As we move towards an energy system based on renewable energy sources, we need to consider their inflexibility to meet sudden peaks in demand. It is therefore important to reduce the peak load placed on our energy system. For individual households this means spreading out the use of high-powered appliances, such as dishwashers and washing machines, throughout the day. Traditional approaches to this problem have relied on differential pricing set by a centralised utility company, but this mechanism has not been effective in promoting widespread shifting of appliance usage. Our previous research investigated a decentralised mechanism where agents receive an initial allocation of time-slots to use their appliances, which they can then exchange with other agents. This was found to be an effective approach to reducing the peak load within a community energy system when we introduced social capital, the tracking of favours given and received, in order to incentivise agents to act flexibly by accepting exchanges that do not immediately benefit them. This system encouraged self-interested agents to learn socially beneficial behaviour in order 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 in order 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 and better overcome the challenges of real-world demand peaks.
|Number of pages||13|
|Publication status||Published - 18 Nov 2022|
Bibliographical noteCopyright the Authors 2022. This article is licensed under a Creative Commons Attribution (CC-BY) license arXiv admin note: text overlap with arXiv:2006.14526