Tanuja Ganu

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.

Hidden costs of power cuts and battery backups

Many developing countries suffer from intense electricity deficits. For instance, the Indian electricity sector, despite having the worlds fifth largest installed capacity, suffers from severe energy and peak power shortages. In February 2013, these shortages were 8.4% (7.5 GWh) and 7.9% (12.3 GW) respectively. To manage these deficits, many Indian electricity suppliers induce several hours of power cuts per day that impact a large number of their customers. Many customers use lead-acid battery backups with inverters and/or diesel generators to power their essential loads during those power cuts. The battery backups exacerbate the deficits by wasting energy in losses (conversion and storage) and by increasing the load (by immediately charging the batteries) when the grid is available. The customers also end up incurring additional costs due to aforementioned losses and due to limited lifetimes of batteries and inverters. In this paper, we discuss the issues with power cuts and backups in detail and illustrate their impact through measurements and simulation results.

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.

Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

Anomaly detection is an important problem in building energy management in order to identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies from the genuine changes in energy consumption due to seasonal variations and changes in personal settings such as holidays. One of the important drawbacks of existing anomaly detection algorithms is that various unknown context variables, such as seasonal variations, can affect the energy consumption of users in ways that appear anomalous to existing time series based anomaly detection algorithms. In this paper, we present a system for monitoring the energy consumption of multiple users within a neighborhood and a novel algorithm for detecting anomalies by combining data from multiple users. For each user, the neighborhood is defined as the set of all other users that have similar characteristics (function, location or demography), and are therefore likely to react and consume energy in the similar way in response to the external conditions. The neighborhood can be predefined based on prior customer information, or can be identified through an analysis of historical energy consumption. The proposed algorithm works as a two-step process. In the first step, the algorithm periodically computes an anomaly score for each user by just considering their own energy consumption and variations in the consumption of the past. In the second step, the anomaly score for a user is adjusted by analyzing the energy consumption data in the neighborhood. The collation of data within the neighborhood allows the proposed algorithm to differentiate between these genuine effects and real anomalous behavior of users. Unlike multivariate time series anomaly detection algorithms, the proposed algorithm can identify specific users that are exhibiting anomalous behavior. The capabilities of the algorithm are demonstrated using several year-long real-world data sets, for commercial as well as residential consumers.

Evaluating demand response programs by means of key performance indicators

Demand response (DR) has received significant attention in recent years and several DR programs are being deployed and evaluated worldwide. DR systems provide a wide range of economic and operational benefits to different stakeholders of the electrical power system including consumers, generators and distributors. DR can be achieved through a number of different mechanisms such as direct-load-control, incentives, pricing signals, or a combination of these schemes. Due to the remarkable variation in demand response systems, it becomes a challenge to evaluate and compare the effectiveness of different DR programs holistically. In this work, we define a number of different performance metrics that could be used to evaluate DR programs based on peak reduction, demand variation and reshaping, and economic benefits.

Individual and Aggregate Electrical Load Forecasting: One for All and All for One

Electrical load forecasting is an important task for utility companies, in order to plan future production and to increase the efficiency of the distribution network. Although load forecasting at the aggregate level has been extensively studied in existing literature, forecasts for individual consumers have been shown to be prone to errors. This paper deals with the problem of electrical load forecasting at multiple scales, from individual consumers to the network as a whole. We use smart meter data from carefully selected sets of consumers for this purpose. First, we consider the problem of forecasting the load for individual consumers at the outermost nodes of the distribution network. We propose an algorithm which considers external available information like calendar or weather contexts along with the energy consumption profiles of different consumers for accurate mid-term and short-term load forecasting. Multiple aggregation approaches are considered for utility level forecasting, in order to characterize their error properties. We show that careful clustering of consumers for aggregation can result in smaller errors. We experiment with two public data sets for demonstrating the advantages of the proposed method over the state-of-the-art approaches.

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.

DC Picogrids as power backups for office buildings

Office buildings in developing countries employ battery backups with inverters and/or diesel generators to power essential loads such as lighting, air conditioning and computing loads during power cuts. Since these backup solutions are expensive and inefficient, they form a significant proportion of the operating expenses. To address this problem, we propose using a personal comfort system (an illustrative configuration can comprise a LED light and a DC desk fan) that is powered by batteries in computing devices. With this approach, cost savings are realized through two mechanisms, (i) by reducing the dependence on high-power lighting and air conditioning during times of power outage, and (ii) by charging the batteries at optimal times, taking advantage of the variable cost of power supply. Simulations show that the expected energy savings from this methodology are in the region of 26%, compared with the current system. In this paper, we present various architectures for the load-battery combination, a dynamic programming based framework that generates optimal charging/discharging schedules, and an experimental evaluation of the proposed approach.

Algorithms for upgrading the resolution of aggregate energy meter data

Metering of the energy supplied to consumers is an important component of operations for utility providers. Several schemes have been employed for this purpose, including traditional postpaid and prepaid metering, and more advanced smart metering technology. Analysis of the data generated by these meters has the potential to provide insights into consumer characteristics and power consumption patterns, including consumer segmentation and anomaly detection. We describe the different types of power purchase and consumption data, as well as the analytics algorithms that can be applied to them. Most applications developed for energy meter data require high resolution information of the type provided by smart meters, thus leaving aggregate prepaid or postpaid meter schemes at a disadvantage. In this paper, we present analytics-based methodologies to upgrade aggregate prepaid and postpaid meter data resolution, which will allow smart meter analytics to be applied without expensive infrastructure upgrades.