STATE OF CHARGE BASED DROOP SURFACE FOR OPTIMAL CONTROL OF DC MICROGRIDS

For a microgrid with a high penetration level of renewable energy, energy storage use becomes more integral to the system performance due to the stochastic nature of most renewable energy sources. This thesis examines the use of droop control of an energy storage source in dc microgrids in order to optimize a global cost function. The approach involves using a multidimensional surface to determine the optimal droop parameters based on load and state of charge. The optimal surface is determined using knowledge of the system architecture and can be implemented with fully decentralized source controllers. The optimal surface control of the system is presented. Derivations of a cost function along with the implementation of the optimal control are included. Results were verified using a hardware-in-the-loop system.

DC MICROGRID STABILIZATION THROUGH FUZZY CONTROL OF INTERLEAVED, HETEROGENEOUS STORAGE ELEMENTS

As microgrid power systems gain prevalence and renewable energy comprises greater and greater portions of distributed generation, energy storage becomes important to offset the higher variance of renewable energy sources and maximize their usefulness. One of the emerging techniques is to utilize a combination of lead-acid batteries and ultracapacitors to provide both short and long-term stabilization to microgrid systems. The different energy and power characteristics of batteries and ultracapacitors imply that they ought to be utilized in different ways. Traditional linear controls can use these energy storage systems to stabilize a power grid, but cannot effect more complex interactions. This research explores a fuzzy logic approach to microgrid stabilization. The ability of a fuzzy logic controller to regulate a dc bus in the presence of source and load fluctuations, in a manner comparable to traditional linear control systems, is explored and demonstrated. Furthermore, the expanded capabilities (such as storage balancing, self-protection, and battery optimization) of a fuzzy logic system over a traditional linear control system are shown. System simulation results are presented and validated through hardware-based experiments. These experiments confirm the capabilities of the fuzzy logic control system to regulate bus voltage, balance storage elements, optimize battery usage, and effect self-protection.

REALIZATION OF A DC MICROGRID USING A HAMILTONIAN BASED CONTROLS SOLUTION

Future power grids are envisioned to be serviced by heterogeneous arrangements of renewable energy sources. Due to their stochastic nature, energy storage distribution and management are pivotal in realizing microgrids serviced heavily by renewable energy assets. Identifying the required response characteristics to meet the operational requirements of a power grid are of great importance and must be illuminated in order to discern optimal hardware topologies. Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) presents the tools to identify such characteristics. By using energy storage as actuation within the closed loop controller, the response requirements may be identified while providing a decoupled controller solution. A DC microgrid servicing a fixed RC load through source and bus level storage managed by HSSPFC was realized in hardware. A procedure was developed to calibrate the DC microgrid architecture of this work to the reduced order model used by the HSSPFC law. Storage requirements were examined through simulation and experimental testing. Bandwidth contributions between feed forward and PI components of the HSSPFC law are illuminated and suggest the need for well-known system losses to prevent the need for additional overhead in storage allocations. The following work outlines the steps taken in realizing a DC microgrid and presents design considerations for system calibration and storage requirements per the closed loop controls for future DC microgrids.

AGENT BASED, DISTRIBUTED CONTROL STRATEGIES AND OPTIMIZATION OF PLUG-IN ELECTRIC VEHICLES ON SMART/MICROGRID ARCHITECTURES

This thesis will present strategies for the use of plug-in electric vehicles on smart and microgrids. MATLAB is used as the design tool for all models and simulations. First, a scenario will be explored using the dispatchable loads of electric vehicles to stabilize a microgrid with a high penetration of renewable power generation. Grid components for a microgrid with 50% photovoltaic solar production will be sized through an optimization routine to maintain storage system, load, and vehicle states over a 24-hour period. The findings of this portion are that the dispatchable loads can be used to guard against unpredictable losses in renewable generation output. Second, the use of distributed control strategies for the charging of electric vehicles utilizing an agent-based approach on a smart grid will be studied. The vehicles are regarded as additional loads to a primary forecasted load and use information transfer with the grid to make their charging decisions. Three lightweight control strategies and their effects on the power grid will be presented. The findings are that the charging behavior and peak loads on the grid can be reduced through the use of distributed control strategies.

A COST-BENEFIT ANALYSIS OF A 25% RPS IN MICHIGAN

Michigan depends heavily on fossil fuels to generate electricity. Compared with fossil fuels, electricity generation from renewable energy produces less pollutants emissions. A Renewable Portfolio Standard (RPS) is a mandate that requires electric utilities to generate a certain amount of electricity from renewable energy sources. This thesis applies the Cost-Benefits Analysis (CBA) method to investigate the impacts of implementing a 25% in Michigan by 2025. It is found that a 25% RPS will create about $20.12 billion in net benefits to the State. Moreover, if current tax credit policies will not change until 2025, its net present value will increase to about $26.59 billion. Based on the results of this CBA, a 25% RPS should be approved. The result of future studies on the same issue can be improved if more state specific data become available.