A fast method for the unit scheduling problem with significant renewable power generation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Osorio, GJ (Autor) Outra; Lujano Rojas, JM (Autor) Outra; Matias, JCO (Autor) Outra; Catalao, JPS (Autor) FEUP

Revista

Título: Energy Conversion and Management

Vol. 94

Páginas: 178-189

ISSN: 0196-8904

Editora: Elsevier

Indexação

ISI Web of Knowledge - 11 Citações

Scopus - 12 Citações

Outras Informações

ID Authenticus: P-00A-5YA

DOI: 10.1016/j.enconman.2015.01.071

Abstract (EN): Optimal operation of power systems with high integration of renewable power sources has become difficult as a consequence of the random nature of some sources like wind energy and photovoltaic energy. Nowadays, this problem is solved using Monte Carlo Simulation (MCS) approach, which allows considering important statistical characteristics of wind and solar power production such as the correlation between consecutive observations, the diurnal profile of the forecasted power production, and the forecasting error. However, MCS method requires the analysis of a representative amount of trials, which is an intensive calculation task that increases considerably with the number of scenarios considered. In this paper, a model to the scheduling of power systems with significant renewable powergeneration based on scenario generation/reduction method, which establishes a proportional relationship between the number of scenarios and the computational time required to analyse them, is proposed. The methodology takes information from the analysis of each scenario separately to determine the probabilistic behaviour of each generator at each hour in the scheduling problem. Then, considering a determined significance level, the units to be committed are selected and the load dispatch is determined. The proposed technique was illustrated through a case study and the comparison with stochastic programming approach was carried out, concluding that the proposed methodology can provide an acceptable solution in a reduced computational time.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12