Jagabondhu Hazra

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.

Identification of Catastrophic Failures in Power System Using Pattern Recognition and Fuzzy Estimation

This paper presents a new approach for finding the sequence of events that may lead to catastrophic failure in a power system. The probable sequences (of events) leading to catastrophic failures are identified using risk indices which incorporate the severity as well as the probability of the contingencies. Probable collapse sequences are identified offline for different possible loading conditions using a modified fast decoupled load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics and stored in a knowledge base. Pattern recognition method and fuzzy estimation are used for online identification of collapse sequences for any operating condition from the stored database (knowledge base).

Real-time hybrid state estimation incorporating SCADA and PMU measurements

This paper proposes a novel hybrid state estimation method using traditional SCADA (Supervisory Control And Data Acquisition) and newly deployed limited PMU (Phasor Measurement Unit) measurements. System states are estimated when a set of SCADA and/or PMU measurements come in. As PMU measurements come much faster (typically one sample in 20ms) than SCADA measurements (typically one sample in 10 seconds), in between two SCADA measurements, system states of PMU unobservable buses are interpolated using an interpolation matrix (H)live PMU measurements. In between two SCADA samples, if PMU measurements change significantly, pre-computed interpolation matrix (H) is compensated with a sensitivity change matrix (ΔH) and system states are estimated using the corrected interpolation matrix. In order to compute the ΔH, the method classified the measurement set into four sub-sets i.e. PMU measurements, SCADA measurements of PMU boundary buses with significant change, SCADA measurements adjacent to the selected boundary buses, and remaining SCADA measurements and run a modified weighted least square method with different weights corresponding to each sub-set of measurements. This compensation improves the estimation accuracy significantly. Effectiveness of the proposed scheme is evaluated on a number of IEEE benchmark test systems and evaluation results are presented in this paper.

Real time contingency analysis for power grids

Modern power grids are continuously monitored by trained system operators equipped with sophisticated monitoring and control systems. Despite such precautionary measures, large blackouts, that affect more than a million consumers, occur quite frequently. To prevent such blackouts, it is important to perform high-order contingency analysis in real time. However, contingency analysis is computationally very expensive as many different combinations of power system component failures must be analyzed. Analyzing several million such possible combinations can take inordinately long time and it is not be possible for conventional systems to predict blackouts in time to take necessary corrective actions. To address this issue, we present a scalable parallel implementation of a probabilistic contingency analysis scheme that processes only most severe and most probable contingencies. We evaluate our implementation by analyzing benchmark IEEE 300 bus and 118 bus test grids. We perform contingency analysis up to level eight (contingency chains of length eight) and can correctly predict blackouts in real time to a high degree of accuracy. To the best of our knowledge, this is the first implementation of real time contingency analysis beyond level two.

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.

Smart grid congestion management through demand response

This paper proposes a novel cost-effective congestion management (CM) scheme for smart grids through demand response (DR). In this congestion management, two objectives i.e. acceptable congestion and congestion cost including DR are optimized by choosing optimal mix of generation rescheduling and DR of participating buses by minimizing the impact on revenues and customer satisfaction. Participating generators for rescheduling and loads for DR are selected using an sensitivity index which combines both biding cost and sensitivity to alleviate the congestion. The scheme employs a meta-heuristic optimization technique called Ant Colony Optimization to optimize the individual options and uses a fuzzy satisfying technique to choose the best compromise solution from the set of Pareto optimal solutions. The proposed system has been evaluated on benchmark IEEE 30 bus test systems and the results of this evaluation are presented in this paper.

Power grid transient stability prediction using wide area synchrophasor measurements

Electric power systems are prone to various kinds of transient disturbances which exist only for a fraction of second and often trigger cascading failures. Hence it is important to detect and prevent them from spreading in time. Conventionally these events are prevented by deploying costly special protection systems (SPS). Unfortunately, in many cases SPSs mis-operate as they could not predict the stability well ahead and are designed to operate based on past experiences and extensive off-line simulations. This paper proposes an online transient stability prediction scheme based on live synchrophasor data. The novelty of the proposed method is that it accurately predicts the transient stability based on only few (10 to 12) sample fault data without solving computationally extensive electromechanical dynamics. Synchrophasor data from geographically distributed Phasor Measurement Units (PMUs) are collected, synchronized, aggregated (if required) and analyzed on a stream computing platform to predict the trajectories of the generators which are then used to predict the transient stability of the grid. Performance of the proposed scheme is evaluated on the benchmark systems and evaluation results are presented in this paper.

Stream computing based synchrophasor application for power grids

This paper proposes an application of stream computing analytics framework to high speed synchrophasor data for real time monitoring and control of electric grid. High volume streaming synchrophasor data from geographically distributed grid sensors (namely, Phasor Measurement Units) are collected, synchronized, aggregated when required and analyzed using a stream computing platform to estimate the grid stability in real time. This real time stability monitoring scheme will help the grid operators to take preventive or corrective measures ahead of time to mitigate any disturbance before they develop into wide-spread. A protptype of the scheme is demonstrated on a benchmark 3 machines 9 bus system and the IEEE 14 bus test system.

CPS-Net: In-network aggregation for synchrophasor applications

Synchrophasors are sensors that sample power grids and publish these measurements over a network to a number of grid applications such as voltage monitoring, state estimation, visualization, etc. The sampled data is QoS sensitive and must be delivered reliably with minimal delays to the target applications. However, during network overloads or grid emergencies when the volume of data transmitted is high, it is important to gracefully degrade performance and data stream delivery in an application-specific manner.

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.