Development and implementation of intelligent controllers for renewable generators and load demands

Abstract

The ever-growing emission of greenhouse gas has exacerbated the climate change, which forces the electric power industries to replace the fossil fuel-based power generation with the eco-friendly renewable generation including but not limited to solar photovoltaic and wind generation. Since power grids in most countries have the obligation to fully receive the electricity generated by renewables, their intermittent characteristic has imposed critical challenges to the power system operation and control. Especially, the increasing penetration of renewable generation may cause the power imbalance and voltage fluctuations at the points of common coupling (PCC). Besides, the occasional grid faults may cause unexpected disconnection of integrated renewable generations, which virtually jeopardize the operation stability of power systems. These issues raise critical doubts over the further deployment of renewable generation and their power grid integration, which craves for effective solutions. To maintain the power grid stability against faults and cope with the voltage fluctuation issues, novel and intelligent controllers based on the power electronic-interfaced renewable generation and load demands are reported in this thesis. Specifically, to ride through the grid faults, the traditional low-voltage-ride-through (LVRT) controllers prevent the overcurrent and provide reactive current injection (RCI) as per the latest grid codes. However, these controllers generally sacrifice energy harvesting, which inevitably leads to compromised voltage-supporting effects when renewables are connected within the distribution networks (DN) with a relatively low X/R ratio. In view of this, the alternative approach of active current injection (ACI) in DN during fault periods is mathematically analyzed in this thesis. Subsequently, a novel LVRT control method is proposed for the photovoltaic (PV) generation system, which distinguishes itself from the existing methods with the dual-objective optimization of ACI and PV energy harvesting. An adaptive DC-link voltage control structure is proposed to reserve extra energy and accelerate the post-fault recovery. Following the similar philosophy, an advanced coordinative LVRT control method is proposed to exploit the maximum potential of energy harvesting in the wind-PV hybrid renewable energy system (HRES). To deal with different voltage dips and working conditions, four control processes on DC-link voltage, turbine rotating speed, PV output power and pitch angle are coordinated in an optimized way. Besides improving the system stability with generation-side control, electric spring, which is connected in series with non-critical loads to form a smart load, is investigated to mitigate node voltage fluctuations from demand-side management. Still, the existing works on ES generally assume predominately inductive line impedances. This assumption is flawed in DN and may lead to deteriorated voltage regulation effects. To address this, an innovative γ control method is proposed to enhance voltage regulation performances of the ES. Equivalent regulation points and optimal operating regions are analytically derived. The proposed γ control embeds a smart-load model (SLM) and enables adaptive control boundaries. Compared with the traditional control, it can significantly avoid the suboptimal or positive-feedback operations resulting from the changing line impedances. Simulation and experimental works have been carried out to verify the effectiveness of the proposed control methods and explore their potential application values.