Open AccessDeterministic proxies for stochastic unit commitment during hurricanes
Author(s) -
Mohammadi Farshad,
Jafarishiadeh Fatemehalsadat,
Xue Jiayue,
SahraeiArdakani Mostafa,
Ou Ge
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12107
Subject(s) - power system simulation , spinning , probabilistic logic , reliability engineering , computer science , monte carlo method , reliability (semiconductor) , proxy (statistics) , grid , stochastic simulation , stochastic modelling , mathematical optimization , operations research , electric power system , power (physics) , engineering , mathematics , statistics , quantum mechanics , physics , mechanical engineering , geometry , artificial intelligence , machine learning
Severe weather threatens the reliability of the power supply by damaging the network. In the case of hurricanes, tens of elements may fail, which would lead to power outages. Under such circumstances, preventive unit commitment methods can model the probabilistic failure forecasts and minimise the power outages. Preventive stochastic unit commitment is an effective method to consider failure forecasts to reduce the power outage. Although stochastic unit commitment produces high‐quality solutions, it is computationally burdensome. Thus, this paper evaluates proxy deterministic methods with lighter computational compared with stochastic unit commitment on both the solution time and quality. Adjusted spinning reserve requirements, engineering judgment‐based rules, and robust preventive operation are among the evaluated methods. Numerical results are obtained for the synthetic grid on the footprint of Texas with 2000 buses. The results suggest that while some proxy methods, such as standard spinning‐reserve and adjusted spinning‐reserve with 6% to 30% of the spinning capacity, may not be as effective as the stochastic method, others, such as robust optimisation, deliver the majority of the stochastic benefits with substantially less (85%) computational time. Monte Carlo simulations are used to evaluate the quality of solutions in reducing the expected unserved load and over‐generation.