Stochastic and robust optimization for resource planning in energy communities considering extreme events

• Grant number: 24/20610-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: March 01, 2025
• End date: September 30, 2028
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: John Fredy Franco Baquero
• Grantee: Dayara Pereira Basso
• Host Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil

Abstract

The transition to decarbonized energy systems is essential to mitigate the impacts of climate change and ensure a more sustainable and resilient energy matrix. Given the increase in the frequency and intensity of extreme events - such as fires, floods, droughts and heat waves - that directly threaten the electricity infrastructure, the need for adaptation and resilience in the electricity sector becomes even more urgent. In this context, Energy Communities (ECs) emerge as fundamental components of the energy transition, offering economic, environmental and social benefits and promoting greater autonomy and efficiency for local communities. However, this potential of ECs can be compromised without robust and efficient resource planning. Therefore, it is crucial to develop approaches that assist in the planning and operation of energy resources, considering the uncertainties associated with extreme events and the growing role of ECs in the electricity sector. This study aims to explore the integration of CEs in infrastructure planning, in addition to investigating how extreme events (fires, floods, droughts, and extreme heat waves) impact CEs and their members from technical, economic, and environmental points of view. The objective of this proposal is to develop a Stochastic Programming model and a Robust Optimization model for resource planning in CEs, taking into account extreme events. The models developed should include the influence of uncertainties in generation and resource planning, with the objective of modeling renewable CEs that promote sustainability and security.