The generation of synthetic inflows via bootstrap to increase the energy efficiency of long-term hydrothermal dispatches

• Published in: Electric power systems research Vol. 124; pp. 33 - 46
• Main Authors: de Castro, Cristina M.B., Marcato, André L.M., Souza, Reinaldo Castro, Silva Junior, Ivo Chaves, Oliveira, Fernando Luiz Cyrino, Pulinho, Tales
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2015
• Subjects: Bootstrap; Hydrothermal planning; Model order; PAR(p); Scenario generation; Stochastic dual dynamic programming; Bootstrap; Stochastic dual dynamic programming; Hydrothermal planning; Scenario generation; PAR(p); Model order
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2015.02.014

•We present a new model applied to the long-term operational planning of an electrical power system.•This model uses stochastic dual dynamic programming (SDDP) together with bootstrap technique and periodic auto-regressive models.•The computation effort and model parsimony decreases with this new algorithm.•Results with Brazilian system are presented.
One of the most important techniques used to study long-term energy operation planning is the stochastic dual dynamic programme (SDDP). In large systems, hydraulic power plants are aggregated in so-called equivalent energy systems, where the inflows into hydro reservoirs are represented by the affluent natural energy (ANE) and the stored volumes are represented by the stored energy. The stochasticity of energy inflows is captured by the historical series ANE. Currently, ANEs are studied using the Box–Jenkins methodology to fit periodic autoregressive models (PAR(p)) and their order (p). A three-parameter log-normal distribution is applied to the residuals generated via PAR(p) modelling to generate synthetic hydrological series similar to the original historical series. However, the log-normal transformation incorporates non-linearities that affect the convergence in SDDP. This study incorporates the bootstrap statistical technique to determine the order p of the PAR(p) model to generate synthetic scenarios that will serve as a basis for SDDP application. The results indicate the adherence of the proposed method on the operational planning of hydrothermal systems. The proposed methodology in this article could successfully be applied in hydro-dominated systems such as Brazilian, Canadian and Nordic systems.