Search

Universal access to electricity is beneficial for the socio-economic development of a country and the development of smart communities. Unfortunately, the electrification of remote off-grid areas, especially in developing countries, is rather slow due to geographic and economic barriers. In the Philippines, specifically, many electrified off-grid areas are underserved, with access to electricity being limited to only a few hours a day. This is mainly due to the high dependence on diesel power plants (DPPs) for electrifying these areas. To address these problems, hybrid renewable energy systems (HRESs) have been considered good electrification alternatives and have been extensively studied for their techno-economic and financial feasibility for Philippine off-grid islands. In this work, articles published from 2012 to 2023 focusing on off-grid Philippine rural electrification were reviewed and classified based on their topic. The taxonomical analysis of collected studies shows that there is a saturation of works focusing on the technical and economic aspects of off-grid electrification. Meanwhile, studies focusing on environmental and socio-political factors affecting HRES off-grid electrification are lagging. A bibliographic analysis of the reviewed articles also showed that there is still a lack of a holistic approach in studying off-grid electrification in the Philippines. There are only a few works that extend beyond the typical techno-economic study. Research works focusing on environmental and socio-political factors are also mainly isolated and do not cross over with technical papers. The gap between topic clusters should be addressed in future works on off-grid electrification.

Hybrid renewable energy systems have garnered considerable attention as sustainable power sources for remote off-grid islands in the Philippines. Consequently, they have been the subject of numerous techno-economic investigations. However, comprehensive explorations into the viability of 100 % renewable energy (RE) systems for these areas have been limited due to their exorbitant initial outlays. In this work, we modelled the prospective transition of off-grid island mini-grids in the Philippines from the contemporary status quo in 2020 to a fully integrated 100 % RE system by 2050. Our approach involves the gradual shift towards RE adoption instead of the abrupt deployment of RE systems. This maneuver serves a twofold purpose: firstly, it mitigates the adverse economic repercussions stemming from the substantial upfront costs inherent to RE technologies; secondly, it leverages the declining future costs of these technologies. We collected an energy generation and consumption dataset across 208 mini-grids in Philippine off-grid islands. We then simulate the RE transition based on prevailing technology costs at five-year intervals from 2020 to 2050. Afterwards, different scenarios that may affect the energy transition are simulated. Our results indicate that an energy transition steered solely by market dynamics cannot achieve a 100 % RE penetration; hence, we analysed alternative scenarios promoting RE utilization. The immediate discontinuation of all diesel generators by 2050 will lead to a substantial cost uptick at the end of the transition. Integrating biodiesel yields a more measured progression of costs, although this relies upon a nascent market, rendering it susceptible to feedstock supply risks. Recognizing the intermittent nature of RE technologies, we posit that allowing for a certain degree of unmet load fosters greater RE penetration. Nevertheless, this approach compromises the reliability of the system. Our work demonstrates that a 100 % RE transition in Philippine off-grid islands is technically and economically feasible. However, the energy trilemma or the tradeoff between affordability, reliability, and sustainability encumbers the realization of this transition.

Hybrid renewable energy systems (HRES) have emerged as a promising solution for delivering sustainable energy to off-grid communities. However, the vulnerability of specific regions to extreme weather events has raised concerns about the resilience of these systems. This study undertakes a techno-economic analysis to assess the impact and significance of incorporating storm hardening measures and insurance into an HRES designed for the Busuanga island cluster. Central to our study is introducing a novel cost metric, the probability-averaged levelized cost of electricity (LCOE), which adeptly captures the inherently probabilistic nature of climate-induced damages to HRES energy assets. This metric serves as the linchpin for comparing the economic viability of HRES configurations, considering both scenarios with and without storm hardening or insurance. Our findings unveil a clear trend: for a solar photovoltaic (PV) panel with an annual probability of damage at 1%, insurance emerges as a financially prudent choice, while storm hardening gains merit at a probability of 4%. The weighted average cost of capital (WACC) is pivotal in shaping investment decisions. HRES setups featuring non-hardened solar PV panels become more economically appealing than their insured or hardened counterparts under higher WACC conditions, under the condition that the solar PV panels can maintain functionality for 15 years without impairment. Our study demonstrates the importance of accounting for often overlooked factors such as storm hardening and insurance premiums for solar PV panels in climate-vulnerable areas, which are commonly disregarded in many techno-economic studies. Moreover, our findings and conclusions on the optimal balance between capital costs, insurance premiums, and storm-hardening markups can readily extend to other climate-vulnerable areas.

In regions heavily affected by recurrent typhoons, the need for more resilient electricity infrastructure is pressing. This emphasizes the importance of integrating resilience assessment, including incorporating resilience metrics, into the planning process of power distribution systems against any disruptive events. Although standardized metrics exist for assessing distribution system reliability, the absence of formalized resilience metrics hampers informed investments in critical infrastructure such as microgrid development. In this work, a set of resilience metrics is proposed by reconceptualizing reliability metrics. The metrics were formulated to account for both the type of extreme event and its specific impact on loads with varying levels of criticality. The effectiveness of the proposed metrics is demonstrated through a Philippine microgrid case study. A Monte Carlo framework incorporating an extreme event model, component fragility model, and system response model was used to quantify the resilience improvement before and after stand-alone microgrid operation of the power distribution system. Results show that the proposed metrics can effectively evaluate resilience enhancement and highlight the value of a holistic approach of considering critical loads and types of extreme events to strengthen societal and community resilience, making a compelling case for strategic investments in infrastructure upgrades such as microgrids.

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.

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.

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.

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.

This data article contains the location, energy consumption, renewable energy potential, techno-economics, and profitability of hybrid renewable energy systems (HRES) in 634 Philippine off-grid islands. The HRES under consideration consists of solar photovoltaics, wind turbines, lithium-ion batteries, and diesel generators. The islands were identified from Google Maps™, Bing Maps™, and the study of Meschede and Ocon et al. (2019) [1]. The peak loads of these islands were acquired from National Power Corporation – Small Power Utilities Group (NPC-SPUG), if available, or estimated from the island population otherwise. Hourly-resolution load profiles were synthesized using the normalized profiles reported by Bertheau and Blechinger (2018) [2]. Existing diesel generators in the islands were compiled from reports by NPC-SPUG, while monthly average global horizontal irradiance and wind speeds were taken from the Phil-LIDAR 2 database. Islands that are electrically interconnected were lumped into one microgrid, so the 634 islands were grouped into 616 microgrids. The HRES were optimized using Island System LCOEmin Algorithm (ISLA), our in-house energy systems modeling tool, which sized the energy components to minimize the net present cost. The component sizes and corresponding techno-economic metrics of the optimized HRES in each microgrid are included in the dataset. In addition, the net present value, internal rate of return, payback period, and subsidy requirements of the microgrid are reported at five different electricity rates. This data is valuable for researchers, policymakers, and stakeholders who are working to provide sustainable energy access to off-grid communities. A comprehensive analysis of the data can be found in our article “Techno-economic and Financial Analyses of Hybrid Renewable Energy System Microgrids in 634 Philippine Off-grid Islands: Policy Implications on Public Subsidies and Private Investments” [3].

In this work, we propose an immersion corrosion measurement method with E10 bioethanol-gasoline blend on austenitic steel (SS304) and aluminum (Al6061). Immersion tests were designed to simulate a weekly refuel cycle and were performed over 4, 8, and 12-week durations. Polarization resistance, surface morphology, and corrosion products were then observed after the immersion tests. Corrosion rates were estimated from polarization resistance measurements via electrochemical impedance spectroscopy. Afterwards, the surface morphology of exposed samples was studied using scanning electron microscopy. Lastly, the corrosion products were characterized using scanning electron microscopy with energy dispersive X-ray analysis. Surface staining was observed in both metal substrates. It was observed that the corrosion rate of SS304 remained relatively constant throughout the immersion time. In contrast, an increment in the corrosion rate of Al6061 was observed after four weeks of immersion due to the peeling of the protective oxide layer.

Sodium-sulfur (NaS) batteries are a promising energy storage technology that features high energy density, high cycle life, and no self-discharge. One of the cell design considerations that can affect the performance and construction of NaS batteries is cell geometry. While the planar geometry has advantages in power output, cell packing, ease of assembly, and thermal management, it has thermo-mechanical issues in sealing, which makes it less commercialized than the tubular geometry. In this work, the first multiphysics model of a NaS cell with a planar geometry was developed by extending an existing multiphysics model for a NaS cell with a tubular geometry. A previously developed multiphysics model for the tubular NaS cell was first adapted into COMSOL Multiphysics®. The model’s results were then validated against experimental discharge profiles and surface temperatures. After this, a planar NaS cell was simulated using the dimensions of an experimental planar cell, with the mass, heat, and charge transfer equations and parameters previously used in the tubular multiphysics model. The results from the planar model were then validated through comparison to experimental discharge profiles for a planar cell. The developed model can be used in future research and development of NaS batteries by comparing performance parameters of the planar geometry such as discharge profiles, energy densities, and temperatures with a multiphysics model of the tubular NaS battery.

Accurately estimating the capacity degradation of lithium-ion (Li-ion) batteries is vital in ensuring their safety and reliability in electric vehicles and portable electronics. Future capacity estimation using machine learning (ML) models allow battery lifetime predictions with minimal cycling data in the train set, well before capacity degradation occurs within the cell. The use of ML methods removes the need for prior knowledge of cell chemistry and the physical and chemical behaviors of batteries. In this paper, the data-driven ML models Gaussian process regression (GPR) and recurrent neural network – long short-term memory (RNN-LSTM) estimated the charge capacity of Li-ion batteries from the Oxford Battery Dataset, using only the battery's cycle index and capacity as input. With only 15 % of the battery’s lifetime as training data, the GPR model achieved a mean average percent error (MAPE) of 8.335 % and an R2 of 0.9755, while the LSTM model achieved a MAPE of 9.984 % and an R2 of 0.9898. These results indicate the goodness of fit and are comparable to results from similar models in the literature (MAPE = 9.1 to 15 %). The methodology may be applied to different features to help establish the relationship between health indicators and capacity fade and can be used in applications that require early capacity prediction such as in space technologies where lifetime and capacity are crucial in ensuring success and safety. This successful estimation highlights the promise and potential of accurately predicting Li-ion battery capacity degradation using a single-feature approach.

The global mission to reduce fossil fuel consumption has led to the escalating demand for electrochemical energy storage (EES) devices such as fuel cells and batteries. Computational techniques like Density Functional Theory (DFT) have recently been coupled with Machine Learning (ML) for high-throughput material screening and discovery. Transition metal surfaces are popular electrocatalyst candidates, but predictive ML regression models have only been applied to select metals such as Pt and Cu. Additionally, characterizing the contributions of each feature is challenging, especially on black-box models. In this work, regression models were trained to predict the adsorption energies of carbon, hydrogen, and oxygen on 27 fcc (111) monometallic surfaces and applied model-agnostic interpretation methods to evaluate feature importance. Over 200 adsorption energies on transition metal surfaces were collected from Catalysis-hub.org, a surface reaction database. A dataset was constructed for each adsorbate, and was composed of ten surface atomic, surface electronic, and adsorbate properties collected from online databases and DFT calculations on adsorbate-free surfaces. Then, the fine-tuned random forest regression, Gaussian process regression, and artificial neural network models predicted atomic adsorption energies while permutation feature importance calculated feature contributions. All ML model accuracies were found to be competitive with those from literature, with Gaussian process regression reporting the lowest errors of the three models. Coordination number was also found to have the largest contributions for all models. ML-DFT methodologies such as this can be expanded to accommodate alloys and more adsorbates for a wider screening of potential EES materials.

The global transition towards net-zero greenhouse gas emissions establishes a need for cleaner energy technologies. Hydrogen is a promising energy carrier whose global demand is steadily increasing and is conventionally produced through steam-methane reforming with carbon capture, or blue H2. Hydrogen production supplied by renewable energy (green H2) is an emerging process, but developing countries are not yet ready for a full transition. Augmenting blue H2 with green H2 production will allow a smoother transition until green H2 costs significantly decrease by 2050. In this work, a novel, low-cost teal hydrogen (teal H2) plant, a mixture of blue and green H2 technologies, located in the Philippines which combines steam-methane reforming, rice husk gasification, and carbon capture by monoethanolamine absorption, is proposed. Setting a production rate of 9,000 kg H2/h, the techno-economic potential of five cases with varying natural gas to rice husk contribution ratios were evaluated using AspenPlus. The levelized cost of the 25:75 teal H2 case at 1.06 USD/kg is cheaper than blue H2 and green H2 by 4.37 and 2.34 USD/kg, respectively. Moreover, the CO2-equivalent emissions of the 25:75 teal H2 case at 0.002 t CO2 -eq/1,000 Nm3 H2 is 57.10 % and 39.25 % lower than those from blue H2 and green H2. As green H2 becomes more economical, rice husk feed to the gasification process can be gradually increased to favor biomass- over petroleum-derived H2. This case study is a successful proof of concept that teal H2 may help transition the energy sector to carbon neutrality.

Ethylene glycol dimethacrylate (EGDMA) – dithiadiamide (DTDA) copolymer was synthesized by condensation reaction between monomeric mercaptoacetamide and ?-dibromoalkanes. This previously designed DTDA was complexed with Pt2+ before direct copolymerization with EGDMA. Pt2+-template was eluted with dilute HCl. The EGDMA-DTDA copolymer was evaluated for Pt2+ adsorption in terms of adsorption isotherm and selectivity. The copolymer exhibited monolayer Langmuir-type adsorption isotherm with qm = 177 mg g-1 at optimum pH = 1. It is selective to Pt2+ in a simulated solution containing the predominant cations present in an acid-leached spent 3-way, gasoline automobile catalytic converter sample (i.e., Al3+, Ba2+, Ce3+, Fe2+, Mg2+, and Zn2+).

Different energy storage technologies have particular applications with advantageous techno-economic characteristics. For this reason, the present and future Levelised Costs Of Storage (LCOS) of commercially mature energy storage technologies have been analysed in the current literature. Emerging energy storage technologies, such as long-duration flywheels, are also vying to capture the energy storage market, but uncertainties linger as to which applications they can capture due to limited and reliable publicly available data. In this work, we determined the future LCOS of a typical 1 MW installation of stationary electrochemical energy storage (lead-acid, sodium-sulphur, and lithium-ion battery) and mechanical energy storage technologies (short-duration flywheel and long-duration flywheel) under different applications from 2020 to 2050 using updated relevant techno-economic parameters. Based on the present costs of energy storage, lithium-ion batteries yield the lowest LCOE across different energy storage applications, corroborating with previous outlooks from different scholarly works. The cost advantage of lithium-ion batteries compared to other storage technologies continues to rise over the years due to their rapid cost decline. In the absence of lithium-ion batteries, long-duration flywheels initially provide the lowest cost for a wide range of applications, but they face stiff competition with sodium-sulphur batteries. By 2040, sodium-sulphur batteries are projected to have a lower LCOS than long-duration flywheels. Promoters and manufacturers of emerging energy storage technologies must find ways to rapidly decrease storage costs to secure their niche in the energy storage market.

Primary alkaline batteries have been widely used in portable electronics due to their low cost and safety. The consumption and disposal of these batteries has prompted notable research on their recycling. Another approach to reducing alkaline battery disposal is to extend their lifetime by increasing their energy density. In this work, the energy density of an AA primary alkaline battery was maximized by determining the optimum amount of electrode materials through multiphysics modeling. An electrochemical model of the alkaline battery is developed in COMSOL Multiphysics® and validated with discharge curves (i.e., voltage vs. time) obtained under constant resistance loads. The electrode thicknesses are then optimized to maximize the energy density of the battery while maintaining its exterior dimensions. The sensitivity of the energy density with respect to the electrode porosities and interfacial areas is then analyzed. The electrochemical model was able to replicate the discharge curves obtained under a 250 mA constant current discharge. The energy density is maximized by decreasing the thickness of the zinc anode. However, this results in anode dissolution near the current collectors and could compromise the electrical continuity in the battery. Increasing the anode thickness prevents dissolution at the current collectors but increases unused mass in the battery. The results of this study can be used to develop longer-lasting alkaline batteries. Furthermore, the model can be improved by considering thermal effects or modified to aid the development of rechargeable alkaline batteries.

Many people around the world rely on groundwater for drinking and sanitation, however, they are exposed to various health risks from the naturally occurring groundwater arsenic (As). Air-cathode Assisted Iron Electrocoagulation (ACAIE) using Carbon Black Pearls 2000® cathode was previously shown catalyse the removal of groundwater As by producing hydrogen peroxide (H2O2). This work explored Vulcan® XC-72, and Printex® L6 Carbon as alternative cathodes for iron electrocoagulation which are more selective towards the 2-electron oxygen reduction reaction into H2O2 compared to Carbon Black Pearls®. The cathodes were tested in an ACAIE set-up to treat synthetic groundwater spiked with 1,500 µg/L of As at different charge dosage rates (CDR) from 1.56 C/L-min to 100 C/L-min with a total charge dosage of 600 C/L for all set-ups. Although the electrocoagulation energies among the cathodes were similar, the use of Printex® cathode for ACAIE remediated the groundwater for all CDR with final As levels below 10 µg/L. This is in contrast with the less selective Carbon Black Pearls® at low CDR, and the less active Vulcan® Carbon at high CDR where the treated groundwater may still have As levels above 10 µg/L. Future research would explore modifications in the carbon materials and reactor configuration to further optimize ACAIE in removing groundwater As.

Acid-alkaline electrolyzers utilize an acidic catholyte and alkaline anolyte, which lower the thermodynamic voltage requirement for water splitting. Experiments have demonstrated the feasibility of acid-alkaline electrolyzers with proton exchange membranes, but a mathematical model has yet to be developed to understand their operation. This work developed a multiphysics model of a batch acid-alkaline electrolyzer with a H2SO4 catholyte, a NaOH anolyte, and a proton exchange membrane. The model was formulated in COMSOL Multiphysics® and validated using experimental current vs. voltage data in literature. The electrolyzer’s reactions and ion transport were analyzed based on the electrolyte potential, concentration profiles, and ion fluxes calculated by the model. The charge imbalance due to the consumption of H+ and OH- in the catholyte and anolyte, respectively, is addressed by Na+ transport from the anolyte to the catholyte. This contradicts the prevailing hypothesis that electroneutrality in a proton exchange membrane acid-alkaline electrolyzers is preserved by the Second Wien Effect, or water splitting in high electric fields. H+ is transported from the catholyte to the anolyte, which results in undesired acid-base neutralization. This is minimized by increasing the applied voltage, which shows a tradeoff between power and reactant consumption. Na+-selective membranes also hinder the neutralization reaction, but their realization is challenging due to the smaller Stokes radius of H+. The proposed model can be used to optimize the parameters of a batch electrolyzer and aid in the design of a continuous electrolyzer stack.

With the rising energy demand, safe and efficient energy storage technologies have been increasing in importance. Lithium Polymer (LiPo) batteries use a gel polymer to act as both separator and electrolyte, which is thermally and electrochemically more stable and safer than conventional liquid electrolytes. Besides exploring new materials, engineering a reliable multiphysics model is vital to exploiting and optimizing existing LiPo batteries' potential. This study developed a multiphysics model for a Lithium Cobalt Oxide (LCO)-graphite- Poly(vinylidene fluoride - hexafluoropropylene) (PVdF-HFP) pouch-type mobile phone LiPo. A pseudo-2-dimensional electrochemical model was coupled with a 3D thermal model using COMSOL Multiphysics® to determine the working voltage and temperature during discharge and was compared with experimental data from a commercial LiPo battery and evaluated using Root Mean Square Error (RMSE). The simulated discharge curve agrees remarkably well with the experimental results. The simulated temperature profile has shown appreciable discrepancies primarily due to the generated entropic change coefficient values that significantly affect the battery's heat generation. Overall, the models can be employed as a design tool to evaluate the component design and estimate the system performance of LiPo batteries for commercial applications. Furthermore, researchers can expand the study to investigate more advanced electrochemical phenomena and performances of state-of-the-art lithium and post-lithium chemistries.

A new class of technology known as transient electronics has the potential to reduce electronic waste with its unique ability to dissolve under a specific condition. Biodegradable batteries are essential to realizing fully integrated and self-sufficient systems. Pure magnesium-based materials are among the widely explored transient electrodes due to their superior mechanical properties. However, such materials have rapid degradation rates. AZ31, an alloy of Mg containing 3 % aluminum (Al) and 1 % zinc (Zn) by weight, has more stable corrosion product layers, making them advantageous as anode materials. In this work, the electrochemical performance of the two full cell combinations of Mg and AZ31 anodes each paired with copper oxide (CuO) cathode were compared in a seawater electrolyte. To produce the cathodes, pristine copper foils were galvanostatically anodized at a current density of 2.0 mA/cm2 for 90 min. Results showed that AZ31-based cell has a higher specific energy of 1.63 J/cm2 compared to pure Mg-based cell which has 1.08 J/cm2. The observed nominal operating voltages for an AZ31-based cell were 1.1 V for the first 0.6 h and remained at 0.8 V for the next 5.6 h when discharged at a current density of 0.25 mA/cm2. The AZ31-based cell also yields a better specific capacity of 2.17 mAh/cm2 and a discharge time of 8.68 h, which is twice the capacity and the discharge life of a pure Mg-based cell. The reported increase in performance is attributed to the presence of alloying components in the anode which limits the parasitic corrosion inherent in a pure Mg anode.

The renewable energy transition requires energy storage technologies for grid-balancing and transportation. Lithium-ion batteries have been widely adopted for these applications, but supply risks due to geopolitical tensions have motivated the search for alternative chemistries less dependent on critical raw materials. Sodium-ion batteries have garnered notable attention as promising post-lithium chemistry due to the relative abundance of sodium and its similar manufacturing process to lithium-ion batteries. This work estimated the cost of producing sodium-ion battery packs from cells optimized via multiphysics modeling for energy or power-based applications. This study replicated a multiphysics model of a pouch format sodium-ion battery from literature in COMSOL Multiphysics®. This model determined the optimal active material used in batteries under 0.1C to 10C discharge rates to maximize the energy density. The cost of battery packs produced from the optimized cells was then determined using the Battery Performance and Cost (BatPaC) model of Argonne National Laboratory, which considers material and manufacturing costs. The optimization results reveal that energy cells have thicker electrodes and lower porosities (217 µm thick 0.11 porosity anode, 237 µm thick 0.10 porosity cathode for 0.1C), which maximize the amount of active material per unit mass. Power cells have thinner electrodes and larger porosities to minimize electrical resistance (58 µm thick 0.32 porosity anode, 63 µm thick 0.31 porosity cathode for 10C), reducing energy losses at high currents. Moreover, we compared the calculated production cost for energy and power applications for sodium-ion batteries, highlighting essential parameters affecting the price. The model observed a 26.42% increase in total material cost per kWh when transitioning from energy to power cells. The model may also be refined by considering sodium-ion batteries with different cathode and anode chemistries in different formats and their applications in different use cases.

The shipping industry is vital for archipelagic countries like the Philippines as they allow transport between islands, but it is a notable contributor of greenhouse gases. In this work, a framework for analyzing the techno-economic potential of hybridizing a sea vessel with solar photovoltaics, lithium-ion batteries, and diesel generators was presented. The roll on/roll off vessel Filipinas Ozamis was considered as a case study due to its commercial use. A 3D model of the roll-on/roll-off vessel was used to measure the ship’s dimensions. The load profile of the vessel was estimated from the ship’s dimensions, operational profile, route, and speed according to the MarineTraffic AIS database. Afterwards, diesel-only and hybrid energy systems were sized in HOMER Pro to power the electrified ship while minimizing its costs and noting the available space on the sea vessel. Lastly, the profitability of the hybrid energy system was determined. The hybrid system was marked with increased capital costs, but the fuel consumption and CO2 emissions were 18.50 % and 27.90 % lower than those of the diesel-only system, respectively. The hybrid system also had lower generation costs and 23.64 % higher net present value than the diesel-only system. This framework can be used in the absence of measured load profiles and can be extended to other sea vessels to conduct a national techno-economic assessment of hybridizing the country’s maritime industry.

The Philippines is home to thousands of off grid islands that are too distant from the mainland and consequently expensive to connect to the main grid. These islands are typically powered by diesel generators, which will require more subsidies as fuel costs continue to increase. Hybrid renewable energy systems (HRES) are an alternative energy source with lower reliance on fuel and generation costs. In this work, the financial sustainability of deploying HRES in Philippine off grid islands of various sizes was evaluated. Patongong Island, Lapinigan Island, Balabac Island, and Sibuyan Island were selected as case studies as their peak electrical demand varies from 4.4 kW in Patongong Island to 3.2 MW in Sibuyan Island, representing a large fraction of off-grid islands in the country. HRES consisting of solar photovoltaics, wind turbines, lithium ion batteries, and diesel generators in these islands were modeled in Island Systems LCOEmin Algorithm (ISLA), an in house energy systems modeling tool. Profitability metrics, such as the net present value, internal rate of return (IRR), and payback period (PBP), were then calculated at varying electricity prices. The large Sibuyan island was already profitable at 0.2 USD/kWh, comparable to the mainland rate, which suggests that subsidies in large islands can be removed. The low 11 % IRR and 13 year PBP may not be attractive to private investors, but this may be alleviated by raising electricity prices. Other islands, however, will still require subsidies as the small Patongong Island becomes profitable only at 1.5 times the mainland rate. This work encourages private sector participation by providing financial insights absent in many techno economic studies. Moreover, this study enlightens the public sector about the necessity of subsidies for providing energy access in small off grid islands.

The archipelagic geography of the Philippines has resulted in thousands of inhabited off grid islands wherein energy and water access are hindered. Electricity in these islands is provided by diesel generators, while water is obtained from rainwater, groundwater, or imports from the mainland. As an alternative to these costly and unsustainable sources, hybrid renewable energy systems (HRES) coupled with reverse osmosis (RO) desalination has been investigated for the cogeneration of electricity and water. In this work, the techno economic potential of coupled RO HRES in selected off grid areas in the Philippines was evaluated. Solar photovoltaics, wind turbines, lithium ion batteries, and diesel generators were considered as HRES components. Dumaran Island and Bantayan Island were chosen as case studies because both islands have faced electricity and water supply concerns. Moreover, these islands present different use cases as Dumaran Island is mainly residential while Bantayan Island has a growing resort industry. Three scenarios were considered in this work to analyze the transition from the status quo to the energy water system: diesel only, HRES, and RO HRES. The islands and scenarios were modeled using the Island Systems LCOEmin Algorithm, an in house energy systems modeling tool written in Python 3. Transition from the diesel only system to HRES decreased the system costs by an average of 15.1 % in both islands. However, the installation of RO was primarily beneficial for the resort island wherein electricity costs increased by only 0.8 % from the HRES scenario and water costs were 80 % lower than current rates in surrounding islands. In contrast, RO deployment in the residential island raised electricity prices by 10.8 %.

Battery energy storage systems are essential for stabilizing the intermittent power generation of renewable energy (RE) technologies. Their integration into RE systems is typically studied using energy systems modeling software that utilize either idealized models or complex models that require experimental data. Reduced order modeling offers minimal experimental costs through the use of a multiphysics model in lieu of experimental battery data. In this work, a previously reported multiphysics model of a lithium ion lithium iron phosphate (Li ion LFP) battery was simulated in COMSOL Multiphysics® and reduced into an equivalent circuit model (ECM). The reduced order ECM was then implemented as a battery systems model in an energy systems modeling tool to perform RE-based hybridization studies. Techno economic case studies were conducted on RE based systems powering a household and an off grid island to validate the reduced order ECM with the idealized battery model with HOMER Pro. Optimal component sizes computed using the two software generally showed good agreement and deviations were attributed to electrical losses. The state of charge (SOC) vs. time graphs generated by the two software had an average root mean square error of 0.00173 SOC units across the different case studies. Discrepancies were observed during rapid charging or high SOC values, which were characteristic of the reduced order ECM. This model reduction framework can be applied to other energy storage and conversion technologies, such as other Li ion chemistries, fuel cells, and supercapacitors, to generate chemistry specific models for energy systems research.

Rational design of new and cost-effective advanced batteries for the intended scale of application is concurrent with cathode materials development. Foundational knowledge of cathode materials’ processing–structure–properties–performance relationship is integral. In this review, we provide an overview of borate-based compounds as possible mixed polyanion cathode materials in organic electrolyte metal-ion batteries. A recapitulation of lithium-ion battery (LIB) cathode materials development provides that rationale. The combined method of data mining and high-throughput ab initio computing was briefly discussed to derive how carbonate-based compounds in sidorenkite structure were suggested. Borate-based compounds, albeit just close to stability (viz., −1), offer tunability and versatility and hence, potential effectivity as polyanion cathodes due to (1) diverse structures which can host alkali metal intercalation; (2) the low weight of borate relative to mature polyanion families which can translate to higher theoretical capacity; and a (3) rich chemistry which can alter the inductive effect on earth-abundant transition metals (e.g., Ni and Fe), potentially improving the open-circuit voltage (OCV) of the cell. This review paper provides a reference on the structures, properties, and synthesis routes of known borate-based compounds [viz., borophosphate (BPO), borosilicate (BSiO), and borosulfate (BSO)], as these borate-based compounds are untapped despite their potential for mixed polyanion cathode materials for advanced batteries.

The cost of unsubsidized electricity in off-grid areas, particularly in the islands dependent on fossil fuels, is expensive. Previous studies and recent installations have proven that renewable energy-based hybrid systems could be suitable alternative to diesel power plants in island grids. In this comprehensive analysis of small island grids in the Philippines, results show that there is a huge economic potential to shift the diesel generation to solar photovoltaics-battery-diesel hybrid systems, with an average cost reduction of around 20% of the levelized cost of electricity. By encouraging private sector participation, hybridization could help provide electrification for twenty-four hours, stabilize the true cost of generation rate with less dependence on imported diesel prices, and reduce greenhouse gas emissions. Further, the declining cost of solar modules and batteries will significantly improve the economics of energy transition in the island grids.

Renewable energy (RE) utilization is expected to increase in the coming years due to its decreasing costs and the mounting socio-political pressure to decarbonize the world’s energy systems. On the other hand, lithium-ion (Li-ion) batteries are on track to hit the target 100 USD/kWh price in the next decade due to economy of scale and manufacturing process improvements, evident in the rise in Li-ion gigafactories. The forecast of RE and Li-ion technology costs is important for planning RE integration into existing energy systems. Previous cost predictions on Li-ion batteries were conducted using conventional learning curve models based on a single factor, such as either installed capacity or innovation activity. A two-stage learning curve model was recently investigated wherein mineral costs were taken as a factor for material cost to set the floor price, and material cost was a major factor for the battery pack price. However, these models resulted in the overestimation of future prices. In this work, the future prices of Li-ion nickel manganese cobalt oxide (NMC) battery packs - a battery chemistry of choice in the electric vehicle and stationary grid storage markets - were projected up to year 2025 using multi-factor learning curve models. Among the generated models, the two-factor learning curve model has the most realistic and statistically sound results having learning rates of 21.18% for battery demand and 3.0% for innovation. By year 2024, the projected price would fall below the 100 USD/kWh industry benchmark battery pack price, consistent with most market research predictions. Techno-economic case studies on the microgrid applications of the forecasted prices of Li-ion NMC batteries were conducted. Results showed that the decrease in future prices of Li-ion NMC batteries would make 2020 and 2023 the best years to start investing in an optimum (solar photovoltaic + wind + diesel generator + Li-ion NMC) and 100% RE (solar photovoltaic + wind + Li-ion NMC) off-grid energy system, respectively. A hybrid grid-tied (solar photovoltaic + grid + Li-ion NMC) configuration is the best grid-tied energy system under the current net metering policy, with 2020 being the best year to deploy the investment.

The industrial sector is a major contributor to the economic growth of the Philippines. However, it is also one of the top consumers of energy, which is produced mainly from fossil fuels. The Philippine industrial sector must therefore be supported economically while minimizing the emissions associated with energy consumption. A potential strategy for minimizing costs and emissions is the installation of solar photovoltaic (PV) modules on the rooftops of industrial facilities, but this approach is hindered by existing energy policies in the country. In this work, we performed a techno-economic assessment on the implementation of rooftop solar PV in Philippine industrial facilities under different policy scenarios. Our study considered 139 randomly sampled industrial plants under MERALCO franchise area in the Philippines. Under the current net metering policy, 132 of the evaluated facilities were economically viable for the integration of rooftop solar PV. This corresponds to an additional 1035 MWp of solar PV capacity and the avoidance of 8.4 million tons of CO2 emissions with minimal financial risk. In comparison, an expanded net metering policy supports the deployment of 4653 MWp of solar PV and the avoidance of 38 million tons of CO2. By enabling an enhanced net metering policy, the widespread application of rooftop solar PV may present considerable savings and emission reduction for energy-intensive industries (electrical and semiconductors, cement and concrete, steel and metals, and textile and garments) and lower generation costs for less energy intensive industries (construction and construction materials, transportation and logistics, and food and beverages).

Several confirmed cases of arsenic (As) poisoning have been reported in Central Luzon, the Philippines, in recent years. There is a growing interest in As research in the Philippines due to the reported As poisoning cases. However, an extensive spatiotemporal As study has not been conducted. In this work, As concentration measurements were conducted in 101 wells in Guagua, Pampanga, in Central Luzon, the Philippines, from November 2018 to November 2019. The wells included 86 public hand pumps, 10 pumping stations, and 5 private, jet-powered pumps. Using hydride generation—inductively coupled plasma—optical emission spectroscopy (HG-ICP-OES), analysis of the wells in 12 barangays in Guagua revealed that 38.7% had average As concentrations beyond the 10 ppb limit with some wells having high Mn (4.0 ppm) and Fe (2.0 ppm) content as well. The high pH and reducing conditions in the wells in Guagua may have contributed to the persistence of As in the groundwater. The mean difference in wet season versus dry season As measurements were −4.4 (As < 10 ppb), −13.2 (10 to 50 ppb As), and −27.4 (As > 50 ppb). Eighty-three wells (82.2%) had higher As concentrations in the dry season, 8 wells (7.92%) had higher As concentrations in the wet season, 7 wells (6.93%) had no significant difference between the wet and dry season, and 3 wells had been decommissioned. These results indicate that there is a significant difference in As concentrations in the wet and dry seasons, and this could have implications in water treatment technology and policy implementation. The work resulted in the first year-long characterization of groundwater As in the Philippines.

Arsenic (As) is a naturally occurring element in the environment that poses significant risks to human health. Several treatment technologies have been successfully used in the treatment of As-contaminated waters. However, limited literature has explored advanced electrocoagulation (EC) processes for As removal. The present study evaluates the As removal performance of electrocoagulation, electrochemical peroxidation (ECP), and photo-assisted electrochemical peroxidation (PECP) technologies at circumneutral pH using electroactive iron electrodes. The influence of As speciation and the role of oxidants in As removal were investigated. We have identified the ECP process to be a promising alternative for the conventional EC with around 4-fold increase in arsenic removal capacity at a competitive cost of 0.0060 $/m3. Results also indicated that the rate of As(III) oxidation at the outset of electrochemical treatment dictates the extent of As removal. Both ECP and PECP processes reached greater than 96% As(III) conversion at 1 C/L and achieved 86% and 96% As removal at 5 C/L, respectively. Finally, the mechanism of As(III) oxidation was evaluated, and results showed that Fe(IV) is the intermediate oxidant generated in advanced EC processes, and the contribution of •OH brought by UV irradiation is insignificant.

Philippine off-grid islands are mostly electrified by diesel generators, resulting in costly electricity that is interrupted by fuel supply disruptions. The archipelagic nature of the country also impedes off-grid electrification due to the high capital cost of grid extension. Transitioning from diesel-only systems to hybrid renewable energy systems and interconnecting the island microgrids can solve these problems while promoting cleaner energy production. In this work, a comparative study on decentralized and clustered hybrid renewable energy system microgrids in the Polillo group of islands in the Philippines, using HOMER Pro, was performed. Microgrids comprising solar photovoltaics, lithium-ion battery energy storage, and diesel generators were designed on each island. Clustered systems encompassing multiple islands in the island group were simulated by also considering the least-cost interconnection paths. The techno-economics of each decentralized or clustered system and the four-island system were evaluated based on the levelized cost of electricity (LCOE). Reliability was assessed using the change in LCOE upon the failure of a component and during weather disturbances. Transitioning from diesel-only systems to hybrid systems reduces generation costs by an average of 42.01% and increases the renewable energy share to 80%. Interconnecting the hybrid systems results in an average increase of 2.34% in generation costs due to the cost of submarine cables but improves system reliability and reduces the optimum solar photovoltaic and lithium-ion storage installations by 6.66% and 8.71%, respectively. This research serves as a framework for the interconnection pre-feasibility analysis of other small off-grid islands.

The electrooxidation kinetics of ethanol is key to making direct ethanol fuel cells and electrocatalytically reforming ethanol viable technologies for a more sustainable energy conversion. In this study, the electrooxidation of ethanol was investigated on nickel hydroxide (Ni(OH)2) catalysts synthesized using a facile solvothermal method. Variations in the temperature, heating time, and the addition of oleylamine in the precursor enabled the phase and morphology control of the catalysts. X-ray diffraction and scanning electron microscopy show that the addition of oleylamine in the precursor resulted in microspheres with a high surface area, but favored the formation of β-phase Ni(OH)2. Elevated temperatures or prolonged periods of heating in a controlled environment, on the other hand, can lead to the formation of the ethanol oxidation reaction-active α-phase. Among the synthesized catalysts, the α-Ni(OH)2 microspheres with nanoflakes achieved the highest activity for ethanol oxidation with a current density of 24.4 mA cm−2 at 1.55 V (vs. RHE, reversible hydrogen electrode) in cyclic voltammetry tests and stable at 40 mA cm−2 in chronoamperometric tests at the same potential, comparatively higher than other Ni-based catalysts found in the literature. While the overpotential is beyond the useful range for direct ethanol fuel cells, it may be useful for understanding the mechanism of ethanol oxidation reactions on transition metal hydroxides at their oxidizing potential for ethanol electroreforming.

Sluggish kinetics in oxygen reduction reaction (ORR) requires low-cost and highly durable electrocatalysts ideally produced from facile methods. In this work, we explored the conversion and utilization of waste biomass as potential carbon support for α-MnO2 catalyst in enhancing its ORR performance. Carbon supports were derived from different waste biomass via hydrothermal carbonization (HTC) at different temperature and duration, followed by KOH activation and subsequent heat treatment. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), energy dispersive X-ray spectroscopy (EDX) and X-Ray diffraction (XRD) were used for morphological, chemical, and structural characterization, which revealed porous and amorphous carbon supports for α-MnO2. Electrochemical studies on ORR activity suggest that carbon-supported α-MnO2 derived from HTC of corncobs at 250 °C for 12 h (CCAC + MnO2 250-12) gives the highest limiting current density and lowest overpotential among the synthesized carbon-supported catalysts. Moreover, CCAC + MnO2 250-12 facilitates ORR through a 4-e‑ pathway, and exhibits higher stability compared to VC + MnO2 (Vulcan XC-72) and 20% Pt/C. The synthesis conditions preserve oxygen functional groups and form porous structures in corncobs, which resulted in a highly stable catalyst. Thus, this work provides a new and cost-effective method of deriving carbon support from biomass that can enhance the activity of α-MnO2 towards ORR.

Energy poverty or lack of access to electricity is still a pressing development challenge worldwide particularly in archipelagic countries like the Philippines and Indonesia. As rural electrification in these remote areas through on-grid extension becomes costly, these communities typically resort to diesel-powered off-grid generators, characterized by high operating costs, unstable supply and price of fuel, and environmental issues. The deployment of clean energy alternatives is clearly needed, but these must be selected systematically using multiple but possibly conflicting criteria. Currently, the decision on which technology to use is derived based on the levelized cost of electricity primarily. In this illustrative case study, a novel multi-criteria decision-making methodology is proposed for the selection of the most appropriate energy system for the off-grid electrification of Marinduque Island. Eight technology combination options were evaluated using six criteria covering socio-economic, environmental, and technical aspects. Fuzzy AHP was used to derive weights of the criteria while addressing ambiguity and subjectivity of decision-makers. Performances of the technology options across different criteria were determined quantitatively via techno-economic simulations, or qualitatively via domain expert estimates. Grey Relational Analysis (GRA) was then used to aggregate the entire range of performance attributes of each alternative into a single score. Results indicate that system reliability and social acceptability are the most important criteria in selecting hybrid energy systems for off-grid electrification. Among the eight alternatives, the fuel saver (diesel-solar PV hybrid) and diesel-solar PV-Li-ion hybrid systems yield the highest performance scores. The prioritization was mainly affected by system reliability and social acceptability, indicating that decision making for the attainment of sustainable island energy supply should not be limited to technical and economic considerations only. This is consistent with the current worldwide trend of implementing the diesel-solar PV-Li-ion hybrid systems in off-grid areas, thus, validating the applicability of the facile methodology developed in this work.