Best-Response Planning of Thermostatically Controlled Loads under Power Constraints

Frits de Nijs, Matthijs T. J. Spaan, and Mathijs M. de Weerdt. Best-Response Planning of Thermostatically Controlled Loads under Power Constraints. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 615–621, 2015.




Renewable power sources such as wind and solar are inflexible in their energy production, which requires demand to rapidly follow supply in order to maintain energy balance. Promising controllable demands are airconditioners and heat pumps which use electric energy to maintain a temperature at a setpoint. Such Thermostatically Controlled Loads (TCLs) have been shown to be able to follow a power curve using reactive control. In this paper we investigate the use of planning under uncertainty to pro-actively control an aggregation of TCLs to overcome temporary grid imbalance. We present a formal definition of the planning problem under consideration, which we model using the Multi-Agent Markov Decision Process (MMDP) framework. Since we are dealing with hundreds of agents, solving the resulting MMDPs directly is intractable. Instead, we propose to decompose the problem by decoupling the interactions through arbitrage. Decomposition of the problem means relaxing the joint power consumption constraint, which means that joining the plans together can cause overconsumption. Arbitrage acts as a conflict resolution mechanism during policy execution, using the future expected value of policies to determine which TCLs should receive the available energy. We experimentally compare several methods to plan with arbitrage, and conclude that a best response-like mechanism is a scalable approach that returns near-optimal solutions.

BibTeX Entry

  author =       {Frits de Nijs and Matthijs T. J. Spaan and Mathijs
                  M. de Weerdt},
  title =        {Best-Response Planning of Thermostatically
                  Controlled Loads under Power Constraints},
  booktitle =    {Proceedings of the Twenty-Ninth AAAI Conference on
                  Artificial Intelligence},
  year =         2015,
  pages =         {615--621}

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