Coordinated control and energy management of distributed generation inverters in a microgrid

published_or_final_versio

Islanded Wind Energy Management System Based on Neural Networks

Wind power, as the main renewable energy source, is increasingly deployed and connected into electrical networks thanks to the development of wind energy conversion technologies. This dissertation is focusing on research related to wind power system include grid-connected/islanded wind power systems operation and control design, wind power quality, wind power prediction technologies, and its applications in microgrids. The doubly fed induction generator (DFIG) wind turbine is popular in the wind industry and thus has been researched in this Dissertation. In order to investigate reasons of harmonic generation in wind power systems, a DFIG wind turbine is modeled by using general vector representation of voltage, current and magnetic flux in the presence of harmonics. In this Dissertation, a method of short term wind power prediction for a wind power plant is developed by training neural networks in Matlab software based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data. Based on the above research work, a microgrid with high wind energy penetration has been designed and simulated by using Matlab/Simulink. Besides wind energy, this microgrid system is operated with assistance of a diesel generator. A three-layer energy management system (EMS) is designed and applied in this microgrid system, which is to realize microgrid islanded operation under different wind conditions. Simulation results show that the EMS can ensure stable operation of the microgrid under varying wind speed situations

Stability analysis and robust damping of multiresonances in distributed-generation-based islanded microgrids

International audienc

Management of Islanded Operation of Microgirds

Distributed generations with continuously growing penetration levels offer potential solutions to energy security and reliability with minimum environmental impacts. Distributed Generations when connected to the area electric power systems provide numerous advantages. However, grid integration of distributed generations presents several technical challenges which has forced the systems planners and operators to account for the repercussions on the distribution feeders which are no longer passive in the presence of distributed generations. Grid integration of distributed generations requires accurate and reliable islanding detection methodology for secure system operation. Two distributed generation islanding detection methodologies are proposed in this dissertation. First, a passive islanding detection technique for grid-connected distributed generations based on parallel decision trees is proposed. The proposed approach relies on capturing the underlying signature of a wide variety of system events on a set of critical system parameters and utilizes multiple optimal decision tress in a parallel network for classification of system events. Second, a hybrid islanding detection method for grid-connected inverter based distributed generations combining decision trees and Sandia frequency shift method is also proposed. The proposed method combines passive and active islanding detection techniques to aggregate their individual advantages and reduce or eliminate their drawbacks. In smart grid paradigm, microgrids are the enabling engine for systematic integration of distributed generations with the utility grid. A systematic approach for controlled islanding of grid-connected microgrids is also proposed in this dissertation. The objective of the proposed approach is to develop an adaptive controlled islanding methodology to be implemented as a preventive control component in emergency control strategy for microgrid operations. An emergency power management strategy for microgrid autonomous operation subsequent to inadvertent islanding events is also proposed in this dissertation. The proposed approach integrates microgrid resources such as energy storage systems, demand response resources, and controllable micro-sources to layout a comprehensive power management strategy for ensuring secure and stable microgrid operation following an unplanned islanding event. In this dissertation, various case studies are presented to validate the proposed methods. The simulation results demonstrate the effectiveness of the proposed methodologies

Microgrid Protection Systems

Micro grids are miniature version of conventional large power grids functioning either autonomously or with inter connection to the main grid. Primary function of micro grid is to serve power at distribution level. Distributed energy resources (DERs) connected to the micro grid enables reliable and efficient operation of micro grid. Protection of micro grids assumed importance due to increased penetration of distributed energy resources. Most of the distribution systems in earlier days are radial in nature and protection systems are designed for that. These protection systems pose serious challenges when applied to present day distribution systems which are mesh connected and fed by the distributed energy resources. Limitation of the conventional protection scheme demands new insights and methodologies for micro grid protection. Due to intermediate current injection from DERs the conventional coordination of over current (O/C) relays is not possible. Further in meshed systems the fault current flow is bidirectional. Hence the protection of micro grid systems with DERs require different approach to ensure faults are cleared in less time and minimal number of consumers connected to the system are affected. A comprehensive analysis of the suitable techniques applicable for micro grid protection is presented in this chapter

The Modeling and Advanced Controller Design of Wind, PV and Battery Inverters

Renewable energies such as wind power and solar energy have become alternatives to fossil energy due to the improved energy security and sustainability. This trend leads to the rapid growth of wind and Photovoltaic (PV) farm installations worldwide. Power electronic equipments are commonly employed to interface the renewable energy generation with the grid. The intermittent nature of renewable and the large scale utilization of power electronic devices bring forth numerous challenges to system operation and design. Methods for studying and improving the operation of the interconnection of renewable energy such as wind and PV are proposed in this Ph.D. dissertation.;A multi-objective controller including is proposed for PV inverter to perform voltage flicker suppression, harmonic reduction and unbalance compensation. A novel supervisory control scheme is designed to coordinate PV and battery inverters to provide high quality power to the grid. This proposed control scheme provides a comprehensive solution to both active and reactive power issues caused by the intermittency of PV energy. A novel real-time experimental method for connecting physical PV panel and battery storage is proposed, and the proposed coordinated controller is tested in a Hardware in the Loop (HIL) experimental platform based on Real Time Digital Simulator (RTDS).;This work also explores the operation and controller design of a microgrid consisting of a direct drive wind generator and a battery storage system. A Model Predictive Control (MPC) strategy for the AC-DC-AC converter of wind system is derived and implemented to capture the maximum wind energy as well as provide desired reactive power. The MPC increases the accuracy of maximum wind energy capture as well as minimizes the power oscillations caused by varying wind speed. An advanced supervisory controller is presented and employed to ensure the power balance while regulating the PCC bus voltage within acceptable range in both grid-connected and islanded operation.;The high variability and uncertainty of renewable energies introduces unexpected fast power variation and hence the operation conditions continuously change in distribution networks. A three-layers advanced optimization and intelligent control algorithm for a microgrid with multiple renewable resources is proposed. A Dual Heuristic Programming (DHP) based system control layer is used to ensure the dynamic reliability and voltage stability of the entire microgrid as the system operation condition changes. A local layer maximizes the capability of the Photovoltaic (PV), wind power generators and battery systems, and a Model Predictive Control (MPC) based device layer increases the tracking accuracy of the converter control. The detail design of the proposed SWAPSC scheme are presented and tested on an IEEE 13 node feeder with a PV farm, a wind farm and two battery-based energy storage systems