As power systems increasingly rely on renewable energy, grid services traditionally supplied by central plants must increasingly be sourced from distributed energy resources (DERs). Virtual power plants (VPPs) aggregate DERs to act as a single entity, but coordination is complicated by information asymmetry, possibly resulting in strategic behaviour. This thesis studies how we can design a mechanism for a commercial VPP, having to satisfy a fixed commitment, while optimising the revenue from the VPP operator.
We first develop a tractable, multi-period VPP model with linear costs, local and temporal constraints for DERs and soft system-wide commitments enforced via deviation penalties. On top of this model we design and compare four mechanisms: first-price sealed bid (FPSB), uniform pricing, Vickrey–Clarke–Groves (VCG) and Arrow–d’Aspremont– Gerard-Varet (AGV). We evaluate them on revenue optimality, weak budget balance, incentive compatibility, individual rationality and scalability. Furthermore, we investigate how the composition of a VPP’s portfolio could inform mechanism design choices. FPSB realises payments equalling costs under truthful reports and remains competitive for small strategic fractions, but overpayment grows with the share of strategic agents and with cost dispersion. Uniform pricing is comparatively insensitive to the strategic fraction but highly sensitive to cost dispersion, often leading to large overpayments. VCG is strategy-proof and insensitive to strategic behaviour, yet externality payments increase with cost dispersion and raise total payouts. AGV keeps the payment-to-cost ratio near or below one by relying on expected externalities and scaling, improving operator viability but potentially violating individual rationality in instances. These results yielded the following guidelines regarding the suitability of mechanisms. FPSB for low strategic participation, uniform pricing for homogeneous portfolios, VCG when truthfulness is vital and external funding is possible, and AGV when operator viability is the hard constraint with safeguards for individual rationality.F

Non-Intrusive Load Monitoring (NILM) is a technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one-shot transfer learning. The research investigates the effectiveness of one-shot transfer learning in fine-tuning a CNN model to accurately detect the ON/OFF state of appliances in households not seen during the training phase of the CNN. The study utilizes the Pecan Street dataset for training and evaluation, which includes detailed energy consumption records from various locations in the United States. The results suggest that one-shot transfer learning could enhance the performance of the NILM algorithm, particularly when multiple data samples are used for fine-tuning. However, the effectiveness of one-shot transfer learning varies strongly depending on the number of samples and the characteristics of the target household.