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

Novel decentralized control of power systems with penetration of renewable energy sources in small-scale power systems

In this paper, the power grid with penetration of renewable energy sources is modeled as a multigenerator interconnected power network. The power grid includes distributed energy resources including conventional synchronous generators and renewable energy sources; here called renewable generators that are connected to the grid via grid-tie inverters (GTIs). With the proposed modeling, the GTI resembles a synchronous generator with excitation control. The modeling takes into account the dc-link capacitor stored energy as a dynamical state, in contrast with the available methods, and through an appropriate controller assures the stability of the dc link and the entire grid without needing an abundant-energy dc link. Next, the power grid comprising the synchronous and renewable generators is converted to decentralized control form with subsystems in Brunovsky canonical form whose interactions with the rest of the grid are unknown. A decentralized adaptive neural network (NN) feedback controller is proposed with quadratic update law to stabilize the rotor speed and dc-link voltage oscillations in asymptotic fashion in the presence of grid disturbances. The proposed controller is then simplified. Though the solar power interacting with conventional synchronous generators is considered in this paper, the proposed modeling and controller design can be applied to many other renewable energy systems. Simulation results on the IEEE 14-bus power system with penetration of solar power are provided to show the effectiveness of the approach in damping oscillations that occur after disturbances

Decentralized discrete-time adaptive neural network control of interconnected DC distribution system

In this paper, the interconnected dc distribution system is represented as a class of interconnected, nonlinear discrete-time systems with unknown dynamics. The dc distribution system comprises several dc sources, here called subsystems, along with resistive and constant-power loads (CPLs.) 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. On the other hand, designing a centralized controller may not be viable due to the distance between the subsystems (dc sources.) Therefore, in this paper the stability of the interconnected dc distribution system is enhanced through decentralized adaptive nonlinear controller design that employs neural networks (NNs) to mitigate voltage and power oscillations after disturbances have occurred. The adaptive NN-based controller is introduced to overcome the unknown dynamics of each subsystem\u27s converter and stabilize the entire grid, assuming that only the local measurements are available to each converter. Simulation results are provided to show the effectiveness of the approach in damping oscillations that occur in the presence of disturbances

Stability of the Small-Scale Interconnected DC Grids via Output-Feedback Control

A decentralized nonlinear model and controller is proposed to stabilize the interconnected small-scale islanded dc grids in the presence of renewable energy sources with proven stability in this paper. The dc interconnected network comprises dc sources along with resistive and constant-power loads (CPLs). Though the dc sources are photovoltaic (PV) in this paper, the proposed controller can be applied to other types of low-inertia intermittent sources as well. All sources and/or CPL loads are connected to the grid through simple dc-dc converters (DDCs) to avoid power electronic complexities. The negative-resistance CPLs can destabilize the grid in the presence of the DDC dynamical components. The decentralized nonlinear output-feedback controller mitigates rapid voltage and power oscillations caused by the disturbances and measurement noises, and stabilizes the grid. Since the proposed output-feedback controller needs only partial knowledge of the local converter states, the number of measure points reduces leading to a simple implementation. Simulation results on a small-scale dc grid are provided to show the performance of the proposed controller