TY - JOUR
T1 - Linear Single- and Three-Phase Voltage Forecasting and Bayesian State Estimation With Limited Sensing
JF - IEEE Transactions on Power Systems
Y1 - 2019/11//
SP - 1674
EP - 1683
A1 - Roel Dobbe
A1 - Werner van Westering
A1 - Stephan Liu
A1 - Daniel Arnold
A1 - Duncan S. Callaway
A1 - Claire Tomlin
AB - Implementing state estimation in low and mediumvoltage power distribution is still challenging given the scale of many networks and the reliance of traditional methods on a large number of measurements. This paper proposes a method to improve voltage predictions in real-time by leveraging a limited set of real-time measurements. The method relies on Bayesian estimation formulated as a linear least squares estimation problem, which resembles the classical weighted least-squares (WLS) approach for scenarios where full network observability is not available. We build on recently developed linear approximations for unbalanced three-phase power flow to construct voltage predictions as a linear mapping of load predictions constructed with Gaussian processes. The estimation step to update the voltage forecasts in real-time is a linear computation allowing fast high-resolution state estimate updates. The uncertainty in forecasts can be determined a priori and smoothed a posteriori, making the method useful for both planning, operation and post-hoc analysis. The method outperforms conventional WLS and is applied to different test feeders and validated on a real test feeder with the utility Alliander in The Netherlands.
VL - 35
IS - 3
JO - IEEE Trans. Power Syst.
ER -
TY - JOUR
T1 - Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
JF - IEEE Transactions on Smart Grid
Y1 - 2020/03//
SP - 1296
EP - 1306
A1 - Roel Dobbe
A1 - Oscar Sondermeijer
A1 - David Fridovich-Keil
A1 - Daniel B. Arnold
A1 - Duncan S. Callaway
A1 - Claire Tomlin
AB - The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near-optimal performance and satisfaction of system constraints. A rate distortion framework enables the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. The methodology provides a natural extension to decide what nodes a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators, focusing on DERs that do not exhibit intertemporal dependencies. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.
VL - 11
IS - 2
JO - IEEE Trans. Smart Grid
ER -