Optimal Power Flow with Four Objective Functions using Improved Differential Evolution Algorithm: Case Study IEEE 57-bus power system

Al-Kaabi, Murtadha; Al-Bahrani, Layth Tawfeeq; Dumbrava, Virgil; Eremia, Mircea

doi:10.1109/ciem52821.2021.9614925

Cited by 16 publications

(6 citation statements)

References 13 publications

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“…The Improved Differential Evolution (IDE) algorithm is an improved version of the Differential Evolution (DE) algorithm. This proposed algorithm proved its effectiveness to solve singleobjective optimal power flow applied on the IEEE 30-bus and IEEE 57-bus [34,35]. In this paper, the authors prove the effectiveness of the proposed algorithm to solve the multi-objective OPF.…”

Section: Multi-objective Improved Differential Evolution Algorithm (M...mentioning

confidence: 58%

Archives of Electrical Engineering

Al-Kaabi

Hasheme

Al-Bahrani

2022

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This article presents a new efficient optimization technique namely the Multi-Objective Improved Differential Evolution Algorithm (MOIDEA) to solve the multiobjective optimal power flow problem in power systems. The main features of the Differential Evolution (DE) algorithm are simple, easy, and efficient, but sometimes, it is prone to stagnation in the local optima. This paper has proposed many improvements, in the exploration and exploitation processes, to enhance the performance of DE for solving optimal power flow (OPF) problems. The main contributions of the DE algorithm are i) the crossover rate will be changing randomly and continuously for each iteration, ii) all probabilities that have been ignored in the crossover process have been taken, and iii) in selection operation, the mathematical calculations of the mutation process have been taken. Four conflicting objective functions simultaneously have been applied to select the Pareto optimal front for the multi-objective OPF. Fuzzy set theory has been used to extract the best compromise solution. These objective functions that have been considered for setting control variables of the power system are total fuel cost (TFC), total emission (TE), real power losses (RPL), and voltage profile (VP) improvement. The IEEE 30-bus standard system has been used to validate the effectiveness and superiority of the approach proposed based on MATLAB software. Finally, to demonstrate the effectiveness and capability of the MOIDEA, the results obtained by this method will be compared with other recent methods.

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Section: Multi-objective Improved Differential Evolution Algorithm (M...mentioning

confidence: 58%

Archives of Electrical Engineering

Al-Kaabi

Hasheme

Al-Bahrani

2022

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“…In the eighteenth case, in order to achieve PFNDSs, the FC, E, and VD were considered as OFs and minimized simultaneously using MOGWO and MOHHO. BCS of FC, E, and VD for the proposed techniques are the following: The best result and OCV for Tri and Quad OFs on this system obtained by MOGWO and MOHHO for Cases (17)(18)(19)(20)(21) are illustrated in Tables 6 and 7. From Figure 14a-d…”

Section: Case #18: Minimization Of Fc E and Vdmentioning

confidence: 94%

“…The twentieth case of this paper is the simultaneous minimization of E, APL, and VD using MOGWO and MOHHO to achieve the PFNDSs. The BCS of E, APL, and VD of the proposed techniques are the following: The best result and OCV for Tri and Quad OFs on this system obtained by MOGWO and MOHHO for Cases (17)(18)(19)(20)(21) are illustrated in Tables 6 and 7. From Figure 14a-d…”

Section: Case #20: Minimization Of E Apl and Vdmentioning

confidence: 95%

“…Lately, many research papers have been published on applications of meta-heuristic optimization methods such as the salp swarm algorithm (SSA) [10], the black widow optimization (BWO) [11], the manta ray foraging optimization (MRFO) [12], Henry gas solubility optimization (HGSO) [13], etc. In power systems, metaheuristic optimization algorithms were proposed to solve OPF problems such as Modified Artificial Bee Colony (MABC) [14], Spatio-temporal distribution (STD) [15], differential evolution (DE) [16], enhanced Particle Swarm optimization (EPSO) and Ant Lion optimization (ALO) [17], improved differential evolution (IDE) [18,19], Harris Hawks Optimization (HHO) [20,21], and Grey Wolf Optimizer (GWO) [22]. The previous methods dealt with the problems of single-objective optimal power flow (SOOPF).…”

Section: Introductionmentioning

confidence: 99%

“…, it can be observed that the PFNDSs obtained by MOGWO are better distributed than the PFNDSs obtained by MOHHO. Using the developed techniques MOGWO and MOHHO, Figure15a-e shows the voltage magnitude values of the Triple and Quad OFs for Cases(17)(18)(19)(20)(21). As seen in Figure15a-e, this figure demonstrates that the solutions found by MOGWO and MOHHO are effective in cases 18-21 (when VD is OFs) and infeasible in case 17 (where VD is not OFs).…”

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confidence: 96%

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Multi Criteria Frameworks Using New Meta-Heuristic Optimization Techniques for Solving Multi-Objective Optimal Power Flow Problems

Al-Kaabi,

Dumbrava,

Eremia

2024

Energies

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This article develops two metaheuristics optimization techniques, Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO), to handle multi-objective optimal power flow (MOOPF) issues. Multi Objective GWO (MOGWO) and Multi Objective HHO (MOHHO) are the names of the developed techniques. By combining these optimization techniques with Pareto techniques, the non-dominated solution set can be obtained. These developed approaches are characterized by simplicity and have few control parameters. Fuel cost, emissions, real power losses, and voltage deviation were the four objective functions considered. The theories used to determine the best compromise solution and organize the Pareto front options are the fuzzy membership equation and the crowding distance approach, respectively. To validate and evaluate the performance of the presented techniques, two standard IEEE bus systems—30-bus and 57-bus power systems—were proposed. Bi, Tri, and Quad objective functions with 21 case studies are the types of objective functions and the scenarios that were applied in this paper. As compared to the results of the most recent optimization techniques documented in the literature, the comparative analysis results for the proposed methodologies demonstrated the superiority and robustness of MOGWO and MOHHO.

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