Optimal Control Design for Operating a Hybrid PV Plant with Robust Power Reserves for Fast Frequency Regulation Services

TL;DR

This paper develops an adaptive model predictive control strategy for hybrid PV systems with BESS to optimize power setpoint tracking and reserve maintenance, validated through real-time simulations with high accuracy.

Contribution

It introduces a novel control approach combining MPC and EKF for hybrid PV-BESS systems, enhancing operational reliability and reserve management.

Findings

Effective power setpoint tracking demonstrated in simulations.

Maintains power reserves during high irradiance variability.

Validates control strategy with real-time EMT simulations.

Abstract

This paper presents an optimal control strategy for operating a solar hybrid system consisting of solar photovoltaic (PV) and a high-power, low-storage battery energy storage system (BESS). A state-space model of the hybrid PV plant is first derived, based on which an adaptive model predictive controller is designed. The controller's objective is to control the PV and BESS to follow power setpoints sent to the the hybrid system while maintaining desired power reserves and meeting system operational constraints. Furthermore, an extended Kalman filter (EKF) is implemented for estimating the battery SOC, and an error sensitivity is executed to assess its limitations. To validate the proposed strategy, detailed EMT models of the hybrid system are developed so that losses and control limits can be quantified accurately. Day-long simulations are performed in an OPAL-RT real-time simulator using second-by-second actual PV farm data as inputs. Results verify that the proposed method can follow power setpoints while maintaining power reserves in days of high irradiance intermittency even with a small BESS storage.