Saiful A. Husain

nPlug: a smart plug for alleviating peak loads

The Indian electricity sector, despite having the worlds fifth largest installed capacity, suffers from a 12.9% peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional DSM strategies may not be suitable for India as the local conditions usually favor only inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present nPlug, a smart plug that sits between the wall socket and deferrable loads such as water heaters, washing machines, and electric vehicles. nPlugs combine real-time sensing and analytics to infer peak periods as well as supply-demand imbalance and reschedule attached appliances in a decentralized manner to alleviate peaks whenever possible. They do not require any manual intervention by the end consumer nor any enhancements to the appliances or existing infrastructure. Some of nPlugs capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage.

Natural flexural vibrations of a continuous beam on discrete elastic supports

Abstract It has been suggested that modern rail systems might exploit so-called floating tracks, to minimise traffic vibration and noise. This paper discusses the transverse deflexion of an infinite Bernoulli–Euler beam mounted on discrete elastic supports, a model considered suitable to explore low-frequency vibrations and associated resonances in such systems. The dynamics is governed by the eigenvalues and eigenvectors of a transfer matrix, which relates the deflexion of any beam span to the deflexions of its neighbours. Important “extensive” contributions, rather than “spatially damped” modes, occur whenever the transfer matrix has one or more eigenvalues of modulus 1. Responses such as the so-called “pinned–pinned resonance” occur when these eigenvalues of modulus 1 are real (i.e., the eigenvalues are ∓1); and further modes corresponding to two complex conjugate eigenvalues coalescing into ∓1 arise at other wavelengths, when the supports are elastic—i.e., in addition to the resonant modes identified in many earlier analyses assuming fixed supports. There is no average energy flux from span to span for any mode defined by a real eigenvector, and we infer that zero-energy transfer between spans is a characteristic of the resonant response of the system to a stationary vibrating source located on some particular span.

nPlug: An Autonomous Peak Load Controller

The Indian electricity sector, despite having the worlds fifth largest installed capacity, suffers from a 12.9% peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional Demand Side Management (DSM) strategies may not be suitable for India as the local conditions usually favor inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present a completely autonomous DSM controller called the nPlug. nPlug is positioned between the wall socket and deferrable load(s) such as water heaters, washing machines, and electric vehicles. nPlugs combine local sensing and analytics to infer peak periods as well as supply-demand imbalance conditions. They schedule attached appliances in a decentralized manner to alleviate peaks whenever possible without violating the requirements of consumers. nPlugs do not require any manual intervention by the end consumer nor any communication infrastructure nor any enhancements to the appliances or the power grids. Some of nPlugs capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage. This technology could potentially be integrated into millions of future deferrable loads: appliances, electric vehicle (EV) chargers, heat pumps, water heaters, etc.

Performance evaluation and optimization of nested high resolution weather simulations

Weather models with high spatial and temporal resolutions are required for accurate prediction of meso-micro scale weather phenomena. Using these models for operational purposes requires forecasts with sufficient lead time, which in turn calls for large computational power. There exists a lot of prior studies on the performance of weather models on single domain simulations with a uniform horizontal resolution. However, there has not been much work on high resolution nested domains that are essential for high-fidelity weather forecasts. In this paper, we focus on improving and analyzing the performance of nested domain simulations using WRF on IBM Blue Gene/P. We demonstrate a significant reduction (up to 29%) in runtime via a combination of compiler optimizations, mapping of process topology to the physical torus topology, overlapping communication with computation, and parallel communications along torus dimensions. We also conduct a detailed performance evaluation using four nested domain configurations to assess the benefits of the different optimizations as well as the scalability of different WRF operations. Our analysis indicates that the choice of nesting configuration is critical for good performance. To aid WRF practitioners in making this choice, we describe a performance modeling approach that can predict the total simulation time in terms of the domain and processor configurations with a very high accuracy (<8%) using a regression-based model learned from empirical timing data.

SocketWatch: An autonomous appliance monitoring system

A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch - an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns. SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage.

Energy delivery networks

Energy storage technologies that are connected to medium- or low-voltage distribution systems are referred to as Distributed Energy Storage (DES). DES are becoming more common as the storage technologies are becoming cheaper. Energy stored on the distribution system, whether it is generated by Distributed Generation (DG) or central generation units, could provide crucial services (such as load leveling, automatic generation control, smoothing fluctuations in intermittent sources, etc) to electricity suppliers. The need of the hour is to effectively utilize these distributed storage devices so as to lower operating costs while offering aforementioned services. In contemporary literature, while DES have been considered, they could only be charged/discharged from/to the grid. The current work marks a significant departure with the goal of allowing storage devices to charge each other. Such battery-to-battery energy transfer is useful for instance in scenarios when generators cannot be run for certain reasons, or that it might cause too much load on the network, if the storage devices were to be charged directly from the power grid. Simulation results on a 30-bus IEEE benchmark system validate the benefits of inter-storage charge transfers.