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Optimal Scheduling of a Solar-Powered Microgrid Using ML-Based Solar and Load Forecasting

Witharama, W.M.N. and Bandara, K.M.D.P. and Azeez, M.I. and Adhikari, Muditha and Bandara, Kasun and LOGEESHAN, V. and Rajakaruna Wanigasekara, Chathura (2023) Optimal Scheduling of a Solar-Powered Microgrid Using ML-Based Solar and Load Forecasting. In: 2023 IEEE World AI IoT Congress, AIIoT 2023. IEEE. 2023 IEEE World AI IoT Congress (AIIoT), 2023-06-07 - 2023-06-10, Seattle, WA, USA. doi: 10.1109/AIIoT58121.2023.10174588. ISBN 979-835033761-7.

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/10174588

Abstract

Microgrids, powered by distributed energy resources, are gaining traction as decentralized power systems. However, optimizing microgrid operation poses challenges due to intermittent renewable energy sources and dynamic load patterns. To tackle this, we propose an AI-driven day-ahead optimal scheduling approach for a grid-connected AC microgrid equipped with a solar panel and a battery energy storage system. Our approach leverages Genetic Algorithm, a popular optimization algorithm, to generate demand response strategies and optimal battery dispatch schedule. Additionally, we utilize LightGBM, a decision tree-based machine learning method, for solar and load forecasting prior to scheduling. Our objective is to minimize operational costs while ensuring the sustainability of the microgrid. Our simulation results showcase the effectiveness of our approach in reducing costs, with a 13.86% decrease in electricity costs observed in the University of Moratuwa microgrid under the tariff structure in Sri Lanka. Our proposed demand response optimizing strategies further contribute to cost reduction. Our approach showcases the power of AI in addressing the challenges of microgrid operation and optimization, with promising results in reducing costs and ensuring sustainability.

Item URL in elib:https://elib.dlr.de/196227/
Document Type:Conference or Workshop Item (Lecture)
Title:Optimal Scheduling of a Solar-Powered Microgrid Using ML-Based Solar and Load Forecasting
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Witharama, W.M.N.University of MoratuwaUNSPECIFIEDUNSPECIFIED
Bandara, K.M.D.P.University of MoratuwaUNSPECIFIEDUNSPECIFIED
Azeez, M.I.University of MoratuwaUNSPECIFIEDUNSPECIFIED
Adhikari, MudithaUniversity of MoratuwaUNSPECIFIEDUNSPECIFIED
Bandara, KasunEnergy AustraliaUNSPECIFIEDUNSPECIFIED
LOGEESHAN, V.University of MoratuwaUNSPECIFIEDUNSPECIFIED
Rajakaruna Wanigasekara, ChathuraUNSPECIFIEDhttps://orcid.org/0000-0003-4371-6108143015322
Date:July 2023
Journal or Publication Title:2023 IEEE World AI IoT Congress, AIIoT 2023
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI:10.1109/AIIoT58121.2023.10174588
Publisher:IEEE
ISBN:979-835033761-7
Status:Published
Keywords:Microgrid, Optimizing, Genetic Algorithm, Ma- chine Learning, Decision Trees
Event Title:2023 IEEE World AI IoT Congress (AIIoT)
Event Location:Seattle, WA, USA
Event Type:international Conference
Event Start Date:7 June 2023
Event End Date:10 June 2023
HGF - Research field:other
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:no assignment
DLR - Program:no assignment
DLR - Research theme (Project):no assignment
Location: Bremerhaven
Institutes and Institutions:Institute for the Protection of Maritime Infrastructures > Reslience of Maritime Systems
Deposited By: Rajakaruna Wanigasekara, Chathura
Deposited On:26 Sep 2023 09:36
Last Modified:27 May 2024 12:42

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