Maximum Reserved Capacity of Aggregated Electric Water Heaters Virtual Battery for Peak Management

Abstract

This paper introduces new insights into the integration of thermostatically controlled loads (TCLs) as hybrid energy sources for grid ancillary and demand response services. Leveraging a generalized virtual battery (VB) model emerges as an effective approach to determine maximum reserve capacity of these aggregated devices. Our research extends the VB model and endeavors to establish a pragmatic framework for EWH control for peak shaving and managing the payback effect. The importance of aggregator capacity in mitigating the impacts of TCL external control, such as customer comfort and safety, is emphasized. Electric water heaters (EWHs) are used as the residential TCL device given their extensive availability and thermal capacity. Two TCL control scenarios, OFF control and ON/OFF control are compared using the Model Predictive Control (MPC) method. The ON/OFF control was found to improve peak shaving capability by approximately 47% when compared to the more rudimentary OFF control mechanism. The main contributions of this study are threefold: assessment of maximum reserve capacity using a modified VB model, creation of a reference control signal based on this result, and development of effective control strategies for managing the payback effect when maximal reserves are utilized. The robustness of the maximum capacity estimation is analyzed through a sensitivity analysis mainly driven by variations in hot water consumption and communication loss. Comprehensive and comparative simulation results show improved capabilities in the utilization of the maximum reserved energy of loads, minimizing the expected payback to avoid additional energy peaks and assessing the impacts of external factors that can affect the expected maximum capacity of the VB.

Keywords

• Demand response
• peak management
• model predictive control
• thermostatically controlled loads
• virtual battery