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1. Virtual Energy Storage from Flexible Loads: Distributed Control with QoS Constraints.

   https://doi.org/10.1007/978-3-319-98310-3_6  

   Barooah, Prabir (September 2018, Smart Grid Control. Power Electronics and Power Systems)

   Stoustrup J., Annaswamy A. (Ed.)

   Loads are expected to help the power grid of the future in balancing the highs and lows caused by intermittent renewables such as solar and wind. With appropriate intelligence, loads will be able manipulate demand around a nominal baseline so that the increase and decrease of demand appears like charging and discharging of a battery, thereby creating a virtual energy storage (VES) device. An important question for the control systems community is: how to control these flexible loads so that the apparently conflicting goal of maintaining consumers’ quality of service (QoS) and providing reliable grid support are achieved? We advocate a frequency domain thinking of handling both of these issues, along the lines of a recent paper. In this article, we discuss some of the challenges and opportunities in designing appropriate control algorithms and coordination architectures in obtaining reliable VES from flexible loads. 

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2. Distributed rate control of smart solar arrays with batteries

   https://doi.org/10.3389/friot.2023.1129367  

   Bovornkeeratiroj, Phuthipong; Lee, Stephen; Iyengar, Srinivasan; Irwin, David; Shenoy, Prashant (June 2023, Frontiers in the Internet of Things)

   Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback of increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion of proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. We also study the benefits of using energy storage when we rate control solar. To do so, we present a decentralized algorithm to charge and discharge batteries for each smart solar. Our evaluation on a city-scale dataset shows that our approach enables 2.6× more solar penetration while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30× faster convergence. Finally, we demonstrate energy storage can help netmeter more solar energy while ensuring fairness and grid constraints are met. 

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3. A model-free method for learning flexibility capacity of loads providing grid support

   https://doi.org/10.23919/ACC50511.2021.9483207  

   Coffman, Austin R.; Barooah, Prabir (May 2021, American Control Conference)

   null (Ed.)

   Flexible loads are a resource for the Balancing Authority (BA) of the future to aid in the balance of power supply and demand. In order to be used as a resource, the BA must know the capacity of the flexible loads to vary their power demand over a baseline without violating consumers' quality of service (QoS). Existing work on capacity characterization is model-based: They need models relating power consumption to variables that dictate QoS, such as temperature in the case of an air conditioning system. However, in many cases the model parameters are not known or are difficult to obtain. In this work, we pose a data driven capacity characterization method that does not require model information, it only needs access to a simulator. The capacity is characterized as the set of feasible spectral densities (SDs) of the demand deviation. The proposed method is an extension of our recent work on SD-based capacity characterization that was limited to the case where the loads dynamic model is completely known. Numerical evaluation of the method is provided, which compares our approach to the model-based solution of our past work. 

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4. A Study of Virtual Energy Storage From Thermostatically Controlled Loads Under Time-Varying Weather Conditions

   Coffman, Austin; Busic, Ana; Barooah, Prabir (January 2018, 5th International High Performance Buildings Conference)

   We propose a control architecture for distributed coordination of a collection of on/off TCLs (thermostatically con- trolled loads), such as residential air conditioners, to provide the same service to the power grid as a large battery. A key constraint is to ensure that consumers’ quality of service (QoS) is maintained. Our proposal involves replac- ing the thermostats at the loads by a randomized controller, following recent proposals in this direction. The new local controller has a tunable parameter that serves as the control command from the balancing authority (BA). Com- pared to prior work in this area, our proposed architecture can handle large disturbances from the outside temperature. Weather-induced disturbance also imposes an algorithm-independent limit on the capacity of the virtual energy storage the loads can provide. This key limitation, which was ignored in prior work, is incorporated in our formulation in a principled manner. 

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5. Large-Scale Invariant Sets for Safe Coordination of Thermostatic Loads

   https://doi.org/10.23919/ACC50511.2021.9483385  

   Jang, Sunho; Ozay, Necmiye; Mathieu, Johanna L. (January 2021, American Control Conference (ACC) 2021)

   Systems often face constraints at multiple levels. For example, in coordinating a collection of thermostatically controlled loads to provide grid services, the controller must ensure temperature constraints for each load (local constraints) and distribution network constraints (global constraints) are satisfied. In this paper, we leverage invariant sets to ensure safe coordination of systems with both local and global constraints. Specifically, we develop a method for constructing a controlled invariant set for a collection of subsystems, modeled as transition systems, to ensure they indefinitely satisfy the constraints, based on cycles in individual transition systems. Then, we develop a control algorithm that keeps the state inside the maximal controlled invariant set.We apply these algorithms to a demand response problem, specifically, the tracking of a power trajectory (e.g., a frequency regulation signal) by a population of homogeneous air conditioners. The algorithm simultaneously maintains local temperature requirements and aggregate power consumption limits, ensuring the control is nondisruptive to consumers and benign to the distribution network. 

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