Dynamic Network Rating for Low Carbon Distribution Network Operation—A U.K. Application

Yang, Jin; Bai, Xuefeng; Strickland, Dani; Jenkins, Lee; Cross, Andrew

doi:10.1109/tsg.2015.2389711

Cited by 36 publications

(18 citation statements)

References 10 publications

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“…The results also showed that for DR application, it is more beneficial to apply a DTR based on Hot Spot Temperature (HST) limit (120 • C) rather than DTR based on design HST (98 • C), which is widely used in other papers on DTR [66][67][68][69]. Specifically, the reader can see in Figure 14 that if DTR based on HST limit is used, then DSO needs to apply less DR volumes both in power and energy terms for studied reserve margins.…”

Section: Discussionmentioning

confidence: 97%

Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime

et al. 2021

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(1) Background: This paper proposes a strategy coupling Demand Response Program with Dynamic Thermal Rating to ensure a transformer reserve for the load connection. This solution is an alternative to expensive grid reinforcements. (2) Methods: The proposed methodology firstly considers the N-1 mode under strict assumptions on load and ambient temperature and then identifies critical periods of the year when transformer constraints are violated. For each critical period, the integrated management/sizing problem is solved in YALMIP to find the minimal Demand Response needed to ensure a load connection. However, due to the nonlinear thermal model of transformers, the optimization problem becomes intractable at long periods. To overcome this problem, a validated piece-wise linearization is applied here. (3) Results: It is possible to increase reserve margins significantly compared to conventional approaches. These high reserve margins could be achieved for relatively small Demand Response volumes. For instance, a reserve margin of 75% (of transformer nominal rating) can be ensured if only 1% of the annual energy is curtailed. Moreover, the maximal amplitude of Demand Response (in kW) should be activated only 2–3 h during a year. (4) Conclusions: Improvements for combining Demand Response with Dynamic Thermal Rating are suggested. Results could be used to develop consumer connection agreements with variable network access.

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Section: Discussionmentioning

confidence: 97%

Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime

et al. 2021

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“…Power systems face several new challenges which includes the sheer complexity that results from the introduction of new devices like phasor measurement units (PMUs), Geographical Information Systems (GIS), sensors and advanced controllers on equipment throughout the system, intelligent electronic devices (IEDs) in substations, electric and hybrid vehicles, smart meters, distributed storage systems, photovoltaic generation and wind generating units [3]. In [4], an application- ISSN: 2088-8708 Int J Elec & Comp Eng, Vol. 9, No.…”

Section: Introductionmentioning

confidence: 99%

Asset management and maintenance: a smart grid perspective

Jung¹,

Ray²,

Salkuti³

2019

IJECE

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<p>This paper presents the importance, issues and challenges related to Smart Grid. It also evaluates various approaches for Smart Grid planning and operation. It discusses tools for asset management and their applicability to the next generation grid. Aging assets, uncertainty in load demand profile and renewable energy resources, and demand management create a challenge for the optimal operation and maintenance of electrical grid. This paper addresses the challenges and opportunities to improve transmission and distribution systems asset maintenance. This paper also presents the asset replacement alternatives. This paper also presents the cost-benefit analysis of asset management using the information/real time data from the utility company. This paper will serve a guide for doing the asset management to the electrification process, investment and recovery to sustain reliable and efficient power delivery.</p>

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“…In this paper, model from [4] is used to determine OHL thermal rating, while [6] presents a comparison of these models. Previous work has shown that application of DTR can increase thermal loading of transmission lines by 5% -15%, [7], and in that way release network capacity for connecting higher number of generation units and supplying more loads, [8], [9], [10]. Implementation of DTR models for optimal power flow (OPF) analysis of networks with wind-based generation is presented in [11] and [12].…”

Section: Introductionmentioning

confidence: 99%

Handling uncertainties with affine arithmetic and probabilistic OPF for increased utilisation of overhead transmission lines

Fang

Zou

Coletta

et al. 2019

Electric Power Systems Research

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Large-scale integration of variable and unpredictable renewable-based generation systems poses significant challenges to the secure and reliable operation of transmission networks. Application of dynamic thermal rating (DTR) allows for a higher utilisation of transmission lines and effectively avoids high-cost upgrading and/or reinforcing of transmission system infrastructure. In order to efficiently handle ranges of uncertainties introduced by the variations of both wind energy sources and system loads, this paper introduces a novel optimization model, which combines affine arithmetic (AA) and probabilistic optimal power flow (P-OPF) for DTR-based analysis of transmission networks. The proposed method allows for the improved analysis of underlying uncertainties on the supply, transmission and demand sides, which are expressed in the form of probability distributions (e.g. for wind speeds, wind directions, wind power generation and demand variations) and related interval values. The paper presents a combined AA-P-OPF method, which can provide important information to transmission system operators for evaluating the trade-off between security and costs at a planning stage, as well as for selecting optimal controls at operational stage. The AA-P-OPF methodology is illustrated for a day-ahead planning, using a case study of a real transmission network and a medium size test distribution network.. .

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Dynamic Network Rating for Low Carbon Distribution Network Operation—A U.K. Application

Cited by 36 publications

References 10 publications

Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime

Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime

Asset management and maintenance: a smart grid perspective

Handling uncertainties with affine arithmetic and probabilistic OPF for increased utilisation of overhead transmission lines

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