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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.

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.

Currently, a wide variety of energy storage alternatives are available, each with a unique set of characteristics advantageous on selective applications. Current studies focus only on levelized costs on predicting the best-fit technology for specific applications. The study addresses this limitation by considering multiple factors on the selection process among technologies for specific applications. A systematic approach on the selection of energy storage technologies based on multiple and possible conflicting factors was proposed in this study for two specific applications: frequency regulation and load levelling. Fuzzy Analytic Hierarchy Process was utilized to generate the relative importance of each criterion. Monte Carlo simulations were performed to reflect the effect of battery characteristics and operating parameters uncertainties on the resulting scores of technologies. Grey Relational Analysis was used to aggregate the performance attributes of alternatives into a single score reflecting the desirability of alternatives. The levelized costs dominated all other criteria for both applications. Lithium ion battery dominated all technologies for both applications resulting from its well-rounded performance across all considered attributes. Results emphasized the importance of considering socio-economic indicators alongside techno-economic parameters on selecting the technology for future deployment. Thorough analysis on the results is important not only for decision-makers but for developers and innovators as well to direct future research.

The Philippines has many off-grid areas relying on diesel generators for energy access, but have high greenhouse gas emissions, high electricity costs, and intermittent operation. An opportunity to decarbonize the energy system of off-grid islands is by harnessing both solar photovoltaic (PV) and wind power. This work evaluates the techno-economic viability of putting up solar PV-wind-battery-diesel hybrid energy systems in 143 existing off-grid island areas operated by the National Power Corporation-Small Power Utilities Group (NPC-SPUG) using HOMER® Pro. The application obtains the optimal system component sizes with the least levelized cost of electricity (LCOE). The results suggest that there are 137 islands capable of using both solar PV and wind generation, 4 islands using solar PV only, and 2 islands using wind only. The hybrid energy systems in the sample islands require USD 774,171,061 (~ PHP 40,643,980,682) worth of investment cost with potential annual savings of USD 132,403,163 (~ PHP 6,951,166,051). The resulting system capacities and their corresponding LCOEs suggest high sensitivity towards wind potential due to lower capital cost of wind and potential higher energy share up to 58.47 %. Wind generation for off-grid islands should be considered alongside solar PV, especially in areas with high wind potential, to provide reliable energy access and reduce greenhouse gas emissions.

Providing water supply in off-grid islands is difficult due to remoteness and high logistics cost. Despite interest in providing energy sustainability in these areas, there is relatively lesser interest in coupling it with sustainable water access. One of the possible solutions is through the use of reverse osmosis (RO) technology for desalination since it has a low energy requirement and high throughput. In this work, the techno-economic viability of incorporating desalination units was elucidated as demand-side management in different dispatch algorithm, accounting water-energy nexus. Different water-energy system configurations were optimized and simulated using ISLA, an open-source microgrid optimizer. Results suggest the viability of installing desalination units with a minimum-level dispatch algorithm yielding the lowest levelized cost of water (LCOW) with only minimal increase in the levelized cost of electricity (LCOE).

The Philippine Industrial Sector contributes USD 124x109 (~PHP 6.5x1012) or about 1/3 in the economy. However, the electricity cost, which is 2nd highest in Asia, constitutes up to 10 % of their total operating expenses. This hinders foreign direct investment to the country. Solar photovoltaic grid-tied hybrid energy systems are one of the emerging ways to reduce electricity expenses of the industrial sector. Current net-metering policy, which enables grid-tied systems, restricts the export of energy to the grid up to 100 kWp with compensation equal to the average generation rate of the distribution utility. This work evaluates the techno-economic viability of putting up solar photovoltaic grid-tied hybrid energy systems for 66 randomly selected industrial establishments classified under electrical/electronics/semiconductors, steel/metal, food/beverages, transportation/logistics and textile/garment sub-sectors using Island System LCOEmin Algorithm (ISLA). ISLA will provide the optimal system component sizes of solar photovoltaic and battery in the least levelized cost of electricity (LCOE) by performing hourly calculations for one reference year using actual load profiles. The results suggest 63 out of 66 sample industrial establishments are viable to put up solar photovoltaic grid-tied hybrid energy systems, with a total solar photovoltaic capacity of 783 MWp. There are 7 establishments that are capable of off-grid solar photovoltaic-battery-diesel configuration. If export restriction in net-metering policy is lifted, the total solar photovoltaic potential will significantly increase up to 3,947 MWp, which corresponds to LCOE reduction to USD 0.14 (~PHP 7.2) per kWh and increase in renewable energy share to 34 %. This work shows that tapping solar rooftop potential and amending the net-metering policy increases operational savings of the Philippine industrial sector.

The energy storage deployment becomes necessary as more renewable energy sources are being installed to achieve sustainable energy access in off-grid areas. Battery prices, however, still hinder massive deployment. One of the energy storage technologies being developed for microgrid applications are flywheels, which stores energy through rotational kinetic energy and are typically suited for high power applications. With the advent of long-discharge flywheels, such as those being marketed by Amber Kinetics® and Beacon Power®, they can be used in microgrids, which are dominated by batteries. This study provides a techno-economic comparison with sensitivity analysis between long-discharge flywheel and utility-scale lithium-ion battery for microgrid applications. The results show lowest levelized cost of electricity (LCOE) for flywheel-based hybrid energy system with 0.345 USD/kWh and renewable share of 62.4 % among tested configurations. The competitiveness of long-discharge flywheel over lithium-ion battery in the microgrid market depends on the diesel prices, expected reduction in lithium-ion battery prices, and improvements in lithium-ion battery lifespan.

In an effort to expedite the electrification in off-grid areas in the Philippines, the Qualified Third Party (QTP) scheme encourages private sector to engage in power generation and distribution business through competitive selection, a process that requires at least two rival bidders with rigorous registration requirements. An exemption is offered for microgrids with sub-100 kW capacity by not undergo competitive selection in order to further attract investors and private sector to engage in these off-grid areas since these areas are deemed highly unviable. The Department of Energy opened around 995 areas waived by electric cooperatives for third party servicing. The sub-100 kW capacity can serve areas with fewer than 500 household connections, which fits the profile of the 995 areas. In this work, the techno-economic feasibility of installation of sub-100 kW microgrids is done in order to know the required level of subsidies, loans, and/or grants to sustainably operate in these areas. The proposed microgrids were evaluated using ISLA, an open-source microgrid optimizer validated by HOMER Pro, by finding the optimal system component sizes of solar PV, battery, and diesel generators with the least levelized cost of electricity (LCOE). Initial results suggest initial investment cost for the establishment of 15 sub-100 kW microgrids ranged from USD 0.5 to 1 M (~PHP 25 to 55 M), with LCOE averaged at PHP 10.26/kWh. This corresponds to 30 % reduction relative to the LCOE from using diesel generator only. Strategies such as partial financing and full grant of capital expenditures show that the former can provide generation rates at par with typical generation rates of existing electric cooperatives at ~PHP 5 to 6 per kWh, while full subsidy can significantly reduce the generation cost to PHP 2 to 3 per kWh. Providing long term and low interest rates from financial institutions to fund these projects will help hasten the deployment of sub-100 kW microgrids. To achieve financial sustainability in these areas, productive use of energy through income generating projects should be highly encouraged in order to give the inhabitants the capacity to pay.

Due to the rapidly declining costs of solar photovoltaic (solar PV) modules and batteries, the possibility of defecting from the grid is starting to become an alternative for some consumers. Should many consumers defect from the grid, given the current rate structures, electricity prices would increase even faster which will further encourage more people to defect from the grid. This positive feedback loop has been called the “utility death spiral”. Previous grid defection studies were conducted in the United States, Australia, as well as some countries in Europe. In this work, the technical feasibility and economic viability of grid parity and defection were determined for residential customers in the major cities of the Philippines (Manila, Cebu and Davao) based on the franchise areas of Manila Electric Company, Visayan Electric Company, and Davao Light and Power Company. The grid defection analysis was divided into customer clustering, levelized cost of electricity (LCOE) calculation, and finally grid parity comparison. Three main clusters were identified based on the k-means clustering by utilizing 18 different features in order to get a more detailed overview on how many customers of each type are more likely to defect based on the representative load profiles from MERALCO. Average silhouette widths of 0.657, 0.587 and 0.585 were obtained for the three clusters. Based on the clusters, the LCOE of optimally sized solar PV-battery systems were calculated using Hybrid Optimization Model for Multiple Energy Resources Software, from 2018 up to 2050. The LCOE data were then compared to the projected retail electricity prices based on the actual data from the mentioned distribution utilities to find the economic viability of grid defection per customer cluster. Results show that grid parity and defection would be possible for residential customers starting in the next 30 y, with customers from Cebu more likely to defect first followed by Manila and then Davao. Based on the clustering, it was observed that the grid parity occurred earliest in Cluster C, followed by Cluster B, and then Cluster A. Different scenarios were also explored depending on the rate of decrease of local prices of photovoltaics, lithium-ion batteries, and a combination of both. Results show that decreasing battery prices play a bigger role achieving grid parity in the country.

A techno-economic assessment was made for thirteen large off-grid islands in the Philippines using HOMER Pro (Hybrid Optimization Model for Electric Renewables Software) to determine the feasibility of integrating waste biomass into their energy systems. Sensitivity analysis on the diesel fuel prices and biomass feedstock prices was performed to determine their effects on the levelized cost of electricity (LCOE) and the renewable energy (RE) share. The results suggest that an average LCOE reduction of around 4.57 %, fuel reduction of 5.71 %, and RE share increase of 4.99 % can be realized by integrating biomass to the existing diesel system even without incorporation of other renewable energy generators such as solar photovoltaics. In cases where biomass is available in large quantities, and the energy demand is relatively low, LCOE reduction, fuel reduction, and RE share increase may even reach up to more than 20 %. This makes the integrated biomass-diesel hybrid system a viable option for reducing diesel consumption in the off-grid islands. And even with the establishment of a feedstock market, the biomass-diesel hybrid system still has a lower LCOE compared to the existing diesel-only systems. This work provides the first systematic techno-economic study on the potential of incorporating waste biomass in off-grid islands.

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.