Multiobjective Optimization of Renewable Distributed Generations in Radial Distribution Networks with Optimal Power Factor

Mmary, Elifuraha Reuben; Marungsri, Boonruang

doi:10.15866/iree.v13i4.15069

Cited by 4 publications

(5 citation statements)

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“…In the following iteration of equation (17), the search agent ( ) p i does not alter its location for the kth design variable. This characteristic enables us to control a search agent's rate of change.…”

Section: Modeling Of the Self-contribution Of An Employeementioning

confidence: 99%

“…Voltage stability index and voltage deviation are rarely mentioned as objectives by authors. The fuzzy-genetic algorithm (FGA) [10], particle swarm optimization [11], gravitational search algorithm [12], Cuckoo search algorithm [13], firefly algorithm (FA) [14], bat algorithm [15], Limaçon-inspired spider monkey optimization algorithm [16], invasive weed optimization method [17], mine blast algorithm [18], self-adapting harmony search algorithm [19], whale optimization algorithm (WOA) [20], Black widow optimizer (BWO) [21] and Flower Pollinated Algorithm (FPA) [22] are a few population-based AI optimization algorithms that have been used to solve OSASC.…”

Section: Introductionmentioning

confidence: 99%

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A novel heap-based optimizer for allocation of shunt capacitors in radial distribution network

Adebayo,

Salimon,

Adejumo

et al. 2024

Eng. Res. Express

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Due to power losses, a sizeable portion of the electrical power produced is lost, which causes voltage deviation and instability as a result of rising load demand at the radial distribution network (RDN). Shunt capacitor penetration will enhance the RDN’s efficiency by reducing power losses, improving the voltage profile and stability. For the best sizing and allocation of shunt capacitors (SCs) in RDN, a novel human-based metaheuristic heap-based optimizer (HBO) influenced by the ideas of corporate rank hierarchy (CRH) of standard organization is presented. The real power loss, voltage variation, and voltage stability index are all minimized while equality and inequality criteria are met by the HBO optimal allocation of SCs. One, two, and three numbers of SCs were investigated when installing the reactive compensating devices. Using typical IEEE 33-bus and 69-bus RDNs, covering a range of scenarios in the form of single- and multi-objective functions, the effectiveness of the HBO technique was examined. The real power loss for the conventional IEEE 33-bus and 69-bus RDN was reduced by 34.46% and 35.50%, respectively compared to their respective base cases. By contrasting the outcomes of the power loss with well-established and new optimization approaches, the efficacy and superiority of the suggested HBO were demonstrated.

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Section: Modeling Of the Self-contribution Of An Employeementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel heap-based optimizer for allocation of shunt capacitors in radial distribution network

Adebayo,

Salimon,

Adejumo

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…The paper specifically focuses on function optimization problems, employing BBA, WOA, and GWO. Through a comparative analysis, the study evaluates the performance of these algorithms, providing valuable insights for researchers and decision-makers in selecting the optimal technique for specific optimization tasks [19], [20]. Next section will explain theoritical background of the selected optimization techniques.…”

Section: Metaheuristic Optimization Algorithmsmentioning

confidence: 99%

Optimal Battery Charging Schedule for a Battery Swapping Station of an Electric Bus With a PV Integration Considering Energy Costs and Peak-to-Average Ratio

Boonraksa,

Pinthurat

et al. 2024

IEEE Access

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Across the globe, the adoption of electric vehicles (EVs), particularly in mass transit systems such as electric buses (E-bus), is on the rise in modern cities. This surge is attributed to their environmentally friendly nature, zero carbon emissions, and absence of engine noise. However, the charging of E-bus batteries could impact the peak demand on the main grid and its overall serviceability, especially when numerous batteries are charged simultaneously. This scenario may also lead to increased energy costs. To address the previously mentioned issue, battery swapping is employed at the charging station in lieu of conventional battery charging. In this paper, the battery swapping approach is utilized to establish the optimal battery charging schedule for E-buses, taking into account both energy costs and the peak-to-average ratio (PAR). The E-bus battery swapping stations incorporate photovoltaic (PV) power generation as their energy source. Three metaheuristic algorithms-namely, the binary bat algorithm (BBA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO)-are employed to identify the optimal conditions. The simulation results demonstrate that integrating the optimal battery charging schedule with a PV power generation system in an E-bus battery swapping station can effectively lower energy costs and the PAR when compared to traditional battery charging methods at charging stations. The optimal charging schedule derived through the GWO technique outperforms those obtained from the WOA and BBA techniques. This resulted in a notable reduction in peak demand from 758.41 to 580.73 kW, corresponding to a 23.43% decrease in peak demand. The integration of the GWO with battery charging scheduling and PV installation resulted in a significant 27.63% reduction in energy costs. As per the simulation results, an optimized battery swapping schedule has the potential to lower energy costs and enhance serviceability for the E-bus battery swapping station.INDEX TERMS Battery charging scheduling, battery swapping stations, electric buses, metaheuristic algorithm, peak-to-average ratio. I. INTRODUCTIONAdvancements in electric vehicle (EV) and battery technology have spurred increased usage, supported by governmental advocacy for clean energy, prompting a shift from internal combustion engine cars to electric vehicles. The development of smart cities includes the expansion of electric bus mass transit systems. Major cities are witnessing a growing preference for electric vehicles over traditional internal combustion engine vehicles, particularly in the form of electric buses (Ebus), offering reductions in diesel fuel usage and air pollution

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“…Distribution networks are major electric components distributing power to loads via conductors called distribution lines (DLs) [1]. DLs have resistance and reactance, causing active power loss, reactive power loss, and voltage drop [2]. For RDNs with low voltage, the losses and voltage drop are significant and methods for reducing loss and improving voltage drop are meaningful.…”

Section: Introductionmentioning

confidence: 99%

Coot Optimization Algorithm for Optimal Placement of Photovoltaic Generators in Distribution Systems Considering Variation of Load and Solar Radiation

Kien

Nga

Phan

et al. 2022

Mathematical Problems in Engineering

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In this paper, photovoltaic generators (PVGs) are placed in radial distribution networks (RDNs) for reducing active power loss of one operation day by using three recently published metaheuristic algorithms including coot optimization algorithm (COOA), transient search algorithm (TSA), and crystal structure algorithm (CRSA). In one operation day, the variation of loads is considered, and the change of solar radiation over daytime is also taken. The study has two main contributions regarding the effectiveness of COOA: energy loss reduction and voltage improvement. COOA can reach high energy loss reduction, better solutions, and faster search speed than TSA and CRSA. In fact, COOA finds better energy loss than the algorithms by 1% and 1.77% for the IEEE 69-node system and 0.75% and 1.4% for the IEEE 85-node system. Furthermore, COOA is at least three times faster than CRSA and two times faster than TSA. As compared to a base system without PVGs, COOA can find better energy loss up to 60.96% and improve voltage up to 4.5%. Thus, COOA is a highly effective optimization tool with the optimal solution, high stability, and fast computation process for placing PVGs in RDNs.

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Multiobjective Optimization of Renewable Distributed Generations in Radial Distribution Networks with Optimal Power Factor

Cited by 4 publications

References 0 publications

A novel heap-based optimizer for allocation of shunt capacitors in radial distribution network

A novel heap-based optimizer for allocation of shunt capacitors in radial distribution network

Optimal Battery Charging Schedule for a Battery Swapping Station of an Electric Bus With a PV Integration Considering Energy Costs and Peak-to-Average Ratio

Coot Optimization Algorithm for Optimal Placement of Photovoltaic Generators in Distribution Systems Considering Variation of Load and Solar Radiation

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