Simultaneous allocation of photovoltaic
            <scp>DG</scp>
            and
            <scp>DSTATCOM</scp>
            for
            <scp>techno‐economic</scp>
            and environmental benefits in electrical distribution systems at different loading conditions using novel hybrid optimization algorithms

Zellagui, Mohamed; Lasmari, Adel; Settoul, Samir; El‐Sehiemy, Ragab A.; El-Bayeh, Claude Ziad; Chenni, Rachid

doi:10.1002/2050-7038.12992

Cited by 37 publications

(29 citation statements)

References 69 publications

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“…It has been noticed that the best performance of PSO is when c 1 is changing in a descending manner while c 2 is changing in ascending manner, and generally this change is between 0 and 2.5 over the full course of iterations. There are various time-varying updating strategies to determine c 1 and c 2 [34]. The formula used in this paper is given in Equations ( 24) and ( 25) as follows:…”

Section: The Proposed Hybrid Methodologymentioning

confidence: 99%

A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

et al. 2021

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Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stochastic, and only the 10-unit system is studied. The proposed technique outperforms the standard EO with cost savings of 40.93 USD. The simulation results demonstrate that MPSO-EO has fairly good performance for solving the UCP with significant total operating cost savings compared to standard EO compared with other reported techniques.

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Section: The Proposed Hybrid Methodologymentioning

confidence: 99%

A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

et al. 2021

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“…4. IEEE 33_bus with three SVC and three DG HFPSO [3] PSO [23] MOPSO [7] BFOA [8] Proposed Method Size in KVAR @bus 390 @12 214.2 @18 524.9 @2 400 @12 370 @13 Size in KVAR @bus 650 @24 375.6 @25 512.3 @11 350 @25 430 @24 Size in KVAR @bus 840 @30 1450.3@30 619.7 @30 850 @30 1080 @29 Size in KW @bus 880 @12 831 @3 720.5 @12 850 @12 730 @14 Size in KW @bus 870 @24 904.7 @14 465.67 @15 750 @25 1010 @24 Size in KW @bus 1030 @30 971.2 @30 1016.2 @28 860 @30 1300 @29 The total real losses are reduced to 58.494KW after utilizing a single SVC and DG. The proposed method determined the optimal location of SVC at bus 30 with size 1260KVAR and the optimal location of DG is obtained at bus 6 with size 2590KW.…”

Section: Case 1: Ieee 33_bus With Only One Svcmentioning

confidence: 99%

Simple Strategy for Finding Optimal Location and Size of Distributed Generators and SVC Devices in Radial Distribution Systems

2022

IJIES

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This study proposed a simple method for determining the best position and size of distributed generation (DG) and static VAR compensator (SVC) in the radial distribution system (RDS), as well as the impact of employing two different types of DG on total losses. The target functions are designed to reduce the system's overall power losses, improve the voltage stability margin (VSM), and reduce drop voltages. This method relies on the deployment of many sizes of DGs or SVCs in each bus and comparing their impacts of them on the system's overall losses and taking optimal sizing and placement of them if that given minimum losses. Moreover, the proposed strategy does not require extra work to set the parameter settings and weighting factors to obtain the objective functions. To assess the performance of the suggested approach, five instances were evaluated during DG and SVC installation in this study. The suggested approach is tested on the RDS's IEEE 33_bus and 69_bus to ensure its practicality. This method decreased losses by 92.72 % for IEEE 33_bus and 96.91 % for IEEE 69_bus. In the 33_bus, the minimum VSM is improved from 0.6674 to 0.9697 p.u, and the minimum bus voltage is improved from 0.9038 to 0.994 p.u. While in the 69_bus, the minimum VSM is enhanced from 0.6835 to 0.9761 p.u and the minimum bus voltage is enhanced from 0.9093 to 0.9958 p.u, with the results compared to other existing methodologies.

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“…The topic of optimal DG integration has been discussed in a number of studies from various perspectives. The firefly algorithm (FA) and particle swarm optimization (PSO) method were presented in [9] to achieve techno-economic and environmental benefits in electrical distribution networks. A PSO and hybrid enhanced gray wolf optimizer (EGWO-PSO) approach was presented in [10] for the optimal DG allocation in order to reduce system costs, emissions, active power losses, and voltage deviation index (VDI), as well as increase voltage stability index (VSI).…”

Section: Literature Reviewmentioning

confidence: 99%

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

et al. 2021

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For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system.

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Simultaneous allocation of photovoltaic DG and DSTATCOM for techno‐economic and environmental benefits in electrical distribution systems at different loading conditions using novel hybrid optimization algorithms

Cited by 37 publications

References 69 publications

A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

Simple Strategy for Finding Optimal Location and Size of Distributed Generators and SVC Devices in Radial Distribution Systems

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

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