Optimal power flow using gravitational search algorithm

Duman, Serhat; Güvenç, Uğur; Sönmez, Yusuf; Yörükeren, Nuran

doi:10.1016/j.enconman.2012.02.024

Cited by 458 publications

(208 citation statements)

References 36 publications

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“…The net increase on the researched interface is the ATC of this simulation state. Such a conventional repeated power flow method only pays attention to system safety, but neglects the economics, so this paper introduces the optimal power flow [25,26] as shown in Equation (10) into every power flow computation of the repeated power flow method to comprehensively consider both safety and economy; it can get a more accurate results and better meet the real application needs in the power market: …”

Section: Atc Calculation Modelmentioning

confidence: 99%

“…In Case J all the parameters are the same as Case H except for the transmission line ones. The differences are that the states of some transmission lines (Lines 3,6,9,10,12,13,14,15,17,18,21,25,26,31,32,35,36,37,40,41) are treated as random fuzzy variables (B = (0.01,0.02,0.06)) and the others are simulated only as random variables by the failure rate B = 0.02.…”

Section: Ieee-30-bus Systemmentioning

confidence: 99%

See 1 more Smart Citation

Credibility Theory-Based Available Transfer Capability Assessment

Zheng

Yang

et al. 2015

Energies

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Since the development of large scale power grid interconnections and power markets, research on available transfer capability (ATC) has attracted great attention. The challenges for accurate assessment of ATC originate from the numerous uncertainties in electricity generation, transmission, distribution and utilization sectors. Power system uncertainties can be mainly described as two types: randomness and fuzziness. However, the traditional transmission reliability margin (TRM) approach only considers randomness. Based on credibility theory, this paper firstly built models of generators, transmission lines and loads according to their features of both randomness and fuzziness. Then a random fuzzy simulation is applied, along with a novel method proposed for ATC assessment, in which both randomness and fuzziness are considered. The bootstrap method and multi-core parallel computing technique are introduced to enhance the processing speed. By implementing simulation for the IEEE-30-bus system and a real-life system located in Northwest China, the viability of the models and the proposed method is verified.

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Section: Atc Calculation Modelmentioning

confidence: 99%

Section: Ieee-30-bus Systemmentioning

confidence: 99%

Credibility Theory-Based Available Transfer Capability Assessment

Zheng

Yang

et al. 2015

Energies

View full text Add to dashboard Cite

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“…In addition, these methods may fall into local minima, hence new optimization algorithms have been proposed to avoid the shortcomings of these methods. From these methods; GA [8,9], MFO [10], DE [11,12], PSO [13], MSA [14], EP [15,16], ABC [17], GSA [18], BBO [19], SFLA [20], forced initialized differential evolution algorithm [21], TS [22], MDE [23], SOS [24], BSA [25] and TLBO [26], decentralized decision-making algorithm [27]. The thermal generation units have multiple valves to control the output generated power.…”

Section: Introductionmentioning

confidence: 99%

“…As the valves of thermal generation units are opened in case of steam admission, a sudden increase in losses is observed which leads to ripples in the cost function curve (known as the valve-point loading effect). Several optimization techniques have been employed for solving the OPF considering the valve-point loading effect such as ABC [17], GSA [18], SFLA [20], SOS [24], BSA [25] and Hybrid Particle Swarm Optimization and Differential Evolution [28].…”

Section: Introductionmentioning

confidence: 99%

Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer

et al. 2018

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Abstract:The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques.

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“…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%

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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Optimal power flow using gravitational search algorithm

Cited by 458 publications

References 36 publications

Credibility Theory-Based Available Transfer Capability Assessment

Credibility Theory-Based Available Transfer Capability Assessment

Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer

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

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