Electricity

This paper presents an experimental investigation of dynamic line rating (DLR) applied to aluminium conductor steel-reinforced (ACSR) overhead line conductors, with a specific focus on wind speed conditions up to 5 m/s. An experimental system was designed and implemented to provide controlled and repeatable cross-flow air conditions along a tested ACSR conductor, enabling direct measurement of wind speed in the immediate vicinity of the conductor surface. Conductor temperature, electrical current, voltage drop per unit length, the phase angle between them, and relevant meteorological parameters were continuously measured under controlled experimental conditions. Based on the measured data, the conductor heat balance was evaluated and the allowable current-carrying capacity was determined. The experimentally obtained conductor temperatures and ampacity values were compared with results calculated using thermal models and correlations recommended by IEEE, CIGRÉ, and IEC standards. The comparison demonstrates that, under low and moderate wind speed conditions, differences between standard-based predictions and experimental results can be significant, leading to deviations in the estimation of allowable current-carrying capacity. The results confirm the high sensitivity of DLR calculations to wind-related assumptions and provide an experimentally validated basis for assessing the applicability and limitations of existing standard thermal models for ACSR conductors under realistic operating conditions. Full article

Medium voltage induction motors (MVIM) are a key component of numerous industries, such as water treatment plants, sewage discharge stations, and chilled water systems. The starting process for these MV motors is critical as it is associated with a major impact on both motor lifetime and power grid quality. In this article, a proposed modified and comprehensive starting scheme of MV three-phase induction motors driving pumps for water stations is introduced. Firstly, the starting performance and its impact on power grid quality will be discussed when all motors are normally started with direct on line connection (DOL), which is already the normal established status. A modified starting scheme based on an optimized coordination of motor starting methods in addition to variable voltage variable frequency drive (VVVFD) drive and control implementation will be discussed. A transition between the starting of variant MV induction motors as well as the starting event coordination principle will be discussed to improve the power quality relative to the obligatory time shift required for the operation. The coordination is based on an algorithm implementation which is achieved using different optimization concepts based on artificial intelligence techniques, properly conducting the transition time in addition to the power delivered by the inverter unit rather than determining the number of DOL and VVVF-implemented motors. A comparison between using the optimized VVVFD soft-starting and the proposed modified scheme is performed, focusing on the power quality improvement rather than optimizing the cost function. The modified scheme is simulated using ETAP power station for brief analysis and study of load flow rather than the complete inspection and power quality assessment. Full article

This paper aims to define the optimal microgrid topology for rural electrification based on the lowest total cost by comparing LVAC and LVDC microgrids across three different scenarios. An LVAC radial topology is first designed using mixed-integer linear programming for phase balancing and the shortest path for connections, then implemented with a genetic algorithm to allocate and size solar home systems, forming an LVAC microgrid. Next, an LVDC topology is then derived from the LVAC structure and integrated with solar home systems under three scenarios: (1) using the same solar home system sizes, locations, and quantities as the LVAC microgrid; (2) using a genetic algorithm to re-determine solar home system sizes and locations, forming an LVDC microgrid; and (3) clustering the LVDC topology into nano-grids, each defined by genetic algorithm for solar home system sizing and placement and connected to the main feeder via bi-directional converters. Finally, all LVAC and LVDC scenarios are simulated over a 30-year planning horizon for analysis. A non-electrified village located in Cambodia has been selected for a case study to validate the proposed methods. The results have been obtained and provide a comparison of performance indicators (i.e., costs, energy production, losses, CO₂ emissions, and autonomous energy) among the microgrids (LVAC and LVDC). The LVAC microgrid produced lower total energy losses than the LVDC microgrid in all scenarios. However, when considering environmental impact, LVDC Scenario 2 is preferable. Based on the total cost results, the LVAC microgrid is considered more economical than the LVDC microgrid in each scenario in this study. Full article

This paper explores a new switching strategy for a recently proposed quadratic boost converter. The topology under study is a high-step-up DC–DC converter with a configuration that allows a portion of the processed energy to be used in what we call a non-series transfer. This characteristic reduces the amount of power processed redundantly. This converter, called a Quadratic Boost Converter based on Non-Series Energy Transfer (QBC-NSET), also has a non-pulsating input current, which is especially desirable for applications like photovoltaic and fuel-cell sources. This paper proposes a different switching strategy that reduces the output voltage ripple without increasing the switching frequency and without increasing the stored energy (inductance in inductors or capacitance in capacitors). The converter has two transistors, originally operated with synchronized signals; the proposed strategy provides independent switching signals with a phase shift between them. This enables the output capacitor to charge in a different switching state, producing a smaller voltage ripple while preserving the advantages of the topology originally presented. Steady-state analysis and voltage gain derivations confirm that the fundamental conversion characteristics remain unchanged. Experimental results obtained from a laboratory prototype validate the effectiveness of the proposed approach, demonstrating the reduction in the output voltage ripple. Full article

With the large-scale integration of renewable energy and the increasing use of power electronics, the issue of wide-band oscillations in power grids has become increasingly prominent. The scarcity and uneven distribution of oscillation samples pose significant challenges for training data-driven models, and traditional generative models struggle to ensure fidelity in both time and frequency domains. To address this, this paper proposes a Time–Frequency Fusion Generative Adversarial Network (TFF-GAN) for generating power grid oscillation risk scenarios. The method constructs a dual-path generation and discrimination framework, where the generator decomposes the signal using Short-Time Fourier Transform (STFT), with time-domain features extracted by a convolutional neural network (CNN) and frequency-domain features extracted from the STFT representation by a dedicated spectral network. These features are then fused using a U-Net structure. The discriminator simultaneously evaluates the authenticity of both the time-domain waveform and the frequency-domain spectrum. A composite loss function, incorporating time-domain loss, frequency-domain loss, and adversarial loss, is used for joint optimization. Experimental results demonstrate that the proposed method generates oscillation scenarios with high fidelity in both time-domain waveforms and frequency-domain spectra, effectively supporting power grid oscillation risk assessment and control strategy validation. Full article

The increasing penetration of low-inertia renewable energy sources and distributed generation has significantly reduced system inertia, making frequency stability a critical challenge in modern power systems. Traditional Under-Frequency Load Shedding (UFLS) schemes often fail to adapt to varying operating conditions and load behaviors, leading to either insufficient or excessive disconnections. This paper presents an optimization-based UFLS scheme that integrates dynamic simulations in DIgSILENT PowerFactory with Python programming through the Particle Swarm Optimization (PSO) algorithm. The proposed methodology optimizes key UFLS parameters—frequency thresholds, intentional delays, and load-shedding percentages—under different ZIP load model configurations (constant power, constant current, and constant impedance). Simulation results on the IEEE 39-bus test system demonstrate that the type of load model has a significant impact on frequency recovery performance and the total amount of load shed. The constant power model achieved system stability with the lowest load disconnection, whereas the constant impedance model required a greater amount of shedding to restore nominal frequency. The results validate the effectiveness of the proposed optimization tool and highlight the importance of considering load characteristics in UFLS design to enhance operational reliability and resilience in modern power systems. Full article

The article presents the development of a large-scale dynamic model of the European power system, including all essential features for wide-area monitoring and control studies. The simulated system includes 3809 nodes, 7343 branches, 618 synchronous machines with 1854 controllers, and 1573 PMUs. The system also integrates inverter-based resources, controlled in either grid-following or grid-forming mode. The model is developed in the phasor-based simulation domain and implemented in MATLAB/Simulink for computation according to a modelling approach that combines vectorized and elementwise operations. The model is publicly available and represents a fundamental tool for investigating transient phenomena and advanced control strategies at a wide-area level. As a demonstration of the possible use of the model, an innovative wide-area damping control is also applied. Numerical experiments are conducted under different configurations, investigating relevant inter-area oscillation phenomena in the European system and assessing the opportunity of the proposed wide-area damping control architectures. The main findings of the case study indicate a definite improvement in the dynamic performance of the system when a wide-area control is applied, leading to a sixfold increase in inter-area oscillation damping, with a reduction of about 80% in the energy involved during the system oscillations. Full article

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management. Full article

The rapid expansion of the high electricity-intensive industries like data center has led to a structural increase in industrial electricity demand, thereby increasing the need for demand response (DR) to enhance power system flexibility. However, in the industrial sector, DR strategies based solely on simple load curtailment can impose productivity losses on participating customers. To address this limitation, this study proposes an SMP-based load shifting linear programming (LP) optimization model that enables DR curtailment to translate into electricity cost reduction through clustered DR resources formed by combining load resources at the industrial complex level. The decision variables representing hourly load shifting are adjusted under constraints defined by the hourly average demand and flexibility of the load resources, and the averages and fluctuations of SMP. The objective function is optimized to minimize the total electricity cost. Since the demand flexibility varies by season, experiments are conducted about various clustered DR resources on a seasonal basis. When resources with similar hourly average demand and flexibility are combined, the resulting load shifting plans are found to yield the greatest electricity cost reduction (Scenario 2—0.79 M KRW). These results confirm that the proposed load shifting LP model can provide a practical approach for DR operation planning. Full article

This paper introduces a hybrid metaheuristic approach for the reconfiguration of electric distribution networks, integrating Simulated Annealing (SA) and Tabu Search (TS) to accelerate convergence and enhance exploration of the solution space. The method employs a selective mesh-based neighbor generation strategy, which substantially reduces the search space while maintaining operational feasibility (radial topology, voltage, and current limits). The approach was implemented in Python and integrated with DIgSILENT PowerFactory, enabling the direct evaluation of losses, voltages, and currents for reproducible and scalable analysis. Validation on 5-, 16- and 33-bus benchmark systems consistently reached the global optimum across 100 simulation runs, demonstrating robustness and computational efficiency. A real-world application was performed on the 10 kV primary distribution network of Huancayo, Peru, where the proposed method achieved a 10.4% reduction in active losses, improved the minimum voltage from 0.931 to 0.949 p.u., and partially relieved feeder overloads. These results confirm the method’s suitability for both academic benchmarking and practical deployment in Latin American distribution systems. Full article

Secondary power supplies are an integral part of any complex device that requires power to different circuit nodes. This includes various kinds of telecommunication equipment, the aerospace industry, battery chargers, etc. Secondary power supplies include the most common pulse converters of both the boost, buck, and buck–boost variety, as well as forward, flyback, and push–pull converters. In particular, a galvanic isolation option may be considered for push–pull types. The use of multi–channel secondary power supplies is relevant for the space industry and satellites, where it is necessary to support the operation of many related devices. The efficiency of such devices is high due to their small number of elements and their simplicity of control. PWM (pulse width modulation) controllers can be considered as the last statement. In turn, the presence of radiation-resistant CMOS technology is required in outer space conditions, which is possessed by the PWM controller considered in this paper. Also, high efficiency and small dimensions can be achieved using planar technology. Here, one such secondary power supply, based on the PWM controller 5315EU015 with a power of 10 W, is considered, as well as the proposed design of a planar transformer. A mathematical model obtained from the algebraization of differential equations method, and from the PSIM software v. 22.2 simulation results and experiments is presented. Full article

Non-intrusive load monitoring (NILM) can provide actionable feedback for demand response (DR) when direct measurements of device states are unavailable. We propose a reproducible, engineering-oriented pipeline for detecting ON/OFF states of end-use load groups from an aggregated active power time series. The method uses robust hysteresis-based labeling with adaptive thresholds derived from the median and median absolute deviation, followed by compact feature engineering restricted to global active power (GAP). After removing collinear features (|r| > 0.98), permutation importance is used to retain informative predictors. Probabilistic binary classifiers (LGBM, Histogram-based Gradient Boosting, XGBoost, and CatBoost) are trained for each target load, and the decision threshold is optimized via F_β to balance missed events and false alarms. A post-processing stage stabilizes predictions by smoothing probabilities and suppressing spurious triggers. Model quality is assessed with both sample-wise metrics and event-based metrics that credit the correct detection of switching intervals within a time tolerance. Experiments on the open “Individual Household Electric Power Consumption” dataset (1-min resolution, 2007–2010) demonstrate that lightweight gradient boosting models, particularly LGBM, deliver reliable and interpretable state estimates suitable for practical DR integration and edge deployment. Full article

This study presents the use of a Battery Energy Storage System (BESS) and a thermal power plant to enhance Primary Frequency Regulation (PFR) in a power system. This integration seeks to mitigate operational challenges, such as the reduction in system inertia and frequency regulation, which are heightened when increasing renewable energy use in power grids with high hydroelectric generation. The proposed solution enables thermal generators to operate at optimal capacity, while the BESS provides a rapid frequency response, thereby enhancing operational efficiency and compliance with national standards. The process was structured in five stages: criteria definition, analysis, design, models, and evaluation. A comprehensive methodological approach was adopted, including dynamic system modeling and BESS sizing based on regulatory parameters. The method was tested with real data from a thermal plant under the conditions of the Colombian electricity market. The simulation results highlight the effectiveness of the proposed BESS, with a response time of approximately 0.6 s and regulation maintenance for over 30 s, reducing mechanical stress and preventing frequency overshoot. The control strategy was designed to maintain the energy neutrality of the BESS, thereby stabilizing its state of charge over the operational horizon. The results show that the BESS targets high-frequency transients and the generator focuses on low-frequency adjustments, managed by an Energy Management System (EMS) with a unified control approach. Full article

Increasing variability in electricity load patterns, driven by end-use behaviour, grid-related technological changes, and socio-economic factors, calls for more accurate and efficient short-term load prediction (STLP) models. This study evaluates the predictive performance of four hybrid models for short-term Amp-load prediction: Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO), as well as convolutional neural networks (CNN) integrated with long short-term memory (LSTM) and extreme gradient boosting (XGB). The models were developed using hourly Amp-load data collected from a power utility substation in Kenya, together with corresponding meteorological data (temperature, wind speed, and humidity) covering a period from January 2023 to June 2024. Results show that the ANFIS-PSO and ANFIS-GA models outperform the CNN-based models, achieving MAPE values of 4.519 and 4.363, RMSE values of 0.3901 and 0.4024, and R² scores of 0.8513 and 0.8481, respectively, due to the adaptive nature of ANFIS, which enables effective modelling of the irregular, nonlinear, and complex temporal behaviour of the Amp load. Enhanced prediction accuracy was observed across all models when variational mode decomposition (VMD) was applied to pre-process the input data. This result was corroborated through further analysis of the Amp-load signals using Taylor plots. Among all of the configurations tested, the CNN-LSTM-VMD model exhibited the highest overall prediction accuracy, with MAPE of 2.625, RMSE of 0.1898, and R² of 0.9702, marginally outperforming the ANFIS-PSO-VMD model, thus making it more suitable for short-term load prediction applications. Full article

The high penetration of wind power, photovoltaic, and energy storage not only presents opportunities for green and low-carbon development, but also poses significant challenges to frequency regulation. During primary frequency regulation (PFR), improper deadband settings may cause delayed or inadequate frequency responses, thereby exacerbating system frequency fluctuations, reducing renewable energy utilization rates, and compromising grid security and stability. This study proposes a parameter optimization method based on deadband to enhance PFR accuracy and improve overall energy conservation and emission reduction benefits. First, the impact of different deadband settings on system frequency fluctuations is analyzed, and the frequency response process is decomposed to quantify its effects on frequency stability and renewable energy integration capacity. Subsequently, the PFR coefficient is modified and optimized with maximum frequency deviation as the objective, thereby strengthening the frequency response capability of renewable energy. Simulation results demonstrate that the modified PFR coefficients reduce the maximum frequency deviation of wind–solar–storage systems by 0.0026 Hz, 0.0036 Hz, and 0.0034 Hz, respectively. This effectively elevates renewable energy integration levels and enhances the low-carbon stability of power system operations. Full article

The use of photovoltaic (PV) water pumping technology offers a viable and sustainable alternative to conventional diesel-driven pumping systems. In PV-based pumping installations, the elimination of bulky transformers significantly reduces the overall system size and weight, which is particularly advantageous for rural and remote irrigation applications. However, removing the transformer can result in high common-mode voltage (CMV) when the induction motor is controlled using a direct torque control (DTC) scheme. This elevated CMV induces leakage currents that may damage the motor, compromise system reliability, and pose potential safety hazards. To ensure a more compact and safer PV pumping system, this paper introduces an improved DTC-based control strategy for induction motors driven by transformerless multilevel PV inverters. The proposed approach effectively suppresses leakage current by mitigating its main source, CMV, while maintaining the simple structure and dynamic performance inherent to conventional DTC. Two new look-up tables (LUTs) are developed to control the stator flux and electromagnetic torque while simultaneously eliminating leakage current. The first method, termed zero-medium vector DTC (ZMV-DTC), employs both zero and medium voltage vectors from the space vector diagram. The second, referred to as medium vector DTC (MV-DTC), utilizes only medium vectors. Numerical simulation results validate the feasibility and superior performance of the proposed algorithms in terms of leakage current suppression. Compared with a conventional DTC (C-DTC) scheme that is designed to limit the CMV, the proposed DTC algorithms achieve a much stronger reduction in the CMV, confining its amplitude to only a few volts, instead of the levels ±V_dc/6 typically produced by the C-DTC. As a result, the leakage current is effectively eliminated, ensuring safer and more reliable operation of the system. Full article

Traditional thermal power systems are merging with distributed generation and renewable energy sources, resulting in complex interconnected power system networks. This results in operational burdens and complexities in thermal power plants that they were not designed to handle. The role of Automatic Voltage Regulation (AVR) is crucial in maintaining the stability and dependability of these complicated power systems. This research provides a comprehensive review of the AVR control strategies within the last five years, considering operational complexities, changing topologies, and evolving challenges, in contemporary power systems. This review first explores the contemporary control strategies used in voltage regulation. Second, it provides an in-depth evaluation of the traditional Proportional Integral Derivative controllers with various improvements, adaptions, and modifications, followed by an examination of supplementary controllers in the AVR framework. Lastly, this paper reviews various optimisation strategies published in the last five years. This paper enriches our understanding of traditional and advanced control strategies in AVR, providing a comprehensive evaluation of their effectiveness and constraints, and aims to provide a valuable resource for researchers in this field. Full article

The rapid integration of renewable energy sources has accelerated the adoption of DC microgrids as an effective platform for flexible and reliable power generation and management. However, conventional droop-based control suffers from inherent limitations, particularly voltage deviations at the DC bus, which compromise stability, power-sharing accuracy, and overall system performance. To address these challenges, this paper presents a distributed secondary control framework for a standalone PV battery-based DC microgrid that achieves bus voltage regulation, precise power distribution, and state-of-charge (SoC) balancing across multiple energy storage units (ESUs). At the primary level, an adaptive mechanism is introduced that dynamically adjusts droop coefficients in response to the real-time SoC of each ESU, promoting balanced utilization of storage resources. At the secondary level, the strategy leverages limited peer-to-peer communication to exchange only aggregate power information, thereby enabling accurate load sharing while preserving scalability and plug-and-play capability. The control architecture further incorporates voltage and current error compensation, with parameters tuned using a Whale Optimization Algorithm to enhance dynamic response. Validation is carried out through a real-time simulation environment developed in MATLAB/Simulink R2024b and executed on a Speedgoat^TM platform. The results demonstrate robust SoC equalization, improved bus voltage stability, and reliable cooperative coordination, positioning the scheme as a practical solution for next-generation DC microgrids. Full article

This study presents an advanced demand-side management framework to optimize energy consumption in smart grids featuring significant intermittent renewable energy integration. The approach leverages real-time data from an advanced metering infrastructure and a predictive model employing a bidirectional long short-term memory network enhanced with attention mechanisms for accurate load and electricity price forecasting. These predictions drive a multi-objective optimization model that harmonizes flexible demands across residential, commercial, and industrial sectors. A novel reference-guided multi-objective particle swarm optimizer is proposed to address the problem’s complexity, promoting improved convergence and diversity in solutions. In benchmarks, RGMOPSO demonstrated superior performance, attaining a fifty-six percent win rate in convergence metrics and a hypervolume of zero point nine three. Simulation results validate the framework’s effectiveness. It achieved a twenty percent reduction in operational costs, a nineteen-point-seven percent lower peak-to-average ratio, and an eighteen percentage point increase in renewable utilization. User-centric benefits included a thirty percent enhancement in comfort and a corresponding reduction in battery degradation. This integrated solution offers a resilient pathway for sustainable smart grid operations amid renewable uncertainties. Full article