Plug-and-Play Control Strategy for Distributed Energy Storage Systems Participating in Frequency Regulation

The frequency stability of new power systems dominated by renewable energy faces severe challenges due to reduced rotational inertia, increased disturbance impacts, and insufficient frequency regulation resources.Distributed energy, typified by distributed energy storage systems (DESSs), has emerged as a novel and effective means to supplement system frequency regulation. However, the conflict between user's autonomy demands and the deterministic requirements of the grid limits the application of DESSs in ancillary frequency regulation. To address this issue, this study first proposes a flexible aggregation and control model for DESSs, in which an aggregator generates real-time incentives based on the deviation between power demand and actual output to guide the response of distributed storage units. Next, prospect theory is applied to analyze the decision-making psychology of distributed energy users. Based on the incentive-response characteristic curve under bounded rationality, a simulation environment that better reflects real-world conditions is constructed for studying DER participation in frequency regulation. Furthermore, a flexible control strategy is proposed, which broadcasts dynamic incentive signals to distributed storage users. By employing dynamic linearization, the nonlinear frequency regulation problem is transformed into a parameter estimation task for a dynamically linearized system. A model-free adaptive incentive control method is employed to address uncertainties in user response and the difficulty of modeling user clusters. This method dynamically linearizes the aggregated response model using real-time data from the cluster control system and outputs an optimal real-time incentive signal. Finally, a case study based on a single-area power system is conducted in Matlab/Simulink. Under both step load fluctuation and continuous load fluctuation scenarios, the proposed method demonstrates effective frequency tracking performance, with the maximum frequency deviation controlled within 0.0015 p.u. This validates the adaptability and effectiveness of the proposed incentive-based control method.