Multi-objective optimization of hybrid renewable energy systems with urban building energy modeling for a prototypical coastal community

Many regions, such as coastal communities, are often located at the end of large transmission lines, leaving them susceptible to power outages and reliant on conventional fuels. We propose a multi-disciplinary, multi-objective optimization method to design a hybrid renewable energy system mix including solar, wind, and marine energy for these regions. The method first simulates demand for Eastport - a prototypical coastal community in Maine (USA) - using urban building energy modeling, and subsequently develops an hourly optimization and time series load-balancing approach to derive an optimal mix of renewable energy systems to meet the region's de-mand. Our results suggest that while different renewable energy system mixes can be viable, optimizing for cost, energy utilization, and power deficit can be increasingly challenging as higher annual demand hours (>90%) are met. This suggests that the final percentages require a disproportionate effort to achieve, and it will be chal-lenging to meet 100% renewable energy deployment for all demand hours across a year. The proposed method is scalable - it can be applied to other regions to help policymakers and city planners make informed, data-driven decisions for various energy and city planning policy analyses and interventions.