Independent microgrids face significant challenges in maintaining frequency stability due to demand fluctuations and the intermittent nature of renewable energy sources, resulting in reduced system inertia and limited external support. Conventional control approaches struggle to maintain dynamic performance under such nonlinear and uncertain operating conditions. To address this, a Fuzzy Logic Controller (FLC)-based Load Frequency Control (LFC) strategy optimized through a Diversity Preserved Multi-Swarm Chaotic Smell Agent Optimization with Adaptive Control Strategy algorithm is proposed for interconnected microgrids. The framework integrates Plug-in Hybrid Electric Vehicle (PHEV) coordination using a State-of-Charge-based deviation mechanism to balance bidirectional energy flow via Vehicle-to-Grid operation. The proposed algorithm simultaneously tunes Fuzzy-assisted Fractional Order Proportional Integral Derivative (PID) and PID controllers, enhancing robustness against parameter uncertainties, nonlinearities, and communication delays. Comprehensive simulations on a two-area microgrid were conducted under four critical scenarios: sudden load disturbances, renewable intermittency, parameter variations, and PHEV participation and validated through real-time OPAL-RT experiments. Results demonstrate that the proposed FLC-based scheme achieves improved frequency regulation, offering a 30–40% reduction in IAE and faster settling times compared with traditional controllers. Overall, the developed control strategy provides a highly responsive, adaptive, and implementation-feasible solution for resilient LFC in modern renewable-integrated microgrids.
Hydrogen is rapidly being viewed as a key energy medium in the global energy shift to a low-carbon and sustainable energy system. The increasing use of renewable sources of energy requires efficient energy storage policies, clean fuels and effective grid stabilization policies, which can be effectively supported with hydrogen. The article presents the current research on the production, storage, and use of hydrogen and specifically green hydrogen produced by electrolysis using renewable energy. It also discusses the implementation of hydrogen fuel cells in transportation and decentralized power production, with the potential of having a substantive reduction of greenhouse emissions. Hydrogen as an alternative to fossil fuel in hard-to-abate industries, including steel, cement and chemical production, is also critically evaluated. Besides, the paper also highlights the integration of hydrogen to power grids to store renewable energy on a large scale and balance the grid. With economic, infrastructural and scalability issues, even with the significant advancement in technology, there is still a lot to be desired in the way of widespread adoption. These hurdles are considered together with investment, policy, and technological directions that can help speeding up the implementation of hydrogen. In general, hydrogen is defined as a pillar of clean energy in the future, and its potential opportunities to contribute to the deep decarbonization of various industries are high.
Transitioning toward global carbon neutrality requires developing energy conversion and storage technologies that surpass current solutions in efficiency, durability, and scalability. In this context, nanocatalysts have emerged as indispensable for advancing clean energy systems, owing to their tunable surface chemistry, structural diversity, and potential for defect engineering. In this review, we present a reproducible roadmap that links nanoscale design strategies to practical applications and eventual industrial adoption. Beginning with fundamental design approaches including morphology manipulation, alloying, heterostructuring, and hierarchical architectures, the review outlines how these strategies enhance electrocatalytic activity, specificity, and stability. The discussion further encompasses nanocatalyst applications in water splitting for sustainable hydrogen production, fuel cells for efficient electrochemical conversion, and advanced energy storage technologies, including batteries, supercapacitors, and hybrid systems. From an industrial perspective, the review also examines scalable synthesis, electrode fabrication, techno-economic evaluation, and life-cycle assessment. Distinct from previous reviews, this work emphasizes the role of artificial intelligence and machine learning in accelerating catalyst development through high-throughput discovery, predictive modeling of catalytic performance, and digital twin-based durability assessment. By integrating nanoscale innovations with pathways for industrial translation, this review provides a comprehensive roadmap for deploying nanocatalyst technologies at scale within renewable energy infrastructure. These insights are critical for mitigating greenhouse gas emissions and fostering sustainable, cost-effective, and commercially viable clean energy technologies.
The growing global demand for sustainable and efficient energy systems has accelerated advancements in solar cell technologies. This review presents a systematic and quantitative analysis of recent developments in solar cells enabled by nanotechnology. A PRISMA-based methodology is adopted to ensure transparency and reproducibility in literature selection. Key nanomaterials such as perovskites, quantum dots, graphene, and carbon nanotubes are critically evaluated based on power conversion efficiency (PCE), stability, cost, and environmental impact. Additionally, nano-enhanced thermal systems including photovoltaic-thermal (PVT) systems, nanofluids, and phase change materials (PCM) are analyzed using thermodynamic principles. The review highlights major challenges such as degradation mechanisms, scalability, and toxicity while proposing a future research roadmap focused on stability engineering and sustainable material design.
Integrated energy networks require advanced flexibility solutions to address the variability of demand and renewable generation. Thermal energy storage (TES) is a key technology for enhancing the flexibility and reliability of combined heat and power (CHP)-based multienergy systems. This article presents a unified modeling framework for TES within CHP-based integrated energy networks, incorporating a detailed TES formulation into the coordinated optimal operation of electrical, natural gas, and district heating subnetworks. The proposed framework models its charging and discharging processes, operational coupling with CHP units and district heating temperature regulation. The results show that TES can significantly smooth fluctuations in CHP-generated electrical and heat power, maintaining a more stable supply to the subnetworks. TES units located at strategic nodes enable effective load shifting, improving the operational flexibility of CHPs and mitigating peak demands. A large-scale case study solved with the Teaching–Learning-Based Optimization algorithm demonstrates that these performance improvements are achieved with a marginal increase in total operational cost (from $121,270 to $121,700, ∼0.35%), while it provides substantial benefits in enhancing system flexibility. Beyond the case study, the proposed framework is generalizable across diverse network topologies and the findings demonstrate that TES integration in the CHP-based networks is a strategic investment for improving overall energy network performance while accommodating variable demand and generation conditions.
