New Swarm and Evolutionary Computation Findings from School of Engineering Discussed (2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results)
- February 18, 2019
- 70 views
By a News Reporter-Staff News Editor at Robotics & Machine Learning -- Fresh data on Computation - Swarm and Evolutionary Computation are presented in a new report. According to news reporting from Porto, Portugal, by VerticalNews journalists, research stated, "This paper summarizes the two testbeds, datasets, and results of the IEEE PES Working Group on Modem 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."
Funders for this research include European Union's Horizon 2020, FEDER Funds, National Funds.
The news correspondents obtained a quote from the research from the School of Engineering, "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 modem heuristic optimization approaches, enabling the achievement of good solutions in much lower execution times, adequate for the envisaged decision-making processes."
According to the news reporters, the research concluded: "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."
For more information on this research see: 2017 IEEE competition on modern heuristic optimizers for smart grid operation: Testbeds and results. Swarm and Evolutionary Computation, 2019;44():420-427. Swarm and Evolutionary Computation can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Swarm and Evolutionary Computation - www.journals.elsevier.com/swarm-and-evolutionary-computation/)
Our news journalists report that additional information may be obtained by contacting J. Soares, Polytechnic Porto, Sch Engn, Gecad Res Grp, Porto, Portugal. Additional authors for this research include F. Lezama, Z. Vale, J. Rueda, S. Rivera and I. Elrich.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.swevo.2018.05.005. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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