A Framework for Green Energy Resources Identification and Integration Supported by Real-Time Monitoring, Control, and Automation Applications

Far Chen, Jong (2025) A Framework for Green Energy Resources Identification and Integration Supported by Real-Time Monitoring, Control, and Automation Applications. PhD thesis, Universiti Malaysia Sarawak.

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Abstract

Sarawak is transitioning to green energy adoption, yet progress is hindered by a lack of comprehensive spatial data to identify optimal locations, inadequate optimization techniques for effective integration of these sites, and insufficient robust Industrial Internet of Things-based real-time monitoring and automation strategies to manage the intermittent nature of green energy resources. To address these challenges, a novel Geographical Information System-based fuzzy Technique for Order Preference by Similarity to Ideal Solution coupled with filtration algorithms was proposed. This two-layered approach effectively filters potential green energy sites. The first layer identified 23 optimal wind energy sites and 138 optimal hydro energy sites. The second layer employed spatial data and the fuzzy Technique for Order Preference by Similarity to Ideal Solution algorithm to refine potential solar energy sites, yielding the top 100 optimal locations. The proposed method demonstrated a 69.01 % alignment when validated against the weighted sum method. Following site identification, an improved Geographical Information System-driven fuzzy Traveling Salesman Problem-Binary Integer Programming algorithm was proposed to integrate these sites into a reliable ring-based system topology, aiming to achieve a zero-carbon footprint. The process involved clustering by divisions and designing optimal electrical power line routing for each cluster, prioritizing minimum total distance, elevation difference, and average ground flash density. Validation against conventional methods and state-of-the-art algorithms confirmed the superior performance of the proposed approach. Additionally, an Industrial Internet of Things-based system utilizing servers, cloud platforms, and Supervisory Control and Data Acquisition systems was developed for real-time monitoring, control, and automation to address green energy intermittency. Hardware prototypes using Raspberry Pi and Industrial Internet of Things components were interfaced with SCADA systems to validate real-world applicability. Experimental results confirmed the effectiveness of the proposed methodologies. In conclusion, the proposed methodologies demonstrate the potential to overcome barriers to green energy implementation, fostering sustainable development in Sarawak. This research offers practical insights for policymakers, energy stakeholders, and researchers advancing green energy initiatives.

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