Application of Firefly Optimization Technique for Solving Convex Economic Load Dispatch of Generation On Nigerian 330 kV, 24-Bus Grid System

Abstract

Abstract-Firefly optimization is a population based technique in which the attractiveness of a firefly is determined by its attractiveness which is then encoded as the objective function of the optimization problems. Firefly algorithm is one of the newest meta-heuristic algorithms based on the mating or flashing behavior of fireflies. Economic load dispatch of generation allocates power generation to match load demand at minimal possible cost without violating power units and system constraints. This paper presents application of firefly optimization technique (FFOT) for solving convex economic load dispatch of generation. The economic load dispatch problem was formulated to minimize the total fuel cost for the heat optimal combination of thermal generators without violating any of the system constraints using quadratic fuel cost model of Sapele, Delta, Afam and Egbin power stations as case studies. The equality and inequality constraints used on the system were the power balance equation and the transmission line constraints respectively. Firefly optimization technique was then developed using appropriate control parameters for a faster convergence of the technique. The optimization technique was tested and validated on the IEEE 30-bus system and Nigerian 24-bus system. The results obtained from the IEEE 30-bus system were compared to published results obtained via Differential Evolution (DE), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The comparison confirms the superiority, fast convergence and proficiency of the algorithm.

A Dynamic Optimal Power Flow of a Power System Based on Genetic Algorithm

“…The two equations had for each load bus given by Equation ( 5) and Equation (6), and one equation for each voltagecontrolled bus given by Equation (5). The terms ( ) and ( ) are the difference between the scheduled and calculated values known as the power residuals, given by:…”

“…Table (5) shows the values of voltage magnitude, Active Power, Reactive Power Generation, Active and Reactive Load at each bus from the application of the Newton-Raphson method. Table (6) shows the values of the production cost, the active and reactive power loss when applying 4.2.2 Genetic Algorithm Optimization Applications: Table (7) shows the values of voltage magnitude, active power, reactive power generation, active and reactive load at each bus from the applications of the Genetic Algorithm Optimization method, the Newton-Raphson method, on the 14-Bus system. Table (8) shows the cost values and the active and reactive losses.…”

“…The objective function was reducing transmission losses. Ajenikoko [6] used the shunt capacitor as an interactive compensation placed at specific generating stations to reduce generating cost and transmission loss. Attia [7] used the modified genetic algorithm with a population size adjustment on a 30-Bus test system to solve the optimal energy flow problem based on different objective functions.…”

Optimization approaches to generation dispatch problems: Review of Nigerian power system

“…It was concluded that PSO generated the lowest cost of generation and power loss when compared with other methods mentioned. Ajenikoko et al (2018) applied Firefly Optimization Technique (FFOT) to solve EDP of generation on Nigerian grid system. The EDP problem was expressed to minimize total fuel cost.…”