Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm

Yuan, Xiao; Zhang, Binqiao; Wang, Pengtao; Liang, Ji; Yuan, Yanbin; Huang, Yuehua; Lei, Xiaohui

doi:10.1016/j.energy.2017.01.071

Cited by 131 publications

(56 citation statements)

References 17 publications

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“…where A i is the alignment of the ith individual, and V j is the velocity of jth neighboring individual. Cohesion, which is the propensity of individuals towards the center of mass of the neighborhood, is formulated by Equation (22).…”

Section: Damentioning

confidence: 99%

A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems

Khunkitti

Siritaratiwat

Premrudeepreechacharn

et al. 2018

Energies

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In this paper, a hybrid optimization algorithm is proposed to solve multiobjective optimal power flow problems (MO-OPF) in a power system. The hybrid algorithm, named DA-PSO, combines the frameworks of the dragonfly algorithm (DA) and particle swarm optimization (PSO) to find the optimized solutions for the power system. The hybrid algorithm adopts the exploration and exploitation phases of the DA and PSO algorithms, respectively, and was implemented to solve the MO-OPF problem. The objective functions of the OPF were minimization of fuel cost, emissions, and transmission losses. The standard IEEE 30-bus and 57-bus systems were employed to investigate the performance of the proposed algorithm. The simulation results were compared with those in the literature to show the superiority of the proposed algorithm over several other algorithms; however, the time computation of DA-PSO is slower than DA and PSO due to the sequential computation of DA and PSO.

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Section: Damentioning

confidence: 99%

A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems

Khunkitti

Siritaratiwat

Premrudeepreechacharn

et al. 2018

Energies

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“…Yuan et al [29] proposed an improved strength Pareto evolutionary algorithm and applied it to the OPF problem.…”

Section: Introductionmentioning

confidence: 99%

p‐Optimality‐Based Multiobjective Root System Growth Algorithms for Multiobjective Applications

Chen

Song

et al. 2019

Mathematical Problems in Engineering

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In this paper, a multiobjective root system growth algorithm-based p-optimality (p-MORSGA) is proposed. The proposed p-MORSGA extended original root system growth algorithm with multiobjective nondomination strategy. To enhance its effect of convergence of solution groups, the p-optimality criterion is employed to determine the solutions of last nondominated front into the next generation group. In the evolution process, global general (GG), concerning the margin information and population density, is selected as the suitable optimality criterion of evaluating the performance of solutions. Application of the new p-MORSGA on several multiobjective benchmark functions shows a marked improvement in performance over the modified classical MOEAs with such criterion. Finally, the proposed p-MORSGA is applied to solve two real-world problems, multiobjective portfolio optimization problems (MOPOPs) and multiobjective optimal power flow (OPF) problems. The experimental results demonstrate that p-MORSGA is extremely effective for real-world application problems.

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“…And at the same time, the power flow characteristics of a modern power system are becoming increasingly complex due to the growing penetration of distributed generations [14][15][16][17] and the deployments of novel power electronic loads [18][19][20][21][22]. In this context, multi-objective OPF (MOPF) has received the extensive attention of researchers in the field of OPF [23][24][25][26][27][28], since it can coordinate different-weight or even conflicting multiple objectives. However, MOPF poses challenges in terms of computational complexity due to its inherent non-linear, non-convex, and non-smooth characteristic [23,24], which is hard to solve directly.…”

Section: Introductionmentioning

confidence: 99%

“…The layout of wind farms is optimized via MOEA in [32]. In particular, MOEAs can be also applied to solve the OPF issue [23][24][25][26][27][28]. Unfortunately, the MOPF can only cope with the optimization issue with two to three objectives, which, to a certain extent, limits the practicality of this type of methods.…”

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Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

2018

Processes

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To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Paretooptimal solutions. The primary contribution of the proposal is the innovative application of manyobjective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.Keywords: optimal power flow; optimal operation; power systems; multi-objective optimization; knee point-driven evolutionary algorithm; decision analysis; best compromise solutions Recent research suggests that multi-objective evolutionary algorithms (MOEAs) are promising tools for addressing various challenging optimization tasks in engineering fields [29][30][31][32]. In order to optimal distributed generation planning, a MOEA is employed in [29]. Reference [30] reviews the most representative MOEAs that have been reported, and MOEA has developed as an effective method to solve such an optimization problem. In [31], MOEA is employed for planning overtime of software engineers. The layout of wind farms is optimized via MOEA in [32]. In particular, MOEAs can be also applied to solve the OPF issue [23][24][25][26][27][28]. Unfortunately, the MOPF can only cope with the optimization issue with two to three objectives, which, to a certain extent, limits the practicality of this type of methods. In addition, many-objective optimization problems (MaOPs), considering four or more objective functions in the OPF problem [33][34][35][36], are commonly existed phenomenon in the practice of real-world power system operation [33]. In [34], a specially tailored MOEA is presented for tackling the current large-scale MaOPs. Another MOEA based on adaptive search strategy is presented for coping with MaOPs in [35]. In [36], six different evolutionary algorithms (EAs) are tested, and the results prove that MOEAs exhibit their own capabilities in dealing with different MaOPs. For this reason, MaOPs have recently gained a great deal of attention as most existing MOEAs are inadequate for solving OPF problems with four or more objectives, and it has become a hotspot to enhance the ability of MOEAs for addressing MaOPs issues [37][38][39]. However, manyobjective OPF (MaOPF) is quite challenging for solving sinc...

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Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm

Cited by 131 publications

References 17 publications

A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems

A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems

p‐Optimality‐Based Multiobjective Root System Growth Algorithms for Multiobjective Applications

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

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