Clustering-based decentralized optimization approaches for DC optimal power flow

This paper studies two decentralized scheme to solve DC optimal power flow (DC-OPF). The first scheme considers the decomposition of DC-OPF based upon augmented Lagrangian relaxation and uses alternating direction method of multipliers (ADMM) algorithm to solve the consensus optimiza tion problem. An adaptive penalty method is proposed for the ADMM algorithm to improve the convergence performance. The second scheme utilizes Karush-Kuhn-Tucker (KKT) conditions and solves the coupled linear equations of DC-OPF directly. We show the impact of different cluster formations on both schemes. Both schemes are evaluated in terms of flexibility, robustness and iteration time using the IEEE 14-bus test system.

[1]  Joshua A. Taylor.  Convex Optimization of Power Systems , 2015 .

[2]  Vladimir Pavlovic,et al.  Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty , 2015, AAAI.

[3]  Sairaj V. Dhople,et al.  Distributed Controllers Seeking AC Optimal Power Flow Solutions Using ADMM , 2018, IEEE Transactions on Smart Grid.

[4]  Lingling Fan,et al.  Consensus ADMM and Proximal ADMM for economic dispatch and AC OPF with SOCP relaxation , 2016, 2016 North American Power Symposium (NAPS).

[5]  Tomaso Erseghe,et al.  Distributed Optimal Power Flow Using ADMM , 2014, IEEE Transactions on Power Systems.

[6]  Gabriela Hug,et al.  Toward Distributed/Decentralized DC Optimal Power Flow Implementation in Future Electric Power Systems , 2018, IEEE Transactions on Smart Grid.

[7]  B. K. Panigrahi,et al.  Hierarchical clustering based zone formation in power networks , 2016, 2016 National Power Systems Conference (NPSC).

[8]  A.G. Bakirtzis,et al.  A decentralized solution to the Security Constrained DC-OPF problem of multi-area power systems , 2005, 2005 IEEE Russia Power Tech.

[9]  Hoay Beng Gooi,et al.  Distributed Congestion Management of Distribution Grids under Robust Flexible Buildings Operations , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[10]  Gabriela Hug,et al.  Distributed State Estimation and Energy Management in Smart Grids: A Consensus${+}$ Innovations Approach , 2014, IEEE Journal of Selected Topics in Signal Processing.

[11]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[12]  B. He,et al.  Alternating Direction Method with Self-Adaptive Penalty Parameters for Monotone Variational Inequalities , 2000 .

[13]  Stephen P. Boyd,et al.  Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[14]  Georgios B. Giannakis,et al.  Distributed Stochastic Market Clearing With High-Penetration Wind Power , 2015, IEEE Transactions on Power Systems.