Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm
AuthorsGil Marcelino, Carolina; Salcedo Sanz, Sancho; Jiménez Fernández, Silvia; Camacho Gómez, Carlos
IdentifiersPermanent link (URI): http://hdl.handle.net/10017/47548
Ministerio de Economía y Competitividad
Comunidad de Madrid
Marcelino, Carolina G.; Camacho-Gómez, Carlos; Jiménez-Fernández, Silvia & Salcedo-Sanz, Sancho. 2021. "Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm", Energies, vol. 14, no. 9, art. no. 2443.
Coral Reefs Optimization algorithm
info:eu-repo/grantAgreement/EC/H2020/754382/EU/GOT Energy Talent/GET
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85887-C2-2-P/ES/NUEVOS ALGORITMOS HIBRIDOS DE INSPIRACION NATURAL PARA PROBLEMAS DE CLASIFICACION ORDINAL Y PREDICCION/
info:eu-repo/grantAgreement/Comunidad de Madrid//S2018%2FEMT4366/ES/PROgrama Microredes INTeligentes-CM/PROMINT-CM
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.