Differential search algorithm for solving multi-objective optimal power flow problem

Abacı, Kadir; Yamaçlı, Volkan

doi:10.1016/j.ijepes.2015.12.021

Cited by 186 publications

(123 citation statements)

References 37 publications

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“…The setting of optimal control variables of IKHA are presented in Table 3, which shows that the minimum of fuel cost emission obtained by IKHA is 0.204818 ton/h. The simulation result is compared with other methods in Table 7, and better than KHA, differential search algorithm (DSA) [9] ABC [31], MSA [22], MGBICA [32] and MSLFA [30]. Compared with the above cases, this objective function is non-linear.…”

Section: Case 3: Minimization Of Fuel Cost Emissionmentioning

confidence: 99%

“…Therefore, to a certain extent, the traditional methods cannot solve the OPF problem successfully. With the development of computers, the intelligent optimization methods based on the combination of computer technology and biological simulation are proposed to overcome many drawbacks of the traditional methods; and these heuristic algorithms include artificial bee colony algorithm (ABC) [7], particle swarm optimization (PSO) [8], differential search algorithm (DSA) [9], differential evolution algorithm (DE) [10], gravitational search algorithm (GSA) [11], etc. Numerous studies indicate that each algorithm has different performance, pros and cons in different cases, so there are more and more modified methods based on intelligent algorithms to solve the OPF problem effectively.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the authors in [12] proposed an improved colliding bodies optimization algorithm (ICBO) which considered three mechanisms in parallel-based colliding bodies optimization (CBO), and the proposed ICBO has been proved to yield better optimization efficacy over CBO. In [9], an efficient inspired search method based on differential search algorithm (DSA) could provide better results compared with DSA for handling OPF problem. A particle swarm optimization with an aging leader and challengers (ALC-PSO) was successfully applied for the solution of OPF problem in [13].…”

Section: Introductionmentioning

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 3: Minimization Of Fuel Cost Emissionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…In 2014, Ghasemi et al [35] presented chaotic invasive weed optimization (CIWO) algorithms based on chaos and examine its performance for optimal settings of OPF and its control variables. In 2016, Abaci and Yamacli [36] presented a differential search based optimization method to solve various types of problems including complex, single, and multiobjective functions within the constraints concerning optimal power flow (OPF). In 2016, Acharjee [37] presented the self-adaptive differential evolutionary (SADE) algorithm for increasing and controlling the power flow using unified power flow controller (UPFC) under practical security constraints (SCs).…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Khan

Singh

2017

Journal of Engineering

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The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

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“…Its principle is simple and it is easy to be understood and implemented with fewer controlled parameters. DE has been used to solve different engineering optimization problems and achieved good effects [17][18][19][20]. However, the control parameters in DE have a great effect on the algorithm performance and their appropriate values must be obtained through numerous tests.…”

Section: Optimization Techniquementioning

confidence: 99%

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

Yuan

Deng

et al. 2016

Energies

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Abstract:In the traditional paradigm, large power plants provide active and reactive power required for the transmission system and the distribution network purchases grid power from it. However, with more and more distributed energy resources (DERs) connected at distribution levels, it is necessary to schedule DERs to meet their demand and participate in the electricity markets at the distribution level in the near future. This paper proposes a comprehensive operational scheduling model to be used in the distribution management system (DMS). The model aims to determine optimal decisions on active elements of the network, distributed generations (DGs), and responsive loads (RLs), seeking to minimize the day-ahead composite economic cost of the distribution network. For more detailed simulation, the composite cost includes the aspects of the operation cost, emission cost, and transmission loss cost of the network. Additionally, the DMS effectively utilizes the reactive power support capabilities of wind and solar power integrated in the distribution, which is usually neglected in previous works. The optimization procedure is formulated as a nonlinear combinatorial problem and solved with a modified differential evolution algorithm. A modified 33-bus distribution network is employed to validate the satisfactory performance of the proposed methodology.

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Differential search algorithm for solving multi-objective optimal power flow problem

Cited by 186 publications

References 37 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

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

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