A fast method for the unit scheduling problem with significant renewable power generation. (April 2015)

• Record Type: Journal Article
• Authors: Osório, G.J.; Lujano-Rojas, J.M.; Matias, J.C.O.; Catalão, J.P.S.
• Is Part Of: Energy conversion and management. Volume 94(2015)
• Page Start: 178
• Page End: 189
• Publication Date: 2015-04
• Subjects: Forecasting error -- Mixed-integer linear programming -- Stochastic unit commitment -- Renewable generation; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2015.01.071
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A model to the scheduling of power systems with significant renewable power generation is provided. A new methodology that takes information from the analysis of each scenario separately is proposed. Based on a probabilistic analysis, unit scheduling and corresponding economic dispatch are estimated. A comparison with others methodologies is in favour of the proposed approach. Abstract: 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 power generation 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.