Yes / Electricity providers aims to deliver uninterrupted
electrical services to their customers at minimum cost while
providing a satisfactory quality service. Therefore, the power
system reliability is essential in power distribution network
planning, design, and operation. This paper proposes a novel
mathematical model to improve the reliability of reconfigurable
distribution networks via investing and operating tie-lines. While
the failure is being repaired, tie-lines allow the network operator
to transfer loads from failed zones to healthy zones. Constructing
new tie-lines could improve the network’s flexibility, aiming to
reduce the cost of expected energy not supplied (EENS). The
objective function of the proposed method is a trade-off between
the investment cost of tie-lines construction in the planning stage,
the cost of tie-lines operation (e.g., opening/closing), and the cost of
EENS in the operational stage. The model simultaneously
evaluates the best combination of investments and network
configuration for each contingency while considering network
constraints. A multistage mathematical model is developed as
mixed-integer linear programming (MILP) to overcome the
computational complexity and maintain solver traceability for
utility-scale realistic networks. The model can handle the network
reconfiguration (NR) considering N-x contingency analysis in the
operation stage while deciding the investment in tie-lines in the
planning stage. The optimal investment and operation in tie-lines,
according to numerical results, can reduce the cost of the
Distribution System (DS) while responding with contingencies by
51 to 70%.
Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19336 |
Date | 03 February 2023 |
Creators | Marquez, Jorge A., Al-Ja’Afreh, Mohammad A., Mokryani, Geev, Kabir, Sohag, Campean, Felician, Dao, Cuong, Riaz, Sana |
Publisher | IEEE |
Source Sets | Bradford Scholars |
Language | English |
Detected Language | English |
Type | Conference paper, Accepted manuscript |
Rights | © 2022 IEEE. Reproduced in accordance with the publisher's self-archiving policy., Unspecified |
Relation | http://www.icsrs.org/icsrs22.html |
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