The arrival of small-scale distributed energy generation in the future smart grid has led to the emergence of so-called prosumers, who can both consume as well as produce energy. By using local generation from renewable energy resources, the stress on power generation and supply system can be significantly reduced during high demand periods. However, this also creates a significant challenge for conventional power plants that suddenly need to ramp up quickly when the renewable energy drops off. In this paper, we propose an energy consumption scheduling problem for prosumers to minimize the peak ramp of the system. The optimal schedule of prosumers can be obtained by solving the centralized optimization problem. However, due to the privacy concerns and the distributed topology of the power system, the centralized design is difficult to implement in practice. Therefore, we propose the distributed algorithms to efficiently solve the centralized problem using the alternating direction method of multiplier (ADMM), in which each prosumer independently schedules its energy consumption profile. The simulation results demonstrate the convergence performance of the proposed algorithms as well as the capability of our model in reducing the peak ramp of the system.
MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
HVAC系统占建筑物能源使用的很大一部分。夜间挫折调度是一种节能措施，其中冷却和加热设定点在空闲期间分别增加和减少，目的是实现节能。然而，需要了解建筑物的实际占用情况，以使这一措施取得最大成功。此外，需要一种可扩展的方式来估计节能措施的节能潜力，该节能措施不受建筑物特定参数和实验或模拟建模投资的限制。在这里，我们提出了MARTINI，这是一个基于居住者的HVAC时间表和节能的sMART meTer driveN评估，它利用了商业建筑中无处不在的能源智能电表和WiFi基础设施。我们估计的时间表，通过集群WiFi导出的占用配置文件，并通过移动斜升和挫折时间观察到的典型/测量的负载配置文件通过集群智能电表能源配置文件，节省能源。我们的案例研究结果显示，当HVAC系统运行与入住率保持一致时，五座建筑物在七个月内的平均冷冻水节能率为8.1%-10.8%（夏季）和0.2%-5.9%（秋季）。我们验证了我们的方法与结果从建筑能源性能模拟（BEPS），发现估计的平均节省的MARTINI是在0.9%-2.4%的BEPS预测。在没有占用信息的情况下，我们仍然可以通过增加爬坡时间和减少倒退开始时间来估计潜在的节省。在51栋教学楼中，我们发现节省潜力在1%-5%之间。
On the Exact Solution to a Smart Grid Cyber-Security Analysis Problem
ACCESS Linnaeus Center and the Automatic Control Lab, the School of Electrical Engineering, KTH Royal Institute of Technology（林奈中心与自动控制实验室，电气工程学院，皇家理工学院）
Dynamic Pricing in Smart Grids under Thresholding Policies: Algorithms and Heuristics
Masdar Institute of Science and Technology（马斯达尔理工学院）；Brigham Young University（杨百翰大学）；New York University, Abu Dhabi（纽约大学阿布扎比分校）；Massachusetts Institute of Technology（麻省理工学院）
