Cloud-Based Optimization for Smart Scheduling of Energy Distribution in modern Power Grids

This study outlines a cloud-based optimization framework for the
scheduling of smart energy distribution, supported by real-time energy data
from smart meters, IoT sensors, and SCADA systems. The framework uses on
board preprocessing techniques to eliminate noise, synchronize, and extract fea
tures from diverse energy datasets. The actual optimization models can be run
on scalable cloud-based platforms. The authors use Mixed Integer Linear Pro
gramming (MILP) for day-ahead and intra-day scheduling optimization and Re
inforcement Learning (RL) for real-time adaptive control. The intelligent
scheduler uses IoT messaging protocols according to NIST standards for secure
updates to grid conditions, to dynamically monitor grid states. The scheduler
will push updated schedules to grid devices to align energy flow with customer
demand-dispatch control. The relevant utility dashboard monitors energy utili
zation efficiency, cost-reduction, grid stability, scheduling latency and CO₂
emission reductions. Comparison with contemporary control techniques, like
time-based, rule-based, heuristic, and edge-based scheduling methods proved
that day ahead and hour ahead smart scheduling in a cloud supported optimiza
tion solution is efficient and effective compared to traditional scheduling meth
ods. This research has confirmed that cloud optimization of energy efficiency
and grid resiliency can be achieved while reducing operational costs and carbon
footprint emissions, representing a viable, multi-industry solution to the future
smart grid complex system.
Keywords: Smart Scheduling, Energy Distribution, Power Grids, Mixed
Integer Linear Programming (MILP) , Reinforcement Learning (RL).