Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
6 results
Search Results
Item Cumulant-based correlated probabilistic load flow considering photovoltaic generation and electric vehicle charging demand(Higher Education Press Limited Company, 2017) Bhat, N.G.; Prusty, B.R.; Jena, D.This paper applies a cumulant-based analytical method for probabilistic load flow (PLF) assessment in transmission and distribution systems. The uncertainties pertaining to photovoltaic generations and aggregate bus load powers are probabilistically modeled in the case of transmission systems. In the case of distribution systems, the uncertainties pertaining to plug-in hybrid electric vehicle and battery electric vehicle charging demands in residential community as well as charging stations are probabilistically modeled. The probability distributions of the result variables (bus voltages and branch power flows) pertaining to these inputs are accurately established. The multiple input correlation cases are incorporated. Simultaneously, the performance of the proposed method is demonstrated on a modified Ward-Hale 6-bus system and an IEEE 14-bus transmission system as well as on a modified IEEE 69-bus radial and an IEEE 33-bus mesh distribution system. The results of the proposed method are compared with that of Monte-Carlo simulation. © 2017, Higher Education Press and Springer-Verlag Berlin Heidelberg.Item Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems(Elsevier Ltd, 2023) Nisha, K.S.; Gaonkar, D.N.; Sabhahit, N.S.Charging of electric vehicles is going to be a major electrical load in the near future, as more and more population shift to electric auto-motives from conventional internal combusted engine-powered vehicles. Integration of electric vehicle charging stations (EVCS) might even burden the existing grid to a point of collapse or grid failure. Establishing charging stations interfaced with bipolar DC microgrids along the roads and highways is the most realistic and feasible solution to avoid the overburdening of the existing power system. The bipolar DC microgrid is a far better microgrid structure than the unipolar microgrid structure in many aspects like reliability, flexibility, and controllability. It can provide multiple voltage level interfaces according to the load demands, which is very apt for different charging levels of electric vehicles (EVs). Operation of multiple sources and multiple loads connected to bipolar DC microgrid will affect DC voltage regulation, capacitance-voltage balancing, and overall stable operation of the grid. In order to mitigate these power quality problems arising in multi-node bipolar DC microgrids, a decentralized model predictive control is proposed in this paper. EV charging load profiles are modeled and developed by considering standard driving cycles, state of charge, and power demand of multiple vehicles to study the effect of unpredictable varying EV loads in the bipolar DC microgrid. EVCS thus modeled are connected to solar photovoltaic-battery energy storage fed bipolar DC microgrid with three-level/bipolar converters and analyzed under dynamic conditions for capacitance–voltage unbalance mitigation, voltage regulation, and the stability of operation with model predictive control. Simulation studies are carried out in MATLAB/Simulink to verify the effectiveness of the system. © 2022 Elsevier LtdItem An efficient optimization algorithm for electric vehicle routing problem(John Wiley and Sons Inc, 2023) Vani, B.V.; Kishan, D.; Ahmad, M.D.W.; Reddy, C.R.P.There has been a steady increase in the prevalence of electric vehicles (EVs) (electric cars) in the transportation industry. The limited range of EVs and the lack of charging facilities make it harder to gain widespread adoption of the EV service. Several optimisation algorithms for EV routes have not yet resolved the problems of trip time and travel cost. The study provides a Bat optimisation technique to reduce trip time and costs while taking into account customer service requests and EV charging schedules. Taking into account battery capacity, charging duration, and delivery/pickup needs, the suggested method may determine the optimal path to supercharger stations. To demonstrate its effectiveness, it is compared to other state-of-the-art algorithms across a variety of benchmark cases. © 2023 The Authors. IET Power Electronics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.Item Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method(MDPI, 2023) Krishnamurthy, N.K.; Sabhahit, J.N.; Jadoun, V.K.; Gaonkar, D.N.; Shrivastava, A.; Rao, V.S.; Kudva, G.In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 106 for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm. © 2023 by the authors.Item Development of Small Signal Model and Stability Analysis of PV-Grid Integration System for EV Charging Application(Institute of Electrical and Electronics Engineers Inc., 2024) Kanimozhi, K.; Koothu Kesavan, K.K.; Nagendrappa, N.; Balasubramanian, B.In this article, grid interactive photovoltaic (PV) system is designed for an electric vehicle (EV) charging application, and the stability of the system is analyzed. The small signal model for the system is derived by averaging and linearizing the state space equations, and the condition for stable operation of PV-integrated charger system is identified from the transfer functions. The proposed charger system implements a coordinated control between the converters to maintain a power balance between the sources and load. System stability is examined using root-locus plots and in addition, the controller is designed to improve the overall stability and reliability of the system. The proposed method provides a general framework for modeling EV charging systems which also details the importance of deriving the model with multiple energy sources. Further, proposed topology has bidirectional capability, which transfers excess PV power to the grid during off-charging hours. The efficacy of the proposed method is verified using the MATLAB Simulink environment for the different scenarios, i.e., variation in the irradiation and disturbances in the grid voltage. The experimental study is conducted on a 1.5-kW laboratory prototype using a low-cost digital signal processing controller (launchpad TMS320F28027F) and the measured results authenticate the simulation findings. © 2020 IEEE.Item Enhanced electric vehicle battery management system employing bat algorithm with chaotic diversification strategies(John Wiley and Sons Inc, 2024) Vani, B.V.; Kishan, D.; Ahmad, Md.W.; Reddy, C.R.P.As the demand for electric vehicles (EV) continues to increase, the need for effective charging and switching of battery systems becomes more important. This article presents a method using the Bat Algorithm (BA) improved by chaotic diversification as well as social education to optimize the power source replacement and the electric vehicle charging procedure. The plan is intended to solve the issues of payment delay and battery management failure. The algorithm searches for better positions by combining chaotic diversity, while social learning supports the coordination of battery stations. Thanks to extensive simulation and real-world testing, our approach shows significant improvements in optimization and a reduced payback period. The results show that the suggested approach outperforms the current algorithms in terms of rotation speed and good solution. This research supports the development of efficient transportation by providing practical solutions to increase the efficiency of electric vehicle transfer and payment and ultimately encourage greater effort. © 2024 The Author(s). IET Power Electronics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.