This article investigates factors affecting the contributions of battery units to fault currents in grid-connected battery storage systems (BSSs). The work in this article is intended to examine the effects of the state-of-charge (SOC) on battery currents that are drawn due to faults. This article also examines the impacts of charger controller actions on the currents drawn from battery units to faults in grid-connected BSSs. The impacts of the SOC and charger controller on battery currents due to faults are examined for the lead-acid, lithium-ion, and nickel–cadmium battery units. Examination results show that the battery currents due to faults are directly dependent on the SOC. Moreover, these results show that actions of charger controller can support the battery terminal voltage, thus preventing the fast reduction of the SOC. The support of the battery terminal voltage helps in limiting the currents drawn from battery units during faults. The effects of the SOC and charger controller are verified using a 1-MW, $3\phi$ grid-connected BSS, which has lead-acid battery units. Several faults have been created during charging and discharging operations, and at different values of SOC. Test results confirm the direct dependence of battery currents (due to faults) on the SOC. In addition, obtained results demonstrate the ability of charger controller to limit the currents drawn from battery units due to faults in different parts of a grid-connected BSS.

The introduction of load-side control actions, to implement smart grid functions or integrate distributed generation units, has created a new source for power system dynamic events. Such events can have the capacity to adversely impact the stability in power systems. The growing interests in load-side control actions mandate the analysis and modeling of their contribution to voltage and frequency dynamics in power systems. This paper presents the analysis, development, and testing of a voltage-survivability based method for modeling the contributions of load-side control actions to power system voltage dynamics and stability. The developed method is structured using a voltage-survivability index $\pmb {\Gamma _{V}}$ that is defined at bus in terms of the difference in reactive power injection before and after a load-side control action. The boundary values of the index $\pmb {\Gamma _{V}}$ are derived in order to identify survivable and non-survivable load-side control actions. The voltage-survivability based method is implemented and tested for the Barbados power system. Performance tests are conducted for integrating distributed generation units, as well as implementing demand response at several load buses. Results of conducted tests demonstrate the ability of voltage-survivability based method to accurately model and quantify the impacts of load-sides activities on the bus voltages in the test power system.

Voltage Stability has emerged in recent decades as one of the most common phenomena, occurrence in Electrical Power Systems. Prior researches focused on the development of algorithm indices to solve the stability problem and in the determination of factors with most influence in voltage collapse to solve the stability problem. This paper evaluates the influence that the load dependence has with the voltage on the phenomenon of the voltage stability and especially on the characteristics the collapse point or instability point. Load modeling used is detailed and comparisons of the results obtained are made with those described in the bibliography and those obtained with commercial software. The results of the load margin are also compared when a constant load or a voltage-dependent load is considered as well as the values obtained at the maximum load point and the point of voltage instability.

This study explores advanced multivariate models that incorporate non-climatic exogenous variables for very short-term photovoltaic energy forecasting. By integrating historical energy data from multiple photovoltaic plants, the research aims to improve the prediction accuracy of a target plant while addressing critical challenges in electric power systems (EPS), such as frequency stability. Frequency stability becomes increasingly complex as renewable energy sources penetrate the grid because of their intermittent nature. To mitigate this challenge, precise forecasting of photovoltaic energy generation is essential for balancing supply and demand in real time. The performance of long short-term memory (LSTM) networks and bidirectional LSTM (BiLSTM) networks was compared over a 5 min horizon. Including energy generation data from neighboring plants significantly improved prediction accuracy compared to univariate models. Among the models, multivariate BiLSTM showed superior performance, achieving a lower root-mean-square error (RMSE) and higher correlation coefficients. Quantile regression applied to manage prediction uncertainty, providing robust confidence intervals. The results suggest that incorporating an exogenous power series effectively captures spatial correlations and enhances prediction accuracy. This approach offers practical benefits for optimizing grid management, reducing operational costs, improving the integration of renewable energy sources, and supporting frequency stability in power generation systems.

Changing energy sources in electricity generation from fossil fuels to green sources is a necessity for achieving global development goals. Agricultural residues are an energy source with great potential worldwide, especially sugarcane and rice residues. At the present, technical–economic studies of electricity generation with biomass limit their field of action to generating systems and perform basic analyses of their impact on electricity power systems (EPS). The present work proposes to implement a procedure that expands these limits with the use of methods such as Contractual Path, Quadratic Cost Functions and Newton’s Fast Uncoupled in calculations of electrical losses, increases in initial investment costs, transmission costs and generation costs. The technical–economic indicators of the current EPS under study without and with the biomass power plants proposed by previous studies are compared, knowing that the introduction of these generators increases the installed capacity from 82.6 MW to 122.1 MW. A reduction of 0.72% in active power losses, 13.2% in transmission costs, 6 USD cents/kWh in generation costs and an increase of 34.41 USD/kW in initial investment costs to adequate the EPS under study (Connecting a power transformer and increasing the capacity of an overhead line) to the increase in generation are reported. Benefits of including biomass in the energy transition from a fossil fuel dependent generation matrix (95%) to a mixed one (51%) are demonstrated. These results demonstrate the ability of the procedure to effectively assess the impact of biomass power plant connection on EPSs.

The increase of energy consumption, global warming, resource depletion and the rise of policies focused on climate change and greenhouse gas emissions reductions, have promoted to countries and industries the implementation of strategies focused on increase energy efficiency and reduce GHG emissions. Compressed Air Systems (CASs) are one of the most widespread systems used in industry. In countries such as China, USA, Australia, France and Italy, CASs accounts around 10% of the overall electricity costs. In Colombia the energy used in the industrial sector, rise the 33 % of the total energy consumption, equal to 481.429 TJ/year; the electricity consumption is a 13 % of this value, equivalent to 13,3 TWh/year. This paper determine the potentials energy saving of CASs for two industrial cities of the Colombian Caribbean Coast region, showing that there is a high energy saving potential, around 50 GWh/year and a reduction of CO₂ emissions of 10,702 tons of CO₂/year, which can be taken in consideration by the government and organizations to develop projects focused on reduce energy consumption and mitigate CO₂ emissions.