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