TY - GEN

T1 - Data-driven power generation design and operation under demand uncertainty

AU - Alkatheri, Mohammed

AU - Rizwan, Muhammad

AU - Alhameli, Falah

AU - ElKamel, Ali

AU - Almansoori, Ali

AU - Douglas, Peter

N1 - Publisher Copyright:
© 2019, IEOM Society International.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85079291218&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85079291218

SN - 9781532359507

T3 - Proceedings of the International Conference on Industrial Engineering and Operations Management

SP - 345

EP - 355

BT - 4th North American IEOM Conference. IEOM 2019

T2 - 4th North American IEOM Conference. IEOM 2019

Y2 - 23 October 2019 through 25 October 2019

ER -