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The increasing demand for batteries’ application in grid-balancing, electric vehicles, and portable electronics has prompted research efforts on improving their performance and safety features. The improvement of batteries involves the comparison of multiple battery designs and the determination of electrochemical and thermal property distributions at the continuum scale. This is achieved by using multiphysics modeling for investigatory battery research, as conventional experimental approaches would be costly and impractical. The fundamental electrochemical models for these batteries have been established, hence, new models are being developed for specific applications, such as thermal runaway and battery degradation in lithium-ion batteries, gas evolution in lead-acid batteries, and vanadium crossover in vanadium redox flow batteries. The inclusion of new concepts in multiphysics modeling, however, necessitates the consideration of phenomena beyond the continuum scale. This work presents a comprehensive review on the multiphysics models of lithium-ion, lead-acid, and vanadium redox flow batteries. The electrochemical models of these chemistries are discussed along with their physical interpretations and common applications. Modifications of these multiphysics models for adaptation and matching to end applications are outlined. Lastly, we comment on the direction of future work with regards to the interaction of multiphysics modeling with modeling techniques in other length and time scales. Molecular-scale models such as density functional theory and kinetic Monte Carlo can be used to create new multiphysics models and predict transport property correlations from first principles. Nanostructures and pore-level geometries can be optimized and integrated into continuum-scale models. The reduction of multiphysics models via machine learning, mathematical simplification, or regression enables their application in battery management systems and energy systems modeling.

Density functional theory was used to investigate the effects of doping alkaline earth metal atoms (beryllium, magnesium, calcium and strontium) on graphene. Electron transfer from the dopant atom to the graphene substrate was observed and was further probed by a combined electron localization function/non-covalent interaction (ELF/NCI) approach. This approach demonstrates that predominantly ionic bonding occurs between the alkaline earth dopants and the substrate, with beryllium doping having a variant characteristic as a consequence of electronegativity equalization attributed to its lower atomic number relative to carbon. The ionic bonding induces spin-polarized electronic structures and lower workfunctions for Mg-, Ca-, and Sr-doped graphene systems as compared to the pristine graphene. However, due to its variant bonding characteristic, Be-doped graphene exhibits non-spin-polarized p-type semiconductor behavior, which is consistent with previous works, and an increase in workfunction relative to pristine graphene. Dirac half-metal-like behavior was predicted for magnesium doped graphene while calcium doped and strontium doped graphene were predicted to have bipolar magnetic semiconductor behavior. These changes in the electronic and magnetic properties of alkaline earth doped graphene may be of importance for spintronic and other electronic device applications.

Traditional understanding of electrocatalytic reactions generally focuses on either covalent interactions between adsorbates and the reaction interface (i.e., electrical double layer, EDL) or electrostatic interactions between electrolyte ions. Here, our work provides valuable insights into interfacial structure and ionic interactions during alkaline oxygen evolution reaction (OER). The importance of inner-sphere OH- adsorption is demonstrated as the IrOx activity in 4.0 M KOH is 6.5 times higher than that in 0.1 M KOH. Adding NaNO3 as a supporting electrolyte, which is found to be inert for long-term stability, complicates the electrocatalytic reaction in a half cell. The nonspecially adsorbed Na+ in the outer compact interfacial layer is suggested to form a stronger noncovalent interaction with OH- through hydrogen bond than adsorbed K+, leading to the decrease of interfacial OH- mobility. This hypothesis highlights the importance of outer-sphere adsorption for the OER, which is generally recognized as a pure inner-sphere process. Meanwhile, based on our experimental observations, the pseudocapacitive behavior of solid-state redox might be more reliable in quantifying active sites for OER than that measured from the conventional EDL charging capacitive process. The interfacial oxygen transport is observed to improve with increasing electrolyte conductivity, ascribing to the increased accessible active sites. The durability results in a liquid alkaline electrolyzer which shows that adding NaNO3 into KOH solution leads to additional degradation of OER activity and long-term stability. These findings provide an improved understanding of the mechanistic details and structural motifs required for efficient and robust electrocatalysis.

Microgrids have emerged as a crucial focus in power engineering and sustainable energy research, with utility-scale microgrids playing a significant role in both developed and developing countries like the Philippines. This study presents a comprehensive framework for utility-scale microgrid planning, emphasizing the sustainable integration of renewable energy resources to the distribution grid. The framework addresses the operational modes of grid-connected and islanded microgrids, emphasizing the seamless transition between these modes to ensure a continuous power supply. By leveraging local distributed energy resources, the microgrid aims to reduce dependence on the main transmission grid while enhancing resilience and reliability. The proposed planning framework not only eases the economic burden of constructing renewable energy sources but also aids distribution utilities in maximizing local resources to achieve sustainable energy goals. Through a detailed network analysis and modeling, the framework provides a robust foundation for optimizing the energy mix and enhancing the overall system performance. This research contributes to advancing microgrid technology as a key driver towards achieving UN Sustainable Development Goals, particularly in promoting clean and affordable energy access. [ABSTRACT FROM AUTHOR]

The recent classification of lithium as a critical raw material surged the research and development (R&D) of post‐lithium batteries (PLBs). The larger cation charge carriers of these PLBs consequently entailed extensive materials R&D for battery components, especially cathode. Prussian Blue (PB) and its analogues (PBAs) have emerged as promising cathode materials for PLBs due to their desirable characteristics, including a three‐dimensional open framework structure that facilitates fast ion diffusion for both monovalent (Li+, Na+, K+) and multivalent (Mg2+, Ca2+, Zn2+, Al3+) ions, stable framework structures, electrochemical tunability, availability of widely used precursors, and ease of synthesis. Our comprehensive review reveals that several challenges are yet to be addressed in employing PBAs as cathode materials for PLBs, viz., vacancies, crystal water, side reactions, and conductivity issues. This review paper provides an exhaustive survey of material development, including the mitigation strategies of the challenges in employing PBAs as cathode materials for advancing PLBs (i. e., sodium‐ion batteries (SIBs), potassium‐ion batteries (KIBs), magnesium‐ion batteries (MIBs), calcium‐ion batteries (CIBs), zinc‐ion batteries (ZIBs), aluminum‐ion batteries (AIBs)) towards commercialization. [ABSTRACT FROM AUTHOR]

An increasing acceptance of microgrid systems is primarily driven by emission reduction, resilience, reliability, and stability of energy systems. This work proposes a utility-scale grid-connected microgrid generation and network planning for a distribution network based on its available local resources and potential for distributed energy resource (DER) generator installation. Enabling distribution utilities to operate as a microgrid allows their systems to function as stand-alone in worst-case transmission grid scenarios. The main steps include identifying planning objectives, generation planning, and network planning. Results of a case study in Kalinga, Philippines show that the distribution utility can lower the levelized cost of electricity (LCOE) by 0.032 USD/kWh (21.38 %) by adding additional renewable energy capacities into the mix to meet its load demand. Results also show resilience and stability improvements with microgrid operation. Additional requirements for the existing distribution network to operate as a microgrid include additional network switches and line reinforcement upgrades. [ABSTRACT FROM AUTHOR]

The continued increase in national energy demand pushes oil and petroleum price prediction efforts for the net oil-importing Philippines to ensure adequate supply. These prices are commonly modeled by data-driven Machine Learning (ML) methods to encompass their extrinsic and volatile nature. However, recent studies have found that new features specific to COVID-19 and the Russia-Ukraine geopolitical conflict have significantly contributed to overseas oil price ML prediction. This work investigated the impact of these factors on seven pump prices in Manila, Philippines, from December 2019 until July 2022. Three ML regression models were chosen to extend the existing literature and ensure price model accuracy: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Random Forest Regression (RFR). Features were listed based on related literature and underwent data preprocessing using p-value testing and Principal Component Analysis. Models were then trained, tested, and optimized using nested splits and hyperparameter tuning. Mean Absolute Percent Error (MAPE) was used to evaluate accuracy. Generated models had MAPE values within the range 3.13 % - 12.67 %, which is within the range of MAPE values in oil and petroleum price prediction literature, 0.131 % - 19.2 %. MLR and SVR models generally exhibited the highest accuracy for each pump price. This study proves that period-specific features may be used for local pump price modeling. Future works may explore other ML models and geographic location effects and investigate newly identified period-specific features. [ABSTRACT FROM AUTHOR]

As climate change is slowly materialising, the importance of alternative fuels has been magnified to aid in reducing carbon emissions. One of the most promising clean alternative fuels is green hydrogen which is produced via water electrolysis powered by renewable energy sources. The solar and wind power potential of the Philippines has been explored in published studies; however, the economic viability of green hydrogen production has not yet been evaluated. In this work, an assessment of the levelized cost of hydrogen (LCOH) produced from solar and wind power in the Philippines was performed. Representative locations within a province were selected based on the annual average resource availability for solar global horizontal irradiance (GHI) and wind speed. LCOH calculations identified the favourable locations for green H2 production by calculating the net present cost (NPC) of solar- and wind-powered electrolysis plants with deionised water as feedstock. Uncertainty analysis was performed via Monte Carlo simulations by perturbing component costs, feedstock water price, and discount rate. Additionally, LCOH calculations and Monte Carlo simulations showed the disparity of resource availability between solar and wind in the Philippines – supported by the calculated capacity factors. It was also shown that favourable conditions such as high solar GHI and high wind speed tend to have a narrower distribution and lower LCOH values – implying less economic risks in developing over these locations. Calculations identified that policy instruments such as subsidies and tax exemptions are still vital in commissioning green hydrogen plants, as LCOH values are generally higher than the cost of grey hydrogen. [ABSTRACT FROM AUTHOR]

With the growing popularity of electric vehicles, longer lasting batteries are necessary for better mileage. Battery chemistries with larger energy densities compared to Lithium-Ion Batteries (LIB) need to be developed. Lithium-Sulfur Battery (LSB) is a good alternative due to its high theoretical and practical energy densities. However, commercialization of LSBs has yet to be realized due to shorter lifetimes caused by non-ideal battery processes such as polysulfide shuttling. Through multiphysics modeling, the effects of shuttling can be observed. Additionally, cost analysis can determine the feasibility of LSBs for mass production and its competitiveness against LIBs. In this work, energy density optimization and LSB cost estimation were done using combined multiphysics modeling and cost modeling approaches. Using COMSOL Multiphysics®, the energy densities of the batteries with and without shuttling were optimized by changing the thickness and porosity of the cathode and separator. Then, a bottom-up cost analysis of the optimized battery cell was conducted using the BatPaC model. It was observed that shuttling leads to a lower optimized energy density. To compensate for the shuttling)induced capacity loss, the model’s optimized battery dimensions had a separator porosity increase by 22.08 % and a cathode thickness and porosity decrease by 23.72 % and 16.21 %. Additionally, shuttling increases battery material costs by 13.35 % because of the larger and more expensive current collectors must compensate for the size of the smaller electrodes. Future works may explore various C-rates and use cases to identify optimal battery parameters for each. [ABSTRACT FROM AUTHOR]