Open AccessOptimal power dispatch considering load and renewable generation uncertainties in an AC–DC hybrid microgrid
Author(s) -
Maulik Avirup,
Das Debapriya
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6502
Subject(s) - microgrid , dispatchable generation , voltage droop , mathematical optimization , control theory (sociology) , renewable energy , particle swarm optimization , computer science , fuzzy logic , distributed generation , engineering , voltage , mathematics , electrical engineering , voltage source , control (management) , artificial intelligence
An AC–DC hybrid microgrid is gradually becoming popular. For economic viability and environmental sustainability, an AC–DC microgrid should be operated optimally. This study introduces an optimal power dispatch strategy for simultaneous reduction of cost and emission from generation activities in an AC–DC hybrid microgrid under load and generation uncertainties. The operational attributes of an AC–DC hybrid microgrid, and load and renewable generation uncertainties are incorporated in the optimal scheduling problem by using a customised power‐flow technique, and by modelling uncertainties by Hong's two‐point estimate method, respectively. The economic and environmental objectives are modelled in the fuzzy domain by fuzzy membership functions. A combination of particle swarm optimisation and fuzzy max–min technique is then employed for obtaining the optimal solution. The static active power droop constants of the dispatchable units are the control variables. Simulation results on a 6‐bus AC–DC hybrid microgrid system demonstrate that optimal scheduling results in 4.26% reduction of operating cost and 13.91% reduction of emission in comparison to capacity based droop settings. Further, a comparison between the proposed method and the elitist multi‐objective GA indicates that the optimised solution lies on the Pareto‐front, thereby validating the proposed technique.