Economic load dispatch using improved particle swarm optimization algorithms

Abstract

The main objective of Economic Load Dispatch (ELD) problem is to schedule the connected generating units of plant outputs so as to fulfill load demands at minimum operating cost while satisfying all operational constraints. Recently particle swarm optimization algorithms inspired by collective behavior of swarm has been applied successfully to solve ELD problem. It is a population based stochastic optimization process driven by the simulation of a social psychological metaphor. In this paper three improved PSO algorithms-IPSO-A, IPSO-B and IPSO-C have been developed and implemented to solve ELD for IEEE 5, 14 and 30 bus systems. Conventional PSO (CPSO) using inertia weight and constriction factor individually as well as simultaneously have been also implemented to solve ELD problem. PSO algorithms have been compared for twenty trial runs. The best, worst, average fitness and their standard deviation for all the algorithms have been determined. The results show that proposed improved PSO techniques gives the optimum operating cost with consistent results in terms of diversity of results.

Development of cloud‐based power system operational data management system

“…The generation of each GS must be varied within its minimum and maximum limits of power generation. If P Gi max and P Gi min be the maximum and minimum limit of power generation of the ith GS, then the inequality constraint of each GS will be given as [20]…”

“…The objective (fitness) function of ELD problem is defined to minimise the total generation cost function and the equality constraint given in (5), where K being the penalty coefficient for the plant due to not fulfilling the load demands to consumer [20]…”

“…Each particle will move iteratively in the search space until all of them converge towards global best position. In conventional PSO, the velocity and position of the particles are updated iteratively by the velocity and position modification rule given by (6) and 7, respectively [20,22]…”

“…In IW-PSO, a new parameter inertia weight (w) is introduced in the velocity modification rule given in (8) with position modification rule remains unaltered [20]…”

“…At time t 1 , GS and DC will compress their next-day availability and load forecasting information by using S-DBEAE and update the respective files present in the cloud. This information is extracted by LDC at time t 2 according to which scheduling of two GS is done by using inertia weight particle swarm optimisation (IW-PSO) [20]. The next-generation allocation of the two GS and load allocation information of the DC are then compressed by using S-DBEAE and are updated in the respective files.…”