Fuzzy satisfying stochastic multi-objective generation' scheduling by weightage pattern search methods

In the multi-objective framework, an interactive fuzzy satisfying method is presented to decide the generation schedule with explicit recognition of statistical uncertainties in system production cost data, NO, pollutant emission data and system load demand. In deciding the optimal schedule, four objectives viz. operating cost, NO, emission and risk due to variance of active and of reactive power generation mismatch are simultaneously minimized. Specific technique is put forth to convert the stochastic models into their respective deterministic equivalents. The weighting method is used to simulate the trade-off relationship between the conflicting objectives in the non-inferior domain. Generally, the weights are either simulated or searched in the non-inferior domain. In the paper, Hooke-Jeeves and evolutionary search techniques are implemented to search the 'preferred' weightage pattern in the non-inferior domain, which corresponds to the 'best' optimal solution. Fuzzy set theory has been exploited to decide the 'preferred' optimal operating point by interacting with the decision maker. The non-inferior solution, which attains maximum satisfaction level from the membership functions of the participating objectives, has been adjudged the 'best' solution. The goal/objectives being of fuzzy nature can be quantified by defining their membership functions. The validity of proposed method has been demonstrated on an 11-node IEEE system having five generators. The results obtained by searching weightage pattern using Hooke-Jeeves and evolutionary search techniques, are compared with the interactive method in which weightage patterns are simulated by giving suitable variation to weights in a specific manner. (C) 2003 Elsevier B.V. All rights reserved.