Modeling and Control of A Hybrid Sustainable Energy System

Abstract

Harnessing of power from non-conventional energy generators such as wind energy conversion system or photovoltaic system is a proven choice for power generation. Some convincing features like zero emission of greenhouse gases, their availability in abundance, and requirement of low maintenance cost are offered by renewable generators. Though generation of power from such sources faces challenge of intermittency due to dependency on variable climatic conditions. Thus, integration of backup sources like fuel cell-electrolyzer, super-capacitors, and battery are inevitable in order to enhance the overall system reliability, efficiency and sustainability of the system. A combination of a renewable energy generator along with back up sources leads to the formation of hybrid sustainable energy system (HSES). If such systems can share a considerable quantum of energy in a geographical area then grid integration of such system is a viable option. Grid integration of HSES enhances the reliability of the utility grid, and such integration attributes to a successful grid support. Yet, the grid integration of such renewable generation-based hybrid energy system faces the challenge of uncertain power system dynamics due the different characteristics of integrated renewable energy resources. Occurrence of faults at the utility end and variation in the load demand at the point of common coupling can affect the operation of HSES. In addition, unpredictable change in climatic conditions may affect the power generation from the sustainable energy generators. In order to solve such challenges, this research work considers design and modeling modalities, fault ride through (FRT) capability enhancement strategies, and power management control strategies in renewable energy-based hybrid energy systems. This thesis majorly considers three distinct configurations of grid-connected hybrid energy systems. The first configuration considers a permanent magnet synchronous generator (PMSG) based wind energy conversion system and a fuel cell (FC) system interconnected at a common dc bus. The second configuration considers a PMSG/Battery/FC-Electrolyzer based hybrid system, while; the third configuration considers doubly-fed induction generator (DFIG)/supercapacitor (SC)/ FC-Electrolyzer based hybrid system. In all the considered configurations, an effort is made to enhance the FRT capability of various active FRT schemes for dc-dc converter control and inverter control system. Specifically, a new feed-forward based FRT control scheme for the inverter current control is recommended where, new current references of the dq-axis frame are derived by tracking the positive sequence power. The regulation of current references is performed by the application of artificial intelligence techniques in order to achieve an enhanced ride through control in the first and third configuration. In order to solve the problem of generation intermittency and variations in the load demand, power management control is essential. Therefore, this thesis also proposes two new power management algorithms that are based on use of reduced-rules by monitoring state of charge of battery and net power of the hybrid system. The request made by the grid operator is kept on priority in all the power management schemes. Further, integrated power management strategy and low-voltage ride through (LVRT) control schemes are also proposed in the second and third configurations. The same is realised by adoption of mode selection-based control between grid feeding mode and LVRT mode in the inverter control structure. The LVRT capability is enhanced through multi-dimensional control approaches in the third hybrid energy system configuration. New control techniques, namely, OFF-MPPT control for the rotor side converter is performed for torque ripple nullification in DFIG during grid voltage sag. Mode selection-based switching control for the dc-dc converter interfaced with SC is adopted for smoothening the dc-link voltage transients under voltage sag scenario. This research work also suggests the adaption of a feed-forward control based adaptive neuro-fuzzy inference system-based control for the grid side converter of DFIG in order to effectively perform LVRT control functionalities. In all the considered hybrid energy system, the proposed power management algorithms and FRT control algorithms are validated by detailing relevant mathematical models. Simultaneously, various case studies such as variable wind speed, load demand, grid power request and occurrence of balanced and unbalanced grid voltage sags are undertaken. The extensive simulation and analysis works are performed using standard MATLAB/Simulink platform. The simulation results obtained from the testbed have validated the design, modeling and control of the proposed HSES architectures.