Transactive Energy and Blockchain: Can Technology Tackle Our Big Challenges?

July 9, 2018
The business world is smitten with blockchain, with energy no exception. Blockchain can solve some challenges posed by distributed energy and transactive energy pricing, but it is just a start. Kay Aikin, CEO of Introspective Systems explains.

The business world is smitten with blockchain, with energy no exception. Blockchain can solve some challenges posed by distributed energy and transactive energy pricing, but it is just a start. Kay Aikin, CEO of Introspective Systems explains.

It’s easy to understand why blockchain is hot — this technology offers a new approach to transaction validation, data sharing, and information security, all of which are challenges many industries face, including energy. Blockchain also has the potential to create new problems, particularly when paired with its digital sister, cryptocurrency. Can blockchain improve transactive energy and distributed energy processes? We believe it can help – as long as we focus on the right problems to solve.

Blockchain is a digital ledger of peer-to-peer transactions that is both public and decentralized. Data blocks are dispersed throughout the network, which makes it hard to change or cheat a transaction entry or perpetrate any other security shenanigans. While often paired with a cryptocurrency such as bitcoin, blockchain itself is not a currency application – it just happens to be very good at verifying and reconciling transactions such as buying and selling currency, or for buying and selling energy.

Once you realize the security and veracity aspects of blockchain, it’s easy to see its application in any number of transaction-oriented markets – it does a lot of what we want from a database with added security and other perks. For energy, blockchain has the potential to be a game-changer for managing energy pricing.  According to research from GTM Research, there are 40 active energy projects using blockchain, with more than $300 million invested in the technology for energy within the last 18 months.

Producer-consumers and distributed energy generation have complicated the traditional straight-unit energy pricing. We have done a great job of opening up new power generation sources and pushing them over expanded distribution networks, but we are far behind on tracking, managing, and billing the transactions between peers in the grid — for example, between one consumer and another.

Consider real-time pricing: utilities pay producers negotiated rates for energy they anticipate they will need days, weeks, even months in advance. Consumers pay set per kWh rates based on some combination of what the market will bear and what the regulators will allow. While smart meters can track time and volume of use, few are using the data for anything meaningful. The data challenges are simply too overwhelming for traditional tools; no utility can track and bill the exact production cost of the energy used by each consumer in minute-level increments.

Transaction pricing is the holy grail of energy markets — it not only levels the pricing playing field, it provides utilities with a powerful incentive to urge consumers to change their consumption habits. Currently, there is absolutely no reason why a business would change an erratic usage pattern; if a utility started billing based on the true cost of purchasing energy to meet that consumers perceived peak demand, then the consumer might consider finding ways to level out consumption. This is no longer an energy production or delivery problem — it’s a data problem.

Energy distribution, too, is a complex, highly dynamic data problem. Numerous energy sources, from traditional to renewable, feed into a single pipeline. Transmission levels vary from source to source. Energy systems need a resilient pipeline, robust security, and an autonomous traffic cop to ensure every kilowatt goes where it’s intended.

Distributed ledgers combined with smart contracts show promise as a new approach for allocating and accounting for the value of electricity. These technology tools can enable transactive energy by supporting machine-to-machine transactions that can be integrated directly into complex grid operations. A fractal approach localizes calculations for micro-transactions – a must for peer-to-peer transactions.

Bottom line: the energy business is becoming a data science business. We need new technologies to manage the variety of data sources and volume of data, the tracking and capture of instantaneous transactions, and the sometimes-unexpected consequences of data interaction. And we need a trusted method for storing and reconciling transactions.

Blockchain can help with the latter aspect of this challenge. It is good for tracking, validating, and reconciling transactions, and for managing the economic side of distributed energy generation. But there is a big if – ironically, the energy costs of processing these huge volumes of microtransactions threaten to swamp billing revenues. Those energy costs rise when blockchain is paired with cryptocurrency for payment. This is not a bug – it is a feature of blockchain, as the massive energy costs create a security barrier. Hackers have little incentive to invest 100x or more of the potential reward in energy costs to run servers needed to alter a transaction. Crime, in this case, does not pay.

But the central data challenge facing the energy industry is determining how much energy we need and where. Managing distributed energy is a data and physics problem – that’s where we need data technologies that incorporate AI for automating rapid decisions at scale, zero-latency data processing, and capacity for processing heterogeneous data from multiple sources.

Is blockchain in energy’s future? Absolutely, yes. But we cannot get sucked into believing the hype that it is our sole savior. Instead, we must continue to focus on using technology wisely to solve our distributed generation and transactional pricing challenges.

Kay Aikin is CEO of Instrospective Systems, the developer of xGraph, a platform technology designed to solve complex and highly dynamic data problems.

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