Impact of Clustering-based Scenario Reduction on the Perception of Risk in Unit Commitment Problem

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

Keko, H (Autor) Outra; Vladimiro Miranda (Autor) FEUP

Ata de Conferência Internacional

Título: 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP)

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00K-C4S

DOI: 10.1109/isap.2015.7325575

Abstract (EN): Optimization problems in electric power systems under high levels of uncertainty have been solved using stochastic programming methods for years. This is especially the case for medium-term problems and systems with a large share of hydro storages. The increased uncertainty in power system operation coming from volatile renewables has made the stochastic techniques interesting in shorter time frames as well. In the stochastic models the uncertainty is typically included by a discretized set of scenarios. This increases the computational burden significantly so a common approach is to preprocess and reduce the number of scenarios. Scenario reduction methods have been shown to function relatively well in expected value stochastic optimization. However, such setting of stochastic optimization is often criticized as being risk-prone so other risk-averse models exist. The evolutionary computation algorithms' flexibility permits the implementation of these risk models with relative simplicity. In this paper, a population-based evolutionary computation algorithm is applied to unit commitment problem under uncertainty and the paper illustrates several approaches to including risk policies in an evolutionary algorithm fitness function and illustrates its flexibility along with the link between scenario reduction similarity metric and risk policy.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6