A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty
ith the high penetration of renewable generation in Smart Grids (SG), the uncertainty behavior associated with the forecast of weather conditions possesses a new degree of complexity in the Energy Resource Management (ERM) problem. In this paper, a Multi-Objective Particle Swarm Optimization (MOPSO) methodology is proposed to solve ERM problem in buildings with penetration of Distributed Generation (DG) and Electric Vehicles (EVs) and considering the uncertainty of photovoltaic (PV) generation. The proposed methodology aims to maximize profits while minimizing CO2 emissions. The uncertainty of PV generation is modeled with the use of Monte Carlo simulation in the evaluation process of the MOPSO core. Also, a robust optimization approach is adopted to select the best solution for the worst-case scenario of PV generation. A case study is presented using a real building facility from Brazil, to verify the effectiveness of the implemented robust MOPSO. ; This work has received funding from the Project NetEffiCity (ANI|P2020 18015), and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.
Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty
ith the high penetration of renewable generation in Smart Grids (SG), the uncertainty behavior associated with the forecast of weather conditions possesses a new degree of complexity in the Energy Resource Management (ERM) problem. In this paper, a Multi-Objective Particle Swarm Optimization (MOPSO) methodology is proposed to solve ERM problem in buildings with penetration of Distributed Generation (DG) and Electric Vehicles (EVs) and considering the uncertainty of photovoltaic (PV) generation. The proposed methodology aims to maximize profits while minimizing CO2 emissions. The uncertainty of PV generation is modeled with the use of Monte Carlo simulation in the evaluation process of the MOPSO core. Also, a robust optimization approach is adopted to select the best solution for the worst-case scenario of PV generation. A case study is presented using a real building facility from Brazil, to verify the effectiveness of the implemented robust MOPSO. ; This work has received funding from the Project NetEffiCity (ANI|P2020 18015), and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.
Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty
Joao Soares (author) / Zita Vale (author) / Nuno Borges (author) / Fernando Lezama (author) / Nelson Kagan (author)
2017-10-19
Conference paper
Electronic Resource
English
DDC:
690
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