Comparison of dynamic programming policies for long-term hydrothermal scheduling of single-reservoir systems in steady-state regime

Abstract

Markov stochastic dynamic programming (MSDP) has been extensively used for long-term hydrothermal systems (LTHS) of single-reservoir systems and has inspired many approaches to multiple-reservoir systems, despite the lack of comprehensive studies that test their performance. This study is concerned with the evaluation of classic dynamic programming policies in LTHS of single-reservoir systems considering the operation in steady-state regime. The Markov stochastic dynamic programming was compared with deterministic dynamic programming (DDP) considering average inflows, and unconditional stochastic dynamic programming (USDP) considering uncorrelated inflows. Performance measures were obtained through simulation over long horizons using historical and synthetically generated inflows. Sensitivity analysis related to hydro plant characteristics, the hydro generation share in the system, and the inflow considered in DDP was performed. Results show that MSDP provides small performance gains whatever the hydro plant considered, that become smaller with the reduction in the share of hydro generation in the system, and the proper consideration of inflows in DDP.