Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation

Genetic algorithms (GAs) are well known optimization methods. However, complicated systems with high dimensional variables, such as long-term reservoir operation, usually prevent the methods from reaching optimal solutions. This study proposes a multi-tier interactive genetic algorithm (MIGA) which decomposes a complicated system (long series) into several small-scale sub-systems (sub-series) with GA applied to each sub-system and the multi-tier (key) information mutually interacts among individual sub-systems to find the optimal solution of long-term reservoir operation. To retain the integrity of the original system, over the multi-tier architecture, an operation strategy is designed to concatenate the primary tier and the allocation tiers by providing key information from the primary tier to the allocation tiers when initializing populations in each sub-system. The Shihmen Reservoir in Taiwan is used as a case study. For comparison, three long-term operation results of a sole GA search and a simulation based on the reservoir rule curves are compared with that of MIGA. The results demonstrate that MIGA is far more efficient than the sole GA and can successfully and efficiently increase the possibility of achieving an optimal solution. The improvement rate of fitness values increases more than 25%, and the computation time dramatically decreases 80% in a 20-year long-term operation case. The MIGA with the flexibility of decomposition strategies proposed in this study can be effectively and suitably used in long-term reservoir operation or systems with similar conditions.     

Study on TMD Control on Stability Improvement of Barge-Supported Floating Offshore Wind Turbine Based on the Multi-Island Genetic Algorithm

A floating offshore wind turbine (FOWT) has a great potential in producing renewable energy as offshore wind resource is rich in deep sea area (water deeper than 60 m) where fixed foundations are cost-effective or deployable. However, compared with a fixed-bottom installation, FOWT has to suffer more extreme loads due to its extra degrees of freedom. Therefore, the stability of an FOWT is a key challenge in exploiting offshore deep-water wind. Focusing on the stability of barge-type FOWT, this paper is to investigate the effect of passive structural control by equipping a tuned mass damper (TMD) on the nacelle. The turbulent wind with sharp fluctuations is established both in velocity and inflow direction based on standard Kaimal turbulence spectrum as suggested in the standard IEC61400-2. The irregular wave is generated according to the Pierson-Moskowitz spectrum. The dynamic structural characteristics of FOWT are calculated based on the fully coupled aero-hydro-servo-elastic solver FAST. Evidence has shown that the proposed method of the nacelle-based TMD is effective in controlling stability of an FOWT, as the sway and roll motions of barge and the side-side displacement of tower top decreased significantly. With the increase of mass, the side-side displacement of tower-top and the amplitude of roll motion of barge reveal a trend of increasing first and then decreasing. The stiffness and damping have little effect. Furthermore, the multi-island genetic optimization algorithm (MIGA) is employed to find globally optimum structural parameters (mass, stiffness and damping) of the TMD. The optimum structure parameters of TMD are achieved when the mass is 21393 kg, damping is 13635 N/(m/s) and stiffness is 6828 N/m. By adopting the optimized TMD, stability of roll motion of barge and side-side displacement of tower-top increase up to 53% and 50% respectively when compared with the normal TMD. The simulation results verify the validity and reliability of the proposed TMD control and the optimization methods.