Coordination of distributed energy resources for a reliable, resilient, and affordable decarbonized grid

Abstract

Rapid decarbonization of the power grid is essential to meet climate goals by reducing emissions and enabling sustainable electrification of sectors like transport and heating. This requires shifting from centralized fossil-fuel generation to variable renewables like wind and solar. The grid must also adapt to a growing number of small-scale, distributed energy resources (DERs) at the edge, such as rooftop solar, batteries, electric vehicles, and heat pumps. This thesis focuses on modeling, optimizing, and coordinating DERs to enable a flexible, resilient, and affordable grid. First, it proposes a novel hierarchical local electricity market for low and medium-voltage distribution grids. This structure enables DER participation through decentralized and distributed optimization, respecting grid physics while preserving privacy and scalability. The market is applicable to both balanced and unbalanced radial grids using two different convex relaxations and power flow models. Grid services are also priced based on duality theory. Numerical simulations show improved dispatch efficiency, reliability, voltage regulation, and lower retail electricity rates. Second, the thesis applies game theory and mechanism design to extract flexibility from autonomous, strategic DER owners. A repeated Stackelberg game with incomplete information and intertemporal constraints yields equilibrium pricing with closed-form solutions. Third, a distributed decision-making framework is developed to coordinate DERs for grid resilience. It mitigates cyber-physical attacks and outages, ranging from 5 to 40% of peak load, using local flexibility and grid reconfiguration, extensively validated through both software and hardware-in-the-loop simulations. Finally, the thesis addresses DER hosting capacity. New algorithms are developed that co-optimize the siting and sizing of diverse DERs under uncertainty using Monte Carlo sampling, stochastic programming, and k-means clustering for scenario reduction. Results show that intelligent DER coordination can defer grid infrastructure upgrades and support greater renewable integration and electrified demand growth. Together, these contributions provide analytical and simulation tools to improve the planning and real-time operation of future distributed, low-carbon power grids.