Distributed State Estimation and Energy Management in Smart Grids: A Consensus${+}$ Innovations Approach

This paper reviews signal processing research for applications in the future electric power grid, commonly referred to as smart grid. Generally, it is expected that the grid of the future would differ from the current system by the increased integration of distributed generation, distributed storage, demand response, power electronics, and communications and sensing technologies. The consequence is that the physical structure of the system becomes significantly more distributed. The existing centralized control structure is not suitable any more to operate such a highly distributed system. Hence, in this paper, we overview distributed approaches, all based on consensus +innovations, for three common energy management functions: state estimation, economic dispatch, and optimal power flow. We survey the pertinent literature and summarize our work. Simulation results illustrate tradeoffs and the performance of consensus +innovations for these three applications.

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

[2]  K. Mani Chandy,et al.  Solving quadratically constrained quadratic programs on acyclic graphs with application to optimal power flow , 2014, 2014 48th Annual Conference on Information Sciences and Systems (CISS).

[3]  Jing Huang,et al.  State Estimation in Electric Power Grids: Meeting New Challenges Presented by the Requirements of the Future Grid , 2012, IEEE Signal Processing Magazine.

[4]  K. Fujisawa,et al.  Semidefinite programming for optimal power flow problems , 2008 .

[5]  Gabriela Hug,et al.  Consensus + Innovations Approach for Distributed Multiagent Coordination in a Microgrid , 2015, IEEE Transactions on Smart Grid.

[6]  Soummya Kar,et al.  Cooperative distributed state estimation: Local observability relaxed , 2011, 2011 IEEE Power and Energy Society General Meeting.

[7]  Jonathan Eckstein.  Parallel alternating direction multiplier decomposition of convex programs , 1994 .

[8]  Gabriela Hug,et al.  Enhanced secondary frequency control via distributed peer-to-peer communication , 2016, 2016 European Control Conference (ECC).

[9]  György Dán,et al.  On the security of distributed power system state estimation under targeted attacks , 2013, SAC '13.

[10]  Steffen Rebennack,et al.  Optimal power flow: a bibliographic survey II , 2012, Energy Systems.

[11]  J. Moura,et al.  Topology for Global Average Consensus , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[12]  Antonio Gómez Expósito,et al.  A Multilevel State Estimation Paradigm for Smart Grids , 2011, Proceedings of the IEEE.

[13]  David Tse,et al.  Distributed algorithms for optimal power flow problem , 2011, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[14]  Dimitri P. Bertsekas,et al.  Constrained Optimization and Lagrange Multiplier Methods , 1982 .

[15]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[16]  H. Vincent Poor,et al.  Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models , 2013, IEEE Journal on Selected Areas in Communications.

[17]  Kevin Tomsovic,et al.  Designing the Next Generation of Real-Time Control, Communication, and Computations for Large Power Systems , 2005, Proceedings of the IEEE.

[18]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part I: Exact Model , 1970 .

[19]  M. Ribbens-Pavella,et al.  A Two-Level Static State Estimator for Electric Power Systems , 1981, IEEE Transactions on Power Apparatus and Systems.

[20]  Anjan Bose,et al.  Transition to a Two-Level Linear State Estimator—Part II: Algorithm , 2011, IEEE Transactions on Power Systems.

[21]  K. Mani Chandy,et al.  Quadratically Constrained Quadratic Programs on Acyclic Graphs With Application to Power Flow , 2012, IEEE Transactions on Control of Network Systems.

[22]  Gabriela Hug,et al.  A benders decomposition approach to corrective security constrained OPF with power flow control devices , 2013, 2013 IEEE Power & Energy Society General Meeting.

[23]  Stephen J. Wright,et al.  Distributed MPC Strategies With Application to Power System Automatic Generation Control , 2008, IEEE Transactions on Control Systems Technology.

[24]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[25]  J. Moura,et al.  Cooperation for aggregating complex electric power networks to ensure system observability , 2008, 2008 First International Conference on Infrastructure Systems and Services: Building Networks for a Brighter Future (INFRA).

[26]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[27]  Francisco J. Prieto,et al.  A Decomposition Methodology Applied to the Multi-Area Optimal Power Flow Problem , 2003, Ann. Oper. Res..

[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]  A. Conejo,et al.  Multi-area coordinated decentralized DC optimal power flow , 1998 .

[30]  H. Vincent Poor,et al.  Distributed joint cyber attack detection and state recovery in smart grids , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[31]  H. Vincent Poor,et al.  Adaptive link selection strategies for distributed estimation in diffusion wireless networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[32]  Zhu Han,et al.  Coordinated data-injection attack and detection in the smart grid: A detailed look at enriching detection solutions , 2012, IEEE Signal Processing Magazine.

[33]  Soummya Kar,et al.  Bad-data detection in smart grid: a distributed approach , 2012 .

[34]  Mo-Yuen Chow,et al.  Convergence Analysis of the Incremental Cost Consensus Algorithm Under Different Communication Network Topologies in a Smart Grid , 2012, IEEE Transactions on Power Systems.

[35]  Georgios B. Giannakis,et al.  Distributed Optimal Power Flow for Smart Microgrids , 2012, IEEE Transactions on Smart Grid.

[36]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[37]  Yun Gu,et al.  Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids , 2014 .

[38]  B.F. Wollenberg,et al.  Toward a smart grid: power delivery for the 21st century , 2005, IEEE Power and Energy Magazine.

[39]  Anna Scaglione,et al.  Robust Decentralized State Estimation and Tracking for Power Systems via Network Gossiping , 2013, IEEE Journal on Selected Areas in Communications.

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

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

[42]  Khosrow Moslehi,et al.  Power System Control Centers: Past, Present, and Future , 2005, Proceedings of the IEEE.

[43]  Georgios B. Giannakis,et al.  Distributed Robust Power System State Estimation , 2012, IEEE Transactions on Power Systems.

[44]  Mohammad Shahidehpour,et al.  Parallel and Distributed State Estimation , 2003 .

[45]  Ali H. Sayed,et al.  Diffusion Strategies for Distributed Kalman Filtering and Smoothing , 2010, IEEE Transactions on Automatic Control.

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

[47]  M. B. Cain,et al.  History of Optimal Power Flow and Formulations , 2012 .

[48]  Marija D. Ilic,et al.  Dynamics and control of large electric power systems , 2000 .

[49]  Stephen P. Boyd,et al.  Dynamic Network Energy Management via Proximal Message Passing , 2013, Found. Trends Optim..

[50]  Soummya Kar,et al.  Gossip Algorithms for Distributed Signal Processing , 2010, Proceedings of the IEEE.

[51]  Stefano Panzieri,et al.  Malicious false data injection in hierarchical electric power grid state estimation systems , 2013, e-Energy '13.

[52]  B. H. Kim,et al.  Evaluation of convergence rate in the auxiliary problem principle for distributed optimal power flow , 2002 .

[53]  George N Korres,et al.  A Distributed Multiarea State Estimation , 2011, IEEE Transactions on Power Systems.

[54]  G. Hug,et al.  Distributed robust economic dispatch in power systems: A consensus + innovations approach , 2012, 2012 IEEE Power and Energy Society General Meeting.

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

[56]  H. Vincent Poor,et al.  Dynamic topology adaptation for distributed estimation in smart grids , 2013, 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[57]  Soummya Kar,et al.  Convergence Rate Analysis of Distributed Gossip (Linear Parameter) Estimation: Fundamental Limits and Tradeoffs , 2010, IEEE Journal of Selected Topics in Signal Processing.

[58]  S. Low.  Convex relaxation of optimal power flow: A tutorial , 2013, 2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid.

[59]  T. Van Cutsem,et al.  Critical Survey of Hierarchical Methods for State Estimation of Electric Power Systems , 1983, IEEE Power Engineering Review.

[60]  José M. F. Moura,et al.  Topology of Sensor Networks in Distributed Detection , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[61]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[62]  Ross Baldick,et al.  Applied Optimization: Formulation and Algorithms for Engineering Systems (Baldick, R.; 2006) , 2008, IEEE Control Systems.

[63]  Ali H. Sayed,et al.  Adaptive Networks , 2014, Proceedings of the IEEE.

[64]  Ali Sayed,et al.  Adaptation, Learning, and Optimization over Networks , 2014, Found. Trends Mach. Learn..

[65]  Soummya Kar,et al.  Consensus + innovations distributed inference over networks: cooperation and sensing in networked systems , 2013, IEEE Signal Processing Magazine.

[66]  Soummya Kar,et al.  Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication , 2008, IEEE Transactions on Information Theory.

[67]  Anna Scaglione,et al.  Convergence and Applications of a Gossip-Based Gauss-Newton Algorithm , 2012, IEEE Transactions on Signal Processing.

[68]  H. Poor,et al.  Fully Distributed State Estimation for Wide-Area Monitoring Systems , 2012, IEEE Transactions on Smart Grid.

[69]  Anjan Bose,et al.  Smart Transmission Grid Applications and Their Supporting Infrastructure , 2010, IEEE Transactions on Smart Grid.

[70]  Steffen Rebennack,et al.  Optimal power flow: a bibliographic survey I , 2012, Energy Systems.

[71]  Soummya Kar,et al.  Topology for Distributed Inference on Graphs , 2006, IEEE Transactions on Signal Processing.

[72]  A. Abur,et al.  Multi area state estimation using synchronized phasor measurements , 2005, IEEE Transactions on Power Systems.

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

[74]  J. Lavaei,et al.  Network Topologies Guaranteeing Zero Duality Gap for Optimal Power Flow Problem , 2013 .

[75]  U. Feige,et al.  Spectral Graph Theory , 2015 .

[76]  H. Vincent Poor,et al.  Distributed Linear Parameter Estimation: Asymptotically Efficient Adaptive Strategies , 2011, SIAM J. Control. Optim..

[77]  Vahid Madani,et al.  Wide-Area Monitoring, Protection, and Control of Future Electric Power Networks , 2011, Proceedings of the IEEE.

[78]  Marija D. Ilic,et al.  Distributed electric power systems of the future: Institutional and technological drivers for near-optimal performance , 2007 .

[79]  S.K. Mazumder,et al.  Joint optimization of control performance and network resource utilization in homogeneous power networks , 2008, 2008 34th Annual Conference of IEEE Industrial Electronics.

[80]  Dong Jia,et al.  Distributed Model Predictive Control for the Mitigation of Cascading Failures , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[81]  Mo-Yuen Chow,et al.  Decentralizing the economic dispatch problem using a two-level incremental cost consensus algorithm in a smart grid environment , 2011, 2011 North American Power Symposium.