Optimal Voltage Regulation of Unbalanced Distribution Networks with Coordination of OLTC and PV Generation

TL;DR

This work addresses voltage regulation in unbalanced distribution networks with high PV penetration by introducing a convex, linearized optimization framework that coordinates PV smart inverter reactive power with OLTC-based voltage regulators. The approach linearizes key power-flow relationships and injection constraints to solve a tractable problem that minimizes voltage deviations and tap operations over a forecast horizon, using forecasts from sky imagers and base-case OpenDSS runs. Validation on the highly unbalanced IEEE 37-bus feeder demonstrates accurate voltage estimation, substantial reductions in voltage deviations and imbalance, and faster solution times compared to non-convex approaches, enabling practical real-time control. The results highlight the practical impact of coordinated VR-PV operation for reliable feeder voltage regulation under variability in PV generation.

Abstract

Photovoltaic (PV) smart inverters can regulate voltage in distribution systems by modulating reactive power of PV systems. In this paper, an optimization framework for optimal coordination of reactive power injection of smart inverters and tap operations of voltage regulators for multi-phase unbalanced distribution systems is proposed. Optimization objectives are minimization of voltage deviations and tap operations. A novel linearization method convexifies the problem and speeds up the solution. The proposed method is validated against conventional rule-based autonomous voltage regulation (AVR) on the highly-unbalanced IEEE 37 bus test system. Simulation results show that the proposed method estimates feeder voltage accurately, voltage deviation reductions are significant, over-voltage problems are mitigated, and voltage imbalance is reduced.

Figures (4)

• Figure 1: Flowchart of the proposed voltage optimization. PV and load forecasts are used to obtain the linearization voltages and currents ($V_0$, $I_0$) for the next 5 mins from base power flow simulations. Then the voltage optimization per Eq. \ref{['optimization_problem']} is formulated and solved, providing decision values for $\Delta V$, $\Delta I$ and $\tau$. The optimal reactive power of the SI is then calculated using Eq. \ref{['reactivepower2']}. The optimal tap positions $\tau$ and SI reactive power are then input into OpenDSS OpenDSS for another power flow. The voltage results from the OpenDSS simulation are then compared to those from the estimation with the proposed method (Eq. \ref{['vamaglin']}).
• Figure 2: Feeder voltage profile at 11:53 (top) and 21:00 (bottom) for the AVR (left) and OVR (right) voltage regulation methods.
• Figure 3: Time series of mean magnitude of node voltage deviation from $1$ p.u..
• Figure 4: Distribution of voltage estimation errors. For readability, the results are aggregated every two hours into 12 groups. For example, the box plot of hour 1 is based on the results from 00:00 to 02:00.