Ancillary Service to the Grid Using Intelligent Deferrable Loads

Ancillary Service to the Grid Using Intelligent Deferrable Loads

Joint research with Prabir Barooah, Ana Bušić, Yue Chen, and Jordan Ehren

Abstract (from our first paper on randomized policies): Renewable energy sources such as wind and solar power have a high degree of unpredictability and time-variation, which makes balancing demand and supply challenging.  One possible way to address this challenge is to harness the inherent flexibility in demand of many types of loads. We focus on pool pumps, and how they can be used to provide ancillary service to the grid for maintaining demand-supply balance.

One Million Pools in Florida that would consume one gigawatt of electricity if they all operate at once.

The control solution is based on the following steps

  1. A Markov Decision Process (MDP) model is introduced for an individual pool pump.
  2. Motivated by Todorov’s formulation, a randomized control architecture is proposed, motivated by the need for decentralized decision making, and the need to avoid synchronization that can lead to large and detrimental spikes in demand.
  3. An aggregate model for a large number of pools is obtained from a mean field limit.
  4. A linearization of the eigenvector problem in (2) provides an LTI-system approximation of the aggregate nonlinear model, with a scalar signal as the input and a measure of the aggregate demand as the output.

The final approximation is convenient for control design at the grid level.  Simulations are provided to illustrate the accuracy of the models and effectiveness of the proposed control approach.

Journal version link here

See bibliography below, and also: From the Sunshine State to the Solar State; Gator Engineers have a plan

2013 Tutorial at Cambridge – link here

2016 Simons Lecture – link here    Search the web for tutorials since 2016

The red plot below is data taken from the Bonneville Power Authority website during a typical week in July, 2013.

Generators are asked to ramp up and down to create these deviations in energy supply. The blue plot is the BPA signal passed through a low pass filter.   This low frequency component can be provided by flexible demand such as pool pumps or flexible manufacturing, such as Alcoa.

2014 ETH Lecture here:

These papers are among many that challenge the emphasis of traditional storage technologies to combat volatility in the power grid.

A high-level view of our work is contained in barbusmey14

The paper meybarbusyueehr14 (on which the abstract above is based) focuses on performance at the grid level, and only evaluates the average quality of service (QoS) to loads.

The sequel yuemeybus13 is an in-depth analysis of the impact of service to an individual load. Additional mechanisms are introduced to ensure strict constraints on QoS. State estimation questions are considered in  chebusmey15b and chebusmey16b

Another sequel busmey14 helps to explain why the grid-level control problem is easy!  Under certain conditions on the nominal model, the input-output system seen at the grid level is passive – a valuable property for control.

It is shown in busmey16a that passivity can be achieved by design

State estimation and Mean-Field Control with application to demand dispatch


Much more has been done since 2016, which means it is too hard to keep this page up to date!  

author = {Bu\v{s}i\'{c}, Ana and Meyn, Sean},
howpublished = {{US Patent 10,692,158 (submitted July, 2015)}},
title = {Using loads with discrete finite states of power to provide ancillary services for a power grid},
year = {2020}}

address = {Gainesville, FL, USA},
author = {Neil Cammardella},
school = {University of Florida},
title = {Creating Virtual Energy Storage Through Optimal Allocation And Control Of Flexible Power Consumption},
year = {2021}}

address = {Gainesville, FL, USA},
author = {Joel Mathias},
school = {University of Florida},
title = {Balancing the power grid with distributed control of flexible loads},
year = {2021}}

address = {Gainesville, FL, USA},
author = {Robert Walton Moye},
school = {University of Florida},
title = {Resource investments in organized markets: a case for central planning},
year = {2021}}

Florida is the sunshine state – why is there so little solar energy?  Why do we import natural gas and coal, when we have so much indigenous energy?

The typical answer:  The sun only shines in the day time,  and the level of solar energy depends on cloud cover.   There is volatility of solar energy (and energy from wind, too).   We are told that volatility is costly, because energy must be stored.   Batteries are very expensive.
Our response:  Storage is everywhere.  We do not need batteries.
Suppose our goal is to achieve 50% solar power in Florida.    We can
  • Use the techniques described in our recent papers to smooth out all of the volatility on time scales less than a few hours.  This is a significant claim – this “regulation” amounts to 10% of the electricity market in regions such as Texas.

There is harmony in this approach – in the case of climate control, the air conditioners gently ramp up when the sun is shining.

  • Use traditional generators to slowly ramp up and down to take care of the very low frequency volatility (the generators might supply very little at noon, and more at 7pm).
  • Follow the lead of Alcoa and other manufacturing companies that are flexible in their energy consumption:  Aluminum cans are produced when the sun is shining!


Title = {Spectral Decomposition of Demand-Side Flexibility for Reliable Ancillary Services in a Smart Grid},
Address = {Kauai, Hawaii},
Author = {Barooah, Prabir and Bu\v{s}i\'{c}, Ana and Meyn, Sean},
Booktitle = {Proc. {48th Annual Hawaii International Conference on System Sciences (HICSS)}},
Publisher = {University of Hawaii},
Year = {2015}}  LINK
Title = {Passive Dynamics in Mean Field Control},
Author = {Bu\v{s}i\'{c}, Ana and Meyn, Sean},
Journal = {ArXiv e-prints: arXiv:1402.4618},
Keywords = {Energy systems, Stochastic optimal control},
Note = {{53rd IEEE Conf. on Decision and Control (Invited)}},
Year = {2014}}  LINK
Title = {Individual risk in mean-field control models for decentralized control, with application to automated demand response},
Author = {Chen, Yue and Bu\v{s}i\'{c}, Ana and Meyn, Sean},
Booktitle = {{Proc. of the 53rd IEEE Conference on Decision and Control}},
Month = {Dec.},
Pages = {6425-6432},
Year = {2014}}  LINK       (and website)   You can find a 2017 extended version published in IEEE Trans. Auto. Control,  and mathematical foundations in Ergodic theory for controlled Markov chains with stationary inputs,  Ann. Appl. Probab., 2018
Author = {{Chen}, Y. and {Bu{\v s}i{\’c}}, A. and {Meyn}, S.},
Title = {Estimation and Control of Quality of Service in Demand Dispatch},
Journal = {ArXiv e-prints and IEEE Transactions on Smart Grid},
Year = 2016}  LINK       (and website)
Title = {Distributed Randomized Control for Demand Dispatch},
Author = {Bu\v{s}i\'{c}, Ana and Meyn, Sean},
Note = {{IEEE Conference on Decision and Control}},
pages = {6964-6971},
Year = {2016}}    LINK
address = {Gainesville, FL, USA},
author = {Yue Chen},
school = {University of Florida},
title = {{Markovian} demand dispatch design for virtual energy storage to support renewable energy integration},
year = {2016}}
Author = {Chen, Yue and Bu\v{s}i\'{c}, Ana and Meyn, Sean},
Journal = {CDC 2015, CoRR, and {IEEE Transactions on Auto. Control}},
Title = {State Estimation for the Individual and the Population in Mean Field Control with Application to Demand Dispatch},
Year = {2015}}
Title = {Ancillary service to the grid from deferrable loads: The case for intelligent pool pumps in {Florida}},
Author = {Meyn, Sean and Barooah, Prabir and Bu\v{s}i\'{c}, Ana and Ehren, Jordan},
Booktitle = {{Proceedings of the 52nd IEEE Conf. on Decision and Control}},
Month = {Dec},
Pages = {6946-6953},
Year = {2013}}  LINK
Author = {Meyn, Sean and Barooah, Prabir and Bu\v{s}i\'{c}, Ana and Chen, Yue and Ehren, Jordan},
Journal = TAC,
Month = {Nov},
Number = {11},
Pages = {2847-2862},
Title = {Ancillary Service to the Grid Using Intelligent Deferrable Loads},
Volume = {60},
Year = {2015}}  LINK
Title = {Smart Fridge / Dumb Grid? Demand Dispatch for the Power Grid of 2020},
Author = {Joel Mathias and Rim Kaddah and Ana Bu\v{s}i\'{c} and Meyn, Sean},
Booktitle = {Proc. {49th Annual Hawaii International Conference on System Sciences (HICSS) }. Online at arXiv:1509.01531},
Year = {2016}}    Link HERE  (and website)