Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

Hachemi, Ahmed T.; Sadaoui, Fares; Arif, Salem; Saim, Abdelhakim; Ebeed, Mohamed; Kamel, Salah; Jurado, Francisco; Mohamed, Emad A.

doi:10.1002/ese3.1605

Cited by 10 publications

(2 citation statements)

References 65 publications

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“…PV is the most widely used technology for distributed generation because of its high efficiency, excellent cost-benefit ratio, and reduced carbon emissions [5,6]. In [7], an adapted reptile search algorithm is introduced, featuring a fitness-distance balance method and Levy flight motion. The primary focus is on optimizing the operation of distribution networks (DNs) by integrating wind turbines and PV units.…”

Section: Introductionmentioning

confidence: 99%

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

Smaili,

Almalawi,

Shaheen

et al. 2024

Mathematics

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

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Section: Introductionmentioning

confidence: 99%

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

Smaili,

Almalawi,

Shaheen

et al. 2024

Mathematics

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“…Enterprises wield a plethora of options to enhance distribution network reliability, including the utilization of Renewable Energy Sources (RESs). RESs, like wind and solar power, present promising low-carbon substitutes for conventional fossil fuels [1,2]. Nevertheless, the intermittent and uncertain characteristics of RESs present complex hurdles to grid design.…”

Section: Introduction 1motivationmentioning

confidence: 99%

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

Hachemi,

Sadaoui,

Saim

et al. 2023

Sustainability

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

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

Elseify,

Kamel,

Nasrat

2024

Cluster Comput

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

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Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

Cited by 10 publications

References 65 publications

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

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

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

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

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