Optimal distribution network reinforcement considering load growth, line loss, and reliability

Ziari, Iman; Ledwich, Gerard; Ghosh, Arindam; Platt, Glenn

doi:10.1109/tpwrs.2012.2211626

Cited by 110 publications

(59 citation statements)

References 33 publications

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“…Furthermore, the efficiency of the PSObased algorithm is 19% better compared to classical linear programming, as investigated in [48]. Moreover, the evaluation of the employed MPSO in this paper against three well-known heuristic methods, called original PSO, GA, and SA for distribution planning shows the better accuracy and robustness of proposed MPSO [49]. In order to improve the accuracy of the solution, a modified version of PSO (MPSO) is proposed by adding the idea of mutation from the genetic algorithm (GA) as in [45], [50] into standard PSO particle update rules.…”

Section: B Modified Particle Swarm Optimization (Mpso)mentioning

confidence: 78%

“…Different approaches have been used in the literature to handle these complexities. Heuristics approaches such as tabu search [3], particle swarm optimization (PSO) [4][5][6][7][8], and genetic algorithm (GA) [9][10][11] have been used to handle large problem sizes. While, decomposition techniques such as forward filling [12], backward pull-out [13], and recursive forward-backward approach [4], [5], [11], [14] have been used for handling time dynamics of MSDEP problems.…”

Section: Introductionmentioning

confidence: 99%

“…A tabu search based MSDEP is presented to optimize the total investment, operational, and reliability of distribution networks by upgrading transformers, lines, and tie lines including switches [3]. A modified PSO is proposed to solve MSDEP considering loss and reliability of networks through the use of dispatchable DGs [8]. For optimal usage of ESSs and voltage control devices such voltage regulators and capacitors along with conventional upgrades, an efficient forward-backward algorithm is given in [4], which is extended to consider the reconfiguration of the network in [5].…”

Section: Introductionmentioning

confidence: 99%

“…where , and , are NPV of fixed and variable investment cost of network solutions at year y, respectively, as provided in [4], [8], [39]. & is the NPV of operation and maintenance (O&M) cost and is the salvage value of , based on the straight line calculation [40].…”

mentioning

confidence: 99%

“…and are cost of energy loss and power loss at year y, respectively. The details of the formulations are provided in [8] [39].…”

mentioning

confidence: 99%

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A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions

Arefi

Abeygunawardana

Ledwich

2016

IEEE Trans. Sustain. Energy

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Abstract-The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary nonnetwork solutions (NNSs) such as demand response (DR) and temporary energy storage system (ESS)/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the "optimal level of demand" for each year at which the network should be upgraded using network solutions (NSs) while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward-backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach.Index Terms-Demand response, Energy storage, Multi-stage electric network distribution planning, Risk-managed cost, Uncertainty, Probability of exceedance.

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Section: B Modified Particle Swarm Optimization (Mpso)mentioning

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

“…and are cost of energy loss and power loss at year y, respectively. The details of the formulations are provided in [8] [39].…”

mentioning

confidence: 99%

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A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions

Arefi

Abeygunawardana

Ledwich

2016

IEEE Trans. Sustain. Energy

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Distribution System

Koutsoukis¹,

Georgilakis²,

Hatziargyriou³

et al. 2020

Applications of Modern Heuristic Optimization Methods in Power and Energy Systems

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The impact of capacity and location of multidistributed generator integrated in the distribution system on electrical line losses, reliability, and interruption cost

Chiradeja

Ngaopitakkul

2015

Env Prog and Sustain Energy

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The primary purpose of this article is to study the impact of distributed generator (DG) integrated into the distribution system in terms of electrical line losses, reliability and interruption cost. The single‐ and multi‐distributed generators (DG) under this study are connected to a 22 kV distribution system of the Provincial Electricity Authority (PEA) that is a part of Thailand's distribution system. Geographic information systems data, including the lengths of the distribution line and the load locations that are key parameters of PEA, are simulated using digital simulation and electrical network calculation program (DIgSILENT). In addition, the capacity and location of DG installed into the distribution system are considered. The system average interruption frequency index (SAIFI), the system average interruption duration index (SAIDI) and the interruption cost are assessed as reliability indices by comparing the SAIFI, SAIDI, and interruption cost of the base case (without DG) and the cases of single‐ and multi‐DGs connected to the distribution system. Moreover, the electrical line loss is considered in terms of active power line loss, which is also compared with the base case. The results can be summarized by focusing on the location of DG, the capacity of DG, the number of DG, the size of the load, and the distance to the load, which are factors capable of impacting the electrical line loss, SAIFI, SAIDI, and interruption cost. © 2015 American Institute of Chemical Engineers Environ Prog, 34: 1763–1773, 2015

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Optimal distribution network reinforcement considering load growth, line loss, and reliability

Cited by 110 publications

References 33 publications

A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions

A New Risk-Managed Planning of Electric Distribution Network Incorporating Customer Engagement and Temporary Solutions

Distribution System

The impact of capacity and location of multidistributed generator integrated in the distribution system on electrical line losses, reliability, and interruption cost

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