Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power systems and ...to help customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using deep reinforcement learning, a hybrid type of methods that combines reinforcement learning with deep learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and deep policy gradient, both of which have been extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly dimensional database includes information about photovoltaic power generation, electric vehicles and buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide real-time feedback to consumers to encourage more efficient use of electricity.
This work estimates the energy embedded in wasted food annually in the United States. We calculated the energy intensity of food production from agriculture, transportation, processing, food sales, ...storage, and preparation for 2007 as 8080 ± 760 trillion BTU. In 1995 approximately 27% of edible food was wasted. Synthesizing these food loss figures with our estimate of energy consumption for different food categories and food production steps, while normalizing for different production volumes, shows that 2030 ± 160 trillion BTU of energy were embedded in wasted food in 2007. The energy embedded in wasted food represents approximately 2% of annual energy consumption in the United States, which is substantial when compared to other energy conservation and production proposals. To improve this analysis, nationwide estimates of food waste and an updated estimate for the energy required to produce food for U.S. consumption would be valuable.
Hydrogen as an energy carrier allows the decarbonization of transport, industry, and space heating as well as storage for intermittent renewable energy. The objective of this paper is to assess the ...future engineering potential for hydrogen and provide insight to areas of research to help lower economic barriers for hydrogen adoption. This assessment was accomplished by creating top-level system models based on energy requirements for end-use services. Those models were used to investigate four case studies that provide a global view augmented with specific national examples. The first case study assesses the potential penetration of hydrogen using a global energy system model. The second applies the dynamic integrated climate–ecosystem–economics model to derive an estimate of the impact of the diffusion of hydrogen as an energy carrier. The third determines the required growth in renewable power and water usage to power transportation in the United States (US) with hydrogen. The fourth assesses the use of hydrogen for heating in the United Kingdom (UK). In all cases, there appeared to be significant potential for hydrogen adoption and net energetic benefit. Globally, hydrogen has the potential to account for approximately 3% of energy consumption by 2050. In the US, using hydrogen for on-road transportation could enable a reduction in rejected energy of nearly 10%. Also, hydrogen might provide the least cost alternative to decarbonizing space heating in the UK. The research highlights a challenge raised by widespread abandonment of nuclear power. It is currently unclear what the removal of nuclear would do to the cost of energy as nations attempt to limit global greenhouse gas emissions. Nuclear power has also been proposed as a source for large scale production of hydrogen. Finally, this analysis shows that with today's technological maturity making the transition to a hydrogen economy would incur significant costs.
•Explores four case studies of the societal penetration of hydrogen.•Identifies the decarbonizing benefits of hydrogen in transport and heating.•Global model identifies nation specific opportunities for hydrogen penetration.•Cost remains a significant challenge to the ushering in of the hydrogen economy.•Cost reductions in hydrogen production, storage and transmission are necessary.
•Monte Carlo simulation is used to estimate plug-in electric vehicle (PEV) charging.•The effect of PEV fleet growth on peak electric load is considered for three regions.•By 2025, PEV charging is ...estimated to increase peak load by less than 2%.•Changes in PEV owner demographics have a limited effect on charging patterns.
With recent changes in the availability and diversity of plug-in electric vehicles (PEVs) in the United States, there is increasing research interest in the interaction between PEVs and the electric grid. Extensive work in the literature examines these interactions with the assumption that the timing of PEV charging will be scheduled, and that charging loads can be adjusted dynamically at the behest of the utility and the system operator. While it might be technically feasible to aggregate the data on driver schedules and historical PEV use and charging decisions, it is unclear whether PEV owners will readily share these data and accept partial third-party control of their vehicle’s charging. Given the uncertainty in the future relationships between electric utilities and PEV owners, this study examines the region-level effects of PEV charging in the absence of the additional data utilities would need to realize these idealized charging scenarios. In particular, this study focuses on temporally-resolved prediction of electricity demand needed to serve PEV charging loads if charge scheduling or control is not widespread.
Vehicle trip data from the National Household Travel Survey (NHTS) were converted into individual vehicle charging profiles. Monte Carlo methods were then used with these profiles to simulate electricity demand for PEV charging. These simulations include accounting for the potential demographic characteristics of PEV drivers and the estimated charging behavior of those drivers. The simulation results were validated using empirical vehicle charging data collected by the Pecan Street Research Consortium from households in Austin, Texas. The simulation results compared favorably with the empirical data, estimating charging behavior to within 7% throughout most of the day. Two different simulation approaches were considered to show that a reduced-order simulation approach yields similar results. Finally, having demonstrated the stability of the simulation to assumptions about PEV owner demographics and PEV type-dependent charging patterns, the simulation results were used to determine the effect of unscheduled PEV charging on peak load in three different regions, Texas, New York, and New England, with three PEV fleet growth projections. These results indicate that for the moderate growth scenario considered, unscheduled charging will increase peak load by less than 1% by 2025 in each of the three regions.
Reducing methane emissions from solid waste is already technically possible
On 20 December 2015, a mountain of urban refuse collapsed in Shenzhen, China, killing at least 69 people and destroying ...dozens of buildings (
1
). The disaster exposed the horrible yet real idea that society’s wastes could pile up uncontrollably, directly threatening our lives. But there is another looming threat from solid waste beyond its sheer volumes and mass: the destabilizing impacts of the greenhouse gases it emits. On page 797 of this issue, Hoy
et al.
(
2
) report that rapid and large reductions of methane emissions from the world’s solid waste sector are needed to meet the global warming limit set by the Paris Agreement. The good news is that this can be achieved with existing technologies and modified behaviors.
Growth of electricity generation from variable renewable resources like wind and solar has raised questions about future grid stability. This paper used several renewable energy penetration scenarios ...to determine when an electric grid might be more vulnerable to frequency contingencies, such as a generator outage. Unit commitment and dispatch modeling was used to quantify system inertia, an established proxy for grid stability. A case study of the Electric Reliability Council of Texas grid was used to illustrate the method. Results from the modeled scenarios showed that the Texas grid is resilient to major grid changes, even with relatively high penetrations (∼30% of annual energy generation compared to 18% in 2017) of renewable energy. However, retiring nuclear power plants and private-use networks in the model led to unstable inertia levels in our results. When the system inertia was constrained to meet a minimum threshold in our model, multiple coal and natural gas combined-cycle plants were dispatched at part-load or at their minimum operating level to maintain stable system inertia levels. This behavior is expected to expand with higher renewable energy penetrations and could occur on other electric grids that are reliant on synchronous generators for inertia support.
•Potential hydrogen demand for light-duty vehicles is estimated for every county.•Optimization models maximize economic value with temporal hydrogen production.•Producing hydrogen in early morning ...hours is favorable when using wind power in TX.•Texas can be an analytical testbed for renewable hydrogen production and demand.
This work developed two methods to investigate the technical and economic potential of hydrogen demand and production: (1) estimating potential hydrogen demand for light-duty vehicles (LDVs) at the county-level using a first-order engineering model, and (2) quantifying temporal renewable hydrogen production from wind energy using a linear programming model. The potential hydrogen demand was primarily evaluated for three geographical regions: (1) the United States, (2) Texas, and (3) the Texas Triangle which is one of the nation’s most important mega-regions. The linear programming model compared marginal electricity and hydrogen prices to maximize revenue over the course of a year. The analysis primarily focused on the Electric Reliability Council of Texas (ERCOT), but also included other six U.S. electricity markets for hypothetical analysis. Results show that the potential hydrogen demand for LDVs in the United States, Texas, and the Texas Triangle are 53.3, 5.3, and 3.9 billion kg per year, respectively. Using the electrolyzer system energy efficiency of 75% and the marginal hydrogen price of $4/kg, the wind energy in Texas as of 2015 could produce nearly 0.84 billion kg of hydrogen, which could supply about 22% of the potential hydrogen demand for LDVs in the Texas Triangle. When the marginal hydrogen price is low (e.g. $1/kg), it is only favorable to produce hydrogen during early morning hours, especially, 1–6 a.m., in ERCOT and other electricity markets except California’s market. These results could provide information for decision makers to better understand the holistic feasibility of a hydrogen economy in the United States.
This paper assesses the environmental impacts of the average American's diet and food loss and waste (FLW) habits through an analysis of energy, water, land, and fertilizer requirements (inputs) and ...greenhouse gas (GHG) emissions (outputs). We synthesized existing datasets to determine the ramifications of the typical American adult's food habits, as well as the environmental impact associated with shifting diets to meet the US Department of Agriculture (USDA) dietary guideline recommendations. In 2010, FLW accounted for 35% of energy use, 34% of blue water use, 34% of GHG emissions, 31% of land use, and 35% of fertilizer use related to an individual's food-related resource consumption, i.e. their foodprint. A shift in consumption towards a healthier diet, combined with meeting the USDA and Environmental Protection Agency's 2030 food loss and waste reduction goal could increase per capita food related energy use 12%, decrease blue water consumption 4%, decrease green water use 23%, decrease GHG emissions from food production 11%, decrease GHG emissions from landfills 20%, decrease land use 32%, and increase fertilizer use 12%.
Desalination is often considered an approach for mitigating water stress. Despite the abundance of saline water worldwide, additional energy consumption and increased costs present barriers to ...widespread deployment of desalination as a municipal water supply. Specific energy consumption (SEC) is a common measure of the energy use in desalination processes, and depends on many operational and water quality factors. We completed multiple linear regression and relative importance statistical analyses of factors affecting SEC using both small-scale meta-data and municipal-scale empirical data to predict the energy consumption of desalination. Statistically significant results show water quality and initial year of operations to be significant and important factors in estimating SEC, explaining over 80% of the variation in SEC. More recent initial year of operations, lower salinity raw water, and higher salinity product water accurately predict lower values of SEC. Economic analysis revealed a weak statistical relationship between SEC and cost of water production. Analysis of associated greenhouse gas (GHG) emissions revealed important considerations of both electricity source and SEC in estimating the GHG-related sustainability of desalination. Results of our statistical analyses can aid decision-makers by predicting the SEC of desalination to a reasonable degree of accuracy with limited data.
•Optimal k-means clustering finds seasonal groups of residential electricity use.•We find that each season has two nominal groups.•One group typically uses more expensive electricity than the ...other.•Regression analysis allows for insight as to which homes will be in which cluster.
Little is known about variations in electricity use at finely-resolved timescales, or the drivers for those variations. Using measured electricity use data from 103 homes in Austin, TX, this analysis sought to (1) determine the shape of seasonally-resolved residential demand profiles, (2) determine the optimal number of normalized representative residential electricity use profiles within each season, and (3) draw correlations to the different profiles based on survey data from the occupants of the 103 homes. Within each season, homes with similar hourly electricity use patterns were clustered into groups using the k-means clustering algorithm. Then probit regression was performed to determine if homeowner survey responses could serve as predictors for the clustering results. This analysis found that Austin homes fall into one of two seasonal groups with some homes using more expensive electricity (from a wholesale electricity market perspective) than others. Regression results indicate that variables such as if someone works from home, hours of television watched per week, and education levels have significant correlations with average profile shape, but might vary across seasons. The results herein also indicate that policies such as time-of-use or real-time electricity structures might be more likely to affect lower income households during some high electricity use parts of the year.
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