A Novel Particle Swarm Optimization for Optimal Scheduling of Hydrothermal System

Abstract

A fuzzy adaptive particle swarm optimization (FAPSO) is presented to determine the optimal operation of hydrothermal power system. In order to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO), the fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on hydrothermal system comprising 1 thermal unit and 4 hydro plants, the comparison is drawn in PSO, FAPSO and genetic algorithms (GA) in terms of the solution quality and computational efficiency. The experiment showed that the proposed approach has higher quality solutions and strong ability in global search.

Optimization Methods and Algorithms for Solving Of HydroThermal Scheduling Problems.

“…According to Atul [1], the operating cost of thermal plant is very high, though their capital cost is low. On the other hand, the operating cost of hydroelectric plant is low, though their capital cost is high, so it has become economical as well as convenient to have both thermal and hydro plants in the same grid.The objective of optimal operation to hydrothermal power is usually to minimize the thermal cost function while satisfying physical and operational constraints [77].…”

“…Particle swarm optimization (PSO) is a computation technique [72] and has been successfully used in many areas [73][74][75][76]. Although PSO has many advantages it has some shortcomings such as premature convergence [77]. To overcome these problems, many methods have been developed, among them is the inertia weight method [78,79].…”

“…This could improve the algorithm but does not truly reflect the actual search process to find the optimum. Chang [77] proposed a fuzzy adaptive particle swarm optimization (FAPSO) to the operation of hydro-thermal power system designed to adjust the inertia weight as the environment changes. Fig 4 presents the flowchart of operations for the FAPSO.…”

“…Fig 4 presents the flowchart of operations for the FAPSO. Chang [77] reports that FAPSO generates better solutions than other methods, mainly because it is implemented to dynamically adjust the inertia weight by using "IF-THEN" rules, this can improve the global and local search ability of the PSO and overcome the disadvantages of the PSO. Although almost all works which use dual decomposition in the Short-Term Scheduling (STS) problem are based on either Lagrangian Relaxation (LR) [42,[80][81][82][83][84][85] or Augmented Lagrangian (AL) [86][87][88], Rodrigues et al [89] proposed a two-phase approach similar to [90,91], but introduced contributions.…”

“…The infeasibility between generation and demand is eliminated and the artificial constraints are satisfied. Figure 4 The framework flow chart of FAPSO [77]. Typically, LR technique relaxes coupling constraints such as demand and reserve requirements.…”