Demand response programmes are essential for the stability of a power system with increasing penetration of renewable generation. However, before the necessary technologies are deployed, the flexibility potential that consumers can offer to the grid operator must be evaluated. Although smart meters can support this process, aggregated consumption profiles do not provide insights into the individual appliances. Non-intrusive load monitoring techniques are an alternative, but most recent works only focus on increasing the performance of primarily supervised learning algorithms, reducing the applicability of their results. This paper proposes an end-to-end methodology to quantify flexibility potential and emissions intensity of large household appliances. The process relies solely on local aggregated measurements and low-complexity unsupervised learning techniques to identify major power blocks. This information is combined with information on wholesale market prices and electricity production mix to derive three novel metrics, dubbed peak price, peak emissions, and critical peak score. The results show that relatively simple unsupervised techniques can help characterise major consumption blocks from aggregated consumption, providing the per appliance flexibility potential.
