Document Type

Dissertation - Open Access

Award Date

2020

Degree Name

Doctor of Philosophy (PhD)

Department / School

Electrical Engineering and Computer Science

First Advisor

Reinaldo Tonkoski

Keywords

Fast-Frequency Support, Low Inertia Power Systems, Microgrids, Model Predictive Control, Moving Horizon Esimation, Virtual Inertia

Abstract

The future electrical energy demand will largely be met by non-synchronous renewable energy sources (RESs) in the form of photovoltaics and wind energy. The lack of inertial response from these non-synchronous, inverter-based generation in microgrids makes the system vulnerable to large rate-of-change-of-frequency (ROCOF) and frequency excursions. This can trigger under frequency load shedding and cause cascaded outages which may ultimately lead to total blackouts. To limit the ROCOF and the frequency excursions, fast-frequency support can be provided through appropriate control of energy storage systems (ESSs). For proper deployment of such fast-frequency control strategies, accurate information regarding the inertial response of the microgrid is required. In this dissertation, a moving horizon estimation (MHE)-based approach is first proposed for online estimation of inertia and damping constants of a low-inertia microgrid. The MHE also provides real estimates of the noisy frequency and ROCOF measurements. The estimates are employed by a model predictive control (MPC) algorithm that computes control actions to provide fast-frequency support by solving a finite-horizon, online optimization problem. The combined MHE-MPC framework allows an ESS operator to provide near-optimal fast-frequency support as a service. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low pass filter and traditional derivative-based (virtual inertia) controllers with high gains. Through simulation results, it has been shown that the proposed framework can provide near-optimal fast-frequency support while incorporating the physical limits of the ESS. The MHE estimator provides accurate state and parameter estimates that help in improving the dynamic performance of the controller compared to traditional derivative-based controllers. Furthermore, the flexibility of the proposed approach to achieve desired system dynamics based on the desired quality-of-service has also been demonstrated.

Library of Congress Subject Headings

Electric power systems -- Control.
Inertia (Mechanics)
Frequency stability.
Microgrids (Smart power grids)
Energy storage.

Format

application/pdf

Number of Pages

132

Publisher

South Dakota State University

Download

Share

COinS
 

Rights Statement

In Copyright