LAUSR

ABSTRACT Contemporary electricity markets necessitate balancing supply and demand in real time, as well as the active involvement of all participating entities in the price-setting procedure. To this end, Demand Response pricing schemes are often introduced, encouraging customers to contribute to power system stability by reducing their load during peak periods. In recent years, various Time-of-Use strategies have been designed and applied in real life scenarios without yielding the expected results. Analysis of several unsuccessful test cases revealed that customers are not willing to alter their consumption habits and reduce their comfort by responding to price signals, unless they are presented with tangible benefits (cost reduction, better energy services, etc.). In this context, current work introduces an optimization methodology for individualized ToU pricing policies, building upon authors' previous work on small-scale consumer activity and response modelling. Using a multi-objective particle swarm optimization mechanism, the appropriate rates for each implemented pricing policy are identified, leading to consumers' cost and peak load reduction, in addition to higher acceptance rates. Experiments indicate the capabilities and effectiveness of the proposed approach on simulated datasets extracted from Power TAC competition platform, as well as real-life measurements acquired from a multi-residential building in Sweden.