Tarik Kousksou’s research while affiliated with Université de Pau et des Pays de l'Adour and other places

Electric vehicles charging infrastructures can have a significant impact on the power grid. Statistical analysis of the charging behavior of PEVs is neces-sary for planning and operation of public charging stations to evaluate the impact of the rapid deployment of PEVs and charging infrastructure on the power grid and to evaluate their potential benefits for future microgrids, especially in reg-ulated (non-liberalized) electricity markets. This paper evaluates the impact of plug-in electric vehicles load and V2G (Vehicle to Grid) on the Moroccan distribution network at different EVs penetration scenarios. Simulation is carried out in MATLAB/SIMULINK. Results shows that the high penetration of EVs (Electric Vehicles) in Morocco might have a significant negative impact on the grid stability, but V2G might be an effective solution for energy dependance in the Kingdom. Results will also enable Moroccan utility company ONEE (National Office of Electricity and Water) anticipate the charging demand of PEV customers, as well as the impact on the electric grid as PEV penetration rates increase.

Anaerobic digestion (AD) is one of the most extensively accepted processes for organic waste cleanup, and production of both bioenergy and organic fertilizer. Numerous mathematical models have been conceived for modeling the anaerobic process. In this study, a new modified dynamic mathematical model for the simulation of the biochemical and physicochemical processes involved in the AD process for biogas production was proposed. The model was validated, and a sensitivity analysis based on the OAT approach (one-at-a-time) was carried out as a screening technique to identify the most sensitive parameters. The model was developed by updating the bio-chemical framework and including more details concerning the physico-chemical process. The fraction XP was incorporated into the model as a particulate inert product arising from biomass decay (inoculum). New components were included to distinguish between the substrate and inoculum, and a surface-based kinetics was used to model the substrate disintegration. Additionally, the sulfate reduction process and hydrogen sulfide production have been included. The model was validated using data extracted from the literature. The model's ability to generate accurate predictions was testified using statistical metrics. The model exhibited excellent performance in forecasting the parameters related to the biogas process, with measurements falling within a reasonable error margin. The relative absolute error (rAE) and root mean square error (RMSE) were both less than 5 %, indicating a high ability of the current model in comparison with the literature. Additionally, the scatter index (SI) was below 10 %, and the Nash-Sutcliffe efficiency (NES) approached one, which affirms the model's accuracy and reliability. Finally, the model was applied to investigate the performances of the AD of food waste (FW). The findings of this study support the robustness of the developed model and its applicability as a virtual platform to evaluate the efficiency of the AD treatment and to forecast biogas production and its quality, CO2 emission, and energy potential across various organic solid waste types.

In response to climate change and the imperative for sustainable energy solutions, this study investigates the feasibility of producing green hydrogen and associated e-fuels (methane, methanol, and ammonia) using a renewable energy hybrid system in Dakhla, Morocco. Utilizing the System Advisor Model (SAM) software for simulation-based analysis, the research evaluates a hybrid system combining concentrated solar power (CSP) and photovoltaic (PV) plants against standalone counterparts. Various simulations comparing standalone PV and CSP plants with a novel CSP/PV hybrid concept were carried out. The hybrid system demonstrates significant promise, exhibiting increased annual energy yield and capacity factors up to 90%, leading to enhanced efficiency , performance, and cost savings with a Levelized Cost of Electricity (LCOE) approximating 17 cents/kWh. Furthermore, this paper conducts an in-depth exploration into the feasible production of hydrogen and its derived synthetic fuels utilizing hybrid renewable systems. The study provides a thorough examination of the production processes, yields, and efficiencies of hydrogen and its derivative e-fuels, with a focus on green hydrogen production through water electrolysis, CO2 hydrogenation, the Sabatier process, and the Haber-Bosch process. It sheds light on the potential applications of these fuels in the transportation sector, including in electric and hydrogen vehicles and aviation. The insights garnered are indispensable for crafting future strategies and policies in sustainable energy planning. These findings not only provide valuable direction for forthcoming initiatives in renewable energy but also underscore the pivotal role of hybridization in enhancing the efficient production and utilization of hydrogen within both renewable energy and transportation sectors.

The recent global energy transition policies emphasize the utilization of renewable sources as a primary energy solution. Solar energy systems, in particular, offer significant advantages in terms of cost-effectiveness and environmental friendliness. This work focuses on the modeling of a photovoltaic-thermal (PV/T) air-based system using machine learning (ML) techniques. The primary contribution lies in estimating the electrical and thermal efficiencies of the PV/T system through the application of the random forest (RF) technique and K-fold cross-validation. The results obtained are notably impressive when compared to the most commonly used techniques reported in the literature: Artificial Neural Networks (ANN), Support Vector Machine (SVM), and linear regression (LR). The assessment demonstrates that Random Forest outperforms these techniques in predicting both Electrical efficiency and Thermal efficiency with R 2 values of 99.9996 % and 81.2 % respectively, and MAE of 0.0034 and 2.64 respectively. Furthermore, selecting the most important features for electrical efficiency not only reduced the processing time but also improved the performance. The Random Forest model exhibited robust stability and demonstrated strong generalization capabilities, as evidenced by its consistent and promising electrical efficiency performance across a 10-fold cross-validation assessment. The proposed method could be a key solution to accurately model the electrical and thermal performances and serve as an alternative to traditional physical modeling approaches.

This research examines distribution networks in detail, both underground and overhead, as well as the layout of distribution pipes. It takes into account both energy and economic aspects. Different installation methods and thermal insulation materials for pipelines are studied, while scientific aspects such as heat and pressure loss modelling, technological advances and strategies for improving cost-effective models are discussed. In addition, regulatory concerns, standards and policies relating to heat distribution are addressed, including Legionella contamination laws and pipe insulation thickness requirements. According to the study’s findings, underground pipes are generally better suited to district heating networks than above-ground pipes and the triple pipes can decrease heat losses by 45% compared to single pipes and by 24% compared to double pipes. This article offers readers a detailed comprehension of the technical, scientific, and regulatory elements of urban heating networks. It highlights the significance of optimizing these networks by employing innovative configurations and adhering to regulatory standards to improve energy efficiency and sustainability in urban areas.