Background: A cost-effective solution for the design of distributed energy storage systems implies the development of battery performance models yielding a suitable representation of its dynamic behaviour under realistic operation conditions. Methods: In this work, a lithium-ion battery (LIB) is tested to be further modelled and integrated into an existing energy management control system. This specific LIB (5.0 kW /9.8 kWh) is integrated with a commercial inverter and solar photovoltaic (PV) system (3.3 kWp) as part of a microgrid that is also encompassing other storage technologies at the University of Évora, Pole of INIESC – National Research Infrastructure for Solar Energy Concentration. The battery and the inverter are fully characterized through the implementation of a testing protocol aiming at better describing the battery performance. Then, a battery model is built upon both the existent LIB description and experimental fitting regression, for real-time predictive optimization control development. Considering the pre-determined efficiency of the inverter, the model allows to obtain the voltage curve, the series resistance (i.e., to describe instantaneous voltage drop/rise and transients), and the state of charge (SOC) and/or energy capacity, based on the current input. The developed model is validated through the comparison with the experimental results. Results: In discharge state, the model approach presented a higher voltage RMSE (root mean square error) of 5.51 V and an MRE (maximum relative error) of 5.68 %. Regarding SOC the MRE obtained was approximately 6.82 %. In charge state, the highest RMSE voltage was 5.27 V, with an MRE of 6.74 %. Concerning SOC, the MRE obtained was approximately 6.53 %. Conclusions: The developed setup allowed us to perform the necessary characterization tests under real operating conditions. Based on computational effort, simplicity of use, and the associated model error compared with the experimental data, generally, the model describes the battery behaviour.

This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.

The global transition toward a low-carbon economy has accelerated the adoption of renewable energy sources. This paper presents the development of a model-based electronic Decision Support System for renewable energy planning, incorporating energy storage and carbon footprint assessment. The tool assists stakeholders in the preliminary evaluation of local wind and solar resources. To validate the model’s credibility, a comparative analysis was conducted, using the Port of Sines, Portugal, as an industrial case study. Solar energy estimations were benchmarked against PVSyst, while wind energy simulations were compared with an INEGI technical study. Results indicate consistency in solar estimates, with maximum deviations of 14% for fixed installations and 13% for vertical barriers, primarily due to terrain orography that was not yet integrated into the algorithm. Regarding wind energy, deviations reached 19% to 25%, largely resulting from the use of aggregated mean values in the reference data and generic turbine models. Overall, this work contributes to energy engineering by formalizing a validated workflow that facilitates early-stage sizing and strategic investment decisions under conditions of data scarcity. The tool proves effective for rapid screening of promising investment options while maintaining a balance between computational complexity and practical usability.

Background: A cost-effective solution for the design of distributed energy storage systems implies the development of battery performance models yielding a suitable representation of its dynamic behaviour under realistic operation conditions. Methods: In this work, a lithium-ion battery (LIB) is tested to be further modelled and integrated into an existing energy management control system. This specific LIB (5.0 kW /9.8 kWh) is integrated with a commercial inverter and solar photovoltaic (PV) system (3.3 kWp) as part of a microgrid that is also encompassing other energy storage technologies at the University of Évora, Pole of INIESC – National Research Infrastructure for Solar Energy Concentration. A testing protocol fully characterizes the battery and the inverter efficiency to describe their performance better. Then, a battery model is built upon both the existent LIB description and experimental fitting regression. The model allows to obtain the voltage curve, the internal resistance (i.e., to describe instantaneous voltage drop/rise and transients), and the state of charge (SOC) and/or energy capacity based on the current input. The developed model is validated through the comparison with the experimental results. Results: The model approach presented a higher voltage RMSE (root mean square error) of 5.51 V and an MRE (maximum relative error) of 5.68 % in the discharge state. Regarding SOC, the MRE obtained was approximately 6.82 %. In the charge state, the highest RMSE voltage was 5.27 V, with an MRE of 6.74 %. Concerning SOC, the MRE obtained was approximately 6.53 %. Conclusions: The developed model is validated through the comparison with experimental results. Based on computational effort, simplicity of use and the associated model error, the approach is validated to the regular conditions of the commercial battery pack to be incorporated in the next research step, following a bottom-up modelling approach for an increasingly more complex smart grid.

Hybrid energy storage systems (HESS) combine different energy storage technologies aiming at overall system performance and lifetime improvement compared to a single technology system. In this work, control combinations for a vanadium redox flow battery (VRFB, 5/60 kW/kWh) and a lithium-ion battery (LIB, 3.3/9.8 kW/kWh) are investigated for the design of a HESS. Four real-time energy management/power allocation scenarios are considered for the operation of the hybrid storage solution through a 15-year economic and energetic analysis. The results obtained for each scenario are compared with a single technology battery performance. In the definition of the scenarios, real electricity generation is considered from two solar photovoltaic installations (3.2 kWp and 6.7 kWp) and an estimated representative load of a services building. HESS performance is evaluated through specific energy and economic key performance indicators. The results obtained indicate that the use of customized energy management strategies (EMSs) renders the VRFB and LIB characteristics complementary, besides enhancing the competitiveness of VRFB, as a single technology. Moreover, the HESS management impacts the seasonality factor, contributing to the overall electric system smart management.