A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power

Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems

“…In this system, P loss is neglected and can be found in [22]. Similarly, the results of the proposed algorithm are compared with those found in literature, such as GA [5], DE [23], modified DE (MDE) [24], chaotic DE (CDE) [23], chaotic sequence based DE (CSDE) [25]" cross-entropy (CE) [26], enhanced CE (ECE) [26], PSO [5], ABC [5], improved chaotic PSO (ICPSO) [27], imperialist competitive algorithm (ICA) [26], hybrid bare-bones PSO [15]. The detailed results, presented in Table 3, show that the algorithm is better than all other algorithms considered.…”

Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

“…The WT dispatch problem has been formulated as a static one in [5,[7][8][9][10][12][13][14][15][16][17][18][19][20][21][22]31,32,36] while a few efforts are also seen towards developing multi-period WT dispatch models [6,11,[23][24][25][26][27][28][33][34][35][36][37][38]. In some papers the WT dispatch problem is formulated for single objective (SO) minimization only [7,8,[10][11][12][13][14]17,19,23,24,28,36,37] whereas in [5,6,9,1...…”

“…Therefore sufficient up/ down reserve capacity is necessary to maintain the demandsupply balance under continuously changing wind and load profiles. In a large number of papers, the cost of over estimation is added in the cost equation as spinning reserve compensation cost and under estimation of wind energy is represented as wind curtailment penalty to include the effect of wind power prediction errors [6,8,9,15,[18][19][20][21][22]25]. A stochastic model for wind power forecast error is proposed using a probabilistic approach to compute the cost of over/under estimation [30].…”

(Lantool) Power Generation Cost Minimization Software Application

“…They include Expert system (ES), Ant Colony search (ACS) [11], Simulated annealing (SA) [12], Artificial Neural networks (ANN) [13,14,15], Fuzzy logic (FL) [16] and Genetic Algorithm (GA) [17]. Others include Meta heuristic methods such as Tabu search (TS) [18], Particle swarm optimization (PSO) [19,20] and Evolutionary programming (EP) [21,22]. Application of these methods depends on the researchers' area of interest as each method has its own advantages and disadvantages.…”