TY - GEN
T1 - Advanced energy storage management in distribution network
AU - Liu, Guodong
AU - Ceylan, Oǧuzhan
AU - Xiao, Bailu
AU - Starke, Michael
AU - Ollis, Ben
AU - King, Daniel
AU - Irminger, Philip
AU - Tomsovic, Kevin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/3/7
Y1 - 2016/3/7
N2 - With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. The proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. The optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. The proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.
AB - With increasing penetration of distributed generation (DG) in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. In this paper, an iterative mixed integer quadratic constrained quadratic programming model to optimize the operation of a three phase unbalanced distribution system with high penetration of Photovoltaic (PV) panels, DG and energy storage (ES) is developed. The proposed model minimizes not only the operating cost, including fuel cost and purchasing cost, but also voltage deviations and power loss. The optimization model is based on the linearized sensitivity coefficients between state variables (e.g., node voltages) and control variables (e.g., real and reactive power injections of DG and ES). To avoid slow convergence when close to the optimum, a golden search method is introduced to control the step size and accelerate the convergence. The proposed algorithm is demonstrated on modified IEEE 13 nodes test feeders with multiple PV panels, DG and ES. Numerical simulation results validate the proposed algorithm. Various scenarios of system configuration are studied and some critical findings are concluded.
KW - Distributed generation (DG)
KW - Linearization
KW - Multiobjective optimization
KW - Sensitivity coefficients
KW - Unbalanced distribution network
KW - Voltage regulation
UR - http://www.scopus.com/inward/record.url?scp=84975454706&partnerID=8YFLogxK
U2 - 10.1109/HICSS.2016.298
DO - 10.1109/HICSS.2016.298
M3 - Conference contribution
AN - SCOPUS:84975454706
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 2381
EP - 2389
BT - Proceedings of the 49th Annual Hawaii International Conference on System Sciences, HICSS 2016
A2 - Sprague, Ralph H.
A2 - Bui, Tung X.
PB - IEEE Computer Society
T2 - 49th Annual Hawaii International Conference on System Sciences, HICSS 2016
Y2 - 5 January 2016 through 8 January 2016
ER -
