A Two Stage Optimal Power Flow Coordination for PVs, Batteries, and EVs in Unbalanced Low Voltage Distribution Networks

Abstract

The increasing penetration of photovoltaic systems, battery storage and electric vehicles in low-voltage distribution networks poses significant operational challenges, including voltage regulation, thermal overload, and energy curtailment. This paper proposes an uncertainty-aware two-stage coordination framework. The first stage employs a LinDist3Flow-based optimisation with Monte Carlo scenario generation to determine day-ahead charging and discharging schedules for batteries and electric vehicles while accounting for uncertainties in load demand, solar irradiance, temperature, and electric vehicle availability. The second stage uses an exact nonlinear alternating current optimal power flow formulation to optimise inverters’ active and reactive power set-points in near real-time operation. The framework is evaluated on a modified IEEE European low-voltage test feeder comprising 165 loads with 100% single-phase photovoltaic penetration. Four operational approaches are compared: local Volt-Watt/Volt-Var control, direct application of scheduled set-points without real-time correction, real-time optimal power flow with fixed time-of-use schedules, and the proposed two-stage framework. The results demonstrate that the proposed approach achieves the lowest total curtailment while maintaining voltage and thermal compliance, reducing the total curtailed energy by approximately 5% compared to the optimal power flow in real-time with fixed schedules. These findings highlight the importance of integrating uncertainty-aware scheduling with real-time coordination in future low-voltage networks with a high penetration of consumer energy resources.