Aggregate capacity for TCLs providing virtual energy storage with cycling constraints

Austin Robert Coffman (1) , Ana Bušić (2, 3) , Prabir Barooah (4)

1 UF - University of Florida [Gainesville]
2 DYOGENE - Dynamics of Geometric Networks
3 LINCS - Laboratory of Information, Network and Communication Sciences
4 UF|MAE - Department of Mechanical and Aerospace Engineering [Gainesville]

The coordination of thermostatically controlled loads (TCLs) is challenging due to the need to meet individual loads quality of service (QoS), such as indoor temperature constraints. Since these loads are usually on/off type, cycling rate is one of their QoS metrics; frequent cycling between on and off states is detrimental to them. While significant prior work has been done on the coordination of air conditioning TCLs, the question of cycling QoS has not been investigated in a principled manner. In this work we propose a method to characterize aggregate capacity of a collection of air conditioning TCLs that respects the loads cycling rate constraints (maximum number of cycles in a given time period). The development is done within the framework of randomized local control in which a load makes on/off decisions probabilistically. This characterization allows us to propose a reference planning problem to generate feasible reference trajectories for the ensemble that respect cycling constraints. The reference planning problem manifests itself in the form a Nonlinear Programming problem (NLP), that can be efficiently solved. Our proposed method is compared to previous methods in the literature that do not enforce aggregate cycling. Enforcing individual cycling constraint without taking that into account in reference generation leads to poor reference tracking.

Domaines: Optimisation et contrôle [math.OC]; Energie électrique; Systèmes et contrôle [cs.SY]

Austin Robert Coffman, Ana Bušić, Prabir Barooah. Aggregate capacity for TCLs providing virtual energy storage with cycling constraints. CDC 2019 - 58th IEEE Conference on Decision and Control, Dec 2019, Nice, France. ⟨hal-02425977⟩