A smart grid (SG) is a modernized electric grid that enhances the reliability, efficiency, sustainability, and economics of electricity services. Moreover, it plays a vital role in modern energy infrastructure. The core challenge faced by SGs is how to efficiently utilize different kinds of front-end smart devices, such as smart meters and power assets, and in what manner to process the enormous volume of data received from these devices. Furthermore, cloud and fog computing provide on-demand resources for computation, which is a good solution to overcome SG hurdles. Fog-based cloud computing has numerous good characteristics, such as cost-saving, energy-saving, scalability, flexibility, and agility. Resource management is one of the big issues in SGs. In this paper, we propose a cloud-fog-based model for resource management in SGs. The key idea of the proposed work is to determine a hierarchical structure of cloud-fog computing to provide different types of computing services for SG resource management. Regarding the performance enhancement of cloud computing, different load balancing techniques are used. For load balancing between an SG user's requests and service providers, five algorithms are implemented: round robin, throttled, artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization. Moreover, we propose a hybrid approach of ACO and ABC known as hybrid artificial bee ant colony optimization (HABACO). Simulation results show that our proposed technique HABACO outperformed the other techniques.
In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users' satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE) and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes (TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.
An unprecedented opportunity is presented by smart grid technologies to shift the energy industry into the new era of availability, reliability and efficiency that will contribute to our economic and environmental health. Renewable energy sources play a significant role in making environments greener and generating electricity at a cheaper cost. The cloud/fog computing also contributes to tackling the computationally intensive tasks in a smart grid. This work proposes an energy efficient approach to solve the energy management problem in the fog based environment. We consider a small community that consists of multiple smart homes. A microgrid is installed at each residence for electricity generation. Moreover, it is connected with the fog server to share and store information. Smart energy consumers are able to share the details of excess energy with each other through the fog server. The proposed approach is validated through simulations in terms of cost and imported electricity alleviation.
Summary This paper presents four routing protocols for Underwater Sensor Networks (USNs): Location Error–resilient Transmission Range adjustment–based protocol (LETR), Mobile Sink–based GEographic and Opportunistic Routing (MSGER), Mobile Sink–based LETR (MSLETR), and Modified MSLETR (MMS‐LETR). LETR considers transmission range levels for finding neighbor nodes. If a node fails to find any neighbor node within its defined maximum transmission range level, it recovers from communication void regions using depth adjustment technology. MSGER and MSLETR avoid depth and transmission range adjustment and overcome the problem of communication void regions using MSs, whereas MMS‐LETR takes into account noise attenuation at various depth levels, elimination of retransmissions using multi‐path communication and load balancing. The performance of our proposed protocols is evaluated through simulations using different parameters. The simulation results show that MSS‐LETR supersedes all counterpart schemes in terms of packet loss ratio. LETR significantly improves network performance in terms of energy consumption, packet loss ratio, fraction of void nodes, and the total amount of depth adjustment.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.