Social cost-benefit analysis of different types of buses for sustainable public transportation

Siddiqui, Osamah; Ishaq, Haris; Khan, Daniyal Ahmed; Fazel, Hesham

doi:10.1016/j.jclepro.2024.140656

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…Due to their many advantages over other transportation technologies, electric vehicles (EVs) have recently attracted a lot of interest (Mohammed et al, 2024;Pamidimukkala et al, 2024). This technology has many benefits, including being environmentally friendly, making very little noise, being extremely efficient with energy, reducing operational costs, using local renewable energy sources, and offering additional services through energy exchange between vehicles and the grid (Misbah Inayat et al, 2023;Siddiqui et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

An innovative method for building electricity energy management in smart homes based on electric vehicle energy capacity

Dodo,

Ibrahim,

Abuhussain

et al. 2024

Front. Energy Res.

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The surging demand for electricity, fueled by environmental concerns, economic considerations, and the integration of distributed energy resources, underscores the need for innovative approaches to smart home energy management. This research introduces a novel optimization algorithm that leverages electric vehicles (EVs) as integral components, addressing the intricate dynamics of household load management. The study’s significance lies in optimizing energy consumption, reducing costs, and enhancing power grid reliability. Three distinct modes of smart home load management are investigated, ranging from no household load management to load outages, with a focus on the time-of-use (ToU) tariff impact, inclining block rate (IBR) pricing, and the combined effect of ToU and IBR on load management outcomes. The algorithm, a multi-objective approach, minimizes the peak demand and optimizes cost factors, resulting in a 7.9% reduction in integrated payment costs. Notably, EVs play a pivotal role in load planning, showcasing a 16.4% reduction in peak loads and a 7.9% decrease in payment expenses. Numerical results affirm the algorithm’s adaptability, even under load interruptions, preventing excessive increases in paid costs. Incorporating dynamic pricing structures like inclining block rates alongside the time of use reveals a 7.9% reduction in payment costs and a 16.4% decrease in peak loads. In conclusion, this research provides a robust optimization framework for smart home energy management, demonstrating economic benefits, peak load reduction potential, and enhanced reliability through strategic EV integration and dynamic pricing.

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Section: Introductionmentioning

confidence: 99%