Towards informed decision-making: A comprehensive statistical study of economic and financing models for sustainable energy transition in urban environments

Abstract

This paper presents a comprehensive approach to optimizing renewable energy allocation in urban environments. Our methodology integrates statistical economic modeling and decision-making techniques to address the complex challenges associated with renewable energy management. By employing stochastic programming and robust optimization, we optimize renewable energy allocation to minimize operational costs, reduce emissions, and enhance system reliability. Our analysis demonstrates significant reductions in operational costs (15.2%) and emissions (20.5% for NOx and 18.9% for CO2) compared to baseline scenarios. Moreover, our approach improves system reliability by reducing load shedding (25.1%) and enhancing overall system reliability (12.3%). These findings underscore the effectiveness of our proposed strategy in fostering economic, environmental, and operational sustainability in urban energy environments.