Multi-objective planning of electrical distribution systems using dynamic programming

Ganguly, Sanjib; Sahoo, Niranjan; Das, Debapriya

doi:10.1016/j.ijepes.2012.10.030

Cited by 94 publications

(53 citation statements)

References 34 publications

(119 reference statements)

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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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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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“…can be calculated by (27) and (28). The severity CC t,mc is calculated by the cost of corrective control actions such as generation rescheduling and load curtailment, as seen in (29).…”

Section: Objectivesmentioning

confidence: 99%

“…The set of all the Pareto optimal solutions is called a Pareto set (PS), and the set of all the Pareto optimal objective vectors is the Pareto frontier (PF). A multi-objective evolutionary algorithm (MOEA) is widely used to approximate the PF [28].…”

Section: Solution Algorithmmentioning

confidence: 99%

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Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

2017

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This paper presents a multi-objective transmission expansion planning (TEP) framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component availability, and incentive-based demand response (IBDR) costs. Specifically, the formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to conduct risk analysis. Thus, a final expansion plan can be selected according to individual risk preferences. Moreover, the economic value of IBDR is modeled and integrated into the cost objective. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and risk are provided. The proposed approach is numerically verified on the Garver's six-bus, IEEE 24-bus RTS and Polish 2383-bus systems. Case study results demonstrate that the proposed approach can effectively reduce cost and hedge risk in relation to increasing wind power integration.

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“…Con respecto a las funciones objetivo consideradas se tienen: costos y confiabilidad (Ramírez y Bernal, 2001); costos, confiabilidad y riesgo de inversión (Ramírez y Domínguez, 2004); costos e índices de fallas del sistema (Carrano, Soarez, Takahashi, Saldanha y Neto, 2006); costos, pérdidas y caída de tensión (Mori y Yamada, 2007); costos y caída de tensión (Mantway y Al-Muhaini, 2008); costos, pérdidas e interrupción del servicio (Kong et ál., 2008); costos y consumo de energía (Kayu y Ooka, 2009); costos, confiabilidad, pérdidas y caída de tensión (Ganguly, Sahoo y Das, 2011); costos de inversión y costos de interrupción del servicio (Ganguly, Sahoo y Das, 2013). Para solucionar el problema se han considerado diversas técnicas multiobjetivo, entre las que se destacan: algoritmos evolutivos (Ramírez y Bernal, 2001;Carrano, Soarez, Takahashi, Saldanha y Neto, 2006;Mori y Yamada, 2007;Kong et ál., 2008;Kayu y Ooka, 2009); búsqueda tabú (Ramí-rez y Domínguez, 2006); cúmulo de partículas (Mantway y Al-Muhaini, 2008;Sahoo, Ganguly y Das, 2012).…”

Section: Introductionunclassified

Planeación óptima de sistemas de distribución considerando múltiples objetivos: costo de inversión, confiabilidad y pérdidas técnicas

Solanilla

Isaza

Rendón

2015

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En este artículo se propone una metodología para solucionar el problema del planeamiento multiobjetivo de sistemas de distribución de energía eléctrica, empleando un algoritmo elitista de ordenamiento no dominado (NSGA-II). Esta metodología considera cuatro modelos matemáticos, donde las funciones objetivo son costos fijos, costos variables, costos fijos y variables, y confiabilidad de la red; como conjunto de restricciones se tienen criterios técnicos y operativos. Al solucionar cada uno de estos modelos se identifican los elementos comunes, los cuales son fijados para la siguiente etapa del proceso; esto se repite hasta hallar una configuración final del sistema. De esta forma, en cada paso del algoritmo se generan especies de cortes, los cuales reducen el espacio de solución. La metodología es verificada con un sistema de distribución de la literatura especializaday se obtienen resultados que muestran la efectividad del método al encontrar un punto de equilibrio entre todos los objetivos considerados.

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Multi-objective planning of electrical distribution systems using dynamic programming

Cited by 94 publications

References 34 publications

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

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

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

Planeación óptima de sistemas de distribución considerando múltiples objetivos: costo de inversión, confiabilidad y pérdidas técnicas

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