Hybrid Spatial-Artificial Intelligence Approach for Renewable Energy Sources Sites Identification and Integration in Sarawak State

Far Chen, Jong (2022) Hybrid Spatial-Artificial Intelligence Approach for Renewable Energy Sources Sites Identification and Integration in Sarawak State. Masters thesis, UNIMAS.

PDF (Please get the password by email to repository@unimas.my , or call ext: 082-583914/3942/3933) Jong Far.pdf Restricted to Registered users only Download (8MB) | Request a copy

Abstract

As many new power infrastructures are planned under Sarawak State, the energy demand is expected to grow exponentially in these coming years. Besides, the minority of the rural villages are still not electrified yet. Fortunately, Sarawak State is blessed with indigenous Renewable Energy such as solar, hydro and wind power but they are scattered in the interior of the Sarawak State. Thus, the first phase is to develop a criteria scheme data for potential Renewable Energy Sources (RES) sites. It is followed by identifying RES sites using spatial data and Multi-Criteria Decision Making-Analytical Hierarchy Process (MCDM-AHP) algorithm. Accordingly, Spatial-Artificial Intelligence (AI) approach is utilised to integrate a high number of RES sites with minimum total distance. The research also proposed a hybrid Spatial-AI approach to integrate a high number of RES sites with minimum total distance and minimum total elevation difference. Initially, the Geographic Information System (GIS) tool is utilised to perform the assessments on current geographical conditions. From this, the spatial criteria scheme data is produced. The MCDM-AHP algorithm is applied to the criteria scheme data to identify the number of RES sites. Four cases were developed for RES sites integration, representing four different arrangements of RES sites. In each case, the Traveling Salesman Problem-Genetic Algorithm (TSP-GA) algorithm is applied to determine a minimum total distance of RES sites integration. Furthermore, a hybrid Spatial-Artificial Intelligence (AI) algorithm is proposed to integrate RES sites with minimum total distance and minimum total elevation difference. This research successfully identifies 55 solar energy sites and 15 wind energy sites. Meanwhile, 155 hydro energy sites were identified using the spatial map from Sarawak Energy Berhad (SEB). The second phase of the research work is to integrate the RES sites. TSP-GA algorithm is applied to generate the transmission line routing among the RES sites with minimum total distance. The minimum total distances in all four cases are acquired and validated as both the TSP-GA algorithm and the Traveling Salesman Problem-Mixed Integer Linear Programming (TSP-MILP) algorithm produced the same routing pattern. In the end, the proposed algorithm is successfully minimized the total distance and total elevation difference. The improved Spatial-AI algorithm showed approximately 15% better compared to ordinary TSP-GA in all four cases.

Actions (For repository members only: login required)

View Item