An improved sine–cosine algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems

Raut, Usharani; Mishra, Sivkumar

doi:10.1016/j.asoc.2020.106293

Cited by 89 publications

(58 citation statements)

References 37 publications

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“…One such endeavor is simultaneous control of DSRs and allocation of DGs. As such, recent studies to the implementation of an effective integration strategy has been presented; for example; manta ray foraging optimization [1]; harmony search algorithm (HSA) with an objective of minimizing real power loss and improving voltage profile [29]; combined GA and branch exchange [30]; artificial bee colony optimizer based on maximization of system loadability [31]; improved spotted hyena algorithm [32], improved elitist-jaya algorithm (IEJAYA) [5], FWA [33], firefly (FF) algorithm [34], sinecosine algorithm [35], Harris Hawks Optimizer (HHO) [36], invasive weed optimizer [37], salp swarm algorithm [38] and an improved beetle swarm optimization algorithm [39]. In [40], multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II) have been applied effectively for DGs allocation in distribution systems.…”

Section: Introductionmentioning

confidence: 99%

Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations

et al. 2021

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This paper presents an Enhanced Marine Predators Algorithm (EMPA) for simultaneous optimal distribution system reconfigurations (DSRs) and distributed generations (DGs) addition. The proposed EMPA recognizes the changes opportunity in environmental and climatic conditions. The EMPA handles a multi-objective model to minimize the power losses and enhance the voltage stability index (VSI) at different loading levels. The proposed EMPA is performed on IEEE 33-bus and large-scale 137-bus distribution systems (DSs) where three distinct loading conditions are beheld through light, nominal and heavy levels. For the 33-bus DS, the proposed EMPA successfully reduces the cumulative losses by 72.4 % compared to 70.36 % for MPA for the three-loading levels simultaneously. As a result, significant voltage improvement is achieved for heavy, nominal and light loadings to be 95.05, 97, 98.3%, respectively. For the 137-bus DS, it successfully minimizes the losses of 81.16 % under small standard deviation 4.76 %. Also, the 83-bus test system is considered for fair comparative between the proposed and previous techniques. The simulation outputs revealed significant improvements in the standard MPA and demonstrated the superiority and effectiveness of the proposed EMPA compared to other reported results by recent algorithms for DSRs associated with DGs integration.

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Section: Introductionmentioning

confidence: 99%

Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations

et al. 2021

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“…Optimal DNR and CBs placement are presented in literature such as improved binary PSO [18], hybrid shuffled frog leaping algorithm in the fuzzy framework [19]. In addition to this, optimal DNR and DGs placement are manifested in literature based on various algorithms such as the dataset approach and water cycle algorithm [4], improved elitist-Jaya algorithm [7], and improved sine-cosine algorithm [20]. Furthermore, optimal DGs and CBs placement in distribution systems has been manifested in various articles using meta-heuristic techniques such as the water cycle algorithm [2], PSO [3], genetic algorithm [6], and enhanced grey wolf algorithm [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

et al. 2021

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Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This paper addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers' satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques. INDEX TERMSDistributed generators, Power losses minimization, switched capacitors, distribution reconfiguration, manta ray foraging algorithm. Nomenclature: Nbn Whole number of the branches in the system OFn Objective function CoV Control variables OT Tie branches which are opened NT Number of branches which could be opened to retain the radial structure of distribution system. Qsc Reactive output power from switching of the capacitors NC Number of the current switched capacitors. Pog Dispatchable output power of DG Ndg Number of located DGs Qsc min Minimum of reactive power resulting from switched capacitors Qsc max Maximum of reactive power resulting from switched capacitors Pog min Minimum of active power resulting from DG Pog max Maximum of active power resulting from DG V Voltage magnitude Ibn Flow of the current in the branches Ibn max Upper limit of the current flow in the branches Pd Active load demand Qd Reactive load demand PRG & PRQ Penetration level which is acceptable from the DGs and CBs Mb Buses number of the system QGsub Substation reactive power

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“…In [ 29 ], harmony search (HS) and teaching–learning-based optimization (TLBO) are hybridized for forming comprehensive teaching learning harmony search optimization algorithm (CTLHSO) and applied to solve simultaneous DGs and reconfiguration problem for minimizing the loss and voltage deviation from reference bus considering different loading levels. In [ 30 ], improved sin-cosine algorithm (SCA) with levy flights is proposed and optimized for multi-objective function with loss and voltage stability index via determining the optimal branches to open and tie-lines to close, for forming optimal reconfiguration and location and sizes of DGs. In [ 31 ], a new thief and police algorithm (TPA) is proposed and applied for solving the renewable DGs and capacitor allocation along with the reconfiguration problem considering loss, voltage stability and operational cost.…”

Section: Introductionmentioning

confidence: 99%

A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

Janamala

2021

SN Appl. Sci.

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A new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications.

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An improved sine–cosine algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems

Cited by 89 publications

References 37 publications

Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations

Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations

Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

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