A Novel Framework for the Operational Reliability Evaluation of Integrated Electric Power–Gas Networks
| Autor: | Enrico Zio, Osama Aslam Ansari, Chi Yung Chung |
|---|---|
| Přispěvatelé: | University of Saskatchewan [Saskatoon] (U of S), Centre de recherche sur les Risques et les Crises (CRC), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL) |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science 020209 energy Monte Carlo method Integrated electric power-gas networks Power system reliability 02 engineering and technology Markov model 7. Clean energy Nonlinear programming Power systems Electric power system 0202 electrical engineering electronic engineering information engineering Monte Carlo simulation Reliability (statistics) Pipelines [PHYS]Physics [physics] Load modeling Computational modeling 020206 networking & telecommunications reliability modeling Natural gas Reliability Reliability engineering operational reliability Cross entropy Electric power Importance sampling |
| Zdroj: | IEEE Transactions on Smart Grid IEEE Transactions on Smart Grid, Institute of Electrical and Electronics Engineers, 2021, 12 (5), pp.3901-3913. ⟨10.1109/TSG.2021.3075918⟩ |
| ISSN: | 1949-3061 1949-3053 |
| Popis: | International audience; This paper proposes a new framework for the operational reliability evaluation of integrated electric power-gas networks (IEPGNs). First, a novel approach for modeling the failure modes of natural gas pipelines is presented. This approach utilizes the concept of virtual nodes and employs a gas release rate model to consider the pinhole, hole, and rupture failure modes of pipelines. Thereafter, a four-state Markov model for natural gas-fired generators (NGFGs) with dual-fuel capabilities is proposed. The area risk method is extended to include the proposed reliability models, and the partial reliability indices of the area risk method are evaluated using a non-sequential Monte Carlo simulation (NSMCS). A nonlinear optimization model is also proposed to calculate electric and gas load curtailments for each system state in NSMCS. This model is linearized to obtain a mixed-integer linear programming (MILP) model for reducing the computational burden. The computational performance of NSMCS is further improved by adopting cross entropy (CE)-based importance sampling (IS). Finally, the efficacy of the proposed framework is demonstrated on three test systems. Case studies validate the importance of considering the proposed reliability models of IEPGNs for operational reliability evaluation. The impacts of operational strategies on the operational reliability indices are also demonstrated. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|