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This paper introduces a comprehensive approach for sizing grid-connected hybrid renewable energy systems tailored for electric bus fleet operations. The study involves two main steps. First, a mathematical model that optimizes the configuration of such systems by considering daily electric bus consumption, solar irradiance, wind speed, and biomass potential is formulated. The model utilizes Pareto frontier multi-objective optimization to minimize the net present cost, the cost of energy, and greenhouse gas emissions. Second, the model is rigorously applied and tested in a real-world case study in Fez, Morocco, using HOMER Pro; the case study centers on the daily energy requirements of the buses, estimated at 2.5 megawatt hours per day, with a peak demand of 345 kilowatts. Two scenarios are explored, revealing a discernible trade-off dilemma between the full hybrid renewable energy scenario (Scenario 1) and the grid-connected hybrid renewable energy scenario (Scenario 2). In Scenario 2, the grid-connected hybrid renewable energy system demonstrates a notable 42.8% reduction in the net present cost, totaling USD 984,624. Similarly, the levelized cost of energy experiences a significant decrease, reaching approximately 0.08 USD/kWh, marking a 38.1% reduction. However, this apparent economic advantage is juxtaposed with a critical consideration—an increase in greenhouse gas emissions from null to 330,418 kg/year.

This research delves into the intricate landscape of energy scheduling and optimization within microgrid and residential contexts, addressing pivotal aspects such as real-time scheduling systems, challenges in dynamic pricing, and an array of optimization strategies. This paper introduces a cutting-edge scheduling algorithm, harnessing the power of artificial neural networks driven by Long Short-Term Memory Networks, and highlights its exceptional performance, boasting a significantly lower Mean Absolute Error of 5.32 compared to conventional models. This heightened predictive accuracy translates into tangible improvements in both energy efficiency and cost savings. This study underscores the delicate balance between user satisfaction, cost reduction, and efficient scheduling for sustainable energy consumption, showcasing a remarkable 38% enhancement in optimized schedules. Further granularity revealed substantial gains in energy efficiency and cost reduction across different scheduling intensities: 11.11% in light schedules, 20.09% in medium schedules, and an impressive 38.85% in heavy schedules. However, this research does not shy away from highlighting challenges related to data quality, computational demands, and generalizability. Future research trajectories encompass the development of adaptive models tailored to diverse data qualities, enhancements in scalability for and adaptability to various microgrid configurations, the integration of real-time data, the accommodation of user preferences, the exploration of energy storage and renewables, and an imperative focus on enhancing algorithm transparency.

The transport sector is one of the main contributors to global CO2 emissions and the transport sector in the Kingdom of Morocco is no exception. To combat this, two important agreements aimed at reducing greenhouse gas emissions were created, the Paris Agreement and the Nationally Determined Contributions. The adoption of battery electric vehicles is one way of helping to reduce transport-related emissions. However, there are several barriers to the adoption of battery electric vehicles in Morocco. The objective of this paper is to identify these barriers and to propose solutions to overcome them based on a survey of 209 responses that were analyzed using the Smart-PLS 4 approach. The study found that the financial attributes, maintenance, design, social reinforcement, and lack of incentives related to battery electric vehicles are the most significant factors that could cause the hindrance of the adoption of battery electric vehicles. Therefore, more affordable and attractive battery electric vehicles should be made more accessible. Moreover, increased technical training facilities should be mobilized to boost further efforts and increase experience in the field of BEV maintenance in Morocco. By implementing these solutions, Morocco can increase the uptake of battery electric vehicles and reduce its greenhouse gas emissions. This will help Morocco reach its Nationally Determined Contributions and protect the environment as well as the health of its population.

The heavy reliance on petroleum-based fuels in the road transport industry, especially public transportation, results in substantial emissions of greenhouse gases. A significant obstacle on the path to a more sustainable public transportation sector is the employment of alternative fuels with lower environmental implications. Although there are several in-depth well-to-wheel studies for developed countries, extrapolating those results to gasoline markets in developing countries presents challenges because of significant differences in local fuel conditions and vehicle engine technologies. Notably, this study provides a national-level environmental life cycle assessment of alternative buses in Morocco and compares current diesel buses to potential hybrid diesel-electric buses, battery-electric buses, and fuel-cell buses from a well-to-wheel perspective. The model and approach used in this study applies to other countries with developing economies. Total energy consumption broken out by fuel type, greenhouse gas emissions, and criterion air pollutants is the primary outcome of this investigation. Findings highlight an energy cut of 44.7% from battery-electric buses, 36.3% from fuel-cell buses, and 31.7% from hybrid buses with regard to the current diesel buses. Additionally, alternative fuel buses proved to be less polluting in terms of greenhouse gas emissions with 50.8% less from battery-electric buses, 46.7% less from fuel-cell buses, and 26.8% less from hybrid buses; consequently, this would lead to reduced climate change, air pollution, acidification, and eutrophication, given the Egalitarian Environmental Impact Assessment. Ultimately, Morocco will have to tackle technological, financial, and institutional barriers to fully implement the change by 2030.

Electric vehicles (EVs) are becoming more commonplace as they cut down on both fossil fuel use and pollution caused by the transportation sector. However, there are a number of major issues that have arisen as a result of the rapid expansion of electric vehicles, including an inadequate number of charging stations, uneven distribution, and excessive cost. The purpose of this study is to enable EV drivers to find charging stations within optimal distances while also taking into account economic, practical, geographical, and atmospheric considerations. This paper uses the Fez-Meknes region in Morocco as a case study to investigate potential solutions to the issues raised above. The scorching, arid climate of the region could be a deterrent to the widespread use of electric vehicles there. This article first attempts to construct a model of an EV battery on MATLAB/Simulink in order to create battery autonomy of the most widely used EV car in Morocco, taking into account weather, driving style, infrastructure, and traffic. Secondly, collected data from the region and simulation results were then employed to visualize the impact of ambient temperature on EV charging station location planning, and a genetic algorithm-based model for optimizing the placement of charging stations was developed in this research. With this method, EV charging station locations were initially generated under the influence of gas station locations, population and parking areas, and traffic, and eventually through mutation, the generated initial placements were optimized within the bounds of optimal cost, road width, power availability, and autonomy range and influence. The results are displayed to readers in a node-link network to help visually represent the impact of ambient temperatures on EV charging station location optimization and then are displayed in interactive GIS maps. Finally, conclusions and research prospects were provided.