• Energy Research

The optimal utilization of ultracapacitor (symmetric electrochemical capacitor) for vehicular applications demands continuous monitoring and maintenance of its state of charge (SOC). The online estimation of SOC plays a vital role in the effective utilization of the energy storage capacity of ultracapacitors. In addition to monitoring SOC, high reliability in vehicle operation requires a predefined SOC level, as per the dynamic load requirement. For the ultracapacitor undergoing series of charge-discharge cycles, proper regulation of the SOC in accordance with the vehicle drive profile, increases the ultracapacitor life by avoiding possible stress due to undercharging/overcharging. Achieving the desired SOC in real-time warrants a mechanism for the optimal charging of the ultracapacitor. In this regard, the present article proposes an optimal charging scheme to attain a predefined SOC from any initial value, within a given time-frame. The proposed noniterative fractional calculus-based approach allows for analytically representing the charging current as a function of the desired SOC, charging duration and ultracapacitor model parameters. The scheme has been extensively validated experimentally for its effectiveness in attaining the desired SOC. Further, the appropriateness of the derived charging profiles for different operating scenarios has also been analyzed in terms of maintaining a tradeoff between faster charging and high charging efficiency.

The intermittency in weather complicates the task of determining the optimal setting of protective relays in hybrid photovoltaic (PV)–wind-based microgrids. Classical overcurrent relays quite often maloperate during fluctuations in wind speed and solar irradiance. Motivated by the significance of reducing the sensitivity of microgrid protection against weather intermittency, this article proposes a protection scheme based on the combined framework of stochastic modeling of weather intermittency using probability distribution function and ensemble-based classifier. Considering the possible similar weather scenario to which both the distributed energy resources are subjected to, the covariability has been considered to develop the joint probabilistic model of variation in wind speed and solar irradiance. The model derived using the historical data allows incorporating the weather intermittency in the formulation of the protection algorithm. The data generated under intermittency are processed using a wavelet transform to derive discriminatory attributes. Using the derived attributes, a rotation forest-based classifier has been developed to perform fault detection/classification and faulty section identification. The scheme has been validated for a wide range of weather and fault scenarios. The comparison of the proposed scheme with other reported techniques reflects its significance in providing resilient and robust protection to the microgrid against weather intermittency.

The integrity of control operations in the smart grid (SG) heavily depend on its monitoring system, which relies on the sensors mounted across the wide-area network (WAN). Utilizing the monitoring capability of SG for a conducive electricity market operation demands high accuracy in real-time sensor measurement. While synchronized measurements from phasor measurement units (PMUs) have improved the reliability compared to the classical sensors (CSs), the high installation cost often hinders the installation of PMUs throughout the WAN. The selection of sensors for wide-area monitoring dependent operations like the electricity market involves maintaining a trade-off between the conflicting objectives of sensor installation cost and accuracy in measurement. Although the objective of the necessary requirement of network observability using sensor data can be fulfilled through various configuration, the impact of sensor information on the electricity market differs as per their location. In this regard, the present work proposes a binary optimization-based approach for identifying strategic sensor placement locations in SG to minimize the disruption in the electricity market. The strategic sensor location aims at improving the resiliency of electricity market operation by minimizing the impact of sensor accuracy on nodal electricity price. The resiliency is achieved while simultaneously meeting the objectives of reduced sensor installation cost and fulfilment of network observability criterion. The effectiveness of the trade-off solution has been validated for standard IEEE 14, 30, and 57 test systems.

Wide area monitoring and control of modern power network demand real-time estimation of state variables from sensor measurements. Maintaining a high degree of reliability and accuracy in the state estimation process is important in avoiding any disruption in the electricity market operation. The market operation in power networks aims at providing a win-win situation for both the utility and consumer. The exposure and vulnerability of cyber components in smart grids allow for manipulating the electricity market by falsifying the state variables. The attacker can cause intentional profit/loss to the utility/consumer by misdirecting the estimated states through the injection of false data into the sensor information. Hence, maintaining integrity in the market operation demands a mechanism for detecting false data injection attack (FDIA). This paper proposes a classification-based approach for detecting FDIAs aiming at electricity market disruption. For any variation in the predicted and real-time nodal electricity price, the proposed decision tree (DT) based ensemble classifier is executed using state information to identify the prevailing scenario as a contingency or FDIA. The effectiveness of the proposed scheme has been extensively validated for various contingency and FDIA scenarios in IEEE 14 bus, 39 bus, and 57 bus test power systems.

Abstract A primary challenge towards the adoption of Polymer Electrolyte Membrane Fuel Cell (PEMFC) on a wider scale involves the extraction of maximum power irrespective of variation is source and loading condition, commonly referred to as Maximum Power Point Tracking (MPPT). The non-linear voltage current relationship and dynamic variation of maximum extractable power with change in operating parameters makes MPPT task quite challenging. Motivated by the need to achieve MPPT over a wider operating range with reduced computational burden, this paper proposes a non-iterative approach for maximum power extraction from PEMFC. Continuous estimation of resistance across the source and load by monitoring the voltage and current, allows reducing the deviation between equivalent resistance across the PEMFC terminals and its internal resistance by regulating the Pulse Width Modulated DC-DC converter. The power converter for MPPT is designed with the objective of simultaneously minimizing the ripple content and achieving faster dynamic response. The proposed approach is compared with the Incremental Resistance Method (INR), in terms of simplicity, MPP efficiency and better dynamic response for both source and load perturbations. The effectiveness of the proposed scheme has been validated experimentally.