Power Reliability Cyber Sensing

Problem 

As businesses, homeowners, and the global economy becomes more electrified in order to mitigate global warming, the importance of understanding, quantifying, and monitoring power system reliability and resilience grows. It is one thing to lose power on a winter night when you have a gas furnace and a gasoline car, but it is another level of concern when your home heating and transportation all rely on electricity. Additionally, as the effects of climate change take hold, we are seeing more frequent and extended power outages in specific regions of the U.S. and the world. 

For 100 years, we have gotten away with stringing power lines along relatively low cost telephone polls for most of the developed and developing world, only paying the extra expense of burying or hardening lines where absolutely necessary. Now utilities around the world are considering spending tens of billions of dollars to harden power lines and install microgrid systems capable of temporary islanding. Some of the largest factors affecting the rate of return electric utilities are allowed to earn are the reliability indicators: SAIFI (average number of interruptions that a customer would experience in a year), SAIDI (average outage duration for each customer served), CAIDI (average restoration time), and MAIFI (average number of momentary interruptions that a customer would experience). However, many of these indexes are self-reported, lacking data, or lacking in spatial granularity (e.g. power interruptions can disproportionality affect certain demographics or regions but be averaged across the whole utility region). It can be hard for regulators to verify these performance metrics, and it can be hard for utilities that lack adequate smart meter infrastructure to identifying the most critical areas for investment. 

Then there is the customer. Corporations looking to site a new manufacturing facility, data center, or other critical component of their supply chain have little visibility into power reliability across utility regions, states, or countries. Further, they have no means today of predicting how that reliability will change as hurricanes, severe storms, flooding, heat waves, polar vortexes, vegetation changes, and other global warming effects worsen. On a smaller scale, prospective or existing homeowners are making decisions about buying backup generators, batteries, and plugin vehicles that can power their homes. Power reliability is at the foundation of a durable economy, and understanding it is only becoming more critical around the world.

Solution

Build a system of software scripts that ping the internet-connected devices in buildings that are highly correlated to power availability (e.g. cable modem boxes, WiFi routers). Increase the frequency of pings during weather events that are likely to cause power outages to improve temporal granularity. Map the IP addresses to geospatial coordinates to increase geospatial granularity. Add additional data sets, such as weather, utility outage reporting, demographics, etc., to draw correlations between outage durations/frequency and potential causes. Project future outage occurrences based on building location. Use additional IP address meta-data to draw insights on demographics, backup power installations, IoT devices type, building type, occupancy, etc.

Business Model

The monetization of a high granularity data set of power outages can potentially be broken into three broad non-mutually exclusive categories: 1) Load-side Services, 2) Supply-side Services, and 3) General Data Services.

Load-side Services:

Businesses, particularly manufacturers, looking to buy or build a new facility are inherently concerned about the power reliability of a potential site as it directly impacts operational performance and free cash flow. This is particularly true for facilities that are sensitive to interruptions (e.g. data centers) and locations that are prone to power interruption (e.g. India). Further, better understanding future power reliability informs investments in backup generation, batteries, electric vehicle fleets, and microgrids for existing sites as well as new ones. This data service could be offered directly to real estate managers for a small fee per assessment and/or used as a lead generation tool for vendors of backup systems (e.g. General, Tesla, Ford, Schneider Electric) 

Homeowners have similar concerns (on a smaller scale) and could also be offered a direct service, but they are more likely to benefit from an API integration into Zillow or marketing resulting from a lead generation tool.

Supply-side Services:

Many smaller U.S. utilities (e.g. municipalities and co-ops) and international utilities that lack smart meter infrastructure would likely pay for a service that provides building-level granularity on outage detection, analytics on likely causes of future outages, and demographics on which populations or building types are impacted most by outages. Further, their state utility regulators may also be interested in a 3rd party service that verifies and Benchmarks SAIFI, CAIDI, and SAIDI scores. This greater visibility can inform rate case approval, capital planning decisions, and operations. Emergency responders would also be interested if the IP cyber sensing can also detect presence of individuals in real time.

General Data Services:

As alluded to earlier, this service could be offered as an API (to integrate with Zillow, Redfin, or other geospatial planning tools) or data subscription (to financial institutions, utilities, or regulators). The most logical monetization and productization path depends on how each user group chooses to engage in the data. Are customers willing to pay per site or do they want to scan all sites simultaneously? Do companies want to build an API into their own software stack or engage with a separate UI? How might financial traders use power interruption to predict earnings or commodities’ performance?