Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Multiobjective Optimization for Frequency Support Using Electric Vehicles: An Aggregator-Based Hierarchical Control Mechanism

Abstract: In the last few years, there has been an exponential increase in the penetration of electric vehicles (EVs) due to their eco-friendly nature, and ability to support bidirectional energy exchanges with the smart grid. Besides serving transportation needs and reducing the carbon footprints in the environment, EVs are widely used for instantaneous grid frequency support. However, the existing research proposals have concentrated majorly on unidirectional vehicle-to-grid (V2G) support using fleet of EVs, which in turn leads to reduced frequency regulation and reserve capacity of participating EVs. Motivated from these facts, in this paper, an “aggregator-based hierarchical control mechanism” for secondary frequency regulation (SFR) using a fleet of EVs has been presented. In the proposed solution, EVs’ scheduling problem has been formulated to provide optimal SFR, while satisfying EVs’ energy demands under battery degradation constraints. This multiobjective primal problem (Mo-PP) under multiple constraints is solved using an approximation approach. This task is achieved by decomposing the complex Mo-PP into four different subproblems (SPs), corresponding to controllers deployed at different layers. The designed SPs are then iteratively solved using interior point method. In summary, the tradeoff between SFR and EV's bidirectional energy demands has been investigated in this paper. Moreover, battery degradation issues induced due to frequent charging and discharging cycles of EVs are also explored. Optimal dispatch of regulation signals among the aggregators and charging stations also takes into account the advantages of conventional droop mechanism. Lastly, widely accepted Pennsylvania–New Jersey–Maryland and ERCOT regulation data have been used to perform extensive simulations. The results obtained demonstrate that the proposed scheme achieves 22.6% and 6.8% better performance in comparison with the existing schemes based on colored petri net and proportional integral derivative controller, respectively.

Cross Layer NOMA Interference Mitigation for Femtocell Users in 5G Environment

Abstract: In this paper, we propose a joint channel allocation and power control algorithm by using cognitive radio non-orthogonal multiple access (CR-NOMA) for femtocell users (FUs). The aim is to maximize the sum rate of the FUs for guaranteed quality of service (QoS). With an aim to have guaranteed QoS for FUs, we use CR-NOMA at the femto base station (FBS). Then, an algorithm for pairing among strong and weak users is proposed by using the channel gain difference. Using pairing, the NOMA interference between them reduces which results in better channel utilization. Moreover, we differentiate the even/odd number of FUs in a femtocell to provide the QoS for weak users also. For this purpose, OMA is used to get a predefined data rate using a greedy channel allocation algorithm. The power of each FBS is controlled by using the successive convex approximation for low complexity (SCALE) protocol with Karush-Kuhn-Tucker (KKT) conditions. Numerical results demonstrate that the proposed scheme enhances the sum rate and provides guaranteed QoS for CR-NOMA based femtocell users in comparison to the existing conventional OMA based-femtocell techniques.

A novel bat flower pollination algorithm for synthesis of linear antenna arrays

Abstract: In this paper, a novel algorithm, namely bat flower pollination (BFP) is proposed for synthesis of unequally spaced linear antenna array (LAA). The new method is a combination of bat algorithm (BA) and flower pollination algorithm (FPA). In BFP, both BA and FPA interact with each other to escape from local minima. The results of BFP for solving a set of 13 benchmark functions demonstrate its superior performance as compared to variety of well-known algorithms available in the literature. The novel proposed method is also used for the synthesis of unequally spaced LAA for single and multi-objective design. Simulation results show that BFP is able to provide better synthesis results than wide range of popular techniques like genetic algorithm, differential evolution, cuckoo search, particle swarm optimization, back scattering algorithm and others.