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

• Published in: Energies (Basel) Vol. 14; no. 16; p. 4987
• Main Authors: Alvarez, David L., Rodriguez, Diego F., Cardenas, Alben, da Silva, F. Faria, Leth Bak, Claus, García, Rodolfo, Rivera, Sergio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2021
• Subjects: Algorithms; Asset management; Contingency; Decision making; Evaluation; health index; Maintenance costs; Management decisions; Methodology; Monte Carlo simulation; Monte Carlo simulations; Optimization; Preventive maintenance; Reliability analysis; Risk assessment; risk index; Risk management; United Kingdom > UK
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14164987

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.