@inproceedings{d50c7e5d985d408895d679b2b0c34e57,
title = "Microgrid energy management system for academic building",
abstract = "In this paper, an optimal energy management system (EMS) for grid-connected microgrid is proposed. The gridconnected microgrid system comprises of photovoltaic (PV) panel, and battery as an energy storage unit. The optimal EMS is aimed to minimize the total operating cost of grid-connected microgrid for academic building. The feedforward neural network with improved salp swarm alogrithm based on weight factor is used to determine the 24-hours ahead data forecasting of load demand and PV power, while improved salp swarm alogrithm based on weight factor (WSSA) is used to perform the day-ahead optimal scheduling to control the power flow between PV, energy storage unit, load and main grid. The proposed microgrid EMS (MGEMS) is simulated using MATLAB/Simulink. The simulation result shows the effectiveness and validity of presented EMS with academic load.",
keywords = "Battery energy storage, Energy management system, Microgrid, Neural network, Photovoltaic panel, Salp swarm optimizer.",
author = "Tayab, {Usman Bashir} and Junwei Lu and Fuwen Yang and Mojaharul Islam and Ali Zia and Jahangir Hossain",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 29th Australasian Universities Power Engineering Conference, AUPEC 2019 ; Conference date: 26-11-2019 Through 29-11-2019",
year = "2019",
month = nov,
doi = "10.1109/AUPEC48547.2019.211931",
language = "English",
series = "2019 29th Australasian Universities Power Engineering Conference, AUPEC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 29th Australasian Universities Power Engineering Conference, AUPEC 2019",
address = "United States",
}