Solar PV, battery, and grid combined joint parameter-state estimation framework: A circuit based I-V formulation algorithm
Abstract
Solar PV and battery storage systems have become integral to modern power grids. Therefore, bulk grid models in real-time operation must include their physical behavior accurately for analysis and optimization. AC state estimation is critical to building real-time bulk power systems models. However, current ACSE techniques do not include detailed physics and measurements for battery and PV systems. This results in sub-optimal estimation results and subsequently less accurate bulk grid models for real-time operation. To address these challenges, we formulate a circuit-theoretic AC state estimator with accurate PV and battery systems physics and corresponding measurements. First, we propose an aggregated equivalent circuit model of the solar PV, battery, and grid components. Next, we add measurements from PV and battery systems to the traditional measurement set to facilitate accurate estimation of the overall grid model. Finally, we develop a joint parameter-state estimation to improve algorithm robustness against erroneous parameters. To demonstrate the efficacy of the proposed framework, we compare the proposed approaches on systems with up to 25k nodes.
Primary Faculty Mentor Name
Mads Almassalkhi
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Electrical Engineering
Primary Research Category
Engineering and Math Science
Solar PV, battery, and grid combined joint parameter-state estimation framework: A circuit based I-V formulation algorithm
Solar PV and battery storage systems have become integral to modern power grids. Therefore, bulk grid models in real-time operation must include their physical behavior accurately for analysis and optimization. AC state estimation is critical to building real-time bulk power systems models. However, current ACSE techniques do not include detailed physics and measurements for battery and PV systems. This results in sub-optimal estimation results and subsequently less accurate bulk grid models for real-time operation. To address these challenges, we formulate a circuit-theoretic AC state estimator with accurate PV and battery systems physics and corresponding measurements. First, we propose an aggregated equivalent circuit model of the solar PV, battery, and grid components. Next, we add measurements from PV and battery systems to the traditional measurement set to facilitate accurate estimation of the overall grid model. Finally, we develop a joint parameter-state estimation to improve algorithm robustness against erroneous parameters. To demonstrate the efficacy of the proposed framework, we compare the proposed approaches on systems with up to 25k nodes.
