Control strategies for hybrid energy storage systems in DC grid

This thesis presents the study of different types of control strategies for the hybrid energy storage system (HESS). The proposed control methods are applied in the standalone and grid-connected mode of operation of HESS. The control strategies studied are mainly focused on the effective power-sharing among the battery and supercapacitor (SC) based HESS, tight DC link voltage regulation with the improved dynamic response and simple controller design. Over the years, DC microgrids have been widely investigated due to their flexibility in integrating the renewable sources and energy storage devices. The photovoltaic (PV) and winds are the two most popular renewable energy sources (RESs). The main issue with these RESs is their intermittent nature. The power generation from these RESs is easily affected by the varying environmental conditions. Thus, the microgrid requires energy storage systems (ESSs) which ensure a continuous power to the load demand. ESSs are the major part of the microgrids. ESSs complement the supply and demand mismatch and ensure the stability of the microgrid. In recent days, ESSs with the different operating characteristics are combined to form a HESS in the microgrid applications. In the HESS, the characteristics of the various ESSs can be effectively and efficiently utilized. The effective utilization of the HESS in the microgrid depends on the control techniques implemented to maintain the power balance through the HESS. Hence, the proper control method is of utmost importance for maintaining the power balance in the system with the load and generation variation. The faster joint control strategy is presented for the effective control of the battery and SC based HESS in the DC microgrid applications. The HESS handles the excess or deficit power to maintain the power balance in the system. In general, the battery supports the slow varying power requirement and the SC supports the high-frequency power demand. To extract the slow varying power demand and the transient power demand from the total power demand low-pass filter (LPF) is used. In conventional control methods, the power decomposition entirely depends on the performance of LPF. It also neglects the uncompensated power from the battery system. In the proposed control method, the uncompensated power from the battery system is handled by the SC system. This improves the overall system response to the PV generation fluctuations and load disturbances providing a faster DC link voltage restoration. The model predictive control (MPC) method has been intensively studied for the industrial process control for more than four decades. The MPC method has been extensively applied in many power electronics topologies in last few decades. The conventional MPC method uses a discrete system model and the complex optimization algorithms to solve the control objectives in the defined time period. This approach requires large computational resources to obtain optimal control parameters. This hinders its utilization for the system which requires a faster control action with less computational burden. The enumeration-based model predictive control (EMPC) methods are generally used to solve these issues. The EMPC methods require less computational resources and can be easily applied in the digital signal processor (DSP) control boards. The enumeration based model predictive current control (EMPCC) method for the battery and SC based HESS in the DC microgrid applications is proposed. The proposed control method is divided into two parts: (i) DC link voltage control loop and (ii) Inner current control loop. The PI compensator is used to design the DC link voltage control loop. The PI controller generates the total current references for the HESS. The inner current control loop is based on EMPCC method. The inner current control regulates the total current references for the HESS. The advantages of the proposed method are less computational complexity, simple controller design, direct manipulation of control signals without the pulse width modulation (PWM) modulator and better dynamic response. The study of the dynamic evolution control (DEC) method for the HESS control is also presented in this thesis. The DEC method uses the pre-defined trajectory known as evolution path to minimize the error state in the system. The difference between the reference value and the measured system output is known as error state. A new power sharing approach for the HESS is also introduced to calculate the current references for the battery and SC. The current is regulated using the DEC method. The control equation of the DEC method consists of a predictive term and a feed-forward term which handle the current from HESS to stabilize the DC link voltage. The performance of the DEC is compared with the conventional PI-based control methods with the simulation and experimental case studies. The results obtained exhibit a better dynamic performance compared to that of the conventional control techniques. The DEC method is simple to implement as it requires less computational resources. The grid-connected mode of operation of the HESS is another aspect of the research work presented in this thesis. A new energy management system (EMS) is proposed which help in effective and efficient utilization of the HESS in the grid-connected mode of operation. The main advantages of the proposed EMS and control method are better DC link voltage restoration to PV generation and load disturbances, efficient power balance between the battery and SC, dynamic power-sharing based on the battery state of charge (SOC), decreased rate of charge/discharge of the battery current, enhanced power quality in AC grid and smooth mode transitions. The scaled down experimental platform is built to verify the proposed control methods. The experimental case studies for the proposed control methods are carefully designed and implemented using the dSPACE 1103 real-time controller platform. The obtained simulation and experimental results are compared with that of the existing control methods. The results validate the effectiveness of the proposed control methods. The results are compared in terms of faster dynamic response and the complexity of the control method. The short-term and long-term experimental case studies are conducted. They show the proposed control methods are effective than the conventional control methods.Doctor of Philosoph