Stochastic economic emission load dispatch
ABSTRACT The economic emission load dispatch (EELD) problem is a multiple non-commensurable objective problem that minimizes both cost and emission together. In the paper a stochastic EELD problem is formulated with consideration of the uncertainties in the system production cost and nature of the load demand, which is random. In addition, risk is considered as another conflicting objective to be minimized because of the random load and uncertain system production cost. The weighted minimax technique is used to simulate the trade-off relation between the conflicting objectives in the non-inferior domain. Once the trade-off has been obtained, fuzzy set theory helps the power system operator to choose the optimal operating point over the trade-off curve and adjust the generation levels in the most economic manner associated with minimum emission and risk. The validity of the method is demonstrated by analysing a sample system comprising six generators.
SourceAvailable from: Prachuab PeerapongGMSARN; 06/2011
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ABSTRACT: The optimization is an important role in wide geographical distribution of electrical power market, finding the optimum solution for the operation and design of power systems has become a necessity with the increasing cost of raw materials, depleting energy resources and the ever growing demand for electrical energy. In this paper, the real coded biogeography based optimization is proposed to minimize the operating cost with optimal setting of equality and inequality constraints of thermal power system. The proposed technique aims to improve the real coded searing ability, unravel the prematurity of solution and enhance the population assortment of the biogeography based optimization algorithm by using adaptive Gaussian mutation. This algorithm is demonstrated on the standard IEEE-30 bus system and the comparative results are made with existing population based methods.Journal of Electrical Engineering and Technology 01/2015; 10(1):56-63. DOI:10.5370/JEET.2015.10.1.056 · 0.52 Impact Factor