Table 5.

A selection of research articles covering energy and power grid digital twins research.

Domain	Physical Asset	Sensors	Physical–Digital Data Flow	Form of Digital Asset	Research Objective	Ref.
Equipment monitoring	Small hydro turbine	Wireless sensor network (e.g., accelerometers, temperature, and inductive current sensors)	Sensor readings, production, and environmental data	Condition indicator visualization and predictive analysis	Build digital twin from various data source for turbine condition monitoring	[41]
Electric power converter	Photovoltaic (PV) dc–dc converter	Thermal camera	Scanning electron microscope (SEM) images, converter tech specs, and temperature data	FEM simulation and prediction of junction, case, and heat sink temperatures	Fast estimation of switching device temperature in PV converters	[42]
Wind engineering	Wind pressure field	Wind pressure sensors	Sensor readings	Wind pressure field visualization and measurements	Develop an optimal sensor placement algorithm to reconstruct wind pressure fields	[43]
Hydropower generation	Hydraulic network (e.g., valve, tank, and pipe)	Pressure sensors	Hydraulic model of the plant and the valve and flap configuration	Hydraulic variables and hydropower generation visualization and analysis	Develop a control system to maximize hydropower production while meeting hydraulic constraints	[44]
Smart grid	Smart grid (e.g., consumer, producer, and regulator)	Event logger	Live semantic annotation (LSA) of events and coordination laws that cause the events to evolve	Autonomous proactive agents on a coordination platform CLEMAP	Develop a series of digital twins that interact and coordinate activities to exchange energy and enhance grid stability	[45]