@article{li_thomas_queiroz_decarolis_2020, title={Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study}, volume={54}, ISSN={["1520-5851"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85078394675&partnerID=MN8TOARS}, DOI={10.1021/acs.est.9b04184}, abstractNote={Rigorous model-based analysis can help inform state-level energy and climate policy. In this study, we utilize an open-source energy system optimization model and publicly available datasets to examine future electricity generation, CO2 emissions, and CO2 abatement costs for the North Carolina electric power sector through 2050. Model scenarios include uncertainty in future fuel prices, a hypothetical CO2 cap, and an extended renewable portfolio standard. Across the modeled scenarios, solar photovoltaics represent the most cost-effective low-carbon technology, while trade-offs among carbon constrained scenarios largely involve natural gas and renewables. We also develop a new method to calculate break-even costs, which indicate the capital costs at which different technologies become cost-effective within the model. Significant variation in break-even costs are observed across different technologies and scenarios. We illustrate how break-even costs can be used to inform the development of an extended renewable portfolio standard in North Carolina. Utilizing the break-even costs to calibrate a tax credit for onshore wind, we find that the resultant wind deployment displaces other renewables, and thus has a negligible effect on CO2 emissions. Such insights can provide crucial guidance to policymakers weighing different policy options. This study provides an analytical framework to conduct similar analyses in other states using an open source model and freely available datasets.}, number={2}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Li, Binghui and Thomas, Jeffrey and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F.}, year={2020}, month={Jan}, pages={665–676} }
 @article{li_queiroz_decarolis_bane_he_keeler_neary_2017, title={The economics of electricity generation from Gulf Stream currents}, volume={134}, ISSN={["1873-6785"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020893529&partnerID=MN8TOARS}, DOI={10.1016/j.energy.2017.06.048}, abstractNote={Hydrokinetic turbines harnessing energy from ocean currents represent a potential low carbon electricity source. This study provides a detailed techno-economic assessment of ocean turbines operating in the Gulf Stream off the North Carolina coast. Using hindcast data from a high-resolution ocean circulation model in conjunction with the US Department of Energy's reference model 4 (RM4) for ocean turbines, we examine resource quality and apply portfolio optimization to identify the best candidate sites for ocean turbine deployment. We find that the lowest average levelized cost of electricity (LCOE) from a single site can reach 400 $/MWh. By optimally selecting geographically dispersed sites and taking advantage of economies of scale, the variations in total energy output can be reduced by an order of magnitude while keeping the LCOE below 300 $/MWh. Power take-off and transmission infrastructure are the largest cost drivers, and variation in resource quality can have a significant influence on the project LCOE. While this study focuses on a limited spatial domain, it provides a framework to assess the techno-economic feasibility of ocean current energy in other western boundary currents.}, journal={ENERGY}, publisher={Elsevier BV}, author={Li, Binghui and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F. and Bane, John and He, Ruoying and Keeler, Andrew G. and Neary, Vincent S.}, year={2017}, month={Sep}, pages={649–658} }
 @article{decarolis_babaee_li_kanungo_2016, title={Modelling to generate alternatives with an energy system optimization model}, volume={79}, ISSN={["1873-6726"]}, DOI={10.1016/j.envsoft.2015.11.019}, abstractNote={Energy system optimization models (ESOMs) should be used in an interactive way to uncover knife-edge solutions, explore alternative system configurations, and suggest different ways to achieve policy objectives under conditions of deep uncertainty. In this paper, we do so by employing an existing optimization technique called modeling to generate alternatives (MGA), which involves a change in the model structure in order to systematically explore the near-optimal decision space. The MGA capability is incorporated into Tools for Energy Model Optimization and Analysis (Temoa), an open source framework that also includes a technology rich, bottom up ESOM. In this analysis, Temoa is used to explore alternative energy futures in a simplified single region energy system that represents the U.S. electric sector and a portion of the light duty transport sector. Given the dataset limitations, we place greater emphasis on the methodological approach rather than specific results.}, journal={ENVIRONMENTAL MODELLING & SOFTWARE}, author={DeCarolis, J. F. and Babaee, S. and Li, B. and Kanungo, S.}, year={2016}, month={May}, pages={300–310} }
 @article{li_decarolis_2015, title={A techno-economic assessment of offshore wind coupled to offshore compressed air energy storage}, volume={155}, DOI={10.1016/j.apenergy.2015.05.111}, abstractNote={A critical challenge associated with renewable energy is managing its variable and intermittent output. Offshore compressed air energy storage (OCAES) is a carbon-free storage technology that can used to support renewable energy generation in marine environments. This paper provides the first economic characterization of OCAES performance when coupled to an offshore wind farm by employing a mixed integer programming model. The model seeks the minimum levelized cost of electricity by optimizing the grid-tied cable capacity and OCAES component sizes across a range of specified cable capacity factors. OCAES can be used to increase the capacity factor of the grid-tied transmission cable, but the resultant levelized cost of electricity strongly depends on the OCAES cost assumptions. Compared to using a land-based gas turbine as backup, OCAES is significantly more expensive, even when the price of carbon exceeds 1000 $/tC.}, journal={Applied Energy}, author={Li, B. H. and DeCarolis, J. F.}, year={2015}, pages={315–322} }