A paper published in Applied Energy by researchers from the Smart Systems Group (SSG) at Heriot-Watt University in Edinburgh, Scotland, has shown that tools from distributed artificial intelligence (specifically multi-agent systems) and co-operative game theory can be used in energy communities to increase use of renewables, protect the lifetime of expensive assets such as batteries, and to devise fair ways to divide joint gains. Their work develops algorithms for smart control of community energy assets to use more locally generated electricity, and to extend the lifetime of energy assets. They compare the case when individual households invest in their own home battery versus investing in a larger community energy storage unit, and show the benefits of a pooled storage approach. Next, they propose several practically applicable and computationally efficient mechanisms to share outputs of these assets between homes fairly. Their work makes use of the key concept of marginal value – borrowed from coalitional game theory and distributed AI, looking at what each member contributes to (and costs) the local community.