Grid Integration of Renewable Energy Through Wind-Pumped Hydro Storage for Load Limiting

Rising energy demand intensifies stress on power systems, necessitating solutions that integrate renewables with storage for grid stability. This study develops an optimal scheduling strategy for a hybrid system combining wind farms with pumped hydro storage, specifically for demand response and peak load reduction. A finite-horizon optimization model is formulated to manage real-time operation using forecasts of wind generation and load demand. A three-layer artificial neural network provides these forecasts from historical data. The week ahead scheduling framework, solved in Pyomo with the IPOPT solver, adheres to all operational constraints. Application to a case study and numerical simulations demonstrate the model's effectiveness in diminishing energy demand and improving grid operational efficiency.