Ancillary Service to the Grid Using Intelligent Deferrable Loads
  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. Introduced in this paper is a technique for decentralized control for automated demand response that can be used by grid operators as ancillary service for maintaining demand-supply balance.   A Markovian Decision Process (MDP) model is introduced for an individual load. 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. An aggregate model for a large number of loads is then developed by examining the mean field limit. A key innovation is 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. This makes the approximation particularly convenient for control design at the grid level.   The second half of the paper contains a detailed application of these results to a network of residential pools. Simulations are provided to illustrate the accuracy of the approximations and effectiveness of the proposed control approach. 
Sean Meyn, Prabir Barooah, Ana Bu\v{s}i\'c, Yue Chen, and Jordan Ehren
math.OC cs.SY