Demand response for real-time congestion management incorporating dynamic thermal overloading cost

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Abstract

Capacity challenges are emerging in the low-voltage (LV) distribution networks due to the rapid proliferation of distributed energy resources (DERs) and increasing electrification of loads. The traditional approach of network reinforcement does not achieve the optimal solution due to the inherent uncertainties associated with the DERs. In this article, a methodology of real-time congestion management of MV/LV transformers is proposed. A detailed thermal model of the transformer is used in order to obtain the costs incurred by overloading. An agent-based scalable architecture is adopted to combine distributed with computational intelligence for the optimum procurement of flexibility. The efficiency of the proposed mechanism is investigated through network simulations for a representative Dutch LV network. Simulation results indicate that the methods can effectively alleviate network congestions, while maintaining the desired comfort levels of the prosumers.
Original languageEnglish
Pages (from-to)65–74
Number of pages10
JournalSustainable Energy, Grids and Networks
Volume10
DOIs
Publication statusPublished - 18 Mar 2017

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Energy resources
Electric potential
Costs
Electric power distribution
Artificial intelligence
Reinforcement
Hot Temperature
Uncertainty

Keywords

  • congestion management
  • Active distribution networks
  • Thermal transformer model
  • Demand response

Cite this

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title = "Demand response for real-time congestion management incorporating dynamic thermal overloading cost",
abstract = "Capacity challenges are emerging in the low-voltage (LV) distribution networks due to the rapid proliferation of distributed energy resources (DERs) and increasing electrification of loads. The traditional approach of network reinforcement does not achieve the optimal solution due to the inherent uncertainties associated with the DERs. In this article, a methodology of real-time congestion management of MV/LV transformers is proposed. A detailed thermal model of the transformer is used in order to obtain the costs incurred by overloading. An agent-based scalable architecture is adopted to combine distributed with computational intelligence for the optimum procurement of flexibility. The efficiency of the proposed mechanism is investigated through network simulations for a representative Dutch LV network. Simulation results indicate that the methods can effectively alleviate network congestions, while maintaining the desired comfort levels of the prosumers.",
keywords = "congestion management, Active distribution networks, Thermal transformer model, Demand response",
author = "A.N.M.M. Haque and H.P. Nguyen and F.W. Bliek and J.G. Slootweg",
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Demand response for real-time congestion management incorporating dynamic thermal overloading cost. / Haque, A.N.M.M.; Nguyen, H.P.; Bliek, F.W.; Slootweg, J.G.

In: Sustainable Energy, Grids and Networks, Vol. 10, 18.03.2017, p. 65–74.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Demand response for real-time congestion management incorporating dynamic thermal overloading cost

AU - Haque, A.N.M.M.

AU - Nguyen, H.P.

AU - Bliek, F.W.

AU - Slootweg, J.G.

PY - 2017/3/18

Y1 - 2017/3/18

N2 - Capacity challenges are emerging in the low-voltage (LV) distribution networks due to the rapid proliferation of distributed energy resources (DERs) and increasing electrification of loads. The traditional approach of network reinforcement does not achieve the optimal solution due to the inherent uncertainties associated with the DERs. In this article, a methodology of real-time congestion management of MV/LV transformers is proposed. A detailed thermal model of the transformer is used in order to obtain the costs incurred by overloading. An agent-based scalable architecture is adopted to combine distributed with computational intelligence for the optimum procurement of flexibility. The efficiency of the proposed mechanism is investigated through network simulations for a representative Dutch LV network. Simulation results indicate that the methods can effectively alleviate network congestions, while maintaining the desired comfort levels of the prosumers.

AB - Capacity challenges are emerging in the low-voltage (LV) distribution networks due to the rapid proliferation of distributed energy resources (DERs) and increasing electrification of loads. The traditional approach of network reinforcement does not achieve the optimal solution due to the inherent uncertainties associated with the DERs. In this article, a methodology of real-time congestion management of MV/LV transformers is proposed. A detailed thermal model of the transformer is used in order to obtain the costs incurred by overloading. An agent-based scalable architecture is adopted to combine distributed with computational intelligence for the optimum procurement of flexibility. The efficiency of the proposed mechanism is investigated through network simulations for a representative Dutch LV network. Simulation results indicate that the methods can effectively alleviate network congestions, while maintaining the desired comfort levels of the prosumers.

KW - congestion management

KW - Active distribution networks

KW - Thermal transformer model

KW - Demand response

U2 - 10.1016/j.segan.2017.03.002

DO - 10.1016/j.segan.2017.03.002

M3 - Article

VL - 10

SP - 65

EP - 74

JO - Sustainable Energy, Grids and Networks

JF - Sustainable Energy, Grids and Networks

SN - 2352-4677

ER -