Active distribution network cost/benefit analysis with multi-objective programming

2009 ◽  
Author(s):  
G. Celli ◽  
F. Pilo ◽  
G.G. Soma ◽  
M. Gallanti ◽  
R. Cicoria
Keyword(s):  
Cost Benefit Analysis ◽  
Distribution Network ◽  
Cost Benefit ◽  
Benefit Analysis ◽  
Multi Objective ◽  
Active Distribution Network ◽  
Network Cost ◽  
Multi Objective Programming ◽  
Objective Programming

Development and Evaluation of Hull-form Stochastic Optimization Methods for Resistance and Operability

2015 ◽  
Author(s):  
Matteo Diez ◽  
Emilio F. Campana ◽  
Frederick Stern
Keyword(s):  
Stochastic Optimization ◽  
Design Optimization ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Optimization Methods ◽  
Regular Wave ◽  
Benefit Analysis ◽  
Sea State ◽  
Hull Form ◽  
Multi Objective

Hull-form stochastic optimization methods are developed and evaluated for resistance reduction and operability increase, addressing stochastic sea state and operations. The cost/benefit analysis of the design-optimization procedure is presented, by comparison of four hierarchical problems, from stochastic most general to deterministic least general. The parent hull is a 100 m Delft catamaran, with geometrical constraints for maximum variation of length, beam, draft and displacement. Problem 1 is used as a benchmark for the evaluation of the other problems. It is as a multi-objective stochastic optimization for resistance and operability, considering stochastic sea state and speed, but limited to head waves. Problem 2 is a multi-objective stochastic optimization for resistance and motions at fixed sea state and speed. Problem 3 is a multi-objective deterministic optimization for resistance and motions using a single regular wave at fixed speed. Problem 4 is a single-objective deterministic optimization for calm-water resistance at fixed speed. The design optimization is based on hull modifications by the Karhunen-Loève expansion of a free-form deformation space, URANS simulations, regular wave approximations for irregular wave, metamodels and multi-objective particle swarm. The design optimization achieves an 8.7, 23, 53, and 10% average improvement for problems 1, 2, 3, and 4, respectively. Comparing to problem 1, problem 2, 3, 4 optimized designs have average performances 1, 2.1 and 1.7% worse, respectively. The most efficient problem, from computational cost/benefit analysis, is problem 3. Nevertheless, problem 1 is needed in order to evaluate and compare the stochastic performance of the design, and assess the optimization cost/benefit.

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