Energy Technologies

Drawing on existing market designs with independent resource participation in electricity markets, this study analyzes participation models for hybrid resources combining renewable generation and storage. Two models are considered: in the first, the components operate independently, with the Independent System Operator (ISO) managing the storage state of charge (SoC); in the second, the hybrid resource acts as an integrated unit, submitting offers as a “black box” and managing its SoC internally. Using a production cost model for the zonal New York Bulk Power System, we evaluate trade-offs in system reliability, market efficiency, and asset profitability. Our results provide several key insights for policymakers, showing that the ISO-managed granular model enhances social welfare through explicit SoC management, while the simpler integrated model is more computationally efficient, but may cause more real-time violations and lower overall profits.

Increasing growth of distributed solar photovoltaics (PV) and electric vehicles (EV) can strain local distribution networks and require costly upgrades. Distributed battery storage, often deployed alongside PV, can be used to mitigate those costs, depending on how batteries are operated. This study evaluates the potential deferral value of distributed battery storage across a range of tariff structures, focusing on the rate structures most commonly available to residential customers today and related variants. Deferrals are evaluated with a least-cost distribution grid expansion optimization model to identify requirements on line reconductoring, transformer upgrades, and voltage regulator installations under each tariff. Results show that TOU rates and net billing tariffs can yield meaningful deferral value, depending on specific tariff structure features. Under the best performing tariff structure tested, storage produced a median annualized deferral value of $7.18 per kW of storage capacity ( kW S ) across all feeders in the sample, though deferral values were considerably larger for feeders with peak loads that coincide with utility system peak, i.e., timing of TOU peak period. In contrast, under an unrestricted TOU design with no restrictions on grid charging or discharging, the median deferral value was $0/ kW S illustrating the critical importance of tariff structure details.

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

With advances in imaging technologies, modern photovoltaic (PV) systems generate large volumes of heterogeneous image data, including visible, electroluminescence (EL), and infrared (IR) images. Existing PV image analysis models, particularly deep learning approaches, are typically task-specific and lack cross-modality generalization. To address this limitation, this paper proposes an open-source large language model (LLM)–based unified framework for heterogeneous PV image diagnostics. Through task-aware diagnostic prompting, the framework enables analysis of visible, EL, and IR images within a single pipeline, supporting both zero-shot and few-shot inference and binary and multiclass classification. It is compatible with state-of-the-art multimodal LLMs, including ChatGPT, Gemini, Claude, Qwen, and CLIP. The framework is evaluated on PV module condition classification (clean, soiling, snow, hail, and bird droppings) using visible images, cell crack detection using EL images, and hotspot detection using IR images. GPT-5.1 in few-shot mode achieves the best performance, with classification accuracy exceeding 97.3%. Open-source models such as Qwen and CLIP also deliver competitive results on visible images (around 90% accuracy), though their performance is more limited on EL and IR modalities. On the full ELPV dataset, the framework achieves 83.5% zero-shot accuracy, within 2.8% of the supervised CNN baseline, confirming scalability to larger benchmarks. Practical aspects such as reproducibility, response latency, and confidence estimation are systematically analyzed. The framework operates across PV image modalities without modality- or task-specific training, making it well suited as a rapid pre-screening tool to support downstream detailed diagnostics. A benchmark dataset of diverse labeled PV images is also released.

Localized high-concentration electrolytes (LHCEs) have been identified as promising electrolyte formulations for lithium metal batteries, due to their effective interphase formation and promotion of compact Li deposition, yet their practical implementation is often limited by reduced ion transport kinetics. In this study, two industrially established fluorinated ethers are identified for the first time in battery research as effective co-diluents as they combine a broad electrochemical stability window with a low viscosity and intrinsic non-flammability. Incorporating these components, commonly used as heat transfer fluids, yields safer, less flammable electrolyte formulations with enhanced ion mobilities. In particular, the ternary co-diluent formulation shows improved ion mobility by reducing the electrolyte's viscosity while limiting excessive ion clustering. Based on the improved electrolyte transport kinetics, lower overvoltages and higher Coulombic efficiencies at current densities ≥ 1 mA cm^-2 are achieved with the ternary co-diluent blend, resulting in markedly extended cycle life in an application-oriented zero-excess pouch cell compared with the baseline system. Complementary electrochemical and ex situ analysis of harvested electrodes at moderate current densities reveals no discernible differences in interphase morphology and composition, suggesting enhanced ion mobility as the primary cause of the improved high-rate performance.

Drawing inspiration from the panther chameleon's sophisticated color-adaptive abilities, we introduce a unified design framework for a new class of bio-inspired mechanochromic materials with tailored reflectivity across the electromagnetic spectrum. While structural coloration in nature relies on the mechanical adjustment of photonic crystal spacing, engineering such systems is often hindered by the barreling effect, the lateral bulging of bulk materials caused by Poisson's effect. To address this, we developed a mechanical metamaterial substrate optimized through a genetic algorithm and modeled it using Timoshenko beam theory to ensure a uniform strain field during deformation. The proposed system features a two-dimensional hexagonal lattice of composite dielectric nanopillars positioned on top of a micro-architected substrate fabricated via Multiphoton Lithography (MPL). The nanopillars consist of an MPL core coated with a high-refractive-index material to achieve complete optical band gaps. By dynamically adjusting the lattice constant through controlled, reversible elastic deformation, the reflected wavelengths can be shifted across broad spectral ranges. We demonstrate the scalability and robustness of this approach through three distinct structures tailored for the visible, mid-wave infrared (MWIR), and long-wave infrared (LWIR) regions. This methodology lays a foundation for scalable, reversible mechanochromic materials with significant potential for applications in adaptive camouflage, radiative thermal management, and wearable electronics.

Abstract Operation-induced surface reconstruction of photoelectrodes is underexplored as a path to control stability and performance. We show how adaptive junctions form via surface reconstruction of BiVO4 during glycerol photooxidation and how these surfaces affect electrolyte-dependent kinetics and durability. Preferential V dissolution in both acidic and alkaline media forms a Bi-rich layer. In situ measurements through a dual-working-electrode platform quantify the changes in photovoltage and charge-transfer resistance derived from adaptive junction formation, while enabling quantitative separation of the driving forces for charge separation and interfacial catalysis. The reconstructed surface in acidic media improves hole-transfer kinetics, functions as a glycerol-oxidation catalyst, and imparts photostability. Surface reconstruction in alkaline media exhibits the opposite behavior, impeding hole injection. This instability is mitigated by trace Ni2+ ions, which drive in situ surface activation without cocatalyst pre-deposition. This study shows the significance of surface reconstruction in the design of durable photoelectrodes for applications targeting organic electro-oxidation.

We show that the pH gradient at the catalyst–ion exchange membrane interface in a seawater bipolar-membrane electrolyzer can be mitigated by reducing the catalyst–membrane distance. We further show how water transport can be balanced at steady state. Bipolar membrane (BPM) electrolyzers offer advantages in the electrolysis of impure-waters by controlling ion flux, yet still suffer from performance and durabilty limitations. Here, we investigate the impact of NaCl electrolyte (nominally simulated seawater) on BPM electrolyzer operation and identify local pH gradients at electrode–membrane interfaces, arising from coupled ion transport and electrode reactions, as one origin of performance loss and degradation. NaCl in the catholyte induces pronounced pH gradients at the cathode|cation-exchange-layer interface, leading to increased voltage, while partial Cl − crossover to the anode becomes detrimental under locally OH − -deficient conditions, promoting the chlorine evolution reaction and accelerating degradation. Direct physical integration of the cathode and anode catalysts onto the cation and anion exchange layers through spray coating and electrodeposition, respectively, mitigates these effects by minimizing the membrane–catalyst distance. The BPM electrolyzer built in this way achieves 0.50 A cm −2 at 2.8 V and shows a degradation rate of 5.5 mV h −1 over 120 h in 0.50 M NaCl electrolyte, compared to degradation of 71 mV h −1 with a typical porous-transport-layer device structure. This work thus establishes control of local pH gradients as a design principle for BPM electrolysis with impure-water feeds.

Low-temperature electrochemical cells reducing iron oxides to metal in alkaline electrolytes can support fully electrified steelmaking processes. Previous studies on these cells have primarily focused on high-surface-area hematite, Fe 2 O 3 , reactants... Low-temperature electrochemical cells reducing iron oxides to metal in alkaline electrolytes can support fully electrified steelmaking processes. Previous studies on these cells have primarily focused on high-surface-area hematite, Fe 2 O 3 , reactants whereas attempts to reduce suspensions of magnetite, Fe 3 O 4 —one of the two feedstocks for existing ironmaking reactors—have generally been limited to low rates of reaction (<30 mA cm -2 ). Here, we control the crystalline domain size of Fe 2 O 3 and Fe 3 O 4 particles in 10 M NaOH electrolytes to study how the nanoscale morphology of oxides controls the rate of electrochemical ironmaking. Rotating-ring disk electrode measurements of Fe 2+ , in-situ Raman spectroscopy of the electrode surface, and ex–situ electron microscopy were consistent with a hypothesized passivation process at Fe 3 O 4 surfaces that may prevent the continuous formation of soluble intermediates. Sufficiently small (<100 nm diameter) oxide particles yielded Fe partial current densities > 160 mA cm -2 , a fivefold increase relative to previously reported rates for Fe 3 O 4 suspensions and comparable to active Fe 2 O 3 . Electron microscopy revealed that electrodeposited films were composed of micron-scale crystalline Fe domains with a porous film of Fe 3 O 4 nanoparticles and supports a model where Fe is grown primarily from soluble Fe 2+ intermediates. Based on these insights, inactive blast-furnace-grade iron-oxides were transformed into high surface area nanoparticles (1.6 to 229.2 m 2 g -1 ) via reprecipitation, leading to a ninefold enhancement in faradaic efficiency and an Fe partial current density of 120 mA cm -2 . When integrated with chlor-iron cells producing reagents for reprecipitation, this approach could lead to a cost-competitive process for electrochemical ironmaking from industrially relevant feedstocks.