Distribution system analysis and the future Smart Grid

Arritt,; Dugan,

doi:10.1109/repcon.2011.5756725

Cited by 32 publications

(35 citation statements)

References 7 publications

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“…In the first iteration, i.e., k = 1, the kW correction factor is 1 so that each load is assigned with the original kW value at instant i, taken from the corresponding load profile curve. These demands are used to run an instantaneous (snapshot) power flow simulation to determine the P (1) s i . If the difference between P (1) s i and P m i is larger than a predefined tolerance error, the correction factor is updated.…”

Section: Load Allocationmentioning

confidence: 99%

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Integration of open source tools for studying large‐scale distribution networks

Valverde

Argüello

Gonzalez

et al. 2017

IET Generation, Transmission & Distribution

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The penetration of Low Carbon Technologies (LCTs) is expected to increase in the near future given the attractive incentives from governments, the cost-effectiveness of the technologies and the appearance of Smart Grid schemes. To understand the corresponding benefits and challenges associated with them, power utilities are expected to run more sophisticated studies supported by more accurate and detailed models of distribution network elements and the network itself. This paper presents the development of two software plugins that allow the integration of a Geographical Information System (GIS) platform with a distribution system simulator. These plugins are intended to provide power utilities free and open source tools to explore the benefits and/or impacts of newly adopted LCTs and to analyze Smart Grid opportunities in large-scale distribution networks. The effectiveness of the plugins is illustrated considering a real DN with 13323 customers in Costa Rica. Results demonstrate that the plugins can successfully support in the creation of detailed network models and studies.

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Section: Load Allocationmentioning

confidence: 99%

“…At present, most distribution system analysis tools are capable of running unbalanced three phase power flows and short circuit analysis, while only a few tools have the capability to perform simulations over a period of time such as day, week, month, or year [1].…”

Section: Introductionmentioning

confidence: 99%

Integration of open source tools for studying large‐scale distribution networks

Valverde

Argüello

Gonzalez

et al. 2017

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

show abstract

“…In light of the above, recent literature has placed significant focus on new planning tools that can properly model and address the use of DR, automation and communications technologies, and network reconfiguration, amongst other smart solutions [7] [8]. The proposed tools rely on significant simplifications and approximations to allow the use of commercial optimisation software (e.g., linear approximations, typical seasonal profiles, limited number of decision nodes, and so forth) [9][10][11], or in a wide variety of complex algorithms based on meta-heuristic approaches [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Practical recursive algorithms and flexible open-source applications for planning of smart distribution networks with Demand Response

Ceseña

Mancarella

2016

Sustainable Energy, Grids and Networks

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-Distribution networks are currently undergoing fundamental changes due to the rise of smart solutions such as for instance Demand Response (DR), which increases network complexity and challenges the adequacy of traditional planning practices. This calls for the use of suitable planning methodologies. However, the planning problem may be too cumbersome for most commercial software tools, or may lead to complex bespoke optimisation models that may not be easy to use by network planners. In this light, this work proposes a recursive function that can be used in practical algorithms for planning of smart distribution networks. The recursive function can emulate business-as-usual planning practices and further optimise them, including DR options as potential substitutes for network reinforcement. Several case studies based on real UK networks highlight the robustness and flexibility of the proposed algorithms to address different problems, including uncertainty analysis and risk management. The results clearly show that the proposed tool leads to increased economic and social benefits, particularly when optimising investment strategies considering smart DR solutions. The model is herein distributed open-source as accompanying material to this paper with the aim of encouraging new practices required for smart distribution network planning beyond the traditional academic applications.

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“…Smart Grid is a concept that encompasses a number of benefits. A Smart Grid helps improve the distribution system and also helps with the planning of the system by monitoring and, controlling it which means that the implementation of a Smart Grid in the distribution system enables greater efficiency in respect to a traditional system [1]. The traditional system did not have a large number of interconnections, its demand was minimal and its most widely used topology was radial as it is easier to control.…”

Section: Introductionmentioning

confidence: 99%

“…However, with the improvement of infrastructure, a closed topology, better control and monitoring was needed to ensure service [2]. Some benefits of the implementation of a Smart Grid worth mentioning are, on the one hand, there is a better distribution of electrical energy in a system which is controlled and monitored in near-real time with high-speed transmission, that is, it implies the existence of an advanced metering infrastructure (AMI) [1] [3]. On the other hand , another key aspect of the implementation of a Smart Grid is to have the ability to provide security to the system [4] against external agents that may be introduced in it in order to destabilize it or hackers who wish to have classified procedure information, control of resources and services provided to different consumers [5] [6].…”

Section: Introductionmentioning

confidence: 99%