Advanced Control Strategies for Enhanced Integration of Renewable and Fossil Fuel Energy Sources

Energy resources, which include both limited and renewable energy supplies, encounter substantial transitions in their design and operating strategies in order to meet future climate targets and electricity demand. While fossil fuel power generation is a major source of electricity, fossil fuels are also a source of harmful emissions. Despite advances in integrating renewable energy sources into the grid, fossil fuels are still in demand for their flexibility in load-following and availability. On the other hand, control synthesis is more demanding for renewable resources to reduce fluctuations and increase their energy contribution smoothly. In order to further decarbonization efforts, new techniques of control must be available in advance for power plant manufacturers.

This Research Topic solicits contributions in the field of control and automation of various energy resources with the aim of faster load demand tracking, increased energy-efficiency, and cleaner operation. Modeling techniques include physics-based or first principle modeling, system identification, machine learning techniques, and deep learning techniques. Parameter identification and verification is crucial for trustable models. Control systems include, but are not limited to, advanced PID controllers, classical or adaptive model predictive control (MPC) and intelligent controllers.

This Research Topic aims to develop models and control methods that enhance the recent responses of energy sources to be significantly faster and more capable than existing options. Thermal plants include subcritical and supercritical generation units fed by gas, oil, or coal. The practical goal is to strengthen the recent networks to be more secure and stable with less pollution and a lower environmental impact. As such, renewables including wind, solar, hydraulic, and geothermal sources have more specific, but generally similar, control goals. However, aside from this general aim, some specific desired outcomes of this Research Topic are as follows: improving online monitoring of safety and energy efficiency, improving fault detection and diagnosis, training technicians and future engineers, upgrading the automation of existing power units, enhancing ancillary services, and helping create an overall robust generation system that increases the feasibility of growing the share of renewables in the energy mix.

To gather further insights in the control and automation of energy resources, we welcome articles addressing, but not limited to, the following themes:
- Modeling by first principles with parameter identification via robust optimization techniques
- Modeling by machine learning and deep learning techniques
- System identification and state estimation (online and offline)
- Robust and adaptive control techniques
- Model predictive control (classical, adaptive, explicit, and so on)
- Intelligent control systems