Integral Planning of Primary–Secondary Distribution Systems Using Mixed Integer Linear Programming

Paiva, P.C.; Khodr, H.M.; Domínguez-Navarro, José A.; Yusta, José M.; Urdaneta, A.J.

doi:10.1109/tpwrs.2005.846108

Cited by 108 publications

(72 citation statements)

References 11 publications

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“…An integral planning methodology of primary-secondary distribution systems is formulated and solved using MILP [6]. A spatial PDP is formulated and solved using MILP [52].…”

Section: Mixed Integer Linear Programming (Milp)mentioning

confidence: 99%

A review of power distribution planning in the modern power systems era: Models, methods and future research

Georgilakis

Hatziargyriou

2015

Electric Power Systems Research

371

175

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“…An integral planning methodology of primary-secondary distribution systems is formulated and solved using MILP [6]. A spatial PDP is formulated and solved using MILP [52].…”

Section: Mixed Integer Linear Programming (Milp)mentioning

confidence: 99%

A review of power distribution planning in the modern power systems era: Models, methods and future research

Georgilakis

Hatziargyriou

2015

Electric Power Systems Research

371

175

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“…Distribution planning under uncertainty is an active research topic, with most publications traditionally focusing on deterministic network design frameworks [8], [9], [10]. Furthermore, in most existing models the deployment pattern of DG sources is regarded either as an input parameter or as a variable to be optimized [11], [12].…”

Section: Strategic Value Of Smart Grid Technologiesmentioning

confidence: 99%

Strategic valuation of smart grid technology options in distribution networks

Konstantelos

Giannelos

Štrbac

2017

2017 IEEE Power &Amp; Energy Society General Meeting

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-The increasing penetration of renewable distributed generation (DG) sources in distribution networks can lead to violations of network constraints. Thus, significant network reinforcements may be required to ensure that DG output is not constrained. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to take fully-informed decisions and integrate DG at a minimum cost. In this paper we propose a novel stochastic planning model that considers investment in conventional assets as well as smart grid assets such as demandside response, coordinated voltage control and soft open points (SOPs). The model also considers the possibility of active power generation curtailment of the DG units. A node-variable formulation has been adopted to relieve the substantial computational burden of the resulting mixed integer non-linear programming (MINLP) problem. A case study shows that smart technologies can possess significant strategic value due to their inherent flexibility in dealing with different system evolution trajectories. This latent benefit remains undetected under traditional deterministic planning approaches which may hinder the transition to the smart grid.

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“…These planning models are able to locate and size primary and secondary circuits, as well as distribution transformers, through mathematical programming or heuristic algorithms. Earlier efforts with mathematical programming methods like mixed integer linear programming [57] or nonlinear programming [58], are being replaced by heuristic methods like genetic algorithms [59], practical heuristic algorithms [60], or particle swarm optimization [61]. The increasing use of heuristic methods over numerical ones is driven by the computational complexity of planning entire distribution systems.…”

Section: Need For Large-scale Test Networkmentioning

confidence: 99%

A Review of Power Distribution Test Feeders in the United States and the Need for Synthetic Representative Networks

et al. 2017

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Abstract:Under the increasing penetration of distributed energy resources and new smart network technologies, distribution utilities face new challenges and opportunities to ensure reliable operations, manage service quality, and reduce operational and investment costs. Simultaneously, the research community is developing algorithms for advanced controls and distribution automation that can help to address some of these challenges. However, there is a shortage of realistic test systems that are publically available for development, testing, and evaluation of such new algorithms. Concerns around revealing critical infrastructure details and customer privacy have severely limited the number of actual networks published and that are available for testing. In recent decades, several distribution test feeders and US-featured representative networks have been published, but the scale, complexity, and control data vary widely. This paper presents a first-of-a-kind structured literature review of published distribution test networks with a special emphasis on classifying their main characteristics and identifying the types of studies for which they have been used. This both aids researchers in choosing suitable test networks for their needs and highlights the opportunities and directions for further test system development. In particular, we highlight the need for building large-scale synthetic networks to overcome the identified drawbacks of current distribution test feeders.

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Integral Planning of Primary–Secondary Distribution Systems Using Mixed Integer Linear Programming

Cited by 108 publications

References 11 publications

A review of power distribution planning in the modern power systems era: Models, methods and future research

A review of power distribution planning in the modern power systems era: Models, methods and future research

Strategic valuation of smart grid technology options in distribution networks

A Review of Power Distribution Test Feeders in the United States and the Need for Synthetic Representative Networks

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