An efficient hybrid approach for optimal allocation of DG in radial distribution networks

Abdel-Mawgoud, Hussein; Kamel, Salah; Ebeed, Mohamed; Aly, Mohamed

doi:10.1109/itce.2018.8316643

Cited by 38 publications

(15 citation statements)

References 16 publications

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“…This factor measures the sensitivity of each bus to the reactive power supplied and its effects on the real loss of RDS [21,48,58]. QLSF can be utilized to choose the superior candidate buses for integrating WT, PV, and BESS in RDS to reduce the simulation time and the number of search agents.…”

Section: Reactive Loss Sensitivity Factor (Qlsf)mentioning

confidence: 99%

A Modified Manta Ray Foraging Optimizer for Planning Inverter-Based Photovoltaic With Battery Energy Storage System and Wind Turbine in Distribution Networks

et al. 2021

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It is widely accepted that the integration of natural sources in distribution networks is becoming more attractive as they are sustainable and nonpolluting. This paper firstly proposes a modified Manta Ray Foraging Optimizer (MMRFO) to enhance the characteristic of MRFO technique. The modified MRFO technique is based on inserting the Simulated Annealing technique into the original MRFO to enhance the exploitation phase which is responsible for finding the promising region in the search area. Secondly, the developed technique is utilized for determining the best sizes and locations of multiple wind turbine (WT) and photovoltaic (PV) units in Radial Distribution System (RDS). The total system losses is taken as a singleobjective function to be minimized, considering the probabilistic nature of PV and WT output generation with variable load demand. Reactive loss sensitivity factor (QLSF) is utilized for obtaining the candidate locations up to fifty percent of total system buses with the aim of reducing the search space. Battery Energy Storage System (BESS) is used with PV to change it into a dispatchable supply. The changes in system performance by optimally integrating PV and WT alone or together are comprehensively studied. The proposed solution approach is applied for solving the standard IEEE 69 bus RDS. The obtained results demonstrate that installing PV and WT simultaneously achieves superior results than installing PV alone and WT alone in RDS. Further, simultaneous integration of WT and PV with BESS gives better results than simultaneous integration of WT and PV without BESS in RDS. The simulation results prove that the total system losses can be reduced by enabling the reactive power capability of PV inverters. The convergence characteristic shows that the modified MRFO gives the best solutions compared with the original MRFO algorithm. INDEX TERMSBattery energy storage, distribution network, Manta Ray Foraging Optimization, optimization, photovoltaic, uncertainty, wind turbine. NOMENCLATURE Acronyms PV photovoltaic WT wind turbine BESS battery energy storage system RDS radial distribution system MRFO manta ray foraging optimizer MMRFO modified manta ray foraging optimizer SA Simulated annealing QLSF reactive loss sensitivity factor PDFs probability distribution functions WOA whale optimization algorithm KHA krill herd algorithm BSOA backtracking search optimization algorithm PSO Particle swarm optimization FFA firefly algorithm GA genetic algorithm CSA cuckoo search algorithm GWO grey wolf optimizer

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Section: Reactive Loss Sensitivity Factor (Qlsf)mentioning

confidence: 99%

A Modified Manta Ray Foraging Optimizer for Planning Inverter-Based Photovoltaic With Battery Energy Storage System and Wind Turbine in Distribution Networks

et al. 2021

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“…The objective functions may be single or multi-objective functions depending on the planning process, and they may include technical or economic issues under some equality, and inequality constraints. Some researches [1]- [25], [27]- [32] have focused on technical issues like power losses (active and reactive), loadability, voltage deviations, voltage stability and emission of generating units while they overlooked the economic issues. Whereas, other researches [21], [33]- [36] focused on economic objectives like fuel cost, active, and reactive power costs and optimal DGs investment cost while they neglected the technical issues.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Novel Crow Search Algorithm Auto-Drive PSO for Optimal Allocation and Sizing of Renewable Distributed Generation

et al. 2020

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The penetration of renewable distributed generations (RDGs) into traditional distribution systems (TDSs) remedies many of its deficiencies and shortcomings. Also, it provides mutual technical, economic and environmental benefits for both electricity companies and their customers as well. With a 25% load increase for the standard IEEE 30-bus system, buses 19, 26 and 30 have the lowest voltage magnitudes among all buses. Therefore, these weak buses are selected initially to allocate RDGs. Three cases, namely, one RDG allocated, two RDGs allocated and three RDGs allocated, of RDGs insertion are covered. A novel crow search algorithm auto-drive particle swarm optimization (CSA-PSO) technique is proposed for the first time in this study to specify the optimal allocation, sizing, and number of RDGs based on the total cost and power losses minimization objectives. A new reduction percent formula is used to estimate the reduction in total cost and the total power losses. These will help us to discern between the best cases based on total cost minimization and those based on total power losses minimization to pick up the best among all best cases. In brief, RDGs allocated on buses 19 and 30 is the best among all cases based on total cost reduction and total power losses reduction. Therefore, buses 19 and 30 are recommended to allocate a wind farm and a solar photovoltaic, respectively based on technical and economic issues. Finally, the simulation findings revealed the superiority of the CSA-PSO algorithm in solving the optimal power flow problem with RDGs compared to the state-of-the-art metaheuristic techniques.

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“…Such a technology brings renewable energy resources into action, as most of the renewable-energy-dependent generation stations are small-scale stations, and of course, this will have a drastic effect on the environment. In other words, the more penetration of renewable-energy-dependent generation stations is achieved, the more reduction of greenhouse gasses (GHG) takes place [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Another optimization method is proposed in [7], regarding the minimization of power losses, where it is simplified so that it does not need excessive computational processes. In [8], a new optimization method, typically the virus colony search (VSC) algorithm was utilized, considering the reliability assessment of the distribution network, where the results are compared with several optimization methods. A hybrid method is proposed in [9] regarding loss minimization and voltage improvement, where the location of DG is identified using an empirical discrete metaheuristic (EDM), while the size is identified by the steepest descent method (SD).…”

Section: Introductionmentioning

confidence: 99%

Developing a Hybrid Optimization Algorithm for Optimal Allocation of Renewable DGs in Distribution Network

et al. 2021

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Distributed generation (DG) is becoming a prominent key spot for research in recent years because it can be utilized in emergency/reserve plans for power systems and power quality improvement issues, besides its drastic impact on the environment as a greenhouse gas (GHG) reducer. For maximizing the benefits from such technology, it is crucial to identify the best size and location for DG that achieves the required goal of installing it. This paper presents an investigation of the optimized allocation of DG in different modes using a proposed hybrid technique, the tunicate swarm algorithm/sine-cosine algorithm (TSA/SCA). This investigation is performed on an IEEE-69 Radial Distribution System (RDS), where the impact of such allocation on the system is evaluated by NEPLAN software.

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An efficient hybrid approach for optimal allocation of DG in radial distribution networks

Cited by 38 publications

References 16 publications

A Modified Manta Ray Foraging Optimizer for Planning Inverter-Based Photovoltaic With Battery Energy Storage System and Wind Turbine in Distribution Networks

A Modified Manta Ray Foraging Optimizer for Planning Inverter-Based Photovoltaic With Battery Energy Storage System and Wind Turbine in Distribution Networks

A Novel Crow Search Algorithm Auto-Drive PSO for Optimal Allocation and Sizing of Renewable Distributed Generation

Developing a Hybrid Optimization Algorithm for Optimal Allocation of Renewable DGs in Distribution Network

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