The decarbonization of hard-to-abate industries is crucial for keeping global warming to below 2°C. Green or renewable hydrogen, synthesized through water electrolysis, has emerged as a sustainable alternative for fossil fuels in energy-intensive sectors such as aluminum, cement, chemicals, steel, and transportation. However, the scalability of green hydrogen production faces challenges including infrastructure gaps, energy losses, excessive power consumption, and high costs throughout the value chain. Therefore, this study analyzes the challenges within the green hydrogen value chain, focusing on the development of nascent technologies. Presenting a comprehensive synthesis of contemporary knowledge, this study assesses the potential impacts of green hydrogen on hard-to-abate sectors, emphasizing the expansion of clean energy infrastructure. Through an exploration of emerging renewable hydrogen technologies, the study investigates aspects such as economic feasibility, sustainability assessments, and the achievement of carbon neutrality. Additionally, considerations extend to the potential for large-scale renewable electricity storage and the realization of net-zero goals. The findings of this study suggest that emerging technologies have the potential to significantly increase green hydrogen production, offering affordable solutions for decarbonization. The study affirms that global-scale green hydrogen production could satisfy up to 24% of global energy needs by 2050, resulting in the abatement of 60 gigatons of greenhouse gas (GHG) emissions - equivalent to 6% of total cumulative CO ₂ emission reductions. To comprehensively evaluate the impact of the hydrogen economy on ecosystem decarbonization, this article analyzes the feasibility of three business models that emphasize choices for green hydrogen production and delivery. Finally, the study proposes potential directions for future research on hydrogen valleys, aiming to foster interconnected hydrogen ecosystems.

In the coming years, major transportation sectors will be electrified, in which more accessible and easy solutions for charging Electric Vehicles (EVs) become vital. Wireless charging is considered one of the best and easiest methods to charge EVs anywhere, even during the driving of cars. To get accurate charging power rates while charging wirelessly, advanced mathematical models are needed to accurately present the charging power. Such models must take into consideration all the coils parameters, shapes, the used compensation topology, and if the system is static or dynamic. This paper presents a comprehensive analysis of the wireless charging systems adapted for EVs by stating the most common charging topologies, and architectures, and by concentrating on the corresponding mathematical models used to calculate the given electrical power as a function of the EV’s situation on streets and its speed. Then, it is possible to evaluate the EV autonomy, and an accurate approximation can be determined when EV is on a wireless charging road. Even with the citation and comparison between the two studied models, this paper opens some perspectives for energy transmitter tools and tries to explain how renewable energy can help the deployment of this technology.

An intelligent control strategy is proposed in this paper which suggests the Optimum Power Quality Enhancement (OPQE) of grid-connected hybrid power systems with solar photovoltaic, wind turbines, and battery storage. Unified Power Quality Conditioner with Active and Reactive power (UPQC-PQ) is designed with Atom Search Optimization (ASO) based Fractional-order Proportional Integral Derivative (FOPID) controller in the proposed Hybrid Renewable Energy Sources (HRES) system. The main aim is to regulate voltage while reducing power loss and reducing Total Harmonic Distortion (THD). UPQC-PQ is used to mitigate the Power Quality (PQ) problems such as sag, swell, interruptions, real power, reactive power and THD reductions related to voltage/current by using ASO based FOPID controller. The developed technique is demonstrated in various modes: simultaneous to improve PQ reinforcement and RES power injection, PRES > 0, PRES = 0. The results are then compared to those obtained using previous literature methods such as PI controller, GSA, BBO, GWO, ESA, RFA, and GA and found the proposed approach is efficient. The MATLAB/Simulink work framework is used to create the model.

Demand-side management (DSM) is a significant component of the smart grid. DSM without sufficient generation capabilities cannot be realized; taking that concern into account, the integration of distributed energy resources (solar, wind, waste-to-energy, EV, or storage systems) has brought effective transformation and challenges to the smart grid. In this review article, it is noted that to overcome these issues, it is crucial to analyze demand-side management from the generation point of view in considering various operational constraints and objectives and identifying multiple factors that affect better planning, scheduling, and management. In this paper, gaps in the research and possible prospects are discussed briefly to provide a proper insight into the current implementation of DSM using distributed energy resources and storage. With the expectation of an increase in the adoption of various types of distributed generation, it is estimated that DSM operations can offer a valuable opportunity for customers and utility aggregators to become active participants in the scheduling, dispatch, and market-oriented trading of energy. This review of DSM will help develop better energy management strategies and reduce system uncertainties, variations, and constraints.

An efficient power control technique for inverter-based distributed generation (DG) in an islanded microgrid is investigated in this work. The objective is to raise the caliber of the electricity pumped from network-connected DGs. The characteristics that are taken into consideration include voltage and frequency control, dynamic response, and steady-state response, particularly when the microgrid is operating in island mode or when there is a load change. The control method consists of an internal current control loop and an external power control loop based on a synchronous reference frame and a conventional PI controller. The power controller is designed based on voltage-frequency (VF) control. In addition, an intelligent search technique that combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is utilized to automatically modify power controller parameters. The control technique in this research is that the DG modifies its control mode to modify the system voltage and frequency when the microgrid is islanded or load conditions change. The simulation results in MATLAB/SIMULINK software show that the proposed control system has been able to improve the power quality well.