A multiobjective placement of switching devices in distribution networks incorporating distributed energy resources

Pombo, A. Vieira; Pina, João Murta; Pires, V. Fernão

doi:10.1016/j.epsr.2015.08.012

Cited by 50 publications

(39 citation statements)

References 23 publications

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“…Much work has been devoted to analyze strategies to determine the optimal location and amount of automated elements considering reliability improvements. Some authors have proposed different optimization techniques, including among others classical optimization, particle swarm and artificial bee colony optimization, and advanced genetic algorithms such as fast elitist nondominated sorting technique, and have presented different Pareto frontiers based on shuffled frog leap, enhanced gravitational search, or genetic algorithms . Most of these works assess the reliability improvement through analytical expressions assuming an average restoration time.…”

Section: Assessing Reliability Improvement In a Smart Grid Environmentmentioning

confidence: 99%

Assessing the potential of MV automation for distribution network reliability improvement

Rodriguez-Calvo

Cossent

Frías

2017

Int Trans Electr Energ Syst

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Summary Distribution network automation is a key functionality in the evolution towards smarter electricity grids. The main driver to deploy this kind of smart grid solutions is the improvement of continuity of supply for network users; thanks to the fact that an enhanced network monitoring and telecontrol can improve the fault management process conducted by distribution system operators. However, the parameters that affect their successful implementation are not completely understood yet. Addressing this gap, this paper presents a detailed analysis to identify the main factors affecting the implementation of automation at medium voltage distribution networks and discusses the regulatory implications. Several realistic case studies are analyzed, corresponding to a number distribution networks that resemble the architectural characteristics representative of the European distribution grids in different contexts. Hence, the results allow deriving general conclusions that can be widely applicable to European distribution networks.

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Section: Assessing Reliability Improvement In a Smart Grid Environmentmentioning

confidence: 99%

Assessing the potential of MV automation for distribution network reliability improvement

Rodriguez-Calvo

Cossent

Frías

2017

Int Trans Electr Energ Syst

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“…Both the number and location of TS and SS need to be optimized [5]. Many mathematical methods and intelligent algorithms, including, 4 simulated annealing [6], genetic algorithms [7][8][9][10], particle swarm optimization (PSO) [11][12][13], ant colony search algorithm [14,15], immune algorithm [16] have been proposed for optimal switch placement. In [11], a novel three-state approach based on PSO was developed for determining the optimal number and locations of two types of switches in distribution systems.…”

Section: Introductionmentioning

confidence: 99%

“…using the random generation method of failure times whose flowchart is shown inFig. 4 10. Assuming a constant failure rate for section z of feeder s, this algorithm produces the number of failures of the intended section in the [0, T] interval.…”

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confidence: 99%

Risk-based Switch Placement in Electric Distribution Network in Presence of Performance Based Regulation

Simab

Moradkhani

2019

Scientia Iranica

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A reward and penalty scheme (RPS) is used for setting up the service quality which exposes the distribution companies to financial benefits caused by demand for the reliability of customers. In this paper, an algorithm for the optimal switch number and placement in distribution networks in the presence of RPS is presented. The primary objective is reliability improvement and minimization of the cost of sectionalizing switches (SS) and tie switches (TS) for a given regulatory period considering acceptable financial risk. In this algorithm, the uncertainty in the reliability appears as financial risk.A genetic algorithm is adopted to solve the optimization problem. The number and location of SS and TS are found while financial incentives of RPS, capital investment and annual operation and maintenance costs are considered. The performance of the proposed approach is assessed and illustrated on a real distribution network. The results show the efficiency of the proposed algorithm. Nomenclature SS t CC Capital investment and installation cost of SS installed in year . TS t CC Capital investment and installation cost of TS installed in year . l Energy price ($/kWh) for load sector . ,, s z k D Average duration of unplanned outage interruption z-th zone of s-th feeder result of failure occurrence in k-th zone of s-th feeder. DRAnnual discount rate. f ( ) i SAIDI Probability distribution of SAIDI for plan . () t f CENS Probability distribution of energy not supplied cost in year . ,, f( ) t s z N Frequency distribution of failures of z-th zone of s-th feeder in year . SS t MC Operation and maintenance cost of SS in year . TS t MC Operation and maintenance cost of TS in year . m Life period of SS and TS. T NC Total number of customers. , sz NC Number of customers located in z-th zone on s-th feeder. ,, NS s z P Expected interrupted load (kW) in z-th zone of s-th feeder. RPI i Probability distribution of reward penalty incentive for plan . RPS( ) SAIDI Reward penalty scheme based on SAIDI index. i SC Switch placement cost for plan T Period of regulation.  VaRValue at risk at a given confidence level (1 − )%.

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“…Planning components associated to each PT in reviewed work are designated with symbols A, B, C, and D, respectively, also shown in an overarching diagram as in Figure 1. The color coding allocated to each PT in the arrangement order includes green (A) for DGP ; orange (B) for VPQ [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70]; blue (C) for CRU [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89] and purple (D) for NTR [90][91][92][93][94][95][96][97][98][99][100]. It is important to note that date o...…”

Section: Composite Review Of Mop Techniques With Taxonomymentioning

confidence: 99%

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

2017

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Distribution networks (DNWs) are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP) strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN) needs to consider choices, primarily distributed generation (DG), network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP) in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs) based on mult-objective optimizations (MOOs) in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT) have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at the multi-objective-based frameworks, are also presented in this paper.Keywords: active distribution network (ADN); distributed generation (DG); distributed energy resources (DERs); distribution network planning (DP); multi-objective optimization (MOO); multi-criteria decision analysis (MCDA); distributed generation placement (DGP); volt-ampere reactive power (VAR) compensation and power quality (VPQ); component reinforcement and up gradation (CRU); network (distribution) topology change and reconfiguration (NTR); planning techniques (PT); multiple objective planning (MOP); multi-objective planning techniques (MOPTs); future distribution networks (FDNs)

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A multiobjective placement of switching devices in distribution networks incorporating distributed energy resources

Cited by 50 publications

References 23 publications

Assessing the potential of MV automation for distribution network reliability improvement

Assessing the potential of MV automation for distribution network reliability improvement

Risk-based Switch Placement in Electric Distribution Network in Presence of Performance Based Regulation

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

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