Optimal power flow by means of improved adaptive differential evolution

Li, Shuijia; Gong, Wenyin; Wang, Ling; Yan, Xuesong; Hu, Chengyu

doi:10.1016/j.energy.2020.117314

Cited by 114 publications

(70 citation statements)

References 44 publications

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“…In 2020, Shuijia Li et al [2], proposed a JADE about the self-adaptive penalty constraint handling method, indicated as EJADE-SP, to attain the best solution of OPF problem. The proposed method was an improved adaptation of JADE, whereas four enhancements were developed to improve the JADE performance while resolving the OPF problem: CR sorting model was developed to permit individuals to inherit additional superior genes; re-randomizing parameters (CR and scale factor) to preserve the search competence and variety; dynamic population lessening approach was exploited to accelerate convergence, and self-adaptive penalty constraint handling method was combined to tackle about constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because of the detail in which there are numerous control variables, and not all, are incessant, such as the shunt capacitor outputs, OPF problem is believed as a multi-modal, non-convex, large-scale, and non-linear constrained optimization problem [13] [14] [15]. In the interim, this as well creates resolving the OPF problem a significant problem in power system optimization [2].…”

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confidence: 99%

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Hybrid Genetic Algorithm and Particle Swarm Optimization Algorithm for Optimal Power Flow in Power System

K.S¹

2019

JCMPS

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In power systems, the Optimal Power Flow (OPF) problem is considered as the majority extensively nonlinear optimization problems. This work presents a new hybrid optimization approach that integrates the advantages of the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) approach to solve the OPF problem. The developed hybrid algorithm is considered to achieve environmental, technical, and economic, advantages. The proposed technique is used to single and multi-objective optimization problems using diverse objective models like minimization of generation cost, reduction of emission, minimization of transmission power loss, maximization of voltage profile, and maximization of voltage stability. To show the ability of developed hybrid optimization method, single-objective cases are used and verified on three standard bus systems. The developed PSO-GA technique attains considerably the efficiency, and the robustness of OPF outcomes for the cases, are represented. The experimentation outcomes show that the developed technique tends to better levels of techno-economic-environmental advantages evaluated. Additionally, the sensitivity analysis study verifies that the proposed hybrid algorithm constructs robust outcomes over parameter variations.

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Hybrid Genetic Algorithm and Particle Swarm Optimization Algorithm for Optimal Power Flow in Power System

K.S¹

2019

JCMPS

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“…The mathematical approach, including linear programming [7], nonlinear programming [8], quadratic programming [9], interior point method [10] and Newton-based methods [11], provides the optimal solution of OPF problems; however, several drawbacks of this approach are also inevitable. For example, some of the noticeable shortcomings of existing mathematical approaches include no guarantee of accuracy for the linear program, poor convergence and algorithm complication for the non-linear programming, the sensitivity of initial guess for the Newton-based algorithm, solving complexities aroused for non-linear and quadratic objective functions in the interior point algorithm [12]. In addition, mathematical approaches have issues in finding global optima (i.e., trapping in local optima) and specific objective function restrictions, such as prohibited operating zones, valve point effects and piece-wise quadratic cost function [3,13].…”

Section: Introductionmentioning

confidence: 99%

“…Hybrid DE and harmony search (HS) algorithms were developed in [28]who replaced operation of the evolution algorithm by a pitch adjustment operation and then a harmony algorithm was used to improve the global convergence. An enhanced adaptive DE algorithm that considers SP constraint handling technique was also developed in [12] to determine the near-optimal solution of the OPF problem. The performance of the algorithm was enhanced using four operators: crossover rate (Cr) sorting mechanism, re-randomizing Cr and scale factor (F), dynamic population reduction strategy, and SP constraint linear technique.…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

Hossain¹,

Sallam²,

Elsayed³

et al. 2021

Preprint

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In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number of solutions evolved by each DE operator based on both the diversity of population and quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering the intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results and thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%.

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“…Differential evolution (DE) [36], proposed by Storn and Price in 1995, is an efficient population-based method. Over the past two decades, due to its simplicity and effectiveness, DE has been widely used in various areas, such as multimodal optimization [37], multiobjective optimization [38], solar cell optimization [39], and dynamic optimization [40], optimal power flow [41]. Also, to enhance the performance of DE, several advanced DE variants have been developed to solve the problems in various areas [42].…”

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confidence: 99%

An Improved Differential Evolution to Extract Photovoltaic Cell Parameters

Liao

et al. 2020

IEEE Access

Self Cite

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Parameter extraction of photovoltaic (PV) models plays a vital role in simulation, evaluation and control of PV systems. It requires to identify the parameters of different PV models quickly and accurately. In this paper, an improved differential evolution by reusing the past individual vectors and adaptive mutation strategy is proposed to extract PV parameters. In the proposed method, the successful difference vectors from previous generations are introduced to produce the offspring in the next generations to improve the performance of differential evolution. In addition, to obtain a nice result, an adaptive mutation strategy is considered to establish a good balance of exploration and exploitation. The proposed method is applied to identify the parameters of different PV models, such as single diode, double diode, and PV models. Comparison results demonstrate that the proposed method obtains the competitive performance on accuracy, reliability and convergence when compared with other state-of-the-art methods.

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Optimal power flow by means of improved adaptive differential evolution

Cited by 114 publications

References 44 publications

Hybrid Genetic Algorithm and Particle Swarm Optimization Algorithm for Optimal Power Flow in Power System

Hybrid Genetic Algorithm and Particle Swarm Optimization Algorithm for Optimal Power Flow in Power System

Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

An Improved Differential Evolution to Extract Photovoltaic Cell Parameters

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