Bio-inspired heuristics hybrid with sequential quadratic programming and interior-point methods for reliable treatment of economic load dispatch problem

Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

“…Test system 1: Three units with convex fuel cost function shown in Equation 1Test system 2: Ten units with convex fuel cost function shown in Equation 1Test system 3: Twenty units with nonconvex fuel cost shown in Equation 2Case 1: Total revenue and total fuel cost are obtained by using Equations (5) and 6Case 2: Total revenue and total fuel cost are obtained by using Equations 7and 8The whole data of the tests and parameters corresponding to the two cases are given in Tables A1-A4 (1) No p = 5 and G max = 5 for test system 1 (2) No p = 20 and G max = 100 for test system 2 (3) No p = 30 and G max = 500 for test systems 3…”

“…The earliest ELD problem did not consider power losses in transmission lines due to effects of resistance and reactance of transmission lines and also ignored valve effects on power increase and decrease process of thermal generation units [2]. Another more complicated ELD problem has taken into account active power loss, the valve effects and complex constraints of thermal generating units such as prohibited working zones [3], ramp rate 2 of 35 limitations [4] and generation limitations [5]. The concerned problem has been solved by applying a huge number of methods such as maximum likelihood optima (MLO) [6], evolutionary particle swarm optimization algorithm (EPSO) [7], improved stochastic fractal search algorithm (ISFSA) [8], improved social spider optimization algorithm (ISSOA) [9], interior search algorithm (ISA) [10], multi-leader comprehensive learning particle swarm optimization with adaptive mutation (MLCL-PSO) [11], dragonfly algorithm (DA) [12] and ameliorated grey wolf optimization (AGWO) [13].…”

“…Different methods have been proposed for the problem such as Lagrange function-based evolutionary programming (LFEP) [19], Tabu search algorithm (TSA) [20], muller approach (MA) [21], memetic optimization algorithm (MOA) [22], modified artificial bee colony optimization algorithm (MABCOA) [23], ant colony optimization algorithm (ACOA) [24], multi-agent model algorithm (MAMA) [25], binary fish optimization algorithm (BFOA) [26], Lagrange function-based invasive weed optimization algorithm (LFIWOA) [27], sine function and cosine function-based algorithm (SCBA) [28], binary whale optimization algorithm (BWOA) [29], expanded Lagrange function-based Hopfield network method (ELF-HNM) [30] and five Lagrange function-based Hopfield neuron network (LF-HNN) methods [31]. The main difference between studies [1][2][3][4][5][6][7][8][9][10][11][12][13] and studies [20][21][22][23][24][25][26][27][28][29][30][31] is competitive electric market. For example, the same authors have published three studies [8,9,31] in Energies journal but only the study [31] has ...…”

“…Thus, operation status and start-up cost of the units are neglected in the paper. As a result, the problem formulation is established in Section 2 by using previous studies [18,19] about competitive electric market and other previous studies [1][2][3][4][5][6][7][8][9] about economic load dispatch problem.…”

A Neuro-Swarming Intelligence-Based Computing for Second Order Singular Periodic Non-linear Boundary Value Problems

“…the designed optimization approach. Some recent submissions of the IPS are mixed complementarity monotone systems [52], active noise control systems [53], simulation of aircraft parts riveting [54], the economic load dispatch model [55], and nonlinear system identification [56].…”