About this Research Topic
grids, and the relentless pursuit of energy efficiency. Advancements in control systems, artificial intelligence, and data analytics provide unprecedented opportunities to optimize energy conservation and conversion processes. Additionally, the rise of distributed energy
resources and the drive toward sustainability underscore the complexity of contemporary energy landscapes. The background thus reflects a convergence of technological progress, environmental consciousness, and the imperative to develop innovative control strategies that not only meet the rising energy demand but also pave the way for a more sustainable and resilient energy future.
The goals of the Research Topic, are multifaceted and forward-looking. Primarily, the research seeks to pioneer groundbreaking control strategies that optimize the utilization of energy across diverse applications, from industrial processes to residential settings. A key
objective is the seamless integration of renewable energy sources into existing grids, mitigating challenges associated with intermittency and ensuring a stable power supply. The development and implementation of advanced control algorithms, drawing from the realms of
control engineering, artificial intelligence, and machine learning, stand as crucial goals, enabling systems to adapt dynamically, self-optimize, and make real-time decisions.
Moreover, the research aspires to enhance grid stability and resilience, addressing disruptions and fluctuations, while simultaneously promoting energy conservation practices through smart technologies and demand-side management. It seeks to minimize environmental impact, with a focus on reducing greenhouse gas emissions and fostering sustainable energy practices.
By fostering intelligent, adaptive systems, the research aims to contribute to sustainable development, ensuring that energy systems evolve to meet the challenges of the future while remaining economically viable and environmentally responsible. In essence, the overarching goals revolve around transforming energy systems into efficient, reliable, and environmentally conscious entities that align with the principles of a sustainable and resilient future.
The scope of the Research Topic is broad and encompasses a multidisciplinary exploration of advanced control strategies aimed at revolutionizing the way we generate, distribute, and consume energy. Researchers in this field delve into the intricate dynamics of energy systems, seeking innovative solutions to optimize efficiency, integrate renewable sources seamlessly, and enhance overall sustainability.
The scope includes;
- The development and application of cutting-edge control algorithms that leverage advancements in artificial intelligence, machine learning, and data analytics.
- Encompassing diverse domains such as smart grids, distributed energy resources, and demand-side management.
- Adaptive and intelligent control systems capable of learning and adapting to dynamic energy landscapes.
The research seeks to address challenges associated with the variability of renewable energy, grid stability, and environmental impact. Emphasis is placed on promoting energy conservation practices, facilitating demand response mechanisms, and contributing to cost-effective solutions.
Ultimately, the research aims to propel the transition towards resilient, efficient, and environmentally conscious energy systems, with a focus on fostering sustainable development and meeting the ever-growing global demand for energy in an era of rapid technological evolution.
Keywords: Energy conservation; Energy conversion; Control systems; Novel control approaches; Smart grids; Renewable energy; Energy efficiency; Power electronics; Demand-side management; Microgrid control; Intelligent energy systems; Adaptive control; Machine learning in energy systems; Energy storage systems; Cyber-physical systems; Internet of Things (IoT) for energy; Distributed energy resources; Advanced control algorithms; Optimal control; Model predictive control; Fuzzy logic control; Neural network control; Energy harvesting; Hybrid energy systems; Virtual power plants; Energy management systems; Power quality control; Load forecasting; Energy-aware scheduling; Fault detection and diagnosis in energy systems; Electric vehicle
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.