HW1

19-704 Applied Data Analysis: Homework

Battery Energy Storage for Maturing Markets: Performance, Cost, Perceptions, and Environmental Impacts

<p>As the use of renewable energy technologies and electric vehicles continues to expand in our electricity generation and transportation sectors, demand for energy storage technologies will only grow. Meeting this increased demand will require both technology innovations, but also new ways of thinking about the costs of implementing these technologies. This dissertation examines electrochemical energy storage technologies at multiple phases of the product cycle to assess how to meet some of the challenges associated with widespread adoption of electrochemical energy storage. Using a process-based cost model to identify the factors that contribute most to battery manufacturing cost, I find that economies of scale cost reductions have largely already been achieved. However, changes in cell design parameters can help to lower the per kWh cost of lithium-ion cells. Looking at a use case for energy storage in a hybrid microgrid, I find that both battery chemistry characteristics and technology costs impact the overall performance of hybrid microgrids and the cost of delivering electricity. As more batteries are produced to meet growing demand, the greenhouse gas emissions associated with battery manufacturing and waste disposal will become increasingly important. Using an attributional life cycle analysis, I compare the emissions associated with two different recycling processes: pyrometallurgical recycling and direct cathode recycling. While pyrometallurgical recycling does not offer emissions reductions, direct cathode recycling does have the potential to reduce greenhouse gas emissions, even if the cathode recovery process has relatively low yield rates. Using these recovered cathode materials is contingent on a market that will accept these recycled materials. A survey of current electric vehicle owners shows that consumer preferences about battery materials differ depending on whether consumers purchased a plug-in hybrid or an all electric vehicle. Overall, plug-in hybrid vehicle owners seem to have a slightly negative perception of recycled battery materials. For electric vehicle owners that have an all-electric vehicle, there are more diverse preferences, with groups that have positive, negative, and indifferent preferences about the type of battery material used in their vehicle. The heterogeneous preferences of different electric vehicle owners could enable different trends in material recovery and reuse as the number of electric vehicles on the road, and the battery energy storage used for transportation, increase.</p

Development of a high-resolution top-down model to estimate actual household-level heat pump electricity consumption

Heat pumps can play an important part in decarbonizing the residential sector due to their use of electricity instead of fossil fuels, and their high efficiency, which often exceeds 100%. However, heat pump performance and energy savings vary with climate and individual household energy usage. Recent studies have used geospatial models to estimate potential heat pump energy consumption across the United States. Yet most studies use generic and oversimplified heat pump models. We contribute to this field with a geospatial model based on manufacturer data and measured test data for 16 different R410A, high efficiency, variable speed compressor heat pumps. Using linear regression, we estimate a market average of COP with respect to ambient temperature. From this, we can identify the variation in efficiency with temperature across this technology class. We also use linear regression to estimate demand for heating and cooling as a function of ambient temperature and household characteristics. We compare the performance of both the predicted energy demand and heat pump efficiency against measured data from a heat pump-equipped house in West Lafayette, Indiana, and find that the model predicts daily heat pump electricity consumption with 27.8% relative error, comparable to other building simulation models. By incorporating high-resolution geospatial data inputs, such top-down models can still maintain a large scope across technologies and diverse climates while increasing spatial and temporal resolution

A minimal information set to enable verifiable theoretical battery research

Batteries are an enabling technology for addressing sustainability through the electrification of various forms of transportation (1) and grid storage. (2) Batteries are truly multi-scale, multi-physics devices, and accordingly various theoretical descriptions exist to understand their behavior (3−5) ranging from atomistic details to techno-economic trends. As we explore advanced battery chemistries (6,7) or previously inaccessible aspects of existing ones, (8−10) new theories are required to drive decisions. (11−13) The decisions are influenced by the limitations of the underlying theory. Advanced theories used to understand battery phenomena are complicated and require substantial effort to reproduce. However, such constraints should not limit the insights from these theories. We can strive to make the theoretical research verifiable such that any battery stakeholder can assess the veracity of new theories, sophisticated simulations or elaborate analyses. We distinguish verifiability, which amounts to “Can I trust the results, conclusions and insights and identify the context where they are relevant?”, from reproducibility, which ensures “Would I get the same results if I followed the same steps?” With this motivation, we propose a checklist to guide future reports of theoretical battery research in Table 1. We hereafter discuss our thoughts leading to this and how it helps to consistently document necessary details while allowing complete freedom for creativity of individual researchers. Given the differences between experimental and theoretical studies, the proposed checklist differs from its experimental counterparts. (14,15) This checklist covers all flavors of theoretical battery research, ranging from atomic/molecular calculations (16−19) to mesoscale (20,21) and continuum-scale interactions, (9,22) and techno-economic analysis. (23,24) Also, as more and more experimental studies analyze raw data, (25) we feel this checklist would be broadly relevant