Optimal Decision Making in Electrical Systems Using an Asset Risk Management Framework

David L. Alvarez, Diego F. Rodriguez, Alben Cardenas, F. Faria da Silva, Claus Leth Bak, Rodolfo García and Sergio Rivera
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David L. Alvarez: Department of Electric and Electronic Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Diego F. Rodriguez: Department of Electric and Electronic Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Alben Cardenas: Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, QC 3592, Canada
F. Faria da Silva: Department of Energy Technology, Aalborg University, 9100 Aalborg, Denmark
Claus Leth Bak: Department of Energy Technology, Aalborg University, 9100 Aalborg, Denmark
Rodolfo García: Deparment of Operation & Maintenance, Enel-Codensa, Bogotá 11010, Colombia
Sergio Rivera: Department of Electric and Electronic Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia

Energies, 2021, vol. 14, issue 16, 1-25

Abstract: In this paper, a methodology for optimal decision making for electrical systems is addressed. This methodology seeks to identify and to prioritize the replacement and maintenance of a power asset fleet optimizing the return of investment. It fulfills this objective by considering the risk index, the replacement and maintenance costs, and the company revenue. The risk index is estimated and predicted for each asset using both its condition records and by evaluating the consequence of its failure. The condition is quantified as the probability of failure of the asset, and the consequence is determined by the impact of the asset failure on the whole system. Failure probability is estimated using the health index as scoring of asset condition. The consequence is evaluated considering a failure impact on the objectives of reliability (energy not supplied -ENS), environment, legality, and finance using Monte Carlo simulations for an assumed period of planning. Finally, the methodology was implemented in an open-source library called PywerAPM for assessing optimal decisions, where the proposed mathematical optimization problem is solved. As a benchmark, the power transformer fleet of the New England IEEE 39 Bus System was used. Condition records were provided by a local utility to compute the health index of each transformer. Subsequently, a Monte Carlo contingency simulation was performed to estimate the energy not supplied for a period of analysis of 10 years. As a result, the fleet is ranked according to risk index, and the optimal replacement and maintenance are estimated for the entire fleet.

Keywords: asset management; decision making; health index; Monte Carlo simulations; risk index (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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