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Application of Markov Models for decision making under uncertainty in the electric utility industry
Tettey, Anyama Hensley, Kim Gholston, Sampson
Tettey, Anyama
Hensley, Kim
Gholston, Sampson
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Issue Date
2020-06
Type
Article
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Keywords
Customer services,Public utilities,Markov chains,Decision making,Planning,Optimization,Planning,Electricity
Subjects (LCSH)
Citation
Anyama, T., Kim, H., P.E., & Sampson, G., PhD. (2020). Application of Markov Models for decision making under uncertainty in the electric utility industry. Journal of Management & Engineering Integration, 13(1), 96-103. https://doi.org/10.62704/10057/24749
Abstract
Planning in the Power Systems distribution involves a formal decision-making process of identifying and prioritizing network improvement projects such as the construction of substations, interconnecting feeder links and general upgrade works, with the aim is of providing an efficient service to the customer without damage to the utility's equipment and customer's property or personnel. This task is plagued with uncertainties associated with load growth and demand due to changing weather conditions and other unforeseen developments, which affects the ability to maximize efficiency and utilization. This paper proposes the use of Markov Models as a more effective technique by utility planners and managers for their decision-making efforts under such uncertainties. The authors develop a load flow modeling approach that takes into consideration the stochastic nature of customer demand and uses the distribution network profiles as fitness values to be optimized. A ranking of the criteria of interest based on the decision makers preferences is the result of the optimization algorithm. This provides a formal process for decision making by the management of utility companies.
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Description
Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, November 2022.
Publisher
Association for Industry, Engineering and Management Systems (AIEMS)
Journal
Book Title
Series
Journal of Management & Engineering Integration
v.13 no.1
v.13 no.1
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Archival Collection
PubMed ID
ISSN
1939-7984