Abstract
In modern power systems, increasing flexibility has become an essential aspect of supporting the integration of renewable energy resources (RERs). The rapid fluctuations in power output from RERs and the reduced reliance on conventional thermal units necessitate flexible energy resources and demand‐side solutions. This study introduces a control framework that utilizes thermostatically controlled loads (TCLs) represented by electric water heaters (EWHs) to support the power system operation in delivering both primary and secondary frequency responses, especially for situations that lack adequate ramping capability from conventional resources. At the operator level, load frequency regulation is performed using a model predictive control (MPC) framework enhanced with integral action to control the frequency in a two‐area power system. The MPC is responsible for adjusting the thermal power plant outputs and providing the reference control signals for demand response agents (DRAs), which are responsible for load aggregation, monitoring, and control. DRAs based on state‐space statistical learning techniques develop an aggregate model within a linear system framework, which is used in a linear quadratic regulator (LQR) to optimally manage device operation and achieve the reference control signals. The developed approach can transform thousands of individual binary devices into a compact, fixed‐dimensional state‐space model. Therefore, the computational burden remains independent of the number of underlying devices, proving its viability for large‐scale power system implementations. This study considers 10,000 heterogeneous EWHs distributed in each control area. MATLAB simulations demonstrate that TCLs can effectively contribute to primary and secondary frequency control, offering a scalable solution for integrating TCLs into future grid operations and improving grid flexibility.