Synergism of Genetic Algorithms and Fuzzy Systems for Power System Applications

The power system of today is a complex interconnected network having four major components – generation, transmission, distribution and loads. Electricity is being generated in large hydro, thermal and nuclear power stations, which are normally located far away from the load centers. Large and long transmission networks are wheeling the generated power from these generating stations to different distribution systems, which ultimately supply the load. The distribution system is that part of the power system which connects the distribution substations to the consumers’ service-entrance.

Earlier the utilities were mainly concerned about the optimal dispatch of active power only, but evolvement of competition has also resulted in the optimal dispatch of reactive power. When only total cost is minimized by real power scheduling of available generator in a system, the optimal power flow (OPF) corresponds to Active Power Dispatch. Some of the solution techniques successfully used for active power dispatch include classical co-ordination methods based on Lagrangian multiplier approach (Chowdhury and Rahman 1990), Linear programming (LP) based methods (Stott and Hibson 1978; Stott and Marinho 1979), quadratic programming (QP) approach (Nanda et al. 1989), Gradient method using steepest descent technique (Dommel and Tinney 1968) and Newton's methods (Sun et al. 1984; Maria and Findlay 1987). A comprehensive review of various optimization techniques available in the literature is reported in references (Happ 1977; Sasson and Merril 1974; Carpantier 1985). The classical method of optimization is relatively simple, fast and requires less memory space but sometimes it is unable to handle the system constraints effectively and sometimes convergence is not obtained. The LP based method involves approximation in linearizing the objective function and constraints and may result in zigzagging of the solution. Gradient based methods compute the derivative of the function at each step. They require a close initial guess and in general suffer from convergence difficulties and may stuck to local minima.