Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.Comment: To be published in IEEE Communications Surveys and Tutorial

From a Barrier to a Bridge: Data-Privacy in Deregulated Smart Grids

The introduction of so-called smart meters involves detailed consumption data. While this data plays a key role in integrating volatile renewable energy sources, a side effect is that it can reveal sensitive personal information. Concerns and protests led to a stopped smart meter rollout yet. In deregulated electricity markets, data-privacy is even more at risk: The UK, Texas and Ontario decided for a nation-wide communication intermediary in order to facilitate the exchange of the vast amount of smart meter data. However, this operational efficiency is achieved by the fact that an intermediary is a single point of failure. We present an approach based on encryption to secure the intermediary against privacy invasions and we can show that our prototypical implementation meets even restrictive requirements for large-scale data handling and processing. By aiming at customers’ confidence in smart metering, our solution might lay the ground for an ecosystem of energy services

UC Berkeley's Cory Hall: Evaluation of Challenges and Potential Applications of Building-to-Grid Implementation

From September 2009 through June 2010, a team of researchers developed, installed, and tested instrumentation on the energy flows in Cory Hall on the UC Berkeley campus to create a Building-to-Grid testbed. The UC Berkeley team was headed by Professor David Culler, and assisted by members from EnerNex, Lawrence Berkeley National Laboratory, California State University Sacramento, and the California Institute for Energy & Environment. While the Berkeley team mapped the load tree of the building, EnerNex researched types of meters, submeters, monitors, and sensors to be used (Task 1). Next the UC Berkeley team analyzed building needs and designed the network of metering components and data storage/visualization software (Task 2). After meeting with vendors in January, the UCB team procured and installed the components starting in late March (Task 3). Next, the UCB team tested and demonstrated the system (Task 4). Meanwhile, the CSUS team documented the methodology and steps necessary to implement a testbed (Task 5) and Harold Galicer developed a roadmap for the CSUS Smart Grid Center with results from the testbed (Task 5a) and evaluated the Cory Hall implementation process (Task 5b). The CSUS team also worked with local utilities to develop an approach to the energy information communication link between buildings and the utility (Task 6). The UC Berkeley team then prepared a roadmap to outline necessary technology development for Building-to-Grid, and presented the results of the project in early July (Task 7). Finally, CIEE evaluated the implementation, noting challenges and potential applications of Building-to-Grid (Task 8). These deliverables are available at the i4Energy site: http://i4energy.org/

Data Analytics for Privacy in Smart Grids

The emergence of smart grids has allowed for integrating new technologies in the power grid, with information flowing across the system allowing for more efficient power delivery and event response. Demand response is a new technology enabled by smart grids, which is a program aiming to reduce or shift peak demand by varying the price of electricity or offering incentives for changing consumption habits.Despite demand response benefits, privacy advocates have raised concerns with information leakages allowed by the type of high-resolution data collected by smart meters, as it can reveal customer usage patterns and different parties can take advantage of that data. In this thesis, a utility vs. privacy framework is developed to maximize the utility of using smart meter data while also minimizing the privacy leakages from the smart meter.Two frameworks are developed, the first, a fault localization technique for radial distribution systems by using alarm processing through binary integer linear programming. The second, a power scheduling tool that uses renewables, a battery, and appliance scheduling to disguise the customer usage patterns by matching it to an average and the resulting collected data is not revealing of any characteristics the customer wants to hide.Fault localization was tested on two radial distribution systems, and locates the fault every time, with the variation in time till detection depending on system size, how the system is branched, fault location, and sampling rate. Power scheduling was tested using simulated home data, different scenarios are run by varying battery, solar, appliance, and privacy parameters, and results are compared for various sampling rates. Both frameworks were successful in hiding privacy leakages based their respective privacy metric.Future research on the fault localization could expand to find two faults simultaneously, along with implementing an emergency mode to find faults quicker in a sampling cycle. The power scheduling framework could expand to include thermostatically controlled load scheduling, by implementing deep learning algorithms on each home and factoring in variables such as historic data of weather, time of day, and day of week to determine how thermostatically controlled loads could fit into the scheduling problem

(Position Paper) Characterizing the Behavior of Small Producers in Smart Grids:A Data Sanity Analysis

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid’s operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges.This work is financially supported by the Danish project RemoteGRID, which is a ForskEL program under Energinet.dk with grant agreement no. 2016-1-12399.Peer ReviewedPostprint (published version

Enabling Machine Understandable Exchange of Energy Consumption Information in Intelligent Domotic Environments

In the 21st century, all the major countries around the world are coming together to reduce the impact of energy generation and consumption on the global environment. Energy conservation and its efficient usage has become a top agenda on the desks of many governments. In the last decade, the drive to make homes automated and to deliver a better assisted living picked pace and the research into home automation systems accelerated, usually based on a centralized residential gateway. However most devised solutions fail to provide users with information about power consumption of different house appliances. The ability to collect power consumption information can lead us to have a more energy efficient society. The goal addressed in this paper is to enable residential gateways to provide the energy consumption information, in a machine understandable format, to support third party applications and services. To reach this goal, we propose a Semantic Energy Information Publishing Framework. The proposed framework publishes, for different appliances in the house, their power consumption information and other properties, in a machine understandable format. Appliance properties are exposed according to the existing semantic modeling supported by residential gateways, while instantaneous power consumption is modeled through a new modular Energy Profile ontolog