TY - JOUR
T1 - Data-Driven Coordination of Distributed Energy Resources for Active Power Provision
AU - Xu, Hanchen
AU - Dominguez-Garcia, Alejandro D.
AU - Sauer, Peter W.
N1 - Funding Information:
Manuscript received July 5, 2018; revised November 9, 2018 and January 3, 2019; accepted February 9, 2019. Date of publication February 14, 2019; date of current version June 18, 2019. This work was supported in part by the Department of Energy under a subcontract from the University of Tennessee Knoxville, in part by the Siebel Energy Institute, and in part by the Advanced Research Projects Agency-Energy (ARPA-E) within the NODES program, under Award DEAR0000695. Paper no. TPWRS-01029-2018. (Corresponding author: Alejandro D. Domínguez-García.) The authors are with the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, Urbana, IL 61801 USA (e-mail:,hxu45@illinois.edu; aledan@illinois.edu; psauer@illinois.edu).
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In this paper, we propose a framework for coordinating distributed energy resources (DERs) connected to a power distribution system, the model of which is not completely known, so that they collectively provide a specified amount of active power to the bulk power system, while respecting distribution line capacity limits. The proposed framework consists of a linear time-varying input-output (IO) system model that represents the relation between the DER active power injections (inputs), and the total active power exchanged between the distribution and bulk power systems (output); an estimator that aims to estimate the IO model parameters; and a controller that determines the optimal DER active power injections so that the power exchanged between both systems equals to the specified amount at a minimum generating cost. We formulate the estimation problem as a box-constrained quadratic program and solve it using the projected gradient descent algorithm. To resolve the potential issue of collinearity in the measurements, we introduce random perturbations in the DER active power injections during the estimation process. Using the estimated IO model, the optimal DER coordination problem to be solved by the controller can be formulated as a convex optimization problem, which can be solved easily. The effectiveness of the framework is validated via numerical simulations using the IEEE 123-bus distribution test feeder.
AB - In this paper, we propose a framework for coordinating distributed energy resources (DERs) connected to a power distribution system, the model of which is not completely known, so that they collectively provide a specified amount of active power to the bulk power system, while respecting distribution line capacity limits. The proposed framework consists of a linear time-varying input-output (IO) system model that represents the relation between the DER active power injections (inputs), and the total active power exchanged between the distribution and bulk power systems (output); an estimator that aims to estimate the IO model parameters; and a controller that determines the optimal DER active power injections so that the power exchanged between both systems equals to the specified amount at a minimum generating cost. We formulate the estimation problem as a box-constrained quadratic program and solve it using the projected gradient descent algorithm. To resolve the potential issue of collinearity in the measurements, we introduce random perturbations in the DER active power injections during the estimation process. Using the estimated IO model, the optimal DER coordination problem to be solved by the controller can be formulated as a convex optimization problem, which can be solved easily. The effectiveness of the framework is validated via numerical simulations using the IEEE 123-bus distribution test feeder.
KW - Data-driven
KW - active power provision
KW - coordination
KW - distributed energy resource
KW - parameter estimation
KW - projected gradient descent
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U2 - 10.1109/TPWRS.2019.2899451
DO - 10.1109/TPWRS.2019.2899451
M3 - Article
AN - SCOPUS:85067811116
VL - 34
SP - 3047
EP - 3058
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 4
M1 - 8642451
ER -