An integral control formulation of Mean-field game based large scale coordination of loads in smart grids

Pressure on ancillary reserves, i.e.frequency preserving, in power systems has significantly mounted due to the recent generalized increase of the fraction of (highly fluctuating) wind and solar energy sources in grid generation mixes. The energy storage associated with millions of individual customer electric thermal (heating-cooling) loads is considered as a tool for smoothing power demand/generation imbalances. The piecewise constant level tracking problem of their collective energy content is formulated as a linear quadratic mean field game problem with integral control in the cost coefficients. The introduction of integral control brings with it a robustness potential to mismodeling, but also the potential of cost coefficient unboundedness. A suitable Banach space is introduced to establish the existence of Nash equilibria for the corresponding infinite population game, and algorithms are proposed for reliably computing a class of desirable near Nash equilibria. Numerical simulations illustrate the flexibility and robustness of the approach.

[1]  Johanna L. Mathieu,et al.  Examining uncertainty in demand response baseline models and variability in automated responses to dynamic pricing , 2011, IEEE Conference on Decision and Control and European Control Conference.

[2]  Minyi Huang,et al.  Large-Population LQG Games Involving a Major Player: The Nash Certainty Equivalence Principle , 2009, SIAM J. Control. Optim..

[3]  Roland P. Malhamé,et al.  A physically-based computer model of aggregate electric water heating loads , 1994 .

[4]  Duncan S. Callaway Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy , 2009 .

[5]  R. Malhamé,et al.  Electric load model synthesis by diffusion approximation of a high-order hybrid-state stochastic system , 1985 .

[6]  Robert R. Bitmead,et al.  Riccati Difference and Differential Equations: Convergence, Monotonicity and Stability , 1991 .

[7]  Peter E. Caines,et al.  Large population stochastic dynamic games: closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle , 2006, Commun. Inf. Syst..

[8]  Jakob Stoustrup,et al.  Contribution of domestic heating systems to smart grid control , 2011, IEEE Conference on Decision and Control and European Control Conference.

[9]  Ioannis Karatzas,et al.  Brownian Motion and Stochastic Calculus , 1987 .

[10]  Ana Busic,et al.  Ancillary service to the grid from deferrable loads: The case for intelligent pool pumps in Florida , 2013, 52nd IEEE Conference on Decision and Control.

[11]  Ana Busic,et al.  State Estimation for the Individual and the Population in Mean Field Control With Application to Demand Dispatch , 2017, IEEE Transactions on Automatic Control.

[12]  Rafael Wisniewski,et al.  Control for large scale demand response of thermostatic loads* , 2013, 2013 American Control Conference.

[13]  R. C. Sonderegger Dynamic models of house heating based on equivalent thermal parameters , 1978 .

[14]  R. Ortega,et al.  Nonlinear PI control of uncertain systems: an alternative to parameter adaptation , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[15]  Ana Busic,et al.  State estimation and mean field control with application to demand dispatch , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[16]  Minyi Huang,et al.  Large-Population Cost-Coupled LQG Problems With Nonuniform Agents: Individual-Mass Behavior and Decentralized $\varepsilon$-Nash Equilibria , 2007, IEEE Transactions on Automatic Control.

[17]  Hyunjoong Kim,et al.  Functional Analysis I , 2017 .

[18]  P. Lions,et al.  Jeux à champ moyen. I – Le cas stationnaire , 2006 .

[19]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.

[20]  Levon Nurbekyan,et al.  A mean-field game economic growth model , 2015, 2016 American Control Conference (ACC).

[21]  B. M. Mitchell,et al.  Electricity Pricing and Load Management: Foreign Experience and California Opportunities , 1977 .

[22]  Ian A. Hiskens,et al.  Achieving Controllability of Electric Loads , 2011, Proceedings of the IEEE.

[23]  Peter E. Caines,et al.  Mean Field Games , 2015 .

[24]  Lennart Söder,et al.  Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs , 2017 .

[25]  Walid Saad,et al.  Prospect theory for prosumer-centric energy trading in the smart grid , 2016, 2016 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[26]  Lion Hirth,et al.  Control power and variable renewables , 2013, 2013 10th International Conference on the European Energy Market (EEM).

[27]  Johanna L. Mathieu,et al.  Variability in automated responses of commercial buildings and industrial facilities to dynamic elec , 2011 .

[28]  Hamidou Tembine,et al.  Mean-Field-Type Games in Engineering , 2016, ArXiv.

[29]  Giorgio Rizzoni,et al.  Residential Demand Response: Dynamic Energy Management and Time-Varying Electricity Pricing , 2016, IEEE Transactions on Power Systems.

[30]  Johanna L. Mathieu,et al.  State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance , 2013 .

[31]  Wei Zhang,et al.  Aggregated Modeling and Control of Air Conditioning Loads for Demand Response , 2013, IEEE Transactions on Power Systems.

[32]  Jianhui Huang,et al.  Social Optima in Robust Mean Field LQG Control: From Finite to Infinite Horizon , 2019, IEEE Transactions on Automatic Control.

[33]  B. Anderson,et al.  Optimal control: linear quadratic methods , 1990 .

[34]  P. Caines,et al.  Individual and mass behaviour in large population stochastic wireless power control problems: centralized and Nash equilibrium solutions , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[35]  Minyi Huang,et al.  Robust Mean Field Linear-Quadratic-Gaussian Games with Unknown L2-Disturbance , 2017, SIAM J. Control. Optim..

[36]  Thomas Ackermann,et al.  Integrating Variable Renewables in Europe : Current Status and Recent Extreme Events , 2015, IEEE Power and Energy Magazine.

[37]  Peter E. Caines,et al.  Mean Field LQG Control in Leader-Follower Stochastic Multi-Agent Systems: Likelihood Ratio Based Adaptation , 2012, IEEE Transactions on Automatic Control.

[38]  Ana Busic,et al.  Ancillary Service to the Grid Using Intelligent Deferrable Loads , 2014, IEEE Transactions on Automatic Control.

[39]  Ana Busic,et al.  Individual risk in mean-field control models for decentralized control, with application to automated demand response , 2014, CDC 2014.