Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty

Esmaeili, M.; Sedighizadeh, Mostafa; Esmaili, Masoud

doi:10.1016/j.energy.2016.02.152

Cited by 150 publications

(66 citation statements)

References 29 publications

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“…Case 4 deals with the non‐uniform variation of all type of load demand for full day as modeled in Esmaeili et al and finds the optimal location of the DG units. The optimal placement of the DG unit with the variation of load without generation variation for a day is under consideration in this case.…”

Section: Resultsmentioning

confidence: 99%

Optimum placement of distributed generation considering economics as well as operational issues

Manna

Goswami

2020

Int Trans Electr Energ Syst

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Summary Distributed generations (DGs) are in increased use because of numerous advantages. While determining the optimum DG placement in the network, issues related to optimum operation and controls have not been taken care of in earlier literatures. The determination of optimal DG locations in distribution network for optimum operation and control in respect of Generation and Load shedding cost (GLC), voltage, and frequency control taking into account of DG type, capacity, variation of DG output power, and load has been presented in this work. A comprehensive solution algorithm has been proposed to find out the optimum set of location of DG units that suit all probable variation of load and DG output power. Lightning search algorithm (LSA) is used to find optimal allocation of DG units in this work. For the multi‐objective function optimization, the weighting factors are optimized by using artificial bee colony (ABC) technique. A comparison study with the NSGA‐II optimization technique has also been reported in this work.

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Section: Resultsmentioning

confidence: 99%

Optimum placement of distributed generation considering economics as well as operational issues

Manna

Goswami

2020

Int Trans Electr Energ Syst

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“…For instance, Tabu Search is combined with Simulated Annealing (SA) to shorten its computational time but maintaining its strength for diversified and extensive solution search [314]; in another case, simplicity of MILP is complemented by excellent global solution exploration skill of SA [347]. In addition, incorporation of FischerBurneister (FB) algorithm is recommended to increase the convergence speed of Self-adapted Evolutionary Strategy (SAES) besides overcoming the infeasible solution generation from mutation step [265], whereas dispersed vector search of PSO and Particle Artificial Bee Colony (PABC) algorithms is helpful to avoid the premature convergence problem of Shuffled Frog Leaping Algorithm (SFLA) [275], Harmony Search (HS) Algorithm [272] and Big Bang -Big Crunch (BB-BC) Algorithm [348]. Similarly, Differential Evolution (DE) is used to compensate the population diversity decay in SFLA with respect to computation iteration number, in order to disperse its search vectors in all possible state spaces for avoiding sub-optimality trap [337].…”

Section: Maleki and Askarzadehmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

231

105

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“…Because most distribution networks operate radially mainly for protection and reliability purposes, radiality is considered a constraint. This purpose is met by (14).…”

Section: Radial Topologymentioning

confidence: 99%

Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations

Zarei

Zangeneh

2017

Int Trans Electr Energ Syst

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Summary With increasing annual energy demand and significant growth in the use of distributed generation (DG), distribution companies are exploring ways to improve the technical specifications of the distribution networks such as power losses, customer interruption costs, and voltage stability and on the other hand to increase the penetration level of DGs. In this paper, a multi‐objective optimization model is proposed for distribution network reconfiguration (DNR) to decrease the total costs of energy losses and energy not supplied (ENS) and increase the voltage stability and DG penetration level. Seasonal hourly load curves are applied, and best configuration is determined for each loading condition. Uncertainties regarding the load variations and volatile power production of wind turbine are modeled using a 2‐point estimate method, and a benefit‐cost analysis is used to assess the effects of depreciation cost in DNR problem. The proposed model is applied on 2 typical test systems.

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Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty

Cited by 150 publications

References 29 publications

Optimum placement of distributed generation considering economics as well as operational issues

Optimum placement of distributed generation considering economics as well as operational issues

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations

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