Engineering Economics

Economic and financial evaluations have become an increasingly important part of our portfolio of services due in part to deregulation of electric utilities. DEI combines state-of-the-art technical problem-solving skills with rigorous financial modeling, which yields not only technical solutions but also an economic basis for the customer's decision-making process. In some cases, we develop computer algorithms that integrate technical analyses with the associated financial model for which the technical results are inputs. This multi-disciplinary approach is extremely flexible, admitting accurate analysis of a wider array of alternatives than would otherwise be possible.

Recent projects in this area include:

Life cycle management of infrastructure and assets
Statistical modeling and risk analysis
Evaluation of steam generator management options

Life Cycle Management
DEI assisted the Electric Power Research Institute (EPRI) in finalizing the life-cycle management planning process for systems, structures, and components—and in developing demonstration plans for three components at a participating utility.

As part of the project, DEI developed the LcmVALUE software package. This package allows utilities to quickly compare the net-present-value cost of alternative life cycle management approaches such as running to failure, increased inspection or maintenance, and component replacement. We have performed life cycle management studies for many components including:

Nuclear plant turbines and generators
Buried piping systems
Steam generators
Buried electrical cabling
Reactor vessel heads

Statistical Modeling and Risk Analysis
In some applications, life cycle management planning can require more than a deterministic best (or conservative) estimate of future failures in order to accurately evaluate the relative economic attractiveness of different alternatives. Statistical analyses are required for these cases. DEI regularly uses statistical distributions (e.g., Weibull and log-normal) to develop predictions of future degradation rates based on both plant-specific and industry-wide experience.

For many applications, we use Monte Carlo simulation techniques to determine the probability of occurrence of important events (e.g., when a first failure occurs) and to develop probability distributions describing both risk and cost. Managers responsible for making major decisions can best make these decisions in the context of the risks of failures occurring as well as the predicted costs.