An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants

• Published in: Energy (Oxford) 2011, Vol.36 (1), p.119-132
• Main Authors: Niknam, Taher, Meymand, Hamed Zeinoddini, Mojarrad, Hasan Doagou
• Format: Article
• Language: English
• Subjects: air pollution; algorithms; Applied sciences; electric power; electricity; Energy; energy costs; Energy. Thermal use of fuels; Exact sciences and technology; Fuel Cell Power Plant (FCPP); fuel cells; Hybrid Modified Honey Bee Mating Optimization (HMHBMO); Installations for energy generation and conversion: thermal and electrical energy; Interactive fuzzy satisfying method; Multi-objective Optimal Operation Management (MOOM); Multi-objective optimization; new methods; Other installations: mhd power plants, fuel cell plants, incineration plants, etc; power plants
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2010.10.062

This paper presents an interactive fuzzy satisfying method based on Hybrid Modified Honey Bee Mating Optimization (HMHBMO). Its purpose is to solve the Multi-objective Optimal Operation Management (MOOM) problem which can be affected by Fuel cell power plants (FCPPs). Minimizing total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources and deviation of bus voltages are the objective functions in this method. A new interactive fuzzy satisfying method is presented to solve the multi-objective problem by assuming that the decision-maker (DM) has fuzzy targets for each of the objective functions. Through the interaction with the DM, the fuzzy goals are quantified by eliciting the corresponding membership functions. Considering the current solution, the DM updates the reference membership values until the best solution can be obtain. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Therefore, evolutionary methods can be used to solve this problem since they are independence of objective function’s type and constraints. Recently researchers have presented a new evolutionary method called Honey Bee Mating Optimizations (HBMO) algorithm. Original HBMO often converges to local optima and this is a disadvantage of this method. In order to avoid this shortcoming we propose a new method. This method improves the mating process and also combines the modified HBMO with a Chaotic Local Search (CLS). Numerical results on a distribution test system have been presented to illustrate the performance and applicability of the proposed method.