<div> Porous lithium ion battery (LIB) electrodes are comprised of electrochemically active material particles that store lithium and a surrounding conductive binder, liquid electrolyte, carbon black mixture that facilitates ionic and electronic transport. Typically, lithium diffusivity is several orders of magnitude smaller in the active material as compared to the surrounding electrolyte, making the electrode microstructure a governing factor in determining the balance between its lithium storage capacity and transport rate. Here, the effects of microstructure on the performance of LIBs are systematically analyzed at three length scales - the single particle length scale, the spatially resolved multiple particle length scale, and the porous electrode layer (homogenized) length scale. At the single particle length scale, a thermodynamically consistent variational framework is presented to examine the effects of crystallographic anisotropy, crystallographic texture, grain size, and grain morphology on the LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) chemistry. The theory was extended to the spatially resolved multiple particle length scale and the porous electrode layer length scale to explain the microstructural origin of experimentally observed instances of apparent phase separation in NMC111. At the electrode length scale, a data driven framework is presented to evaluate the electrochemical performance of a wide range of particle morphologies and battery architectures. Specifically, microstructural characteristics of 53 356 microstructures are assessed, and strategies to optimize electrode design parameters such as active particle morphology, spatial orientation, electrode porosity, and cell thickness are presented.</div><p></p>

<p dir="ltr">Distributed energy resources (DERs), such as rooftop solar generation and energy storage systems, are becoming more prevalent in distribution systems. DERs are connected to the distribution system via power electronic converters, introducing faster dynamics in the system. Understanding the system dynamics under a high penetration of inverter-based DERs is critical for power system researchers and practitioners, driving the development of modeling techniques and simulation software. Aiming to reduce computational complexity, existing tools and techniques often employ various approximations. Meanwhile, modern advancements in computational hardware capabilities provide opportunities to include the faster time-scale dynamics. To address this, the primary objective of this thesis is to develop an open-source Python simulation package, Dynamic Simulation using Sparse Tableau Approach in Python, DynaSTPy (pronounced “dynasty”), capable of capturing the dynamics of all components in a distribution feeder. The distribution feeder is modeled as a system of Differential-Algebraic Equations (DAEs). Further, each component in the feeder is modeled based on the Sparse Tableau Approach (STA), which involves the representation of component model equations using sparse matrices, facilitating a systematic procedure to model the components and construct the system DAEs. In sinusoidal steady state, the DAEs can be represented in phasor form, extending the approach to perform power flow analysis of distribution feeders.</p>

<p>  </p> <p>Organic-inorganic halide perovskites are at the brink of commercialization as the next generation of light-absorbing materials for solar energy harvesting devices. Perovskites have large absorption coefficients, long charge-carrier lifetimes and diffusion lengths, and a tunable absorption spectrum. Furthermore, these materials can be low-temperature solution-processed, which transfers to low-cost manufacturing and cost-competitive products. The remarkable material properties of perovskites enable a broad product-market fit, encompassing traditional and new applications for solar technology. Perovskites can be deposited on flexible substrates for flexible solar cells, applied in thermochromic windows for power generation and building cooling, or tuned for tandem solar cell application to include in high-performance solar panels. However, perovskites are intrinsically unstable, which has so far prevented their commercialization. Despite large research efforts, including over two thousand publications per year, perovskite solar cells degrade in under one year of operation. In a saturated research field, new ideas are needed to inspire alternative approaches to solve the perovskite stability problem. In this dissertation, we detail research efforts surrounding the concept of a self-healing perovskite solar cell.</p> <p>     A self-healing perovskite solar cell can be classified with two distinctions: mechanically healing and molecularly healing. First, mechanically self-healing involves the material’s ability to recover its intrinsic properties after mechanical damage such as tares, lacerations, or cracking. This type of healing was unique to the organic polymer community and ultra-rare in semiconducting materials. By combining a self-healing polymer with perovskite material, we developed a self-healing semiconducting perovskite composite material which can heal using synergistic grain growth and solid-state diffusion processes at slightly elevated temperatures. The material is demonstrated in flexible solar cells with improved bending durability and a power conversion efficiency reaching 10%. The addition of fluidic polymer enables macroscopic perovskite material movement, which is otherwise brittle and rigid. The results inspire the use of polymer scaffolds for mechanically self-healing solar cells.</p> <p>     The second type of healing, molecular healing, involves healing defects within the rigid crystal domains resulting from ion migration. The same phenomenon which leads to device degradation, also assists the recovery of the device performance after resting the device in the dark. During device operation, perovskite ions diffuse in the perovskite lattice and accumulate at the device interfaces where they undergo chemical reactions or leave the perovskite layer, ultimately consuming the perovskite precursors. The photovoltaic performance can be recovered if irreversible degradation is limited. Ideally, degradation and recovery can match day and night cycling to dramatically extend the lifetime of perovskite solar cells. In this dissertation, we introduce the application of chalcogenide chemistry in the fabrication of perovskite solar cells to control the thin film crystallization process, ultimately to reduce defects in the perovskite bulk and introduce surface functionality which extends the device stability. This new strategy will help improve molecularly self-healing perovskite solar cell by reducing irreversible degradation. Lastly, we present a few other new ideas to inspire future research in perovskite solar cells and assist in the commercialization of the next generation of photovoltaics.</p>

<p dir="ltr">The urgent global shift towards decarbonization necessitates the development of robust frameworks to navigate the complex technological, financial, and regulatory challenges emerging in the clean energy transition. Furthermore, the increased adoption of renewable energy sources (RES) is correlated to the exponential growth in weather data research over the last few years. This circular relationship, where big data drives renewable growth, which feeds back the data pipeline, serves as the primary focus of this study: the development of computational tools across diverse spatial and temporal scales for the optimal design and operation of renewable energy-based systems. Two scales are considered, differentiated by their primary objectives and techniques used. </p><p dir="ltr"> In the first one, the integration of probabilistic forecasts into the operations of RES microgrids (MGs) is studied in detail. It is revealed that longer scheduling horizons can reduce dispatch costs but at the expense of forecast accuracy due to increased prediction accuracy decay (PAD). To address this, a novel method that determines how to split the time horizon into timeblocks to minimize dispatch costs and maximize forecast accuracy is proposed. This forms the basis of an optimal rolling horizon strategy (ORoHS) which schedules distributed energy resources over varying prediction/execution horizons. Results offer Pareto-optimal fronts, showing the trade-offs between cost and accuracy at varying confidence levels. Solar power proved more cost-effective than wind power due to lower variability, despite wind’s higher energy output. The ORoHS strategy outperformed common scheduling methods. In the case study, it achieved a cost of \$4.68 compared to \$9.89 (greedy policy) and \$9.37 (two-hour RoHS). The second study proposes the Caribbean Energy Corridor (CEC) project, a novel, ambitious initiative that aims to achieve total grid connectivity between the Caribbean islands. The analysis makes use of thorough data procedures and optimization methods for the resource assessment and design tasks needed to build such an infrastructure. Renewable energy potentials are quantified under different temporal and spatial coverages to maximize usage. Prioritizing offshore wind development, the CEC’s could significantly surpass anticipated growth in energy demand, with an estimated installed capacity of 34 GW of clean energy upon completion. The corridor is modeled as an HVDC grid with 32 nodes and 31 links. Underwater transmission is optimized with a Submarine-Cable-Dynamic-Programming (SCDP) algorithm that determines the best routes across the bathymetry of the region. It is found that the levelized cost of electricity remains on the low end at \$0.11/kWh, despite high initial capital investments. Projected savings reach \$ 100 billion when compared with ”business-as-usual” scenarios and the current social cost of carbon. Furthermore, this infrastructure has the potential to create around 50,000 jobs in construction, policy, and research within the coming decades, while simultaneously establishing a robust and sustainable energy-water nexus in the region. Finally, the broader implications of these works are explored, highlighting their potential to address global challenges such as energy accessibility, prosperity in conflict zones, and sharing these discoveries with the upcoming generations.</p>