How Big Data Wants To Help Businesses Save On Electricity: Limejump

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To Ning Zhang, the co-founder of Limejump, operating a “virtual power plant” is a lot like managing a hedge fund.

“It’s all about hedging, risk management, and power asset optimization,” he says.

Limejump is a “technology-driven utility” based in London that delivers a demand-response solution that it calls a virtual power plant. Demand-response is a type of energy management that changes energy distribution on the demand side – where electricity is consumed, not generated – in response to changes in the price of electricity or when the reliability of the grid system is threatened.

For example, if millions of residents suddenly turn on their T.V. to watch the Oscars, the amount of energy demand will spike. That’s known as a “peak load”, something power producers use to estimate how much electricity they have to generate. Instead of powering up energy generation, demand-response solutions redistribute energy in the grid system to make sure that demand is met without having to pump up supply. This prevents unnecessary electricity generation and potential blackouts in the grid.

“We noticed there’s a lot of flexibility in the market,” says Zhang, referring to the energy market. “For example, if you’re a big corporation, you always have a spare load you [can] control. If you’re a hospital, you always have a backup generator [that] you can turn on. Flexibility in financial terms can be a real options.”

To “alleviate network imbalance and take advantage of market prices,” Limejump’s virtual power plant aggregates flexible demand from an interconnected network of businesses and generators. For example, Limejump worked with Planet Ice & Silver Blades Ice Rinks, a chain of ice rinks in the U.K, to power down its compressors for short periods of time during moments of peak electricity usage in the National Grid. Planet Ice & Silver Blades Ice Rinks halved its energy costs without having to increase temperature of its ice, according to Limejump.

“Three years ago, the biggest challenge for either trading or risk management for hedging [is that] energy cannot be stored,” says Zhang. “[You had to] match supply and demand within a half hour. The price becomes a lot more difficult to manage.”

That’s why data and data analytics are vital to demand-response solutions like Limejump’s. Without an accurate and real-time pricing model for electricity, Limejump’s “virtual power plant” cannot optimize prices for its clients or energy savings in the grid.

“The key goal is billing, so you have to measure it carefully,” says Zhang. “The focus is not only about [data] measurement, but communication. How quickly the data can be communicated and aggregated, [and] how quickly we can use that aggregated data to make a decision on the cloud.”

Not surprisingly, Limejump has invested a lot of research and development (R&D) into this area. According to Zhang, the first phase of Limejump’s R&D focused on capturing real-time data through IoT (internet-of-things) hardware. The second phase is more about fintech (financial tech). In the future, the company wants to create different kinds of demand-response products and enable its clients to do real-time trading based on their energy portfolio.