Energy allocation strategies for common property load connected to shared solar and battery storage systems in strata apartments

Common property (CP) is a significant consumer of electricity in apartment buildings. Although some apartments in Australia have adopted shared microgrid configurations to offset grid consumption, the characteristics and load patterns of CP are rarely discussed due to lack of available data. As common areas normally constitute part of owner corporations, energy distribution in these premises requires attention. This paper presents empirical analysis of the CP load connected to shared solar and battery storage for three apartment complexes located in Perth Australia. Load patterns for CP over a defined dataset period were analyzed, and grid usage reduction was examined by implementing and comparing three energy allocation strategies based on surplus energy utilization. The findings indicated significant grid usage reduction for CP load in different apartments after implementation of three strategies. Instantaneous consumption decreased 72%, and surplus allocation strategy reduced 91%, while consumption-based allocation reduced 76%, of grid electricity. Moreover, consumption-based allocation offered improved cost benefits compared to the other two strategies. The results further revealed the usefulness of energy allocation and effectiveness of surplus energy utilization. Based on outcomes, the strategies provide consolidation with conventional energy trading mechanisms and broadly link to the virtual power plant concept for coordinating energy flows between multiple generators

Design of control tools for use in microgrid simulations

2018 Summer.Includes bibliographical references.New technologies are transforming the way electricity is delivered and consumed. In the past two decades, a large amount of research has been done on smart grids and microgrids. This can be attributed to two factors. First is the poliferation of internet. Internet today is as ubiquitous as electricity. This has spawned a new area of technology called the internet of things (IoT). It gives us the ability to connect almost any device to the internet and harness the data. IoT finds use in smart grids that allow utiliy companies to deliver electricity efficiently. The other factor is the advancement in renewable sources of electricty and high power semiconductors coupled with their decreasing cost. These new sources disrupt the traditional way of electicity production and delivery, putting an increased focus on distributed power generation and microgrids. A microgrid is different from a utility grid. The difference is in the size of the grid, power level, a variety of possible sources and the way these are tied together. These characteristics lead to some unique control challenges. Today's appliances and consumer goods are powered using a standardized AC power. Thus a microrid must deliver uninterrupted and high quality power while at the same time taking into account the vastly different nature of the microsurces that produce the power. This work describes control system tools for different power converters that will be used in simulating microgrids.\ Simulations are important tool for any researcher. It allows researchers to test their research and theories at a greatly reduced cost. The process of design, testing and verification is an iterative process. Simulations allow a cost effective method of doing research, substituting the actual process of building experimental systems. This greatly reduces the amount of manpower and capital investment. A microgrid consists of several building blocks. These building blocks can be categorized into microsources, energy stores, converters and the loads. Microsources are devices that produce electric power. For example, a photovoltaic panel is a mirosource that produces DC power. Converters act as an interface between microsources and the grid. The constituent chapters in the document describe microsources and converters. The chapters describe the underlying control system and the simulation model of the system designed in Simulink. Some of the tools described are derived from the MATLAB/Simulink Examples library. Original authors of the simulation models and systems have been duly credited. Colorado State University has a vibrant research community. The tools described in this thesis are geared to be used for research into microgrids. The tools are developed in a simulation software called Simulink. The tools would allow future researchers to rapidly build microgrid simulations and test new control system implementations etc. The research described in the thesis builds upon the research by Han on natural gas engine based microgrid. The control tools described here are used to construct a microgrid simulation. The microgrid is built around a natural gas engine. Due to the transport lag in delivering fuel, a natural gas engine exhibits significant deviation in the AC grid frequency when subjected to step load. The microgrid setup along with the control system described here, minimizes the frequency deviation, thus stabilizing the microgrid. Simulation results verify the working of the tools

Development of Robust and Dynamic Control Solutions for Energy Storage Enabled Hybrid AC/DC Microgrids

Development of Robust and Dynamic Control Solutions for Energy Storage Enabled Hybrid AC/DC Microgrid

Local and Central Controllers for Microgrids

The main objective of this thesis is to serve as a guide, so readers are able to learn about microgrids and to design simple controllers for different AC microgrid applications. In addition, this thesis has the objective to provide examples of simulation cases for the hierarchical structure of a basic AC microgrid which can be used as a foundation to build upon, and achieve more complex microgrid structures as well as more sophisticated power-converter control techniques. To achieve these objectives, the modeling of voltage source converters and control design in the z-domain are presented. Moreover, the implementation and transient analysis of the power-converter operating modes are executed through MATLAB/SimulinkTM simulations. Then, an energy management case for the central controller of the AC microgrid is performed utilizing real-time simulation tools, Typhoon HIL software and hardware devices in addition to Texas instruments digital signal processors as local controllers

Advanced control methods on three-phase inverters in distributed energy resources

“This research is an endeavor to apply new and well-established control methodologies to improve transient response, stability and reliability of three-phase inverters in grid-connected and isolated mode of operation. In the course of studying the effect of these methodologies, model-based control is introduced and is extensively applied which is relatively a new approach. In addition, the application of this concept has been studied on developing “grid-forming” controls to allow wind and solar inverters to support voltage and frequency levels like traditional generators. This research encloses the details of three major works of this research and their possible contributions on improving the performance of three-phase inverters in gridconnected and isolated mode of operation. The first one employs the concept of adaptive control using multiple models and a hierarchical control approach to smoothly switch between isolated and grid-connected modes of operation. In the second work, the features of the first research work have been applied and more nourished to control a grid-forming unit. The interactions of this grid-supporting converter with a grid- forming unit is the main subject of discussion in this work. The last work applies the concept of internal-model control to introduce a new control methodology in power-synchronization method. This approach has tackled the non-minimum phase issue attributed to power-synchronization methodology and offers a robust solution. Furthermore, in this research, detailed stability analysis of all the proposed control structures have been presented. Along with all simulation verification, FPGA-Based Hardware-in-the-Loop (HIL) has been utilized to verify the performance of the discrete control structure. The details of plant modeling, controller design, HIL and experimental results are presented for all of the proposed schemes in each section”--Abstract, page iv