Multi-objective Bi-level Programming for Environmental Constrained Electric Power Generation and Dispatch via Genetic Algorithm (original) (raw)

This article presents how multiobjective bilevel programming (MOBLP) in a hierarchical structure can be efficiently used for modeling and solving environmental-economic power generation and dispatch (EEPGDD) problems through Fuzzy Goal Programming (FGP) based on genetic algorithm (GA) in a thermal power system operation and planning horizon. In MOBLP formulation, first the objectives associated with environmental and economic power generation are considered two optimization problems at two individual hierarchical levels (top level and bottom level) with the control of more than one objective, that are inherent to the problem each level. Then, the optimization problems of both the levels are described fuzzily to accommodate the impression arises for optimizing them simultaneously in the decision situation. In the model formulation, the concept of membership functions in fuzzy sets for measuring the achievement of highest membership value (unity) of the defined fuzzy goals in FGP formulation to the extent possible by minimising under-deviational variables associated with membership goals defined for them on the basis of their weights of importance is considered. Actually, the modeling aspects of FGP are used here to incorporate various uncertainties arises in generation of power and dispatch to various locations. In the solution process, a GA scheme is used in the framework of FGP model in an iterative manner to reach a satisfactory decision on the basis of needs in society in uncertain environment. The GA scheme is employed at two different stages. At the first stage, individual optimal decisions of objectives are determined for fuzzy goal description of them. At the second stage, evaluation of goal achievement function to arrive at the highest membership value of the fuzzy goals in the order of hierarchical of optimizing them in the decision situation. The effective use of the approach is tested on the standard IEEE 6-Generator 30-Bus System.