A New Derivation for Newton-Based Optimal Power Flow Solution

Radman, Ghadir; Shultz, J.

doi:10.1080/15325000590885577

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…Several conventional optimization techniques were used to solve the OPF problem such as Newton-Raphson [2], linear programming [3], quadratic programming [4], and interior 1548 Downloaded by [New York University] at 22:12 30 July 2015…”

Section: Introductionmentioning

confidence: 99%

Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms

El‐Fergany

Hasanien

2015

Electric Power Components and Systems

261

110

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Section: Introductionmentioning

confidence: 99%

Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms

El‐Fergany

Hasanien

2015

Electric Power Components and Systems

261

110

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“…These approaches are categorized into classical and artificial intelligence-based approaches. Classical optimization techniques include linear programming [7], non-linear programming [8], interior point method [9], quadratic programming [10], Newton-Raphson (NR) [11], and semi-definite method [12]. These techniques are reported to (a) possibly get trapped in local minima instead of achieving the global optimum solution of the OPF problem, (b) be computationally demanding, and (c) the best solution is strongly affected by the initial guess of the problem [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-Regional Optimal Power Flow Using Marine Predators Algorithm Considering Load and Generation Variability

et al. 2021

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This paper introduces the application of a newly developed heuristic nature-inspired optimization technique, viz, tuned Marine Predator Algorithm (MPA), to solve the optimal power flow (OPF) problem of multi-regional systems. The paper proposes MPA parameters' tuning to enhance the algorithm performance. The paper takes into account the variability of different types of renewable energy resources (RERs) and loads. Two modeling approaches are presented: holistic (multi-regions are modeled as one large network) and inter-bounded (modeling the regional interfaces). The MPA is applied to the IEEE-48 bus connected system, and the results are compared with another well-established heuristic algorithm, namely the Genetic Algorithm (GA). The results demonstrate the validation, applicability and effectiveness of using the MPA for solving multi-region OPF problem considering renewable energy sources and load variability.

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“…More than one conventional method has been proposed in the literature survey to handle the OPF problem such as Newton-Raphson (NR) [3], linear programming [4], quadratic programming [5], interior point method [6], and a semi-definite programming [7]. The previously used NR method has a drawback which is the need for a solution to a new linear system at every individual iteration [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Flow of Power Systems Including Distributed Generation Units Using Sunflower Optimization Algorithm

et al. 2019

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This article introduces a new attempt of utilizing the sunflower optimization (SFO) algorithm in solving the problem of optimal power flow (OPF) in the field of power systems. The principle target is to optimize the generating units' fuel cost under the system constraints. At initial stage, the objective function is solved to find the optimal siting of Distributed Generation (DG) units within the system under study. Then, different scenarios are performed to solve the OPF problem including and excluding DG units. The generators' real output power defines the exploration field for the OPF problem. The SFO algorithm is used to minimize the fitness function and yields the best solutions of the problem. More than one electric grid is tested to check the validity of the proposed algorithm such as the IEEE 14-bus, and 30-bus networks. Simulations included different scenarios are implemented in these two networks. To obtain a realistic result, real daily load curve is considered in this study. The results of simulations are investigated and analyzed. Results confirm the flexibility, validation, and applicability of the introduced SFO-based OPF methodology when compared with the genetic algorithm. INDEX TERMS Optimal power flow, optimization, power systems, renewable energy.

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A New Derivation for Newton-Based Optimal Power Flow Solution

Cited by 12 publications

References 9 publications

Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms

Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms

Multi-Regional Optimal Power Flow Using Marine Predators Algorithm Considering Load and Generation Variability

Optimal Power Flow of Power Systems Including Distributed Generation Units Using Sunflower Optimization Algorithm

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