Search Results (451)

The study underpins the geothermal energy potential of Cambrian reservoirs in Lithuania, which utilizes the use of reactive transport modelling to examine how different reinjection temperatures and flow rates affect mineral changes. The results are then applied to design field development plans, using petroleum engineering methods such as horizontal wells and induced fracturing. The research study indicates that there are some changes in porosity and permeability over time due to mineral dissolution and precipitation because of injection rates, but no adverse effect of re-injection temperature was observed. Hence, a reinjection temperature of 40 °C is geochemically stable and suitable for long-term operation, with no significant effect on mineral behavior. Moreover, application of horizontal wells proves that there is a significant increase in water production and power (thermal) output due to improved reservoir exposure. Hydraulic fracturing further enhanced the performance and flow rates, concluding that, among all the sites studied, Nausodis demonstrated the highest thermal output, while Genciai showed the lowest potential due to limited reservoir temperature and productivity. Thus, research aims to improve power output by studying how well design, reinjection methods, and chemical reactions affect the reservoir, and it shows that using horizontal wells, fracturing, and proper reinjection temperature can help increase geothermal energy recovery in Lithuania. Full article

This paper quantifies the steady-state voltage-compliance impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, i.e., ≈20% and ≈40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking ($\partial V/\partial Q$), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow assessment is performed using Monte Carlo sampling ($N=100$), jointly modeling residential-demand variability and stochastic EV charging activation. Across the four cases, the worst-hour minimum voltage (uncompensated) ranges from 0.803 to 0.902 p.u., indicating a persistent under-voltage risk under dense and/or high-power charging. When the expected minimum-hourly voltage violates the 0.95 p.u. limit, a closed-form, sensitivity-guided reactive compensation is computed at the critical bus, and the power flow is re-solved. The proposed mitigation increases the minimum-voltage trajectory by approximately 0.03–0.12 p.u. (about 3.0–12.0% relative to 1 p.u.), substantially reducing the depth and duration of violations. The maximum required reactive support reaches 6.35 Mvar in the most stressed case (12 chargers at 1 MW), whereas limiting the unit charger power to 350 kW lowers both the severity of under-voltage and the compensation requirement. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-compliance assessment and targeted steady-state mitigation in EV-rich radial distribution networks. Full article

Virtual synchronous machine (VSG) technology provides a robust framework for integrating electric vehicle energy storage into modern microgrids. Nonetheless, conventional VSG control often suffers from intense interaction between active and reactive power flows, which can trigger persistent steady-state errors, power fluctuations, and potential system collapse. This research addresses these challenges by developing a 5th-order electromagnetic dynamic model tailored for a two-stage cascaded bridge inverter. By synthesizing a 3rd-order power regulation loop with a 2nd-order output stage, the proposed model captures stability boundaries across an extensive parameter spectrum. Unlike traditional 3rd-order “quasi-steady-state” approaches—which overlook essential dynamics under weak-damping or low-inertia conditions—this study utilizes the 5th-order model to derive an adaptive dynamic virtual impedance decoupling technique. This strategy facilitates real-time compensation of the cross-coupling between active and reactive channels, significantly boosting the inverter’s damping ratio. Quantitative analysis confirms that this approach curtails overshoot by 85.6% and accelerates the stabilization process by 42%, markedly enhancing the overall dynamic performance of the grid-connected system. Full article

This work presents a lightweight and interpretable framework for the early warning of voltage stability degradation in distribution networks, based on fractal and spectral features from flow measurements. We propose a Fast Voltage Stability Index (FVSI), which combines four independent indicators: the Detrended Fluctuation Analysis (DFA) exponent α (a proxy for long-term correlation), the width of the multifractal spectrum Δα, the slope of the spectral density β in the low-frequency range, and the $c_2$ curvature of multiscale structure functions. The indicators are calculated in sliding windows on per-node series of voltage in per unit Vpu and reactive power Q, standardized against an adaptive rolling/first-N baseline, and anomalies over time are accumulated using the Exponentially Weighted Moving Average (EWMA) and Cumulative SUM (CUSUM). A full online pipeline is implemented with robust preprocessing, automatic scaling, thresholding, and visualizations at the system level with an overview and heat maps and at the node level and panel graphs. Based on the standard IEEE 13-node scheme, we demonstrate that the Fractal Voltage Stability Index (FVSI_Fr) responds sensitively before reaching limit states by increasing α, widening Δα, a more negative $c_2$, and increasing β, locating the most vulnerable nodes and intervals. The approach is of low computational complexity, robust to noise and gaps, and compatible with real-time Phasor Measurement Unit (PMU)/Supervisory Control and Data Acquisition (SCADA) streams. The results suggest that FVSI_Fr is a useful operational signal for preventive actions (Q-support, load management/Photovoltaic System (PV)). Future work includes the calibration of weights and thresholds based on data and validation based on long field series. Full article

Distributed energy resources (DERs) can improve the performance of radial distribution systems. The nonlinear power flow constraints, multi-objective trade-offs, and network reconfiguration scenarios for DER placement and sizing call for the formulation of optimization problems. Most of the times optimization algorithms suffer from premature convergence and poor exploration-exploitation balance. These problems exhibit an inherent internal structural symmetry. In order to overcome the above problem, this study uses the Multi-Objective Archimedes Optimization Algorithm (MAOA) to optimally allocate DERs in the Radial Distribution Networks (RDNs), moreover the performance of the proposed MAOA is compared with the other well established algorithms including Particel Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Shuffled Frog Leaping Algorithm (SFLA), Atom Search Optimization (ASO), and Butterfly Optimization Algorithm (BOA) on the IEEE-33 RDN. The comparison is made for the four cases (S1: DER Only), (S2—Network Reconfiguration Only), (S3—DER Followed by Reconfiguration), and (S4—Reconfiguration Followed by DER) considering factors like voltage profile, network reconfiguration, active and reactive power loss reduction, carbon emission DER utilization and Cost reduction. The MAOA is observed to provide better results among all the other benchmark algorithms. In S3, the active power loss is reduced by 68.41%, whereas the reactive power loss is reduced by 57.44% and the MAOA algorithm improves the voltage by 3.98%. The minimum voltage of the network is also improved by 6.28%. The algorithm improves convergence with a percentage of 18.50% enhancing the system’s operational symmetry and stability, while satisfying all constraints. At Bus 3 and Bus 6 of IEEE-33 bus radial distribution network (Baran–Wu test system), DG capacity is allocated to be 3.8 MW and 2.1 MW, respectively. Full article

Recent progress in hydrogen combustion indicates that hydrogen could partially or fully replace traditional fuels in power plants, but maintaining stable flames remains a major challenge for many combustion systems. This study presents the effect of hydrogen enrichment of Liquid Petroleum Gas (LPG) on the low-swirl flame structure and flame temperature at different hydrogen mass fractions and equivalence ratios (φ = 0.501 and 1.04). The experimental observations for low-swirl flames under various conditions, including the effect of increasing hydrogen enrichment from 0% to ~20%, were discussed. Experiments were performed using a swirl burner, flame photography, and temperature measurements to evaluate the dynamic swirl flame, stability, and flame temperature distribution. The results show that moderate hydrogen enrichment (5–15%) improves flame stability and delays blow-off. In contrast, very high hydrogen concentrations may destabilize the flame due to higher reactivity and enhanced sensitivity to flow perturbations. Also, hydrogen enrichment up to ~20% enhances flame compactness, intensifies heat release, and reduces oscillatory instability without triggering blow-off or flashback, making hydrogen blending a promising strategy for stabilizing swirl flames at rich operating conditions. Finally, hydrogen enrichment consistently increases swirl flame temperature at both equivalence ratios. Full article

While organolithium reactions hold great promise in synthetic chemistry, their high reactivity, strong exothermicity, and the instability of intermediates often limit their application, making the effective control of reaction processes difficult in traditional batch reactors. This review systematically summarizes the latest advances in utilizing flow chemistry technology to address process challenges related to organolithium reactions from 2014 to 2025. From a process perspective, we systematically discuss the literature cases regarding three key themes: the synthesis of organic compounds applied in the pharmaceutical field, the development of novel methods centered on effective process control (reaction temperature, residence time, phase state, multi-step reaction sequence, and safety), and fundamental process research on continuous flow organolithium reactions. Analysis shows that continuous flow systems provide a powerful platform for fully realizing the potential of organolithium chemistry by enhancing heat/mass transfer and precisely controlling reaction parameters. This review emphasizes how flow chemistry technology not only improves process safety and efficiency but also enables transformations and process scaling that are difficult or impossible in batch modes, thus providing a novel process intensification method for modern synthetic chemistry. Full article

This paper presents an Artificial Neural Network (ANN) approach for estimating total real and reactive power losses in electrical power systems. Three network architectures were explored: the Multilayer Perceptron (MLP), the Radial Basis Function (RBF) network, and the Generalized Regression Neural Network (GRNN). The main advantage of the proposed methodology lies in its ability to rapidly compute power loss values throughout the system. ANN models are especially effective due to their capacity to capture the nonlinear characteristics of power systems, thus eliminating the need for iterative procedures. The applicability and effectiveness of the approach were evaluated using the IEEE 14-bus test system and compared with the continuation power flow method, which estimates losses using conventional numerical techniques. The results indicate that the ANN-based models performed well, achieving mean squared error (MSE) values below the predefined threshold during both training and validation (0.001). Notably, the networks accurately estimated the total power losses within the expected range, with residuals on the order of 10⁻⁴. Among the models tested, the RBF network showed slightly superior performance in terms of error metrics, requiring fewer centers to meet the established criteria compared to the MLP and GRNN models (11 centers). However, the GRNN achieved the shortest processing time; even so, all three networks produced satisfactory and consistent results, particularly in identifying the critical points of electrical power systems, which is of fundamental importance for ensuring system stability and operational reliability. Full article

Static voltage stability (SVS) is a critical aspect of the safe and efficient operation of electrical power systems (EPS), as it reflects the system’s ability to maintain adequate voltage levels in the face of progressive increases in demand under steady-state conditions. Traditionally, improving SVS has been addressed by compensating reactive power using FACTS devices. However, this research introduces an alternative methodology based on the hybridization of transmission technologies, integrating HVAC and HVDC links in parallel, to increase the stability margin and optimize performance in the event of contingencies. The proposed methodology is based on the resolution of the optimal AC power flow (OPF-AC) and the analysis of P-V curves to evaluate the displacement of the critical collapse point. The validity of the approach was verified through simulations in the Generation-Infinite Busbar and IEEE 9-busbar models, using the DIgSILENT PowerFactory environment. The results obtained show significant improvements in the SVS margin: an increase of 4.6% in the infinite busbar generation system, 9.5% in the critical busbar of the IEEE 9-busbar system, and 7.6% in the critical busbar of the IEEE 30-busbar system. In addition, the hybrid scheme showed a 17.1% reduction in real power losses and a more efficient redistribution of energy flows, which translates into a decrease in line load capacity. It should be noted that, under an N-1 contingency scenario, the hybrid system showed a 13.3% improvement in maximum power transfer before collapse, confirming its effectiveness under critical conditions. These findings position HVAC/HVDC hybridization as a robust and scalable alternative for strengthening voltage stability in modern electrical systems subject to operational variability. Full article

To address active voltage control in photovoltaic (PV)-integrated distribution networks characterized by weak voltage support conditions, this paper proposes a multi-agent deep reinforcement learning (MADRL)-based coordinated control method for PV clusters. First, the voltage control problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and a centralized training with decentralized execution (CTDE) framework is adopted, enabling each inverter to make independent decisions based solely on local measurements during the execution phase. To balance voltage compliance with energy efficiency, two barrier functions are designed to reshape the reward function, introducing an adaptive penalization mechanism: a steeper gradient in violation region to accelerate voltage recovery to the nominal range, and a gentler gradient in the safe region to minimize excessive reactive regulation and power losses. Furthermore, six representative MADRL algorithms—COMA, IDDPG, MADDPG, MAPPO, SQDDPG, and MATD3—are employed to solve the active voltage control problem of the distribution network. Case studies based on a modified IEEE 33-bus system demonstrate that the proposed framework ensures voltage compliance while effectively reducing network losses. The MADDPG algorithm achieves a Controllability Ratio (CR) of 91.9% while maintaining power loss at approximately 0.0695 p.u., demonstrating superior convergence and robustness. Comparisons with optimal power flow (OPF) and droop control methods confirm that the proposed approach significantly improves voltage stability and energy efficiency under model-free and communication-constrained weak grid conditions. Full article

The penetration of distributed energy resources (DERs), such as photovoltaic (PV) generation and electric vehicles (EVs), in distribution systems has been increasing rapidly. At the same time, load demand is rising due to the proliferation of data centers and the growing use of artificial intelligence. These trends have introduced new operational challenges: reverse power flow from PV generation during the day and low-voltage conditions during periods of peak load or when PV output is unavailable. To address these issues, this paper proposes a two-stage adaptive rolling horizon (ARH)-based model predictive control (MPC) framework for coordinated voltage and power factor (PF) control in distribution systems. The proposed framework, designed from the perspective of a distributed energy resource management system (DERMS), integrates EV charging and discharging scheduling with PV- and EV-connected inverter control. In the first stage, the ARH method optimizes EV charging and discharging schedules to regulate voltage levels. In the second stage, optimal power flow analysis is employed to adjust the voltage of distribution lines and the power factor at the substation through reactive power compensation, using PV- and EV-connected inverters. The proposed algorithm aims to maintain stable operation of the distribution system while minimizing PV curtailment by computing optimal control commands based on predicted PV generation, load forecasts, and EV data provided by vehicle owners. Simulation results on the IEEE 37-bus test feeder demonstrate that, under predicted PV and load profiles, the system voltage can be maintained within the normal range of 0.95–1.05 per unit (p.u.), the power factor is improved, and the state-of-charge (SOC) requirements of EV owners are satisfied. These results confirm that the proposed framework enables stable and cooperative operation of the distribution system without the need for additional infrastructure expansion. Full article

The inverter is the key element that converts the intermittent DC power of the PV array into a quality AC flow to the grid and simultaneously performs functions such as power factor control, reactive services, and grid code compliance. Therefore, the detailed operating profile of the inverter, how the power, dynamics, power quality, and efficiency evolve over time, is critical for both the scientific understanding of the system and the daily operation (O&M). Monitoring only aggregated energy indicators or single KPIs (e.g., PR) is often insufficient: it does not distinguish weather-related variations from technical limitations (clipping, curtailment), does not show dynamic loads (ramp rate), and does not provide confidence in the quality of the injected energy (PF, P–Q behavior). These deficiencies motivate research that simultaneously covers the physical side of the conversion, the operational dynamics, and the climatic reference of the resource. The analysis covers the window of 25 January–15 April 2025 (winter→spring). Due to the pronounced seasonality of the solar resource and temperature regime, all quantitative results and conclusions regarding efficiency, dynamics, clipping, and degradation are valid only for this window; generalizations to other seasons require additional data. In the next stage, we will add ≥12 months of data and perform a comparable seasonal analysis. Full specifications of the measuring equipment (DC/AC current/voltage, clock synchronization, separate high-frequency $PQ$-logger) and quantitative uncertainty estimates, including distribution to key indicators ($\eta$, $PR$, $THD$, $I_{DC}$), are presented. The PVGIS per-kWp climate reference is anchored to the nameplate DC peak and cross-checked against percentile scaling; $a\pm \epsilon$ scale error shifts $PR$ by $\epsilon$ and changes $\Delta E$ proportionally only on hours with $\widehat{P}>P$. The capacity for the climate reference (PVGIS per-kWp) is calibrated to the tabulated DC peak power $C_{cert}$ and is cross-validated using a percentile scale ($Q_{0.99}$). Full article

This study investigates the optimal operation of distribution systems incorporating Photovoltaic (PV) units, Electric Vehicle Charging Stations (EVCSs), and DSTATCOM devices using the Starfish Optimization Algorithm (SFOA). The main goal of the SFOA is to minimize a combined function that encompasses three key objectives: reducing system losses, increasing PV capacity, and enhancing EVCS power. By applying the SFOA within a multi-objective optimization framework, the optimal locations and sizes of PV units, EVCSs, and DSTATCOMs are identified to meet these objectives. This study analyzes and compares several case studies with different numbers of EVCSs, focusing on the operation of a modified 51-bus distribution system over 24 h. Results show that PV hosting energy increases to 21.73, 23.83, and 29.22 MWh for cases with 1, 2, and 3 EVCSs, respectively. EVCS energy also rises to 12.41, 19.50, and 37.23 MWh for the same cases. The corresponding optimized DSTATCOM reactive powers are 11.02, 12.02, and 13.74 MVarh. Throughout all cases, system constraints—such as voltage limits, utility current, and power flow equations—remain within acceptable ranges. The findings demonstrate the SFOA’s effectiveness in optimizing distribution systems with various devices, ensuring efficient operation and meeting all key objectives while adhering to system constraints. Full article

This work proposes an intelligent strategy for the coordinated management of active and reactive power in Battery Energy Storage Systems (BESSs) within AC microgrids operating under both grid-connected (GCM) and islanded (IM) modes to minimize daily operational costs. The problem is formulated as a mixed-variable optimization model that explicitly leverages the control capabilities of BESS power converters. To solve it, a Parallel Particle Swarm Optimization (PPSO) algorithm is employed, coupled with a Successive Approximation (SA) power flow solver. The proposed approach was benchmarked against parallel implementations of the Crow Search Algorithm (PCSA) and the JAYA algorithm (PJAYA), both in parallel, using a realistic 33-node AC microgrid test system based on real demand and photovoltaic generation profiles from Medellín, Colombia. The strategy was evaluated under both deterministic conditions (average daily profiles) and stochastic scenarios (100 daily profiles with uncertainty). The proposed framework is evaluated on a 33-bus AC microgrid that operates in both grid-connected and islanded modes, with a battery energy storage system dispatched at both active and reactive power levels subject to network, state-of-charge, and power-rating constraints. Three population-based optimization algorithms are used to coordinate BESS schedules, and their performance is compared based on daily operating cost, BESS cycling, and voltage profile quality. Quantitatively, the PPSO strategy achieved cost reductions of 2.39% in GCM and 1.62% in IM under deterministic conditions, with a standard deviation of only 0.0200% in GCM and 0.2962% in IM. In stochastic scenarios with 100 uncertainty profiles, PPSO maintained its robustness, reaching average reductions of 2.77% in GCM and 1.53% in IM. PPSO exhibited consistent robustness and efficient performance, reaching the highest average cost reductions with low variability and short execution times in both operating modes. These findings indicate that the method is well-suited for real-time implementation and contributes to improving economic outcomes and operational reliability in grid-connected and islanded microgrid configurations. The case study results show that the different strategies yield distinct trade-offs between economic performance and computational effort, while all solutions satisfy the technical limits of the microgrid. Full article

When conducting research on the static voltage stability of AC/DC systems with voltage source converter-high voltage direct current (VSC-HVDC) transmission lines, the focus is often given to reactive power control, neglecting the potential from active power support. Based on the minimum modulus eigenvalue, this paper proposes to coordinately control active and reactive power of VSC-HVDC to improve the static voltage stability of AC/DC systems. Firstly, the converter loss is quantified and taken into account to solve the power flow of the AC/DC system. Secondly, the minimum modulus eigenvalue of the system is calculated based on the Jacobian matrix in the power flow solution process to characterize the static voltage stability of the system. Then, taking the minimum modulus eigenvalue of the AC/DC system as the optimization objective, with power flow, node voltage, and converter power as constraints, and with the active and reactive power injections of HVDC as optimization variables, an optimization model is built to determine the optimal adjustment of active and reactive power of VSC-HVDC. Finally, the particle swarm optimization algorithm is utilized to solve the optimization model. Simulations in MATLAB show that compared with only active power control and only reactive power control, the proposed control method can significantly improve the static voltage stability of the system while ensuring its safe operation. Full article