M.S. Syed - Department of EEE, VLITS, Vadlamudi, Guntur. Dt, AP ۵۲۲۲۱۳, India
C.V. Suresh - Department of EEE, VVIT, Nambur, Guntur. Dt, AP ۵۲۲۵۰۸, India
S. Sivanagaraju - EEE Department, UCEK, JNTUK, Kakinada, E.G.Dt, AP ۵۳۳۰۰۳, India

In this paper, Renewable energy sources (RES) are incorporated into the electricity grid. A real-time Andhra Pradesh ۱۴ bus system is considered in which, windy and sunny locations are identified for this study. A new algorithm called Persistence - Extreme Learning Machine (P-ELM) is suggested. The suggested methodology is used to predict wind speed and solar insolation in the selected regions across the short-term and long-term time period horizons. The load flow problem is handled in ۱۲ distinct by penetrating the wind and solar power into the system. The research findings are examined in terms of voltage variation and active power loss. The results obtained observed as, with wind and solar integration, the voltage variation is higher in both the short and long-term time frames, but the active power losses are lower than in the other cases.

Forecasting, Persistent-extreme learning machine algorithm, Power flow solution, renewable energy sources, Renewable penetration