@article{faria_queiroz_decarolis_2023, title={Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies}, volume={270}, ISSN={["1873-6785"]}, url={http://dx.doi.org/10.1016/j.energy.2023.126946}, DOI={10.1016/j.energy.2023.126946}, abstractNote={This work proposes an analytical decision-making framework considering scenario generation using artificial neural networks and risk-averse stochastic programming to define renewable offshore portfolios of wind, wave, and ocean current technologies. For the scenario generation, a generative adversarial neural network is developed to generate synthetic energy scenarios considering resources distributed over large geographic regions. These scenarios are then fed to a stochastic model, which objective to determine the optimal location and number of turbines for each technology. In the stochastic model formulation, a representation of the limits in the portfolio Levelized Cost of Energy and the maximization of the five percent lower energy generation conditions, also known as Conditional Value at Risk, is presented. The framework proposed here is tested considering data from a portion of the U.S. East coast, where the generative model was successful in creating energy scenarios statistically consistent with the historical data for wind, wave, and ocean current resources at more than 500 sites. Furthermore, the Conditional Value at Risk portfolio optimization model was used to construct efficient frontiers for a combination of different technologies, showing the significance of resource diversification as a tool to improve system security.}, journal={ENERGY}, publisher={Elsevier BV}, author={Faria, Victor A. D. and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F.}, year={2023}, month={May} }
 @article{ford_queiroz_decarolis_sankarasubramanian_2022, title={Co-Optimization of Reservoir and Power Systems (COREGS) for seasonal planning and operation}, volume={8}, ISSN={["2352-4847"]}, url={https://doi.org/10.1016/j.egyr.2022.06.017}, DOI={10.1016/j.egyr.2022.06.017}, abstractNote={Climate variability accounts for distinct seasonal differences in electricity demand and streamflow potential, which power systems rely on to assess available hydropower and to cool thermal power plants. Understanding the interactions between reservoir and power networks under varying climate conditions requires an integrated analysis of both systems. In this study, we develop Co-Optimization of Reservoir and Electricity Generation Systems (COREGS), a generalized, open-source, modeling framework that optimizes both systems with respect to reducing power generation costs using a multireservoir model (GRAPS) and an electricity system model (TEMOA). Three optimization schemes of varying degrees of model integration are applied to Tennessee Valley Authority's reservoir and electricity systems for the summer and winters from 2003 to 2015. We find that co-optimization of the systems results in more efficient water allocation decisions than separate optimization. Co-optimization solutions reduce reservoir spill and allocate water for hydropower only when and where it is beneficial to the power system as compared to stand-alone water system optimization. As the penetration of solar and wind power continues to increase, power systems will be more reliant on flexible reliable generating services such as reservoir systems and co-optimization of both systems will become more essential for efficient seasonal planning and operation.}, journal={ENERGY REPORTS}, publisher={Elsevier BV}, author={Ford, Lucas and Queiroz, Anderson and DeCarolis, Joseph and Sankarasubramanian, A.}, year={2022}, month={Nov}, pages={8061–8078} }
 @article{sioshansi_denholm_arteaga_awara_bhattacharjee_botterud_cole_cortes_queiroz_decarolis_et al._2022, title={Energy-Storage Modeling: State-of-the-Art and Future Research Directions}, volume={37}, ISSN={["1558-0679"]}, url={http://dx.doi.org/10.1109/tpwrs.2021.3104768}, DOI={10.1109/TPWRS.2021.3104768}, abstractNote={Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.}, number={2}, journal={IEEE TRANSACTIONS ON POWER SYSTEMS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sioshansi, Ramteen and Denholm, Paul and Arteaga, Juan and Awara, Sarah and Bhattacharjee, Shubhrajit and Botterud, Audun and Cole, Wesley and Cortes, Andres and Queiroz, Anderson de and DeCarolis, Joseph and et al.}, year={2022}, month={Mar}, pages={860–875} }
 @article{ravishankar_booth_hollingsworth_ade_sederoff_decarolis_brendan t. o'connor_2022, title={Organic solar powered greenhouse performance optimization and global economic opportunity}, volume={15}, ISSN={["1754-5706"]}, url={https://doi.org/10.1039/D1EE03474J}, DOI={10.1039/d1ee03474j}, abstractNote={This work integrates greenhouse energy demand, solar power production, and plant growth modeling to assess the economic opportunity of organic solar powered greenhouses. Results show these systems have positive economic outlook across broad climates.}, number={4}, journal={ENERGY & ENVIRONMENTAL SCIENCE}, publisher={Royal Society of Chemistry (RSC)}, author={Ravishankar, Eshwar and Booth, Ronald E. and Hollingsworth, Joseph A. and Ade, Harald and Sederoff, Heike and DeCarolis, Joseph F. and Brendan T. O'Connor}, year={2022}, month={Mar} }
 @article{patankar_eshraghi_queiroz_decarolis_2022, title={Using robust optimization to inform US deep decarbonization planning}, volume={42}, ISSN={["2211-4688"]}, url={http://dx.doi.org/10.1016/j.esr.2022.100892}, DOI={10.1016/j.esr.2022.100892}, abstractNote={US energy system development consistent with the Paris Agreement will depend in part on future fuel prices and technology costs, which are highly uncertain. Energy system optimization models (ESOMs) represent a critical tool to examine clean energy futures under different assumptions. While many approaches exist to examine future sensitivity and uncertainty in such models, most assume that uncertainty is resolved prior to the model run. Policy makers, however, must take action before uncertainty is resolved. Robust optimization represents a method that explicitly considers future uncertainty within a single model run, yielding a near-term hedging strategy that is robust to uncertainty. This work focuses on extending and applying robust optimization methods to Temoa, an open source ESOM, to derive insights about low carbon pathways in the United States. A robust strategy that explicitly considers future uncertainty has expected savings in total system cost of 12% and an 8% reduction in the standard deviation of expected costs relative to a strategy that ignores uncertainty. The robust technology deployment strategy also entails more diversified technology mixes across the energy sectors modeled.}, journal={ENERGY STRATEGY REVIEWS}, publisher={Elsevier BV}, author={Patankar, Neha and Eshraghi, Hadi and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F.}, year={2022}, month={Jul} }
 @article{bennett_trevisan_decarolis_ortiz-garcia_perez-lugo_etienne_clarens_2021, title={Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico}, volume={6}, ISSN={["2058-7546"]}, DOI={10.1038/s41560-020-00758-6}, number={3}, journal={NATURE ENERGY}, author={Bennett, Jeffrey A. and Trevisan, Claire N. and DeCarolis, Joseph F. and Ortiz-Garcia, Cecilio and Perez-Lugo, Marla and Etienne, Bevin T. and Clarens, Andres F.}, year={2021}, month={Mar}, pages={240–249} }
 @article{mueller_thomas_johnson_decarolis_call_2021, title={Life cycle assessment of salinity gradient energy recovery using reverse electrodialysis}, volume={25}, ISSN={["1530-9290"]}, DOI={10.1111/jiec.13082}, abstractNote={Abstract}, number={5}, journal={JOURNAL OF INDUSTRIAL ECOLOGY}, author={Mueller, Katelyn E. and Thomas, Jeffrey T. and Johnson, Jeremiah X. and DeCarolis, Joseph F. and Call, Douglas F.}, year={2021}, month={Oct}, pages={1194–1206} }
 @article{brown_siddiqui_avraam_bistline_decarolis_eshraghi_giarola_hansen_johnston_khanal_et al._2021, title={North American energy system responses to natural gas price shocks}, volume={149}, ISSN={["1873-6777"]}, DOI={10.1016/j.enpol.2020.112046}, abstractNote={As of 2020, North American natural gas extraction and use in the electricity sector have both reached all-time highs. The combination of North America's increased reliance on natural gas with a potential disruption to the natural gas market has several energy security implications. Additionally, policymakers interested in economic resiliency will find this study's results useful for informing the implications of the energy sectors' long-term planning and investment decisions. This paper evaluates how both the electricity and natural gas sectors could respond to hypothetical gas price shocks under different system configurations. We impose unforeseen natural gas price shocks under reference and alternative configurations resulting from a renewable generation mandate or variations to renewable capacity costs. Results from several different models are presented for the electricity and natural gas sectors separately for Canada, Mexico, and the United States. Generally, the US becomes more (less) reliant on electricity imports from Canada given a high (low) gas price shock but increases (decreases) exports to Mexico. The renewable mandate is demonstrated to buffer electricity price increases under high price shocks but price reductions under the low price shocks are dampened given less flexibility to take advantage of the low-priced natural gas. The United States is demonstrated to reduce natural gas production and net exports with high natural gas price shocks given a reduction in demand.}, journal={ENERGY POLICY}, author={Brown, Maxwell and Siddiqui, Sauleh and Avraam, Charalampos and Bistline, John and Decarolis, Joseph and Eshraghi, Hadi and Giarola, Sara and Hansen, Matthew and Johnston, Peter and Khanal, Saroj and et al.}, year={2021}, month={Feb} }
 @article{henry_eshraghi_lugovoy_waite_decarolis_farnham_ruggles_peer_wu_queiroz_et al._2021, title={Promoting reproducibility and increased collaboration in electric sector capacity expansion models with community benchmarking and intercomparison efforts}, volume={304}, ISSN={["1872-9118"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85114748043&partnerID=MN8TOARS}, DOI={10.1016/j.apenergy.2021.117745}, abstractNote={Electric sector capacity expansion models are widely used by academic, government, and industry researchers for policy analysis and planning. Many models overlap in their capabilities, spatial and temporal resolutions, and research purposes, but yield diverse results due to both parametric and structural differences. Previous work has attempted to identify some differences among commonly used capacity expansion models but has been unable to disentangle parametric from structural uncertainty. Here, we present a model benchmarking effort using highly simplified scenarios applied to four open-source models of the U.S. electric sector. We eliminate all parametric uncertainty through using a common dataset and leave only structural differences. We demonstrate how a systematic model comparison process allows us to pinpoint specific and important structural differences among our models, including specification of technologies as baseload or load following generation, battery state-of-charge at the beginning and end of a modeled period, application of battery roundtrip efficiency, treatment of discount rates, formulation of model end effects, and digit precision of input parameters. Our results show that such a process can be effective for improving consistency across models and building model confidence, substantiating specific modeling choices, reporting uncertainties, and identifying areas for further research and development. We also introduce an open-source test dataset that the modeling community can use for unit testing and build on for benchmarking exercises of more complex models. A community benchmarking effort can increase collaboration among energy modelers and provide transparency regarding the energy transition and energy challenges, for other stakeholders such as policymakers.}, journal={APPLIED ENERGY}, publisher={Elsevier BV}, author={Henry, Candise L. and Eshraghi, Hadi and Lugovoy, Oleg and Waite, Michael B. and DeCarolis, Joseph F. and Farnham, David J. and Ruggles, Tyler H. and Peer, Rebecca A. M. and Wu, Yuezi and Queiroz, Anderson and et al.}, year={2021}, month={Dec} }
 @article{koecklin_longoria_fitiwi_decarolis_curtis_2021, title={Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland}, volume={151}, ISSN={["1873-6777"]}, DOI={10.1016/j.enpol.2021.112185}, abstractNote={This paper analyses how people's attitudes towards onshore wind power and overhead transmission lines affect the cost-optimal development of electricity generation mixes, under a high renewable energy policy. A power systems generation and transmission expansion planning model is used for the analysis, combined with a novel additional modelling constraint incorporating public acceptance of energy infrastructure. In the scenarios examined the least cost solutions increase by as much as 33% compared to a base case where the constraint on public acceptance of energy infrastructure is excluded. In the most extreme public acceptance scenario considered, the greatest share of additional costs (>80%) is related to value of lost load, while additional investment and operational costs associated with public acceptance constraints for new energy infrastructure are between 5–6% of base case costs. The results are indicative of the cost that power systems face in reflecting the public's preferences for new energy infrastructure in generation and grid expansion planning. Power system modelling that ignores the public's acceptance of new energy infrastructure may offer generation or transmission pathways that are likely to be sub-optimal in practice.}, journal={ENERGY POLICY}, author={Koecklin, Manuel Tong and Longoria, Genaro and Fitiwi, Desta Z. and DeCarolis, Joseph F. and Curtis, John}, year={2021}, month={Apr} }
 @article{esraghi_queiroz_sankarasubramanian_decarolis_2021, title={Quantification of climate-induced interannual variability in residential U.S. electricity demand}, volume={236}, ISSN={["1873-6785"]}, url={http://dx.doi.org/10.1016/j.energy.2021.121273}, DOI={10.1016/j.energy.2021.121273}, abstractNote={We assess the sensitivity of residential electricity demand in 48 U S. states to seasonal climate variations and structural changes pertaining to state-level household electricity demand. The main objective is to quantify the effects of seasonal climate variability on residential electricity demand variability during the winter and summer seasons. We use state-level monthly demographic, energy, and climate data from 2005 to 2017 in a linear regression model and find that interannual climate variability explains a significant share of seasonal household electricity demand variation: in 42 states, more than 70% and 50% of demand variability in summer and winter, respectively, is driven by climate. Our work suggests the need for new datasets to quantify unexplained variance in the winter and summer electricity demand. Findings from this study are critical to developing seasonal electricity demand forecasts, which can aid power system operation and management, particularly in a future with greater electrification of end-use demands.}, journal={Energy}, publisher={Elsevier BV}, author={Esraghi, H. and Queiroz, Ade and Sankarasubramanian, A. and DeCarolis, J.}, year={2021}, month={Dec}, pages={121273} }
 @article{cawthorne_rodrigo de queiroz_eshraghi_sankarasubramanian_decarolis_2021, title={The Role of Temperature Variability on Seasonal Electricity Demand in the Southern US}, volume={3}, ISSN={["2624-9634"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85123099133&partnerID=MN8TOARS}, DOI={10.3389/frsc.2021.644789}, abstractNote={The reliable and affordable supply of energy through interconnected systems represent a critical infrastructure challenge. Seasonal and interannual variability in climate variables—primarily precipitation and temperature—can increase the vulnerability of such systems during climate extremes. The objective of this study is to understand and quantify the role of temperature variability on electricity consumption over representative areas of the Southern United States. We consider two states, Tennessee and Texas, which represent different climate regimes and have limited electricity trade with adjacent regions. Results from regression tests indicate that regional population growth explains most of the variability in electricity demand at decadal time scales, whereas temperature explains 44–67% of the electricity demand variability at seasonal time scales. Seasonal temperature forecasts from general circulation models are also used to develop season-ahead power demand forecasts. Results suggest that the use of climate forecasts can potentially help to project future residential electricity demand at the monthly time scale.}, journal={Frontiers in Sustainable Cities}, author={Cawthorne, D. and Rodrigo de Queiroz, A. and Eshraghi, H. and Sankarasubramanian, A. and DeCarolis, J.F.}, year={2021}, month={Jun} }
 @article{sodano_decarolis_queiroz_johnson_2021, title={The symbiotic relationship of solar power and energy storage in providing capacity value}, volume={177}, ISSN={["1879-0682"]}, DOI={10.1016/j.renene.2021.05.122}, abstractNote={Ensuring power system reliability under high penetrations of variable renewable energy is a critical task for system operators. In this study, we use a loss of load probability model to estimate the capacity credit of solar photovoltaics and energy storage under increasing penetrations of both technologies, in isolation and in tandem, to offer new understanding on their potential synergistic effects. Increasing penetrations of solar PV alter the net load profile on the grid, shifting the peak net load to hours with little or no solar generation and leading to diminishing capacity credits for each additional increment of solar. However, the presence of solar PV decreases the duration of daily peak demands, thereby allowing energy-limited storage capacity to dispatch electricity during peak demand hours. Thus, solar PV and storage exhibit a symbiotic relationship when used in tandem. We find that solar PV and storage used together make a more significant contribution to system reliability: as much as 40% more of the combined capacity can be counted on during peak demand hours compared to scenarios where the two technologies are deployed separately. Our test case demonstrates the important distinction between winter and summer peaking systems, leading to significantly different seasonal capacity values for solar PV. These findings are timely as utilities replace their aging peaking plants and are taking energy storage into consideration as part of a low carbon pathway.}, journal={RENEWABLE ENERGY}, author={Sodano, Daniel and DeCarolis, Joseph F. and Queiroz, Anderson Rodrigo and Johnson, Jeremiah X.}, year={2021}, month={Nov}, pages={823–832} }
 @article{hollingsworth_ravishankar_o'connor_johnson_decarolis_2020, title={Environmental and economic impacts of solar-powered integrated greenhouses}, volume={24}, ISSN={["1530-9290"]}, DOI={10.1111/jiec.12934}, abstractNote={Abstract}, number={1}, journal={JOURNAL OF INDUSTRIAL ECOLOGY}, author={Hollingsworth, Joseph A. and Ravishankar, Eshwar and O'Connor, Brendan and Johnson, Jeremiah X. and DeCarolis, Joseph F.}, year={2020}, month={Feb} }
 @article{jaunich_levis_decarolis_barlaz_ranjithan_2020, title={Exploring alternative solid waste management strategies for achieving policy goals}, volume={53}, ISSN={0305-215X 1029-0273}, url={http://dx.doi.org/10.1080/0305215X.2020.1759578}, DOI={10.1080/0305215X.2020.1759578}, abstractNote={The authors previously analysed a real-world solid waste management (SWM) system using the solid waste optimization life-cycle framework (SWOLF) to identify optimal SWM strategies that meet modelled objectives (e.g. cost, environmental impacts, landfill diversion). While mathematically optimal strategies can support SWM decision making, they may not be readily implementable because of unmodelled objectives (e.g. practical limitations, social preferences, political and management considerations). A mathematical programming technique extending SWOLF is used to systematically identify, for several scenarios, different ‘optimal’ SWM strategies that are maximally different from each other in terms of waste flows, while meeting modelled objectives and constraints. The performance with respect to unmodelled issues was analysed to demonstrate the flexibility in potential strategies. Practitioner feedback highlighted implementation challenges due to existing practices; however, insights gained from this exercise led to more plausible and acceptable strategies by incrementally modifying the initial SWM alternatives generated.}, number={5}, journal={Engineering Optimization}, publisher={Informa UK Limited}, author={Jaunich, Megan K. and Levis, James W. and DeCarolis, Joseph F. and Barlaz, Morton A. and Ranjithan, S. Ranji}, year={2020}, month={Jun}, pages={1–14} }
 @article{yue_patankar_decarolis_chiodi_rogan_deane_o'gallachoir_2020, title={Least cost energy system pathways towards 100% renewable energy in Ireland by 2050}, volume={207}, ISSN={["1873-6785"]}, DOI={10.1016/j.energy.2020.118264}, abstractNote={Studies focusing on 100% renewable energy systems have emerged in recent years; however, existing studies tend to focus only on the power sector using exploratory approaches. This paper therefore undertakes a whole-system approach and explores optimal pathways towards 100% renewable energy by 2050. The analysis is carried out for Ireland, which currently has the highest share of variable renewable electricity on a synchronous power system. Large numbers of scenarios are developed using the Irish TIMES model to address uncertainties. Results show that compared to decarbonization targets, focusing on renewable penetration without considering carbon capture options is significantly less cost effective in carbon mitigation. Alternative assumptions on bioenergy imports and maximum variability in power generation lead to very different energy mixes in bioenergy and electrification levels. All pathways suggest that indigenous bioenergy needs to be fully exploited and the current annual deployment rate of renewable electricity needs a boost. Pathways relying on international bioenergy imports are slightly cheaper and faces less economic and technical challenges. However, given the large future uncertainties, it is recommended that further policy considerations be given to pathways with high electrification levels as they are more robust towards uncertainties.}, journal={ENERGY}, author={Yue, Xiufeng and Patankar, Neha and Decarolis, Joseph and Chiodi, Alessandro and Rogan, Fionn and Deane, J. P. and O'Gallachoir, Brian}, year={2020}, month={Sep} }
 @article{decarolis_jaramillo_johnson_mccollum_trutnevyte_daniels_akin-olcum_bergerson_cho_choi_et al._2020, title={Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts}, volume={4}, ISSN={["2542-4351"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85097654384&partnerID=MN8TOARS}, DOI={10.1016/j.joule.2020.11.002}, abstractNote={The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts. Individual author biographies can be found on the project website: https://openenergyoutlook.org/.}, number={12}, journal={JOULE}, publisher={Elsevier BV}, author={DeCarolis, Joseph F. and Jaramillo, Paulina and Johnson, Jeremiah X. and McCollum, David L. and Trutnevyte, Evelina and Daniels, David C. and Akin-Olcum, Gokce and Bergerson, Joule and Cho, Soolyeon and Choi, Joon-Ho and et al.}, year={2020}, month={Dec}, pages={2523–2526} }
 @article{jaunich_decarolis_handfield_kemahlioglu-ziya_ranjithan_moheb-alizadeh_2020, title={Life-cycle modeling framework for electronic waste recovery and recycling processes}, volume={161}, ISSN={["1879-0658"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85086084470&partnerID=MN8TOARS}, DOI={10.1016/j.resconrec.2020.104841}, abstractNote={Policies and regulations such as Extended Producer Responsibility (EPR) have been implemented to potentially increase the recycling rate of electronic waste (e-waste), but the cost and environmental impacts of associated collection, transportation, material recovery, material re-processing, and disposal could outweigh the benefits of recycling if the e-waste management system is not effectively designed and implemented. This paper presents a quantitative, holistic framework to systematically estimate life-cycle impacts and costs associated with e-waste management. This new framework was tested using data from the state of Washington's EPR program to represent e-waste collection, transportation, processing and disposal. Sensitivity of process-level life-cycle model outputs to parameter and input variability was also conducted. Drop-off using fossil-fuel-powered personal vehicles was found to be a key contributor to cost and carbon dioxide emissions. Decision-makers must account for drop-off and consider the feasibility of alternate e-waste aggregation strategies to ensure life-cycle benefits of e-waste recycling are maximized.}, journal={RESOURCES CONSERVATION AND RECYCLING}, author={Jaunich, Megan Kramer and DeCarolis, Joseph and Handfield, Robert and Kemahlioglu-Ziya, Eda and Ranjithan, S. Ranji and Moheb-Alizadeh, Hadi}, year={2020}, month={Oct} }
 @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{patankar_queiroz_decarolis_bazilian_chattopadhyay_2019, title={Building conflict uncertainty into electricity planning: A South Sudan case study}, volume={49}, ISSN={["0973-0826"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85060864438&partnerID=MN8TOARS}, DOI={10.1016/j.esd.2019.01.003}, abstractNote={This paper explores electricity planning strategies in South Sudan under future conflict uncertainty. A stochastic energy system optimization model that explicitly considers the possibility of armed conflict leading to electric power generator damage is presented. Strategies that hedge against future conflict have the greatest economic value in moderate conflict-related damage scenarios by avoiding expensive near-term investments in infrastructure that may be subsequently damaged. Model results show that solar photovoltaics can play a critical role in South Sudan's future electric power system. In addition to mitigating greenhouse gas emissions and increasing access to electricity, this analysis suggests that solar can be used to hedge against economic losses incurred by conflict. While this analysis focuses on South Sudan, the analytical framework can be applied to other conflict-prone countries.}, journal={ENERGY FOR SUSTAINABLE DEVELOPMENT}, author={Patankar, Neha and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F. and Bazilian, Morgan D. and Chattopadhyay, Debabrata}, year={2019}, month={Apr}, pages={53–64} }
 @article{sun_wang_decarolis_barlaz_2019, title={Evaluation of optimal model parameters for prediction of methane generation from selected US landfills}, volume={91}, ISSN={["0956-053X"]}, DOI={10.1016/j.wasman.2019.05.004}, abstractNote={In practice, methane generation at U.S. landfills is typically predicted by using the EPA's Landfill Gas Emissions Model (LandGEM), which includes two parameters, the methane production potential (L0, m3 CH4 Mg-1 wet waste) and the first-order decay rate constant (k, yr-1). Default parameters in LandGEM (L0 = 100 and k = 0.04) were determined using data that reflect landfill management practices in the 1990s. In this study, methane collection data from 21 U.S. landfills were used to estimate the best fit k by inverse modeling of measured methane collection data in consideration of a time-varying gas collection efficiency. Optimal values of k were identified at a range of L0s between 55 and 160. The best fit k was greater than the U.S. EPA's default parameter of 0.04 yr-1 at 14 of the 21 landfills studied. Surprisingly, the best fit k was often observed at L0 values greater than 100 m3 CH4 Mg-1 wet waste which again is the U.S. EPA default. The results show that there is wide variation in the best estimate of k. While there was a tendency for landfills, or sections of landfills that received more moisture to exhibit higher decay rates, the results were not consistent. Some landfills exhibited high decay rates even though the data suggested that they were relatively dry while some wet landfills exhibited low decay rates. The results suggest that L0 captures many factors and that the data may be most useful for site specific analysis as opposed to general landfill predictions.}, journal={WASTE MANAGEMENT}, author={Sun, Wenjie and Wang, Xiaoming and DeCarolis, Joseph F. and Barlaz, Morton A.}, year={2019}, month={May}, pages={120–127} }
 @article{rossi_oliveira favretto_grassi_decarolis_cho_hill_soares chvatal_ranjithan_2019, title={Metamodels to assess the thermal performance of naturally ventilated, low-cost houses in Brazil}, volume={204}, ISSN={["1872-6178"]}, DOI={10.1016/j.enbuild.2019.109457}, abstractNote={Building performance simulation [BPS] tools are important in all design stages. However, barriers such as time, resources, and expertise inhibit their use in the early design stages. This study aims to develop, as part of decision-support framework, metamodels to assess the thermal discomfort in a naturally ventilated Brazilian low-cost house during early design. The metamodels predict the degree-hours of discomfort by heat and/or by cold as a function of design parameters for three Brazilian cities: Curitiba, São Paulo, and Manaus. The key design parameters, related with passive design strategies, are building orientation, shading devices position and dimensions, thermal properties of the walls and roof, window-to-wall ratio, and effective window ventilation area. The method consists of three main stages: (i) baseline model development; (ii) Monte Carlo simulation; (iii) multivariate regression. Overall, the metamodels showed R2 values higher than 0.95 for all climates, except the ones predicting discomfort by heat for Curitiba (R2 =0.61) and São Paulo (R2 =0.75). The proposed metamodels can quickly and accurately assess the thermal performance of naturally ventilated low-cost houses. They can be used to guide professionals during the early design stages, and for educational purposes in building design pedagogy.}, journal={ENERGY AND BUILDINGS}, author={Rossi, Michele Marta and Oliveira Favretto, Ana Paula and Grassi, Camila and DeCarolis, Joseph and Cho, Soolyeon and Hill, David and Soares Chvatal, Karin Maria and Ranjithan, Ranji}, year={2019}, month={Dec} }
 @article{de queiroz_mulcahy_sankarasubramanian_deane_mahinthakumar_lu_decarolis_2019, title={Repurposing an energy system optimization model for seasonal power generation planning}, volume={181}, ISSN={0360-5442}, url={http://dx.doi.org/10.1016/j.energy.2019.05.126}, DOI={10.1016/j.energy.2019.05.126}, abstractNote={Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate electricity demand and utilizing energy models to estimate monthly electricity generation and associated operational costs. The objective of this paper is to develop and test a computationally efficient model that can support seasonal planning while preserving key aspects of system operation over hourly and daily timeframes. To do so, an energy system optimization model is repurposed for seasonal planning using features drawn from a unit commitment model. Different scenarios utilizing a well-known test system are used to evaluate the errors associated with both the repurposed energy system model and an imperfect load forecast. The results show that the energy system optimization model using an imperfect load forecast produces differences in monthly cost and generation levels that are less than 2% compared with a unit commitment model using a perfect load forecast. The enhanced energy system optimization model can be solved approximately 100 times faster than the unit commitment model, making it a suitable tool for future work aimed at evaluating seasonal electricity generation and demand under uncertainty.}, journal={Energy}, publisher={Elsevier BV}, author={de Queiroz, A.R. and Mulcahy, D. and Sankarasubramanian, A. and Deane, J.P. and Mahinthakumar, G. and Lu, N. and DeCarolis, J.F.}, year={2019}, month={Aug}, pages={1321–1330} }
 @article{jaunich_levis_decarolis_barlaz_ranjithan_2019, title={Solid Waste Management Policy Implications on Waste Process Choices and Systemwide Cost and Greenhouse Gas Performance}, volume={53}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/acs.est.8b04589}, DOI={10.1021/acs.est.8b04589}, abstractNote={Solid waste management (SWM) is a key function of local government and is critical to protecting human health and the environment. Development of effective SWM strategies should consider comprehensive SWM process choices and policy implications on system-level cost and environmental performance. This analysis evaluated cost and select environmental implications of SWM policies for Wake County, North Carolina using a life-cycle approach. A county-specific data set and scenarios were developed to evaluate alternatives for residential municipal SWM, which included combinations of a mixed waste material recovery facility (MRF), anaerobic digestion, and waste-to-energy combustion in addition to existing SWM infrastructure (composting, landfilling, single stream recycling). Multiple landfill diversion and budget levels were considered for each scenario. At maximum diversion, the greenhouse gas (GHG) mitigation costs ranged from 30 to 900 $/MTCO2e; the lower values were when a mixed waste MRF was used, and the higher values when anaerobic digestion was used. Utilization of the mixed waste MRF was sensitive to the efficiency of material separation and operating cost. Maintaining the current separate collection scheme limited the potential for cost and GHG reductions. Municipalities seeking to cost-effectively increase landfill diversion while reducing GHGs should consider waste-to-energy, mixed waste separation, and changes to collection.}, number={4}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Jaunich, Megan K. and Levis, James W. and DeCarolis, Joseph F. and Barlaz, Morton A. and Ranjithan, S. Ranji}, year={2019}, month={Jan}, pages={1766–1775} }
 @article{yue_pye_decarolis_li_rogan_gallachóir_2018, title={A review of approaches to uncertainty assessment in energy system optimization models}, volume={21}, ISSN={2211-467X}, url={http://dx.doi.org/10.1016/J.ESR.2018.06.003}, DOI={10.1016/J.ESR.2018.06.003}, abstractNote={Energy system optimization models (ESOMs) have been used extensively in providing insights to decision makers on issues related to climate and energy policy. However, there is a concern that the uncertainties inherent in the model structures and input parameters are at best underplayed and at worst ignored. Compared to other types of energy models, ESOMs tend to use scenarios to handle uncertainties or treat them as a marginal issue. Without adequately addressing uncertainties, the model insights may be limited, lack robustness, and may mislead decision makers. This paper provides an in-depth review of systematic techniques that address uncertainties for ESOMs. We have identified four prevailing uncertainty approaches that have been applied to ESOM type models: Monte Carlo analysis, stochastic programming, robust optimization, and modelling to generate alternatives. For each method, we review the principles, techniques, and how they are utilized to improve the robustness of the model results to provide extra policy insights. In the end, we provide a critical appraisal on the use of these methods.}, journal={Energy Strategy Reviews}, publisher={Elsevier BV}, author={Yue, Xiufeng and Pye, Steve and DeCarolis, Joseph and Li, Francis G.N. and Rogan, Fionn and Gallachóir, Brian Ó.}, year={2018}, month={Aug}, pages={204–217} }
 @article{eshraghi_queiroz_decarolis_2018, title={US Energy-Related Greenhouse Gas Emissions in the Absence of Federal Climate Policy}, volume={52}, ISSN={["1520-5851"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85052916806&partnerID=MN8TOARS}, DOI={10.1021/acs.est.8b01586}, abstractNote={The planned US withdrawal from the Paris Agreement as well as uncertainty about federal climate policy has raised questions about the country's future emissions trajectory. Our model-based analysis accounts for uncertainty in fuel prices and energy technology capital costs and suggests that market forces are likely to keep US energy-related greenhouse gas emissions relatively flat or produce modest reductions: in the absence of new federal policy, 2040 greenhouse gas emissions range from +10% to -23% of the baseline estimate. Natural gas versus coal utilization in the electric sector represents a key trade-off, particularly under conservative assumptions about future technology innovation. The lowest emissions scenarios are produced when the cost of natural gas and electric vehicles declines, while coal and oil prices increase relative to the baseline.}, number={17}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={American Chemical Society (ACS)}, author={Eshraghi, Hadi and Queiroz, Anderson Rodrigo and DeCarolis, Joseph F.}, year={2018}, month={Sep}, pages={9595–9604} }
 @inproceedings{sun_thomas_singh_li_baran_lubkeman_decarolis_queiroz_white_watts_et al._2017, title={Cost-benefit assessment challenges for a smart distribution system: A case study}, volume={2018-January}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85046337981&partnerID=MN8TOARS}, DOI={10.1109/pesgm.2017.8274167}, abstractNote={The FREEDM system is a technology for a smarter and resilient distribution system that facilitates a higher level of distributed energy resource (DER) integration by offering effective voltage regulation, reactive power compensation and real time monitoring and control. This paper provides a framework for conducting a cost-benefit analysis for such a smart distribution system. The method first identifies the benefits, and then quantifies and monetizes them. OpenDSS time-series based power flow simulation is used to quantify the benefits accurately. The costs associated with the new components of the system are estimated based on prototype units. A cost-benefit analysis is adopted to identify the scenarios where employing such a system by a utility becomes economically attractive.}, booktitle={2017 ieee power & energy society general meeting}, author={Sun, L. S. and Thomas, J. and Singh, S. and Li, D. X. and Baran, M. and Lubkeman, David and DeCarolis, J. and Queiroz, A. R. and White, L. and Watts, S. and et al.}, year={2017}, pages={1–5} }
 @article{galik_decarolis_fell_2017, title={Evaluating the US Mid-Century Strategy for Deep Decarbonization amidst early century uncertainty}, volume={17}, ISSN={1469-3062 1752-7457}, url={http://dx.doi.org/10.1080/14693062.2017.1340257}, DOI={10.1080/14693062.2017.1340257}, abstractNote={The recent change in US presidential administrations has introduced significant uncertainty about both domestic and international policy support for continued reductions in GHG emissions. This brief analysis estimates the potential climate ramifications of changing US leadership, contrasting the Mid-Century Strategy for Deep Decarbonization (MCS) released under the Obama Administration, with campaign statements, early executive actions, and prevailing market conditions to estimate potential emission pathways under the Trump Administration. The analysis highlights areas where GHG reductions are less robust to changing policy conditions, and offers brief recommendations for addressing emissions in the interim. It specifically finds that continued reductions in the electricity sector are less vulnerable to changes in federal policy than those in the built environment and land use sectors. Given the long-lived nature of investments in these latter two sectors, however, opportunities for near-term climate action by willing cities, states, private landowners, and non-profit organizations warrant renewed attention in this time of climate uncertainty. Key policy insights The recent US presidential election has already impacted mitigation goals and practices, injecting considerable uncertainty into domestic and international efforts to address climate change. A strategic assessment issued in the final days of the Obama Administration for how to reach long-term climate mitigation objectives provides a baseline from which to gauge potential changes under the Trump Administration. Though market trends may continue to foster emission declines in the energy sector, emission reductions in the land use sector and the built environment are subject to considerable uncertainty. Regardless of actions to scale back climate mitigation efforts, US emissions are likely to be flat in the coming years. Assuming that emissions remain constant under President Trump and that reductions resume afterwards to meet the Obama Administration mid-century targets in 2050, this near-term pause in reductions yields a difference in total emissions equivalent to 0.3–0.6 years of additional global greenhouse gas emissions, depending on the number of terms served by a Trump Administration.}, number={8}, journal={Climate Policy}, publisher={Informa UK Limited}, author={Galik, Christopher S. and DeCarolis, Joseph F. and Fell, Harrison}, year={2017}, month={Jul}, pages={1046–1056} }
 @article{martinez-sanchez_levis_damgaard_decarolis_barlaz_astrup_2017, title={Evaluation of Externality Costs in Life-Cycle Optimization of Municipal Solid Waste Management Systems}, volume={51}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/acs.est.6b06125}, DOI={10.1021/acs.est.6b06125}, abstractNote={The development of sustainable solid waste management (SWM) systems requires consideration of both economic and environmental impacts. Societal life-cycle costing (S-LCC) provides a quantitative framework to estimate both economic and environmental impacts, by including "budget costs" and "externality costs". Budget costs include market goods and services (economic impact), whereas externality costs include effects outside the economic system (e.g., environmental impact). This study demonstrates the applicability of S-LCC to SWM life-cycle optimization through a case study based on an average suburban U.S. county of 500 000 people generating 320 000 Mg of waste annually. Estimated externality costs are based on emissions of CO2, CH4, N2O, PM2.5, PM10, NOx, SO2, VOC, CO, NH3, Hg, Pb, Cd, Cr (VI), Ni, As, and dioxins. The results indicate that incorporating S-LCC into optimized SWM strategy development encourages the use of a mixed waste material recovery facility with residues going to incineration, and separated organics to anaerobic digestion. Results are sensitive to waste composition, energy mix and recycling rates. Most of the externality costs stem from SO2, NOx, PM2.5, CH4, fossil CO2, and NH3 emissions. S-LCC proved to be a valuable tool for policy analysis, but additional data on key externality costs such as organic compounds emissions to water would improve future analyses.}, number={6}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Martinez-Sanchez, Veronica and Levis, James W. and Damgaard, Anders and DeCarolis, Joseph F. and Barlaz, Morton A. and Astrup, Thomas F.}, year={2017}, month={Mar}, pages={3119–3127} }
 @article{decarolis_daly_dodds_keppo_li_mcdowall_pye_strachan_trutnevyte_usher_et al._2017, title={Formalizing best practice for energy system optimization modelling}, volume={194}, ISSN={0306-2619}, url={http://dx.doi.org/10.1016/J.APENERGY.2017.03.001}, DOI={10.1016/J.APENERGY.2017.03.001}, abstractNote={Energy system optimization models (ESOMs) are widely used to generate insight that informs energy and environmental policy. Using ESOMs to produce policy-relevant insight requires significant modeler judgement, yet little formal guidance exists on how to conduct analysis with ESOMs. To address this shortcoming, we draw on our collective modelling experience and conduct an extensive literature review to formalize best practice for energy system optimization modelling. We begin by articulating a set of overarching principles that can be used to guide ESOM-based analysis. To help operationalize the guiding principles, we outline and explain critical steps in the modelling process, including how to formulate research questions, set spatio-temporal boundaries, consider appropriate model features, conduct and refine the analysis, quantify uncertainty, and communicate insights. We highlight the need to develop and refine formal guidance on ESOM application, which comes at a critical time as ESOMs are being used to inform national climate targets.}, journal={Applied Energy}, publisher={Elsevier BV}, author={DeCarolis, Joseph and Daly, Hannah and Dodds, Paul and Keppo, Ilkka and Li, Francis and McDowall, Will and Pye, Steve and Strachan, Neil and Trutnevyte, Evelina and Usher, Will and et al.}, year={2017}, month={May}, pages={184–198} }
 @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{pfenninger_decarolis_hirth_quoilin_staffell_2017, title={The importance of open data and software: Is energy research lagging behind?}, volume={101}, ISSN={["1873-6777"]}, DOI={10.1016/j.enpol.2016.11.046}, abstractNote={Energy policy often builds on insights gained from quantitative energy models and their underlying data. As climate change mitigation and economic concerns drive a sustained transformation of the energy sector, transparent and well-founded analyses are more important than ever. We assert that models and their associated data must be openly available to facilitate higher quality science, greater productivity through less duplicated effort, and a more effective science-policy boundary. There are also valid reasons why data and code are not open: ethical and security concerns, unwanted exposure, additional workload, and institutional or personal inertia. Overall, energy policy research ostensibly lags behind other fields in promoting more open and reproducible science. We take stock of the status quo and propose actionable steps forward for the energy research community to ensure that it can better engage with decision-makers and continues to deliver robust policy advice in a transparent and reproducible way.}, journal={ENERGY POLICY}, author={Pfenninger, Stefan and DeCarolis, Joseph and Hirth, Lion and Quoilin, Sylvain and Staffell, Iain}, year={2017}, month={Feb}, pages={211–215} }
 @article{al gharably_decarolis_ranjithan_2016, title={An enhanced linear regression-based building energy model (LRBEM plus ) for early design}, volume={9}, ISSN={["1940-1507"]}, DOI={10.1080/19401493.2015.1004108}, abstractNote={The design community lacks simple, data-driven energy assessment tools to explore energy-efficient alternatives during the early stages of building design. A promising option is to utilize a whole building energy simulation engine (e.g. EnergyPlus) within a Monte Carlo simulation framework to develop a linear regression-based building energy model (LRBEM) that can predict idealized heating and cooling loads based on parameters relevant to early design. Previous work was limited to medium-sized US commercial office buildings with rectangular geometries. A key limitation is addressed in this paper by considering complex geometries. A reformulated model, LRBEM+, is developed and tested with a suite of building geometries that represent limiting cases. The resultant relative error between LRBEM+ and EnergyPlus is generally less than 10%. Furthermore, LRBEM+ correctly predicts the direction and magnitude of changes in heating and cooling loads in response to changes in the most influential early design parameters.}, number={2}, journal={JOURNAL OF BUILDING PERFORMANCE SIMULATION}, author={Al Gharably, Maged and DeCarolis, Joseph F. and Ranjithan, S. Ranji}, year={2016}, month={Mar}, pages={115–133} }
 @article{jaunich_levis_decarolis_gaston_barlaz_bartelt-hunt_jones_hauser_jaikumar_2016, title={Characterization of municipal solid waste collection operations}, volume={114}, ISSN={0921-3449}, url={http://dx.doi.org/10.1016/j.resconrec.2016.07.012}, DOI={10.1016/j.resconrec.2016.07.012}, abstractNote={Solid waste collection contributes to the cost, emissions, and fossil fuel required to manage municipal solid waste. Mechanistic models to estimate these parameters are necessary to perform integrated assessments of solid waste management alternatives using a life-cycle approach; however, models are only as good as their parameterization. This study presents operational waste collection data that can be used in life-cycle models for areas with similar collection systems, and provides illustrative results from a collection process model using operational data. Fuel use and times associated with various aspects of waste collection were obtained for vehicles collecting mixed residential (residual) waste, recyclables, and yard waste from single-family residences in selected municipalities. The total average fuel economy for similarly-sized diesel collection vehicles was 0.6-1.4 km/L (1.4–3.3 mpg (miles per gallon)) for residual waste and 0.8–1 km/L (1.9–2.4 mpg) for recyclables. For residual waste and recyclables collection stops, the average time to collect at each residence using automated collection was 11–12 s and 13–17 s, respectively. The average time between stops was 11–12 s and 10–13 for residuals and recyclables, respectively. A single yard waste route was observed, and all collection times were longer than those measured for either recycling or residual waste. Unload or tip times were obtained or measured at a landfill, transfer station, and material recovery facility (MRF). Average time to unload was 7–9 min at a MRF, 14–22 min at a landfill, and 11 min at a transfer station. Commercial and multi-family collection vehicles tend to have longer stops and spend more time between stops than single-family collection, and a larger portion of fuel is used while driving relative to single-family collection. Roll-off vehicles, which collect more waste per stop, spend longer at each stop and drive longer distances between stops than front-loader vehicles. Diesel roll-offs averaged 2.4 km/L (5.7 mpg) and front-loaders averaged 1.4 km/L (3.3 mpg).}, journal={Resources, Conservation and Recycling}, publisher={Elsevier BV}, author={Jaunich, Megan K. and Levis, James W. and DeCarolis, Joseph F. and Gaston, Eliana V. and Barlaz, Morton A. and Bartelt-Hunt, Shannon L. and Jones, Elizabeth G. and Hauser, Lauren and Jaikumar, Rohit}, year={2016}, month={Nov}, pages={92–102} }
 @article{jaunich_levis_barlaz_decarolis_2016, title={Lifecycle Process Model for Municipal Solid Waste Collection}, volume={142}, ISSN={0733-9372 1943-7870}, url={http://dx.doi.org/10.1061/(ASCE)EE.1943-7870.0001065}, DOI={10.1061/(ASCE)EE.1943-7870.0001065}, abstractNote={AbstractA process model was developed using a lifecycle approach to estimate the cost and energy use associated with municipal solid waste collection, which is the most fuel-intensive and often the most costly aspect of solid waste management. The model divides collection service areas into single-family residential, multi-family residential, and commercial sectors with sector-specific, user-defined characteristics, including population, waste generation, and waste composition. Waste is collected by a set of processes (e.g., residual waste, recyclables collection) defined by costs, collection activity parameters, and energy use. The model overpredicted fuel use by ~25% compared with data obtained from actual single-family residential collection routes and their average fuel efficiencies, but was within 10% when modal fuel efficiencies (e.g., driving, idling) were considered. Adding recyclables or yard waste collection to a mixed waste collection program increased fuel consumption by approximately 75% per ...}, number={8}, journal={Journal of Environmental Engineering}, publisher={American Society of Civil Engineers (ASCE)}, author={Jaunich, Megan K. and Levis, James W. and Barlaz, Morton A. and DeCarolis, Joseph F.}, year={2016}, month={Aug}, pages={04016037} }
 @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{hodge_levis_decarolis_barlaz_2016, title={Systematic Evaluation of Industrial, Commercial, and Institutional Food Waste Management Strategies in the United States}, volume={50}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/acs.est.6b00893}, DOI={10.1021/acs.est.6b00893}, abstractNote={New regulations and targets limiting the disposal of food waste have been recently enacted in numerous jurisdictions. This analysis evaluated selected environmental implications of food waste management policies using life-cycle assessment. Scenarios were developed to evaluate management alternatives applicable to the waste discarded at facilities where food waste is a large component of the waste (e.g., restaurants, grocery stores, and food processors). Options considered include anaerobic digestion (AD), aerobic composting, waste-to-energy combustion (WTE), and landfilling, and multiple performance levels were considered for each option. The global warming impact ranged from approximately -350 to -45 kg CO2e Mg(-1) of waste for scenarios using AD, -190 to 62 kg CO2e Mg(-1) for those using composting, -350 to -28 kg CO2e Mg(-1) when all waste was managed by WTE, and -260 to 260 kg CO2e Mg(-1) when all waste was landfilled. Landfill diversion was found to reduce emissions, and diverting food waste from WTE generally increased emissions. The analysis further found that when a 20 year GWP was used instead of a 100 year GWP, every scenario including WTE was preferable to every scenario including landfill. Jurisdictions seeking to enact food waste disposal regulations should consider regional factors and material properties before duplicating existing statutes.}, number={16}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Hodge, Keith L. and Levis, James W. and DeCarolis, Joseph F. and Barlaz, Morton A.}, year={2016}, month={Jul}, pages={8444–8452} }
 @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} }
 @article{pressley_levis_damgaard_barlaz_decarolis_2015, title={Analysis of material recovery facilities for use in life-cycle assessment}, volume={35}, ISSN={0956-053X}, url={http://dx.doi.org/10.1016/j.wasman.2014.09.012}, DOI={10.1016/j.wasman.2014.09.012}, abstractNote={Insights derived from life-cycle assessment of solid waste management strategies depend critically on assumptions, data, and modeling at the unit process level. Based on new primary data, a process model was developed to estimate the cost and energy use associated with material recovery facilities (MRFs), which are responsible for sorting recyclables into saleable streams and as such represent a key piece of recycling infrastructure. The model includes four modules, each with a different process flow, for separation of single-stream, dual-stream, pre-sorted recyclables, and mixed-waste. Each MRF type has a distinct combination of equipment and default input waste composition. Model results for total amortized costs from each MRF type ranged from $19.8 to $24.9 per Mg (1Mg=1 metric ton) of waste input. Electricity use ranged from 4.7 to 7.8kWh per Mg of waste input. In a single-stream MRF, equipment required for glass separation consumes 28% of total facility electricity consumption, while all other pieces of material recovery equipment consume less than 10% of total electricity. The dual-stream and mixed-waste MRFs have similar electricity consumption to a single-stream MRF. Glass separation contributes a much larger fraction of electricity consumption in a pre-sorted MRF, due to lower overall facility electricity consumption. Parametric analysis revealed that reducing separation efficiency for each piece of equipment by 25% altered total facility electricity consumption by less than 4% in each case. When model results were compared with actual data for an existing single-stream MRF, the model estimated the facility's electricity consumption within 2%. The results from this study can be integrated into LCAs of solid waste management with system boundaries that extend from the curb through final disposal.}, journal={Waste Management}, publisher={Elsevier BV}, author={Pressley, Phillip N. and Levis, James W. and Damgaard, Anders and Barlaz, Morton A. and DeCarolis, Joseph F.}, year={2015}, month={Jan}, pages={307–317} }
 @article{wang_nagpure_decarolis_barlaz_2015, title={Characterization of Uncertainty in Estimation of Methane Collection from Select US Landfills}, volume={49}, ISSN={["1520-5851"]}, DOI={10.1021/es505268x}, abstractNote={Methane is a potent greenhouse gas generated from the anaerobic decomposition of waste in landfills. If captured, methane can be beneficially used to generate electricity. To inventory emissions and assist the landfill industry with energy recovery projects, the U.S. EPA developed the Landfill Gas Emissions Model (LandGEM) that includes two key parameters: the first-order decay rate (k) and methane production potential (L0). By using data from 11 U.S. landfills, Monte Carlo simulations were performed to quantify the effect of uncertainty in gas collection efficiency and municipal solid waste fraction on optimal k values and collectable methane. A dual-phase model and associated parameters were also developed to evaluate its performance relative to a single-phase model (SPM) similar to LandGEM. The SPM is shown to give lower error in estimating methane collection, with site-specific best-fit k values. Most of the optimal k values are notably greater than the U.S. EPA's default of 0.04 yr(-1), which implies that the gas generation decreases more rapidly than predicted at the current default. We translated the uncertainty in collectable methane into uncertainty in engine requirements and potential economic losses to demonstrate the practical significance to landfill operators. The results indicate that landfill operators could overpay for engine capacity by $30,000-780,000 based on overestimates of collectable methane.}, number={3}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Wang, Xiaoming and Nagpure, Ajay S. and DeCarolis, Joseph F. and Barlaz, Morton A.}, year={2015}, month={Feb}, pages={1545–1551} }
 @article{babaee_nagpure_decarolis_2014, title={How Much Do Electric Drive Vehicles Matter to Future U.S. Emissions?}, volume={48}, ISSN={["1520-5851"]}, DOI={10.1021/es4045677}, abstractNote={Hybrid, plug-in hybrid, and battery electric vehicles--known collectively as electric drive vehicles (EDVs)--may represent a clean and affordable option to meet growing U.S. light duty vehicle (LDV) demand. The goal of this study is 2-fold: identify the conditions under which EDVs achieve high LDV market penetration in the U.S. and quantify the associated change in CO2, SO2, and NOX emissions through midcentury. We employ the Integrated MARKAL-EFOM System (TIMES), a bottom-up energy system model, along with a U.S. data set developed for this analysis. To characterize EDV deployment through 2050, varying assumptions related to crude oil and natural gas prices, a CO2 policy, a federal renewable portfolio standard, and vehicle battery cost were combined to form 108 different scenarios. Across these scenarios, oil prices and battery cost have the biggest effect on EDV deployment. The model results do not demonstrate a clear and consistent trend toward lower system-wide emissions as EDV deployment increases. In addition to the trade-off between lower tailpipe and higher electric sector emissions associated with plug-in vehicles, the scenarios produce system-wide emissions effects that often mask the effect of EDV deployment.}, number={3}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Babaee, Samaneh and Nagpure, Ajay S. and DeCarolis, Joseph F.}, year={2014}, month={Feb}, pages={1382–1390} }
 @article{pressley_aziz_decarolis_barlaz_he_li_damgaard_2014, title={Municipal solid waste conversion to transportation fuels: a life-cycle estimation of global warming potential and energy consumption}, volume={70}, ISSN={["1879-1786"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84898919815&partnerID=MN8TOARS}, DOI={10.1016/j.jclepro.2014.02.041}, abstractNote={This paper utilizes life cycle assessment (LCA) methodology to evaluate the conversion of U.S. municipal solid waste (MSW) to liquid transportation fuels via gasification and Fischer-Tropsch (FT). The model estimates the cumulative energy demand and global warming potential (GWP) associated with the conversion of 1 Mg (1 Mg = 1000 kg) of MSW delivered to the front gate of a refuse-derived fuel (RDF) facility into liquid transportation fuels. In addition, net energy production is reported to quantify system performance. The system is expanded to include substituted electricity and fuel. Under a set of default assumptions, the model estimates that 1 Mg of MSW entering the RDF facility yields 123 L of gasoline, 57 L of diesel, 79 kg of other FT products, and 193 kWh of gross electricity production. For each Mg of MSW, the conversion process consumes 4.4 GJ of primary energy while creating fuels and electricity with a cumulative energy content of 10.8 GJ. Across a range of waste compositions, the liquid fuels produced by gasification and FT processing resulted in a net GWP ranging from −267 to −144 kg CO2e per Mg MSW, including offsets for conventional electricity and fuel production. The energy requirement associated with syngas compression for FT processing was significant and resulted in high levels of process-related GWP. The model demonstrates that an increased biogenic MSW fraction, assumed to be carbon neutral, reduced the GWP. However, a greater GWP reduction could be obtained through reduced FT pressure requirements, increased gas reaction rates, or a less carbon intensive power mix.}, journal={JOURNAL OF CLEANER PRODUCTION}, author={Pressley, Phillip N. and Aziz, Tarek N. and DeCarolis, Joseph F. and Barlaz, Morton A. and He, Feng and Li, Fanxing and Damgaard, Anders}, year={2014}, month={May}, pages={145–153} }
 @article{levis_barlaz_decarolis_ranjithan_2014, title={Systematic Exploration of Efficient Strategies to Manage Solid Waste in U.S. Municipalities: Perspectives from the Solid Waste Optimization Life-Cycle Framework (SWOLF)}, volume={48}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/es500052h}, DOI={10.1021/es500052h}, abstractNote={Solid waste management (SWM) systems must proactively adapt to changing policy requirements, waste composition, and an evolving energy system to sustainably manage future solid waste. This study represents the first application of an optimizable dynamic life-cycle assessment framework capable of considering these future changes. The framework was used to draw insights by analyzing the SWM system of a hypothetical suburban U.S. city of 100 000 people over 30 years while considering changes to population, waste generation, and energy mix and costs. The SWM system included 3 waste generation sectors, 30 types of waste materials, and 9 processes for waste separation, treatment, and disposal. A business-as-usual scenario (BAU) was compared to three optimization scenarios that (1) minimized cost (Min Cost), (2) maximized diversion (Max Diversion), and (3) minimized greenhouse gas (GHG) emissions (Min GHG) from the system. The Min Cost scenario saved $7.2 million (12%) and reduced GHG emissions (3%) relative to the BAU scenario. Compared to the Max Diversion scenario, the Min GHG scenario cost approximately 27% less and more than doubled the net reduction in GHG emissions. The results illustrate how the timed-deployment of technologies in response to changes in waste composition and the energy system results in more efficient SWM system performance compared to what is possible from static analyses.}, number={7}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Levis, James W. and Barlaz, Morton A. and DeCarolis, Joseph F. and Ranjithan, S. Ranji}, year={2014}, month={Mar}, pages={3625–3631} }
 @article{levis_barlaz_decarolis_ranjithan_2013, title={A generalized multistage optimization modeling framework for life cycle assessment-based integrated solid waste management}, volume={50}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2013.08.007}, DOI={10.1016/j.envsoft.2013.08.007}, abstractNote={Solid waste management (SWM) is an integral component of civil infrastructure and the global economy, and is a growing concern due to increases in population, urbanization, and economic development. In 2011, 1.3 billion metric tons of municipal solid waste (MSW) were generated, and this is expected to grow to 2.2 billion metric tons by 2025. In the U.S., MSW systems processed approximately 250 million tons of waste and produced 118 Tg of CO2e emissions, which represents over 8% of non-energy related greenhouse gas (GHG) emissions, and 2% of total net GHG emissions. While previous research has applied environmental life cycle assessment (LCA) to SWM using formal search techniques, existing models are either not readily generalizable and scalable, or optimize only a single time period and do not consider changes likely to affect SWM over time, such as new policy and technology innovation. This paper presents the first life cycle-based framework to optimize—over multiple time stages—the collection and treatment of all waste materials from curb to final disposal by minimizing cost or environmental impacts while considering user-defined emissions and waste diversion constraints. In addition, the framework is designed to be responsive to future changes in energy and GHG prices. This framework considers the use of existing SWM infrastructure as well as the deployment and utilization of new infrastructure. Several scenarios, considering cost, diversion, and GHG emissions, are analyzed in a 3-stage test system. The results show the utility of the multi-stage framework and the insights that can be gained from using such a framework. The framework was also used to solve a larger SWM system; the results show that the framework solves in reasonable time using typical hardware and readily available mathematical programming solvers. The framework is intended to inform SWM by considering costs, environmental impacts, and policy constraints.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Levis, James W. and Barlaz, Morton A. and DeCarolis, Joseph F. and Ranjithan, S. Ranji}, year={2013}, month={Dec}, pages={51–65} }
 @article{hunter_sreepathi_decarolis_2013, title={Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa)}, volume={40}, ISSN={["1873-6181"]}, DOI={10.1016/j.eneco.2013.07.014}, abstractNote={This paper introduces Tools for Energy Model Optimization and Analysis (Temoa), an open source framework for conducting energy system analysis. The core component of Temoa is an energy economy optimization (EEO) model, which minimizes the system-wide cost of energy supply by optimizing the deployment and utilization of energy technologies over a user-specified time horizon. The design of Temoa is intended to fill a unique niche within the energy modeling landscape by addressing two critical shortcomings associated with existing models: an inability to perform third party verification of published model results and the difficulty of conducting uncertainty analysis with large, complex models. Temoa leverages a modern revision control system to publicly archive model source code and data, which ensures repeatability of all published modeling work. From its initial conceptualization, Temoa was also designed for operation within a high performance computing environment to enable rigorous uncertainty analysis. We present the algebraic formulation of Temoa and conduct a verification exercise by implementing a simple test system in both Temoa and MARKAL, a widely used commercial model of the same type. In addition, a stochastic optimization of the test system is presented as a proof-of-concept application of uncertainty analysis using the Temoa framework.}, journal={ENERGY ECONOMICS}, author={Hunter, Kevin and Sreepathi, Sarat and DeCarolis, Joseph F.}, year={2013}, month={Nov}, pages={339–349} }
 @article{wang_nagpure_decarolis_barlaz_2013, title={Using observed data to improve estimated methane collection from select US landfills}, volume={47}, DOI={10.1021/es304565m}, abstractNote={The anaerobic decomposition of solid waste in a landfill produces methane, a potent greenhouse gas, and if recovered, a valuable energy commodity. Methane generation from U.S. landfills is usually estimated using the U.S. EPA's Landfill Gas Emissions Model (LandGEM). Default values for the two key parameters within LandGEM, the first-order decay rate (k) and the methane production potential (L0) are based on data collected in the 1990s. In this study, observed methane collection data from 11 U.S. landfills and estimates of gas collection efficiencies developed from site-specific gas well installation data were included in a reformulated LandGEM equation. Formal search techniques were employed to optimize k for each landfill to find the minimum sum of squared errors (SSE) between the LandGEM prediction and the observed collection data. Across nearly all landfills, the optimal k was found to be higher than the default AP-42 of 0.04 yr(-1) and the weighted average decay for the 11 landfills was 0.09 - 0.12 yr(-1). The results suggest that the default k value assumed in LandGEM is likely too low, which implies that more methane is produced in the early years following waste burial when gas collection efficiencies tend to be lower.}, number={7}, journal={Environmental Science & Technology}, author={Wang, X. M. and Nagpure, A. S. and DeCarolis, J. F. and Barlaz, Morton}, year={2013}, pages={3251–3257} }
 @article{welsch_howells_bazilian_decarolis_hermann_rogner_2012, title={Modelling elements of Smart Grids - Enhancing the OSeMOSYS (Open Source Energy Modelling System) code}, volume={46}, ISSN={["1873-6785"]}, DOI={10.1016/j.energy.2012.08.017}, abstractNote={‘Smart Grids’ are expected to help facilitate a better integration of distributed storage and demand response options into power systems and markets. Quantifying the associated system benefits may provide valuable design and policy insights. Yet many existing energy system models are not able to depict various critical features associated with Smart Grids in a single comprehensive framework. These features may for example include grid stability issues in a system with several flexible demand types and storage options to help balance a high penetration of renewable energy. Flexible and accessible tools have the potential to fill this niche. This paper expands on the Open Source Energy Modelling System (OSeMOSYS). It describes how ‘blocks of functionality’ may be added to represent variability in electricity generation, a prioritisation of demand types, shifting demand, and storage options. The paper demonstrates the flexibility and ease-of-use of OSeMOSYS with regard to modifications of its code. It may therefore serve as a useful test-bed for new functionality in tools with wide-spread use and larger applications, such as MESSAGE, TIMES, MARKAL, or LEAP. As with the core code of OSeMOSYS, the functional blocks described in this paper are available in the public domain.}, number={1}, journal={ENERGY}, author={Welsch, M. and Howells, M. and Bazilian, M. and DeCarolis, J. F. and Hermann, S. and Rogner, H. H.}, year={2012}, month={Oct}, pages={337–350} }
 @article{hygh_decarolis_hill_ranji ranjithan_2012, title={Multivariate regression as an energy assessment tool in early building design}, volume={57}, ISSN={0360-1323}, url={http://dx.doi.org/10.1016/j.buildenv.2012.04.021}, DOI={10.1016/j.buildenv.2012.04.021}, abstractNote={This paper presents a new modeling approach to quantify building energy performance in early design stages. Building simulation models can accurately quantify building energy loads, but are not amenable to the early design stages when architects need an assessment tool that can provide rapid feedback based on changes to high level design parameters. We utilize EnergyPlus, an existing whole building energy simulation program, within a Monte Carlo framework to develop a multivariate linear regression model based on 27 building parameters relevant to the early design stages. Because energy performance is sensitive to building size, geometry, and location, we model a medium-sized, rectangular office building and perform the regression in four different cities—Miami, Winston-Salem, Albuquerque, and Minneapolis—each representing a different climate zone. With the exception of heating in Miami, all R2 values obtained from the multivariate regressions exceeded 96%, which indicates an excellent fit to the EnergyPlus simulation results. The analysis suggests that a linear regression model can serve as the basis for an effective decision support tool in place of energy simulation models during early design stages. In addition, we present standardized regression coefficients to quantify the sensitivity of heating, cooling, and total energy loads to building design parameters across the four climate zones. The standardized regression coefficients can be used directly by designers to target building design parameters in early design that drive energy performance.}, journal={Building and Environment}, publisher={Elsevier BV}, author={Hygh, Janelle S. and DeCarolis, Joseph F. and Hill, David B. and Ranji Ranjithan, S.}, year={2012}, month={Nov}, pages={165–175} }
 @article{bazilian_rice_rotich_howells_decarolis_macmillan_brooks_bauer_liebreich_2012, title={Open source software and crowdsourcing for energy analysis}, volume={49}, ISSN={["0301-4215"]}, DOI={10.1016/j.enpol.2012.06.032}, abstractNote={Informed energy decision making requires effective software, high-quality input data, and a suitably trained user community. Developing these resources can be expensive and time consuming. Even when data and tools are intended for public re-use they often come with technical, legal, economic and social barriers that make them difficult to adopt, adapt and combine for use in new contexts. We focus on the promise of open, publically accessible software and data as well as crowdsourcing techniques to develop robust energy analysis tools that can deliver crucial, policy-relevant insight, particularly in developing countries, where planning resources are highly constrained—and the need to adapt these resources and methods to the local context is high. We survey existing research, which argues that these techniques can produce high-quality results, and also explore the potential role that linked, open data can play in both supporting the modelling process and in enhancing public engagement with energy issues.}, journal={ENERGY POLICY}, author={Bazilian, Morgan and Rice, Andrew and Rotich, Juliana and Howells, Mark and DeCarolis, Joseph and Macmillan, Stuart and Brooks, Cameron and Bauer, Florian and Liebreich, Michael}, year={2012}, month={Oct}, pages={149–153} }
 @article{decarolis_hunter_sreepathi_2012, title={The case for repeatable analysis with energy economy optimization models}, volume={34}, ISSN={["1873-6181"]}, DOI={10.1016/j.eneco.2012.07.004}, abstractNote={Energy economy optimization (EEO) models employ formal search techniques to explore the future decision space over several decades in order to deliver policy-relevant insights. EEO models are a critical tool for decision-makers who must make near-term decisions with long-term effects in the face of large future uncertainties. While the number of model-based analyses proliferates, insufficient attention is paid to transparency in model development and application. Given the complex, data-intensive nature of EEO models and the general lack of access to source code and data, many of the assumptions underlying model-based analysis are hidden from external observers. This paper discusses the simplifications and subjective judgments involved in the model building process, which cannot be fully articulated in journal papers, reports, or model documentation. In addition, we argue that for all practical purposes, EEO model-based insights cannot be validated through comparison to real world outcomes. As a result, modelers are left without credible metrics to assess a model's ability to deliver reliable insight. We assert that EEO models should be discoverable through interrogation of publicly available source code and data. In addition, third parties should be able to run a specific model instance in order to independently verify published results. Yet a review of twelve EEO models suggests that in most cases, replication of model results is currently impossible. We provide several recommendations to help develop and sustain a software framework for repeatable model analysis.}, number={6}, journal={ENERGY ECONOMICS}, author={DeCarolis, Joseph F. and Hunter, Kevin and Sreepathi, Sarat}, year={2012}, month={Nov}, pages={1845–1853} }
 @article{howells_rogner_strachan_heaps_huntington_kypreos_hughes_silveira_decarolis_bazillian_et al._2011, title={OSeMOSYS: The Open Source Energy Modeling System An introduction to its ethos, structure and development}, volume={39}, ISSN={["0301-4215"]}, DOI={10.1016/j.enpol.2011.06.033}, abstractNote={This paper discusses the design and development of the Open Source Energy Modeling System (OSeMOSYS). It describes the model’s formulation in terms of a ‘plain English’ description, algebraic formulation, implementation—in terms of its full source code, as well as a detailed description of the model inputs, parameters, and outputs. A key feature of the OSeMOSYS implementation is that it is contained in less than five pages of documented, easily accessible code. Other existing energy system models that do not have this emphasis on compactness and openness makes the barrier to entry by new users much higher, as well as making the addition of innovative new functionality very difficult. The paper begins by describing the rationale for the development of OSeMOSYS and its structure. The current preliminary implementation of the model is then demonstrated for a discrete example. Next, we explain how new development efforts will build on the existing OSeMOSYS codebase. The paper closes with thoughts regarding the organization of the OSeMOSYS community, associated capacity development efforts, and linkages to other open source efforts including adding functionality to the LEAP model.}, number={10}, journal={ENERGY POLICY}, author={Howells, Mark and Rogner, Holger and Strachan, Neil and Heaps, Charles and Huntington, Hillard and Kypreos, Socrates and Hughes, Alison and Silveira, Semida and DeCarolis, Joe and Bazillian, Morgan and et al.}, year={2011}, month={Oct}, pages={5850–5870} }
 @article{decarolis_2011, title={Using modeling to generate alternatives (MGA) to expand our thinking on energy futures}, volume={33}, ISSN={["1873-6181"]}, DOI={10.1016/j.eneco.2010.05.002}, abstractNote={Energy-economy optimization models – encoded with a set of structured, self-consistent assumptions and decision rules – have emerged as a key tool for the analysis of energy and climate policy at the national and international scale. Given the expansive system boundaries and multi-decadal timescales involved, addressing future uncertainty in these models is a critical challenge. The approach taken by many modelers is to build larger models with greater complexity to deal with structural uncertainty, and run a few highly detailed scenarios under different input assumptions to address parametric uncertainty. The result is often large and inflexible models used to conduct analysis that offers little insight. This paper introduces a technique borrowed from the operations research literature called modeling to generate alternatives (MGA) as a way to flex energy models and systematically explore the feasible, near-optimal solution space in order to develop alternatives that are maximally different in decision space but perform well with regard to the modeled objectives. The resultant MGA alternatives serve a useful role by challenging preconceptions and highlighting plausible alternative futures. A simple, conceptual model of the U.S. electric sector is presented to demonstrate the utility of MGA as an energy modeling technique.}, number={2}, journal={ENERGY ECONOMICS}, author={DeCarolis, Joseph F.}, year={2011}, month={Mar}, pages={145–152} }
 @article{vijay_decarolis_srivastava_2010, title={A bottom-up method to develop pollution abatement cost curves for coal-fired utility boilers}, volume={38}, ISSN={["1873-6777"]}, DOI={10.1016/j.enpol.2009.12.013}, abstractNote={This paper illustrates a new method to create supply curves for pollution abatement using boiler-level data that explicitly accounts for technology cost and performance. The Coal Utility Environmental Cost (CUECost) model is used to estimate retrofit costs for five different NOx control configurations on a large subset of the existing coal-fired, utility-owned boilers in the US. The resultant data are used to create technology-specific marginal abatement cost curves (MACCs) and also serve as input to an integer linear program, which minimizes system-wide control costs by finding the optimal distribution of NOx controls across the modeled boilers under an emission constraint. The result is a single optimized MACC that accounts for detailed, boiler-specific information related to NOx retrofits. Because the resultant MACCs do not take into account regional differences in air-quality standards or pre-existing NOx controls, the results should not be interpreted as a policy prescription. The general method as well as NOx-specific results presented here should be of significant value to modelers and policy analysts who must estimate the costs of pollution reduction.}, number={5}, journal={ENERGY POLICY}, author={Vijay, Samudra and DeCarolis, Joseph F. and Srivastava, Ravi K.}, year={2010}, month={May}, pages={2255–2261} }
 @article{kaplan_decarolis_thorneloe_2009, title={Is It Better To Burn or Bury Waste for Clean Electricity Generation?}, volume={43}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/es802395e}, DOI={10.1021/es802395e}, abstractNote={The use of municipal solid waste (MSW) to generate electricity through landfill-gas-to-energy (LFGTE) and waste-to-energy (WTE) projects represents roughly 14% of U.S. nonhydro renewable electricity generation. Although various aspects of LFGTE and WTE have been analyzed in the literature, this paper is the first to present a comprehensive set of life-cycle emission factors per unit of electricity generated for these energy recovery options. In addition, sensitivity analysis is conducted on key inputs (e.g., efficiency of the WTE plant landfill gas management schedules, oxidation rate, and waste composition) to quantify the variability in the resultant life-cycle emissions estimates. While methane from landfills results from the anaerobic breakdown of biogenic materials, the energy derived from WTE results from the combustion of both biogenic and fossil materials. The greenhouse gas emissions for WTE ranges from 0.4 to 1.5 MTCO2e/MWh, whereas the most agressive LFGTE scenerio results in 2.3 MTCO2e/MWh. WTE also produces lower NO(x) emissions than LFGTE, whereas SO(x) emissions depend on the specific configurations of WTE and LFGTE.}, number={6}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Kaplan, P. Ozge and DeCarolis, Joseph and Thorneloe, Susan}, year={2009}, month={Mar}, pages={1711–1717} }
 @article{decarolis_keith_2006, title={The economics of large-scale wind power in a carbon constrained world}, volume={34}, ISSN={0301-4215}, url={http://dx.doi.org/10.1016/j.enpol.2004.06.007}, DOI={10.1016/j.enpol.2004.06.007}, abstractNote={The environmental impacts of fossil-fueled electricity drive interest in a cleaner electricity supply. Electricity from wind provides an alternative to conventional generation that could, in principle, be used to achieve deep reductions (>50%) in carbon dioxide emissions and fossil fuel use. Estimates of the average cost of generation—now roughly 4¢/kWh—do not address costs arising from the spatial distribution and intermittency of wind. The greenfield analysis presented in this paper provides an economic characterization of a wind system in which long-distance electricity transmission, storage, and gas turbines are used to supplement variable wind power output to meet a time-varying load. We find that, with somewhat optimistic assumptions about the cost of wind turbines, the use of wind to serve 50% of demand adds ∼1–2¢/kWh to the cost of electricity, a cost comparable to that of other large-scale low carbon technologies. Even when wind serves an infinitesimal fraction of demand, its intermittency imposes costs beyond the average cost of delivered wind power. Due to residual CO2 emissions, compressed air storage is surprisingly uncompetitive, and there is a tradeoff between the use of wind site diversity and storage as means of managing intermittency.}, number={4}, journal={Energy Policy}, publisher={Elsevier BV}, author={DeCarolis, Joseph F. and Keith, David W.}, year={2006}, month={Mar}, pages={395–410} }
 @article{decarolis_keith_2005, title={The Costs of Wind's Variability: Is There a Threshold?}, volume={18}, ISSN={1040-6190}, url={http://dx.doi.org/10.1016/j.tej.2004.12.006}, DOI={10.1016/j.tej.2004.12.006}, abstractNote={Managing wind's intermittency entails costs even when wind power supplies a small fraction of load. If electric power systems evolve efficiently as wind capacity grows, the costs of managing intermittency will grow smoothly with increasing penetration, allowing wind power to provide deep reductions in CO2 emissions at costs that are competitive with other mitigation options.}, number={1}, journal={The Electricity Journal}, publisher={Elsevier BV}, author={DeCarolis, Joseph F. and Keith, David W.}, year={2005}, month={Jan}, pages={69–77} }
 @article{keith_decarolis_denkenberger_lenschow_malyshev_pacala_rasch_2004, title={The influence of large-scale wind power on global climate}, volume={101}, ISSN={0027-8424 1091-6490}, url={http://dx.doi.org/10.1073/PNAS.0406930101}, DOI={10.1073/PNAS.0406930101}, abstractNote={
            Large-scale use of wind power can alter local and global climate by extracting kinetic energy and altering turbulent transport in the atmospheric boundary layer. We report climate-model simulations that address the possible climatic impacts of wind power at regional to global scales by using two general circulation models and several parameterizations of the interaction of wind turbines with the boundary layer. We find that very large amounts of wind power can produce nonnegligible climatic change at continental scales. Although large-scale effects are observed, wind power has a negligible effect on global-mean surface temperature, and it would deliver enormous global benefits by reducing emissions of CO
            2
            and air pollutants. Our results may enable a comparison between the climate impacts due to wind power and the reduction in climatic impacts achieved by the substitution of wind for fossil fuels.
          }, number={46}, journal={Proceedings of the National Academy of Sciences}, publisher={Proceedings of the National Academy of Sciences}, author={Keith, D. W. and DeCarolis, J. F. and Denkenberger, D. C. and Lenschow, D. H. and Malyshev, S. L. and Pacala, S. and Rasch, P. J.}, year={2004}, month={Nov}, pages={16115–16120} }