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Optimal design and dispatch of hybrid co-generation microgrid systems with resilience considerations
Grymes, James C.
Grymes, James C.
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2023
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Abstract
The decision to supplement conventional energy generation with an on-site microgrid consisting of a mix of distributed generation resources and energy storage devices is motivated by several factors, including reducing costs, increasing sustainability, and improving the resilience and reliability of an energy system. The procurement and operational costs of installing distributed generation are traditionally the impetus behind long-term decisions. While costs remain a key component, decision-makers are now interested in other benefits such as improved resilience and reliability, reduction in emissions, and public sentiment.
An open-sourced webtool, REopt, is a mixed-integer linear program that exists to provide users the ability to conduct parametric analysis under many scenarios. Often commercial optimization software struggles to obtain fast and reliable solutions. Therefore we develop a Matheuristic that yields objective function values within 5% of an exogenously produced optimal in fewer than 30 seconds for 90% of test cases compared to only 10% by a traditional optimization solver.
We then embellish REopt, to explore the tradeoffs between cost and resilience for a coastal wastewater treatment facility. We find that the facility can reduce life-cycle energy costs by 3.1% through the installation of a hybrid combined-heat-and-power, photovoltaic, and storage system. Furthermore, when paired with existing diesel generators, this system can sustain full load for seven days while saving $664,000 over 25 years and reducing diesel fuel use by 48% compared to the diesel-only solution.
Finally, we extend the concepts of the first two works by incorporating emerging technologies (fuel cells) into a distributed generation multi-objective model that simultaneously minimizes costs while ensuring a communities' critical load is satisfied during a utility service disruption. This extension requires the incorporation of challenging non-linear constraints that inhibit state-of-the-art optimization software from finding solutions within 15%, on average, after two hours for realistic instances encompassing five technologies and a year-long time horizon at hourly fidelity. We devise a multi-stage methodology resulting in, on average, an 8% decrease in objective function value. Additionally, solutions obtained using our methodology result in fuel cell utilization three times more often than solutions obtained with commercial solvers.
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