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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].

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.

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.

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.

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.