An iterative approach to large-scale decentralised optimised demand response considering user comfort

• The paper discusses the large-scale implementation of demand response for residential end-users. • The physical characteristic of the appliance controlled were maintained to ensure user comfort is considered. • A strategy consider ing maximising user comfort while minimising excess generation demand side was developed. • For fast convergence and reduced complexity of the large scale implementation, a decentralised optimisation approach was selected. This study presents a scalable, decentralised approach to optimised large-scale Demand Response (DR) in electrical distribution networks with significant renewable energy integration, focusing on user comfort. By modelling thermostatically controlled Electric Water Heaters (EWHs) as flexible loads in residential installations, the proposed direct control DR scheme coordinates activation signals through local hubs, optimising both renewable energy utilisation and user comfort. A multi-objective genetic algorithm (GA) was used to iteratively optimise EWH activation time and duration, balancing curtailment reduction against comfort impact. Simulation results on a representative network indicate that aggressive control strategies significantly reduce renewable energy curtailment, up to 37 %, though this comes at the cost of user comfort, with up to 7 % of cold-water events compared to a 0.12 % reference. In contrast, less aggressive strategies deliver lower curtailment benefits but with a reduced impact on comfort. The proposed decentralised optimisation framework enhances scalability, communication efficiency, and computational feasibility compared to a centralised alternative. The findings highlight the potential for coordinated DR to reduce reliance on traditional energy storage, improve grid flexibility, and maintain end user comfort, with practical insights for implementation and future research directions addressing forecast uncertainties and extended evaluation horizons.