Estimation of constraint parameters in optimal power flow data sets

Large-scale electric power system analysis depends upon representation of vast numbers of components whose individual models must be populated with parameters. The challenge of populating such component models is particularly apparent in optimal power flow applications, in which incorrect parameters and/or constraint limits can yield overall system representations with either unrealistically large feasible regions or an empty feasible set. Unfortunately, many data sets, particularly those of publicly available test cases, were originally developed to illustrate simpler “power flow only” applications, and may contain unrealistic values or wholly omit important constraint limits. This paper describes engineering-based approaches to obtain credible estimates for parameters and limits associated with line-flow constraints and generator capability curves, as may be employed in a number of steady state analyses such as the optimal power flow. These can substitute for missing or unrealistic data in test systems for which more fully detailed, “real-world” component specifications and limits are not available, and thereby make such test systems more valuable as research tools.