Enhanced Multi-Objective Energy Optimization by a Signaling Method

João Soares, Nuno Borges, Zita Vale and P.B. De Moura Oliveira
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João Soares: GECAD, 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, Portugal
Nuno Borges: GECAD, 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, Portugal
Zita Vale: GECAD, 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, Portugal
P.B. De Moura Oliveira: INESC Technology and Science, UTAD University, Quinta de Prados, Vila Real 5000-801, Portugal

Energies, 2016, vol. 9, issue 10, 1-23

Abstract: 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 (CO 2 ) 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.

Keywords: electric vehicle (EV); emissions; energy resources management (ERM); multi-objective optimization; virtual power player (VPP); smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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