Annual Energy Production Maximization for Tidal Power Plants with Evolutionary Algorithms

Genetic Algorithm in Tidal Range Schemes’ Optimisation

“…Furthermore, Child and Venugopal showed that superior results can be obtained using GAs for the optimisation of wave energy converters layout [26]. Their results were consistent with the findings of Kontoleontos et al which proved that it was possible to predict tidal stream energy using EAs with a satisfactory result [27]. However, there was no study found in the literature using GAs or similar EAs to optimise the flexible operation scenarios of the tidal range structures.…”

“…EAs and particularly Genetic Algorithms (GAs), have been demonstrated to be a competitive method in the power market [24]. However, the application of GAs to marine renewable energy has been limited [25][26][27][28]. Leite Neto et al [28] and Sullivan et al [25] investigated the potential of using GAs for the optimisation of dispatch and arrangement of tidal turbines, respectively.…”

Design of tidal range energy generation schemes using a Genetic Algorithm model

“…Evolutionary Algorithms (EAs) have been developed to solve a wide range of optimisation problems in power systems [19,20], with Genetic Algorithms (GAs) constituting one of the metaheuristic EAs frequently used to combine variables to analyse optimisation problems, and providing a high level of satisfactory results [21], especially when compared with the brute force algorithms including the grid search method [22]. EAs have been used in the optimisation of tidal energy generated from TRSs, and have already been used to provide design details in terms of the number of turbines and sluice gates to use for such schemes [18,23,24]. Leite Neto et al discussed the maximum energy gains obtained with dispatchable turbines by using GAs for optimisation [24].…”

“…More recently, GAs have been refined to determine operational optimisation with the findings being possible for the implementation of GAs in the optimal design of TRSs for higher efficiency [17], particularly in comparison with other alternative algorithms [18]. Furthermore, Kontoleontos and Weissenberger [23] highlighted the scope for using EAs in optimising multiple variables, including operational modes, speed of turbines etc., to maximise energy generation. However, so far as the authors are aware no studies have been published to-date using GAs, or similar EAs, in simultaneously designing and optimising TRSs, where different numbers and combinations of turbines and sluice gates have been considered, as well as consideration being given to flexible operation linked to the tidal characteristics and pumping at high and low water to increase energy generation.…”

Innovative Projects and Technology Implementation in the Hydropower Sector

“…The cost of a predictive system for one unit (development and implementation at HPP) is about 200,000 EUR [40]. An additional increase and optimization could be achieved with the use of software that uses genetic algorithms such as EASY [41]. To improve further the flexibility of hydropower plants, a number of researchers [42] investigated their stability properties by means of transfer functions representing the dynamic behavior of the reservoir, penstock, surge tank, hydro-turbine, and the generator.…”