An application of improved salp swarm algorithm for optimal power flow solution considering stochastic solar power generation

Sulaiman, Mohd Herwan; Mustaffa, Zuriani

doi:10.1016/j.prime.2023.100195

Cited by 12 publications

(2 citation statements)

References 36 publications

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“…The adjustment of followers' positions is governed by a simplified version of Newton's law of motion, allowing for a dynamic representation of the movement of the swarm through iterations. This novel application of salp chain dynamics to optimization problems not only fills a notable gap in the literature but also enriches the toolbox of swarm intelligence techniques available for complex problem solving [55].…”

Section: B Salp Swarm Algorithm (Ssa)mentioning

confidence: 91%

Development of a Novel Hybrid Salp Swarm-Kepler Algorithm for Engineering Problems: Optimizing Microgrid Sizing with Integration of Photovoltaic, Wind, Battery, and Supercapacitor Systems

Güven

2024

Preprint

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Economic growth accelerates, leading to an increasingly important need for efficient resource use in engineering design due to the resulting supply and demand imbalance. This study introduces an integrated hybrid application of the Salp Swarm Algorithm (SSA) and the Kepler Optimization Algorithm (KOA) to optimize engineering design problems. The integration of SSA’s mathematical modeling of salp movement and leader-follower dynamics, along with KOA's optimization mechanisms based on Kepler’s laws, provides significant advancements in avoiding local optimum traps and achieving balance in the search space. The hybrid SSAKOA algorithm is capable of quickly reaching optimal or near-optimal solutions to optimization problems while efficiently working across parameters. Experimental results show that SSAKOA outperforms other algorithms in terms of optimum performance, solution stability, and applicability, effectively reducing resource wastage in engineering designs. The optimization capability of the SSAKOA has been verified for 23 different function problems, revealing that SSAKOA offers higher convergence speed, precision, and robustness than other algorithms. Moreover, this algorithm has yielded the most optimal results compared to 12 different algorithms in sizing a microgrid consisting of grid-connected batteries, supercapacitors, wind turbines, and photovoltaic panel components, which poses a significant engineering challenge. This study demonstrates the effectiveness of the proposed algorithm by applying it to a hybrid renewable energy system on a Turkish university campus. The application reveals an annual cost of $572,369.93 and an energy cost of $0.23996/kWh, achieving a renewable energy fraction of 78.54%. This indicates that the system not only offers a cost-effective alternative to Turkey's conventional grid rate of $0.35/kWh but also underscores the algorithm’s potential in enhancing the economic and environmental sustainability of renewable energy projects. Such findings lay a foundational framework for future advancements in renewable energy applications.

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Section: B Salp Swarm Algorithm (Ssa)mentioning

confidence: 91%

Development of a Novel Hybrid Salp Swarm-Kepler Algorithm for Engineering Problems: Optimizing Microgrid Sizing with Integration of Photovoltaic, Wind, Battery, and Supercapacitor Systems

Güven

2024

Preprint

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“…This method has numerous benefits, such as simple encoding, integer discrete handling, rapid convergence, and high-quality feasible solutions for various OPF issues [14]. Besides, OPF problem is optimized by an enhanced version of the salp swarm algorithm (SSA) called ISSA technique [15]. A modified Rao-2 (MRao-2) technique is applied to find the best solution for OPF, as well as wind and solar.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Power Flow Analysis With Renewable Energy Resource Uncertainty: A Hybrid AEO-CGO Approach

Hassan,

Kamel,

Alateeq

et al. 2023

IEEE Access

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Over the last decade, significant advancements have occurred in global electricity networks due to the widespread adoption of renewable energy resources (RES). While these sources offer numerous benefits such as cost-effective operation of solar photovoltaic and wind power stations and reduction of environmental hazards related to traditional power sources, they have also introduced various challenges to power network scheduling and operation. The traditional optimal power flow (OPF) problem, which is inherently complex, has become even more intricate with the integration of RES alongside traditional thermal power generators. This complexity arises from the unpredictable and intermittent nature of those resources. To tackle the intricacies of incorporating RES into conventional electric power systems, this study utilizes a pair of probability distribution functions to predict the power generation of wind and solar PV systems, respectively. The comprehensive OPF, which includes RES components, is expressed as a singular objective problem encompassing multiple goals including reducing fuel costs, emissions, real transmission losses, and voltage deviations. To tackle this challenge, a novel hybrid metaheuristic optimization algorithm (ACGO) is introduced. The ACGO algorithm combines Chaos game optimization (CGO) with the artificial ecosystem-based optimization (AEO) method to obtain the optimum solution for the OPF problem considering stochastic RES. This technique aims to enhance solution precision by increasing solution diversity through an optimization process. The modified optimizer's validation begins by examining its performance using well-known benchmark optimization functions, demonstrating its superiority over CGO, AEO, and other competitive algorithms. Subsequently, the modified optimizer is applied to a combined model of a wind and PV-incorporated IEEE 30-bus system. The ACGO technique proves to be highly effective, yielding the lowest fitness values of 781.

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Optimizing power management for wind energy integration with SVC support using hybrid optimization

Mahdad

2024

Electr Eng

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An application of improved salp swarm algorithm for optimal power flow solution considering stochastic solar power generation

Cited by 12 publications

References 36 publications

Development of a Novel Hybrid Salp Swarm-Kepler Algorithm for Engineering Problems: Optimizing Microgrid Sizing with Integration of Photovoltaic, Wind, Battery, and Supercapacitor Systems

Development of a Novel Hybrid Salp Swarm-Kepler Algorithm for Engineering Problems: Optimizing Microgrid Sizing with Integration of Photovoltaic, Wind, Battery, and Supercapacitor Systems

Optimal Power Flow Analysis With Renewable Energy Resource Uncertainty: A Hybrid AEO-CGO Approach

Optimizing power management for wind energy integration with SVC support using hybrid optimization

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