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

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 ene...

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Vydáno v:Energy (Oxford) Ročník 36; číslo 1; s. 119 - 132
Hlavní autoři: Niknam, Taher, Meymand, Hamed Zeinoddini, Mojarrad, Hasan Doagou
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 2011
Elsevier
Témata:
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
methodology
Multi-objective Optimal Operation Management (MOOM)
Multi-objective optimization
new methods
Other installations: mhd power plants, fuel cell plants, incineration plants, etc
power plants
Multi-objective Optimal Operation Management (MOOM)
Multi-objective optimization
Interactive fuzzy satisfying method
Fuel Cell Power Plant (FCPP)
Hybrid Modified Honey Bee Mating Optimization (HMHBMO)
ISSN:0360-5442
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Shrnutí: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.
Bibliografie:http://dx.doi.org/10.1016/j.energy.2010.10.062
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ISSN:0360-5442
DOI:10.1016/j.energy.2010.10.062