Electricity

The transition to decentralized renewable energy systems has highlighted the role of AC microgrids and battery energy storage systems in achieving operational efficiency and sustainability. This study proposes an improved energy management system for AC MGs based on a tuned Parallel Population-Based Genetic Algorithm for the optimal operation of batteries under variable generation and demand. The optimization framework minimizes power losses, emissions, and economic costs through a master–slave strategy, employing hourly power flow via successive approximations for technical evaluation. A comprehensive assessment is carried out under both grid-connected and islanded operation modes using a common test bed, centered on a flexible slack bus capable of adapting to either mode. Comparative analyses against Particle Swarm Optimization and the Vortex Search Algorithm demonstrate the superior accuracy, stability, and computational efficiency of the proposed methodology. In grid-connected mode, the Parallel Population-Based Genetic Algorithm achieves average reductions of 1.421% in operational cost, 4.383% in power losses, and 0.183% in CO₂ emissions, while maintaining standard deviations below 0.02%. In islanded mode, it attains reductions of 0.131%, 4.469%, and 0.184%, respectively. The improvement in cost relative to the benchmark exact methods is 0.00158%. Simulations on a simplified 33-node AC MG with actual demand and generation profiles confirm significant improvements across all performance metrics compared to previous research works. Full article

Large-scale wind power grid-connected systems can trigger the risk of power system instability. In order to enhance the stability margin of grid-connected systems, this paper accurately characterizes the topology of the global boundary of stability domain (BSD) of the grid-connected system based on BSD theory, using the method of combining the manifold topologies and singularities at infinity. On this basis, the effect of large-scale doubly fed induction generators (DFIGs) replacing synchronous units on the BSD of the system is analyzed. Simulation results based on the IEEE 39-bus system indicate that the negative impedance characteristics and low inertia of DFIGs lead to a contraction of the stability domain. The principle of singularity invariance (PSI) proposed in this paper can effectively expand the BSD by adjusting the inertia and damping, thereby increasing the critical clearing time by about 5.16% and decreasing the dynamic response time by about 6.22% (inertia increases by about 5.56%). PSI is superior and applicable compared to traditional energy functions, and can be used to study the power angle stability of power systems with a high proportion of renewable energy. Full article

This paper addresses the economic–environmental dispatch (EED) problem in DC power grids integrating thermoelectric and photovoltaic generation. A multi-objective optimization model is developed to minimize both fuel costs and CO₂ emissions while considering power balance, voltage constraints, generation limits, and thermal line capacities. To overcome the non-convexity introduced by quadratic voltage products in the power flow equations, a convex reformulation is proposed using second-order cone programming (SOCP) with auxiliary variables. This reformulation ensures global optimality and enhances computational efficiency. Two test systems are used for validation: a 6-node DC grid and an 11-node grid incorporating hourly photovoltaic generation. Comparative analyses show that the convex model achieves objective values with errors below 0.01% compared to the original non-convex formulation. For the 11-node system, the integration of photovoltaic generation led to a 24.34% reduction in operating costs (from USD 10.45 million to USD 7.91 million) and a 27.27% decrease in CO₂ emissions (from 9.14 million kg to 6.64 million kg) over a 24 h period. These results confirm the effectiveness of the proposed SOCP-based methodology and demonstrate the environmental and economic benefits of renewable integration in DC networks. Full article

Transmission lines are constantly exposed to changes in climatic conditions and aging which affect the parameters and change the characteristics of the three-terminal circuit over time. In this paper we propose a fault location algorithm for three-terminal transmission lines to solve this problem. The algorithm utilizes the positive components of the voltage and current signals measured synchronously from the terminals. In this work no prior knowledge of the line parameters was required when calculating the fault location and the use of fault classification algorithms was not necessary. In addition, the proposed method determines the parameters of the line segment and fault location based on a solid mathematical basis and has been verified through simulation results using SIMULINK/MATLAB R2018a software. The fault location results demonstrate the high accuracy and efficiency of the algorithm. Moreover, this method can estimate the characteristic impedance and propagation constants of the transmission lines and determine the location of the fault, which is not affected by different fault parameters including fault location, and fault resistance. Full article

This study presents a novel methodology for determining zonal electricity generation and capacity requirements corresponding to forecasted annual production in an integrated power system (IPS). The proposed model combines the statistical analysis of historical daily load patterns with a calibration technique to translate forecast total demand into zonal powers (base, semi-peak and peak). A representative reference daily electrical load graph (ELG) is selected from retrospective data using least squares criteria, and a calibration factor α = W_x/W_ie scales its zonal outputs to match the forecasted annual generation W_x. The innovation lies in this combination of historical ELG identification and calibration for accurate zonal power prediction. Applying the model to Ukrainian IPS data yields high accuracy: a zonal power error below 1.02% and a generation error below 0.39%. Key contributions include explicitly stating the research questions and hypotheses, providing a schematic procedural description and discussing model limitations (e.g., treatment of renewable variability and omission of meteorological/astronomical factors). Future work is outlined to incorporate unforeseen factors (e.g., post-war demand shifts, electric vehicle adoption) into the forecasting framework. Full article

This study evaluates and compares centralized and distributed reactive power compensation strategies using Static Var Compensators (SVCs) to enhance the performance of a high-voltage transmission system in the Caribbean region of Colombia. The methodology comprises four stages: system characterization, assessment of the uncompensated condition under peak demand, definition of four SVC-based scenarios, and steady-state analysis through power flow simulations using DIgSILENT PowerFactory. SVCs were modeled as Thyristor-Controlled Devices (“SVC Type 1”) operating as PV nodes for voltage regulation. The evaluated scenarios include centralized SVCs at the Slack node, node N4, and node N20, as well as a distributed scheme across load nodes N51 to N55. Node selection was guided by power flow analysis, identifying voltage drops below 0.9 pu and overloads above 125%. Technically, the distributed strategy outperformed the centralized alternatives, reducing active power losses by 37.5%, reactive power exchange by 46.1%, and improving node voltages from 0.71 pu to values above 0.92 pu while requiring only 437 MVAr of compensation compared to 600 MVAr in centralized cases. Economically, the distributed configuration achieved the highest annual energy savings (36 GWh), the greatest financial return (USD 5.94 M/year), and the shortest payback period (7.4 years), highlighting its cost-effectiveness. This study’s novelty lies in its system-level comparison of SVC deployment strategies under real operating constraints. The results demonstrate that distributed compensation not only improves technical performance but also provides a financially viable solution for enhancing grid reliability in infrastructure-limited transmission systems. Full article

The optimal integration of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks is important to reduce their operating costs, improve their voltage profiles, and enhance their power quality. To this effect, this paper proposes a mixed-integer convex (MI-Convex) optimization model for the optimal siting and sizing of PV systems and D-STATCOMs, with the aim of minimizing investment and operating costs in electrical distribution networks. The proposed model transforms the traditional mixed-integer nonlinear programming (MINLP) formulation into a convex model through second-order conic relaxation of the nodal voltage product. This model ensures global optimality and computational efficiency, which is not achieved using traditional heuristic-based approaches. The proposed model is validated on IEEE 33- and 69-bus test systems, showing a significant reduction in operating costs in both feeders compared to traditional heuristic-based approaches such as the vortex search algorithm (VSA), the sine-cosine algorithm (SCA), and the sech-tanh optimization algorithm (STOA). According to the results, the MI-convex model achieves cost savings of up to 38.95% in both grids, outperforming the VSA, SCA, and STOA. Full article

Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication systems poses serious data security risks. Meanwhile, the complexity of energy scheduling presents challenges in obtaining feasible solutions. To address these issues, this paper proposes an innovative two-stage security optimization scheduling framework aimed at simultaneously improving the security and economy of the system. Firstly, the framework employs a CNN-LSTM hybrid model (WOA-CNN-LSTM) optimized using the whale optimization algorithm to achieve real-time detection of false data injection attacks (FDIAs) and post-attack data recovery. By deeply mining the spatiotemporal characteristics of the measured data, the framework effectively identifies anomalies and repairs tampered data. Subsequently, based on the improved multi-objective whale optimization algorithm (IMOWOA), rapid optimization scheduling is conducted to ensure that the system can maintain an optimal operational state following an attack. Simulation results demonstrate that the proposed framework achieves a detection accuracy of 0.9864 and a recovery efficiency of 0.969 for anomaly data. Additionally, it reduces the ship’s operating cost, power loss, and carbon emissions by at least 1.96%, 5.67%, and 1.65%, respectively. Full article

In this paper, we formulate optimization problems and successive convex relaxations to perform optimal transmission switching (OTS) in order to operate power transmission grids more efficiently. OTS may be crucial in future power grids with much higher penetrations of renewable energy sources, which will introduce more variability and intermittency in generation. Similarly, OTS can potentially help mitigate the effects of unpredictable demand fluctuations (e.g., due to extreme weather). We explore and compare several different formulations for the OTS problem in terms of the computational performance and optimality. In particular, we build upon the literature by considering more complex and accurate power flow formulations for OTS and introducing novel convex relaxations. This allows us to model the grid physics more accurately than prior works and generalize to several different types of networks. We also apply our methods to small transmission test cases as a proof of concept to determine the effects of applying OTS. Full article

The integration of battery energy storage systems (BESSs) within active distribution networks (ADNs) entails optimized day-ahead charge/discharge scheduling to achieve effective peak shaving.The primary objective is to reduce peak demand and mitigate power deviations caused by intermittent photovoltaic (PV) output. Quasi-static time-series (QSTS) co-simulations for determining optimal heuristic solutions at each time interval are computationally intensive, particularly for large-scale systems. To address this, a two-stage intelligent BESS scheduling approach implemented in a MATLAB–OpenDSS environment with parallel processing is proposed in this paper. In the first stage, a rule-based decision tree generates initial charge/discharge setpoints for community BESS units. These setpoints are refined in the second stage using an optimization algorithm aimed at minimizing community net load power deviations and reducing peak demand. By assigning each ADN community to a dedicated CPU core, the proposed approach utilizes parallel processing to significantly reduce the execution time. Performance evaluations on an IEEE 8500-node test feeder demonstrate that the approach enhances peak shaving while reducing QSTS co-simulation execution time, utility peak demand, distribution network losses, and point of interconnection (POI) nodal voltage deviations. In addition, the use of smart inverter functions improves BESS operations by mitigating voltage violations and active power curtailment, thereby increasing the amount of energy shaved during peak demand periods. Full article

Power systems need to meet the ever-increasing demand for higher quality and reliability of electricity in distribution systems while remaining sustainable, secure, and economical. The globe is moving toward using renewable energy sources to provide electricity. An evaluation of the influence of artificial intelligence (AI) on the accomplishment of SDG7 (affordable and clean energy) is necessary in light of AI’s development and expanding impact across numerous sectors. Microgrids are gaining popularity due to their ability to facilitate distributed energy resources (DERs) and form critical client-centered integrated energy coordination. However, it is a difficult task to integrate, coordinate, and control multiple DERs while also managing the energy transition in this environment. To achieve low operational costs and high reliability, inverter control is critical in distributed generation (DG) microgrids, and the application of artificial neural networks (ANNs) is vital. In this paper, a power management strategy (PMS) based on Inverter Control and Artificial Neural Network (ICANN) technique is proposed for the control of DC–AC microgrids with PV-Wind hybrid systems. The proposed combined control strategy aims to improve power quality enhancement. ensuring access to affordable, reliable, sustainable, and modern energy for all. Additionally, a review of the rising role and application of AI in the use of renewable energy to achieve the SDGs is performed. MATLAB/SIMULINK is used for simulations in this study. The results from the measures of the inverter control, m, VL-L, and Vph_rms, reveal that the power generated from the hybrid microgrid is reliable and its performance is capable of providing power quality enhancement in microgrids through controlling the inverter side of the system. The technique produced satisfactory results and the PV/wind hybrid microgrid system revealed stability and outstanding performance. Full article

As renewable energy penetration and extreme weather events increase, accurately predicting power system behavior is essential for reducing risks and enabling timely interventions. This study presents a physics-informed learning approach to forecast transient voltage angles in power systems with integrated wind energy under gusty wind conditions. We developed a simulation framework that generates wind power profiles with significant gust-induced variations over a one-minute period. We evaluated the effectiveness of physics-informed neural networks (PINNs) by integrating them with LSTM (long short-term memory) and GRU (gated recurrent unit) architectures and compared their performance to standard LSTM and GRU models trained using only mean squared error (MSE) loss. The models were tested under three wind energy penetration scenarios—20%, 40%, and 60%. Results show that the predictive accuracy of PINN-based models improves as wind penetration increases, and the best-performing model varies depending on the penetration level. Overall, this study highlights the value of physics-informed learning for dynamic prediction under extreme weather conditions and provides practical guidance for selecting appropriate models based on renewable energy integration levels. Full article

The growing integration of photovoltaic (PV) energy systems and electric vehicles (EVs) introduces new challenges in managing energy flow within smart grid environments. The intermittent nature of solar energy and the variable charging demands of EVs complicate reliable and efficient power management. Existing strategies for grid-connected PV–battery systems often fail to effectively handle bidirectional power flow between EVs and the grid, particularly in scenarios requiring seamless transitions between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. This paper presents a novel neural network-based model predictive control (NN-MPC) approach for optimizing energy management in a grid-connected PV–battery–EV system. The proposed method combines neural networks for forecasting PV generation, EV load demand, and grid conditions with a model predictive control framework that optimizes real-time power flow under various constraints. This integration enables intelligent, adaptive, and dynamic decision making across multiple objectives, including maximizing renewable energy usage, minimizing grid dependency, reducing transient responses, and extending battery life. Unlike conventional methods that treat V2G and G2V separately, the NN-MPC framework supports seamless mode switching based on real-time system status and user requirements. Simulation results demonstrate a 12.9% improvement in V2G power delivery, an 8% increase in renewable energy utilization, and a 50% reduction in total harmonic distortion (THD) compared to PI control. The results highlight the practical effectiveness and robustness of NN-MPC, making it an effective solution for future smart grids that require bidirectional energy management between distributed energy resources and electric vehicles. Full article

In the future, higher penetrations of electrical loads in low-voltage distribution grids are to be expected. To prevent grid overload, a possible solution is coordination of controllable loads. Typical examples might be charging of electric vehicles or operation of electric heat pumps. Such loads are associated with specific requirements that should be fulfilled if possible. However, at the same time, a safe grid operation must be ensured. To this end, a corresponding optimal power flow optimization problem might be formulated and solved. This article gives a comprehensive review of the state of the art of optimal power flow formulations. It is investigated which constraint handling techniques are used and how hyper parameters are tuned when solving optimal power flow problems using metaheuristic solvers and how controllable loads and fluctuating renewable production are incorporated into optimal power flow formulations. Therefore, the literature is reviewed for pre-defined criteria. The results show possible gaps to be filled with future research: extended optimal power flow formulations to account for controllable loads, investigation of effects of choosing constraint handling techniques or hyper parameter tuning on the performance of the metaheuristic solver and automated methods for determining optimal values for hyper parameters. Full article

Balancing electricity production and distribution remains a central challenge in modern energy systems, especially with the increasing penetration of renewable sources that introduce variability and uncertainty. In this context, accurate forecasting of electricity demand is essential for grid stability and operational efficiency. This study addresses the problem of hourly electricity demand forecasting in Italy using recurrent neural networks (RNNs), particularly long short-term memory (LSTM) models, which are designed to capture complex temporal dependencies in time series data. Utilizing real consumption data from Terna—Rete Elettrica Nazionale S.p.A.—for the years 2022 and 2023, we developed and tested an LSTM model capable of predicting national hourly demand with Root Mean Squared Error (RMSE) consistently below 2%. The model’s forecasts show strong agreement with official data provided by Terna, accurately capturing demand peaks and seasonal trends over both short- and medium-term horizons. In addition to evaluating predictive performance, this work proposes a reproducible methodology applicable to other national contexts or similar forecasting problems. Our findings suggest that, while data-driven models offer robust and replicable results, further improvements may require the integration of system-specific knowledge to address persistent limitations in forecasting extreme events or structural anomalies. Full article

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. Full article

This paper analyzes the power quality in a 400 kWp grid-connected solar photovoltaic system with storage (BESS), considering standards IEEE Std 519TM, IEEE Std 1159TM, and IEC 61000-4-30. For system analysis, a photovoltaic array model is developed. Neplan-Smarter Tools software is used for model validation, and experimental measurements are performed on the actual photovoltaic system, recording total harmonic distortion (THDi/THDv). A class B power quality monitor was used to measure three-phase electrical variables: current, voltage, power, power factor, and THD. The THD level was generated at an energy level below 20% of the rated power, resulting in high THDi. The recorded THDv remained below 2.5%, which means that its value is limited by the IEEE 519 standard. When the BESS was connected to the PCC grid, the voltage level remained regulated, and the electrical system appeared to be stable. This paper contributes a methodology and procedure for measurement and power quality assessment, allowing for THD identification and enabling designers to configure better designs and energy system protections when integrating solar photovoltaic energy into an electrical distribution network. Full article