A probabilistic approach to solve the economic dispatch problem with intermittent renewable energy sources. (15th March 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. Volume 82(2015)
• Page Start: 949
• Page End: 959
• Publication Date: 2015-03-15
• Subjects: Economic dispatch problem -- Greenhouse gas emissions -- Power system reliability -- Wind power forecasting error -- Probability distribution function; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2015.01.104
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In this paper, a methodology for solving the ED (economic dispatch) problem considering the uncertainty of wind power generation and generators reliability is presented. The corresponding PDF (probability distribution function) of available wind power generation is discretized and introduced in the optimization problem in order to probabilistically describe the power generation of each thermal unit, wind power curtailment, ENS (energy not supplied), excess of power generation, and total generation cost. The reliability of each unit is incorporated by estimating the joint PDF of power generation and failure events, while the PDF of ENS is incorporated by convoluting the PDF of ENS due to the forecasting error and any failure event. The performance of the proposed approach is analyzed by studying two power systems of 5 and 10 units. The proposed method is compared to MCS (Monte Carlo Simulation) approach, being able to reproduce the PDF in a reasonable manner, specifically when system reliability is not taken into account. Highlights: A methodology for solving the economic dispatch problem is presented in this paper. It considers the uncertainty of wind power generation and generators reliability. The PDF (probability distribution function) of available wind power generation is considered. Reliability is incorporated by estimating the joint PDF of power generation and failure events. The proposed approach is compared to a Monte Carlo Simulation, being able to reproduce the PDF.