Deterministic proxies for stochastic unit commitment during hurricanes
| Autor: | Jiayue Xue, Ge Ou, Mostafa Sahraei-Ardakani, Fatemehalsadat Jafarishiadeh, Farshad Mohammadi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
TK1001-1841
021103 operations research Distribution or transmission of electric power Computer science 020209 energy 0211 other engineering and technologies Energy Engineering and Power Technology 02 engineering and technology TK3001-3521 Power system simulation Production of electric energy or power. Powerplants. Central stations Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Econometrics Electrical and Electronic Engineering |
| Zdroj: | IET Generation, Transmission & Distribution, Vol 15, Iss 8, Pp 1357-1370 (2021) |
| ISSN: | 1751-8687 1751-8695 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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