Browsing by Subject "Energy modeling"

Now showing 1 - 7 of 7
  • Thumbnail Image
    ItemOpen Access
    A bottom-up electricity demand model for the residential sector in Querétaro, Mexico
    (2016-12-12) Hernandez, Mauricio; Cao, Sunzhe
    There is a growing interest in reducing energy consumption and its associated greenhouse gas emissions from every sector of the economy. The residential sector is a substantial consumer of energy in every country and understanding which factors affect the energy consumption in this sector is crucial for policy makers, investors, utilities, and other stakeholders. This study contributes to a better understanding of these factors through a case study in Queretaro, Mexico. Particularly, this study sheds light on two different methods for collecting detailed information on the end uses of energy in homes, and for assessing the relationship between energy consumption and demographic and socioeconomic indicators. The data used for this study were collected through face-to-face surveys in 32 households and by tracking real-time electricity consumption in a subset of these households. Finally, a bottom-up model was used to estimate the electricity demand of the households surveyed, with the results suggesting a correlation with income, number of lightbulbs, dwelling size, and the number of days the household was occupied. The results of the study motivated the authors to recommend the extension and replication of this study, to assess the role of private transportation in energy consumption, and to improve the use of technology for the collection and analysis of data.
  • Thumbnail Image
    ItemOpen Access
    A Bottom-Up Model of Residential Electricity Demand in North and South Carolina
    (2017-04-28) Hollis, John
    Residential electricity is a significant component of total electricity use in the United States, and the residential market is also a key demographic for energy efficiency (EE) and distributed generation growth. Uncertainty in residential load growth is driven by the impact of changes in technology, policy, demographic and life-style changes. Using a bottom-up engineering model, we begin the construction of a tool to facilitate analyzing the effects of these factors. We use data from the EIA’s Residential Electricity Consumption Survey (RECS), in conjunction with EnergyPlus and BEopt, publicly available software from NREL, to construct 22 archetypical residential buildings characterizing North and South Carolina’s housing stock. We then model energy use for these buildings, and extrapolate these results to the larger housing stock. Projections are accurate for a benchmark year using actual weather data. We identify a number of potential improvements to the model and ways in which the uncertainty on future projections of energy use can be bound. Our conclusions follow: - The archetypical model is a reasonable solution for regional scale residential electricity modeling which minimizes computational needs. - The model delivers disaggregated energy demand, and hourly demand, estimates which are useful for future analysis of energy policy cost effectiveness. - Weather data is a driving source of uncertainty, and hence input weather data should be carefully considered. Projections should use varied weather data to bound uncertainty. - Despite being less computationally demanding than other methods, this model would benefit from an automated method of archetype alteration to ease sensitivity analysis. BEopt supports this through python and XML input files.
  • Thumbnail Image
    ItemOpen Access
    Benefits of Energy Efficiency Standards: The Case of Residential Refrigerators
    (2021-04-29) Jain, Abhishek Sanjay
    An integral part of the modern American household, refrigerators are ubiquitous. Characterized by their constant load profile, refrigerators account for a substantial part of residential energy demand. Improving energy efficiency standards for refrigerators can offer a sizeable demand reduction and therefore be a pivotal part of climate change mitigation strategy. This study quantifies the benefits of implementing energy efficiency standards for residential refrigerators by considering three policy scenarios. An in-house Monte-Carlo model is developed to replicate important considerations in the rulemaking process through which the Department of Energy (DOE) sets the conservation standards. Our results indicate that the existing standards for refrigerators can realize savings equal to the electricity consumption of about 400,000 average U.S. homes by 2025. While these savings are critical, the benefits of this conservation standard are inequitable. Our study provides insights into how the benefits are realized across all the consumers in the country and provides recommendations for improving the rulemaking process.
  • Thumbnail Image
    ItemOpen Access
    Modeling the Economics of Offshore Wind in the Southeastern United States
    (2019-04-26) Nayem, Tasfia; Browning, Morgan; Burton, Eric; Wan, Yuejiao
    This study analyzes the future deployment of offshore wind in the southeastern U.S. states of North Carolina, South Carolina, and Virginia. There has been a flurry of recent activity surrounding this topic. State governments have shown interest in developing policy to attract offshore wind to their shores, and an offshore wind developer has already purchased the rights to build a farm off the coast of North Carolina. At the early stages of a potentially significant trend like this, it is important to examine what could occur under different scenarios. Our research aims to inform the conversation on offshore wind by addressing three main sets of questions on the future of offshore wind in the southeastern U.S.: 1. What is the landscape of offshore wind in the U.S.? 2. How much offshore wind will be developed in the Southeast in the coming decades? What policies could have the greatest impact? 3. What is public opinion on offshore wind in the Southeast? To perform each of our three analyses, we employed literature and policy reviews, energy forecasting and modeling, and expert interviews and survey reviews, respectively. Our exploration of the first question revealed that state-level policy-makers, particularly in the northeast, are aggressively pursuing and considering policies to accelerate offshore wind deployment in their states. In the Southeast, Virginia is leading the charge, driven by the green jobs potential associated with the offshore wind. State leaders in Virginia are convening task forces to explore manufacturing potential; incorporating offshore wind into its renewable energy portfolio goals; and demonstrating the potential of the industry with the first offshore wind initiative in the mid-Atlantic. To address our second set of questions, we conducted an energy analysis using the AURORA modeling software over a study period of 2018–2040. Various policy scenarios were modeled, including multiple carbon prices and an offshore mandate. In addition, we coupled each policy scenario with both high-cost and low-cost price scenarios for offshore wind technology. Model results reveal two key conclusions. The first is that the cost of offshore wind technology will be crucial in determining the level of deployment. Model runs at the higher cost scenario did not show any offshore capacity unless the model was forced to build it with a mandate. Of the selected policy scenarios, the offshore mandate was the most effective. With high costs, it was the only modeled scenario in which any capacity was added, and under low costs, it produced the highest level of capacity of any scenario. A separate stakeholder analysis was performed to address our third research question. We examined how different groups in the region may react to and impact offshore wind deployment. As the scale of the offshore wind expands in the future in U.S., there is an emerging trend that more opposition will come from minority groups such as local commercial fishermen. However, overall, the majority of the general public supports offshore wind development. In summary, our analysis finds that costs and policy options should not be considered in isolation. The development cost for offshore wind is the key factor in determining whether capacity is built; therefore, it is imperative that offshore wind policies specifically aim to lower the upfront capital investments required. Additional consideration must be given to stakeholder concerns. The termination of previously approved wind farms, due in part to public opinion, have demonstrated the importance of stakeholder management. Communication with interested parties must begin early in the development process and should be supported with objective evaluation and monitoring of existing projects.
  • Thumbnail Image
    ItemOpen Access
    New England's Installed Electric Generation Forecast 2013-2025
    (2014-04-24) DeMarco, Elizabeth; Osteen, C. Alex; Song, Jiayin; Wang, Yuan
    The aim of this Master’s Project, as identified by our client the C Three Group, LLC, was to forecast installed electric capacity in the ISO New England region through the year 2025 under different scenarios including varying natural gas prices and RPS programs. ISO New England is the Independent System Operator of New England and oversees electric generation and transmission in the New England States. Our team built a basic supply model and, using linear optimization, we estimated ways for the ISO New England region to expand its supply to meet the growth in forecast demand. We ran our model under different scenarios, including varying natural gas prices and RPS programs. We took into account announced changes to capacity as well as possible scenarios that may affect further changes in the makeup of capacity. The final results showed continued expansion of natural gas and wind generation, the low-cost leaders, as well as new development of demand response. As we varied the future prices of natural gas, more electricity began to be imported from Canada. We believe that future carbon prices and stricter RPS standards may further ratchet up imports and renewables, in place of natural gas. Finally, our model predicts possible future coal retirements and is doubtful of new nuclear. Our client will potentially use the explanation of our models and written report of our findings in future research and consulting for their business.
  • Thumbnail Image
    ItemOpen Access
    Simulating Financial Returns of Arbitrage Opportunities using Lithium-Ion Battery Storage Paired with Photovoltaic Systems in 3 U.S. Wholesale Electricity Markets
    (2018-04-26) Miller, Nicole
    Interest in battery storage as a means to support renewable energy integration has recently increased as more developers continue to build utility-scale renewable energy resources. Yet, the ability for large-scale battery projects to be deployed depends on the ability to make the projects financially attractive.This analysis seeks to determine whether a battery energy storage system paired with a 110MW utility-scale solar PV installation can result in net present value (NPV) positive returns when engaging in electricity price arbitrage. The analysis examines the revenues and costs that would be incurred under 12 different scenarios that vary in assumptions regarding the location of the market -focusing on ERCOT, PJM and CAISO, the intra-day variations in electricity prices, the energy storage capacity of the battery, and the length of the battery charge and discharge. Overall, the results show that the financial viability of large battery storage projects receiving revenues exclusively from electricity price arbitrage requires high intra-day price differentials. Therefore, battery storage remains unprofitable without subsidies at current prices and further means of extracting value from batteries (such as ancillary services), and reduction in capital costs are needed before large-scale Lithium-Ion battery operations become widespread in the United States energy market.
  • Thumbnail Image
    ItemOpen Access
    The Impact of Electricity Storage on Energy Sector Emissions
    (2011-04-29) Keaveny, Brian
    The composition of the U.S. electrical power system reflects competing objectives, as investments are driven by ratepayers who demand access to electricity that is both low cost and reliable. Maintaining generation ready for fluctuations in demand currently requires the inefficient use of generation assets, driving up total energy system costs. During the night, low demand for electricity can force the underutilization of wind farms and baseload coal-fired plants. During the day, load-following natural gas-fired plants are often operated at partial capacity so they can be ramped up to track changing demand. One means to address these two issues is electricity storage. Electricity storage technologies are capable of shifting surplus low cost nighttime electricity to times of higher daytime demand. Whether time-shifting electricity storage technologies may enable lower energy system costs depends upon the parameters of these technologies and future conditions. In this study, a least-cost optimization energy model (MARKAL) managed by U.S. EPA is used to explore the potential future role of electricity storage under varying conditions. Scenarios model a stricter national renewable portfolio standard (RPS), varying natural gas prices, and a national limit on CO2 emissions from the energy system. Scenario results are analyzed to discern the impact of electricity storage on generation output and on the associated energy sector emissions of CO2, SO2, NOx, and PM10. Four trends emerge when examining the impacts of time-shifting electricity storage on the national energy system. First, electricity storage enables an increasing utilization of baseload generation and a corresponding decreasing reliance on daytime load-following generation. Second, and to a lesser degree, nighttime natural gas-fired generation increases, making use of existing capacity. Third, the overall decreasing natural gas use by load-following generation leads to the increasing use of natural gas in the industrial sector. Fourth, the use of time-shifting electricity storage does not result in net increases of electricity output from renewable power sources. The impacts of these four trends on generation investments and emissions vary by the future conditions modeled. In many instances the use of electricity storage results in a less expensive energy system with higher emissions of CO2, SO2, NOx, and PM10.