Abstract
Microgrid parameter estimation is essential to enable optimal voltage and frequency control using distributed energy resources (DER). Microgrid parameters vary through time, e.g., when generation is re-dispatched/committed, during microgrid reconfiguration. Furthermore, sensor measurements are noisy and preservation of the fast dynamics measurements is required, which is difficult to achieve with a lowpass filter. In this paper, a moving horizon estimation (MHE) approach is applied to estimate microgrid parameters for voltage and frequency support. The proposed approach estimates the states i.e., frequency, rate of change of frequency, grid voltage and current, and system parameters i.e., inertia, damping, and equivalent impedance. The MHE is formulated as an optimization problem using data over a fixed past horizon and solved online such that the sum of the square of measurement noise and process noise is minimized. Results showed that the proposed approach was able to estimate microgrid states, parameters, and disturbances within 5% for most values, which is sufficient to use in microgrid voltage and frequency control.
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781665435970 |
| DOIs | |
| State | Published - 28 Jun 2021 |
| Externally published | Yes |
| Event | 2021 IEEE Madrid PowerTech, PowerTech 2021 - Madrid, Spain Duration: 28 Jun 2021 → 2 Jul 2021 |
Publication series
| Name | 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings |
|---|
Conference
| Conference | 2021 IEEE Madrid PowerTech, PowerTech 2021 |
|---|---|
| Country/Territory | Spain |
| City | Madrid |
| Period | 28/06/21 → 2/07/21 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Energy storage systems
- microgrids
- moving horizon estimation
- optimal state and parameter estimation
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