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Multi-level optimization framework for resilient distribution system expansion planning with distributed energy resources

Zakernezhad, Hamid; Nazar, Mehrdad Setayesh; Shafie-khah, Miadreza; Catalão, João P.S (2021-01-01)

 
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https://doi.org/10.1016/j.energy.2020.118807

Zakernezhad, Hamid
Nazar, Mehrdad Setayesh
Shafie-khah, Miadreza
Catalão, João P.S
Elsevier
01.01.2021
doi:10.1016/j.energy.2020.118807
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020120198845

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©2021 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/
Tiivistelmä
A multi-stage optimization framework is proposed in this paper for the resilient electric distribution system expansion planning problem. The Non-utility Distributed Energy Resources (NDERs) can deliver their electricity to the distribution system in normal and external shock conditions. However, the NDERs bidding strategies in external shock conditions are an important issue and they can withhold their electricity generation in a contingent condition. The distribution system must tolerate the external shocks and determine the optimal contribution scenarios of NDERs in these conditions. The proposed algorithm determines the initial topology and system parameters of the planning horizon, at the first stage of optimization. Then, it explores the bidding strategies of NDERs in the second stage. At the third stage, the procedure calculates different market power indices to determine the optimal price of NDERs contributions in its different operational conditions and contracts with the selected NDERs. The problem has different sources of uncertainty that are modelled in the proposed algorithm. To assess the proposed method, 21-bus and 123-bus test systems are considered and the introduced procedure reduced the aggregated investment and operational costs of systems by about 11.82% and 23.74%, respectively, in comparison with the custom expansion planning exercise.
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