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Unidirectional chargers, valued for their simplicity and cost-effectiveness, are widely deployed. In contrast, bidirectional chargers enable advanced functionalities such as Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) but come with greater complexity, higher costs, and design challenges. This aim of this research is to analyze unidirectional and bidirectional charging systems integrated with renewable energy, from both economic and environmental perspectives. Additionally, the research conducts a technical analysis of different EV charging technologies via Polysun software, considering a predefined mobility profile that includes charging times and kilometers driven. The study focuses on households with renewable energy systems connected to the grid, evaluating energy consumption, grid reliance, CO₂ emissions, and financial viability across scenarios with varying numbers of EVs (1–3) over one year. While bidirectional EV setups enhance self-consumption and reduce dependence on the external grid, they face financial challenges, including higher initial costs and a lower net present value (NPV) due to maintenance expenses. In Jordan the time-of-use (TOU) pricing system has applied for EVs charging. This study reveals that the bidirectional EV charging improves energy efficiency and reduces CO 2 emissions by optimizing PV energy utilization in Jordan to charge EVs, however, its increased initial costs under TOU pricing highlight the need for supportive policies to encourage wider adoption.

The transition to electric vehicles (EVs) plays a pivotal role in achieving decarbonization within the transportation sector. However, the widespread adoption of EVs faces multifaceted challenges, particularly concerning infrastructure development. This paper investigates the intersection of sustainability, decarbonization, and EV adoption, with a focus on identifying and analyzing the challenges associated with infrastructure deployment. Strictly adhering to the methodological principles and process of systematic literature reviews, this paper analyzes research spanning the fields of engineering, energy, computer science, environmental science, social sciences, and others to elucidate the barriers hindering EV adoption, ranging from technological limitations to regulatory complexities and market dynamics. Furthermore, it examines the critical role of infrastructure, encompassing charging networks, grid integration, and supportive policies, in facilitating EV uptake and maximizing environmental benefits. The findings are finally used to present the implications for theory, practice, and policies and to highlight the avenues for future research.

This study focuses on the development of electric vehicles (EV) in the private passenger vehicle fleet in Beijing (China), analyzes how EVs will penetrate in the market, and estimates the resulting impacts on energy consumption and CO2 emissions up to 2030. A discrete choice model is adopted with consideration of variables including vehicle technical characteristics, fuel prices, charging conditions and support policies. Results show that by 2030, without technological breakthrough and support policies, the market share of EV will be less than 7%, with gasoline dominating the energy structure. With fast technological progress, charging facility establishment, subsidies and tax breaks, EVs will account for 70% of annual new vehicle sales and nearly half of the vehicle stock by 2030, resulting in the substitution of nearly 1 million tons of gasoline with 3.2 billion kWh electricity in 2030 and the reduction of 0.6 million tons of CO2 emission in 2030. Technological progress, charging conditions and fuel prices are the top three drivers. Subsidies play an important role in the early stage, while tax and supply-side policies can be good options as long-term incentives.

Dynamically charging electric vehicles (EVs) have the potential to significantly reduce range anxiety and decrease the size of battery required for acceptable range. However, with the main driver for progressing EV technology being the reduction of carbon emissions, consideration of how a dynamic charging system would impact these emissions is required. This study presents a demand-side management method for allocating resources to charge EVs dynamically considering the integration of local renewable generation. A multi-objective optimisation problem is formulated to consider individual users, an energy retailer and a regulator as players with conflicting interests. A 19% reduction in the energy drawn from the power grid is observed over the course of a 24 h period when compared with a first-come-first-served allocation method. This results in a greater reduction in CO2 emissions of 22% by considering the power grid's make-up at each time interval. Furthermore, a 42% reduction in CO2 emissions is achieved compared to a system without local renewable energy integration. By varying the weights assigned to the players’ goals, the method can reduce overall demand at peak times and produce a smoother demand profile. System fairness is shown to improve with an average Gini coefficient reduction of 4.32%.