RT info:eu-repo/semantics/article T1 Dynamic electric dispatch for wind power plants: a new automatic controller system using evolutionary algorithms A1 Gil Marcelino, Carolina A1 Avancini, Joäo V.C. A1 Delgado, C.A.D.M. A1 Fialho Wanner, Elizabeth A1 Jiménez Fernández, Silvia A1 Salcedo Sanz, Sancho K1 Offshore wind power K1 Optimization K1 Energy efficiency K1 Energy resources K1 Clean energies K1 Informática K1 Computer science K1 Energías Renovables/Energías Alternativas K1 Alternative energies AB In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation. PB MDPI SN 2071-1050 YR 2021 FD 2021-10-28 LK http://hdl.handle.net/10017/49808 UL http://hdl.handle.net/10017/49808 LA eng NO European Commission DS MINDS@UW RD 29-mar-2024