Advanced Control of Small-Scale Power Systems with Penetration of Renewable Energy Sources

Stability, protection, and operational restrictions are important factors to be taken into account in a proper integration of distributed energy. The objective of this research is presenting advanced controllers for small-scale power systems with penetration of renewable energy sources resources to ensure stable operation after the network disturbances. Power systems with distributed energy resources are modeled and controlled through applying nonlinear control methods to their power electronic interfaces in this research. The stability and control of both ac and dc systems have been studied in a multi-source framework. The dc distribution system is represented as a class of interconnected, nonlinear discrete-time systems with unknown dynamics. It comprises several dc sources, here called subsystems, along with resistive and constant-power loads (which exhibit negative resistance characteristics and reduce the system stability margins.) Each subsystem includes a dc-dc converter (DDC) and exploits distributed energy resources (DERs) such as photovoltaic, wind, etc. Due to the power system frequent disturbances this system is prone to instability in the presence of the DDC dynamical components and constant-power loads. On the other hand, designing a centralized controller may not be viable due to the distance between the subsystems (dc sources.) In this research it is shown that the stability of an interconnected dc distribution system is enhanced through decentralized discrete-time adaptive nonlinear controller design that employs neural networks (NNs) to mitigate voltage and power oscillations after disturbances have occurred. The ac power system model is comprised of conventional synchronous generators (SGs) and renewable energy sources, here, called renewable generators (RGs,) via grid-tie inverters (GTI.) A novel decentralized adaptive neural network (NN) controller is proposed for the GTI that makes the device behave as a conventional synchronous generator. The advantage of this modeling is that all available damping controllers for synchronous generator, such as AVR (Automatic Voltage Regulator) + PSS (Power System Stabilizer), can be applied to the renewable generator. Simulation results on both types of grids show that the proposed nonlinear controllers are able to mitigate the oscillations in the presence of disturbances and adjust the renewable source power to maintain the grid voltage close to its reference value. The stability of the interconnected grids has been enhanced in comparison to the conventional methods

Discrete time modeling and control of DC/DC switching converter for solar energy systems

Distributed generation networks including micro grids benefit from solar cells that are controlled by dc-dc converters. In this research a nonlinear discrete-time model for a buck converter tied to a solar system is derived with unknown internal dynamics. Then, adaptive neural network (NN) controller is employed to enhance stability of dc-dc converter connected to grid-tie inverter (GTI) in the presence of power system disturbances. The NN weights are tuned online by using a novel update law. By using Lyapunov techniques, all signals can be shown to be uniformly ultimately bounded (UUB). In addition, the interaction of the converter with the GTI is investigated to assure stability of the entire interconnected system while the GTI is controlled via a novel stabilizer similar to power system stabilizer (PSS). The proposed nonlinear discrete-time converter controller along with the GTI, equipped with PSS, is simulated in Matlab Simulink environment. The results have highlighted the effectiveness of the proposed modeling and controller design

Sliding mode control for Maximum Power Point Tracking of photovoltaic inverters in microgrids

In this paper the design of sliding mode controllers for Maximum Power Point Tracking (MPPT) of a photovoltaic inverter in microgrids is presented. A master-slave configuration of the microgrid is considered in islanded operation mode where the photovoltaic Distributed Generation unit (DGu) serves as a slave. The DGu is also affected by nonlinearities, parameters and modelling uncertainties, which make the use of the sliding mode control methodology particularly appropriate. Specifically, a sliding mode controller, relying on the so-called unit vector approach, is first proposed to control the photovoltaic inverter. Then, a Second Order Sliding Mode (SOSM) controller, adopting a Suboptimal SOSM algorithm, is proposed to alleviate the chattering phenomenon and feed a continuos modulating signal into the photovoltaic inverter. Simulation tests, carried out on a realistic scenario, confirm satisfactory closed-loop performance of the proposed control scheme

Offset-free feedback linearisation control of a three-phase grid-connected photovoltaic system

In this study, a state feedback control law is combined with a disturbance observer to enhance disturbance rejection capability of a grid-connected photovoltaic (PV) inverter. The control law is based on input-output feedback linearisation technique, while the existing disturbance observer is simplified and adopted for the system under investigation. The resulting control law has a proportional-integral (PI)/almost PI-derivative-like structure, which is convenient for real-time implementation. The objective of the proposed approach is to improve the DC-bus voltage regulation, while at the same time control the power exchange between the PV system and the grid. The stability of the closed-loop system under the composite controller is guaranteed by simple design parameters. Both simulation and experimental results show that the proposed method has significant abilities to initiate fast current control and accurate adjustment of the DC-bus voltage under model uncertainty and external disturbance

DISTRIBUTION NETWORK OPERATION WITH SOLAR PHOTOVOLTAIC AND ENERGY STORAGE TECHNOLOGY

Among distributed energy resources, solar photovoltaic (PV) generation has the largest penetration in the distribution networks. Serving electric vehicles (EV) with renewable resource generation would further reduce the carbon footprint of the energy supply chain for electric vehicles. However, the integration of solar PV and EVs in the unbalanced distribution network introduces several challenges including voltage fluctuations, voltage imbalances, reverse power flow, and protection devices’ malfunctions. The uncertainties associated with solar PV integration and electric vehicles operation require significant effort to develop accurate optimization methodologies in the unbalanced distribution systems operation. In this thesis, in order to cope with the uncertainties, we first developed a two-stage optimization problem, to identify the feasible dispatch margins of photovoltaic generation considering the distribution network operation constraints. The dispatch margins of photovoltaic generation are quantified considering the worst-case realization of demand in the distribution network. The linear and the second-order cone mathematical problem formulation is procured to solve the optimal power flow problem. Second, a data-driven distributionally robust optimization framework is proposed for the operation of the unbalanced distribution network considering the uncertainties associated with the interconnected EV fleets and solar PV generation, and the proposed framework leverages the column-and-constraint generation approach. Moreover, to minimize the operation cost and improve the ramping flexibility, a continuous-time optimization problem, is developed and reformulated to a linear programming problem using Bernstein polynomials. Here, a generalized exact linear reformulation of the data-driven distributionally robust optimization is used to capture the worst-case probability distribution of the net demand uncertainties. Furthermore, in this thesis, an interconnection of multi microgrids (MGs) technology is considered a promising solution to handle the variability of the distributed renewable energy resources and improve the energy resilience in the distribution network. The coordination among the microgrids in the distribution network could improve the operation cost, reliability, and security of the distribution network. Therefore, an adaptive robust distributed optimization framework is developed for the operation of a distribution network with interconnected microgrids considering the uncertainties in demand and solar PV generation