Due to global warming and climate change, it is essential to produce power using renewable sources, such as solar, wind, fuel cells, etc. The traditional grid shifts towards the smart grid by infusing digital communication techniques and information technology. As the current power system is shifting towards a smart grid, the utility and prosumers participate in the energy trading process. Due to the distributed nature of the smart grid, providing a fair price among them is becoming a difficult task. The article introduces a model for energy trading in a smart grid by allowing participants to negotiate in multiple stages using a game-theory-based multi-stage Nash Bargaining Solution (NBS). The model’s application of game theory enables the participants to decide on a mutually acceptable price, thereby encouraging the utility, private parties and prosumers (those who are able to generate and consume energy) to participate in the trading process. Since all parties participate in the trading procedure, greenhouse gas emissions are reduced. The proposed model also balances the benefits of consumers and producers in the final agreed fixed price. To demonstrate the efficacy of the proposed work, we compare the analytical results with feed-in-tariff (FiT) techniques in terms of consumers’ energy bills and producers’ revenue. For experimental analysis, 20 participants are considered, where the percentage reduction in the bill of each consumer and the percentage increment of revenue of each producer are compared to FiT. On average, the overall bill of the consumer is reduced by $32.8\%$, and the producers’ revenue is increased by $64.83\%$ compared to FiT. It has been shown further that the proposed model shows better performance as compared to FiT with an increase in the number of participants. The analysis of carbon emission reduction in the proposed model has been analyzed, where, for 10 participants, the carbon emission reduction is approximately 28.48 kg/kWh, and for 100 participants is 342.397 kg/kWh.

The IEEE 802.15.6 standard is a prominent communication protocol for Wireless Body Area Networks (WBANs), enabling seamless and reliable data transmission between wearable sensor devices. Energy efficiency is a critical concern in WBANs due to the limited battery capacity of these devices. To address this challenge, this letter proposes an energy-aware duty cycle adjustment mechanism within the superframe of IEEE 802.15.6 Medium Access Control (MAC) protocol to conserve more energy by the sensor devices and the coordinator without compromising other important quality of service (QoS) metrics such as reliability and throughput. We dynamically adjust the length of the active and inactive phases of the superframes based on the current traffic load and the previous superframe's slot utilizations. Moreover, we designed a priority-based scheduled slot allocation algorithm for sensor devices in Managed Access Phase (MAP). We achieved better network lifetime compared to IEEE 802.15.6 MAC and other benchmarks. In comparison, the proposed approach achieves 40% improvement in network lifetime over IEEE 802.15.6 MAC.

In recent times, as many people have started using an electric vehicle (EV) as it provides benefits such as low fuel economy, reduced emission, etc., the deployment of fixed electric vehicle charging stations has been a popular method for supplying EVs with charging services. The charging requirement of EVs varies from vehicle to vehicle. Various schemes have been introduced recently regarding electric vehicles’ energy trading, charging station deployments, etc. Selecting an optimal charging station for booking charging slots by electric vehicle is one of the main concerns to avoid inappropriate services. To address this challenge, we proposed an optimal charging station selection scheme to book charging slots by EV. The proposed method used a multi-criteria decision-making technique called technique for order performance by similarity to ideal solution (TOPSIS) based on various parameters of electric vehicle charging stations (EVCSs). In this, the performance score of each EVCS is determined, and the ranking process is made according to their performance score. Then, EV selects the EVCS with the highest ranked and books the charging slot. Further, an analysis of the proposed scheme is carried out for 10 EVCSs and 3 EVs.