Some of our previous researches available with prominent peer-reviewed journals and conferences have been stated below. To explore in details, just follow the external links.
Pranay Kumar Saha; Nilotpal Chakraborty; Arijit Mondal; Samrat Mondal
Due to the steep rise in global population, urbanization, and industrialization, most of the cities in the world today are witnessing increased carbon footprints and reduced per capita space. In such a scenario, vehicle sharing and carpooling systems, specifically with electric vehicles (EV), can significantly help due to the reduced cost of ownership, maintenance, and parking space. In this article, we study the challenging problem of optimal sizing and efficient routing for an electric vehicle-on-demand system. Users demand EVs at the pooling stations at different time instances with individual deadlines to reach the destinations. The objective is to fulfill all the demands respecting the deadlines with minimum investment, which essentially translates to minimizing the total number of EVs. We define the problem formally using mixed-integer linear programming formulation and propose a set of intelligent and efficient heuristic algorithms to solve it efficiently. The proposed algorithms’ performances are tested and validated in a simulated environment on a reasonable size city network with many EV demands. The results obtained show that the proposed heuristic algorithms are competent by reducing 200–360 EVs per day on a network of 282 charging ports, indicating their scalability to be implemented in real-world scenarios.
Pranay Kumar Saha; Arijit Mondal
Renewable resources are prime contributors to energy in a microgrid. Fluctuation in the renewable outcome is inevitable because of strong dependence on the atmospheric condition. Energy storage systems (ESSs) are often used to mitigate variability issues. Development of efficient methodology to supply necessary electrical energy to the load from a combination of ESS and utility grid with minimal cost is the need of the hour. The variable pricing scheme adds another dimension to the problem. In this article, we propose a mixed-integer linear programming (MILP) formulation to find an optimal operation schedule of charging/discharging of an ESS offline. As MILP formulation fails to scale for large inputs, we propose a heuristic algorithm, namely, intelligent day-ahead schedule with predicted renewable energy heuristic algorithm, to quickly find a good solution. We propose another online heuristic strategy, namely, online scheduling algorithm (OSA), which can take account of real-time fluctuation. We provide an extensive comparative study of the different proposed approaches using the Pecan Street dataset. We observe that the OSA can provide a reasonably acceptable solution considering its minimal requirement of computing resources. Under a significant fluctuation in the renewable outcome, a maximum of 18% deviation was observed for the OSA compared to every slot optimal results, i.e., OSETon
Ritam Sarkar; Pranay Kumar Saha; Arijit Mondal; Samrat Mondal
We consider the problem of efficient scheduling of vehicle-to-grid (V2G), grid-to-vehicle (G2V), vehicle-to-vehicle (V2V) operations in a smart microgrid equipped with renewable energy resources and multiple types of charging ports to cater the requirements of Electric Vehicles (EVs) and essential loads of households. Our objective is to schedule the operations such that the total price paid to the utility-grid for the electrical energy is minimum. We propose a mixed integer linear programming (MILP) based formulation to obtain an optimal schedule of those operations. We present a heuristic approach to find a reasonable solution quickly.
Pranay Kumar Saha; Nilotpal Chakraborty; Arijit Mondal; Samrat Mondal
We propose a coordinated scheduling approach for controllable electrical appliances along with the optimal usage of heterogeneous energy sources to minimize the energy drawn from the grid and thereby reducing electricity cost for the users. A mixed integer linear programming (MILP) formulation for the problem is presented to produce the optimal solutions, and an efficient heuristic algorithm is proposed to obtain results efficiently.
Nilotpal Chakraborty, Arijit Mondal, and Samrat Mondal
We propose an efficient charge scheduling and routing mechanism for a set of electric vehicles with the objective to optimize average time and energy consumption for a tour. We propose a graph-based offline heuristic scheduling algorithm, whose performance evaluation is done in comparison to optimal results on graphs depicting real world scenario. Obtained preliminary results indicate that the proposed algorithm is highly efficient and effective in producing solutions that are significantly closer to optimal solutions.
Arun Vikram, Samrat Mondal, Jimson Mathew and Ariijt Mondal
In this problem, we have considered a city map network with a Centralised Charging Station (CCS) and a number of Battery Swapping Stations (BSSs). The CCS owns a set of delivery trucks with limited capacity. Each BSS can demand some fully charged batteries from the CCS, which needs to figure out a routing strategy for each of the delivery trucks. The strategy fulfills the demand of each BSS and at the same time optimizes the overall cost of delivery in terms of the distance travelled. A constrained programming based approach has been proposed to obtain an optimal routing of the delivery trucks considering the partial delivery of batteries.
Sanghamitra Mishra, Samrat Mondal and Arijit Mondal
In this paper, we address one of the many challenges of electric vehicles(EVs) that is the optimal placement of charging stations(citing) and allocating a certain number of charging ports(sizing) at these stations. We formulate a real life problem in the form of a city network that accounts for the uncertainty in the arrival of EVs, mathematically represented in the form of an optimization problem. The uncertainty in the flow of EVs can be approximated in a statistical distribution which can be analysed to determine the optimal allocation of the charging ports. Experiments suggest that our approach performs better when compared using certain performance metric with the existing research works.
Sanghamitra Mishra, Samrat Mondal and Arijit Mondal
The problem of charge scheduling of Electric Vehicles (EVs) at charging stations remains one of the significant challenges due to high charging time and insufficient charging infrastructure leading to unfulfilled demands. Moreover, most public charging stations (CSs) are equipped with charging ports that serve only a fixed charging rate. The installation of adaptable ports, that can vary their rate of charging with time, has been observed to alleviate these challenges. Hence, we propose an efficient EV charge scheduling plan, for a CS equipped with adaptable charging ports, to improve its performance. The CS aims at maximizing not only its profit but also its total customer satisfaction. Also, it is assumed that, upon being unable to fulfill their total energy demands, the CS pays an incentive to the EV owners. Such incentives reduce the profit margins of the CSs. Hence, we formulate a bi-objective optimization EV scheduling model that drives the CSs toward maximizing their profit and customer satisfaction. Satisfiability Modulo Theory (SMT) solver and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) evolutionary algorithm are used to obtain the optimal and approximate Pareto fronts respectively. We further propose a charging action replacement-based heuristic approach to speed up the process of obtaining an approximate set of non-dominated solutions. We run several simulations and observe that the proposed algorithm results in a near-optimal set of solutions compared to the actual Pareto front with a much less computation time.