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Open image in new window Fig. 6 Total cost against different values of α with different total load curtailment thresholds Open image in new window Fig. 7 Total cost against different total installed wind power capacity with different values of α.
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This load curtailment threshold is common in industrial practice.
We set the upper bound of load curtailment threshold as a percentage of the total annual energy demand, i.e. r max = 0.1%.
In our probabilistic approach, the loss of load item in (1) is replaced by a percentage of having load curtailment over the threshold, as given in (9) and (10).
We model the optimal protection decision problem as a mixed integer linear programming (MILP) problem to minimize the investment costs such that the risk of the load curtailment exceeding a certain threshold value, after an attack to the power grid, be less than the risk tolerance value, defined by power system operators.
In order to consider uncertainties in TEP, we propose a probabilistic formulation to reflect the level of load curtailment bounded by a threshold, r max.
As mentioned in Section 2.1, the probability α is defined as the lower bound for each planning scheme the possibility of having load curtailment below the required threshold, and α ∊ [0, 1].
Because the 97% probability exceeds the 95% threshold for stopping, stochastic curtailment halts the questionnaire in favor of an immediate "screened in" result.
Other details of the curtailment.
I aspire to total curtailment.
California curtailment hit a record high this spring.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com