Advancing renewable energy scenarios with graph theory and ensemble meta-optimized approach

Abstract

Transitioning to renewable energy (RE) in Iran is crucial for reducing its dependence on fossil fuel revenues and for advancing global climate goals. This article presents the Ensemble Meta-Optimized Scenario Graph Planning (EMOSGP) method to explore future RE scenarios.The EMOSGP framework applies Micmac and k-means techniques to identify the key factors influencing renewable energy scenarios. By integrating the graph theory with scenario planning, EMOSGP employs a variety of algorithms, including hybrid k-means models enhanced by Particle Swarm Optimization (PSO) and the Artificial Hummingbird Algorithm (AHA), to provide insightful analyses through ensemble spectral graph partitioning of trend interactions. Moreover, the EMOSGP offers a novel approach for creating a comprehensive ensemble dataset derived from multiple spectral graph partitioning results, along with an advanced technique for weighting the foundational algorithms. Additionally, the strategic application of trend weights in feature weighting significantly improves the performance of the ensemble clustering process. By utilizing the ensemble learning through simple k-means, the EMOSGP method effectively addresses clustering limitations in scenario planning, resulting in the generation of reliable scenarios. Among the five scenarios produced, one stands out as particularly optimistic.