Open AccessOptimal optimisation‐based microgrid scheduling considering impacts of unexpected forecast errors due to the uncertainty of renewable generation and loads fluctuation
Author(s) -
Lee ChunYao,
Tuegeh Maickel
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2019.0635
Subject(s) - microgrid , renewable energy , photovoltaic system , scheduling (production processes) , mathematical optimization , inertia , computer science , particle swarm optimization , wind power , control theory (sociology) , engineering , mathematics , electrical engineering , physics , control (management) , classical mechanics , artificial intelligence
Unlike conventional power plants, wind farm and solar photovoltaic (PV) module operations involved uncontrollable factors such as wind and solar irradiation, making the dispatch problem more complex. This study proposes optimal scheduling using a modification inertia weight of the particle swarm optimisation (PSO) algorithm and take into account unexpected forecast errors due to uncertainty in renewable distributed generators and loads in the day‐ahead market in a microgrid. Modified inertia weight makes the PSO algorithm have a strong global searching ability to solve the scheduling. Meanwhile, the microgrid coordinates the realistic production of its power plant to get the optimal total cost. The uncertainty of renewable distributed generators is modelled based on forecast data. The Simulation result shows the advantages of the proposed method.