Optimising distributed energy: Artificial intelligence and the modern grid

Connected assets — from household thermostats to large grid-connected solar farms — produce data every second on how much energy they consume or generate. There is immense value to the grid if this data is intelligently collected, aggregated, analysed, and enables decision-making by grid operators.

For example, using machine learning algorithms, one can forecast if a transformer will get overloaded or if there will be congestion in a certain part of the grid. Not only is this helpful for long-term grid planning, but it also offers real-time situational visibility alongside control of other DERs to mitigate any power quality issues.

Forecasts typically serve as critical inputs to downstream control and optimisation modules used by utilities and energy companies to drive enhanced grid operations. Examples of this include leveraging software applications to schedule customer demand response, reserve battery state of charge, or to guide operations of combined heat and power facilities to improve grid performance. 

Take a typical city street as a simplistic example, where you might find a house with a solar photovoltaic (PV) system and an energy storage asset, a large office building, and a hospital with a backup generation system. Let’s say they are all enrolled in a utility demand response programme. Each building on the street has its own unique energy needs, ranging from the everyday to the critical, different rate tariffs, and varying abilities to moderate energy consumption. Given the capability to predict and control the consumption patterns of each building, would you choose to save on the energy bill for the end consumer or participate in demand response events for the utility and get paid? How about doing both? What is the optimal strategy?

This is not a straightforward problem to solve because the economic optimal control is often not intuitive and rule-based approaches typically fail to scale. Imagine doing this, not just on one street, but for every utility feeder or substation, where many also have DERs like solar contributing to the grid energy mix.

This is where AI is able to handle the complexity and drive scale — load and generation forecasts feed into optimisers that provide outputs on the best way to operate. For example, for the battery in your home, the optimisation ensures that you can not only save on your bill but also make money from demand response incentives.

In the real world, scalability is essential for a comprehensive AI energy application. The modern grid, with an ever-changing pattern of generation and consumption, needs control strategies that account for the specific constraints of each site (for example, not turning off critical backup in a hospital).

At the same time, there is tremendous potential to create enough grid flexibility with control of on-site DERs. In other words, AI unlocks the flexibility of the assets and combines them in such a way that they become a reliable and dispatchable source of capacity for grid use — a virtual power plant (VPP)!

Grid operators can then utilise the aggregated DER portfolio to make decisions — either to delay building expensive infrastructure (non-wires alternatives) or reduce system peak to avoid turning on fossil-fuel based reserves. Complex solutions like wholesale market trading, ancillary services and increasing hosting capacity for renewables may also address this issue.