Current efforts in weather and system resilience predictive models provide forecasts of adverse weather by locale and expected electricity infrastructure damages and outage durations. Economic literature generates likely economic costs by duration but is not industry specific by locale. There is a need to tie databases on industry locales to above vulnerable locales and supplement economic estimates with industry specific cost impacts. Relevant questions are:
Therefore, it is critical that Eversource be able to make the economic argument that the investments in resiliency will pay off in ways that translate into a more competitive economy and defend those choices–as they imply rate increases at least in the short-run–to PURA.
The Connecticut Center for Economic Analysis (CCEA) uses the REMI model, which is a dynamic forecasting and policy analysis tool (referred to as an econometric model or a computable general equilibrium model). The model forecasts the future of a regional economy, and it predicts the effects on that same economy when the user implements a change.
The data REMI can draw is the OPM-driven system projections on disruption (as well as, if desired), pole breakage, expanded renewables, etc. with detailed county-level economic data to bridge the technical resiliency to recommendations for interventions.
To that end, work on System Resilience Modeling will continue to update and augment fragility curves by incorporating correlation of parameters, such as soil, weather, wind, pole conditions, and vegetation status. This will allow the development of multivariable fragility curve (i.e. response surface — fragility surface). Statistics of scenario-based extreme parameters will be used for outage predictions and time-series of weather events and outages will be predicted by the OPM team and will inform system model predictions as time-series of power outages under different interventions or conditions scenarios.
Comparisons of different intervention techniques will inform economic analysis (performed over 5 year periods or more). This analysis will be based on counterfactual modeling vs. pre-post analysis. Results generated by counterfactual scenarios will be compared against the observed reality (or paired factual model). The relevant metrics for comparison will be power outages or economic indicators without ETT or other interventions.
Analysis will provide a) the cost of savings for the utility company, and b) how this might affect long-term state and regional economy (societal effects, loss of business and loss of revenue).
Date for outages from 2005 to present will allow the analysis of several 5-year window scenarios including the reference (2005-2009) that had no trimming. For the economic analysis outage duration will be provided by the OPM team including shorter than 5 minute outages (likely a noisy dataset). Eversource data on customer type for outages will also be needed.
This work will consolidate current research on system performance modeling that has developed methods for combining weather forecasts with electric grid components via fragility curves and link to short- and long-term economic analysis. Deliverables from the new integrated project will include: