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.

翻译：随着我们向可再生能源过渡，解决其在高峰需求期间缺乏灵活性的问题变得至关重要。因此，降低能源系统的高峰负荷显得尤为重要。对于家庭而言，这意味着将洗碗机、洗衣机等高功率电器的使用分散到全天。传统的分散使用方式依赖于集中式公用事业公司设定的差异化定价，但这种方法效果不佳。我们此前的研究探讨了一种去中心化机制：智能体获得初始时间段来使用其电器，并可与其他智能体交换这些时间段。研究发现，当引入社会资本（即对互惠行为的追踪）以激励智能体接受对其无直接利益的交换时，这种方法能有效降低高峰负荷。该系统鼓励自利的智能体学习有益于社会的行为以积累社会资本，并利用这些资本在未来改善自身表现。本文通过引入真实家庭电器使用数据扩展了该工作，以确保我们的机制能够适应真实家庭具有挑战性的需求模式。我们还证明，较小且更多样化的人口群体比大型社区能源系统能更有效地进行优化。