Impact of Fuel Cell Power Plants on Multi-objective Optimal Operation Management of Distribution Network

• Published in: Fuel cells (Weinheim an der Bergstrasse, Germany) Germany), 2012-06, Vol.12 (3), p.487-505
• Main Authors: Niknam, T., Zeinoddini-Meymand, H.
• Format: Article
• Language: English
• Subjects: Fuel Cell Power Plant (FCPP); Hybrid Modified Honey Bee Mating Optimization and Differential Evolution (MHBMO-DE); Interactive Fuzzy Satisfying Method; Multi-Objective Optimal Operation Management (MOOM); Multi-Objective Optimization
• ISSN: 1615-6846; 1615-6854
• DOI: 10.1002/fuce.201100167

This paper presents an interactive fuzzy satisfying method based on hybrid modified honey bee mating optimization and differential evolution (MHBMO‐DE) to solve the multi‐objective optimal operation management (MOOM) problem, which can be affected by fuel cell power plants (FCPPs). The objective functions are to minimize total electrical energy losses, total electrical energy cost, total pollutant emission produced by sources, and deviation of bus voltages. A new interactive fuzzy satisfying method is presented to solve the multi‐objective problem by assuming that the decision‐maker (DM) has fuzzy goals for each of the objective functions. Through the interaction with the DM, the fuzzy goals of the DM are quantified by eliciting the corresponding membership functions. Then, by considering the current solution, the DM acts on this solution by updating the reference membership values until the satisfying solution for the DM can be obtained. The MOOM problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. Recently researchers have presented a new evolutionary method called honey bee mating optimization (HBMO) algorithm. Original HBMO often converges to local optima, in order to overcome this shortcoming, we propose a new method that improves the mating process and also, combines the modified HBMO with DE algorithm. Numerical results for a distribution test system have been presented to illustrate the performance and applicability of the proposed method.