Diagonal Quadratic Approximation for Decentralized Collaborative TSO+DSO Optimal Power Flow

Collaborative operation of electricity transmission and distribution systems improves the economy and reliability of the entire power system. However, this is a challenging problem given that transmission system operators (TSOs) and distribution system operators (DSOs) are autonomous entities that are unwilling to reveal their commercially sensitive information. This paper presents a decentralized decision-making algorithm for collaborative TSO+DSO optimal power flow (OPF) implementation. The proposed algorithm is based on analytical target cascading for multilevel hierarchical optimization in complex engineering systems. A local OPF is formulated for each TSO/DSO taking into consideration interactions between the transmission and distribution systems while respecting autonomy and information privacy of TSO and DSOs. The local OPF of TSO is solved in the upper-level of hierarchy, and the local OPFs of DSOs are handled in the lower-level. A diagonal quadratic approximation (DQA) and a truncated DQA are presented to develop iterative coordination strategies in which all local OPFs are solved in a parallel manner with no need for a central coordinator. This parallel implementation significantly enhances computations efficiency of the algorithm. The proposed collaborative TSO+DSO OPF is evaluated using a 6-bus and the IEEE 118-bus test systems, and promising results are obtained.

[1]  Mohammad E. Khodayar,et al.  A Hierarchical Electricity Market Structure for the Smart Grid Paradigm , 2016, IEEE Transactions on Smart Grid.

[2]  Hongbin Sun,et al.  Coordinated Transmission and Distribution AC Optimal Power Flow , 2017, IEEE Transactions on Smart Grid.

[3]  Qinglai Guo,et al.  Coordinated Economic Dispatch of Coupled Transmission and Distribution Systems Using Heterogeneous Decomposition , 2016 .

[4]  Henrik Sandberg,et al.  A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems , 2017, IEEE Transactions on Smart Grid.

[5]  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.

[6]  Amin Kargarian,et al.  Decentralized Implementation of Unit Commitment With Analytical Target Cascading: A Parallel Approach , 2018, IEEE Transactions on Power Systems.

[7]  Masoud Rais-Rohani,et al.  Analytical Target Cascading Framework using the Exponential Method of Multipliers , 2012 .

[8]  M. Rais-Rohani,et al.  Comparison of Alternative Strategies for Multilevel Optimization of Hierarchical Systems , 2012 .

[9]  Hongbin Sun,et al.  A new LMP-sensitivity-based heterogeneous decomposition for transmission and distribution coordinated economic dispatch , 2017, 2017 IEEE Power & Energy Society General Meeting.

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

[11]  S. Low,et al.  Zero Duality Gap in Optimal Power Flow Problem , 2012, IEEE Transactions on Power Systems.

[12]  Jeremy J. Michalek,et al.  Diagonal Quadratic Approximation for Parallelization of Analytical Target Cascading , 2007, Design Automation Conference.

[13]  M. Rais-Rohani,et al.  Exponential penalty function formulation for multilevel optimization using the analytical target cascading framework , 2013 .

[14]  S. Chowdhury,et al.  Microgrids and Active Distribution Networks , 2009 .

[15]  Panos Y. Papalambros,et al.  Convergence properties of analytical target cascading , 2002 .

[16]  J. E. Rooda,et al.  An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers , 2006 .

[17]  Gabriela Hug,et al.  Distributed Approach for DC Optimal Power Flow Calculations , 2014 .

[18]  G. Cohen Auxiliary problem principle and decomposition of optimization problems , 1980 .

[19]  A. Bakirtzis,et al.  A decentralized solution to the DC-OPF of interconnected power systems , 2003 .

[20]  M. Shahidehpour,et al.  Microgrid-Based Co-Optimization of Generation and Transmission Planning in Power Systems , 2013, IEEE Transactions on Power Systems.

[21]  Stephen Boyd,et al.  Security Constrained Optimal Power Flow via proximal message passing , 2014, 2014 Clemson University Power Systems Conference.

[22]  G. Andersson,et al.  Decentralized Optimal Power Flow Control for Overlapping Areas in Power Systems , 2009, IEEE Transactions on Power Systems.

[23]  B. H. Kim,et al.  A comparison of distributed optimal power flow algorithms , 2000 .

[24]  Angelo Ferrante,et al.  Lines of Convergence: R&D for Transmission and Distribution: Coordination and the Regulatory Challenge , 2015, IEEE Power and Energy Magazine.

[25]  Lorenzo Kristov,et al.  A Tale of Two Visions: Designing a Decentralized Transactive Electric System , 2016, IEEE Power and Energy Magazine.

[26]  Gabriela Hug,et al.  Intelligent Partitioning in Distributed Optimization of Electric Power Systems , 2016, IEEE Transactions on Smart Grid.

[27]  Ross Baldick,et al.  Coarse-grained distributed optimal power flow , 1997 .

[28]  Gabriela Hug,et al.  Role of communication on the convergence rate of fully distributed DC optimal power flow , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[29]  Yong Fu,et al.  Chance-Constrained System of Systems Based Operation of Power Systems , 2016, IEEE Transactions on Power Systems.

[30]  Yong Fu,et al.  System of Systems Based Security-Constrained Unit Commitment Incorporating Active Distribution Grids , 2014, IEEE Transactions on Power Systems.

[31]  Yong Fu,et al.  Optimal Operation of Active Distribution Grids: A System of Systems Framework , 2014, IEEE Transactions on Smart Grid.

[32]  Lei Wu,et al.  Distributed optimization approaches for emerging power systems operation: A review , 2017 .

[33]  Mahmoud-Reza Haghifam,et al.  Load management using multi-agent systems in smart distribution network , 2013, 2013 IEEE Power & Energy Society General Meeting.

[34]  Hongbin Sun,et al.  Coordinated Economic Dispatch of Coupled Transmission and Distribution Systems Using Heterogeneous Decomposition , 2016, IEEE Transactions on Power Systems.

[35]  Zhao Yuan,et al.  Hierarchical coordination of TSO-DSO economic dispatch considering large-scale integration of distributed energy resources , 2017 .

[36]  Jhi-Young Joo,et al.  Efficient Coordination of Wind Power and Price-Responsive Demand—Part I: Theoretical Foundations , 2011, IEEE Transactions on Power Systems.

[37]  Balasubramaniam Natarajan,et al.  Hierarchical Architecture for Integration of Rooftop PV in Smart Distribution Systems , 2018, IEEE Transactions on Smart Grid.

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

[39]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Suboptimal Control: A Survey from ADP to MPC , 2005, Eur. J. Control.

[40]  Andrzej Ruszczynski,et al.  On Convergence of an Augmented Lagrangian Decomposition Method for Sparse Convex Optimization , 1995, Math. Oper. Res..

[41]  Jianhui Wang,et al.  Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis , 2015, IEEE Transactions on Smart Grid.