Optimal Dispatch of Residential Photovoltaic Inverters Under Forecasting Uncertainties

TL;DR

This paper presents a novel uncertainty-aware optimal inverter dispatch framework that manages PV inverter services under forecast errors, ensuring grid reliability while balancing risk and power reserves.

Contribution

It introduces a convex relaxation approach for a complex nonconvex optimization problem, incorporating risk measures like conditional value-at-risk for PV forecast uncertainties.

Findings

Effective risk management of PV forecast errors demonstrated

Convex relaxation achieves computational efficiency

Framework ensures reliable inverter dispatch under uncertainties

Abstract

Efforts to ensure reliable operation of existing low-voltage distribution systems with high photovoltaic (PV) generation have focused on the possibility of inverters providing ancillary services such as active power curtailment and reactive power compensation. Major benefits include the possibility of averting overvoltages, which may otherwise be experienced when PV generation exceeds the demand. This paper deals with ancillary service procurement in the face of solar irradiance forecasting errors. In particular, assuming that the forecasted PV irradiance can be described by a random variable with known (empirical) distribution, the proposed uncertainty-aware optimal inverter dispatch (OID) framework indicates which inverters should provide ancillary services with a guaranteed a-priori risk level of PV generation surplus. To capture forecasting errors, and strike a balance between risk of overvoltages and (re)active power reserves, the concept of conditional value-at-risk is advocated. Due to AC power balance equations and binary inverter selection variables, the formulated OID involves the solution of a nonconvex mixed-integer nonlinear program. However, a computationally-affordable convex relaxation is derived by leveraging sparsity-promoting regularization approaches and semidefinite relaxation techniques. The proposed scheme is tested using real-world PV-generation and load-profile data for an illustrative low-voltage residential distribution system.