2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results

Fernando Lezama, João Soares, Zita Vale, Jose Rueda, Sergio Rivera, István Elrich

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
2 Downloads (Pure)

Abstract

This paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modern Heuristic Optimization (WGMHO) 2017 Competition on Smart Grid Operation Problems. The competition is organized with the aim of closing the gap between theory and real-world applications of evolutionary computation. Testbed 1 considers stochastic OPF (Optimal Power Flow) based Active-Reactive Power Dispatch (ARPD) under uncertainty and Testbed 2 large-scale optimal scheduling of distributed energy resources. Classical optimization methods are not able to deal with the proposed optimization problems within a reasonable time, often requiring more than one day to provide the optimal solution and a significant amount of memory to perform the computation. The proposed problems can be addressed using modern heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes. Results from the competition show that metaheuristics can be successfully applied in search of efficient near-optimal solutions for the Stochastic Optimal Power Flow and large-scale energy resource management problems.

Original languageEnglish
Pages (from-to)420-427
Number of pages8
JournalSwarm and Evolutionary Computation
Volume44
DOIs
Publication statusPublished - 2019

Keywords

  • Evolutionary computation
  • Metaheuristics
  • Optimization
  • Power systems
  • Smart grids
  • Swarm intelligence

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