Biblio

Transactive or market-based coordination strategies have recently been proposed to control the aggregate demand of a large number of electric loads. While several operational benefits can be achieved, such as reducing the demand below distribution feeder capacity limits and providing users with flexibility to consume energy based on the price they are willing to pay, our work focuses on studying the impact of market based coordination mechanisms on load synchronization and power oscillations. We adopt the transactive energy framework and apply it to a population of thermostatically controlled loads (TCLs). We present a modified TCL switching logic that takes into account market coordination signals, alongside the natural switching conditions. Our studies suggest that several factors, in a market-based coordination mechanism, could contribute to load synchronism, including sharp changes in market prices broadcast to loads, lack of diversity in user specified bid curves, feeder limits being encountered periodically and being set too low, and the form of user bid curves. All these factors can contribute in various ways to synchronization of TCL behavior and lead to power oscillations. The case studies provide novel insights into challenges associated with market-based coordination strategies, thereby providing a basis for modifications that address those issues.

Aggregate models are used in the analysis and control of large populations of thermostatically controlled loads (TCLs), such as air-conditioners and water heaters. The fidelity of such models is studied by analyzing the influences of noise and parameter heterogeneity on TCL aggregate dynamics. While TCLs can provide valuable services to the power systems, control may cause their temperatures to synchronize, which may then lead to undesirable power oscillations. Recent works have shown that the aggregate dynamics of TCLs can be modeled by tracking the evolution of probability densities over discrete temperature ranges or bins. To accurately capture oscillations in aggregate power, such bin-based models require a large number of bins. The process of obtaining the Markov state transition matrix that governs the dynamics can be computationally intensive when using Monte Carlo based system identification techniques. Existing analytical techniques are further limited as noise and heterogeneity in several thermal parameters are difficult to incorporate. These challenges are addressed by developing a fast analytical technique that incorporates noise and heterogeneity into bin-based aggregate models. Results show the identified and the analytical models match very closely. Studies consider the influence of model error, noise and parameter heterogeneity on the damping of oscillations. Results demonstrate that for a specific bin width, the model can be invariant to quantifiable levels of noise and parameter heterogeneity. Finally, a discussion is provided of cases where existing bin models may face challenges in capturing the influence of heterogeneity.