Asea Brown Boveri Ltd. (ABB) Award - 2025 - Rahul Gupta

For proposing visionary frameworks extending from real-time grid-aware control to nationwide planning of distributed energy resources, this thesis constitutes a seminal contribution to the advancement of power systems engineering, with enduring scientific and societal impact.

Modern power systems are increasingly challenged by the rapid integration of decentralized renewable generation, resulting in power imbalances, increased reserve requirements, voltage violations, and line congestion. This thesis develops advanced methodologies for the control and planning of distributed energy resources (DERs) in active distribution networks (ADNs), addressing these technical challenges.
First, real-time control frameworks are developed to minimize imbalances while enforcing nodal voltage and branch flow constraints. To overcome the computational complexity of non-convex optimal power flow (OPF) problems, linearized OPF models and model predictive control (MPC) strategies are developed. These frameworks are experimentally validated on real microgrids and extended with hierarchical two-layer MPC, tackling intra-day uncertainties.
Second, model-less control strategies are proposed by estimating network models from phasor measurements, using both admittance-matrix and voltage–power sensitivity coefficients estimation. These approaches enable reliable voltage control under incomplete or uncertain network information.
Finally, the thesis develops large-scale planning tools to assess countrywide PV hosting capacity and optimally allocate battery energy storage systems (BESSs). It is achieved by generating synthetic distribution network models, followed by grid-aware optimization of PV hosting capacity and BESS. The scheme is applied for the case of Switzerland to obtain countrywide cost-optimal integration of PV and BESS units.