Stochastic Optimization of Energy Resources in Smart Homes

Author(s)

Silva, Jessica Alice A. ; Tairo, Derian C. ; Chagas, Guilherme Souto ; Rider, Marcos J.

• Document type: Journal article
• Source: 2024 IEEE ANDESCON; v. N/A, p. 6-pg., 2024-01-01.

Abstract

Managing energy resources effectively in smart homes presents a multifaceted challenge due to the dynamic nature of renewable energy sources, unpredictable load profiles, and the integration of electric vehicles. This paper addresses this challenge by proposing a mixed-integer linear programming (MILP) model for efficient energy management. The model integrates battery energy storage systems (BESS), photovoltaic (PV) generation, and electric vehicle (EV) charging, aiming to address the complexities of energy resource coordination. We introduce a stochastic approach to account for uncertainties associated with PV generation, load profiles, and EV charging while also considering the probability of network outages. The proposed MILP model offers a systematic solution to the challenges posed by energy management in smart homes; by optimizing energy utilization and cost reduction, our approach enables efficient coordination of multiple energy resources. The application of the model in a smart home test scenario demonstrated that the BESS is employed to minimize operational expenses by charging it with excess PV generation and discharging it during peak periods. Finally, our approach offers a practical and scalable solution for sustainable energy usage, contributing to the advancement of smart home technology. (AU)

• FAPESP's process: 21/11380-5 - CPTEn - São Paulo Center for the Study of Energy Transition
• Grantee: Luiz Carlos Pereira da Silva
• Support Opportunities: Research Grants - Science Centers for Development
• FAPESP's process: 23/13444-6 - Optimal operation of microgrids through distributed energy management using the alternating multiplier direction method
• Grantee: Jéssica Alice Alves da Silva
• Support Opportunities: Scholarships in Brazil - Post-Doctoral