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The Smart Grid Revolution and IT


The biggest transformation in the electrical power grid is underway since Thomas Edison’s invention of the incandescent light and the subsequent electric power distribution. Two global factors are impacting this revolution.

Data Storage Costs in a Free Fall

In addition to the rapid and sustained drop in the price of solar, wind and other renewable energy sources, the early stages of the penetration of Electric Vehicles and the advent of distributed storage, there are two IT factors strongly impacting the smart grid revolution we are currently experiencing around the world.

The first is the freefall in the cost of data storage. With the average price per GB going at $0.019 less than a quarter of what it was only 6 years ago, storage has become a negligible cost in the overall expense report of utilities.

That is not to say that the cost of implementing data-lakes or Big Data platforms are insignificant. It is only that the actual data storage component, the MB, GB, TB or PB of storage has become irrelevant for the enterprise.

Trends in the Price of Computing

In recent years, cloud computing providers have been involved in a “race to zero”. AWS has cut its price more than 44 times in the past 6 years with Microsoft and Google following suite to keep up. The average cost per GB or RAM went from $189 in 2005 to $4.37 in 2015, according to Statistic Brain Research Institute.

Our utility executive clients tell us their utility is producing in one day, the same amount of data that it took a full year to produce only 5 years ago.

Over a 4-5-year time frame, a ca. 365x growth in data generation. No less amazing is that that data explosion is continuing to grow. The entire energy supply chain (this holds true in the water, oil & gas, and transportation industries), is already or will be online with volumes of data being measured, generated, and collected at every step of the increasingly complex and connected way.

Every single node, even before generation, through generation, transmission, distribution, and consumption are all connected to millions of sensors measuring and collecting operational data.

Yet the availability of data is one part of the equation. Data in and of itself has little value.

Data has to be used, manipulated, correlated, compared, calculated, communicated, analyzed, and visualized to create value.

When data is correlated with other relevant data, both structured and unstructured, the data enables predictions. Historical data can be used to predict how grids will behave in similar circumstances, what tasks carry the highest risk of injury for certain employees in certain weather and so on. And the ability to increase predictability in data drives greater efficiency, higher quality services at lower costs and an increase in profits for business. A comprehensive view of enterprise data addresses any manager’s concerns which is the fear of the unknown.

Example: Creating Value out of a Weak Grid

A weak asset or node on a grid that has been failing repeatedly causes a ripple effect on the rest of the grid. Not knowing the cause of the failure creates inefficiencies and increases operational costs.

If we connect the data from the weak asset to other data sources such as the maintenance history of the asset, similar asset performance, its cost, who has been maintaining it, and other parameters that could be relevant to its operation – we can create value.

When we can connect, correlate, analyze, and visualize a multitude of data sources, in real or near real-time, we infuse brain-like cognition into the operation of the grid. Now we can make use of all the data and operational controls we have at our fingertips.

Thanks to technology, utilities can more efficiently predict grid behavior and preempt predictable, and preventable failures through Condition Based Maintenance. They can optimize their grid and avoid over engineering which for years has been used, in lieu, of easily accessible data, to enhance grid resilience and reliance.

Getting the Data to Talk

The examples we encounter at App Orchid are many and unending. What we typically see is that most of the databases across organizations stand-alone. They are not readily and easily communicating with their neighboring silos, which also means the data is not creating value for the business.

We have seen Enterprise Resource Planning (ERP) systems that track the lifecycle of utility assets not communicating with the state estimation systems that track (but do not always store) real-time operational data obtained from SCADA and EMS systems.

When all data is connected, such as, operational IOT data with historical process data, manuals, e-mails, field notes, guidelines and manuals with the experiences and insights of employees (Tribal Knowledge), we provide utilities the ability to become a hyper connected, hyper efficient and thus hyper profitable enterprise.