A Computer Engineering Approach to the Smart Grid

智能电网的概念诞生于电力能源系统和最新的信息和通信技术的融合，包括宽带有线和无线网络，传感器，数据收集和处理。目标是减少我们的碳足迹，提高基础设施成本效率（特别是通过减少峰值），并通过集成分布式发电、需求侧管理、储能元件和新负载（例如，电动车）。其目的是将现有的“能源丰富，控制贫乏，信息贫乏”的电网转换为“能源节约，控制丰富，信息丰富”的系统。其结果将是一个大规模的异构分布式系统，由成千上万的间歇源，双向流，和一组弹性负载和存储元件，有很多的相似之处（和一些显着的差异）与互联网。该提案旨在通过充分利用互联网的经验（成功和失败）来解决智能电网面临的几个基本挑战和系统问题。作为一个例子，互联网的鲁棒性来自于其简单的分层架构和使用分布式控制机制来防止拥塞，这些机制是实现需求侧管理的高潜力候选者。互联网研究人员不得不为未知设计，内容的“生成”和需求都是随机的，新的应用程序定期出现以测试互联网设计的鲁棒性。现有的电网是相对集中和可预测的，而智能电网将是高度分布和随机的，这使得未知的设计成为必要。在现有电网中，电力公司控制供电，使其跟随（准不受控制的）负载。与智能电网相关的主要范式转变之一是供应跟踪，即，由于新的间歇性能源（例如，风能和太阳能）以及对成本效率的需求，将需要控制负载以匹配可变发电，同时最小化对消费者的影响（通过利用其固有的灵活性），或者将需要通过能量存储执行“阻抗匹配”。我们的长期目标是通过考虑两种（互补）方法来解决供应挑战，一种是基于储能，另一种是基于表征和利用负载灵活性。更具体地说，我们将解决以下5个中期目标：a）使用随机微积分技术表征和量化负载灵活性。B）使用分布式算法、控制和凸分析的技术来定义管理负载灵活性的机制和协议。c）使用优化和实时分布式算法技术，使用能量存储聚合器的快速时标供应跟踪。d）使用优化和实时分布式算法技术，研究涉及多个储能聚合器的现实供应跟踪场景。e）智能电网的全新方法：从新的服务模式到增强的架构。这项研究的结果将显着推进供应跟踪和智能电网的一般艺术的状态。这将是加拿大和其他地方智能电网发展的关键。它将帮助加拿大工业（已经是电力部门的重要全球参与者）继续在这一对我们经济至关重要的领域发挥领导作用。这些项目还将提供优秀的HQP培训机会，在博士和硕士的水平。

The concept of the Smart Grid is born from the convergence of electrical energy systems and the latest Information and Communications technologies including broadband wireline and wireless networks, sensors, data collection and processing. The objectives are to reduce our carbon footprint, improve the infrastructure cost efficiency (in particular by reducing peaks) and put the customers in the loop by integrating disruptive technologies such as distributed generation, demand side management, energy storage elements, and new loads (e.g., electrical vehicles). The purpose is to convert the existing grid which is 'energy rich, control poor, and information poor' into an 'energy frugal, control rich, and information rich' system. The result will be a large scale heterogeneous distributed system made of hundreds of thousands of intermittent sources, two way flows, and a set of elastic loads and storage elements which has a lot of similarities (and some striking differences) with the Internet. This proposal aims to address several of the fundamental challenges and system issues facing the Smart Grid by taking full advantage of the experiences (successes and failures) drawn from the Internet. As an example, the Internet robustness comes from its simple layered architecture and the use of distributed control mechanisms to prevent congestion and these mechanisms are high potential candidates for enabling demand side management. Internet researchers have had to design for the unknown, both the 'generation' of content and the demands are random, and new applications come regularly to test the robustness of the Internet design. The existing grid is relatively centralized and predictable, while the Smart Grid will be highly distributed and stochastic making the design for the unknown a necessity. In the existing grid, the electric utility controls the supply so that it follows the (quasi-uncontrolled) loads. One of the main paradigm shifts linked to the Smart Grid is supply following, i.e., the fact that due to the high penetration of new intermittent energy sources (e.g., wind and solar) and the need for cost efficiency, loads will need to be controlled to match variable generation while minimizing the impact on the consumers (by taking advantage of their inherent flexibility) or 'impedance matching' via energy storage will need to be performed. Our long term objective is to address the challenge of supply following by considering two (complementary) approaches, one based on energy storage and one based on characterizing and taking advantage of load flexibility. More specifically, we will address the following 5 medium-term objectives: a) Characterizing and quantifying load flexibility using techniques from stochastic calculus. b) Defining mechanisms and protocols to manage load flexibility using techniques from distributed algorithms, control, and convex analysis. c) Fast time-scale supply following using an energy storage aggregator using optimization and real-time distributed algorithms techniques. d) Studying a realistic supply following scenario involving several energy storage aggregators using optimization and real-time distributed algorithms techniques. e) A clean-slate approach to the smart grid: from new service models to an enhanced architecture. The outcome of this research will significantly advance the state of the art on supply following and the Smart Grid in general. It will be key to the development of Smart Grids in Canada and elsewhere. It will help Canadian industries (which are already important global players in the power sector) to continue to lead in this area that is critical to our economy. The projects will also provide excellent HQP training opportunities at the PhD and Master's levels.