Data-driven power generation design and operation under demand uncertainty

Mohammed Alkatheri, Muhammad Rizwan, Falah Alhameli, Ali ElKamel, Ali Almansoori, Peter Douglas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


A Data-driven stochastic optimization framework that leverages big data in design and operation of power generation units is proposed. A k-means clustering algorithm is adopted to generate uncertainty scenarios for the stochastic optimization framework. In order to do this, the power generating design and operation problem is formulated as a two-stage stochastic programming model. The first stage variables are associated with design decisions, whereas the second stage variables are associated with unit commitment operation (i.e. scheduling). The historical demand data was first collected and reprocessed. After that, the processed electrical demand (uncertain parameter) is processed and recognized using unsupervised machine learning. K-means clustering algorithm is used to produce electrical demand scenario profiles. These scenarios are used as inputs to the stochastic model. The proposed model is formulated as a mixed integer linear programming (MILP) and solved using GAMS. The stochastic data driven method enjoys the following features: it is based on information derived from real data without explicitly knowing the data distribution and it applies the recent advances of data analysis tools (e.g. machine learning) to generate a reduced size data set (i.e. clusters) integrated into mathematical model (i.e. design and planning model) that leads to a computationally tractable problem.

Original languageBritish English
Title of host publication4th North American IEOM Conference. IEOM 2019
Number of pages11
StatePublished - 2019
Event4th North American IEOM Conference. IEOM 2019 - Toronto, Canada
Duration: 23 Oct 201925 Oct 2019

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management
ISSN (Electronic)2169-8767


Conference4th North American IEOM Conference. IEOM 2019


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