Energy Strategy Reviews (Jan 2024)
Exploring decision-making techniques for evaluation and benchmarking of energy system integration frameworks for achieving a sustainable energy future
Abstract
Energy Systems Integration (ESI) involves coordinating and planning energy systems to provide reliable and affordable energy services while minimizing environmental harm. It optimizes interactions among different energy sources to achieve sustainability goals and promotes efficient resource usage. However, evaluating and benchmarking ESI frameworks to select the most suitable and transparent ones is a complex Multi-Criteria Decision-Making (MCDM) problem. This complexity arises from trade-offs, conflicts, and importance considerations of the six ESI evaluation characteristics: Multidimensional, Multivectoral, Systemic, Futuristic, Systematic, and Applied. Hence, this study aims to address this complexity by integrating Fuzzy-Weighted Zero-Inconsistency (FWZIC) and Multi-Attributive Border Approximation Area Comparison (MABAC). The proposed methodology consists of two phases. Firstly, the development of a Dynamic Decision Matrix (DDM) to handle 26 ESI frameworks as alternatives and the six ESI characteristics criteria. Secondly, the integration of mathematical processes is formulated based on the FWZIC-MABAC methods. Using the FWZIC technique, the ESI evaluation criteria were weighted based on the preferences of twelve experts. ESI-C2 (Multivectoral) and ESI-C1 (Multidimensional) criteria received the highest weights of 0.195 and 0.190, respectively, while the ESI-C5 (Systematic) criterion received the lowest weight of 0.110. The remaining criteria, namely ESI-C3 (Systemic), ESI-C6 (Applied), and ESI-C4 (Futuristic) obtained weights of 0.189, 0.168, and 0.147, respectively. The MABAC benchmarking results showed that A11 (Energy Security) and A15 (Energy Security under decarbonization) ranked first with the highest score value of 0.28081 for both. Conversely, A19 (EJM) had the lowest score value of −0.17022. The systematic rank and sensitivity analysis assessments were conducted to verify the efficiency of the proposed methodology. We benchmarked the proposed methodology against three other benchmark studies and achieved a score of 100 % across three key perspectives. This methodology offers valuable support in making informed and sustainable decisions in the energy sector.
