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The value of stochastic optimization in reducing the cost of day-ahead wind forecast uncertainty

University of British Columbia

Wind power is gaining popularity around the globe mainly because of its advantages such as its renewability and its low environmental impact. However, wind power has some operational disadvantages. Wind power is uncertain, variable, and non-dispatchable. These three properties add cost on wind power producers and electric utilities. In this thesis, stochastic optimization (SO) is applied to minimize the cost of day-ahead (DA) wind forecast uncertainty. SO requires the statistical properties of an uncertain parameter to be accurately represented. In this thesis, scenarios were used to represent the wind forecast error (WFE). To generate WFE scenarios that properly characterize the wind forecast uncertainty, first the statistical properties of the DA WFE was investigated. Then two competing scenario generation algorithms, one based on Markov chain (MC) and the other based on autoregressive moving average (ARMA), were proposed and tested. Both models were successful in generating representative WFE scenarios, but the ARMA-based model was found to be better at recreating the statistical properties of the WFE. These two algorithms required the generation of numerous scenario to confidently capture the WFE probability distribution. Since using all the generated scenarios in a SO problem was infeasible, a scenario reduction algorithm based on probability distance was applied to reduce the number of scenarios while preserving the information they contain. The scenarios obtained after the reduction process were used as inputs into a two-state linear SO model. The results of the optimization showed that, under normal operating conditions, SO can reduce the cost of DA wind forecast uncertainty by up to 70%. The SO models were also found to be better at avoiding load shedding and wind power curtailment. When comparing the two scenario generation algorithms with respect to cost savings, the MC-based model resulted in higher cost savings because the scenarios it generated were better at capturing extreme WFEs, which led to less load shedding events. Sensitivity analysis conducted by varying input assumptions showed that the savings from SO vary considerably based on the modeling assumptions and that care should be taken when designing a cost savings evaluation strategy.

Text

2018-07-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66571

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/