Next Article in Journal
Dependency-Aware Clustering of Time Series and Its Application on Energy Markets
Next Article in Special Issue
Multi-Objective Distribution Network Expansion Incorporating Electric Vehicle Charging Stations
Previous Article in Journal
Recovery and Utilization of Lignin Monomers as Part of the Biorefinery Approach
Previous Article in Special Issue
A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier
Open AccessArticle

Enhanced Multi-Objective Energy Optimization by a Signaling Method

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
INESC Technology and Science, UTAD University, Quinta de Prados, Vila Real 5000-801, Portugal
Author to whom correspondence should be addressed.
Academic Editor: Chunhua Liu
Energies 2016, 9(10), 807;
Received: 2 August 2016 / Revised: 19 September 2016 / Accepted: 22 September 2016 / Published: 10 October 2016
(This article belongs to the Collection Smart Grid)
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. View Full-Text
Keywords: electric vehicle (EV); emissions; energy resources management (ERM); multi-objective optimization; virtual power player (VPP); smart grid electric vehicle (EV); emissions; energy resources management (ERM); multi-objective optimization; virtual power player (VPP); smart grid
Show Figures

Figure 1

MDPI and ACS Style

Soares, J.; Borges, N.; Vale, Z.; Oliveira, P.D.M. Enhanced Multi-Objective Energy Optimization by a Signaling Method. Energies 2016, 9, 807.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop