Articles

This charge-ahead paper suggests that transitioning the automotive industry towards a zero-carbon ecosystem from material to end-of-life can be accomplished through disruptive zero-carbon manufacturing in the broad area of all-electric vehicle production technology. To accomplish zero carbon emission automotive manufacturing in the vehicle assembly domain, future paradigms must converge on the decoupling of carbon dioxide emissions from automobile manufacturing and use the design, processing, and manufacturing conditions. The envisioned zero carbon emission vehicle manufacturing domain consists of two complementary components: (a) making more efficient use of energy and (b) reducing carbon in energy use. This paper presents the status of key scientific and technological advancements to bring the manufacturing model of today to a zero-carbon ecosystem for the entire automotive industry of tomorrow. This paper suggests the groundbreaking application of dynamic and distributed predictive scheduling algorithms and open sensing and visualization technology to meet the zero carbon emission vehicle manufacturing goals. Power-aware high-performance computing clusters have recently become a viable solution for sustainable production. Advances in scalable and self-adaptive monitoring, predictive analytics, timeline-based machine learning, and digital replica of cyber-physical systems are also seen co-evolving in the zero carbon manufacturing future. These methods are inspired by initiatives to decouple gross domestic product growth and energy-related carbon dioxide emissions. Stakeholders could co-design and implement shared roadmaps to transition the automotive manufacturing sector with relevant societal and environmental benefits. The automated mobility sector offers a program, an industry-leading example of transforming an automotive production facility to carbon neutrality status. The conclusions from this paper challenge automotive manufacturers to engage in industry offsetting and carbon tax programs to drive continuous improvement and circular vehicle flows via a multi-directional zero-carbon smart grid.

The traditional electric grid of the City of Kumba has been experiencing a constant failure which leads inhabitant to experience constant blackout. This constant blackout persists and stays for a long time due to the lack of communication between equipment, consumer and supplier. Whenever there is a fault, the repairing agents walk along the feeder to find the fault. This manual fault finding increases the restauration time which leads to the augmentation of the blackout period. Factors responsible for the failure of the line are complex to be controlled. It is necessary to reduce restauration time by introducing Information and Communication Technologies (ICT) and sensing system in the grid and making it to be smart. ICT in this smart grid, sensors and smart meters are meant to assure two-way communication between the supplier and the consumer. They send real time information which is computed at the control center to optimize the entire grid. Distributed generation is also introduced in the system for two purposes. To complete the lag in power demand of the grid and to take over the supply when the main feeder is faulty. Various distributed generation sources studied led to the choice of solar power plants thanks to their low production of Greenhouse Gas (GHG) and availability of their resources in the city. A model has been proposed for the distributed generation and optimization of the smart grid. The system indexes obtained without distributed generation in the grid are different from that with. The difference in these indexes proved that the grid has been optimized. However, the reliability of the grid is enhanced after the introduction of distributed generation into the system. This enhancement in reliability declares that with distributed generation into the grid, the population of Kumba has a reliable power supply, which makes them to have energy throughout.

This article describes the optimization models recently applied to the design and operation of power systems towards forming smart grids and identifies trends, barriers, and possible gaps in this area. Models are described to optimize the design and operation of power systems considering renewable energies, distributed generation, microgrids, demand management, and energy storage systems. It was concluded that it is necessary to validate many of the models formulated recently to optimize the operation through tests with real data and on a large scale. Furthermore, demand management and microgrids are aspects in which it is necessary to develop models for optimal power flow. Finally, it is necessary to predict stochastic variables with greater precision so that these models adapt to the real behavior of the system.