A real-time co-simulation framework for the validation of optimal energy scheduling in hybrid microgrids under uncertainty

• Grant number: 25/12681-0
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: February 01, 2026
• End date: January 31, 2027
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: John Fredy Franco Baquero
• Grantee: Milton Patricio Agudo Guiracocha
• Supervisor: Thiago Batista Soeiro
• Host Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil
• Institution abroad: University of Twente (UT), Netherlands
• Associated to the scholarship: 23/16829-6 - Optimal Operation of Smart Microgrids within Local Energy Markets with Uncertainties in Distributed Renewable Resources and Demand, BP.DR

Abstract

The integrated deployment of renewable energy sources, energy storage systems, and electric vehicles, among other resources, offer promising alternatives for meeting microgrid demand in an environmentally sustainable way. These technologies reduce dependence on the main grid and enhance the resilience of electrical networks. However, the stochastic nature of renewable energy sources, dynamic energy pricing, and demand fluctuations pose significant challenges to the optimal management of smart microgrids. To address these issues, mathematical programming techniques are commonly employed to determine optimal scheduling strategies for microgrid resources. Although these models offer rigorous and efficient solutions in theory, they may not fully capture the dynamic behavior and real-time constraints of actual systems. With these considerations, the present research proposes a real-time co-simulation framework for the experimental validation of optimal scheduling strategies derived from mathematical programming models. The proposed approach aims to bridge the gap between theoretical optimizationand practical implementation through hardware-in-the-loop simulations. This framework allows for assessing the performance of dispatch decisions in a dynamic and realistic environment, considering operational delays, system non-idealities, and uncertainties. In addition, it will help to identify potential limitations in the optimization-based schedules, enabling the development of model improvement strategies. The integration of thisvalidation layer is expected to expand the initial doctoral research project by improving the applicability and robustness of model-based methods in practical scenarios, resulting in a valuable decision-making tool for microgrid operation. The research will be conducted at the University of Twente, promoting international collaboration and knowledge exchange between the participating institutions. (AU)