Version 1
: Received: 31 August 2023 / Approved: 31 August 2023 / Online: 31 August 2023 (04:18:56 CEST)
How to cite:
Murshed, M.; Chamana, M.; Schmitt, K.; Pol, S.; Adeyanju, O.; Bayne, S. Renewable Energy Integration for Power Outage Mitigation: A Data-Driven Approach in Advancing Grid Resilience Strategies. Preprints2023, 2023082119. https://doi.org/10.20944/preprints202308.2119.v1
Murshed, M.; Chamana, M.; Schmitt, K.; Pol, S.; Adeyanju, O.; Bayne, S. Renewable Energy Integration for Power Outage Mitigation: A Data-Driven Approach in Advancing Grid Resilience Strategies. Preprints 2023, 2023082119. https://doi.org/10.20944/preprints202308.2119.v1
Murshed, M.; Chamana, M.; Schmitt, K.; Pol, S.; Adeyanju, O.; Bayne, S. Renewable Energy Integration for Power Outage Mitigation: A Data-Driven Approach in Advancing Grid Resilience Strategies. Preprints2023, 2023082119. https://doi.org/10.20944/preprints202308.2119.v1
APA Style
Murshed, M., Chamana, M., Schmitt, K., Pol, S., Adeyanju, O., & Bayne, S. (2023). Renewable Energy Integration for Power Outage Mitigation: A Data-Driven Approach in Advancing Grid Resilience Strategies. Preprints. https://doi.org/10.20944/preprints202308.2119.v1
Chicago/Turabian Style
Murshed, M., Olatunji Adeyanju and Stephen Bayne. 2023 "Renewable Energy Integration for Power Outage Mitigation: A Data-Driven Approach in Advancing Grid Resilience Strategies" Preprints. https://doi.org/10.20944/preprints202308.2119.v1
Abstract
This article presents a comprehensive study on enhancing grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, Long Short-Term Memory (LSTM) networks for forecasting solar irradiance and load profiles, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of Battery State of Charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The results highlight the potential of the proposed approach in strengthening grid resilience, reducing downtime, and fostering sustainable energy utilization.
Keywords
Grid resilience; Power outage prediction; Monte Carlo simulation; LSTM forecasting; Hybrid LSTM-PSO model; Battery State of Charge; Microgrid integration; Techno-economic analysis; Renewable energy; Energy independence
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
