IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 7, JULY 2013 1205 nPlug: An Autonomous Peak Load Controller Tanuja Ganu, Deva P. Seetharam Member, IEEE, Vijay Arya Member, IEEE, Jagabondhu Hazra, Deeksha Sinha, Rajesh Kunnath, Liyanage Chandratilake De Silva, Saiful A. Husain, and Shivkumar Kalyanaraman Fellow, IEEE Abstract—The Indian electricity sector, despite having the world’s 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 in- expensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present a completely autonomous DSM controller called the nPlug 1 . 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 nPlug’s 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. Index Terms—Smart Plug, Demand Response, Peak Loads, Scheduling, Multiple Access I. I NTRODUCTION A S OF NOVEMBER 2011, the Indian electricity sector, despite having the world’s ﬁfth largest installed capacity of 185.5 GW, suffers from a 12.9% peaking shortage and 10.3% energy shortage [2]. The situation could worsen with the current trends in population and income growth, industrial- ization, and urbanization. Electricity consumption is expected to increase substantially in the coming decades as well [3]. Considering that electricity cannot easily be stored in large scale, peak shortage can be alleviated by increasing supply or by reducing demand. Supply can be increased through the use of “peaker” power plants that operate on fast-starting fuels. Peaker plants operate only during the peak, for a small Manuscript received October 8, 2012; revised March 18, 2013. T. Ganu, D.P. Seetharam, V. Arya and J. Hazra are with IBM Research, India (e-mail: tanuja.ganu@in.ibm.com). D. Sinha is a student at IIT Mumbai, India. This work was done while she interned at IBM Research, India. R. Kunnath is with Radio Studio, India. S. A. Husain and L. C. De Silva are with Universiti Brunei Darussalam. S. Kalyanaraman is a Senior Manager at IBM Research, India. He is also a visiting professor at Universiti Brunei Darussalam. Digital Object Identiﬁer 10.1109/JSAC.2013.130705. 1 An earlier version of the paper was presented at ACM e-Energy conference 2012 [1]. This paper extends the work on nPlug Software Components and presents additional analysis and experimental results. fraction of time, so their electricity is inherently expensive. It is estimated that if India were to add peakers to the existing generation portfolio, the average supply cost might increase by over 35% [4]. Clearly, there is a signiﬁcant role and potential for demand side management (DSM) programmes in India. The Government of India, through new Energy Conservation legislation, is also seeking to implement a host of such programmes within the country [5]. 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 [6]. To address this need, we developed an autonomous DSM system based on smart plugs called nPlugs [1] that “sit” between deferrable loads and wall sockets. An nPlug senses line voltage and frequency to infer the load level and supply- demand imbalance in the grid respectively. It processes the sensed data using resource-efﬁcient data mining algorithms to identify the peak/off-peak periods and imbalance conditions of the power grid. It runs the attached load(s) during user-speciﬁed time intervals while avoiding unfavorable grid conditions (peak load hours and supply-demand imbalance conditions) as much as possible. As a result, each nPlug runs a decentralized load rescheduling algorithm that contributes to peak load reduction by distributing the loads over time. The beneﬁts of our approach are: Network free - Since nPlugs don’t require any network infrastructure for sensing or control, they can be completely autonomous. This makes nPlugs particularly appropriate for locations where communication infrastructure is underdeveloped. For instance, in India, as there is spectrum crunch to serve the data/voice communication needs of humans, there may not be sufﬁcient bandwidth to support machine-to-machine communications that would be required by centralized DSM solutions. Even wired networks may not be widely applicable as only 11.3% of Indian households have access to Internet [7]. Location-speciﬁc load management - nPlugs sense the line voltage to determine whether the incoming feeder is congested or not. As the line voltage reﬂects the load on the local transformer and load on the grid that feeds that transformer, nPlugs can alleviate the local load levels even if the overall grid is not congested. It is important to note that the gains from decentralized demand reduction could add up and alleviate grid-level load issues as well. Brownﬁeld innovation - nPlugs don’t require any changes to the grid or to the appliances that they manage. This approach is particularly suitable for a mature system like the power grid and for the millions of appliances already in use. 0733-8716/13/$31.00 c  2013 IEEE