PhD thesis defense to be held on June 19, 2025 at 10:00 (via internet)
Thesis title: Data-driven Methods for the Integration of Flexible Loads in Demand-Side Management Schemes
Abstract: The transition towards a sustainable and resilient energy system in the European Union (EU) has accelerated the integration of Distributed Energy Resources (DERs), introducing challenges to grid stability and reliability. Demand-Side Management (DSM) emerges as a key strategy for balancing supply and demand by leveraging flexible loads. This doctoral dissertation presents data-driven methods to enhance the integration of flexible loads into DSM schemes, aligning with the goals of the Marie-Curie EU Horizon 2020 GECKO project. A multi-faceted approach was followed, starting with a review of Particle Swarm Optimization (PSO) methods in residential energy management, highlighting objectives such as cost reduction, time preferences and thermal comfort. Building on these insights, the dissertation developed two innovative smart EV charging strategies using Deep Q-Network (DQN) models. The proposed strategies offer end-users a choice between a "cost savings" policy, which prioritizes reducing electricity bills, and a "user-oriented" policy that aligns with historical charging habits. The latter approach achieves notable results, with cost savings of up to 49.83% while maintaining an 86.22% consistency with historical charging patterns. To address the challenge of Very Short-Term Load Forecasting (VSTLF) at the appliance level, a Long Short-Term Memory (LSTM) model with Feed-Forward Error Correction (FFEC) is compared with State of the Art models such as LightGBM and XGBoost in 15-minute A/C and EV aggregate residential load forecasting. The research further proposes a "Fair Demand Response (DR)" framework to enhance residential participation in DSM programs. This approach addresses the limitations of traditional performance-based selection methods, which often concentrate opportunities among a small group of participants. Finally, in advancing the concept of capacity reserve markets, the dissertation explores the participation of EV aggregators in a newly proposed energy reserves market product. A mathematical formulation of energy reserves maximization for EV aggregators is proposed, considering EV users’ preferences. A novel Six-Criteria selection approach is introduced, enabling System Operators (SOs) to efficiently preselect aggregators, reducing the administrative burden for System Operators.
Supervisor: Professor Pavlos Georgilakis
PhD Student: Christoforos Menos-Aikateriniadis