Optimal coordinated generation scheduling considering day‐ahead PV and wind power forecast uncertainty

Economic operation and reliable supply-demand balance are problems of paramount importance in power grids with a massive share intermittent renewable energy sources (RESs) great interest. This paper sought an optimal coordinated generation scheduling for day-ahead system considering RESs storage units. Renewable generation, particularly, wind photovoltaic uncontrollable, whereas can be predicted using forecasting models. Within the proposed framework, hyperparameter-optimized long short-term memory (LSTM) regression model is employed to forecast weather from historical time-series data. Eventually, empirical formula used estimate conversion forecasts selected PV module turbine. The objective framework keep delicate at lowest possible cost while maintaining prevailing constraints. A variance measure uncertainty handling-based grey wolf optimizer (GWO) technique find schedules dispatches under uncertainty. examined on IEEE 6 30-bus systems. In studied scenarios, generators decrease total operating modified 6-bus by 2.57% compared supplying electricity conventional alone. Likewise, all portfolios was reduced 6.93% during base case simulation (supply only dispatchable thermal units) system. Moreover, studies show that mitigate variability problem, provide secure operation, minimize generation.