An efficient, robust optimization model for the unit commitment considering renewable uncertainty and pumped-storage hydropower

Abstract

This paper presents a new model for allocating pumped-storage hydropower units in the unit commitment program's next day's market. Because the market prices of the next day are highly volatile and have high uncertainty, the time series Autoregressive integral Moving Average (ARIMA) and Stationary Autoregressive integral Moving Average (SARIMA) have been used to predict the market price of the next day. A triple scenario tree is also used to cover electricity market uncertainties. The problem of allocating pumped-storage hydropower plants is planned as a mixed-integer linear programming (MILP) to maximize the power plant profit on a certain operating horizon. Because the variable nature of wind farms is also difficult to accurately estimate the output power of these power plants, the proposed method can be proposed on MNHPSO to consider wind power plant uncertainties with the presence of pumped-storage hydropower plant storage in the unit commitment program. The proposed strategy is simulated in MATLAB software. The simulation results show that in this paper while considering an accurate model for various uncertainties, in the unit commitment planning, it shows that SARIMA can predict the daily prices of the next day market with acceptable accuracy.