Enhanced Multi-Objective Energy Optimization by a Signaling Method
In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/9/10/807 |
| _version_ | 1798041568015286272 |
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| author | João Soares Nuno Borges Zita Vale P.B. de Moura Oliveira |
| author_facet | João Soares Nuno Borges Zita Vale P.B. de Moura Oliveira |
| author_sort | João Soares |
| collection | DOAJ |
| description | In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus). It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs), several distributed generation (DG), customers with demand response (DR) contracts and energy storage systems (ESS). The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality. |
| first_indexed | 2024-04-11T22:23:22Z |
| format | Article |
| id | doaj.art-fc0b3670af094369ae4c2a5d7e4bcb67 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:23:22Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-fc0b3670af094369ae4c2a5d7e4bcb672022-12-22T03:59:59ZengMDPI AGEnergies1996-10732016-10-0191080710.3390/en9100807en9100807Enhanced Multi-Objective Energy Optimization by a Signaling MethodJoão Soares0Nuno Borges1Zita Vale2P.B. de Moura Oliveira3GECAD, Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 431, Porto 4200-072, PortugalGECAD, Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 431, Porto 4200-072, PortugalGECAD, Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 431, Porto 4200-072, PortugalINESC Technology and Science, UTAD University, Quinta de Prados, Vila Real 5000-801, PortugalIn this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus). It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs), several distributed generation (DG), customers with demand response (DR) contracts and energy storage systems (ESS). The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality.http://www.mdpi.com/1996-1073/9/10/807electric vehicle (EV)emissionsenergy resources management (ERM)multi-objective optimizationvirtual power player (VPP)smart grid |
| spellingShingle | João Soares Nuno Borges Zita Vale P.B. de Moura Oliveira Enhanced Multi-Objective Energy Optimization by a Signaling Method Energies electric vehicle (EV) emissions energy resources management (ERM) multi-objective optimization virtual power player (VPP) smart grid |
| title | Enhanced Multi-Objective Energy Optimization by a Signaling Method |
| title_full | Enhanced Multi-Objective Energy Optimization by a Signaling Method |
| title_fullStr | Enhanced Multi-Objective Energy Optimization by a Signaling Method |
| title_full_unstemmed | Enhanced Multi-Objective Energy Optimization by a Signaling Method |
| title_short | Enhanced Multi-Objective Energy Optimization by a Signaling Method |
| title_sort | enhanced multi objective energy optimization by a signaling method |
| topic | electric vehicle (EV) emissions energy resources management (ERM) multi-objective optimization virtual power player (VPP) smart grid |
| url | http://www.mdpi.com/1996-1073/9/10/807 |
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