Application of Improved PSO Technique for Short Term Hydrothermal Generation Scheduling of Power System

Modified Differential Evolution Algorithm: A Novel Approach to Optimize the Operation of Hydrothermal Power Systems while Considering the Different Constraints and Valve Point Loading Effects

“…Step 2: Initialize a population of N p solutions using Equations (25) and (26) for the first problem and Equations (30) and (31) for the second problem.…”

“…Among the optimization problems related to the optimal scheduling of hydrothermal systems, the fixed-head, short-term HTS problem has been widely and successfully studied so far . The fixed-head, short-term HTS problem has been classified into two sub-problems with different hydraulic constraints, namely the amount of discharged water available via the turbine [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] and reservoir volume limits [2,3,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. In the two sub-problems, the water head of reservoirs is not supposed to be changed over the scheduled time, thus the water discharge function of the two problems has the same quadratic function model with respect to hydro generation and coefficients; however, the set of hydraulic constraints taken into account in the problems are almost completely different.…”

Optimal Scheduling of Large-Scale Wind-Hydro-Thermal Systems with Fixed-Head Short-Term Model

“…The problem has been studied so far and obtained many intentions from researchers. Several algorithms, such as Gradient Search Algorithm (GSA) [2], Newton-Raphson Method (NRM) [3], Hopfield Neural Networks (HNN) [4], Simulated Annealing Algorithm (SAA) [5], Evolutionary Programming Algorithm (EPA) [6][7][8], Genetic Algorithm (GA) [9], modified EPA (MEPA) [10], Fast Evolutionary Programming Algorithm (FEPA) [10], Improved FEPA (IFEPA) [10], Hybrid EPA (HEPA) [11], Particle Swarm Optimization (PSO) [12], Improved Bacterial Foraging Algorithm (IBFA) [13], Self-Organization Particle Swarm Optimization (SOPSO) [14], Running IFEPA (RIFEPA) [15], Improved Particle Swarm Optimization (IPSO) [16,17], Clonal Selection Optimization Algorithm (CSOA) [18], Full Information Particle Swarm Optimization (FIPSO) [19], One-Rank Cuckoo Search Algorithm with the applications of Cauchy (ORCSA-Cauchy) and Lévy distribution (ORCSA-Lévy) [20], Cuckoo Search Algorithm with the applications of Gaussian distribution (CSA-Gauss), Cauchy distribution (CSA-Cauchy), and Lévy distribution (CSA-Lévy) [21], Adaptive Cuckoo Search Algorithm (ACSA) [22], Improved Cuckoo Search Algorithm (ICSA) [23], Modified Cuckoo Search Algorithm (MCSA) [24], and Adaptive Selective Cuckoo Search Algorithm (ASCSA) [24] have been applied to solve the problem of hydrothermal scheduling. Almost all of the above-mentioned methods are mainly meta-heuristic algorithms, excluding GSA and NRM.…”

“…Only one-thermal and one-hydropower plant system and quadratic fuel cost function is employed as the case study for running those methods. In order to improve the conventional PSO successfully, weight factor [16] and constriction factor [17] are respectively used to update new velocity and new position. The improvement also leads to an optimal solution with shorter execution time, but the two research studies report an invalid optimal solution, since the water discharge violates the lower limit.…”

Adaptive Cuckoo Search Algorithm for Short-Term Fixed-Head Hydrothermal Scheduling Problem with Reservoir Volume Constraints

“…The improvement of EP can improve the solution and speed up convergence and they are superior to conventional EP via only one test system. The application of improved PSO has been presented in [12][13] when a weight factor and a constriction factor were suggested to update new velocity and new position. The weight factor is adaptive meanwhile the constriction factor is fixed at a value depending on the selection of two acceleration constants.…”