Dynamic performance improvement of an isolated wind-diesel system with intelligently controlled supercapacitor energy storage system

This paper aims to develop a supervisory control scheme to enhance the effectiveness and profitability of a small-rating super capacitor energy storage system (SCESS) used in load-frequency-control applications. The proposed approach, which uses one-step-ahead adaptive predictive control (APC), adeptly handles the operational limitations of the SCESS. For the purpose of online estimation of system parameters, the recursive least square (RLS) algorithm has been employed. The system can be characterized as a two input two output system, wherein the real and reactive powers required by the SCESS serve as control signals issued by the controller. The SCESS voltage is subject to constraints to limit energy trade within specific bounds. The proposed control scheme successfully maintains the voltage constraints of the SCESS while significantly reducing frequency and voltage deviations in the presence of two disturbances, viz; load disturbance and wind disturbance. The effectiveness of the proposed scheme is demonstrated through simulation experiments on an isolated hybrid wind diesel power system, which addresses several modeling and design aspects.