Day-Ahead Operation Scheduling of Microgrids Considering Conservation Voltage Reduction and Uncertainty-Based Demand Response Programs
Subject Areas : International Journal of Smart Electrical Engineering
Tahere
Daemi
1
(Faculty of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran)
Shahram
Pourfarzin
2
(Faculty of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran)
Hamidreza
Akbari
3
(Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran.)
Keywords: Demand Response, Uncertainty, CVR, IGDT.,
Abstract :
The planning and operation of microgrids have become very important challenges in the electricity industry due to the expansion of distributed generation (DG) resources and the development of demand response programs (DRPs). Microgrids generally include renewable DG resources whose generation is random. This leads to uncertainty in system planning. This study discusses microgrid operation management considering DRPs and implementation of conservation voltage reduction (CVR) in the future operation horizon. For this purpose, a stochastic operation planning model for the next day is designed, which is associated with the implementation of DRPs, CVR, and the presence of DG resources to optimize the performance of a smart microgrid to increase reliability and reduce costs. In this study, DRPs are implemented using time-of-use (TOU) and incentive-based programs. Incentive-based programs are used to deal with uncertainty in the commitment of renewable resources, and TOU programs are used to deal with the fluctuation of generation of renewable resources by establishing a relationship between uncertainty and the fluctuation of generation of these resources. Besides, CVR is applied and voltage-dependent load modeling is performed considering innovation in addition to the format of DRPs to further reduce peak loads. The uncertainty of DG resources is modeled using the information-gap decision theory (IGDT) method. This optimization is carried out on a sample microgrid using genetic algorithm (GA). According to the results, the implementation of uncertainty-based DRPs leads to cost reduction and improvement of microgrid reliability.
