A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-objective Particle Swarm Optimization to Solve Energy Scheduling with Vehicle-to-Grid in Office Buildings Considering Uncertainties
This paper presents a Multi-Objective Particle Swarm Optimization (MOPSO) methodology to solve the problem of energy resource management in buildings with a penetration of Distributed Generation (DG) and Electric Vehicles (EVs). The proposed methodology consists in a multi-objective function, in which it is intended to maximize the profit and minimize CO2 emissions. This methodology considers the uncertainties associated with the production of electricity by the photovoltaic and wind energy sources. This uncertainty is modeled with the use of a robust optimization approach in the metaheuristic. A case study is presented using a real building facility from Portugal, in order to verify the feasibility 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 Particle Swarm Optimization to Solve Energy Scheduling with Vehicle-to-Grid in Office Buildings Considering Uncertainties
This paper presents a Multi-Objective Particle Swarm Optimization (MOPSO) methodology to solve the problem of energy resource management in buildings with a penetration of Distributed Generation (DG) and Electric Vehicles (EVs). The proposed methodology consists in a multi-objective function, in which it is intended to maximize the profit and minimize CO2 emissions. This methodology considers the uncertainties associated with the production of electricity by the photovoltaic and wind energy sources. This uncertainty is modeled with the use of a robust optimization approach in the metaheuristic. A case study is presented using a real building facility from Portugal, in order to verify the feasibility 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 Particle Swarm Optimization to Solve Energy Scheduling with Vehicle-to-Grid in Office Buildings Considering Uncertainties
Nuno Borges (author) / João Soares (author) / Zita Vale (author)
2017-10-18
Conference paper
Electronic Resource
English
Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty
| BASE | 2017
Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty
| BASE | 2017
| American Institute of Physics | 2018