Grantee Research Project Results
Final Report: Web-Based Tool to Reduce GHG Emissions from Coal
EPA Grant Number: SU836794Title: Web-Based Tool to Reduce GHG Emissions from Coal
Investigators: Castillo-Villar, Krystel K
Institution: The University of Texas at San Antonio
EPA Project Officer: Aja, Hayley
Phase: I
Project Period: November 1, 2016 through October 31, 2017
Project Amount: $15,000
RFA: P3 Awards: A National Student Design Competition for Sustainability Focusing on People, Prosperity and the Planet (2016) RFA Text | Recipients Lists
Research Category: Sustainable and Healthy Communities , P3 Awards , P3 Challenge Area - Air Quality
Objective:
Problem Description: Coal provides about 30% of electricity in the United States [1]. Coal power plants are confronted with the challenge of reducing greenhouse gas emissions. How can power plants continue to produce a third of the nation’s electricity while reducing emissions? By cofiring coal with sustainable harvested biomass.
A recent report from the U.S. Department of Energy (DoE) [2] identified that a key barrier to increasing biomass use for electricity generation is the high cost of biomass based on both supply and logistical challenges. Previous cofiring efforts lead by DoE and individual utilities found that at least 10% biomass can be mixed with ground coal to be pulverized in the current coal milling and particle entrainment feed line. Replacing 10% of the coal used by an average coal power plant with an equivalent amount of switchgrass can reduce yearly CO2 emissions by nearly 3 million tons. This study [2] highlighted as a gap the need for advanced logistical systems that include depots where biomass is processed to achieve a cofiring rate of 20%. To date, there is a lack of integrated sophisticated optimization models that can assist decision makers with the process/logistics design that minimizes the overall cost while quantifies the greenhouse gas reduction benefits of substituting high volumes of biomass (i.e., 20%) in coal power plants. Our proposed project aims to overcome this challenge. The developing of a sophisticated expert system to cofire biomass in power plants will aid not only developed countries but also the developing world to reduce their greenhouse gas emissions and take a proactive role towards climate change.
The long-term goal of this project is to develop a robust response to the following P3 challenge: the nexus of greenhouse gas emissions and electricity generation by (1) developing an expert system that assists coal power plant decision makers and stakeholders to design an optimal production and logistics network to cofire coal with switchgrass; (2) increasing awareness of the different ways that coal power plants can minimize their greenhouse gas emissions by cofiring biomass and the impact of cofiring from the environmental, economic, and social perspectives; (3) enhancing the quality of education in STEM degrees by providing students with experiential learning opportunities; and (4) promoting the ethnic diversity of those receiving STEM degrees because The University of Texas at San Antonio (UTSA) is a minority and Hispanic serving institution (71% of the students involved in Phase I identify themselves as underrepresented students and 29% were females students).
The main objective of this proposal is to increase public awareness of greenhouse gas emissions for energy production and assess the cost and greenhouse gas reduction benefits of cofiring large amounts of biomass in existing coal power plants. To achieve this main objective, four sub-objectives were defined for Phase I:
- Modeling and Optimizing Logistics. An optimization model to design a biomass supply system able to meet the cofiring demand at minimum harvesting, storage and logistics costs.
- Testing the Models in a Real-life Case Study. A case study based on a power plant in Bexar County, which currently purchases coal from Wyoming. The proposed biomass is switchgrass (i.e., an herbaceous biomass) to be grown and harvested in two counties nearby the power plant.
- Assessing Sustainability Considering Climate Change. Four climate change scenarios used to evaluate the impact of climate change in switchgrass yields and logistics.
- Creating a Preliminary Web-based Expert System. This tool materializes the proposed optimization model, data obtained from simulations, and collected cost data into a decision support system, which will expand the use and applicability of the models and data to a broader community, that is, students, academicians, industry, government, and government policy makers.
Summary/Accomplishments (Outputs/Outcomes):
In Phase I, we proposed an optimization model and developed a real-life case study. In total, we developed five cases: baseline case (2015-2025), 2050 climate change A2 (not favorable future), 2050 climate change B2 (favorable future), 2080 climate change A2 (not favorable future), and 2080 climate change B2 (favorable future). Each of these cases was tested using biomass cofiring rates of 10, 15 and 20 percent. In total, 15 scenarios were generated and analyzed. From the 15 scenarios, 4 of them (i.e., baseline at 10% and 20%, 2050 A2 and B2 at 20%) are representative of insightful behavior and are summarized next. The baseline scenario at cofiring rates of 10% and 20% cover all the biomass demand utilizing the locally grown and harvested switchgrass transported within the network (i.e., logistics infrastructure), whereas climate change scenario 2050 A2 (not favorable) and B2 (favorable) at 20% cofiring rate require biomass supplied by a third-party vendor (i.e., switchgrass supplier outside the state of Texas) at a cost of $70M and $39M, respectively. Because the yield in climate change scenarios is not sufficient to offer a competitive harvesting cost, some biomass would need to be obtained from a third-party supplier (e.g., in this case, the state of Tennessee). In the baseline scenario with 20% cofiring rate, the logistics infrastructure is economically feasible because it consolidates biomass in three depots. Based on the results, scaling-up the cofiring rate is viable because approximately one-fourth of the available parcels are utilized at 20% cofiring rate. Increasing the switchgrass cofiring, and consequently the demand, will allow economies of scale as the number/capacity of the depots is increased. Decreasing the harvesting and logistics costs will potentially lead to a cost-competitive production of clean electricity. To make cofiring a sustainable option, the future climate change scenarios should be more favorable than the current yield estimations so that enough biomass is available in 2050 and 2080.
Quantitative benefits to people, prosperity and the planet. To quantify the benefits from reductions in emissions, the Social Cost of Carbon (SCC) is considered, which is an estimate of economic damages associated with CO2 emissions, accounting for climate change damages, changes in net agricultural productivity, human health, property damage from increased flood risk, and changes in energy system costs. SCC is estimated using three different discount rates that calculate damages from the time of CO2 release until 2050. In this calculation, 3 percent was used [3]. The net coal emissions were calculated by subtracting the estimated CO2 output of transporting biomass from the amount of CO2 released by burning coal. The total SCC was calculated after converting the 2007 dollars provided in the report for inflation to March 2017 dollars, $43.36 per metric ton of CO2 [4]. Therefore, the social costs of the net emissions ($79.3M) using 20% coal are greater than the cost of cofiring 20% of switchgrass ($52.7M). The Clean Air Taskforce assessed the negative health impacts and calculates the likelihood of health risks from fine particle pollution from 500 power plants across the United States [5]. Using data from 2012, at 20 percent biomass cofiring rate, about six lives would be saved by the reduction of emissions alone.
Conclusions:
During Phase I, significant progress was achieved toward developing the foundation of a web-based tool that materializes the data generated through simulations, optimization models, and advanced visualization tools. The alpha version can be found on our project’s website. The tool is divided into three modules. Data Depot module is a repository of the simulation runs of the models ALMANAC, SSURGO, and SWAT. These sanitized data sets enable reproducible research. The Visualization module displays the solutions from the optimization model (i.e., optimal supply and logistics network). The Discovery Room module aims to serve as an outreach portal where presentations, videos from workshops, papers, and reports generated from this project are posted. The Decision Support System (DSS) is publicly available and will be an inexpensive means for coal-fired power plants to gain understanding of the biomass cofiring potential to reduce the GHG emissions.
References:
1. Energy Information Administration. Short-term Energy Outlook. http://www.eia.gov/forecasts/steo/report/prices.cfm.
2. Boardman, R. D.; Cafferty, K. G.; Nichol, C.; Searcy, E. M.; Westover, T.; Wood, R.; Bearden, M. D.; Cabe, J. E.; Drennan, C.; Jones, S. B. Logistics, Costs, and GHG Impacts of Utility Scale Cofiring with 20% Biomass; Pacific Northwest National Laboratory (PNNL), Richland, WA (US), 2014.
3. U.S. Environtmental Protection Agency. Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units. 2015, 80.
4. Bureau of Labor Statistics. CPI Inflation Calculator 2017.
5. Clean Air Taskforce. The Toll From Coal: An Updated Assessment of Death and Disease from America’s Dirtiest Energy Source; 2010.
Journal Articles:
No journal articles submitted with this report: View all 1 publications for this projectSupplemental Keywords:
Clean air; GHG emissions, green energy; power plants; cloud-based decision support system.The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.