Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones

Duman, Serhat

doi:10.1007/s00521-016-2265-0

Cited by 100 publications

(76 citation statements)

References 55 publications

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“…The developed code has been written using MATLAB 2015 and the simulation run on a PC equipped with a core i5 processor, 2.50 GHz and 4 GB RAM. The upper and lower operating ranges and coefficients of generators are given in Table 1 [24]. The parameters of DGWO technique are selected as; number of populations = 50, maximum iteration = 100, b = 1, K min = 0.00001 and K max = 0.1.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…It is clear that DGWO has stable and rapid convergence characteristic. [14] 800.5099 NA NA SOS [24] 801.5733 801.7251 801.8821 ABC [17] 800.6600 800.8715 801.8674 TS [22] 802.290 NA NA MDE [23] 802.376 802.382 802.404 IEP [15] 802.465 802.521 802.581 TS [15] 802.502 802.632 802.746 EP [16] 802.62 803.51 805.61 TS/SA [15] 802.788 803.032 803.291 EP [15] 802.907 803.232 803.474 ITS [15] 804.556 805.812 806.856 GA [9] 805.937 NA NA…”

Section: Case1: Opf Solution Without Considering the Valve Point Effectsmentioning

confidence: 99%

“…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%

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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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Section: Simulation Resultsmentioning

confidence: 99%

Section: Case1: Opf Solution Without Considering the Valve Point Effectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Finding the best compromising solution in view of all the objectives also becomes difficult since the optimization algorithms are originally designed to optimize a single objective. 34 In recent years, SOS algorithm and some of its variants have been widely applied in solving various power engineering optimization problems, such as economic load dispatch, [35][36][37][38] optimal power flow, 39 congestion management, 40 and optimal DG allocation problem. In the weighted sum approach, a single solution is obtained that depends on the selected weights for each objective.…”

Section: Introductionmentioning

confidence: 99%

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

Saha

Mukherjee

2019

Int Trans Electr Energ Syst

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Summary This article presents a new method to decide the optimal locations and sizes of distributed generators (DGs) in radial distribution system (RDS). This task is associated with minimization of total voltage deviation and power loss and maximization of voltage stability index of the RDS. To solve this multiobjective DG allocation problem, a novel metaheuristic algorithm, namely, multiobjective chaotic symbiotic organisms search (SOS) (MOCSOS), is proposed in this article. The proposed MOCSOS is an improved version of the traditional SOS, where a chaotic local search technique is incorporated within the traditional SOS framework. The MOCSOS utilizes a fast nondominated sorting strategy to achieve the Pareto solutions. The constraints associated with this DG allocation problem are handled using a binary tournament selection–based approach. The performance of MOCSOS is validated on 33‐ and 69‐node RDSs. The efficacy of the MOCSOS is established by comparing its performance with other nature‐inspired state‐of‐the‐art algorithms.

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A novel approach towards uncertainty modeling in multiobjective optimal power flow with renewable integration

Saha

Bhattacharya

Das

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a novel methodology for solving multiobjective optimal power flow (MOPF) considering uncertain renewable generation. Two‐point estimate method (2PEM) is employed to take care of the uncertainty in renewable generation. Optimal power flow (OPF) is a very challenging optimization problem to solve due to its nonlinear nature. To overcome the constraints faced by classical optimization techniques, a novel hybrid metaheuristic algorithm is designed and applied to solve the MOPF problem. Since uncertainty in generation is considered, a probabilistic approach is required. Five different algorithms have been applied to the MOPF problem using 2PEM for the sake of comparison, and results show superiority of the proposed metaheuristic in achieving optimal results.

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Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones

Cited by 100 publications

References 55 publications

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

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

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

A novel approach towards uncertainty modeling in multiobjective optimal power flow with renewable integration

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