Distributed Control Design for Balancing the Grid Using Flexible Loads

Inexpensive energy from the wind and the sun comes with unwanted volatility, such as ramps with the setting sun or a gust of wind. Controllable generators manage supply-demand balance of power today, but this is becoming increasingly costly with increasing penetration of renewable energy. It has been argued since the 1980s that consumers should be put in the loop: “demand response” will help to create needed supply-demand balance. However, consumers use power for a reason and expect that the quality of service (QoS) they receive will lie within reasonable bounds. Moreover, the behavior of some consumers is unpredictable, while the grid operator requires predictable controllable resources to maintain reliability. The goal of this chapter is to describe an emerging science for demand dispatch that will create virtual energy storage from flexible loads. By design, the grid-level services from flexible loads will be as controllable and predictable as a generator or fleet of batteries. Strict bounds on QoS will be maintained in all cases. The potential economic impact of these new resources is enormous. California plans to spend billions of dollars on batteries that will provide only a small fraction of the balancing services that can be obtained using demand dispatch. The potential impact on society is enormous: a sustainable energy future is possible with the right mix of infrastructure and control systems.

[1]  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.

[2]  Emanuel Todorov,et al.  Efficient computation of optimal actions , 2009, Proceedings of the National Academy of Sciences.

[3]  Ana Busic,et al.  Ordinary Differential Equation Methods for Markov Decision Processes and Application to Kullback-Leibler Control Cost , 2018, SIAM J. Control. Optim..

[4]  Jean-Yves Le Boudec,et al.  A Generic Mean Field Convergence Result for Systems of Interacting Objects , 2007, Fourth International Conference on the Quantitative Evaluation of Systems (QEST 2007).

[5]  P. Lions,et al.  Mean field games , 2007 .

[6]  Johanna L. Mathieu Modeling, Analysis, and Control of Demand Response Resources , 2012 .

[7]  Sean P. Meyn,et al.  Ancillary Service to the Grid Through Control of Fans in Commercial Building HVAC Systems , 2014, IEEE Transactions on Smart Grid.

[8]  Emanuel Todorov,et al.  Linearly-solvable Markov decision problems , 2006, NIPS.

[9]  L. Kadano More is the Same; Phase Transitions and Mean Field Theories , 2009 .

[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.  Passive dynamics in mean field control , 2014, 53rd IEEE Conference on Decision and Control.

[12]  Ana Busic,et al.  Demand Dispatch with Heterogeneous Intelligent Loads , 2016, HICSS.

[13]  Fred Schweppe,et al.  Homeostatic Utility Control , 1980, IEEE Transactions on Power Apparatus and Systems.

[14]  Ana Busic,et al.  Ergodic Theory for Controlled Markov Chains with Stationary Inputs , 2016, ArXiv.

[15]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[16]  Goran Andersson,et al.  Primary frequency control with refrigerators under startup dynamics and lockout constraints , 2015, 2015 IEEE Power & Energy Society General Meeting.

[17]  Miroslav Kárný,et al.  Towards fully probabilistic control design , 1996, Autom..

[18]  Munther A. Dahleh,et al.  Volatility of Power Grids Under Real-Time Pricing , 2011, IEEE Transactions on Power Systems.

[19]  Anne Condon,et al.  On the undecidability of probabilistic planning and related stochastic optimization problems , 2003, Artif. Intell..

[20]  Sean P. Meyn,et al.  Efficiency and marginal cost pricing in dynamic competitive markets with friction , 2007, 2007 46th IEEE Conference on Decision and Control.

[21]  Neil Immerman,et al.  The Complexity of Decentralized Control of Markov Decision Processes , 2000, UAI.

[22]  Ana Busic,et al.  Spectral Decomposition of Demand-Side Flexibility for Reliable Ancillary Services in a Smart Grid , 2015, 2015 48th Hawaii International Conference on System Sciences.

[23]  Ana Busic,et al.  Smart Fridge / Dumb Grid? Demand Dispatch for the Power Grid of 2020 , 2015, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[24]  Michael Chertkov,et al.  Ensemble Control of Cycling Energy Loads: Markov Decision Approach , 2017, ArXiv.

[25]  Peter Fairley Energy storage: Power revolution , 2015, Nature.

[26]  U. Rieder,et al.  Markov Decision Processes , 2010 .

[27]  Mario Paolone,et al.  GECN: Primary Voltage Control for Active Distribution Networks via Real-Time Demand-Response , 2014, IEEE Transactions on Smart Grid.

[28]  F. C. Schweppe,et al.  Power systems `2000': hierarchical control strategies , 1978, IEEE Spectrum.

[29]  Tyrone L. Vincent,et al.  Aggregate Flexibility of Thermostatically Controlled Loads , 2015, IEEE Transactions on Power Systems.

[30]  P. Weiss L'hypothèse du champ moléculaire et la propriété ferromagnétique , 1907 .

[31]  Sean P. Meyn,et al.  How demand response from commercial buildings will provide the regulation needs of the grid , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[32]  Duncan S. Callaway,et al.  State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance , 2013, IEEE Transactions on Power Systems.

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

[34]  Luminita Cristiana Totu Large Scale Demand Response of Thermostatic Loads , 2015 .

[35]  Arman C. Kizilkale,et al.  A class of collective target tracking problems in energy systems: Cooperative versus non-cooperative mean field control solutions , 2014, 53rd IEEE Conference on Decision and Control.

[36]  Arman C. Kizilkale,et al.  Mean field based control of power system dispersed energy storage devices for peak load relief , 2013, 52nd IEEE Conference on Decision and Control.

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

[38]  Ana Busic,et al.  Distributed control of a fleet of batteries , 2017, 2017 American Control Conference (ACC).

[39]  S. Mitter,et al.  Optimal control and nonlinear filtering for nondegenerate diffusion processes , 1982 .

[40]  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..

[41]  Ana Busic,et al.  Distributed randomized control for demand dispatch , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

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

[43]  Johanna L. Mathieu,et al.  State Estimation and Control of Heterogeneous Thermostatically Controlled Loads for Load Following , 2012, 2012 45th Hawaii International Conference on System Sciences.

[44]  Goran Strbac,et al.  Decentralized Control of Thermostatic Loads for Flexible Demand Response , 2015, IEEE Transactions on Control Systems Technology.

[45]  Yue Chen,et al.  Estimation and Control of Quality of Service in Demand Dispatch , 2018, IEEE Transactions on Smart Grid.