Control of SMES systems in distribution networks with renewable energy integration: A perturbation estimation approach

Abstract

Electrical energy storage system (EESS) plays a crucial role to handle the intermittency and randomness of renewable energy, such that the power reliability can be significantly enhanced. This paper attempts to design an adaptive fractional-order sliding-mode control (AFOSMC) approach for a typical EESS technology, e.g., superconducting magnetic energy storage (SMES) systems, to improve its dynamical responses against varirous operation conditions. At first, a sliding-mode state and perturbation observer (SMSPO) is applied to estimate the combined effect of unmodelled dynamics, parameter uncertainties, and external disturbances of SMES systems. Then, a fractional-order sliding-mode control (FOSMC) is utilized to completely compensate the perturbation estimat, such that a noticeable robustness can be achieved. Moreover, only the dq-axis currents need to be measured while the perturbation estimate replaces its upper bound, thus AFOSMC be easily achieved with appropriate control costs. For the purpose of validating its control performance, a distribution network involving SMES system with renewable energy penetration is studied. The control performance of conventional proportional-integral-derivative (PID) control, interconnection and damping assignment passivity-based control (IDA-PBC), sliding-mode control (SMC), and fractional-order sliding-mode control (FOSMC) is compared to that of AFOSMC under three scenarios. Simulation results show that AFOSMC can greatly outperform other approaches in both tracking speed and overall costs, e.g., its active power error is only 63.55%, 83.44%, 69.60%, and 76.67% of that of PID control, IDA-PBC, SMC, and FOSMC under reactive and active power supply, while the required control costs is only 76.69%, 91.28%, 83.50, and 86.76% to the above three controllers. Finally, a hardware-in-the-loop (HIL) test based on dSpace is implemented to verify its practicability under various scenarios.