Friday, 21 December 2018

CFP: IEEE CEC 2019 Special Session on Evolutionary Algorithms for Complex Optimization in the Energy Domain

Keeping the world at current pace in what concerns energy demand is not possible with the limited world resources. Sustainability and efficiency are a crucial part of the process to enable this sustained growth. Moreover, adequate methods of energy optimization are highly relevant in the current paradigm playing a key role in the planning, operation, and control of energy systems. However, several optimization problems in the energy domain are complex by nature since they are highly constrained and face issues related to high-dimensionality, lack of information, noisy and corrupted data as well as real-time requirements. Under these circumstances, achieving good solutions in a reasonable amount of time remains a challenge in most problems. Even the most sophisticated exact solutions require workarounds that often lead to unsatisfactory performance and applicability of the algorithms. Due to the difficulties of traditional algorithms to find feasible solutions for those complex problems in real-world conditions, Evolutionary Computation (EC) has emerged and demonstrated satisfactory performance in a wide variety of applications in the energy domain.

Scope and Topics

This is a follow-up of the previous special session in WCCI2018. Research work is welcome concerning complex real-world applications of EC in the energy domain. The problems can be focused on different parts of the energy chain (e.g., heating, cooling, and electricity supply) and different consumer targets (e.g., residential or industrial level). Problems dealing with uncertainty, dynamic environments, many-objectives, and large-scale search spaces are important for the scope of this special session. This special session aims at bringing together the latest applications of EC to complex optimization problems in the energy domain. Besides, this special session is linked to the competition on “Evolutionary Computation in Uncertain Environments: A Smart Grid Application”. Therefore, participants are also welcome to submit the results of their algorithm to our session.

List of topics

Topics must be related to EC in the energy domain including, but not limited to:

  • Electric and plug-in hybrid vehicles
  • Electricity markets
  • Energy scheduling
  • Heat and electricity joint optimization problems
  • Hydrogen economy problems
  • Multi/many-objective problems in the energy domain
  • Natural gas optimization problems
  • Optimal power flow in distribution and transmission
  • Residential, industrial and district cooling/heating problems
  • Smart grid and micro-grid problems
  • Solar and wind power integration and forecast
  • Super grids problems (continental and trans-continental transmission system) 
  • Transportation & energy joint problems
  • Distributed evolutionary approaches in the energy domain

How to submit a paper

Select our SS name under the main topic in the upload paper section 

Organizers

  • João Soares, Polytechnic of Porto, PT, joaps@isep.ipp.pt
  • Fernando Lezama, Polytechnic of Porto, PT, flzcl@isep.ipp.pt
  • Zita Vale, Polytechnic of Porto, PT, zav@isep.ipp.pt
  • Markus Wagner, Adelaide University, AU, markus.wagner@adelaide.edu.au

Further related bibliography

[1] Joao Soares, Bruno Canizes, M. A. Fotouhi Gazvhini, Zita Vale, and G. K. Venayagamoorthy, “Two-stage Stochastic Model using Benders’ Decomposition for Large-scale Energy Resources Management in Smart grids,” IEEE Transactions on Industry Applications, 2017.

[2] Fernando Lezama, Joao Soares, Enrique Munoz de Cote, L. E. Sucar, and Zita Vale, “Differential Evolution Strategies for Large-Scale Energy Resource Management in Smart Grids,” in GECCO ’17: Genetic and Evolutionary Computation Conference Companion Proceedings, 2017.

[3] João Soares, Mohammad Ali Fotouhi Ghazvini, Marco Silva, Zita Vale, Multi-dimensional signaling method for population-based metaheuristics: Solving the large-scale scheduling problem in smart grids, Swarm and Evolutionary Computation, 2016.

[4] Joao Soares, Hugo Morais, Tiago Sousa, Zita Vale, Pedro faria, Day-ahead resource scheduling including demand response for electric vehicles, IEEE Transactions on Smart Grid 4 (1), 596-605, 2013.

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