STATCOM Allocation Using Firefly Algorithm for Loss Minimization and Voltage Profile Enhancement

Ayanlade, Samson Oladayo; Ogunwole, Emmanuel Idowu; Jimoh, Abdulrasaq; Ezekiel, Sunday Olufemi; Owolabi, Dolapo Eniola; Jimoh, Abdulsamad Bolakale

doi:10.1109/iceccme55909.2022.9988475

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…In general, the wind turbine's service life and the photovoltaic generator is nearing the end of the system's lifetime, resulting in a zero-replacement cost. The capital cost can be expressed in (2) and the definition of all parameters involved are as in (3)- (6).…”

Section: Methods 21 Problem Formulationmentioning

confidence: 99%

“…15, No. 2, June 2024: 1147-1157 1148 optimizing the hybrid system design requires determining the best values for the renewable source's rated power, peak power, and storage capacity to meet the required reliability conditions for the system [3], [6]. To provide affordable and effective alternative energy for rural areas, many optimization techniques have been used, including traditional methods such as genetic algorithm (GA) and analytical, statistical, and numericalbased methods, as well as meta-heuristic methods like salp swarm optimization (SSA), firefly algorithm (FA) [7] dingo optimization algorithm [8].…”

Section: Introductionmentioning

confidence: 99%

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Economic optimization of hybrid renewable energy resources for rural electrification

Adebayo,

Sun,

Awal

2024

IJPEDS

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In rural areas, grid expansions and diesel generators are commonly used to provide electricity, but their high maintenance costs and CO2 emissions make renewable energy sources (RES) a more practical alternative. Traditional methods such as analytical, statistical, and numerical-based techniques are inadequate for designing an energy-efficient RES. Therefore, this study utilized the bat algorithm (BA) to optimize the use of hybrid RES for rural electrification. A feasibility study was conducted in the village of Kalema to assess energy consumption, and a diesel-only system was modeled to serve the entire community. The BA was used to determine the optimal size and cost-effectiveness of the hybrid RES, with MATLAB R (2021a) utilized for simulation. The BA's performance was compared with diesel only and GA using cost of energy (COE) and CO2 emissions as metrics. Diesel generators only produced a COE of $6,562,000 and 1679.6 lb/hr of CO2 emissions. COE with BA was $356,9781.37 (a 45.6% reduction) and CO2 emissions were 635.29 lb/hr (a 62.2% drop). Genetic algorithm (GA) resulted in $364,3122.46 COE and 652.69 lb/hr CO2 emissions, indicating 61.1% and 44.5% decreases, respectively. BA significantly reduced COE and CO2 emissions over GA, according to the analysis.

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Section: Methods 21 Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Economic optimization of hybrid renewable energy resources for rural electrification

Adebayo,

Sun,

Awal

2024

IJPEDS

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“…Up until the stopping condition is satisfied, the aforementioned procedures are repeated. The largest and best-predicted position and capacity are represented by the brightest firefly [25].…”

Section: ( ) (mentioning

confidence: 99%

Metaheuristic Optimization Algorithm Performance Comparison for Optimal Allocation of Static Synchronous Compensator

Jimoh¹,

Ayanlade²,

Ogunwole³

et al. 2023

Adv. sci. technol. eng. syst. j.

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The relevance of static synchronous compensator (STATCOM) controllers in controlling power network parameters is causing them to be included in contemporary networks. But for the intended objectives to be attained, the best device positioning and parameter settings are essential. This work compares the performance of the particle swarm optimization (PSO) and firefly algorithm (FA) in sizing and placing a STATCOM device for the dual objectives of loss reduction and voltage deviation abasement. The effective mitigation of network loss and voltage fluctuations in the network will be achieved by the deployment of the efficient method during device allocation. While PSO and FA were taken into consideration due to their computational efficiency among other metaheuristic algorithms, STATCOM was chosen from among the Flexible Alternating Current Transmission System (FACTS) controllers as a consequence of its reactive power compensation capability. The MATLAB software was used to implement the simulations on an IEEE 14-bus system. When STATCOM was optimized with PSO and FA, it resulted in active power loss reductions of 432 and 733 kW, respectively, and reactive power loss reductions of 1622 and 2100 kVAr, respectively. As a result, the reductions in voltage variation and power losses in this instance show some benefits of FA over PSO. Additionally, this work has shown that metaheuristic algorithms are beneficial for allocating FACTS devices.

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STATCOM Allocation Using Firefly Algorithm for Loss Minimization and Voltage Profile Enhancement

Cited by 2 publications

References 23 publications

Economic optimization of hybrid renewable energy resources for rural electrification

Economic optimization of hybrid renewable energy resources for rural electrification

Metaheuristic Optimization Algorithm Performance Comparison for Optimal Allocation of Static Synchronous Compensator

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