Control of Adaptive Renewable Energy System with Distributed Energy Storage

Abstract

Nowadays, energy management in a standalone system consists of distributed renewable energy sources, and distributed energy storage has been a significant challenge. An adaptable system architecture addresses this issue. The thesis proposes a solar photovoltaic system (SPVS) connected to distributed energy storage (battery) using two DC-DC converters. The proposed system would consist of a DC-DC boost converter (BC) and a bidirectional switched quasi-Z-source DC-DC converter (BSQZSDC). These converters coordinate the management of power sources, energy storage systems, and DC loads. The boost converter is connected to the SPVS to harvest maximum power from the PV panel using the maximum power point tracking (MPPT) algorithm. The MPPT perturbation period is largely affected by the actual insolation conditions at weather in the PV generation area. In practice, the traditional MPPT approaches often use a perturbation step size that is set according to the trade-off between efficiency and tracking speed requirements. Apart from this, during sudden changes in irradiance conditions, the conventional P&O algorithm gets confused when tracking the MPP and drifts away. This effect is observed to be severe for a rapid change in irradiance conditions. Thus, it is necessary to modify these algorithms to have an adaptive step size for tracking MPP at a faster rate without having any deviations. Therefore, to determine the optimum MPPT perturbation period and the magnitude of the perturbation voltage, it is necessary to analyze the performance of the MPPT controller for actual insolation conditions in a real weather environment. So, an MPPT algorithm with an improved variable step size (adaptive) is implemented in this research work. The proposed BSQZSDC can provide high gain at a moderate duty cycle. The low-voltage side of the BSQZSDC is connected to the distributed energy storage (battery), and the high-voltage side is connected to the DC-link side. The BSQZSDC operates as a boost converter when load demand exceeds the energy generated by the PV source and acts as a buck converter when the load is less than the generation. The proposed SPVS system with distributed energy storage has many operational modes. In each operating mode, regulating the voltage is the primary goal. Different controller techniques are used to regulate dc-link voltage. Firstly, the performance of the proposed system with distributed energy storage is examined using a dual loop proportional Integral controller (DLPIC). The linear control design techniques were used when the converter remained at an 16 operating point. The DLPIC is commonly designed by linearizing the model at a particular operating point. The transient behaviors of BSQZSDC with PI controllers are poor under large-signal perturbations. A nonlinear controller can be used to solve the above problem. Several nonlinear controller approaches were developed for DC-DC converters. Nonlinear controllers such as double integral sliding mode (DISMC) and Lyapunov function-based controllers have also been designed for the entire system. The DISMC has a quicker response for a wide range of operating circumstances with a significant reduction in steady state error (SSE). However, the transient response is significantly improved due to the Lyapunov-function-based controller’s global exponential stable feature. The performances of these controllers have been evaluated and compared in terms of transient and steady-state responses. Finally, flexible architecture develops coordination in proper energy management among PV generation, energy storage systems, and DC load. This flexible architecture presented in this thesis provides proper energy management in the stand-alone system. The major contributions of the proposed work are the development of an adaptive MPPT technique for effective operation in actual climate conditions, designing a bidirectional converter that provides wide gain at a moderate duty cycle, and designing linear and nonlinear controllers for energy management in the proposed system.