Optimal power flow using an Improved Colliding Bodies Optimization algorithm

Bouchekara, Houssem R. E. H.; Chaib, A.E.; Abido, M. A.; El‐Sehiemy, Ragab A.

doi:10.1016/j.asoc.2016.01.041

Cited by 225 publications

(147 citation statements)

References 40 publications

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“…The ant lion optimization algorithm (ALOA) [31] was also a new method with few applications, and the method in the study has not shown its potential search ability persuasively due to simple test employment. In addition, many new methods have been developed for solving the problem such as modified symbiotic organisms search algorithm (MSOSA) [32], mine blast algorithm (MBA) [33], clonal algorithm (CA) [34], mathematical programming algorithm (MPA) [35], improved quantuminspired evolutionary algorithm (IQIEA) [36], cuckoo optimization algorithm (COA) [37], improved colliding bodies optimization algorithm (ICBOA) [38], flower pollination algorithm (FPA) [39], natural updated harmony search (NUHS) [40], lightning flash algorithm (LFA) [41,42], moth swarm algorithm (MSA) [43], and orthogonal learning competitive swarm optimization algorithm (OLCSOA) [44]. [45].…”

Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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This paper presents a novel improved firefly algorithm (IFA) to deal the problem of the optimal operation of thermal generating units (OOTGU) with the purpose of reducing the total electricity generation fuel cost. The proposed IFA is developed based on combining three improvements. The first is to be based on the radius between two solutions, the second is updated step size for each considered solution based on different new equations, and the third is to slightly modify a formula producing new solutions by using normally distributed random numbers and canceling uniform random numbers of conventional firefly algorithm (FA). The effect of each proposed improvement on IFA is investigated by executing five benchmark functions and two different systems. The performance of IFA is investigated on six other study cases consisting of different types of objective function and complex level of constraints. The objective function considers single fuel with quadratic form and nonconvex form, and multifuels with the sum of several quadratic and nonconvex functions while a set of constraints taken into account are power loss, prohibited zone, ramp rate limit, spinning reserve, and all constraints in transmission power networks. The obtained results indicate the proposed improvements in terms of high optimal solution quality, stabilization of search ability, and fast convergence compared with FA. In addition, the comparisons with other methods also lead to a conclusion that the proposed method is a very promising optimization tool for systems with quadratic fuel cost function and with complicated constraints.

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Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

2018

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“…In addition, the best costs, mean cost, standard deviation and N FES from CCSA, and the proposed method for the three sub-cases are also given in Table 6 for subcase 3.1, in Table 7 for subcase 3.2 and in Table 8 for subcase 3.3 for comparisons with those from other methods. Observations from such sub-cases show that the proposed method yields better minimum cost than CCSA and most methods excluding MCBOA [34] for sub-cases 3.1, 3.2, and 3.3, HIGA-BM [20], and EADPSO [24] for sub-case 3.3; however, MCBOA has used much high value N FES with 25,000 (for sub-cases 3.1 and 3.2) and 45,000 (for sub-case 3.3) while that of HIGA-BM [20] and EADPSO [24] are 12,000 and 12,500, respectively, but the value from the proposed method is only 2000. Besides, as we check the optimal solution reported in [24], the solution is valid but the exact cost is $956.2325, which is much higher than the reported number of $629.4692.…”

Section: Case 3: Ieee-30 Bus Power Systemmentioning

confidence: 99%

“…For sub-case 3.2, there is an important note that HIGA-BM [20] has only reported active power of generators for optimal solution, leading to a restriction for checking validation, and the effectiveness of the proposed method cannot be evaluated. The comparison of N FES to the proposed method and other methods indicates that the proposed method is much faster than them because the proposed method used only N FES of 2000 while others have used from N FES of 4560 (HIGA [19]) to 45,000 (MCBOA [34]). For comparisons of mean cost and standard deviation, the three subcases have the same result that those values of other methods are better than those of the proposed method, but the deviation is not insignificant.…”

Section: Case 3: Ieee-30 Bus Power Systemmentioning

confidence: 99%

“…The key variables corresponding to the best fitness function yielded by the proposed method for case 3 are given in Appendix A. proposed method used only NFES of 2000 while others have used from NFES of 4560 (HIGA [19]) to 45,000 (MCBOA [34]). For comparisons of mean cost and standard deviation, the three subcases have the same result that those values of other methods are better than those of the proposed method, but the deviation is not insignificant.…”

Section: Case 3: Ieee-30 Bus Power Systemmentioning

confidence: 99%

“…Most methods are the family of meta-heuristic algorithms in which conventional methods, modified methods, combination of two different methods, and hybrid methods have been developed widely. In fact, there have been a huge number of applied methods such as the integration of improved genetic algorithm and effective decoupled quadratic load flow (IGA-EDQLF) [18], hybrid IGA with incremental power flow model (HIGA) [19], HIGA with boundary method (HIGA-BM) [20], differential evolution [21,22], conventional PSO [23], Evolving ant direction particle swarm optimization (EADPSO) [24], PSO with Pseudo-Gradient and constriction factor (PG-CF-PSO) [25], Biogeography-based optimization algorithm (BBOAA) [26] and adaptive real-coded biogeography-based optimization algorithm (ARCBBOA) [27], teaching-learning-based optimization algorithm (TLBO) [28], improved TLBO (ITLBO) [29], gravitational search algorithm (GSA) [30], Artificial bee colony algorithm (ABCA) [31], Grey wolf optimizer (GWO) [32], modified electromagnetism-like mechanism algorithm (MELMA) [33], modified Colliding Bodies Optimization algorithm (MCBOA) [34], moth swarm algorithm (MSA) [35], improved imperialist competitive algorithm (IICA) [36], cuckoo optimization algorithm (COA) [37], Gaussian bare-bones imperialist competitive algorithm (GBBICA) [38], and mathematical programming algorithm (MPA) [39]. In [18][19][20], different variants of GA have been developed in which GA has been improved first and then combined with another method for handling constraints of OPF problem.…”

Section: Introductionmentioning

confidence: 99%

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Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Nguyen

Quynh

et al. 2018

Energies

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Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems with 30, 57, and 118 buses considering transmission power network constraints. The specific evaluation of the HPCSA is compared to that of Lagrange optimization-based methods (LMS), neural network-based methods (NNMS), CCSA, and other popular methods such as Particle swarm optimization (PSO) variants, Differential evolution (DE) variants, Genetic Algorithm (GA) variants, and state-of-the-art methods. In comparison with CCSA, the proposed method is always more effective and more robust since the proposed method can find most solutions with better quality and faster convergence speed. In comparison with LMS and NNMS, the proposed method can also find solutions with approximate or equal quality. In comparison with popular methods and state-of-the-art methods, the proposed method has more potential since it can reach faster convergence to valid solutions with approximate or better quality. Consequently, it can be concluded that the proposed HPCSA is an effective optimization tool for dealing with ELD problems.

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Modeling of retailer's behavior for participation in the capacity market

Jalili

Eslami

Moghaddam

et al. 2019

Int Trans Electr Energ Syst

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Summary Due to economic reasons, only way for demand side management resources participation in capacity market is their participation by load serving entities, energy service companies, or retailers. To achieve the optimal participation of retailers in the capacity market, it is crucial to model the effective parameters in retailers’ behavior model and how they trade with the independent system operator and the consumers. So a novel method is proposed in this paper for modeling retailers’ behavior, considering demand side management resources availability for their participation in long term commitments. The capacity product of the retailer and its behavior are comprehensively modeled to trade in the capacity market. The competition of retailers is also considered and its effects on the profit of the retailer and consumers are analyzed. A genetic algorithm has been used to solve this problem due to its ability for solving large‐scale real nonlinear Non‐deterministic Polynomial‐time hard (NP‐hard) optimization problems.

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Optimal power flow using an Improved Colliding Bodies Optimization algorithm

Cited by 225 publications

References 40 publications

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

Improved Firefly Algorithm: A Novel Method for Optimal Operation of Thermal Generating Units

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Modeling of retailer's behavior for participation in the capacity market

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