in public parking lots and fast charging stations. The proposed CAREER plan integrates research with teaching and training activities that provide exposure to exciting opportunities in power systems engineering to students at University of California - Santa Barbara, both at the undergraduate and graduate levels. Our outreach efforts will introduce smart grid concepts to local middle and high school students.The intellectual merit of this project is to build on algorithmic techniques for online learning and control of stochastic systems with unknown parameters in order to generate novel systematic tools for demand response and retail market operation in the presence of high levels of uncertainty. Due to the direct involvement of humans in the DR control loop and high levels of renewable integration, a principal challenge we now face is how we can optimize system operations and dispatch resources in the absence of: 1) algebraic models of the system, e.g., due to unknown price response of customers; 2) a characterization of the uncertainty faced by the system in the future, e.g., due to renewables. We will develop real-time dispatch and pricing solutions that provide optimality guarantees in the face of uncertainty about users demand flexibility as well as grid conditions. Our proposed learning and optimization techniques will be integrated with grid reliability and cyber-security constraints. Furthermore, to study the practical impact of this proposal for an important application, we study online learning and dispatch methods for the mobility-aware real-time electric vehicle (EV) demand management problem, which suffers from both of the challenges highlighted above.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.