Differential Evolution Strategy for Optimal Design of Gas Transmission Network

Abstract

This paper presents the application of Differential Evolution (DE), an evolutionary computation technique for the optimal design of gas transmission network. As a gas transmission system includes source of gas, delivery sites with pipeline segments and compressors, the design of efficient and economical network involves lot of parameters. In addition, there are many equality and inequality constraints to be satisfied making the problem highly non-linear. Hence an efficient strategy is needed in searching for the global optimum. In this study, DE has been successfully applied for optimal design of gas transmission network. The results obtained are compared with those of nonlinear programming technique and branch and bound algorithm. DE is able to find an optimal solution with a cost that is less than reported in the earlier literature. The proposed strategy takes less computational time to converge when compared to the existing techniques without compromising with the accuracy of the parameter estimates.

Optimal Bidding and Generation Scheduling of Utilities Participating in Single Side Auction Markets Including Ancillary Services

“…DE/rand/1/bin in which DE represents differential evolution, rand is any randomly chosen vector for perturbations, 1 represents the number of difference vectors to be perturbed and bin is the binomial type of crossover used. The seventh strategy is the most successful and widely used in optimization problems such as emission constrained economic dispatch [12], optimal power flow [13] and optimal design of gas transmission network [14].…”

Evolutionary Algorithms for Optimal Operation of Gas Networks

“…This model has been used to identify the characteristics of compressor stations such as the optimal location and type of compressors, and optimal pipe dimensions, length and topology to be installed. Babu et al (2003), develop a nonlinear optimization model to minimize the total investment cost of transmission networks, considered the yearly operating and maintenance costs of compressors, and solve the model using the DE Algorithm.…”

“…He also applies GAs to Larson and problem formulation for his case study. However, application of this and other heuristic algorithms did not find their way into the gas network optimization literature until Babu et al (2003) successfully applied the DE Algorithm to the optimal design of a gas transmission network with a complex nonlinear optimization model. They compare their final solution(s) with solutions using Nonlinear Programming and a Branch and Bound Mixed Integer Nonlinear Programming Algorithm.…”

Simulation and Optimization of Wiped-Film Poly-Ethylene Terephthalate (PET) Reactor Using Multiobjective Differential Evolution (MODE)

“…Differential Evolution (DE), which is an improved version of GA, is found to give better results than simple GA for single objective optimization problems [28][29][30][31][32][33][34]. DE has also been successfully applied to different variants of Multiobjective Differential Evolution (MODE) to solve MOO problems [35][36][37][38][39][40][41].…”