Transactions on Machine Intelligence

Transactions on Machine Intelligence

Analysis of the Impact of Electricity Price Variations on Self-Healing Improvement in Smart Distribution Networks Considering Consumer Behavior

Document Type : Original Article

Authors
S. Karimi Talkhouncheh S. Karimi Talkhouncheh 1
A. Fereidunian 2
A. Moshari 3
1 MSc Student, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
2 Assistant Professor, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 Assistant Professor, Department of Power System Planning and Operation, Niroo Research Institute, Tehran, Iran
10.47176/TMI.2019.108
Abstract
Self-healing is a capability of smart electricity distribution networks that enables the automatic restoration of the network in the event of a persistent fault. To restore the power supply to loads downstream of the fault location, adjacent feeders can be utilized. However, the use of backup feeders is subject to network technical constraints such as bus voltage levels and permissible line currents. One method to prevent the violation of network constraints during load restoration via backup feeders is the implementation of demand response programs. This paper examines the impact of Critical Peak Pricing (CPP), a price-based program, on the self-healing of smart distribution networks. Two models, exponential and linear, have been considered for modeling the CPP program. Additionally, the impact of different price surges on self-healing improvement has been analyzed. To make the results more realistic, various consumer participation rates in demand response programs have been taken into account. The proposed model has been evaluated using bus number 4 of the Roy-Billinton test system.
Keywords
Demand Response
Self-Healing
Self-Healing Improvement
Smart Grid
Critical Peak Pricing Program
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Transactions on Machine Intelligence
Volume 2, Issue 2
Spring 2019
Pages 108-119

  • Receive Date 03 February 2019
  • Revise Date 07 March 2019
  • Accept Date 28 May 2019