Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation

Murty, V. V. S. N.; Kumar, Ashwani

doi:10.1049/iet-stg.2018.0146

Cited by 53 publications

(28 citation statements)

References 55 publications

(138 reference statements)

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“…eir objective functions were to minimize voltage stability and power loss. Murty et al [24] suggested the hybrid method of the General Algebraic Modelling System and Gravitational Search Algorithm for DNR and ODGP problem. e objectives of this problem were to maximize annual cost savings and minimize power loss.…”

Section: Introductionmentioning

confidence: 99%

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

Truong¹,

Tuan

2021

Mathematical Problems in Engineering

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This paper aims to propose a new multiobjective algorithm for multiobjective distributed network reconfiguration (DNR) with the placements of distributed generation (DG) in radial distribution networks (RDNs). The new proposed algorithm, called the nondominated sorting stochastic fractal search (NSSFS), is a new multiobjective version of the original SFS algorithm. NSSFS incorporated fast nondominated sorting strategies, crowding distance computation, and selection mechanism into SFS to find and maintain the best nondominated solutions. The proposed NSSFS algorithm was tested with eight multiobjective benchmark test functions to validate its performance. The NSSFS was then implemented to define the optimal network configuration, positions, and sizes of DG units in the RDNs, where real power loss, voltage profile, and voltage stability index were optimized simultaneously. The implementation of multiobjective DNR-DG (MODNR-DG) significantly enhanced the performance of the system. Based on the comparison outcomes, the NSSFS algorithm obtained better solution quality than other multiobjective techniques, proving the effectiveness of NSSFS in dealing with the MODNR-DG problem.

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Section: Introductionmentioning

confidence: 99%

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

Truong¹,

Tuan

2021

Mathematical Problems in Engineering

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“…In [19], the authors have employed the CDE optimizer for the DGs' placement. In [20], the authors have proposed a mixture of GA-GSA for a DG incorporation. In [21], a mixed WIPSO-GSA is proposed to install two DGs along with capacitors in the above-mentioned test networks.…”

Section: Introductionmentioning

confidence: 99%

“…In [33], an ASFLA for the optimal allocation of three DGs is selected. In [34], a combined GSA-GAMS has been availed for the optimal deployment of three DGs in the distribution networks. Similarly, the authors in [35], have tested the QOCSOS algorithm to search for the optimal locations of three DGs.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Optimal Deployment of Several DGs in Distribution Networks Using Plant Propagation Algorithm

et al. 2020

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In recent years, the substantial upsurge of electricity demand has directly impacted the performance of the distribution networks concerning the active power losses and voltage drops. In such circumstances, the distributed generators (DGs) could uphold these concerns if they are optimally deployed in terms of sizing and placement. For this reason, in current research, the optimal deployment of DGs has been proposed with the plant propagation algorithm (PPA) to simultaneously maximize the total active power loss reduction and to upgrade the magnitude of the minimum bus voltage. Alongside, the authors have examined four rounds of DGs. In that context, the optimal deployment of one DG is investigated in the first round. In each succeeding round, the number of DGs is increased: in the second round, this investigation is carried out for two DGs, for three DGs in the third round, and finally, for four DGs in the fourth round of the investigation. The effectiveness of the proposed PPA has been tested on IEEE 33 and 69-bus test networks in the load flow analysis, and results are compared with the standard optimization algorithms. Thereafter, a post deployment economic assessment based on loss calculation has been undertaken out as well. The ANOVA test has also been performed for statistical evaluation of standard algorithms. The simulation results exhibit that the proposed algorithm outdo other algorithms both technically and economically. It has been seen that as the deployment of DGs is increased, the total active power losses and voltage drops are also reduced. In terms of economic assessments, the total cost decreases with the increased deployment of DGs in IEEE 33-bus test network, whereas, the total cost increases with the increased deployment of DGs in IEEE 69-bus test network. INDEX TERMS Distributed generators, load flow analysis, sizing and placement of DGs, total active power losses, voltage drops, test networks, plant propagation algorithm (PPA).

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“…Fossil fuel resource depletion and environmental interests have encouraged the integration of renewable distributed generator (DG) units (eg, wind and solar) in distribution networks. These units have offered several advantages such as total loss reduction, improved total system efficiency, voltage stability and voltage profile improvement, enhanced reliability, green‐house gas emission reduction and they have a significant impact on the power flow, continuity, short circuit level, and power quality . However, the inappropriate choice of locations and sizes of DGs in electric power systems has adverse effects on voltages profile, transmission losses, feeder loading, stability, and system reliability .…”

Section: Introductionmentioning

confidence: 99%

Assessment of optimal allocation of renewable distributed generator sources in distribution network

Nasef

Osheba

Khattab

et al. 2020

Engineering Reports

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The integration of distributed generators (DGs) into distribution networks in optimal allocation is one of the main issues facing power system engineers to ensure improved stability and economic operation. This article presents a detailed analysis of the impacts of the optimal allocation and the number of DGs on both system steady-state and transient performances of distribution networks. An oscillatory particle swarm optimization (OPSO) algorithm was used to find the optimal allocations of DGs via minimizing various objective functions that deal with Total Transmission Losses, Voltage Regulation, and Power Performance Index. The OPSO is used to optimize these functions as a single and as a multiobjective optimization problem. The effectiveness of the method is demonstrated with the IEEE-14 bus as an example of distribution networks with a 50% increase in system loading. Two penetration scenarios have been considered, the optimal sizes and locations of DGs are obtained, and the results are presented.In addition, the impact of the penetration level of Photovoltaic and Wind Energy sources on transient performance is obtained using detailed nonlinear models of both synchronous machines and DGs sources. The system response is then obtained when the system is subjected to a three-phase short circuit fault for six cycles and the results are presented in a comparative form for different penetration levels. The techniques and results presented in the article form a useful base for power system engineers in planning and operating distribution systems with high penetration levels of RDG sources. K E Y W O R D Sconcentrated and distributed DG (C-DG, D-DG), distributed generation (DG), optimal DG allocations, oscillatory PSO, penetration level.

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Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation

Cited by 53 publications

References 55 publications

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

A Nondominated Sorting Stochastic Fractal Search Algorithm for Multiobjective Distribution Network Reconfiguration with Distributed Generations

Analysis of Optimal Deployment of Several DGs in Distribution Networks Using Plant Propagation Algorithm

Assessment of optimal allocation of renewable distributed generator sources in distribution network

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