In this thesis we study the method of approximation of the sample mean in the unit effort problem with stochastic demand. Using several instances with different standard deviation values and number of generators, we tested the performance of this sampling method when the amount of scenarios to be evaluated is too large for CPLEX to handle easily. These tests have been implemented in Python with the Gurobi solver. The results showed a significant decrease in the time needed to find an acceptable solution to the problem. This thesis provides computational data on the performance of the sample mean approximation.
In questa tesi si studia il metodo di Sample Average Approximation nel Unit Commitment Problem con domanda stocastica. Utilizzando diverse istanze con diversi valori di deviazione standard e numero di generatori, abbiamo testato le prestazioni di questo metodo di campionamento quando la quantità di scenari da valutare è troppo grande per essere gestita facilmente da CPLEX. Questi test sono stati implementati in Python con il risolutore Gurobi. I risultati hanno mostrato una significativa diminuzione del tempo necessario per trovare una soluzione accettabile al problema. Questa tesi fornisce dati computazionali sulle prestazioni dell'approssimazione della media campionaria.
Sample Average Approximation for the Unit Commitment Problem with Stochastic Demand
Ramos Lara, Tomas
2021/2022
Abstract
In this thesis we study the method of approximation of the sample mean in the unit effort problem with stochastic demand. Using several instances with different standard deviation values and number of generators, we tested the performance of this sampling method when the amount of scenarios to be evaluated is too large for CPLEX to handle easily. These tests have been implemented in Python with the Gurobi solver. The results showed a significant decrease in the time needed to find an acceptable solution to the problem. This thesis provides computational data on the performance of the sample mean approximation.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/203053