Big Data Analytics Video: Utilities Smart Grid Demo – Starview Enterprise Platform
The anticipated proliferation of electric cars is expected to create a stress fracture on existing electric grids. In this video, Starview helps a utilities company simulate demand scenarios and devise automated strategies for distributing the electric load across the grid during peak demand times – for example in the evenings when electric vehicle owners return home from work and plug in their vehicles to recharge – in order to avoid system overloads that could result in neighborhood blackouts.
Starview’s Operational Intelligence platform introduces a revolutionary new approach to solving the most complex problems facing by today’s data-drenched enterprises. As organizations continue to invest in information-generating and intelligent infrastructure, the need for real-time analysis of large and often rapid data alongside accompanying actions triggered in the face of present-moment conditions is becoming more apparent.
It is within this landscape where complex decisions based on real time data can be so difficult that the best next action will produce a significant impact on operational viability and efficiency. This is where Starview’s platform succeeds in revolutionizing the way that businesses are viewing Big Data as an opportunity rather than a problem.
In this demonstration, we’ll show how Starview’s deployment within a Smart Grid simulates the resolution of a real world utilities business issue. The anticipated proliferation of electric vehicles within metropolitan areas is expected to create a stress factor of sorts for utilities companies. Some existing infrastructure on the low-voltage distribution grid is not equipped to handle the expected increase in energy usage associated with charging these vehicles and the risk of reoccurring and systemic power outages is a very real threat during peak hours when most vehicle owners are arriving home after the work day and plugging in their cars.
Electric vehicles are expected to grow in popularity, which means that the added strain on infrastructure will only increase as time passes. The solution? Either plan for the worst case scenario and invest additional millions to upgrade existing substation transformers, but which ones? Or find a smarter solution to the problem. Starview offers the latter.
What you’re looking at in this simulation is a custom management interface developed specifically for this use case. In the top left System Events window each of the households with a smart meter is represented. The pins on the map correspond with the households that have an electric vehicle – each color representing a different charge state.
The Load Profile graph at the bottom of the dashboard shows how electrical load is distributed across a 24-hour period condensed into one minute. Let’s drill down to a specific neighborhood and examine a scenario where ten percent of households own an electric vehicle.
The electrical substation responsible for powering this neighborhood has a maximum load as represented by the thicker red line in the Load Profile graph. If the total load on the transformer exceeds this critical threshold caused by the addition of electric vehicles, the neighborhood may suffer from power outages.
The goal then is to distribute the load efficiently so that the critical threshold level is not breached. The difficulty in achieving this objective revolves around the fact that in any given hour the load is dynamically changing around real-world conditions and distribution decisions need to be analyzed in real time to determine the “best next action.”
Existing conditions can not satisfy the need for a rapid analysis of present-moment data coupled with predictive modeling based on historical trends that triggers “best next actions” in real time. As this simulation shows, load distribution begins to approach critical threshold levels at the five o’clock hour as electric vehicle owners arrive home from work and plug in their vehicles to recharge.
In this scenario, vehicle owners have the option of selecting from economy and premium charging. With economy charging, tariffs are lower and the vehicle owners are consenting to the utility company to control when to charge their vehicle. Premium charging removes the flexibility, but vehicle owners pay a higher rate for their consumption.
Without Starview, even though the utility company may be able to influence the vehicle owners’ charging behavior by allowing them to choose between premium and economy tariffs, there are no mechanisms in place to analyze real-time load distribution and trigger reallocation based on real-world variables. Without this ability, households in the neighborhood, regardless of whether they owned an electric vehicle, would endure power outages at several points throughout a normal evening.
Using Starview’s platform, pre-determined business rules will trigger as load approaches the threshold line. By combining historical data with real-time consumption data, Starview is able to proactively reallocate economy charging based on actual circumstances – intelligently re-distributing economy load to lower burden hours which will prevent power outages while still delivering premium charge when requested so as to maximize company revenues.
Notice that the System Events window also shows the actions being taken and sent back to the smart meters. For instance, observe the real-time suspension of economy charging to offset load burden as critical levels are approached. And then, as load begins to decrease, actions are triggered to resume the suspended economy charging. This is all dynamic based on actual conditions that are continually in flux and occurring in real time based on the pre-determined business logic.
This demonstration provides one example of how the Starview Enterprise Platform can be intelligently deployed to solve real-world business problems. Have a problem that we might be able to help resolve? Interested in learning more? Download our white paper or reach out to us today for more information.