Data-Enabled Distribution Grid Management

In 2020, U.S. electric utilities installed more than 94 million advanced meters, which brought the percentage of residential customers equipped with smart meters to 75%. This significant investment allows collecting extensive customer data at the distribution level, however, the data are not currently leveraged effectively to help with system operations. This dissertation aims to use the smart meters’ data to improve the grid’s reliability, stability, and controllability by solving two of the most challenging problems at the distribution level, namely distribution network phase identification and outage identification. Distribution networks have typically been the least observable and most dynamic and locally controlled elements in the power grid. Complete information about the network topology is continuously changing and is not always readily available when needed. Lack of phase connectivity information is a challenge, especially when rebalancing the grid and also in the aftermath of outages caused by extreme events. Traditionally, phase identification is executed manually. In this dissertation, a machine learning-based data mining method for accurate and efficient phase identification of residential customers is proposed by leveraging power consumption data collected through smart meters. The proposed method uses a high-pass filter to remove the redundant and irrelevant segments of the power consumption time series, and accordingly identifies the residential customers’ phase connectivity through a modified clustering algorithm. Accurate connectivity information among customers is essential for outage identification and management in distribution networks. Extreme weather events can cause significant damage to electric power grid infrastructure and lead to widespread power outages. The frequency and the intensity of these events are continuously increasing as a direct result of climate change. Identifying grid components that are damaged is the first step to recovering from extreme weather-related power outages. An effective data mining method in identifying distribution network line outages is presented in this dissertation by leveraging data collected through AMI. The line outage identification method is developed based on a Multi-Label Support Vector Machine (ML-SVM) classification scheme that utilizes the status of customers’ smart meters as input data and identifies the outage/operational status of distribution lines. Numerical simulations demonstrate the effectiveness of the proposed models and their respective viability in achieving the targeted operational objectives

A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Architectures for smart end-user services in the power grid

Abstract-The increase of distributed renewable electricity generators, such as solar cells and wind turbines, requires a new energy management system. These distributed generators introduce bidirectional energy flows in the low-voltage power grid, requiring novel coordination mechanisms to balance local supply and demand. Closed solutions exist for energy management on the level of individual homes. However, no service architectures have been defined that allow the growing number of end-users to interact with the other power consumers and generators and to get involved in more rational energy consumption patterns using intuitive applications. We therefore present a common service architecture that allows houses with renewable energy generation and smart energy devices to plug into a distributed energy management system, integrated with the public power grid. Next to the technical details, we focus on the usability aspects of the end-user applications in order to contribute to high service adoption and optimal user involvement. The presented architecture facilitates end-users to reduce net energy consumption, enables power grid providers to better balance supply and demand, and allows new actors to join with new services. We present a novel simulator that allows to evaluate both the power grid and data communication aspects, and illustrate a 22% reduction of the peak load by deploying a central coordinator inside the home gateway of an end-user

Load Balancing with Energy Storage Systems Based on Co-Simulation of Multiple Smart Buildings and Distribution Networks

In this paper, we present a co-simulation framework that combines two main simulation tools, one that provides detailed multiple building energy simulation ability with Energy-Plus being the core engine, and the other one that is a distribution level simulator, Matpower. Such a framework can be used to develop and study district level optimization techniques that exploit the interaction between a smart electric grid and buildings as well as the interaction between buildings themselves to achieve energy and cost savings and better energy management beyond what one can achieve through techniques applied at the building level only. We propose a heuristic algorithm to do load balancing in distribution networks affected by service restoration activities. Balancing is achieved through the use of utility directed usage of battery energy storage systems (BESS). This is achieved through demand response (DR) type signals that the utility communicates to individual buildings. We report simulation results on two test cases constructed with a 9-bus distribution network and a 57-bus distribution network, respectively. We apply the proposed balancing heuristic and show how energy storage systems can be used for temporary relief of impacted networks

Modeling the Temperature Bias of Power Consumption for Nanometer-Scale CPUs in Application Processors

We introduce and experimentally validate a new macro-level model of the CPU temperature/power relationship within nanometer-scale application processors or system-on-chips. By adopting a holistic view, this model is able to take into account many of the physical effects that occur within such systems. Together with two algorithms described in the paper, our results can be used, for instance by engineers designing power or thermal management units, to cancel the temperature-induced bias on power measurements. This will help them gather temperature-neutral power data while running multiple instance of their benchmarks. Also power requirements and system failure rates can be decreased by controlling the CPU's thermal behavior. Even though it is usually assumed that the temperature/power relationship is exponentially related, there is however a lack of publicly available physical temperature/power measurements to back up this assumption, something our paper corrects. Via measurements on two pertinent platforms sporting nanometer-scale application processors, we show that the power/temperature relationship is indeed very likely exponential over a 20{\deg}C to 85{\deg}C temperature range. Our data suggest that, for application processors operating between 20{\deg}C and 50{\deg}C, a quadratic model is still accurate and a linear approximation is acceptable.Comment: Submitted to SAMOS 2014; International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV

Smart grids for rural conditions and e-mobility - Applying power routers, batteries and virtual power plants

Significant reductions of greenhouse gas emission by use of renewable energy sources belong to the common targets of the European Union. Smart grids address intelligent use and integration of conventional and renewable generation in combination with controllable loads and storages. Two special aspects have also to be considered for smart grids in future: rural conditions and electric vehicles. Both, the increasing share of renewable energy sources and a rising demand for charging power by electrical vehicles lead to new challenges of network stability (congestion, voltage deviation), especially in rural distribution grids. This paper describes two lighthouse projects in Europe (“Well2Wheel” and “Smart Rural Grid”) dealing with these topics. The link between these projects is the implementation of the same virtual power plant technology and the approach of cellular grid cells. Starting with an approach for the average energy balance in 15 minutes intervals in several grid cells in the first project, the second project even allows the islanded operation of such cells as a microgrid. The integration of renewable energy sources into distribution grids primary takes place in rural areas. The lighthouse project “Smart Rural Grid”, which is founded by the European Union, demonstrates possibilities to use the existing distribution system operator infrastructure more effectively by applying an optimised and scheduled operation of the assets and using intelligent distribution power routers, called IDPR. IDPR are active power electronic devices operating at low voltage in distribution grids aiming to reduce losses due to unbalanced loads and enabling active voltage and reactive power control. This allows a higher penetration of renewable energy sources in existing grids without investing in new lines and transformers. Integrated in a virtual power plant and combined with batteries, the IDPR also allows a temporary islanded mode of grid cells. Both projects show the potential of avoiding or postponing investments in new primary infrastructure like cables, transformers and lines by using a forward-looking operation which controls generators, loads and batteries (mobile and stationary) by using new grid assets like power routers. While primary driven by physical restrictions as voltage-band violations and energy balance, these cells also define and allow local smart markets. In consequence the distribution system operators could avoid direct control access by giving an incentive to the asset owners by local price signals according to the grid situation and forecasted congestions.Peer ReviewedPostprint (published version