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Innovative Microgrid Management: Balancing Costs, CO2 Emissions, and EV Charging Events

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Abstract

Microgrids are small-scale, localized systems operating independently or in conjunction with the main electrical grid. They have a variety of potential applications and offer a promising future for reducing carbon emissions associated with electricity generation and lowering consumer electricity costs. As we move towards transportation electrification, the role of microgrids in reducing greenhouse gas emissions (GHG) and energy costs for electric vehicles (EVs) becomes increasingly vital. This dissertation takes a comprehensive approach to designing and testing microgrid control algorithms that reduce consumer costs and lower GHG emissions associated with electricity. The control algorithms are rigorously tested through simulations that model microgrids in a versatile "plug and play" fashion, allowing for robust testing and addressing current challenges within the microgrid framework. The microgrid model has been validated through experiments conducted on a full-sized microgrid testbed at a university office building, assuring the thoroughness of this research.

Using this powerful modeling tool, the dissertation analyzes and improves microgrid energy management strategies, beginning with a sensitivity analysis of the impact of passenger EV charging. Multiple scenarios were assessed to evaluate the costs and carbon dioxide (CO2) emissions associated with varying battery sizes and charging speeds for EVs. The dissertation then introduces microgrid control strategies that prioritize electric utility costs while simultaneously lowering CO2 emissions by converting CO2 emissions into a cost factor that the microgrid prioritizes alongside electric utility expenses. Furthermore, the dissertation advances the field by developing a co-optimization framework to decarbonize building and commercial transportation operations and optimize electric truck route planning algorithms.

The results of these experiments show that fast charging (direct current Level 3) for passenger vehicles poses significant challenges in electric cost and power management compared to standard (Level 2) EV chargers. To address these challenges, microgrids can effectively reduce electric utility costs and CO2 emissions for commercial facilities, even with additional energy demand from electric buildings. The experiments reveal that a microgrid optimized solely for CO2 reduction achieves its objectives but typically incurs high operational costs. In contrast, a more pragmatic approach combining CO2 reduction and cost efficiency results in significant CO2 reductions with minimal electric costs to the operator. This control algorithm can be applied in different scenarios by adjusting the values of constraints in the objective function and the carbon emissions rate of electricity generation.

Additionally, this dissertation considers the interaction between microgrid energy management and electric vehicle routing, helping a microgrid effectively manage its electric demand while decarbonizing electric transportation. The electric truck co-optimization framework found that managing electric trucks on local routes is surprisingly straightforward when using a microgrid. One advantage of electric truck charging is that a truck's arrivals and departures can be planned within the framework when the microgrid has abundant renewable energy sources or sufficient energy stored to charge the trucks. These experiments underscore the pivotal role that microgrid control algorithms play in decarbonizing electricity usage and transportation while simultaneously reducing electric billing costs.

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This item is under embargo until January 31, 2026.