Comparative Analysis of Economic Viability with Distributed Energy Resources on Unit Commitment

• Published in: Electric power components and systems Vol. 44; no. 14; pp. 1588 - 1607
• Main Authors: Govardhan, Manisha, Roy, Ranjit
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 26.08.2016; Taylor & Francis Ltd
• Subjects: Algorithms; Alternative energy sources; Cost engineering; distributed energy resources; Distributed generation; Electric utilities; Electric vehicles; Electricity distribution; Electricity generation; emergency demand response program; Emergency vehicles; Energy resources; modified teaching-learning-based optimization algorithm; Optimization; Optimization algorithms; plug-in electric vehicles; Probability distribution; teaching-learning based optimization algorithm; Test systems; Unit commitment; unit commitment problem; Viability; Weibull probability distribution function; Wind power
• ISSN: 1532-5008; 1532-5016
• DOI: 10.1080/15325008.2016.1174907

Classic unit commitment is the important and challenging task of allocating generating units subject to basic constraints over a scheduled time horizon to obtain the least generation cost. Penetration of distributed energy resources in modern power systems makes generation planning more complex. This article presents the individual and combined effect of three distributed energy resources, namely wind power generator as a renewable energy source, plug-in electric vehicles, and emergency demand response program on unit commitment. The inconsistent nature of wind speed and wind power is characterized by the Weibull probability distribution function considering overestimation and underestimation costs of stochastic wind power. The comprehensive comparative analysis of the economic viability on unit commitment is carried out to minimize the total cost of the entire system. To obtain the optimum solution, a modified teaching-learning-based optimization algorithm is used. The IEEE standard ten-unit test system is used for this study. To validate the efficacy of the modified teaching-learning-based optimization algorithm, a 26-unit reliability test system is also considered. It is found that the collective effect of wind power generator, plug-in electric vehicles, and emergency demand response program on unit commitment provides significant reduction in the total cost.