Multi-objective electric distribution network reconfiguration solution using runner-root algorithm

Nguyen, Thuan Thanh; Nguyen, Thang Trung; Truong, Anh; Nguyen, Quyen Thi; Phung, Anh Tuan

doi:10.1016/j.asoc.2016.12.018

Cited by 98 publications

(68 citation statements)

References 43 publications

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“…In an attempt to expose the predominance of mGWO algorithm in tackling DNR problem, the viable indices that have obtained by FWA [16], CSA [17], RRA [18], IAICA [21], MBFOA [22] and AWIDPSO [25] techniques for test systems-I, and CSA [17], IAICA [21], MBFOA [22] and AWIDPSO [25] for test systems-II including mGWO were contrasted in Tables 3 and 4 respectively. The improvement in PLRI of mGWO over FWA [16], CSA [17], RRA [18], IAICA [21], MBFOA [22] and AWIDPSO [25] is 0.0375, 00286, 0.0353, 0.0357, 0.0104 and 0.0065 respectively in case of test system-I. Similarly, the PLRI has minimized by mGWO over CSA [17], IAICA [21], MBFOA [22], AWIDPSO [25] is 0.0074, 0.008, 0.0072 and 0.0048 respectively in case of test system-II.…”

Section: Comparison Of Viable Solutionmentioning

confidence: 99%

“…Likewise, enhancement in NVDI of mGWO over FWA [16], CSA [17], RRA [18], IAICA [21] and AWIDPSO [25] is 0.0035, 0.0024, 0.0036, 0.007 and 0.0006 respectively in case of test system-I. At the same time, the NVDI has improved by mGWO is 0.0104, 0.0099 and 0.0077 higher than CSA [17], IAICA [21] and AWIDPSO [25] respectively in case of test system-II.…”

Section: Comparison Of Viable Solutionmentioning

confidence: 99%

“…Later stage, a metaheuristic optimization technique based on either population or nature inspired algorithms have been employed for minimizing power loss and maximizing node voltage magnitude of the DS were presented, such as: genetic algorithm (GA) [8], ant colony search algorithm (ACSA) [9], a new codification with genetic operators [10], artificial bee colony algorithm (ABC) [11], evolutionary algorithms [12], plant growth simulation algorithm (PGSA) [13], bacterial foraging optimization algorithm (BFOA) [14], artificial immune system (AIS) [15], fireworks algorithm (FA) [16], a cuckoo search algorithm (CSA) [17] and runner-root algorithm (RRA) [18].…”

Section: Introductionmentioning

confidence: 99%

“…Namely, GA [8], ACSA [9], ABC [11], EP [12], PGSA [13], BFOA [14] and AIS [15] algorithms have nicely reconfigured the DS for minimizing power loss. On the other hand FA [16], CSA [17] and RRA [18] have competently minimized power loss and improving the voltage profile of the distribution network; the first two simultaneously minimize both net real power loss reduction and voltage deviation index and later has exercised in performing single objective and multiobjective optimization.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Nataraj¹,

Loganathan²,

Veerasamy³

et al. 2018

IJIES

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A novel metaheuristic optimization procedure for optimizing electrical distribution system is proposed that may help the system planners to know the minimum and maximum objective values. In which the optimal distribution network reconfiguration (DNR) framework and implementation of modified grey wolf optimization (mGWO) algorithm to minimize power loss reduction and node voltage deviation indices have delineated. The DNR involves nonlinear and multimodal function that has been optimized under practical constraints. For the purpose, mGWO algorithm is employed for ascertaining optimum switching position while reconfiguring the distribution system at a minimum fitness value. In fact grey wolf's update its position linearly from a higher value to zero in the search vicinity that provides perfect balance among intensification and diversification to ascertain the best fitness function and exhibits quick and steady convergence. Moreover, the proposed method seems to be a promising optimization tool for the electrical utilities, thereby modifying their operating strategy of distribution system under steady state conditions. This study is conducted on standard IEEE 33-bus and 69-bus distribution systems, the simulated results have analyzed and compared with several recent methods. It shows that the numerical results have provided, by mGWO is superior among the contestant algorithms.

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Section: Comparison Of Viable Solutionmentioning

confidence: 99%

Section: Comparison Of Viable Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Nataraj¹,

Loganathan²,

Veerasamy³

et al. 2018

IJIES

View full text Add to dashboard Cite

show abstract

“…However, both the studies 52,53 did not consider renewable DG and reconfiguration. Researchers have also used other optimization techniques such as simulated annealing, 54 artificial immune algorithm, 55,56 vaccine-enhanced artificial immune system, 57 modified plant growth simulation algorithm, 58 PSO, 59,60 GA 61-63 , runner root algorithm, 64 harmony search algorithm, 65 artificial bee colony algorithm, 66,67 hybrid Harmony search and particle artificial bee colony algorithm, 68 interval analysis, 69 stochastic MILP, 70 Ant Colony Optimization, 71 hybrid receding horizon control and scenario analysis, 72 nondominated sorting GA, 73 fuzzy mutated GA, 74 tabu search, 75 Benders decomposition approach, 76 and evolutionary programming. 77 Summary of the reviewed literature is presented in Tables 1 and 2. From the above literature survey, it is seen that most of the researchers have studied either DG installation or network reconfiguration for power loss minimization.…”

Section: Introductionmentioning

confidence: 99%

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Hesaroor

Das

2019

Int Trans Electr Energ Syst

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Summary Renewable energy sources are becoming popular day by day for distributed generation as technology is becoming cheaper and modular. Power loss reduction is a powerful incentive for a distribution system operator to promote distributed generation. This paper presents a method to minimize annual energy losses through the integration of nondispatchable renewable distributed generation (DG) units and network reconfiguration under varying load demand condition. The optimal location and DG power level are calculated, and then these data are used to determine DG plant size taking into account the nonavailability of renewable energy at certain times. A network reconfiguration strategy is proposed, which takes into account both the time‐varying load and the renewable energy source such that annual energy loss is minimum. The proposed methodology is applied to 33‐node and 118‐node test distribution system with different scenarios. Results show a substantial reduction in energy loss. A cost‐benefit analysis is also carried out.

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Improving voltage profile and reducing power losses based on reconfiguration and optimal placement of UPQC in the network by considering system reliability indices

Dashtdar

Bajaj

Hosseinimoghadam

et al. 2021

Int Trans Electr Energ Syst

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Distribution networks have problems such as an increase in active losses, overload on distribution substations, voltage drop, decrease in network reliability, increase in load unbalance and unbalance in line current due to variable characteristics in terms of load consumption. In this article, to reduce losses, improve the voltage profile and increase the reliability of the system, the UPQC (Unified Power Quality Controller) optimal placement and reconfiguration method have been used in the distribution network. The two objective functions of losses and the mean voltage deviation are defined by considering the reliability indices. Since the reconfiguration of distribution networks is a large-scale nonlinear hybrid optimization problem with several constraints, the genetic algorithm combines genes including key numbers and UPQC location, UPQC series power size, UPQC shunt power size used to solve the problem. The proposed method is implemented in three steps on IEEE 33-bus and 69-bus standard networks by considering the load model List of Symbols and Abbreviations: agr, agriculture load; com, commercial load; C i , number of loads connected to the ith bus; D, load coefficient; F1, F2, objective functions; fraci,k, the amount of load lost in the ith bus; gen, general load; I EAF_a , main current component of a phase; Ii, current of feeders; Ii, input current to bus i; ind, industrial load; i p , reference current phase component for active power; i q , reference current phase component for reactive power; I ref_a , current reference of the a phase; I s , source current; n, number of buses; P 0i , Q 0i , active and reactive power at a per-unit voltage of bus i; Pi, Q i , active and reactive power passing through branch i; P ref , reference active power; P rk , probability of losing the ith bus; P total,loss , total active network losses; Q a , reactive power of a phase; Q L , reactive power demand; res, residential load; ri, resistance of branch i; S, tnumber of UPQCs installed in the network; V DC_ref , treference DC voltage; V i , voltage of Bus i; V trans_a , effective value of the voltage of the a phase; V s , source voltage; Y ij , admittance matrix; Z s , source impedance; Δδ i , changes in voltage angle of bus, changes in voltage magnitude of bus i; ΔP i , changes in active power of bus i; ΔQ i , changes in reactive power of bus i; γ, τ, active and reactive charge change coefficient; ACS, ant colony search; ACIFi, average fixed number of each customer (1/a) per year; AENS, average energy not supplied per customer; AI

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Multi-objective electric distribution network reconfiguration solution using runner-root algorithm

Cited by 98 publications

References 43 publications

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

Improving voltage profile and reducing power losses based on reconfiguration and optimal placement of UPQC in the network by considering system reliability indices

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