This paper shows the technical–economic, operational and environmental feasibility of four off-grid hybrid power systems to supply energy to the Cerrito de los Morreños community in Ecuador. These configurations consist of combinations of diesel generators, solar photovoltaic systems, and battery energy storage systems. Each configuration was simulated and the results were analyzed for two different load conditions: (1) the existing load profile and (2) a reduced load profile by incorporating an energy efficiency initiative. Homer Pro software was used to perform the simulations. The planning horizon for the simulations was selected to be 15 years. The results showed the diesel/photovoltaic/battery configuration with energy efficiency showed the best performance, which was achieved with a photovoltaic system of 160 kWp, the existing generator of 165 kW and a storage system of 283 kWh. The stand-alone diesel generator and photovoltaic/diesel configurations showed higher Net Present Costs, instability problems and higher CO₂ emissions. Additionally, the configurations without energy efficiency had increases of between 15% and 40% higher costs compared to their respective energy-efficient scenarios. The information in this work could be useful for some organizations in Ecuador who are interested in investing in rural electrification projects with renewable energy to reduce and/or compensate their CO₂ emissions.

This study assessed the techno-economic and environmental feasibility of a grid-connected PV system on a university building, with a focus on potential revenue from carbon credit sales. The analysis assumes a regulated CO₂ emissions market in Ecuador and references carbon credit prices from the European Union, New Zealand, China, and the Republic of Korea. Seven PV system configurations, varying in size and capacity, were modeled using Homer Pro and assessed for their techno-economic feasibility and environmental performance. The results indicated that the 166 kWp system was the most promising, supplying approximately 74% of the building’s electricity demand. Thus, this system was selected as the baseline for evaluating potential revenues from carbon credit sales in international markets, based on average carbon prices in 2022. The selected markets generated annual revenues of USD 4410.68, USD 2587.55, USD 446.34, and USD 958.37, respectively. While these additional revenues improved the Net Present Value (NPV) of the 166 kWp system, the overall NPV remained negative due to the high initial investment costs.

This paper models the operation of a Lithium-ion battery pack when undergoing three current cycling profiles. In these scenarios, the battery model is undergoing charge and discharge regimes within applications such as a constant current and constant voltage process, a simulated frequency regulation current profile, and an electric vehicle current profile. The temperature conditions were kept constant. The battery model was developed using an open-source Python package, used default settings for internal resistances and the nominal terminal voltage and capacity of a commercial Li-ion battery pack. The simulation results show the impact of the battery cycling including selected degradation metrics and capacity loss. The battery degradation as a function of cycle number is represented in terms of the change in solid electrolyte interface (SEI) thickness, loss of capacity to SEI, and internal cell resistance. The battery model exercised under the electric vehicle current profile showed a considerable degradation and capacity loss when compared to the other current cycling profiles. In addition, the model undergoing the constant current and constant voltage profile presented the lowest degradation based on the analyzed metrics.

The telecommunications industry relies on a stable power supply for continuous operation. When grid reliability is uncertain, energy storage systems are often used as backup power. Lithium-ion (Li-ion) batteries have become the preferred choice for this purpose due to their high energy density, rapid power delivery, and minimal self-discharge. These advantages make them particularly suitable for the growing energy demands of data centers. Ensuring the long-term reliability of Li-ion batteries in telecom applications requires understanding their capacity degradation, particularly under deep discharge cycling. However, experimentally assessing degradation under varying conditions requires extensive cycling, making the process time-consuming and resource-intensive. Developing a predictive model for battery lifespan provides a practical alternative. This study builds upon previous research in which Li-ion cells were characterized using electrochemical impedance spectroscopy. Charge-discharge data from a deep discharge profile (90%–0% SOC) at a C/2 rate under ambient conditions, performed four times daily to simulate telecom applications with unreliable grid electricity, is used to develop an electrochemical model through parameter estimation in COMSOL v6.2. In many cases, detailed information required for physical modeling is unavailable due to experimental constraints (e.g., lack of specialized measurement equipment) or proprietary restrictions (e.g., undisclosed electrode chemistry). To address these challenges, this study proposes an electrochemical modeling methodology using a limited dataset, including manufacturer specifications (dimensions, nominal capacity, cut-off voltage, and maximum voltage) and experimental charge-discharge curves. A generic 1D isothermal model from the COMSOL library, which assumes standard Lithium manganese oxide (LiMn₂O₄) chemistry, serves as the starting point, followed by a sensitivity study to assess the influence of various parameters on charge-discharge behavior. An iterative parameter estimation approach is then employed to refine the model which integrates the electrical behavior of the cell, mass conservation, mass transport, and reaction kinetics (P2D electrochemical model). This methodology addresses a gap in the literature, as most Li-ion battery models focus on lower-capacity cells (< 25 Ah), whereas this study examines a high-capacity (115 Ah) battery designed for telecom applications. Initial results successfully replicated the first charge cycle of the experimental data, demonstrating that the model can capture the essential electrochemical behavior even without the actual battery parameters. While the first cycle has been validated, further cycles and additional experimental data will also be used for further validation and model refinement. This successful replication provides a promising validation, laying a solid foundation for future improvements. Future work will focus on enhancing the model’s ability to predict capacity degradation over time and estimate cycle life. Additionally, different battery chemistries will be studied to assess their impact on the model’s performance.

Energy storage has been used for backup power in the telecommunications industry, data centers, and other areas where reliable power is required continuously. Depending on the reliability of grid power, the backup battery may be called into play a few times a day or a few times a year. While most backup batteries presently in service are lead-acid batteries, there is a transition in the industry to lithium-ion batteries. They are appealing for numerous grid applications due to their positive attributes including minimal self-discharge, high power delivery capability, high energy efficiency (>90%), and high energy density. Lithium-ion batteries are also becoming widely used as a backup power source for UPS, which are equipment in traditional or edge data centers that guarantee the uptime of mission-critical IT and/or network infrastructure. In the present study, we have been characterizing Li-ion cells designed for telecom applications in regions where grid power is unreliable and so the batteries would be deeply discharged on a regular basis. In this study we cycled a lithium-ion cell, at room temperature through a deep discharge profile four times a day and compared its behavior to three cells stored at three different temperatures (0°C, 25°C, and 45°C). Capacity loss measurements have been made over 650 cycles to date and Electrochemical Impedance Spectroscopy (EIS) measured every 50-100 cycles. The EIS tests were done using the EchemLab XM with a 50V 5A power booster equipment and the XM studio ECS software. The measurements were made in potentionstatic mode with an applied 20 mV rms AC sinusoidal voltage, ranging from 1 kHz to 10 mHz, and measuring the corresponding current response. The collected EIS data was used to develop an equivalent-circuit model for each of the four cells to estimate the impact of cycling and calendar aging on the degradation of the tested Li-ion batteries. Equivalent circuit models have been extracted from the EIS data for the four different cells. A comparison of the mechanisms for degradation of the cells were inferred from the equivalent circuit parameter changes for the cells. This model has considered open-circuit voltages, the state-of-charge dependence, equivalent series resistance, diffusion voltages and Warburg impedance. Early results indicate that among the cells subjected simply to calendar aging (not undergoing any cycling), the highest degradation occurred in the cell stored at 45°C, followed by the cell stored at 0°C, and finally the cell stored at 25°C. When comparing calendar aging with cycling aging, it was observed that the cell cycled for over 650 cycles exhibited a higher capacity than the cell rested at 45°C but a lower capacity than the cell stored under similar conditions at 25°C. Additionally, electrochemical impedance spectroscopy (EIS) results revealed changes in the Nyquist and Bode plots over time for all four cells. Future work will focus on using the collected data to develop a computational tool that can model the performance and estimate the remaining number of cycles for a Li-ion cell in telecom applications where the grid electricity is not very reliable. The model will incorporate different battery parameters such as battery State-of-Charge (SOC) and State-of-Health (SOH), voltage, current, impedance spectra, and cell temperature.