Multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm

Ghasemi, Maryam; Ghavidel, Sahand; Ghanbarian, Mohammad Mehdi; Gitizadeh, Mohsen

doi:10.1016/j.ins.2014.09.051

Cited by 103 publications

(60 citation statements)

References 39 publications

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“…NA means that the datum is not reported in the referred literature. From the tables, it is clear that the minimization of quadratic fuel cost obtained by IKHA is better than KHA, modified shuffle frog leaping algorithm (MSLFA) [30], ABC [31], moth swarm algorithm (MSA) [22], modified Gaussian bare-bones imperialist competitive algorithm (MGBICA) [32], Jaya [33] and adaptive real coded biogeography-based optimization (ARCBBO) [34]. The single iteration computation times of ABC [31] and Jaya [33] are longer than IKHA, which shows the search efficiency of IKHA.…”

Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

“…According to the experimental data of the three situations, the proposed method can successfully solve the non-differential and non-continuous OPF problem which contains the discrete and continuous variables. [30] 802.2870 NA/100 ABC [31] 800.6600 130.16/200 MSA [22] 800.5099 NA/100 MGBICA [32] 801.1409 NA/NA ARCBBO [34] 800.5159 NA/200 Jaya [33] 800.479 72.4/100 GSA [35] 798.6751 a 10.7582/200 BBO [36] 799.1116 a NA/200 a Infeasible solution. …”

Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

“…It can be seen that the minimization of voltage magnitude deviation of IKHA is 0.0892 p.u., which is less by 0.0137, 0.0347, 0.0082 and 0.0059 p.u. comparing with KHA, MGBICA [32], lévy teaching-learning-based optimization (LTLBO) [23] and BBO [36], respectively. Moreover, the voltage magnitude deviation in this case is decreasing from 0.9215 p.u.…”

Section: Case 2: Minimization Of Voltage Magnitude Deviationmentioning

confidence: 99%

“…Obviously, the proposed method can guide the individual to find the better location in the viable domain. [32] 0.1239 NA/NA LTLBO [23] 0.0974 20.17/100 BBO [36] 0.0951 NA/200 …”

Section: Case 2: Minimization Of Voltage Magnitude Deviationmentioning

confidence: 99%

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Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Chen

Zhang

2018

Energies

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Abstract:As one of the most important tools used in operation and planning of power systems, the optimal power flow (OPF) problem considering the economy and security is large-scale, complex and hard to solve. In this paper, an improved krill herd algorithm (IKHA) has been proposed. In IKHA, the onlooker search mechanism is introduced to reduce the probability of falling into local optimum; and the parameter values of the proposed algorithm including inertia weight and step-length scale factor are varied according to the iteration of evolutionary process, which improves the exploration and exploitation capabilities. Moreover, a novel constraint handling method is proposed to guide the individual to the feasible space and ensure that the optimal solution satisfies the security constraints. Then, IKHA is combined with the novel constraint handling method to solve the multi-constrained OPF problem, and its performance is tested on the IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems for 10 different simulation cases containing linear and non-linear objective functions. The simulation results demonstrate that the proposed method can solve the OPF problem successfully and obtain better solutions compared with other methods reported in the recent literatures, which prove the feasibility and effectiveness of the improvements in this work.

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Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

Section: Case 1: Minimization Of Quadratic Fuel Cost Functionmentioning

confidence: 99%

Section: Case 2: Minimization Of Voltage Magnitude Deviationmentioning

confidence: 99%

“…Obviously, the proposed method can guide the individual to find the better location in the viable domain. [32] 0.1239 NA/NA LTLBO [23] 0.0974 20.17/100 BBO [36] 0.0951 NA/200 …”

Section: Case 2: Minimization Of Voltage Magnitude Deviationmentioning

confidence: 99%

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Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Chen

Zhang

2018

Energies

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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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Impact of demand response for frequency regulation in two-area thermal power system

Singh

Samuel

Kishor

2016

Int. Trans. Electr. Energ. Syst.

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Summary In future power system operation, wide area monitoring applications will rely heavily on demand response (DR) because of its inherent characteristics mainly in ancillary services (AS). The DR is widely applied in power system; however, impact of DR loop with communication delay on dynamic performance of two‐area power system, mainly load frequency control (LFC), have not been reported by researchers. This paper presents the impact of DR control loop with communication delay in two‐area thermal power systems for improvement in frequency deviation profile. The optimal power sharing between supplementary and DR loop control is presented for different load variations. Further, efficacy of controller performance is also validated by introducing non‐linearities in generation in terms of generation rate constraint (GRC) and governor dead band (GDB). The simulations are performed with inclusion of DR for LFC control problem in (i) single‐area, (ii) two‐area thermal and (iii) thermal power system integrated with pumped hydropower system to improve the dynamic performance of system and hence minimize the frequency deviation. The controller in supplementary loop and DR loop is optimized using particle swarm optimization (PSO) technique. Copyright © 2016 John Wiley & Sons, Ltd.

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Multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm

Cited by 103 publications

References 39 publications

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

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

Impact of demand response for frequency regulation in two-area thermal power system

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