Editorial: Assessment of Renewable Energy Systems for Energy Conversion and Storage

Tabish Alam^*Injamamul HaqueNaushad Ali

• Central Building Research Institute (CSIR), Roorkee, India

Editorial: Assessment of Renewable Energy Systems for Energy Conversion and Storage Tabish Alama, Injamamul Haquea, Naushad Alia a CSIR-Central Building Research Institute, Roorkee 247667, Uttarakhand, India Keywords: Renewable Energy, Energy Storage, Optimization, Electrolyser, Energy Conversion The global pursuit of decarbonizing power systems has driven transition from fossil fuels to renewable energy systems that is also critical for achieving sustainable development. Renewable sources offer virtually inexhaustible energy, and yet their variable nature poses challenges for reliable supply of electricity [1]. To harness energy from these sources, advanced energy conversion systems are required, such as photovoltaic cells, wind turbines, and gasification plants. Under optimal conditions these systems may offer efficiencies up to 20-45% but it is worth noting the intermittency of solar and wind power which leads to energy storage solutions to balance supply and demand [1]. Energy storage and advanced conversion technologies have emerged as key drivers of renewable energy penetration, offering flexibility, risk mitigation, and multi-service value. It is also essential to innovate for current energy demands, improve operation of conversion system and optimise future designs. In order to address the challenges related to extreme environments and nature of produced energy, the team of editors proposed the Research Topic “Assessment of Renewable Energy Systems for Energy Conversion and Storage” in the journal Frontiers in Energy Research. Finally, 4 articles are collected, covering the numerical and experimental studies on renewable energy, materials and systems under extreme environments. Accurate modelling of individual renewable sources provides the foundation, but real-world energy systems require coordination of multiple sources, in this pursuit a novel algorithmic approach for the precise extraction unknown parameters of solar is utilised by Hussain et al., they introduce the Archimedes Optimization Algorithm (AOA) in their study, for extraction of unknown parameters of solar photovoltaic (PV) cell models using both Single Diode Model (SDM) and Double Diode Model (DDM)[2]. Manufacturers typically omit essential model parameters, hindering accurate performance prediction. Novel formulation of root mean square error (RMSE) is introduced, which computes the error between estimated and experimental currents at each I–V point. Results suggest AOA outperforms other metaheuristic methods in extraction of parameters, offering strong, accurate modelling for PV system design and control. AOA based PV parameter extraction offers a future ready approach, enhancing simulation accuracy enabling efficient, scalable integration of renewable energy systems into the grid by enabling reliable renewable energy modelling and precise solar performance prediction. Research submitted by Cai et al. present an advancement in the field by enhancing energy systems integration via two-tier coordinated optimal scheduling framework that manage wind, PV, hydropower, and energy storage systems more efficiently. Their work utilises an elevated Generative Adversarial Network (GAN) for creation of scenarios, then an improved Coati Optimization Algorithm (COA) compute solution, this method addresses uncertainty and variability of renewable sources. By use of studied model, researchers were able to minimize load fluctuations and optimise the energy dispatch thus achieving 51.7% reduction in active network losses. The study is validated on a modified IEEE 30 bus system and provides solutions which are relevant for future smart grid development, where multi source integration is essential for sustaining transition toward low carbon and resilient energy infrastructure[3]. There are a lot of integration challenges that are revealed through multi energy coordination directly highlighting the critical importance of intelligent energy storage configuration strategies. A bi-level programming optimising model addresses these needs by simultaneously considering flexible requirements and operational risk through conditional value at risk methodology presented by Hui et al. the upper level of model optimises the energy storage system, does planning and makes decisions while lower level of model simulates daily operations under a typical scenario to incorporate flexibility requirements and constraints to characterize required flexibility of power system. This approach transforms the original bi-level programming model into single direct solvable single level Mixed Integer Linear Programming model through association of constraints, providing an efficient solution while maintaining planning accuracy. The results effectively demonstrate how optimised storage system provide the flexible backbone necessary for future development and reducing operational risk in power system[4]. Some studies suggest advancement beyond the traditional storage solution; Cozzolino and Bella reviewed electrolyser-based systems with evolutionary grid support technologies, enabling dual purpose capabilities. Being based on electrolysers, they can produce hydrogen for end user while providing ancillary services. These innovative systems deliver frequency control, voltage control, congestion management, and black-start capabilities, leveraging rapid response measures in milliseconds than enable ultrafast grid support that surpasses the conventional generation units[5]. These progresses in the field of renewable energy incorporating precise renewable energy modelling, to multi-source energy coordination, and intelligent storage unit to advanced electrolyser-based grid services have created a comprehensive ecosystem for sustainable energy transition. The submitted papers demonstrate innovative optimisation and modelling approaches for renewable energy conversions and storage. A review on electrolyser-based system underscores how power to hydrogen conversion can provide both hydrogen and grid balance services. Each article contributes to advances in scenario-based optimisation, which are flexible and does aware plannings. Collectively, they illustrate how coordinated multi source operation, advanced conversion technology can be integrated to improve energy conversion and storage utilisation. These articles critically assesses the performance, environmental impacts, and techno-economic viability of leading renewable energy conversion and storage systems, providing insights to guide future research, deployment strategies, and policy measures. References [1]Hemeida MG, Hemeida AM, Senjyu T, Osheba D. Renewable Energy Resources Technologies and Life Cycle Assessment: Review. Energies (Basel) 2022;15. https://doi.org/10.3390/en15249417. [2]Hussain MT, Hussan MR, Tariq M, Sarwar A, Ahmad S, Poshtan M, et al. Archimedes optimization algorithm based parameter extraction of photovoltaic models on a decent basis for novel accurate RMSE calculation. Front Energy Res 2023;11. https://doi.org/10.3389/fenrg.2023.1326313. [3]Cai C, Li Y, He Y, Guo L. Two-tier coordinated optimal scheduling of wind/PV/hydropower and storage systems based on generative adversarial network scene generation. Front Energy Res 2023;11. https://doi.org/10.3389/fenrg.2023.1266079. [4]Hui Z, Yan H, Li B, He W, Wu X. Optimal configuration of energy storage considering flexibility requirements and operational risks in a power system. Front Energy Res 2024;12. https://doi.org/10.3389/fenrg.2024.1351569. [5]Cozzolino R, Bella G. A review of electrolyzer-based systems providing grid ancillary services: current status, market, challenges and future directions. Front Energy Res 2024;12. https://doi.org/10.3389/fenrg.2024.1358333.