Extremum-Seeking Adaptive-Droop for Model-free and Localized Volt-VAR Optimization

TL;DR

This paper introduces a model-free, local Volt-VAR control method using extremum-seeking and adaptive-droop techniques to optimize power loss minimization in distribution systems without communication, ensuring stability and voltage regulation.

Contribution

It presents a novel extremum-seeking based local control algorithm combined with adaptive-droop for stable, communication-free Volt-VAR optimization in distribution networks.

Findings

Achieves network loss minimization without communication.

Maintains voltage within limits during variable DER scenarios.

Proven convergence under convex loss minimization conditions.

Abstract

In an active power distribution system, Volt-VAR optimization (VVO) methods are employed to achieve network-level objectives such as minimization of network power losses. The commonly used model-based centralized and distributed VVO algorithms perform poorly in the absence of a communication system and with model and measurement uncertainties. In this paper, we proposed a model-free local Volt-VAR control approach for network-level optimization that does not require communication with other decision-making agents. The proposed algorithm is based on extremum-seeking approach that uses only local measurements to minimize the network power losses. To prove that the proposed extremum-seeking controller converges to the optimum solution, we also derive mathematical conditions for which the loss minimization problem is convex with respect to the control variables. Local controllers pose stability concerns during highly variable scenarios. Thus, the proposed extremum-seeking controller is integrated with an adaptive-droop control module to provide a stable local control response. The proposed approach is validated using IEEE 4-bus and IEEE 123-bus systems and achieves the loss minimization objective while maintaining the voltage within the pre-specific limits even during highly variable DER generation scenarios.