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• This is a case study of a small modular reactor supplying steam and electricity to a pulp and paper mill. • Resulting steam conditions in the mill are analyzed using steady-state and dynamic models. • Models of the paper mill were built in Aspen HYSYS and Dymola, a Modelica Ecosystem. • Integrated SMRs with pulp and paper mills is feasible but may present cost limitations. Small modular reactors (SMRs) are reactor designs producing less than 300 MWe and are generally planned for deployment as multimodule nuclear power plants. The possibility of factory-manufactured, flexibly sized plants expands the opportunities for nuclear power to different communities and industries, including manufacturing plants that currently utilize fossil fuels to produce both steam and electricity. This paper examines the feasibility of coupling a NuScale SMR with a midsize pulp and paper mill in the Southeastern United States. A steady-state mill model was developed in Aspen HYSYS, based on real data from the operation of the mill, and modified it to include the SMR while maintaining steam quality requirements and making as few changes as possible to existing equipment. Dynamic plant models were also developed Dymola to demonstrate possible plant conditions, using three configurations. Preliminary results suggest that, while SMR coupling is physically feasible, its economic feasibility is limited by the differences in steam and electricity demands. Because of limitations in the amount of steam the mill can take from the SMR, sizing the SMR for the plant's steam demand may result in an electricity deficit, or vice versa. Dynamic analyses show that the addition of a thermal storage system could reduce such deficits, but this entails its own challenges. Each plant must determine the best configuration and control scheme for itself, based on its electricity and heat needs, including the peak duration and intensity for both. Ultimately, an implementation of SMRs with manufacturing processes would benefit from partnering with a local utility to purchase excess electricity generated by the SMR. This will help manufacturing facilities meet their environmental and cost-savings goals, in addition to meeting the need for cost-effective baseload power across the United States. [ABSTRACT FROM AUTHOR]

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation's energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events. [Display omitted] • A systematic summer droughts generating capacity assessment framework is developed. • Capacity derating models for various generation technologies are presented. • The impacts of summer droughts on generating fleet in PJM and SERC are studied. • Impact of extreme summer drought on usable capacity of generators is significant. [ABSTRACT FROM AUTHOR]

• Decoupling of GDP from all energy sources under multiple principles are examined. • World economy achieved weak energy decoupling in most years during 2000–2011. • World economy coupled with coal use and tended to couple with cleaner energy use. • None of studied economies achieved long-term GDP-embodied energy decoupling. • Decoupling delusions occurred when only considering direct and total energy use. Decoupling indicators are widely used to understand links between economic growth and energy use. However, traditional decoupling analyses mostly focus on domestic energy consumption (i.e., the production-based principle) and neglect off-site energy use across global supply chains to satisfy an economy's final consumption (i.e., the consumption-based principle). Moreover, analyses for total primary energy conceal an economy's preference for different energy sources. Therefore, this paper evaluates decoupling states of GDP from all types of primary energy use under consumption-based principle, for world economy and eight typical economies during 2000–2011. Regarding total primary energy, world economy witnessed weak decoupling in most years, and most economies studied (e.g., USA, Japan and China) achieved decoupling initially but performed negative decoupling finally. For EU, USA, Japan, Russia and India, production-based decoupling performances were generally better than consumption-based ones. Decoupling phenomena detected under production-based principle even became coupling or negative decoupling under consumption-based principle in some cases. As for each energy source, world economy decoupled from oil use, but still coupled with coal use, and gradually showed a trend to couple with natural gas and renewables use. Different energy sources showed distinct decoupling degrees from GDP, affected by individual embodied energy requirement structure. This paper uncovers potential energy decoupling delusions to deepen the understanding of relationships between energy use and economic growth. [ABSTRACT FROM AUTHOR]

• Energy simulation of AIS using experimentally validated thermal resistances. • AIS evaluation at varying orientations, climate zones, and switching ratios. • Investigation of AIS switching and wall thermal conductivity/heat transfer. • Application of freeze timer to reduce the number of switches of AIS. Active insulation systems (AISs) in buildings are envelopes that integrate thermal insulation, thermal energy storage, and controls. Although different designs for AISs have been proposed in the literature, a comprehensive analysis of feasible AISs is lacking. This paper discusses the energy performance, peak demand reduction potential, and performance characteristics of an AIS that uses a concrete wall as thermal mass sandwiched between two solid-state thermal switches (STSs). These STSs change their thermal conductivity using an on/off metal switch to create or break a thermal bridge across the STS. This paper first describes the experimental setup, used to determine the ratio of thermal resistance during R-high (low thermal conductivity) and R-low (high thermal conductivity) states of the STSs. This ratio was then used in whole-building energy simulations to evaluate the performance of AIS walls across different climate zones with/without a freeze timer of 60 min. The timer was added to reduce the number of switches of STSs from one state to another, and hence the energy needed for these switches. Analysis of the switching frequency and interval of STSs, thermal conductivity of walls, impact of wall orientation, and heat transfer through the wall from the use of AIS at different climate zones/locations were performed. The simulation results show that the AIS can achieve energy savings ranging from ∼ 980 to 2,290 kWh in a single-family home with a floor area of ∼ 220 m2 compared with an IECC 2018 baseline. The energy savings was higher in dry climate zones which represent 17% of residential buildings in the United States, compared to humid or marine climate. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of existing financial incentive policies in the U.S. for integrated photovoltaic and battery energy storage (PV-BES) systems. A mathematical model of PV-BES system to evaluate annual energy performance is developed in this paper. Four types of buildings (i.e., hospital, large office, large hotel, and secondary school) located in four different states, which each has their own PV and/or BES incentives, are selected and analyzed. Based on the energy performance data for each building type, the simply payback period (PBP) for the PV-BES system in different locations is calculated according to the local incentive policies. The PBP is chosen as an indicator to evaluate the effectiveness of incentive policies for different locations and building types by comparing it to the PBP for the same PV-BES systems without incentive policies. The reduction of carbon dioxide emission (CDE) due to the PV generation is also investigated since it indicates the potential to reduce the PBP for a further step when a high carbon credit is available. Furthermore, a parametric analysis is conducted to determine the sensitivity and contribution of parameters such as the capacity of the PV-BES system, the capital cost of PV module and the battery storage on the performance of the PV-BES system. Results show that for all the evaluated buildings in California and Hawaii, the existing incentive policy could reduce the PBP effectively below 10 years. However, the PBP for most of the evaluated buildings in New Jersey and New York were high even when both the PV and BES incentive policies were taken into account (approximately from 11 to 29 years). [ABSTRACT FROM AUTHOR]

• Aggregation and cooperation among peers will expand beyond prosumers in microgrids. • Growth in IoT and metering infrastructure is empowering end-use consumers. • Methodology allows aggregated consumers to schedule load and share energy. • Application demonstrates a significant cost reduction while assuring fairness. • Offers a novel cost-sharing strategy guided by cost minimization and Nash bargaining. End-use consumers (peers) are being empowered to aggregate for direct demand-side participation through load scheduling and energy sharing. This is the result of the growth of Internet of Things (IoT) enabled loads, availability of advanced metering infrastructure, and the move towards real-time (RT) pricing of electricity. Peer-to-peer (P2P) cooperation has received significant interest in recent years, though the focus of this growing body of research is on modeling prosumer behavior in microgrids. Hence, there is a need for new methodologies to examine empowerment of all end-use consumers (not limited to prosumers) to form aggregations and develop fair rules of cooperation to reduce cost. This paper offers an optimization based methodology to address the above need for power systems. It minimizes the total cost and considers fairness using a Nash bargaining approach. Since cost and fairness are often in conflict, trade-off strategies are also presented. The model to asses fairness is nonlinear. Hence, it is transformed into a second order cone program (SOCP) and solved using GUROBI software version 7.5.2. The methodology is implemented on a sample 5-bus network, built using price and demand data from one of the load zones of Pennsylvania, New Jersey, and Maryland (PJM) power network in the United States. It is shown that two aggregations of peers participating in the sample network can reduce their total cost by 14.17% and 22.7%, while maintaining fairness. Concluding remarks highlight some of the limitations of the methodology. [ABSTRACT FROM AUTHOR]

• A case study based on the national BCycle bike-sharing program was presented. • The bike usage patterns were compared among different membership groups. • The spatio-temporal trends of empty and full bike stations were examined. • The current system configuration was optimized under three hypothetical scenarios. • This study has important implications to other cities in the BCycle program. Many cities around the world have integrated bike-sharing programs into their public transit systems to promise sustainable, affordable transportation and reduce environmental pollution in urban areas. Investigating the usage patterns of shared bikes is of key importance to understand cyclist's behaviors and subsequently optimize bike-sharing programs. Based on the historical trip records of bike users and station empty/full status data, this paper evaluated and optimized the bike-sharing program BCycle in the city of Boulder, Colorado, the United States, using a combination of different methods including the Potential Path Area (PPA) and the Capacitated Maximal Covering Location Problem (CMCLP). Results showed significantly different usage patterns between membership groups, revealed diverse imbalance patterns of bike supply and demand across stations in the city and provided three system upgrading strategies about maximizing the service coverage. This case study is committed to future energy conservation and sustainable energy systems nationwide and ultimately worldwide, by holding immerse potential to adapt the resulting optimization strategies to the cities with a similar urban context across the United States, as well as more emerging bike-sharing programs in other countries, such as China. [ABSTRACT FROM AUTHOR]

• We develop a microeconomic model of VMT choice under time and budget constraints. • Using NHTS data, we estimate VMT elasticities with respect to fuel and time costs. • We use these elasticities to forecast CAV-induced travel and energy rebound. • A 38% drop in time cost offsets energy savings from a 20% fuel efficiency rise. Connected and automated vehicles (CAVs) are expected to yield significant improvements in safety, energy efficiency, and time utilization. However, their net effect on energy and environmental outcomes is unclear. Higher fuel economy reduces the energy required per mile of travel, but it also reduces the fuel cost of travel, incentivizing more travel and causing an energy " rebound effect." Moreover, CAVs are predicted to vastly reduce the time cost of travel, inducing further increases in travel and energy use. In this paper, we forecast the induced travel and rebound from CAVs using data on existing travel behavior. We develop a microeconomic model of vehicle miles traveled (VMT) choice under income and time constraints; then we use it to estimate elasticities of VMT demand with respect to fuel and time costs, with fuel cost data from the 2017 United States National Household Travel Survey (NHTS) and wage-derived predictions of travel time cost. Our central estimate of the combined price elasticity of VMT demand is −0.4, which differs substantially from previous estimates. We also find evidence that wealthier households have more elastic demand, and that households at all income levels are more sensitive to time costs than to fuel costs. We use our estimated elasticities to simulate VMT and energy use impacts of full, private CAV adoption under a range of possible changes to the fuel and time costs of travel. We forecast a 2–47% increase in travel demand for an average household. Our results indicate that backfire – i.e., a net rise in energy use – is a possibility, especially in higher income groups. This presents a stiff challenge to policy goals for reductions in not only energy use but also traffic congestion and local and global air pollution, as CAV use increases. [ABSTRACT FROM AUTHOR]

Power split configurations exhibit potential on decreasing fuel consumption. However, how to select the best power split configuration among all the possible configurations still need to be explored. In this paper, a one-dimensional searching ECMS (Equivalent Consumption Minimization Strategy) is developed to evaluate fuel consumption for input power split configuration. Additionally, a battery degradation model based on experimental data is applied to evaluate the battery degradation for input power split configuration. According to one-dimensional searching ECMS and battery degradation model, the impact of different input power split configurations on fuel consumption and battery degradation is investigated. Simulations show that configuration T1 has fuel consumption and battery degradation advantages for PHEV city bus application in China. These advantages are observed because the system efficiency operation points of configuration T1 are closer to the high efficiency area than the system efficiency operation points of other input power split configurations. Furthermore, according to the lever model and one-dimensional searching ECMS method, a power split configuration selection method is provided in this paper. The simulation results based on configuration selection method still indicate that configuration T1 appears to have the best fuel economy for PHEV city bus application. Moreover, configuration T5, with a lever length close to 0, exhibits the worst fuel consumption and battery degradation. [ABSTRACT FROM AUTHOR]

• Substitution of 2nd generation for 1st generation biofuel is investigated. • An optimization model for the 2nd generation biofuel supply chain is proposed. • Both economic and environmental performance measures are examined. • 2nd generation biofuel is more cost effective than 1st generation biofuel. • Considering stover handling or not leads to different environmental performances. Multiple generations of biofuel manufacturing technologies have been proposed and developed, among which first generation technology is the most mature. It typically utilizes corn grain as feedstock in bio-refineries in many countries such as the United States. However, the use of edible matter of crops like corn may lead to a food-versus-fuel competition. To address such an issue, second generation biofuel manufacturing technology using the non-edible matter of crops has been developed. In this paper, the economic viability and environmental sustainability are analyzed and quantified, when restructuring the biofuel supply chain from employing first generation manufacturing technology to the one utilizing second generation technology considering the existing supply chain infrastructure. Two supply chain restructuring strategies, i.e., distributed and centralized preprocessing deployment, are modeled for implementing biomass preprocessing for second generation biofuel manufacturing. Bi-objective optimization formulations for the corn stover-sourced biofuel (a typical second generation biofuel) supply chain considering both economic and environmental aspects are proposed under both strategies. Different decision variables such as the locations selected for constructing preprocessing centers and the corresponding corn stover handling capacities of such centers, as well as the material flows between farms, preprocessing centers, and bio-refinery plants are identified. A case study based on the state of Missouri in the United States is implemented to illustrate the effectiveness of the proposed model and analyze the performance of different strategies. The results of the case study show that when corn stover is used as feedstock in bio-refineries, the unit cost of bioethanol production can be reduced, while the unit emission is increased compared to the corn-sourced supply chain under both supply chain restructuring strategies. Specifically, in Missouri where the daily stover handling amount per bio-refinery plant is relatively low and the stover collection radius is relatively small, the centralized strategy outperforms the distributed one with a higher cost reduction and a smaller increase in emissions. It reduces the unit cost by 27.39%, while increasing the unit emission by 24.42%, compared to the corn-sourced supply chain. [ABSTRACT FROM AUTHOR]

• We analyze time-of-use rates with respect to model predictive controls. • We show that thermal storage potential can vary greatly for differing climates. • We build a model predictive control framework for indoor temperature setpoints. • We analyze of the impacts of different variable electric rates. • The framework is capable of reducing cooling electricity costs by 30%. Heating, ventilation and air conditioning for residential and commercial buildings requires a substantial share of electric energy, and ultimately drives summer peak demand in the United States. Variable electric rates are becoming more common in the residential market, as utilities try to encourage users to shift their energy demand. Model predictive controls, one method of reducing energy usage, employ an optimization model to minimize peak demand, energy usage, or electricity costs. This paper details the development of a co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls. The framework integrates commercially available software to: (i) simulate all energy interactions between the building, internal gains, outdoor environment, and heating and cooling systems via a building energy simulation program (EnergyPlus), (ii) algebraically formulate an optimization problem (with AMPL) using a black-box, reduced-order model for rapid calculations, (iii) employ Simulink as the environment that links calls to EnergyPlus and AMPL, and (iv) solve the optimization model (with CPLEX) to minimize electricity costs and user discomfort. Variable electric time-of-use rates are analyzed in the context of total cooling electricity costs, thermal comfort of users, and peak demand shedding. The framework uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule. [ABSTRACT FROM AUTHOR]

Highlights • We analyze the economic value of a U.S windfarm using long-term LCOE information. • Long-term LCOE of wind energy is obtained from two decades of history. • Monte Carlo and Risk-Neutral valuation is used to valuate a reference windfarm. • Managerial flexibilities and generation cost uncertainty make windfarms more valuable. • Real Option accounts for the flexibility of decision-making with stochastic costs. Abstract Renewable energies are a natural replacement for conventional or fossil fuel energy generation. One of these sources, wind energy, has exhibited significant cost reductions during the last decades. Even though this historical cost reduction trend is well known in the industry and in the academia, information on this cost trend and volatility has been scarcely incorporated in previous works, thus a natural concern arises when assessing their influence on the economic valuation of wind farms. Moreover, wind projects are modular in size and have a short time-to-market, providing a valuable managerial flexibility to defer investment under non-attractive market conditions, such as high development cost scenarios. Traditional evaluation tools often consider those scenarios, wrongly assuming that wind-developers would move forward with the project under such conditions. Accordingly, this paper analyzes the effect of incorporating both generation cost history and investment flexibility in the valuation of wind farms in the United States using Real Options methodology. Taking two decades of cost data and a reference wind farm, this "uncertainty-flexibility" effect is incorporated and valued, finding that the value of the wind project is 14.2% larger than the one neglecting such effect – where cost stochasticity and managerial flexibility are ignored. Therefore, traditional tools for project valuation, such as discounted cash flow and scenario analysis can no longer solely be used, as they do not properly account for the cost reduction trend, uncertainty, and managerial flexibility that a wind farm could face. [ABSTRACT FROM AUTHOR]

Highlights • Future residential demand with heat pumps are estimated in four cities. • Local wind and solar generation mixes are compared with and without storage. • Cost comparisons of distributed technology options including storage are studied. Abstract To reduce greenhouse gas emissions, the electricity sector is going through two main transitions. First, the electric grid is integrating variable renewable generation, such as wind and solar. Second, demands are changing as heating systems are shifting from gas-based to high efficiency electric heat pumps. This paper provides a comparative analysis of future energy scenarios with distributed technology options including (1) wind and solar generation; (2) heat pumps for heating and cooling; and (3) battery and thermal storage in representative residential blocks in four cities, including New York City, New York; Minneapolis, Minnesota; Tallahassee, Florida; and Fort Collins, Colorado. These cities are located in three climate regions with different weather patterns which result in different demand profiles and different local renewable resources. Future energy demand scenarios with 100% penetration of air source or ground source heat pumps for heating and cooling are estimated for the four residential city blocks. Under a future scenario with all electric demand with air source heat pumps and high renewable energy penetration, this study finds that (1) the optimal wind and solar generation mix varies with location and amount of storage and (2) battery storage is more cost effective than thermal storage, ground source heat pumps, and overbuilt renewable generation. [ABSTRACT FROM AUTHOR]

• Backup capability of solar and storage differs by region in the U.S. • Small batteries can meet a limited set of critical loads in most counties. • Electric cooling and heating loads are typically hardest loads to backup. • Household building attributes like efficiency can impact results up to 20% • Resiliency value needs to be high to motivate adoption of solar-plus-storage. Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer's resilience value of PVESS must be high to motivate adoption of these systems. [ABSTRACT FROM AUTHOR]

The market for solar energy in the US has grown exponentially due to increased consumer demand resulting from price reduction via economies of scale, technological progress, and a variety of incentives from federal and state governments as well as utility companies. The installation of solar power in Tennessee has more than doubled each year from 2009 to 2011. In this paper, we focus on the behavior of the Tennessee Solar Value Chain (TNSVC) to study the factors that influence growth of solar industry in the state. The impact of existing incentives on the TNSVC is analyzed. The TNSVC is simulated based on inputs from on-site survey to estimate economic impact in terms of the number of jobs added and the tax revenue generated in the state. In addition, a sensitivity analysis for the impact on the TNSVC under different policies those may be adopted by the state of Tennessee in the future is conducted. This paper employs a holistic model which can predict PV installation demand, understand solar value chain capacity, and estimate the revenue generation. It should be noted that the method employed in this study is not unique to the solar energy industry in Tennessee. The data utilized in this study is a combination of public domain information and surveys of suppliers, manufacturers, distributors, and installers. This makes the model in this paper flexible enough to be applied to assess the solar value chain in other state or country. [ABSTRACT FROM AUTHOR]

This paper presents the Dynamic Upstream Gas Model (DYNAAMO); a new, global, bottom-up model of natural gas supply. In contrast to most “static” supply-side models, which bracket resources by average cost, DYNAAMO creates a range of dynamic outputs by simulating investment and operating decisions in the upstream gas industry triggered in response to investors’ expectations of future gas prices. Industrial data from thousands of gas fields is analysed and used to build production and expenditure profiles which drive the economics of supply at field level. Using these profiles, a novel methodology for estimating supply curves is developed which incorporates the size, age and operating environment of gas fields, and treats explicitly the fiscal, abandonment, exploration and emissions costs of production. The model is validated using the US shale gas boom in the 2000s as a historic case study. It is found that the modelled market share of supply by field environment replicates the observed trend during the period 2000–2010, and that the model price response during the same period – due to lower capacity margins and the financing of new projects – is consistent with market behaviour. [ABSTRACT FROM AUTHOR]

Occupants are integral elements of a building ecosystem and their behavior can have a substantial impact on energy consumption in buildings. A wide range of energy feedback programs have been developed to make energy consumption more visible and interpretable to occupants and help them learn how to control and save energy. In this paper, we conduct a critical review of the literature related to energy feedback and identify four key methodological approaches to designing and studying energy feedback programs: experiments, analytics, surveys and simulation. Our meta-analysis reveals five research gaps and opportunities for future methodological fusion at the intersection between such approaches, including the analytics-survey, experiments-analytics, experiments-analytics-surveys, simulation-experiments and analytics-simulation interfaces. Future research at these crucial interfaces could provide the deeper understanding necessary to develop energy feedback programs that yield substantial and persistent energy savings. [ABSTRACT FROM AUTHOR]

The uncertainties in renewable power generators and the proliferation of price-responsive load aggregators make it a challenge for independent system operators (ISOs) to manage the energy trading in the power markets. Hence, a centralized framework for the energy trading market may not be remained practical for the ISOs mainly due to violating the privacy of different entities, i.e., load aggregators and generators. It can also suffer from the high computational burden in a market with a large number of entities. Instead, in this paper, we focus on proposing a decentralized energy trading framework enabling the ISO to incentivize the entities toward an operating point that jointly optimize the cost of load aggregators and profit of the generators, as well as the risk of shortage in the renewable generation. To address the uncertainties in the renewable resources, we apply a risk measure called the conditional value-at-risk (CVaR) with the goal of limiting the likelihood of high renewable generation shortage with a certain confidence level. Then by considering the risk attitude of the ISO and the generators, we develop a decentralized energy trading algorithm with some control signals that properly coordinate the entities toward the market operating point of the ISO’s centralized approach. Simulation results on the IEEE 30-bus test system show that the proposed decentralized algorithm converges to the solution of the ISO’s centralized problem in a timely fashion. Furthermore, the load aggregators can help their consumers reduce their electricity cost by 18 % on average through managing their loads using locally available information. Meanwhile, the generators can benefit from 17.1 % increase in their total profit through decreasing their generation cost. [ABSTRACT FROM AUTHOR]

Addressing greenhouse gas emissions and the associated global temperature rise will be one of the key issues of the 21st century. The supply and use of energy is the single largest contributor to anthropogenic greenhouse gas emissions through the burning of fossil fuels. Given the role fossil fuel use plays in the global economy, curtailing their use in order to mitigate climate change will be very difficult. This has led to proposals to deploy various types of geoengineering as a means of mitigating the global temperature increase. Current integrated assessment models (IAMs) do not readily incorporate geoengineering options. They are also complex to set up and run. This has led the authors to develop the Combined Energy and Geoengineering Optimization Model (CEAGOM) © . CEAGOM provides an easy to use software platform that explicitly includes geoengineering as potential options as part of the optimization of the energy resource mix for meeting a specified energy demand while still meeting specified climate targets. This paper describes the CEAGOM model and shows that its results compare quite favorably with those from a more complex IAM. [ABSTRACT FROM AUTHOR]

Building energy consumption makes up 40% of the total energy consumption in the United States. Given that energy consumption in buildings is influenced by aspects of urban form such as density and floor-area-ratios (FAR), understanding the distribution of energy intensities is critical for city planners. This paper presents a novel technique for estimating commercial building energy consumption from a small number of building features by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS). Our results show that gradient boosting regression models perform the best at predicting commercial building energy consumption, and can make predictions that are on average within a factor of 2 from the true energy consumption values (with an r 2 score of 0.82). We validate our models using the New York City Local Law 84 energy consumption dataset, then apply them to the city of Atlanta to create aggregate energy consumption estimates. In general, the models developed only depend on five commonly accessible building and climate features, and can therefore be applied to diverse metropolitan areas in the United States and to other countries through replication of our methodology. [ABSTRACT FROM AUTHOR]

Market penetration of energy-efficient technologies can be estimated using energy optimization models that minimize cost; however, such models typically estimate the minimum cost of optimal pathways under a certain set of non-dynamic assumptions, so technology penetrations determined for the long-term do not fully respond to changing circumstances or costs. In this study, investment costs of energy-efficient technologies are modeled dynamically in the Industrial Sector Energy-Efficiency Model (ISEEM) using a technological learning formula. Results from 24 energy-efficient technologies – 14 existing, 10 emerging – selected from the United States (U.S.) iron and steel sector show that when technological learning is incorporated into the model, total energy consumption of this sector is expected to decrease by 13% (180 PJ) in 2050 compared to energy consumption in a non-learning scenario. Average energy intensity of the steel production improves from 12.3 GJ/t in the non-learning scenario to 10.7 GJ/t in the learning scenario in 2050. This decrease represents a cost savings of US$1.6 billion and a carbon dioxide emissions reduction potential of 14.9 billion tonnes. Results discussed in this paper focus on the U.S. iron and steel sector, but the proposed framework can be applied to study new technology development in any other industrial processes and regions. [ABSTRACT FROM AUTHOR]

• A framework identifies energy contexts where racial injustice is more likely. • The 2019 American Housing Survey is used to assess five energy outcomes. • Housing-wealth explains 37% of racial inequality in solar uptake by US homeowners. • Racial differences exist for having washing machines, dryers, or broken equipment. • Black-alone households in public housing may have lower access to solar panels. This paper presents a framework to identify which household-energy outcomes are more likely to display injustice. Inequity is more likely when variation is pronounced at the lower end of wealth distributions. This paper compares solar-panel uptake with four other energy outcomes using the 2019 American Housing Survey. Analysis shows that solar-panel uptake in the United States has mainly been by high-wealth homeowners, across racial groups. Housing wealth explains around 37% of the solar-uptake difference between "Black alone" and other homeowners. In contrast, there are large racial differences for having washing machines or dryers. In addition, uncomfortably cold home temperatures and broken equipment are more likely for "Black alone" homeowners. Similar patterns are mostly observed for renters. However, "Black alone" households in public housing may have been less likely to benefit from solar panels than other public-housing tenants. There are three main implications. Solar policies targeting low wealth, rather than income, have greater potential for reducing racial inequality. Also, policymakers could focus more on equality in solar-panel access for public housing. Further, there are larger racial differences to address in non-solar contexts. [ABSTRACT FROM AUTHOR]

Long-term Load Forecasting (LTLF) plays a vital role in the planning of electric utilities. In the long run, utilities face various uncertainties caused by economic and environmental factors. These uncertainties have made LTLF more complex and inaccurate, thus, amplifying financial risks of utilities. A potent contributor to such losses in LTLF accuracy is economic shocks. This study proposes two probabilistic Time-Varying (TV) approaches to capture such shocks in LTLF and minimise accuracy loss, namely TV-XGB-X and TV-PR, and their combinations considering economic policy uncertainty. Both eXtreme Gradient Boosting (XGB) and Polynomial Regression (PR) are extended to include the long-run TV effects of economic shocks as eXogeneous predictors in this paper. These models and their combinations are compared with various non-TV approaches through experiments on the monthly electricity consumption of eight energy-intensive states in the United States. The results reveal that the proposed combined approaches outperform stand-alone models on all datasets. The findings of this study can help utilities in hedging financial risks under shocks. • Proposed Time-Varying framework to detect and capture economic shocks for LTLF. • Framework includes probabilistic TV-PR, TV-XGB-X, their variants and combinations. • Compared the point and probabilistic accuracies of proposed and benchmark models. • Proposed models outperform existing benchmark models. [ABSTRACT FROM AUTHOR]

Electricity prices change substantially over time in the wholesale markets. The fluctuations are mainly caused by power grid states, fuel price fluctuations, and market conditions. Real-time pricing (RTP) is an effective way to reduce risks utility companies face from volatile electricity prices. RTP allows participating customers to reduce electric bills by changing use patterns. This paper analyzes the pricing components and characteristics of representative RTP programs for industrial customers based on publicly available information. Our main goal is to study the reasons behind their success and failure. Knowing the details about RTP programs increases customers' awareness of their advantages and assists them in deciding whether they should enroll. It also helps utility companies develop new programs or improve existing ones. Case studies in the manufacturing sector are presented and the savings of RTP are compared with another popular dynamic pricing program, time-of-use (TOU) pricing under different scenarios. The results show that the savings by switching from flat rates to TOU and RTP are highly program dependent. Eighteen out of the 35 base-case scenarios resulted in positive savings by switching to TOU, and 29 out of 35 base-case scenarios resulted in positive savings by switching to RTP. [ABSTRACT FROM AUTHOR]

Electricity power is a basic industrial component which plays an important role in the economy of a nation. In this paper, the correlations evolution of electricity prices among 50 states and the District of Columbia are studied based on random matrix theory (RMT) Four regime shifts are identified from January 1990 to August 2014 in the U.S. residential, commercial and industrial electricity markets. Then, the genetic algorithm (GA) is applied to analyze the clusters of evolution. The results show that, the correlations of electricity prices increased continually in the three departments. However, it decreased in 2012 which further confirms its sensitivity to fuel market. Besides, four regime shifts exist in the three departments though the different times of occurrence caused by price level. And, the fluctuation of community evolution is consistent with four regime shifts. The final part is a summary of the research analyzed and results. [ABSTRACT FROM AUTHOR]

This paper evaluates potential changes in the power system associated with sustained growth in wind generation in the United States to 35% of end-use demand by 2050; Wiser et al. (2016) evaluate societal benefits and other impacts for this same scenario. Under reference or central conditions, the analysis finds cumulative wind capacity of 404 gigawatts (GW) would be required to reach this level and drive 2050 incremental electricity rate and cumulative electric sector savings of 2% and 3% respectively, relative to a scenario with no new wind capacity additions. Greater savings are estimated under higher fossil fuel costs or with greater advancements in wind technologies. Conversely, incremental costs are found when fossil fuel costs are lower than central assumptions or wind technology improvements are more-limited. Through 2030, the primary generation sources displaced by new wind capacity include natural gas and coal-fired generation. By 2050, wind could displace other renewables. Incremental new transmission infrastructure totaling 29 million megawatt-miles is estimated to be needed by 2050. In conjunction with related societal benefits, this work demonstrates that 35% wind energy by 2050 is plausible, could support enduring benefits, and could result in long-term consumer savings, if nearer-term (pre-2030) cost barriers are overcome; at the same time, these opportunities are not anticipated to be realized in their full form under “business-as-usual” conditions. [ABSTRACT FROM AUTHOR]

We model scenarios of the U.S. electric sector in which wind generation reaches 10% of end-use electricity demand in 2020, 20% in 2030, and 35% in 2050. As shown in a companion paper, achieving these penetration levels would have significant implications for the wind industry and the broader electric sector. Compared to a baseline that assumes no new wind deployment, under the primary scenario modeled, achieving these penetrations imposes an incremental cost to electricity consumers of less than 1% through 2030. These cost implications, however, should be balanced against the variety of environmental and social implications of such a scenario. Relative to a baseline that assumes no new wind deployment, our analysis shows that the high-penetration wind scenario yields potential greenhouse-gas benefits of $85–$1,230 billion in present-value terms, with a central estimate of $400 billion. Air-pollution-related health benefits are estimated at $52–$272 billion, while annual electric-sector water withdrawals and consumption are lower by 15% and 23% in 2050, respectively. We also find that a high-wind-energy future would have implications for the diversity and risk of energy supply, local economic development, and land use and related local impacts on communities and ecosystems; however, these additional impacts may not greatly affect aggregate social welfare owing to their nature, in part, as resource transfers. [ABSTRACT FROM AUTHOR]

• More accurate and explanatory model is proposed for natural gas storage deliverability. • A hybrid Sparse Autoencoder and Random Forest model shows on average of 5.7% increase in prediction accuracy. • The first use of machine learning and explanatory methods (XAI) in deliverability prediction for natural gas storage. Numerous subsurface factors, including geology and fluid properties, can affect the connectivity of the storage spaces in depleted reservoirs; hence, fluid flow simulations become more complicated, and predicting their deliverability remains challenging. This paper applies Machine Learning (ML) techniques to predict the deliverability of underground natural gas storage (UNGS) in depleted reservoirs. First, three baseline models were developed based on Support Vector Regression (SVR), Artificial Neural Network (ANN), and Random Forest (RF) algorithms. To improve the accuracy of the RF model as the best-performing baseline model, a unified framework, referred to as SARF, was developed. SARF combines the capabilities of Sparse Autoencoder (SA) and that of Random Forest (RF). To achieve this, the internal representations of the SA, which constitute extracted features of the input variables, are used in RF to develop the proposed SARF framework. The predictive capabilities of the baseline models and the proposed SARF model were validated using 3744 real-world storage data samples of 52 active storage reservoirs in the United States. The experimental result of this study shows that the proposed SARF model achieved an average 5.7% increase in accuracy on four separate data partitions over the baseline RF model. Furthermore, a set of eXplainable Artificial Intelligence (XAI) methods were developed to provide an intuitive explanation of which factors influence the deliverability of reservoir storage. The visualizations developed using the XAI method provide an easy-to-understand interpretation of how the SARF model predicted the deliverability values for separate reservoirs. [ABSTRACT FROM AUTHOR]

Abstract: Optimization of combined cooling, heating, and power (CCHP) systems operation commonly focuses only on energy cost. Different algorithms have been developed to attain optimal utilization of CCHP units by minimizing the energy cost in CCHP systems operation. However, other outcomes resulting from CCHP operation such as primary energy consumption and emission of pollutants should also be considered during CCHP systems evaluation as one would expect these outcomes can be subject to regulation. This paper presents an optimization of the operation of CCHP systems for different climate conditions based on operational cost, primary energy consumption (PEC), and carbon dioxide emissions (CDE) using an optimal energy dispatch algorithm. The results for the selected cities demonstrate that in general there is not a common trend among the three optimization modes presented in this paper since optimizing one parameter may reduce or increase the other two parameters. The only cities that show reduction of PEC while also reducing the CDE are Columbus, MS; Minneapolis, MN; and Miami, FL. For these cities the operational cost always increases when compared to the reference case consisting of using a vapor/compression cycle for cooling and natural gas for heating. On the other hand, for San Francisco and Boston, CCHP systems increase the CDE. In general, if CCHP systems increase the cost of operation, as long as energy savings and reduction of emissions are guaranteed, the implementation of these systems should be considered. [Copyright &y& Elsevier]

Four major themes will dominate United States energy-security in the 21st century. One is the need to diversify the fuel mix (i.e., oil, natural gas, nuclear power, hydro-electricity and other renewable and alternative energy resources). Another is the need to diversify the geographic origin of energy. A third theme is conservation and energy efficiency. And the final theme is devising new ways of managing growing dependence on oil imports rather than aiming at achieving “energy independence”. This paper will examine the new frontiers of the United States’ energy security in the 21st century. It will argue that fossil fuels will continue to dominate US energy needs well into the 21st century and that the Gulf region will maintain its strategic importance for US energy security for the foreseeable future. The paper will conclude that US energy-security could be better served by devising ways of managing dependence on oil imports rather than engaging in meaningless debate over energy independence. [Copyright &y& Elsevier]

The rise in power shutdowns triggered by severe weather due to deteriorating climate change has expedited the research in enhancing community resilience. Several researchers and policy-makers have contributed to the characterization and parameterization of energy resilience and reliability in particular, which requires accumulated and coordinated studies to underline the outcomes and reflect those in future works on grid resilience and reliability enhancement. The concept of both the resilience and reliability of the grid systems should be defined and distinguished so that the systems can be clearly comprehended, assessed, and operated to maintain flawless operation and ensure environmental sustainability. This paper meets the mentioned objectives to discuss grid resilience and reliability, their quantification metrics, and their enhancement techniques in detail. The paper also categorizes the United States into four tiers based on grid reliability and grid resilience using Monte Carlo Simulations and the discussed metrics. Two novel terminologies named resilience risk factor and grid infrastructure density are propounded in this work, which will serve as vital parameters to determine grid resilience. • Impact of natural disasters on the electricity grid is studied. • Grid resilience and reliability metrics and enhancement strategies are described. • Grid resilience and reliability of the United States are assessed. • The United States map is categorized based on grid resilience and reliability. • Resilience risk factor and grid infrastructure density are newly coined terms. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the performance of Model Predictive Control (MPC) for a typical commercial building heating, ventilation and air conditioning (HVAC) system across a wide range of climate and weather conditions. The motivation of the study comes from the fact that although there is a large body of work on MPC for HVAC systems, there is a lack of studies that examine the range of possible performance of MPC, in terms of both energy savings and maintaining indoor climate (temperature and humidity) as a function of outdoor weather. A challenge in conducting such a study is developing an MPC controller that can be used in a wide range of weather. The root cause of this challenge is the need for a tractable cooling and dehumidification coil model that can be used by the MPC controller, since the coil may operate in quite distinct modes depending on weather. We present such an MPC controller, and then leverage it to conduct an extensive simulation campaign for fourteen climate zones in the United States and four weather conditions (winter, spring, summer, and fall) in each climate zone. The performance of the proposed controller is compared with not only a rule-based baseline controller but also with a simpler MPC controller that ignores humidity and latent heat considerations. There are several results the arise from this comparative study. One such result is that energy savings from MPC over baseline can vary dramatically based on climate and season. Another is that the effect of ignoring humidity in the MPC formulation can lead to poor indoor humidity control more in milder weather rather than in hot weather. The results from this study can help practitioners and researchers assess costs and benefits of proposed MPC formulations for HVAC control. • Performance of two MPC controllers for HVAC systems is studied. • The study spans a wide range of climate and weather conditions, a first of its kind. • MPC controller can perform poorly unless latent heat is considered in design. • Poor performance occurs typically in moist and marine climate zones. [ABSTRACT FROM AUTHOR]

Quantitative measurements of historic improvements in fuel efficiency technology help to illuminate the feasibility of future fuel economy standards. Past investigations have produced widely varying estimates of this rate of improvement, though all seem to indicate that fuel consumption reductions implied by the 2025 U.S. CAFE standards cannot be met solely through technological improvements at historic rates. In this paper, we use the characteristics of U.S. cars between 1975 and 2009 to estimate that holding all else equal, a 1% increase in weight increases a car’s fuel consumption by 0.69%, and a 1% reduction in 0–97 km/h acceleration time increases fuel consumption by 0.44%. These tradeoff parameters are combined with the results of related work by the authors and others, yielding a more comprehensive measure of technological improvements than has been previously reported. When accounting for all of these sources of improvement, we conclude that the per-mile (or per-kilometer) fuel consumption of new cars in the U.S. could have been reduced by 5% per year from 1975 to 1990, if acceleration, features, and functionality had remained at their 1975 levels. Approximately 80% of this potential was realized as actual reductions in fuel consumption. Between 1990 and 2009, in contrast, technological improvement averaged just 2.1% per year, only 34% of which was realized as actual fuel consumption reductions. To meet the 2025 CAFE standards for cars without sacrificing capabilities that consumers have come to expect, technology must improve quickly enough to reduce fuel consumption by 4.3% per year for 14 years — considerably faster than has occurred since 1990, but consistent with the pace of improvements observed between 1975 and 1990. [ABSTRACT FROM AUTHOR]

In the United States, buildings account for 35% of total energy-related carbon dioxide emissions, making them important contributors to decarbonization. Carbon intensities in the power grid are time-varying and can fluctuate significantly within hours, so shifting building loads in response to the carbon intensities can reduce a building's operational carbon emissions. This paper presents a rule-based carbon responsive control framework that controls the setpoints of thermostatically controlled loads responding to the grid's carbon emission signals in real time. Based on this framework, four controllers are proposed with different combinations of carbon accounting methods and control rules. To evaluate their performance, we performed simulation studies using models of a 27-home, all-electric, net zero energy residential community located in Basalt, Colorado, United States. The carbon intensity data of four future years from the Cambium data set are adopted to account for the evolving resource mix in the power grid. Various performance metrics, including energy consumption, carbon emission, energy cost, and thermal discomfort, were used to evaluate the performance of the controllers. Sensitivity analysis was also conducted to determine how the control thresholds and intervals affect the controllers' performance. Simulation results indicate that the carbon responsive controllers can reduce the homes' annual carbon emissions by 6.0% to 20.5%. However, the energy consumption increased by 0.9% to 6.7%, except in one scenario where it decreased by 2.2%. Compared to the baseline, the change in energy cost was between −2.9% and 3.4%, and thermal discomfort was also maintained within an acceptable range. The little impact on energy cost and thermal discomfort indicates there are no potential roadblocks for customer acceptance when rolling out the controllers in utility programs. [ABSTRACT FROM AUTHOR]

This paper examines the asymmetry of changes in CO 2 emissions over business cycle recessions and expansions using yearly data from 1949 and monthly data from 1973 for the United States (US). In addition, decomposition analysis is applied to investigate the relative roles of various proximate contributing factors to observed changes in total and per capita CO 2 emissions and emissions intensity, over business cycle phases. The results suggest, inter alia , that aggregate emissions and emissions intensity reduce much faster in contractions than they increase in expansions. In addition, unlike the three previous expansions, in the most recent post-GFC US expansion, emissions per capita have continued to decline, and at a rate very similar to the rate of reduction in preceding contractions. This suggests the real possibility that the most recent contraction may have had an ongoing impact on the path of per capita emissions well beyond the immediate impact experienced during the contraction itself. [ABSTRACT FROM AUTHOR]

Eco-innovations are being recognized as fundamental means to foster sustainable development, as well as to create new business opportunities. Nowadays, the eco-innovation concept is gaining ground within both academic and practitioner studies with the attempt to better understand the main dynamics underlying its nature and guide policymakers and companies in supporting its development. This paper contributes to the extant literature on eco-innovation by providing a comprehensive overview of the evolution of a specific type of eco-innovations that are playing a crucial role in the current socio-economic agenda, namely low-carbon energy technologies. Accordingly, we focus our attention on the related patenting activity of different countries and organizations over time, as well as on influencing policy initiatives and events. Hence, we collected 131,661 patents granted at the United States Patent and Trademark Office (U.S.PTO.) between 1971 and 2010, and belonging to the “Nuclear power generation”, “Alternative energy production”, and “Energy conservation” technological classes, as indicated by the International Patent Classification (IPC) Green Inventory. Our findings report the development trends of low-carbon energy technologies, as well as identify major related environmental programs, historical events, and private sector initiatives explaining those trends, hence revealing how these different circumstances have significantly influenced their development over time. [ABSTRACT FROM AUTHOR]

In the early 2000s high expectations existed regarding the potential contribution of biofuels to the reduction of greenhouse gas emissions and substitution of fossil fuels in transport. In recent years sobering judgments prevailed. The major barriers for a further expansion of biofuels are their high costs (compared to fossil fuels), moderate ecological performances, limited feedstocks for biofuel production and their competition with food production. The objective of this paper is to investigate the market prospects of biofuels up to the year 2030. It focuses on the three currently most important regions for biofuels production and use: the US, EU and Brazil which in 2010 accounted together for almost three-quarters of global biofuel supply. Our method of approach is based on a dynamic economic framework considering the major cost components of biofuels and corresponding technological learning with respect to capital costs. Moreover, for the analysis of the competitiveness of biofuels with fossil fuels also taxes are considered. The most important result is that under existing tax policies biofuels are cost-effective today and also for the next decades in the regions investigated. However, their potentials are restricted especially due to limited crops areas, and their environmental performance is currently rather modest. The major final conclusions are: (i) To reveal the real future market value of biofuels, a CO 2 based tax system should be implemented for all types of fuels providing a neutral environmental incentive for competition between all types of fossil and renewable fuels; (ii) Moreover, the research and development for all types of biofuels, but especially for second generation biofuels, has to be intensified. [ABSTRACT FROM AUTHOR]

• Developed a publicly available high temperature (60–200 °C) heat pump model. • Heat pump performance was coupled to economics using US energy prices. • Energy prices applicable to international audiences with low fossil energy prices. • Model comparison includes heat pump and an equivalent gas-boiler economic analysis. • Model produces levelized cost of heat, payback period, and internal rate of return to demonstrate the economic competitiveness of heat pump systems for industrial decarbonization. Heat pumps represent an important opportunity for energy savings and decarbonization. This work investigates the techno-economic performance of high-temperature heat pumps (HTHPs) for use in the U.S. dairy industry. The studied heat pump performs a 50 °C temperature lift on a waste heat stream of cleaning water and applies the upgraded heat stream to a fluid milk pasteurization process. This work involved the creation of a HTHP model that estimated the coefficient of performance (COP), internal rate of return (IRR), net present value (NPV), and payback period (PBP), and emissions saved for a heat pump replacing a natural gas boiler. Capital costs, operations, and maintenance (O&M) cost, heat pump lifetime, electricity prices, natural gas prices, and a cost of carbon were varied to perform a parametric study on the factors affecting the break-even price of HTHPs. The results show that HTHP economics are highly sensitive to COP and energy price environment, and less sensitive to capital and O&M cost variance, leading to a large scatter of positive and negative NPVs based on U.S. location. PBPs demonstrate a defined threshold, based on energy price environment, below which favorable two-to-three-year PBPs predominate. This work is focused on the U.S. dairy industry, but international application in relation to fossil vs. electricity price regimes. Heat pumps have seen wider adoption in regions with a high ratio of fossil energy to electricity prices ($/MMBTU vs. $/kWh). The U.S. has plentiful natural gas resulting in lower fossil energy prices which has reduced heat pump adoption. This paper identifies potential first mover industries for HTHP adoption and their associated price regimes even in regions with lower ratios of fossil energy to electricity prices that exist many places globally. [ABSTRACT FROM AUTHOR]

• A framework for data center to bid into electricity market and follow regulation signal in real-time. • A risk-constrained bidding strategy is developed to determine the optimal energy and frequency regulation bids. • Dummy load is introduced to increase the amount of regulation service provision of data center. • Bi-linear server power consumption model and rule-based control enable data center to follow regulation signal accurately. • The proposed regulation service provision framework reduces electricity bill by 12%. The rapid adoption of cloud storage and computing services led to unprecedented growth of data centers in the world. As bulk energy consumers, large-scale data centers in the U.S. rack up billions in electricity costs annually. Fortunately, the operational flexibility of data centers can be leveraged to provide valuable frequency regulation services in smart grids to mitigate the indeterminacy of the renewable generation resources. Specifically, this paper aims to leverage computational flexibility provided by servers, such as dynamic voltage frequency scaling and dummy loads. This paper develops a comprehensive framework for data center's frequency regulation service provision in both hour-ahead market and real-time operations. A risk constrained hour-ahead bidding strategy along with a real-time data center power consumption control algorithm are developed to minimize electricity bills and the total response time of the requests. The introduction of dummy load, realistic bi-linear server power consumption model, and probabilistic forecast of electricity and frequency regulation service prices enable the data center to accurately follow frequency regulation signals, while reducing the financial risks associated with electricity market participation. The simulation results show that the proposed frequency regulation provision framework results not only in significant cost reduction for data centers, but also limits degradation in quality of service. Meanwhile, the stability and reliability of a power grid will be improved by the frequency regulation service provision. [ABSTRACT FROM AUTHOR]

• COVID-19 modified when, where, and how we use electricity. • The impact of COVID-19 varied across spatiotemporal scales. • There are multiple offsetting effects that can mask the impact of COVID-19 on electricity demand. Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class. [ABSTRACT FROM AUTHOR]

• Detailed cost estimation of advanced nuclear plants methodology extended to HTGRs. • Large HTGRs shown to have 32% overnight capital costs than recent LWRs. • The HC-HTGR reduced overnight capital costs 20% from large HTGR costs. • Discussion of required engineering of new systems for the HC-HTGR. • Low-carbon high temperature process heat in the range of $6.13–12.48/GJ. In 2018, nuclear energy generated 55% of United States' and one third of the world's carbon free electricity, making nuclear energy a key tool in efforts to mitigate climate change before 2050. However, the current nuclear technology, light water reactors (LWRs), is limited to 300°C, so it cannot be used to decarbonize industrial process heat which accounts for 12% of US greenhouse gas emissions. High temperature gas reactors (HTGRs) can meet the high temperature demand with carbon free nuclear heat. The estimated cost of HTGRs, such as the Next Generation Nuclear Plant (NGNP), are even higher than state-of-the-art LWRs. In this paper, we expanded our nuclear cost estimating tool to include HTGRs and find that the NGNP overnight capital costs were 32% higher than an advanced LWR per unit capacity. The higher cost will naturally result in larger risk to cost overrun as recently experienced by larger LWRs in western nations. With a design-to-build mindset to minimize cost and construction risk, we introduce the horizontal, compact HTGR (HC-HTGR). The reactor core and steam generator are mounted horizontally on rails and in-line with one another, decreasing the size of the reactor building relative to the power capacity four times when compared to traditional HTGRs. The HC-HTGR reduced overnight civil structure costs by 42%, indirect costs by 38%, and total capital costs by 20% from NGNP. We discussed the required engineering of new systems for the HC-HTGR including vessel supports, the reactor cavity cooling system, and steam generator design. Finally, we estimated the fuel and operations costs of the HC-HTGR, and a survey of low-carbon industrial process heat technology showed the HC-HTGR can deliver a highly competitive levelized cost of heat in the range of $6.13–12.48/GJ. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the effects of oil price shocks in the last 30years on the Brazilian and American inflation rate and rhythm of economic activity. The Brazilian and the United States economies are interesting polar cases, since they had a completely different path on the oil import dependence rate. While the oil import dependence rate has increase sharply in the United States (US), it has decreased substantially in Brazil. We found that output growth volatility in the United States has been decreasing over time as well as the contribution of oil price shocks to such volatility, despite the increase in oil import dependence. Inflation volatility has also been decreasing but oil price shocks are accounting for a larger fraction of this volatility in the US. In Brazil, such shocks do not seem to have a clear impact on output growth and they account for a very small fraction of the Brazilian inflation and output growth rate volatility. We finally run some counterfactual experiments to analyze how real output growth in the United States would had been if net oil import share in the United States behaved similarly to what was observed in Brazil. We conclude that output level would be roughly the same, however, it would be about 10% less volatile if the US had the actual Brazilian oil import share. [Copyright &y& Elsevier]

Abstract: To meet rising energy demands in a sustainable manner, the United States Renewable Fuel Standard 2 (RFS2) mandates the production of 3.78billion L (1billion gallons) of biomass-based biodiesel per year by 2020. Urban marginal lands, herein defined as lots with poor agricultural potential and unfit for residential purposes, can be used to cultivate biodiesel feedstock, which can contribute toward the RFS2. GIS is particularly well suited to identify the amount and location of these urban marginal lands. Biofuel crop production is one way for urban neighborhoods to reclaim vacant, blighted properties and also generate green energy for consumption. Combining soil classification data with urban development data, a GIS framework was developed to determine the amount of urban marginal land available in Pittsburgh, Pennsylvania. This location was chosen because small-scale sunflower crops are already being grown on urban marginal lands for biodiesel production. This paper evaluates two methods for identifying marginal lands for biofuel cultivation. The first is based on NRCS soil classifications derived from previous literature approaches. When compared with the locations of a nonprofit that is actually cultivating marginal lands, the NRCS soil classification method captures less than 35% of the marginal lands being cultivated in Pittsburgh, and 2400ha (6000acres) of marginal land in total that could be used to grow sunflowers for biodiesel production. We modify the NRCS marginal soil classification method to include certain types of urban commercial lands and lands not currently classified as marginal, and create a framework that identifies almost 90% of the marginal lands currently under cultivation in Pittsburgh, with approximately 3500ha (8700 acres) of available marginal lands. Pittsburgh’s marginal lands could be used to produce up to 129,000L (34,100gallons) of sunflower-based biodiesel. This could contribute a maximum of 0.1% to Pennsylvania’s biofuel mandate and 0.003% to the RFS2. While a city’s individual contribution may be incremental on the national scale, multiple cities and regions may be able to contribute to the RFS2. In addition, there is an unquantified social benefit that results from community engagement and revitalization, which is part of the mission of the Pittsburgh nonprofit. [Copyright &y& Elsevier]

Abstract: In this paper using historical monthly data on the US oil stocks (Crude Oil and Petroleum Products Ending Stock-coppes), industrial production, energy use for transportation, oil production, and oil imports, we examine whether supply and demand shocks explain the apparent decline in the volatility of the growth of COPPES since about the mid-1980s. We find that nearly 70% of the variation in the US COPPES growth is explained by its supply and demand factors, each sharing about half of this variation. This is on account of sharp decline in the contribution of persistence to the US COPPES growth variation from about 47% in the pre-break period to about 17% in the post-break period. This reduction is taken up by increased contribution of demand and supply factors since mid 1980s, of which growth variances have declined on net since then. This in turn contributes to the stability of the US COPPES growth fluctuations. [Copyright &y& Elsevier]

Abstract: Off-farm work is a growing reality in the US agricultural sector as a whole. Another staple program in the US agriculture is the use of crop insurance. This paper assesses hitherto unaddressed issues of fuel consumption and hence pollution generated by farm households associated with off-farm work and crop insurance. We applied a quantile regression method on a unique national farm-level survey data to address the fuel consumption issues. Results indicate that off-farm work by operators tends to decrease fuel expenses. In contrast, households with crop insurance had higher fuel consumption thereby increasing fuel usage. Finally, our study shows that the net effect of these two activities resulted in an increase in the pollution level. [ABSTRACT FROM AUTHOR]

Abstract: The goal of this paper is to examine whether the volatility of the growth in the US oil stocks has changed overtime, and if it has then whether or not this change is real. We find that the growth in volatility of oil stocks has declined overtime. We conduct a Monte Carlo simulation exercise to investigate whether this decline is real or an artefact of the growth definition. Our findings support the fact that the decline in growth volatility of oil stocks is an artefact of the growth definition. This is because a data generating process having a unit root with drift has a tendency to grow and thereby pulls the variance of growth down with time. [Copyright &y& Elsevier]

Abstract: Since the turn of the 21st century, the onshore wind industry has seen significant growth due to the falling cost of wind generated electricity. This growth has coincided with an interest in the development of offshore wind farms. In Europe, governments and developers have begun establishing small to medium sized wind farms offshore to take advantage of stronger and more constant winds and the relative lack of landowner conflicts. In the U.S., several developers are in the planning and resource evaluation phases of offshore wind farm development, but no wind farms are currently operational or under construction. In this paper, we analyze the patterns of development in Europe and compare them to the U.S. We find significant differences in the patterns of development in Europe and the U.S. which may impact the viability of the industry in the U.S. We also discuss the policies used by European nations to stimulate offshore wind development and we discuss the potential impacts of similar policies in the U.S. [Copyright &y& Elsevier]

[Display omitted] • A new convolutional recurrent neural network is used for spatiotemporal prediction. • A new probabilistic deep learning model is proposed for solar radiation prediction. • A data preprocessing method for solar radiation uncertainty assessment is proposed. • The developed model performs very well in accuracy and uncertainty. High-quality and reliable solar radiation spatiotemporal probabilistic prediction is very significant for the planning and management of solar energy. In this paper, convolutional shared weight long short-term memory network is proposed to deal with solar radiation spatiotemporal prediction problems. The model can minimize the training time of model without significantly reducing prediction accuracy. Furthermore, a spatiotemporal probabilistic prediction model that combines convolutional shared weight long short-term memory network and deep ensemble method is proposed to deal with the solar radiation probabilistic prediction problem. The model obtains the uncertainty estimation of predictands by adjusting the network structure and optimizes the uncertainty by employing a proper scoring rule. In addition, a combination of ensembles are used in the model to improve the robustness of probabilistic prediction. The new spatiotemporal probabilistic prediction model is applied to predict solar radiation in a real area of the United States. Five state-of-the-art models and seven evaluation indicators are used for comparation. The comparation results show the proposed model is able to provide accurate point prediction, reasonable prediction interval and reliable probabilistic prediction results for a whole area. [ABSTRACT FROM AUTHOR]

• Renewable hydrogen can compete with lithium-ion batteries for seasonal storage. • Hydrogen's competitiveness depends on heat rate of plant it is replacing. • Net present value of hydrogen-fired gas turbine varies based on operation of plant. • Lack of infrastructure will impede demand growth for hydrogen in the United States. Non-emitting variable renewable energy (VRE) resources are needed on the power grid if the United States is to "deeply decarbonize" the power sector. The intermittent nature of these resources makes them difficult to integrate into the power system. Existing energy storage technologies, such as lithium-ion (LI) batteries, could be used to aid the integration of these resources, but these technologies are sized to produce power for hours at a time before needing to be charged again. While these energy storage technologies could address daily imbalances between supply and demand for electric power, they cannot address the seasonal nature of power production of VREs. This paper details a methodology to estimate the levelized cost of energy (LCOE) of meeting this seasonal imbalance with either a hydrogen-fired gas turbine (HFGT) or lithium-ion battery system (LI) as a measure of economic efficiency of the technologies. Applying our model, we find the average LCOE associated with meeting this seasonal imbalance is $2400/MWh using a HFGT fueled with green hydrogen and $3000/MWh using a LI. If we allow the model to operate the HFGT with blue hydrogen the average LCOE decreases to $1560/MWh. However, we find the power prices required to justify investment in an HFGT to replace a natural gas-fired gas turbine are considerably higher than those seen in the market today. [ABSTRACT FROM AUTHOR]

• Temporal characterization of marine energy resources, at varying time scales. • Characterized wave, tidal, and ocean currents, and compared to wind and solar. • Data based exposition of grid benefits for wave power at high renewable scenarios. • Data based exposition of higher capacity adequacy and stability for wave power. • Detailed discussion on market implications of grid integration of marine energy. In this paper, the applicability of marine renewable energy (MRE) for potential grid applications is presented. We show that many of the unique value streams from marine-based electricity generation resources stem from their inherent temporal characteristics, especially when compared to wind and solar. Specifically, in this work, we evaluate the timing value for three types of MRE resources: (a) tidal, (b) wave, and (c) ocean currents. First, through a suite of novel metrics, such as resource availability, persistence, and versatility, we evaluate the temporal value characteristics of these resources. Second, through a more grid-oriented numerical study, we comment on the potential ramifications of those temporal characteristics in context of energy balancing and effective load carrying capability for one marine-based resource i.e., wave. Finally, we further our understanding of the relative advantages that may be leveraged by operating wave-based generation in tandem with more established renewable resources, such as wind and solar. Our results indicate that compared to wind and solar, MRE resources are consistently more available and persistent on an hourly level throughout an entire year of operation. In addition, wave resources are also seen to reduce the balancing requirements within the power system. Our work focuses on sites specific to the United States (US) and a parallel study for a location in Great Britain (GB). Results are found to be consistent for sites in both the US and GB, implying that the grid benefits discussed in this work could apply to a number of locations globally. [ABSTRACT FROM AUTHOR]