Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm

Mathematics, Год журнала: 2024, Номер 12(5), С. 625 - 625

Опубликована: Фев. 20, 2024

This work presents an optimal methodology based on augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning utilizes most effective response with goal improving ability search. Two different scenarios are investigated. First, suggested ASABT used considering shunt capacitors only losses. Second, simultaneous placement and sizing both PV units handled. Applications ASAB performed two distribution systems. practical Egyptian system considered. The results simulation show has significant 56.4% decrease in over original scenario using only. By incorporating addition capacitors, energy reduced from 26,227.31 10,554 kW/day high reduction 59.75% 4.26% compared initial case SABT alone, respectively. Also, emissions produced substation greatly 110,823.88 kgCO2 79,189 kgCO2, 28.54% case. standard IEEE 69-node added application. Comparable indicate significantly reduces (5.61%) as enhances minimum voltage (2.38%) substantial (64.07%) For investigated systems, proposed outcomes Coati optimization algorithm, Osprey algorithm (OOA), dragonfly (DA), methods; shows superior outcomes, especially deviation obtained

Язык: Английский

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An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 5, 2024

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, MMGs became complex strenuous with high penetration renewable resources due stochastic nature these along load fluctuations. In this regard, paper aims solve EM problem optimal inclusion photovoltaic (PV) systems, wind turbines (WTs), biomass systems. proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving 85-bus MMGS system minimize total cost, performance improvement concurrently. algorithm based on Weibull Flight Motion (WFM) Fitness Distance Balance (FDB) mechanisms tackle stagnation conventional JSO technique. EJSO tested standard CEC 2019 benchmark functions obtained results are compared optimization techniques. As per results, powerful method other like Sand Cat Swarm Optimization (SCSO), Dandelion (DO), Grey Wolf (GWO), Whale Algorithm (WOA), (JSO). reveal that by suggested can reduce cost 44.75% while voltage profile stability 40.8% 10.56%, respectively.

Язык: Английский

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Dynamic operation of distribution grids with the integration of photovoltaic systems and distribution static compensators considering network reconfiguration

Energy Reports, Год журнала: 2024, Номер 12, С. 1623 - 1637

Опубликована: Июль 31, 2024

Язык: Английский

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Optimum Fractional Tilt Based Cascaded Frequency Stabilization with MLC Algorithm for Multi-Microgrid Assimilating Electric Vehicles

Fractal and Fractional, Год журнала: 2024, Номер 8(3), С. 132 - 132

Опубликована: Фев. 23, 2024

An important issue in interconnected microgrids (MGs) is the realization of balance between generation side and demand side. Imbalanced load demands lead to security, power quality, reliability issues. The frequency control (LFC) accountable for regulating MG against generation/load disturbances. This paper proposed an optimized fractional order (FO) LFC scheme with cascaded outer inner loops. controller based on a one plus tilt derivative (1+TD) loop FO integrator-derivative filter (FOTIDF) loop, forming (1+TD/FOTIDF) controller. 1+TD/FOTIDF achieves better disturbance rejection compared traditional methods. optimally designed using modified version liver cancer optimization algorithm (MLCA). In this paper, new (MLCA) overcome shortcomings standard Liver (LCA), which contains early convergence local optima debility its exploration process. MLCA three improvement mechanisms, including chaotic mutation (CM), quasi-oppositional learning (QOBL), fitness distance (FDB). method simultaneously adjusts selects best parameters achieve performance MGs. Obtained results are other FOTID, TI/FOTID, TD/FOTID controllers. Moreover, contribution electric vehicles high penetration renewables considered system parameter uncertainty test stability technique. obtained under different possible load/generation scenarios confirm superior response improved MLCA-based

Язык: Английский

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Optimal Operation of Distribution Networks Considering Renewable Energy Sources Integration and Demand Side Response

Sustainability, Год журнала: 2023, Номер 15(24), С. 16707 - 16707

Опубликована: Дек. 10, 2023

This paper demonstrates the effectiveness of Demand Side Response (DSR) with renewable integration by solving stochastic optimal operation problem (OOP) in IEEE 118-bus distribution system over 24 h. An Improved Walrus Optimization Algorithm (I-WaOA) is proposed to minimize costs, reduce voltage deviations, and enhance stability under uncertain loads, generation, pricing. The I-WaOA utilizes three strategies: fitness-distance balance method, quasi-opposite-based learning, Cauchy mutation. optimally locates sizes photovoltaic (PV) ratings wind turbine (WT) capacities determines power factor WT DSR. Using Monte Carlo simulations (MCS) probability density functions (PDF), uncertainties energy load demand, costs are represented. results show that approach can significantly improve stability, mitigate deviations. total annual reduced 91%, from 3.8377 × 107 USD 3.4737 106 USD. Voltage deviations decreased 63%, 98.6633 per unit (p.u.) 36.0990 p.u., index increased 11%, 2.444 103 p.u. 2.7245 when contrasted traditional methods.

Язык: Английский

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An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Cluster Computing, Год журнала: 2024, Номер 27(10), С. 14767 - 14810

Опубликована: Авг. 1, 2024

Abstract Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature speed solar radiation. These intermittencies have posed considerable challenges to grids, including oscillation, increased losses, voltage instability. To overcome these challenges, battery storage (BES) supports PV unit, while biomass aids WT mitigating fluctuations boosting supply continuity. Therefore, main innovation this study presenting an improved moth flame optimization algorithm (IMFO) capture optimal multiple dispatchable non-dispatchable DGs loss in considering different dynamic load characteristics. The IMFO comprises new update position expression based on roulette wheel selection strategy as well Gaussian barebones (GB) quasi-opposite-based learning (QOBL) mechanisms enhance exploitation capability, global convergence rate, solution precision. algorithm's success rate effectiveness are evaluated using 23rd benchmark functions compared with basic MFO other seven competitors rigorous statistical analysis. developed optimizer then adopted performance 69-bus 118-bus distribution deterministic stochastic DG's planning. findings reflect superiority against its rivals, emphasizing influence types varying generations DG Numerically, deployment BES + significantly maximizes reduction percent 68.3471 98.0449 69-bus's commercial type 54.833 52.0623 118-bus's type, respectively, confirming efficacy maximizing diverse situations.

Язык: Английский

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Simultaneous Allocation of PV Systems and Shunt Capacitors in Medium Voltage Feeders Using Quadratic Interpolation Optimization‐Based Gaussian Mutation Operator

International Journal of Energy Research, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

This study introduces an enhanced version of quadratic interpolation optimization (QIO) merged with Gaussian mutation (GM) operator for optimizing photovoltaic (PV) units and capacitors within distribution systems, addressing practical considerations discrete nature capacitors. In this regard, the variations in power loading productions from PV sources are taken into consideration. The QIO is inspired by generalized (GQI) method mathematics GM that randomness solution to explore search space avoid premature convergence. proposed QIO‐GM tested on Egyptian standard IEEE demonstrating its effectiveness minimizing energy losses. Comparative studies against QIO, northern goshawk (NGO), optical microscope algorithm (OMA), as well other reported algorithms, validate QIO‐GM’s superior performance. Numerically, first system, designed achieves 2.5% improvement over a 4.4% NGO, 9.2% OMA, leading substantial reduction carbon dioxide (Co 2 ) emissions 110,823.886 79,402.82 kg, reflecting commendable 28.35% decrease. Similarly, second demonstrates significant Co 72,283.328 54,627.65 28.3% These results underscore not only losses but also contributing environmental benefits through reduced emissions.

Язык: Английский

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Optimal Placement of Distributed Generators and Electric Vehicles Using Whale Optimization Algorithm (WOA)

Опубликована: Апрель 26, 2024

Integrating Distributed Generation (DG) sources and Electric Vehicles (EVs) into radial distribution systems (RDS) is a promising approach to enhancing power system efficiency reliability. This trend driven by the deregulation of electric sector technical constraints in extending transmission networks some areas. By carefully selecting optimal location sizing for DG vehicles (EVs), it possible minimize losses, improve voltage profiles, enhance overall study suggests integration multiple DGs EV s how PLoss get affected along with profiles. However, increase if number exceeds their level. integrates 3 EVs RDS separately. The are obtained help stability index, sizes were using Whale Optimization Algorithm (WOA). It implemented IEEE 33 test verify its robustness effectiveness. results show loss reduction 41.96%, 57.52%, 64.73% bus one, two three DGs, respectively slight concerning as load increases. In addition, index improved significantly but was case EVs.

Язык: Английский

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Reliability and line loading enhancement of distribution systems using optimal integration of renewable energy and compressed air energy storages simultaneously under uncertainty

Journal of Energy Storage, Год журнала: 2024, Номер 101, С. 113921 - 113921

Опубликована: Окт. 2, 2024

Язык: Английский

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Optimal reconfiguration of a distribution network integrated photovoltaic and wind systems using blood-sucking leech optimizer

STUDIES IN ENGINEERING AND EXACT SCIENCES, Год журнала: 2024, Номер 5(2), С. e11477 - e11477

Опубликована: Дек. 3, 2024

This paper presents a planning strategy for integrating renewable distributed generation (DG) units into distribution network, incorporating network reconfiguration to enhance the network's technical, economic, and environmental performance. Utilizing novel meta-heuristic algorithm, Blood-Sucking Leech Optimizer (BSLO), study addresses multi-objective optimization problem aimed at determining optimal placement sizing of DG units, as well most effective topology. approach seeks minimize active power losses, improve voltage profiles, reduce installation costs, lower greenhouse gas emissions. The model accounts variable load demands, climatic factors (such ambient temperature, solar irradiation, wind speed), fluctuating energy prices, reflecting realistic operating conditions. Tested on IEEE 69-bus BSLO algorithm demonstrated rapid convergence global optimum by effectively balancing exploration exploitation phases. Compared other methods, such Grey Wolf Optimizer, Gorilla Troops Walrus Optimization Algorithm, Artificial Hummingbird consistently achieved superior accuracy faster convergence, resulting in higher precision efficiency. deployment two PV generators turbines, combined with selective line switch openings, resulted an 87.66% reduction 73.30% decrease deviation, 51.91% overall system 62.74% emissions compared base case.

Язык: Английский

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