Assessing solution quality and computational performance in the long-term generation scheduling problem considering different hydro production function approaches

摘要

The long-term generation scheduling (LTGS) problem aims at finding a generation policy that minimizes an objective function over a multi-year planning horizon. A crucial aspect of this problem is the Hydropower Production Function (HPF), which relates power with head, turbined outflow, and efficiency of the generating units. Given that the LTGS is a large-scale stochastic optimization problem, the HPF is modeled in a simplified manner. However, considering the high-performance computers currently available and the recent advances in stochastic optimization algorithms, it is possible to enhance the HPF modeling to use the energy resources more efficiently. This paper proposes a piecewise linear model of HPF that considers the plant generation as a function of the volume and the total outflow. Unlike previous works, the HPF also considers the (nonlinear) efficiency function of each generating unit. The paper also presents a comparison between the proposed HPF and a one-dimensional HPF known as constant productivity. The generation policy and the computational burden are analyzed using an optimization simulation process based on Stochastic Dual Dynamic Programming algorithm. The computational tests use data of a large-scale electrical power system, which corresponds to about 90% of the Brazilian system. (C) 2018 Elsevier Ltd. All rights reserved.