Energy Environment and Economy
Energy, Environment and Sustainable Sciences | online ISSN 3069-0935
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Volume 1 Number 1 2023
RESEARCH ARTICLE (Open Access)
Integrated Artificial Intelligence and Stochastic Optimization Framework for Resilient and Low Carbon Renewable Energy Manufacturing Systems
Shipon Chandra Barman1*, Anisul Islam Opy2
Energy Environment and Economy 1 (1) 1-8 https://doi.org/10.25163/energy.1110684
Submitted: 09 November 2022 Revised: 19 January 2023 Accepted: 22 January 2023 Published: 24 January 2023
Abstract
Background: As the power grid embraces renewable energy, systems for manufacturing these are being confronted with more and more uncertainty from both fluctuating market demand and sporadic supply of variable renewables to equipment breakdowns as well as strict low carbon emission requirements. Although AI and stochastic optimization appear promising in isolation, their combined application for improving the resilience and low-carbon level of production is still scarce. Methods: An AI-based stochastic optimization framework that combines machine-learning predictions with two-stage stochastic programming was designed. Artificial neural networks anticipated demand, and Random Forest models predicted equipment failures. Probabilistic outputs were implemented in order to optimize production, energy usage, and emissions with operational (2018–2022) and survey data provided by 210 U.S. solar manufacturing professionals evaluated by means of reliability, factor, and scenario analyses. Results: The resulting framework achieved substantial improvement over the results of deterministic planning models. Total operating costs were decreased by 16.2%, with energy expenditure reduced by around 20%. Carbon emissions were down by almost 20%, and renewable energy use went up from 41% to 56.8%. Operational resilience also increased as unmet demand decreased from 9.8 to 7.3% and recovery time following a severe disruption decreased from 7.2 to 4.9 days. Conclusions: AI-integrated prediction together with stochastic optimization makes a self-sustainability system possible in terms of both economic efficiency and resilience along with low-carbon performance, providing such scaled solutions for the sustainable renewable energy manufacturing systems.
Keywords: Stochastic Optimization, Artificial Intelligence, Renewable Energy Manufacturing, Operational Resilience, Low-Carbon Production
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