Why artificial intelligence could be key to future-proofing the grid
Both types of demand response are happening already. Some industrial power customers and certain other companies such as hotel operators have contracts for reducing power, while National Grid has been attracting much bidder interest for power storage schemes and has some underway in parts of the country. This storage is an alternative to deploying large-scale batteries, and promises to be much more economical if we can make it work on a large enough scale.
The problem is that these schemes get more complicated once the pool of customers gets beyond a certain size. Knowing which customers to sign up and what tariffs to offer requires understanding to what extent devices will be available and at what cost, for example.
Once a pool of customers is set up, some devices might not always be available for storage or reducing demand when needed. This needs to be factored into the calculations both to minimize grid disruption and incentivize customers to participate at these times.
There can also be undesired effects, such as large simultaneous rebounds in consumption. For example many refrigerators will draw extra power to get their internal temperature below the required level when a demand response period ends.
Finally there’s a potential major security issue: a central system that collects data about energy usage from many devices may be prone to malicious attacks and information tampering. This could undermine both grid balancing and keeping track of what customers are owed.
How AI can help
Emerging artificial intelligence technologies look like providing answers to these challenges. To select the best participants, for example, grid operators will be able to use sophisticated machine-learning techniques to model the behavior of individual devices and battery storage units by reviewing data from smart meters and sensors.
Once signed up for grid storage, it should be possible to estimate the useful lifetime of a battery pack or unit by applying prognostic algorithms to its charging/discharging data. Owners will then receive appropriate compensation, plus the added incentive of knowing how long their battery will last.
When it comes to managing devices in the pool, people used to think we could use individual smart meters or control devices to feed a central server in the cloud. But meters are expensive and the short response times require the cloud server to analyse data in milliseconds, which looks unfeasible once many thousands of units are in a pool.
An alternative is to have metering devices which detect demand levels on the grid themselves and reduce power accordingly. These take pressure off the central server and it only requires metering at site level, rather than for every electrical device. But it still leaves you with a complex control problem in coordinating all these individual decisions. We at Heriot-Watt are working on a solution to this using AI-based algorithms.
Another line of AI research draws on insights from algorithmic game theory to develop reward/penalty mechanisms which ensure enough customers in the pool are willing to participate, and actually respond when necessary. Researchers are also optimistic that blockchain protocols, using the same technology as Bitcoin, could underpin a decentralized ledger system that would get round the security risk of having a single storage point for user data.
Numerous AI research groups, both in the UK and elsewhere, have been addressing these challenges, while a number of start-ups have started developing such systems in practice – relatively simple versions of machine learning are now beginning to be used, for instance. The UK has a good chance to be at the forefront of international efforts to make smarter demand response a reality over the next few years.
Valentin Robu is Lecturer in Smart Grids, Heriot-Watt University. This article is published courtesy of The Conversation (under Creative Commons-Attribution / No derivative).
