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In Part I of Fig. 3, with the forecasted wind power, the master problem provides the UC and dispatching solution to minimize the operating cost.
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It can be easily seen that different levels of risk imply different dispatching solutions, and ultimately, different transmission loss under the uncertain wind power environment.
In essence, the final dispatch solution is determined mainly by the dispatchers' attitudes toward the trade-off between profit and risk.
The commitment schedule is validated with solar scenario analysis, i.e., Monte-Carlo simulations are conducted to test the proposed dispatch solution.
The proposed fuzzy decision based dispatch solution is implemented fully considering the power generation and consumption relationship, the remaining amount of freshwater the state of charge of storage units, and without the requirement of accurate system models.
The difference of the dispatch solution for each experiment is presented in Table 3.
In this way, the final dispatch solution can obtain the best average value.
The expected transmission loss achieved by a conservative dispatch solution (λ = 0.5) is 2.2045 MW/h whereas the expected fuel cost with the maximum risk is equal to 1.4431 MW/h (λ = 0), corresponding to the aggressive dispatch solution.
Once a dispatch solution is determined, then the mean and variance of the profit function can be calculated.
With this approach, optimal dispatch solution can be obtained adaptively and the operation reliability of power grid would be enhanced.
Simulation studies show that HICOS could effectively reach the global optimal dispatch solution with non-MGC center.
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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