This comprehensive review explores the applications and challenges of Digital Twin (DT) technology in smart grids. As power grid systems rapidly evolve to meet the increasing energy demands and the new requirements of renewable source integration, DTs offer promising solutions to enhance the monitoring, control, and optimization of these systems. In this paper, we examine the concept of DTs in the context of smart grids, and their requirements, challenges, and integration with the Internet of Things (IoT) and Artificial Intelligence (AI). We also discuss different applications in asset management, system operation, and disaster response. This paper analyzes current challenges, including data management, interoperability, cost, and ethical considerations. Through case studies from various sectors in Canada, we illustrate the real-world implementation and impact of DTs. Finally, we discuss emerging trends and future directions, highlighting the potential of DTs to revolutionize smart grid networks and contribute to more efficient, reliable, and sustainable power systems.

The increasing demand for sustainable transportation has positioned Electric Vehicles (EVs) as a key application area for photovoltaic (PV) systems. This study aims to enhance PV performance under Partial Shading Conditions (PSCs) by employing advanc...

In-pit fast charging of battery-electric haul trucks can overload a mine substation whose N-1 firm capacity, rated in MVA, is shared with the process load, while every charging restriction costs haulage. This paper proposes a grid-aware charging coordination for twelve 2.6 MWh trucks and four 6 MW chargers on a representative 33 kV open-pit network with a 30 MVA firm capacity, an on-load tap changer, pit overhead lines and trailing cables. The policy combines state-of-charge thresholds, deadline-based break charging, a fleet power cap, staggered meal breaks, charger reactive power and a forecast-based apparent-power headroom constraint that converts the firm capacity into a per-step charging limit. Its twelve parameters are tuned with L-SHADE on eight random days and tested on 1000 independent days. Uncontrolled charging exceeds the firm capacity every day (mean peak 43.0 MVA). The coordinated policy holds the mean peak at 29.7 MVA, lowers the peak import by 32 % and the losses by 10.9 %, and reduces the mean lost haulage from 30.2 to 29.5 truck-hours per day; with a 98 % margin, it exceeds 30 MVA on 2.9 % of the days, whereas strict-compliance settings never do on the 1000 test days (29.6–29.7 truck-hours per day). The cap lowers the peak import by about 1 MW; its haulage benefit depends on the charger-allocation rule. Staggered breaks give most of the haulage gain. Charger reactive power acts as an apparent-power margin and becomes decisive at 25 MVA. Stress and forecast-error tests show that the haulage gain is an average, not a daily guarantee, and that volatile process loads require metered feedback and larger margins.