  As we transition to renewable energy sources, addressing their inflexibility
during peak demand becomes crucial. It is therefore important to reduce the
peak load placed on our energy system. For households, this entails spreading
high-power appliance usage like dishwashers and washing machines throughout the
day. Traditional approaches to spreading out usage have relied on differential
pricing set by a centralised utility company, but this has been ineffective.
Our previous research investigated a decentralised mechanism where agents
receive an initial allocation of time-slots to use their appliances, which they
can exchange with others. This was found to be an effective approach to
reducing the peak load when we introduced social capital, the tracking of
favours, to incentivise agents to accept exchanges that do not immediately
benefit them. This system encouraged self-interested agents to learn socially
beneficial behaviour to earn social capital that they could later use to
improve their own performance. In this paper we expand this work by
implementing real world household appliance usage data to ensure that our
mechanism could adapt to the challenging demand needs of real households. We
also demonstrate how smaller and more diverse populations can optimise more
effectively than larger community energy systems.
