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
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Energies 2016, 9(10), 807; doi:10.3390/en9100807

Enhanced Multi-Objective Energy Optimization by a Signaling Method

1
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
2
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
Received: 2 August 2016 / Revised: 19 September 2016 / Accepted: 22 September 2016 / Published: 10 October 2016
(This article belongs to the Collection Smart Grid)
View Full-Text   |   Download PDF [4924 KB, uploaded 10 October 2016]   |  

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 (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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Soares, J.; Borges, N.; Vale, Z.; Oliveira, P.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.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top