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.

In this work, we propose a multi-objective optimal scheduling strategy for air-conditioning devices to optimize both energy consumption and thermal comfort for the users. We propose a graph-based modeling for the problem and utilize Johnson's all elementary circuit finding algorithm to obtain the desired solutions. The proposed methodology has been experimented on test cases that mimic real-world scenario, and further, the applicability of Karp's minimum mean cycle algorithm is also studied in this problem set-up.

The continuous decrease in the cost of mobile devices and mobile Internet has triggered an exponential growth of overall IP devices, which has consequently increased the demand for network mobility (NEMO), which is required to ensure seamless connectivity of the mobile devices with the Internet on the go. To support this growing demand, the concept of nested-NEMO has evolved over the past few years. However, due to the inherent architecture of nested-NEMO, it is inefficient in terms of hand-off delay and throughput which hinder it to deploy as a highly scalable network. In this paper, we address the scalability issue of nested-NEMO and propose a hierarchical node attachment process for the mobile devices. The performance of the proposed mechanism has been evaluated where we computed the hand-off delay and throughput and observed that the proposed mechanism supports a considerable degree of scalability as compared to the existing nested network mobility architecture.

Existing electrical grid systems have a limited amount of real-time monitoring and controlling capabilities of energy generation and consumption facilities, which trigger various technical issues including voltage overloading, demand–supply mismatch, peak load consumption, etc. Some of the primary reasons for these key issues have been identified to be the inefficient utilization of energy infrastructure and uncoordinated power consumption pattern among the consumers. In this paper, we propose a coordinated load scheduling and controlling algorithm to schedule controllable appliances with the objective to minimize peak load consumption. For this purpose, we model the problem into the strip packing problem, a well-known NP-hard problem, and discuss the applicability of existing heuristics in our problem setup. We then discuss a new offline heuristic solution, named MinPeak, specifically designed for load scheduling problem. We have conducted comprehensive simulation studies using available benchmark data sets and have performed extensive comparative analyses of the proposed algorithm with some of the well-known heuristics for strip packing problem. Furthermore, experiments have been carried out using practical electricity consumption data to evaluate the performance of the algorithm in real life. The results obtained are very encouraging in terms of reducing peak load consumption and overall efficiency of the system.

Residential, commercial, and industrial buildings have been reported to consume a large portion of the generated energy. With the introduction of smart grid and its energy optimization techniques, it is now possible to efficiently manage and control consumers’ energy usage to fulfil their demands with the existing energy generation infrastructure, which otherwise seems to be a backbreaking challenge. This paper presents an efficient energy management solution for buildings with a large number of thermostatic devices (air conditioners) that maintain the temperature of different thermal zones in a predefined range. The primary objective of this paper is to schedule the thermostatic devices in order to reduce total energy consumption by these devices when they are in operation for a very long duration of time, while maintaining the other constraints. We formulate it as a graph problem where minimum mean cycle will provide the desired solution. The proposed methodology ensures that at no point in time the power consumption goes beyond a certain peak power consumption limit. We also enhance the methodology to reduce peak load consumption. Furthermore, a fast greedy approach has been developed to efficiently scale up the aforementioned scheduling scheme for a large number of devices. Experimental results show that significant improvements can be obtained by the proposed approaches over existing algorithms in reducing average energy consumption.