Advanced control in smart microgrids
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This thesis presents various advanced control strategies in smart microgrid applications. In recent years, due to the rapid depletion of fossil fuels, increasing demand of electricity, and more strict compulsory government policies on reduction of greenhouse gas emissions, renewable energy technologies are attracting more and more attentions and various types of distributed generation (DG) sources, such as wind turbine generators and solar photovoltaic (PV) panels, are being connected to low-voltage distribution networks. Because of the intermittent nature of the renewable energy sources, it would be a good idea to connect these DG units together with energy storage units and loads to form a local micro power system, known as microgrid. This PhD thesis project aims to develop new and competitive control methods for microgrid applications. Based on a review of the state of the art of the wind power techniques, a new predictive direct control strategy of doubly fed induction generator is proposed. This method can achieve fast and smooth grid synchronization, and after grid connection, the active and reactive power can be regulated flexibly, which enables the wind power systems contributing to the grid voltage support and power quality improvement. The proposed strategy is simple and reliable, and presents excellent steady-state and dynamic performance. A new control approach using the model predictive scheme is developed for a PV system in microgrid applications. In the islanded operation, the inverter output voltage is controlled stably for the local loads. A simple synchronization scheme is introduced to achieve seamless transfer, and after being connected to the utility grid, the PV system can inject both active and reactive power into the grid flexibly within its capacity. As the capacity of DGs getting larger, the power conversion efficiency becomes more important. In order to reduce the switching loss, a multi-objective model-predictive control strategy is proposed for the control of high power converters. By revising the cost function properly, the switching frequency can be reduced considerably without deteriorating the system performance. The control strategy is simplified using a graphical algorithm to reduce the computational burden, which is very useful in practical digital implementation where high sampling frequency is required. The proposed method is very flexible and can be employed in both AC/DC and DC/AC energy conversions in microgrids. For a microgrid consisting of several DG units, various system level control methods are studied. A novel flux droop control approach is developed for parallel-connected DGs by drooping the inverter flux instead of drooping the inverter output voltage. The proposed method can achieve autonomous active and reactive power sharing with much lower frequency deviation and better transient performance than the conventional voltage droop method. Besides, it includes a direct flux control (DFC) algorithm, which avoids the use of proportional-integral (PI) controllers and PWM modulators. For a microgrid system consisting of a 20 kW PV array and a 30 kW gas microturbine, a coordinated control scheme is developed for both islanded and grid-connected operations. The experimental results from a renewable energy integration facility (REIF) laboratory confirmed the feasibility of the control strategy. The response of this microgrid under the condition of grid faults is investigated and the relevant protection mechanism is proposed. Given the intermittent nature of the renewable energy sources, and the fluctuated load profile, an appropriate solution is to use energy storage systems (ESS) to absorb the surplus energy in the periods when the power production is higher than the consumption and deliver it back in the opposite situation. In order to optimize the power flow, a model predictive control (MPC) strategy for microgrids is proposed. This method can flexibly include different constraints in the cost function, so as to smooth the gap between the power generation and consumption, and provide voltage support by compensating reactive power during grid faults.
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