Multi-objective generation dispatch using Particle Swarm Optimisation

Bacterial foraging algorithm for optimum economic-emission dispatch

“…Recently, heuristic non-classical methods have been proposed to solve this problem. These include evolutionary programming [5], genetic algorithm [6,7], and particle swarm optimization [8][9][10][11]. These non derivative-based methods demonstrate good performance in solving the EED problem regardless of the non-linear and non-smooth shape of the input-output characteristics of the thermal generating unit [4].…”

“…This swarming is expressed as follows: d attract , ω attract , h repellant and ω repellant are coefficients represent the characteristics of the attractant is the size of the bacteria population. The function(9) which represents the cell-to-cell signaling effect is added to the process is performed after taking a maximum number of chemotactic steps, Nc. The population is halved so that the least healthy half dies and each bacterium in the other healthiest one splits into two bacteria which takes the same position.…”

Multi Objective Dispatch Problem with Valve Point Effect Loading of Fuel Cost and Emission Criterion

“…The methods used or developed to solve the multi objective dispatch problems formulated by using penalty factors are described in the paper. .The combined fuel cost function and emission function without valve point effect is formulated by Weighted Sum Method (WSM) using PSO algorithm in [17], [18] and Evolutionary programming in [19].The combined fuel cost function and emission function without valve point effect is formulated by various price penalty factors approaches using recursive algorithm in [20]. MaxMax price penalty factor method using quadratic programming in [21], security constrained unit commitment problem algorithm in [22], sequential approach with matrix frame work in [23], PSO in [24], Improved back propagation neural network in [25], and Lagrange's method in [26].…”

Optimized generation costs using a modified particle swarm optimization version

“…A probabilistic transmission planning model was evaluated the expansion and reinforcement of transmission system using an adequacy linear programming model in the liberalized electricity markets [7]. Modern heuristics optimization techniques were considered as practical tools for non-linear optimization problems [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The Particle Swarm Optimization (PSO) technique was invented by Kennedy and Eberhart in 1995.…”

“…Recently, PSO has been successively applied to various fields of power system optimization problems such as for economic dispatch problem considering generation constraint [8], for minimizing the non-smooth cost function of economic dispatch problem [9], scheduling the generation outputs considering lagrangian relaxation method [10], reactive power and voltage control [11][12][13], optimal design of power system stabilizer [14], optimal power flow [15], state estimation [16], and for unit commitment problem [17]. The multiobjective generation dispatch using PSO with multiple fuel option were presented in [18] while, in [19], the multi-objective generation dispatch using PSO was presented for electricity markets. Reference [20] presented a procedure using PSO for obtaining the optimal design of a neuro-sliding mode controller for the transient stability enhancement of multimachine power systems with UPFC.…”