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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.

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.

Energy system optimization models often incorporate climate change impacts to examine different energy futures and draw insights that inform policy. However, increased risk of extreme weather events from climate change has proven more difficult to model. Here, we present an energy system optimization model that incorporates hurricane risks by combining storm probabilities with infrastructure fragility curves, and demonstrate its utility in the context of Puerto Rico, an island territory of the United States that had its energy system severely damaged by Hurricane Maria in 2017. The model assesses the potential to change grid architecture, fuel mix and grid hardening measures considering hurricane impacts as well as climate mitigation policies. When hurricane trends are included, 2040 electricity cost projections increase by 32% based on historical hurricane frequencies and by 82% for increased hurricane frequencies. Transitioning to renewables and natural gas reduces costs and emissions independent of climate mitigation policies. In order to assess the impact of climate change on energy systems, models need to incorporate the increased risk of extreme weather events. Here, Bennett et al. provide a framework to integrate increasing extreme event risk in grid expansion planning models and apply the method to hurricane risks in Puerto Rico.

This study is the first comprehensive life cycle assessment (LCA) of reverse electrodialysis (RED), a technology that converts salinity gradient energy into electricity. Our goal is to identify RED system components of environmental concern and provide insights on potential environmental impacts. We conduct an attributional LCA of two RED scenarios: large‐scale energy generation from natural bodies of water and smaller‐scale energy generation from industrial processes. A functional unit of 1 MWh of net electricity production enables comparison to existing renewable energy technologies, including wind and solar photovoltaics. Under theoretical, favorable conditions, environmental impacts from RED are found to be comparable to, and often lower than, established renewable energy technologies. Processes associated with membrane manufacture are primary contributors to six of the nine evaluated impact categories. Under baseline assumptions, impacts are an average of 50% higher for the Natural Water scenario compared to the Concentrated Brine scenario because of the increased power density achieved with concentrated brines. This early‐stage LCA demonstrates that the expected environmental impacts of RED are comparable to existing renewable technologies and a large improvement over fossil‐based generation. However, eutrophication, ecotoxicity, and carcinogenic impacts are larger for RED than other technologies under some assumptions.

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 modelle...

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., In press; 24 pages, 4 figures. Environmental Science & Technology (2019)

Energy system optimization models (ESOMs) are designed to examine the potential effects of a proposed policy, but often represent energy-efficient technologies and policies in an overly simplified way. Most ESOMs include different end-use technologies with varying efficiencies and select technologies for deployment based solely on least-cost optimization, which drastically oversimplifies consumer decision-making. In this paper, we change the structure of an existing ESOM to model energy efficiency in way that is consistent with microeconomic theory. The resulting model considers the effectiveness of energy-efficient technologies in meeting energy service demands, and their potential to substitute electricity usage by conventional technologies. To test the revised model, we develop a simple hypothetical case and use it to analyze the welfare gain from an energy efficiency subsidy versus a carbon tax policy. In the simple test case, the maximum recovered welfare from an efficiency subsidy is less than 50% of the first-best carbon tax policy., 31 pages, 7 figures, 2 tables (main body)

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.Capsule Summary: Seasonal temperature forecasts from GCMs can potentially help in predicting season-ahead residential power demand forecasts for states in the Southern US.

Greenhouse vegetable production plays a vital role in providing year‐round fresh vegetables to global markets, achieving higher yields, and using less water than open‐field systems, but at the expense of increased energy demand. This study examines the life cycle environmental and economic impacts of integrating semitransparent organic photovoltaics (OPVs) into greenhouse designs. We employ life cycle assessment to analyze six environmental impacts associated with producing greenhouse‐grown tomatoes in a Solar PoweRed INtegrated Greenhouse (SPRING) compared to conventional greenhouses with and without an adjacent solar photovoltaic array, across three distinct locations. The SPRING design produces significant reductions in environmental impacts, particularly in regions with high solar insolation and electricity‐intensive energy demands. For example, in Arizona, global warming potential values for a conventional, adjacent PV and SPRING greenhouse are found to be 3.71, 2.38, and 2.36 kg CO2 eq/kg tomato, respectively. Compared to a conventional greenhouse, the SPRING design may increase life cycle environmental burdens in colder regions because the shading effect of OPV increases heating demands. Our analysis shows that SPRING designs must maintain crop yields at levels similar to conventional greenhouses in order to be economically competitive. Assuming consistent crop yields, uncertainty analysis shows average net present cost of production across Arizona to be $3.43, $3.38, and $3.64 per kg of tomato for the conventional, adjacent PV and SPRING system, respectively.

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.

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., 30 pages, 9 figures

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 h...

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.

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., Highlights • 100% renewable energy can be achieved by multiple pathways with similar cost levels. • Focusing only on renewables is not the most cost-effective path to decarbonization. • Pathways relying on bioenergy import are more susceptible to uncertainties. • Indigenous bioenergy needs to be exploited to its full potential. • Much greater efforts than current national action plans are required.

The planned US withdrawal from the Paris Agreement as well as uncertainty about federal climate policy have 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 tradeoff, particularly under conservative assumptions about future technology innovation. The lowest emissions scenarios are produced when the cost of natural gas and electric vehicles decline while coal and oil prices increase relative to the baseline., 28 pages, 7 figures

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. Keywords: Energy system modelling, Uncertainty, Monte Carlo analysis, Stochastic programming, Robust optimization, Modelling to generate alternatives

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.

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.

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.

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 acti...

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.

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.

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., 22 pages, 5 figures

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, Sao 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 Sao 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.

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.

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/. DeCarolis et al. articulate the benefits of forming collaborative teams with a wide array of disciplinary and domain expertise to conduct analysis with macro-energy system models. Open-source models, tools, and datasets underpin such efforts by enabling transparency, accessibility, and replicability among team members and with the broader modeling community.

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).

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. Modeling to generate alternatives (MGA) is applied to an energy system model.Temoa, an open source energy model, and a two sector U.S. dataset are utilized.MGA is used to explore different cost- and CO2-constrained futures.Results highlight the value of iterative analysis to probe the model decision space.

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.

This paper provides an economic comparison of three technologies that enable high PV penetration on the distribution system: Medium voltage solid state transformer (SST), grid edge regulator and smart inverter. In order to compare these three technologies, simulations are performed on a representative utility feeder using OpenDSS. Quasi-static time series analysis over a year is used to predict the effectiveness of high PV penetration and more aggressive conservation voltage reduction enabled by each technology. Comprehensive cost benefit analyses are conducted based on these simulation results. The cost benefit analysis results indicate that the medium voltage solid state transformer (MV SST) is the most cost effective option with a 3 year payback period for utilities that are trying to accommodate high PV penetration in the distribution grid.

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.

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.

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 (1 Mg = 1 metric ton) of waste input. Electricity use ranged from 4.7 to 7.8 kWh 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. (C) 2014 Elsevier Ltd. All rights reserved.

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.

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.

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 CO"2e 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.

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.

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.

Energy Policy, 101, ISSN:0301-4215

A 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 ...

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.