Kyle O’Connor is the CEO of kWantera, a company that uses advanced predictive analytics and machine learning to identify the optimal price, time and location at which to buy, sell or consume power in deregulated markets across the world. Kyle runs kWantera as its CEO, managing the strategic direction and growth in addition to its day-to-day operations, employees, and services. He joined kWantera as a former employee of GE, where he most recently led the company’s global sales force effectiveness, data science & analytics, commercial operations, and training/development efforts for a 2,000-person sales organization.
Historically, companies held monopolies in the electric generation, transmission and distribution supply chain. This created consistency and certainty around energy prices, but the lack of competition left consumers and generators with minimal flexibility in how electricity was bought or sold.
That’s beginning to change.
To understand how energy markets are evolving because of deregulation, a great parallel exists in the 1980’s telecommunications industry. At that time, consumers had no choice in their phone company, and they paid regulated usage rates. While customers could reduce the number of calls they placed, they had little control over the total cost of their phone service.
Telecom deregulation allowed consumers to choose their providers, control their expenses and select flexible usage and price structures.
Electricity deregulation now allows power consumers, intermediaries and generators the same opportunity.
Because electricity can’t be reliably stored en masse, it has to be consumed as it is produced. This means supply and demand must be continuously balanced. That’s is no easy task, and power prices are therefore subject to considerable fluctuations caused by the weather, demand at the time, fuel costs and a host of other factors.
Thanks to big data, machine learning and advanced algorithms, technology allows us to quickly analyze all of these factors and identify the optimal price, time and location to buy and sell energy in these deregulated markets.
So how does this help generators increase revenue on their energy sales?
Through dynamic energy scheduling, asset owners can place their power into the optimal market at the optimal time, avoiding price volatility or capturing price volatility according to their preferences.
Now, while the idea of a dynamic pricing can cause frayed nerves, this is where technology comes in. Imagine if you were able to accurately predict the price of energy – and more confidently participate economically without any adjustments to your physical operations.
This challenge is meaningful for intermittent resource generators such as wind or solar, given their limited control over output. That resource uncertainty generally means that operators refrain from scheduling dynamically in the markets, which causes them to leave revenue opportunity on the table.
With an optimized and forward-looking understanding of power prices, generators can now make smarter, more well-informed decisions about how to sell their power.