Power Management and Control of DC Microgrid with Hybrid Energy Storage System

Abstract

This thesis presents the study on power management of DC microgrids, which comprises of control strategies for hybrid energy storage system (HESS) and maximum power point tracking method for photovoltaic (PV) source. The target of the control strategies for HESS proposed in this thesis is regulating the output voltage with less dynamic error and settling time, and the MPPT method developed in this thesis is focusing on finding the maximum power point fast and accurately. Future electrical power system focuses on increasing the utilisation of renewable energy sources (RES), owing to the depletion of fossil fuel based energy sources. However, the RES cannot account for the load demand alone because of their instability and intermittent. For example, the generation of PV is affected by temperature, irradiance and shadings effects easily, which may cause the system to be unstable. Microgrid is a solution for the reliable integration of distributed renewable generation sources, utilising the energy storage system (ESS). In microgrids, HESS, which consists of batteries and supercapacitors (SC), is used to maintain power balance between the generation and load side. A sudden large change in the RES generation or the load demand might result in long rise/fall-time and large over/undershoot, causing high dynamic error in the output voltage. Hence, developing the efficient control strategies to control the charging/discharging behaviors of HESS is very important in microgrids. Rising and falling edge compensation based control strategy obtains the current reference of the HESS by PI controllers. Then it is translated to a positive falling signal, which can be compensated by an adaptive compensation related to the dynamic voltage error. The proposed control strategy significantly reduces the rise and settling-times, making a faster response, with reduced voltage fluctuation. In model predictive (MPC) and iterative learning control (ILC) based hybrid control method, ILC is designed to correct the dynamic current references of the HESS to compensate for the steady-state error caused by the power loss in the power electronic devices. MPC is used to track the current reference of the HESS. An improved quadratic programming algorithm is proposed to reduce the iterations in online optimization. MPC based on dynamic power loss prediction method is a dual layer MPC method to control the charging/discharging behavior effectively and efficiently. The dynamic power loss can be predicted by the predicted current and duty ratio in the primary layer MPC. The predicted dynamic power loss is one of the state variables in the secondary layer MPC to generate optimal power reference for primary layer MPC, which can compensate the voltage ripple caused by dynamic power loss and reduce the dynamic error and settling time. A hybrid MPPT method based on ILC and perturb & observe (P&O) algorithm is proposed in this thesis. ILC can deal with the periodic variations to eliminate the steady-state oscillations and errors, when the operation point is close to the MPP or a small irradiance variation occurs. In the proposed hybrid MPPT technique, a high frequency power P&O method without deadtime is used to improve the dynamic response when the irradiance changes rapidly. A 48V microgrid setup with PV source and HESS is built for this study, validations of all control strategies are done by simulation and hardware experiments.