Browsing by Author "Mahanta, Pinakeswar"
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Item Charging Station Placement for Electric Vehicles : A Case Study of Guwahati City, India(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019) Deb, Sanchari; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar; Indian Institute of Technology Guwahati; Department of Mechanical EngineeringThe ever-increasing population of India accompanied by the recent concerns regarding fossil fuel depletion and environmental pollution has made it indispensable to develop alternate mode of transportation. Electric vehicle (EV) market in India is expanding. For acceptance of EVs among the masses, development of charging infrastructure is of paramount importance. This paper formulates and solves the charging infrastructure-planning problem for Guwahati, India, that will develop as a smart city soon. The allocation of charging station problem was framed in a multi-objective framework considering the economic factors, power grid characteristics, such as voltage stability, reliability, power loss, as well as EV user's convenience, and random road traffic. The placement problem was solved by using a Pareto dominance-based hybrid algorithm amalgamating chicken swarm optimization (CSO) and the teaching learning-based optimization (TLBO) algorithm. Finally, the Pareto optimal solutions were compared by fuzzy decision-making.Item A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-05-29) Deb, Sanchari; Tammi, Kari; Gao, Xiao Zhi; Kalita, Karuna; Mahanta, Pinakeswar; Department of Mechanical Engineering; Indian Institute of Technology Guwahati; University of Eastern FinlandA new hybrid multi-objective evolutionary algorithm is developed and deployed in the present work for the optimal allocation of Electric Vehicle (EV) charging stations. The charging stations must be positioned on the road in such a way that they are easily accessible to the EV drivers and the electric power grid is not overloaded. The optimization framework aims at simultaneously reducing the cost, guaranteeing sufficient grid stability and feasible charging station accessibility. The grid stability is measured by a composite index consisting of Voltage stability, Reliability, and Power loss (VRP index). A Pareto dominance based hybrid Chicken Swarm Optimization and Teaching Learning Based Optimization (CSO TLBO) algorithm is utilized to obtain the Pareto optimal solution. It amalgamates swarm intelligence with teaching-learning process and inherits the strengths of CSO and TLBO. The two level algorithm has been validated on the multi-objective benchmark problems as well as EV charging station placement. The performance of the Pareto dominance based CSO TLBO is compared with that of other state-of-the-art algorithms. Furthermore, a fuzzy decision making is used to extract the best solution from the non dominated set of solutions. The combination of CSO and TLBO can yield promising results, which is found to be efficient in dealing with the practical charging station placement problem.Item A New Teaching–Learning-based Chicken Swarm Optimization Algorithm(Springer Verlag, 2019) Deb, Sanchari; Gao, Xiao Zhi; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar; Department of Mechanical Engineering; Indian Institute of Technology Guwahati; University of Eastern FinlandChicken Swarm Optimization (CSO) is a novel swarm intelligence-based algorithm known for its good performance on many benchmark functions as well as real-world optimization problems. However, it is observed that CSO sometimes gets trapped in local optima. This work proposes an improved version of the CSO algorithm with modified update equation of the roosters and a novel constraint-handling mechanism. Further, the work also proposes synergy of the improved version of CSO with Teaching–Learning-based Optimization (TLBO) algorithm. The proposed ICSOTLBO algorithm possesses the strengths of both CSO and TLBO. The efficacy of the proposed algorithm is tested on eight basic benchmark functions, fifteen computationally expensive benchmark functions as well as two real-world problems. Further, the performance of ICSOTLBO is also compared with a number of state-of-the-art algorithms. It is observed that the proposed algorithm performs better than or as good as many of the existing algorithms.Item A Novel Chicken Swarm and Teaching Learning based Algorithm for Electric Vehicle Charging Station Placement Problem(PERGAMON-ELSEVIER SCIENCE LTD, 2021-04-01) Deb, Sanchari; Gao, Xiaozhi; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar; Department of Mechanical Engineering; Indian Institute of Technology Guwahati; University of Eastern FinlandThe current concern about the ever-escalating demand for energy, exhaustive nature of fossil fuels, global warming accompanied by climate change has necessitated the development of an alternate pollution-free mode of commute. Electric Vehicles (EV) are an environmentally friendly alternative to reduce the reliance on fossil fuel and pollution. For public acceptance of EVs, functionality and accessibility of charging stations is of paramount importance. Improper planning of EV charging stations, however, is a threat to the power grid stability. EV charging stations must be placed in the transport network in such a way that the safe limit of distribution network parameters is not violated. Thus, charging station placement problem is an intricate problem involving convolution of transport and distribution networks. A novel and simple approach of formulating the charging station placement problem is presented in this work. This approach takes into account integrated cost of charging station placement as well as penalties for violating grid constraints. For obtaining an optimal solution of this placement problem, two efficient evolutionary algorithms, such as Chicken Swarm Optimization (CSO) and Teaching Learning Based Optimization algorithm (TLBO) are amalgamated together thereby extracting the best features of the both algorithms. The efficacy of the proposed algorithm is tested by solving selected standard benchmark problems as well as charging station placement problem. The result of this hybrid algorithm is further compared with other algorithms used for this purpose.Item Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018)(Springer Netherlands, 2020-03-01) Deb, Sanchari; Gao, Xiao Zhi; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar; Department of Mechanical Engineering; Indian Institute of Technology Guwahati; University of Eastern FinlandSolving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm Optimization Algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.Item Review of recent trends in charging infrastructure planning for electric vehicles(WILEY PERIODICALS, INC, 2019-11) Deb, Sanchari; Tammi, Kari; Kalita, Karuna; Mahanta, Pinakeswar; Department of Mechanical Engineering; Indian Institute of TechnologyThe exhaustive nature of fossil fuels and environmental concerns associated with greenhouse gases are the major causes of the paradigm shift from conventional vehicles to electric vehicles (EVs). The electrification of the transportation sector and the increasing popularity of the EVs have driven scientists and researchers to delve into charging stations. Underdeveloped charging infrastructure, optimal placement of charging stations, and charge scheduling in the charging stations are the major concerns for the large-scale deployment of EVs. Even few questions related to EVs, such as the vehicle price and driving range, can be partially solved with a well-developed charging infrastructure. Existing old infrastructure may bring severe limitations in the realization of the optimal placement of charging stations as EVs have not existed when the road and grid infrastructure have been developed. For the past few years, the studies associated with the optimal placement and sizing of EV charging stations have drawn the consideration of the researchers. The researchers have used different approaches, objective functions, and applied different optimization algorithms while dealing with the problem of charging station placement. The complex and dynamic nature of the problem has led researchers to apply different optimization algorithms for the solution of the problem. In this particular review study, the research works bearing on the charging infrastructure planning for the EVs are considered. Thus, this review work will endow the research community with the latest developments and research findings in the paradigm of charging infrastructure planning for EVs. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Energy and Urban Design > Systems and Infrastructure Energy and Transport > Science and Materials Energy Systems Analysis > Science and MaterialsItem A Robust Two-Stage Planning Model for the Charging Station Placement Problem Considering Road Traffic Uncertainty(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-07) Deb, Sanchari; Tammi, Kari; Gao, Xiao Zhi; Kalita, Karuna; Mahanta, Pinakeswar; Cross, Sam; Department of Mechanical Engineering; Mechatronics; Energy Conversion; VTT Technical Research Centre of Finland; University of Eastern Finland; Indian Institute of Technology GuwahatiThe current critical global concerns regarding fossil fuel exhaustion and environmental pollution have been driving advancements in transportation electrification and related battery technologies. In turn, the resultant growing popularity of electric vehicles (EVs) calls for the development of a well-designed charging infrastructure. However, an inappropriate placement of charging stations might hamper smooth operation of the power grid and be inconvenient to EV drivers. Thus, the present work proposes a novel two-stage planning model for charging station placement. The candidate locations for the placement of charging stations are first determined by fuzzy inference considering distance, road traffic, and grid stability. The randomness in road traffic is modelled by applying a Bayesian network (BN). Then, the charging station placement problem is represented in a multi-objective framework with cost, voltage stability reliability power loss (VRP) index, accessibility index, and waiting time as objective functions. A hybrid algorithm combining chicken swarm optimization and the teaching-learning-based optimization (CSO TLBO) algorithm is used to obtain the Pareto front. Further, fuzzy decision making is used to compare the Pareto optimal solutions. The proposed planning model is validated on a superimposed IEEE 33-bus and 25-node test network and on a practical network in Tianjin, China. Simulation results validate the efficacy of the proposed model.