Optimal Power Flow Using Flower Pollination Algorithm: A Case Study of 500 kV Java-Bali Power System

Sakti, Fredi Prima; Sarjiya, Sarjiya; Hadi, Sasongko Pramono

doi:10.22146/ijitee.28363

Cited by 4 publications

(2 citation statements)

References 11 publications

(14 reference statements)

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“…Research on optimal power flow in several electrical systems has been widely carried out, both based on conventional methods and artificial intelligence methods, including the following. Rajaei et al [1][2][3] discusses Power Flow Analysis Using Newton Raphson's Method, [4] discusses Power Flow Analysis Using the Flower Pollination Method, [5] discusses Power Flow Analysis Using Bisection Method, [6] discusses Power Flow Analysis Using Gravitational Search Method. Meanwhile, Power Flow research on the Sulselrabar electrical system, previously there has also been carried out, [7] discusses Power Flow Analysis and Transmission Disturbances (Using Newton Raphson's Method), and [8] discusses Power Flow and Short Circuit Analysis (Using Newton Raphson's Method).…”

Section: Optimal Power Flow For a Non-smooth Cost Function Using Part...mentioning

confidence: 99%

Optimal Power Flow For Non-Smooth Cost Function Using Particle Swarm Optimization On 150 Kv System

Rais¹,

Bustamin²,

Sirad³

2023

protk

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optimal power flow by considering the nonsmooth cost curve using the meta-heuristic algorithm method, namely particle swarm optimization (PSO), in the 150 kV Sulselrabar electrical system. In this study, the PSO algorithm was used to optimize optimal power flow so that the cheapest generation price was obtained with a non-smooth cost curve while still considering the limitations of similarity and inequality. In this study, the PSO algorithm was used to optimize optimal power flow so that the cheapest generation price was obtained with a non-smooth cost curve while still considering the limitations of similarity and inequality. From the results of generation optimization using the particle swarm method, it produces the lowest generation costs compared to other methods, namely Rp. 93,498,916.10/hour to generate power of 270.14 MW with losses of 25.73 MW. The Particle Swarm Optimization (PSO) method is able to reduce the cost of generating the Sulselrabar system by Rp. 34,382,857.58 per hour, or 26.89%. From the results of generation optimization using the ant colony method, it resulted in a total generation cost of Rp. 94670335.98 per hour to generate power of 270,309 MW with losses of 25,91 MW. The ant colony method is able to reduce the cost of generating the Sulselrabar system by Rp. 33,211,437.70 per hour, or 25.98%. From the results of generation optimization using the Lagrange method, it resulted in a total generation cost of Rp. 117,121,631.08 per hour to generate power of 339.4 MW with losses of 25,016 MW. The Lagrange method is able to reduce the cost of generating the Sulselrabar system by Rp. 10,760,142.60 per hour, or 8.41%. The artificial intelligence method based on Particle Swarm Optimization (PSO) can well perform optimization of optimal power flow, based on the results of the analysis, which obtained the cheapest generation cost compared to the comparison methods, the Lagrange Method and the Ant Colony Artificial Intelligence Method.

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Section: Optimal Power Flow For a Non-smooth Cost Function Using Part...mentioning

confidence: 99%

Optimal Power Flow For Non-Smooth Cost Function Using Particle Swarm Optimization On 150 Kv System

Rais¹,

Bustamin²,

Sirad³

2023

protk

View full text Add to dashboard Cite

show abstract

“…These techniques are categorized into evolutionary-based, swarm-based, and physics-based strategies. Some of these techniques were used to solve the OPF problem, e.g., genetic algorithm (GA) [16], modified particle swarm optimization (PSO) [17], artificial bee colony (ABC) [18], grey wolf optimizer [19], flower pollination algorithm (FPA) [20], moth-flame optimization (MFO) [21], ant colony optimization (ACO) [22], gravitational search algorithm (GSA) [23], whale optimization algorithm (WOA) [24], [25], multi-objective dragonfly algorithm (MODA) [26], shuffled frog leaping algorithm (SFLA) [27], cuckoo Optimization Algorithm (COA) [28], Jaya optimizer [29], tree seed algorithm (TSA) [30], Sine-Cosine algorithm [31], and sunflower optimization (SFO) [32]. In [33], improved GA is applied to solve the OPF problem of a single area system by considering the presence of renewable energy resources and energy storage units.…”

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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AC&DC optimal power flow incorporating centralized/decentralized multi-region grid control employing the whale algorithm

Swief

Hassan

Hasanien

et al. 2021

Ain Shams Engineering Journal

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Optimal Power Flow Using Flower Pollination Algorithm: A Case Study of 500 kV Java-Bali Power System

Abstract: Abstract-Flower

Cited by 4 publications

References 11 publications

Optimal Power Flow For Non-Smooth Cost Function Using Particle Swarm Optimization On 150 Kv System

Optimal Power Flow For Non-Smooth Cost Function Using Particle Swarm Optimization On 150 Kv System

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

AC&DC optimal power flow incorporating centralized/decentralized multi-region grid control employing the whale algorithm

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