Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem

Subbaraj, P.; Rengaraj, R.; Salivahanan, S.; Senthilkumar, T.R.

doi:10.1016/j.ijepes.2010.02.003

Cited by 53 publications

(19 citation statements)

References 33 publications

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“…The losses are calculated using B-loss coefficient matrix. The [15], PSO-MSAF [43], GA-AFI [44] and TVAC-EPSO [45] methods and are shown in Table 3. From the comparison of results, it is observed that the proposed HPSO method gives minimum fuel cost than the other methods.…”

Section: Resultsmentioning

confidence: 99%

Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints

Prabakaran¹,

Senthilkuma²,

Baskar³

2015

Journal of Electrical Engineering and Technology

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-This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

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Section: Resultsmentioning

confidence: 99%

Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints

Prabakaran¹,

Senthilkuma²,

Baskar³

2015

Journal of Electrical Engineering and Technology

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“…The results show that the MPSO-TVAC provides the minimum cost with less computational time compared to other methods . For 15-generators system, the results obtained by the MPSO-TVAC are compared with GA [14], PSO [14], BF [37], SOH_PSO [17],GA-API [6], PSO-MSAF [38], PSO-TVAC [36] and FA [34] in Table 9. It shows that MPSO-TVAC can produce a better cost and less computational time compared to other methods.…”

Section: Comparison Of Best Solutionmentioning

confidence: 99%

Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch with Generator Constraints

Abdullah

Bakar

Rahim

et al. 2014

Journal of Electrical Engineering and Technology

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-This paper proposes a Modified Particle Swarm Optimization with Time VaryingAcceleration Coefficients (MPSO-TVAC) for solving economic load dispatch (ELD) problem. Due to prohibited operating zones (POZ) and ramp rate limits of the practical generators, the ELD problems become nonlinear and nonconvex optimization problem. Furthermore, the ELD problem may be more complicated if transmission losses are considered. Particle swarm optimization (PSO) is one of the famous heuristic methods for solving nonconvex problems. However, this method may suffer to trap at local minima especially for multimodal problem. To improve the solution quality and robustness of PSO algorithm, a new best neighbour particle called 'rbest' is proposed. The rbest provides extra information for each particle that is randomly selected from other best particles in order to diversify the movement of particle and avoid premature convergence. The effectiveness of MPSO-TVAC algorithm is tested on different power systems with POZ, ramp-rate limits and transmission loss constraints. To validate the performances of the proposed algorithm, comparative studies have been carried out in terms of convergence characteristic, solution quality, computation time and robustness. Simulation results found that the proposed MPSO-TVAC algorithm has good solution quality and more robust than other methods reported in previous work.

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“…[17,56] 121423.6300 121814.9400 NA PSO-MSAF [57] 121423.2300 NA NA ICA-PSO [9] 121422.1000 NA NA DE/BBO [18] 121420.8948 121420.8952 121420.8963 FAPSO-NM [20] 121418.3000 121418.8030 121419.8000 FA [21] 121415.0500 121416.5700 121424.5600 PS [23] 121415.1400 122332.6500 125486.2900 MDE [58] 121414.7900 121418.4400 121466.0400 CSOMA [59] 121414.6978 121415.0479 121417.8045 DEvol [60] 121412.9100 NA 121464.400 FAMPSO [61] 121412.5700 121413.3879 121415.7800 NAPSO [62,63] 121412.5700 NA NA CIHBMO [64] 121412.5700 121412.5919 121412.63 MGSO [65,66] 121412.5693 NA NA QGSO [67] 121412.5512 121423.5200 121438.6850 IPSO-TVAC [68] 121412.5450 121419.3000 121423.8000 CCPSO [69] 121412.5362 121454.3269 121534.4934 MTLA [70] 121412 population have been carried out to test the consistency of the SDE algorithm. Due to the inherent randomness involved the performance of heuristic search algorithms are judged out of a number of trials.…”

Section: Average Cost ($/ H)mentioning

confidence: 99%

Shuffled differential evolution for economic dispatch with valve point loading effects

Reddy¹,

Vaisakh

2013

International Journal of Electrical Power & Energy Systems

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Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem

Cited by 53 publications

References 33 publications

Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints

Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints

Modified Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch with Generator Constraints

Shuffled differential evolution for economic dispatch with valve point loading effects

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