In the wake of hurricane Sandy and mounting concern about climate-related infrastructure vulnerability, many state and federal policy makers are focusing on smart grid and microgrid strategies to improve grid resiliency against future severe weather events, cyber-attack, etc. Mr. Lecar will present recent work at GE Energy Consulting focused on the economics of reliability-oriented Distribution Automation (DA) and the connections between ordinary distribution reliability (as measured by the conventional industry indices, SAIDI, SAIFI, CAIDI, MAIFI, etc.) and major storm resiliency (major events are explicitly excluded from the indices). The central focus of this work is on identifying the circuit-level characteristics that present the most attractive opportunities for automation investment.
A significant challenge in modeling the business case for DA is that no two utility systems are alike. Each contains a unique mix of different circuit types with different customers that may see different benefits from automation. For example, rural circuits with many line miles between customers can suffer poor baseline reliability, as measured by the indices. Reliability can be improved greatly by adding automated sensing and switching to more rapidly detect and isolate faults and restore service to customers more quickly. Urban circuits, where baseline reliability is generally better, have a higher density of customers per line mile and will therefore experience more customer outage minutes per event, contributing disproportionately to improvement in the indices when they are upgraded even modestly. Circuits with large commercial and industrial customers that attribute high costs to the damages caused by even short duration outages can be good candidates for automation when these values are appropriately taken into account.
GE Energy Consulting has developed a Distribution Automation Reliability Tool (DART) in order to provide utilities a way to quickly evaluate automation investment on circuits of different types. DART uses the results of distribution simulations that were performed with a set of real circuits as prototypes, to represent a range of circuit geography and customer mix. Simulations were conducted for scenarios of increasing levels of automation, including combinations of both centralized software (OMS, DMS, ADMS) and intelligent field devices. Finally, a configuration engine was built to quickly map DART to a given utility service territory (or subarea) with a “best fit” mix of our prototype circuits. Using DART, utilities can examine the business case for incremental automation starting from a user-specified base case. The results are expressed in terms of improvement in the standard reliability metrics, as well as dollar benefits, using the DOE Interruption Cost Estimation (ICE) calculator (developed by LBNL).
Finally, Mr. Lecar will connect this work with on-going projects in New York and New Jersey that look at best practices for post-Sandy storm hardening, grid resiliency, and the application of microgrids to protect critical public infrastructure.