The objective of this paper is to provide electric utilities with a concept for developing and applying effective decision support metrics via integrated risk-informed asset management (RIAM) programs for power stations and generating companies. RIAM is a process by which analysts review historical performance and develop predictive logic models and data analyses to predict critical decision support figures-of-merit (or metrics) for generating station managers and electric utility company executives. These metrics include, but are not limited to, the following: profitability, net benefit, benefit-to-cost ratio, projected return on investment, projected revenue, projected costs, asset value, safety (catastrophic facility damage frequency and consequences, etc.), power production availability (capacity factor, etc.), efficiency (heat rate), and others. RIAM applies probabilistic safety assessment (PSA) techniques and generates predictions probabilistically so that metrics information can be supplied to managers in terms of probability distributions as well as point estimates. This enables the managers to apply the concept of “confidence levels” in their critical decision-making processes.
Developing a model for improving the productivity and energy production of small-scale power plants using the physical asset management model in a fuzzy environment