Assessing local economic impact of urban energy transition through optimized resource allocation in renewable energy

Abstract

This paper introduces an innovative approach to optimizing the allocation of local workforce and resources in the renewable energy sector through the application of the Modified Bat Algorithm (MBA). By leveraging MBA, the study achieves enhanced precision in decision-making, maximizing the positive impact of urban energy transition on local employment while respecting various constraints. To address the inherent uncertainties associated with the energy transition process, the paper employs a stochastic framework grounded in the Unscented Transform (UT). By incorporating UT, the study captures and analyzes the probabilistic nature of factors such as economic growth, energy production, and environmental impact. This framework provides a robust foundation for decision-making under uncertainty, ensuring the reliability of the proposed assessment model. Recognizing the critical importance of data integrity and security in the assessment of local economic and employment effects, this paper integrates blockchain technology to enhance the trustworthiness of the data transaction process. The use of blockchain ensures transparency, immutability, and security of data, thereby safeguarding the integrity of the results and fostering trust in the assessment outcomes.