Fair voltage regulation and energy management in smart distribution grids

The energy transition toward decentralised, renewable-based generation is reshaping electricity distribution networks. In particular, the rapid growth of photovoltaic (PV) systems introduces new operational challenges such as overvoltage, voltage unbalance, and power quality (PQ) degradation. These problems are amplified by the intermittent nature of renewable generation and the limited hosting capacity of low-voltage (LV) grids. Conventional grid operation methods, designed for unidirectional power flows, are no longer sufficient. At the same time, most existing control approaches focus solely on technical feasibility, often overlooking fairness in how grid resources and constraints are shared among prosumers.

This thesis develops fairness-incorporated control strategies to maintain grid operation standards while ensuring equity in LV grids. It begins by outlining the technical and socio-economic implications of high PV penetration and then introduces new approaches for fair voltage regulation and unbalance reduction. Two active power curtailment (APC) methods are proposed to distribute curtailment equitably among prosumers, with one designed for computational efficiency in real-time applications. While fairness entails higher total power curtailment compared to traditional droop control, it prevents disproportionate burdens on individual users.

To address both unbalance and overvoltage, reactive power control (RPC) and neutral current compensation (NCC) are integrated with fairness-driven APC into a global control algorithm. This approach reduces neutral currents, improves grid balance, and lowers curtailment needs, and demonstrates the alternate voltage unbalance factor (AVUF) as a more accurate metric for unbalance in LV grids.

The thesis further introduces proportional voltage fairness (PVF), a scalable fairness metric that accounts for voltage sensitivity across diverse network configurations. PVF-based voltage regulation enables targeted curtailment, reduces overall power losses, and provides distribution system operators (DSOs) with a transparent mechanism to make a trade-off between fairness and efficiency.

Finally, a data-driven energy management system (EMS) is presented to coordinate demand, storage, and generation in real time. By combining Bayesian inference-based decision trees with a hierarchical control framework, the EMS achieves predictive and interpretable control, balancing local prosumer needs with system-wide requirements.