Risk Cost Optimized Autonomous Switching for Weather-Driven Renewable Integrated Smart Grids

Meyyappan M; Avirajamanjula P; Marimuthu P; Bharathiraja M

doi:10.5935/jetia.v12i59.3848

Authors

Meyyappan M Department of Electrical and Electronics Engineering, Ponnaiyah Ramajayam Institute of Science and Technology (PRIST University), Thanjavur, 613403, India
Avirajamanjula P Department of Electrical and Electronics Engineering, Ponnaiyah Ramajayam Institute of Science and Technology (PRIST University), Thanjavur, 613403, India
Marimuthu P Department of Mechanical Engineering, Sri Krishna College of Engineering, Arakkonam, 631003, India.
Bharathiraja M Department of Mechatronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, 638401, India.

DOI:

https://doi.org/10.5935/jetia.v12i59.3848

Abstract

Growing penetration of solar photovoltaic and wind energy in smart grids introduces operational uncertainty and power quality challenges due to weather-driven variability. Conventional switching mechanisms rely on deterministic thresholds and local meteorological sensors, leading to reactive behavior, increased infrastructure cost, and excessive switching operations. A sensorless IoT-enabled weather-informed control framework is developed to enhance renewable coordination without dedicated environmental sensors. Cloud-based meteorological information is integrated with real-time electrical measurements to infer renewable variability. A probabilistic operational risk model is formulated using power deficit probability and severity indices derived from voltage deviation, frequency variation, and total harmonic distortion. A multi-objective optimization structure simultaneously minimizes operational risk and economic cost while enforcing voltage, frequency, and IEEE 519 harmonic compliance constraints. MATLAB/Simulink validation under varying renewable scenarios demonstrates substantial reduction in total harmonic distortion, decreased switching frequency, improved voltage stability, and lower operational cost compared with conventional and rule-based IoT strategies. The proposed framework enables proactive power quality regulation and scalable renewable integration in intelligent smart grid environments.