Electric power, water, natural gas and other utilities are served to consumers via functional sources such as electric power substations, pumps and pipes. Understanding the impact of service outages is vital to decision making in response and recovery efforts. Often, data pertaining to the source-sink relationships between service points and consumers is sensitive or proprietary, and is, therefore, unavailable to external entities. As a result, during emergencies, decision makers often rely on estimates of service areas produced by various methods. This paper, which focuses on electric power, assesses the accuracy of four methods for estimating power substation service areas, namely the standard and weighted versions of Thiessen polygon and cellular automata approaches. Substation locations and their power outputs are used as inputs to the service area calculation methods. Reference data is used to evaluate the accuracy in approximating a power distribution network in a mid-sized U.S. city. Service area estimation methods are surveyed and their performance is evaluated empirically. The results indicate that the performance of the approaches depends on the type of analysis employed. When the desired analysis includes aggregate economic or population predictions, the weighted version of the cellular automata approach has the best performance. However, when the desired analysis involves facility-specific predictions, the weighted Thiessen polygon approach tends to perform the best.