Hybrid stochastic/robust optimization model for resilient architecture of distribution networks against extreme weather conditions
Shahbazi, Amid; Aghaei, Jamshid; Pirouzi, Sasan; Shafie-khah, Miadreza; Catalão, João P.S. (2021-03-01)
Shahbazi, Amid
Aghaei, Jamshid
Pirouzi, Sasan
Shafie-khah, Miadreza
Catalão, João P.S.
Elsevier
01.03.2021
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020120198866
https://urn.fi/URN:NBN:fi-fe2020120198866
Kuvaus
vertaisarvioitu
©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/
©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
This paper expresses the planning model of the backup distributed generation (DG) and lines hardening and tie lines in distribution networks according to resilient architecture (RA) strategy under natural disaster conditions such as earthquakes and floods. Indeed, the proposed deterministic problem of resilient distribution system planning considers the minimization of the daily investment, operation and resiliency (repair and load shedding) costs as objective functions subject to constraints of AC power flow equations, system operation limits, planning and operation model of backup DG and hardening and tie lines, as well as network reconfiguration formulation. The problem formulation is based on a mixed integer non-linear programming (MINLP) model, which is converted to a mixed integer linear programming (MILP) model on the basis of Benders decomposition (BD) approach using linearization approaches to achieve the optimal solution with the lower computational efforts and error. Besides, a hybrid stochastic/robust optimization (HSRO) based on the bounded uncertainty-based robust optimization (BURO) and a scenario-based stochastic optimization is used to model the uncertainties of load, energy price and availability of the network equipment under the extreme weather conditions. Finally, the proposed RA strategy is applied on 33-bus and 119-bus distribution test systems to investigate its capabilities in different case studies.
Kokoelmat
- Artikkelit [2821]