Stochastic multiobjective generation allocation using pattern-search method
In a stochastic multiobjective framework, fuzzy decision-making methodology is exploited to decide the generation schedule of committed thermal stations. The multiobjective problem is formulated considering objectives like fuel cost, gaseous pollutant emission, variance of active and reactive generation mismatch, and a voltage profile minimisation to avoid violation of active power-line flow limits with explicit recognition of statistical uncertainties in the thermal generation cost, gaseous emission curves, real and reactive power demand and voltage magnitude at each bus, which are random variables. Equality and inequality network constraints are also incorporated as objectives to be optimised. Objectives of a fuzzy nature are quantified by defining their membership functions. The problem is solved sequentially in the decoupled form. The Hooke–Jeeves method has been employed to generate noninferior solutions within minimum and maximum limits of power generation. The min–max technique is used to select the optimal solution interactively. This technique has the advantage to maximise the most underachieved objective also. A IEEE five-generator, 25-bus and 35-line power system has been used to demonstrate the applicability of the method.
