A probabilistic method to quantify the capacity value of load transfer

Ilias Sarantakos*, David M. Greenwood, Natalia Maria Zografou-Barredo, Vahid Vahidinasab, Phil C. Taylor

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)

Abstract

When a primary substation reaches its capacity limit reinforcement is required, usually via additional circuits. Load transfer constitutes an alternative solution to this problem, as it can provide substantial capacity support at little, or even zero, capital expenditure. This paper provides a probabilistic method which quantifies the capacity value of load transfer using the Effective Load Carrying Capability methodology within a Sequential Monte Carlo Simulation framework. Load transfer is mathematically formulated as a mixed-integer second-order cone programming problem, which can be efficiently solved using commercial solvers. The proposed methodology is applied to a realistically sized distribution network considering three different redundancy levels, namely N-1, N-0.75, and N-0.5. The results show a maximum capacity value of 25% and 37% of the base case demand for manual and remote control load transfer, respectively, for the N-0.5 case with 4.21 MWh/year. The results also show that the capacity value of load transfer is significantly higher if the initial level of reliability of the network is lower, indicating that the network operator is prepared to accept a higher level of risk.

Original languageEnglish
Article number106238
JournalInternational Journal of Electrical Power and Energy Systems
Volume123
DOIs
Publication statusPublished - Dec 2020

Bibliographical note

Funding Information:
The first author would like to thank Mrs Antigone Mondelou for her valuable advice and support. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK (award reference 1645763), Siemens PLC , UK (reference 14220176), Northern Powergrid, UK as part of the Pragmatic Security Assessment NIA Project (NIA_NPG_029), and the EPSRC Supergen Energy Networks Hub (EP/S00078X/1). The authors would also like to thank seven anonymous reviewers for their valuable time to examine this paper.

Publisher Copyright:
© 2020 The Authors

Keywords

  • Capacity value
  • Distribution network
  • Load transfer
  • Probabilistic model
  • Security of supply

Fingerprint

Dive into the research topics of 'A probabilistic method to quantify the capacity value of load transfer'. Together they form a unique fingerprint.

Cite this