Demand response is a critical part of renewable integration and energy cost reduction goals across the world. Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand. Our work is inspired by a novel connection we make to crowdsourcing mechanisms. The proposed mechanism incorporates realistic features of the demand response problem including time varying and quadratic cost function. The mechanism marries auctions, that allow users to report their preferences, with online algorithms, that allow distribution companies to learn user-specific parameters. We show that MAB-MDR is dominant strategy incentive compatible, individually rational, and achieves sublinear regret. Such mechanisms can be effectively deployed in smart grids using new information and control architecture innovations and lead to welcome savings in energy costs.
Mid-century writers often discussed flying cars and massive space settlements, but how many predicted mobile phones? Still, some trends are clear, and few would argue that smart technology is going to play an increasing, and perhaps even dominating, role in our cities' futures. Will these trends lead to better quality of life? What are the potential downsides? Although cities are changing at a rapid pace, many of these changes aren't immediately visible.
AT&T has announced a project that can potentially offer low-cost and multi-gigabit Internet connectivity to urban, rural and underserved parts of the world. Called Project AirGig, the wireless technology consists of using traditional power lines to deliver ultra-fast wireless connectivity to a home or handheld device using a special transmitter. AT&T Labs will be field testing AirGig, which is faster than standard broadband, in 2017. "Project AirGig has tremendous potential to transform internet access globally – well beyond our current broadband footprint and not just in the United States," said John Donovan, AT&T's chief strategy officer and group president, in a statement. "The results we've seen from our outdoor labs testing have been encouraging, especially as you think about where we're heading in a 5G world.
We consider the problem of tracking an intruder using a network of wireless sensors. For tracking the intruder at each instant, the optimal number and the right configuration of sensors has to be powered. As powering the sensors consumes energy, there is a trade off between accurately tracking the position of the intruder at each instant and the energy consumption of sensors. This problem has been formulated in the framework of Partially Observable Markov Decision Process (POMDP). Even for the simplest model considered in , the curse of dimensionality renders the problem intractable. We formulate this problem with a suitable state-action space in the framework of POMDP and develop a reinforcement learning algorithm utilising the Upper Confidence Tree Search (UCT) method to mitigate the state-action space explosion. Through simulations, we illustrate that our algorithm scales well with the increasing state and action space.
The ability to capture insights from data in real-time, with potentially no latency issues is disrupting many industries and offering great benefits to companies and consumers. In this article, we'll be looking at five different use case examples of edge computing solutions in use today. So, let's jump straight in! Smart Grid, as we know, is essentially the concept of establishing a two way communication between distribution infrastructure, consumer and the utility head end using Internet Protocol. Fast developing industrial IoT landscape is offering a number of technologies to monitor, manage and control a variety of functions within an electric grid's distribution infrastructure.