Scenario generation quality assessment for two-stage stochastic programs

Sari, Didem

doi:10.31274/etd-180810-5034

Cited by 2 publications

(1 citation statement)

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“…Generally, the calculation time and the quality of the solution depend on the number of modelled scenarios. For any specific scenario generation method, modelling a larger number of scenarios will result in higher quality solutions, but it will cost more in terms of calculation time and required hardware [19]. Hence, the scenario set should be selected in a way that the uncertain future is well represented within a small number of scenarios.…”

Section: Introductionmentioning

confidence: 99%

Deterministic proxies for stochastic unit commitment during hurricanes

Mohammadi

Jafarishiadeh

Xue

et al. 2020

IET Generation Trans & Dist

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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.

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Section: Introductionmentioning

confidence: 99%