Economic analysis of unit commitment with distributed energy resources

• Published in: International journal of electrical power & energy systems Vol. 71; pp. 1 - 14
• Main Authors: Govardhan, Manisha, Roy, Ranjit
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2015
• Subjects: Distributed energy resources (DERs); Emergency demand response program (EDRP); Plug-in electric vehicles (PEVs); Teaching–learning based optimization (TLBO) algorithm; Unit commitment problem (UCP); Emergency demand response program (EDRP); Distributed energy resources (DERs); Plug-in electric vehicles (PEVs); Teaching–learning based optimization (TLBO) algorithm; Unit commitment problem (UCP)
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2015.01.028

•Unit commitment problem (UCP) is solved using distributed energy resources (DERs).•This paper evaluates the individual and combined effect DERs on UCP.•Wind power generator, electric vehicle and demand response are considered as DERs.•Teaching–learning based optimization algorithm is used to solve UCP. Classic unit commitment (UC) is an important and exciting task of distributing generated power among the committed units subject to several constraints over a scheduled time horizon to obtain the minimum generation cost. Large integration of distributed energy resources (DERs) in modern power system makes generation planning more complex. This paper presents the individual and collective impact of three distributed energy resources (DERs), namely, wind power generator as a renewable energy source, plug-in electric vehicles (PEVs) and emergency demand response program (EDRP) on unit commitment. In this paper, an inconsistent nature of wind speed and wind power is characterized by the Weibull probability distribution function considering overestimation and underestimation cost model of the stochastic wind power. The extensive economic analysis of UC with DERs is carried out to attain the least total cost of the entire system. To obtain the optimum solution, Teaching–learning based optimization (TLBO) algorithm is employed to solve the unit commitment problem considering IEEE standard 10 unit test system in this study. It is found that the combined effect of wind power generator, plug-in electric vehicles and emergency demand response program on UC significantly lessen the total cost of the system.