Distributed generation in power systems is currently attracting significant attention as a means to reduce energy generation costs through the exploitation of low-cost decentralized sources. Cost reduction becomes especially relevant when a large portion of the energy supplied by a centralized generator is replaced by energy from a few high-power distributed sources. However, the effectiveness and safety of this approach critically depend on the proper allocation of distributed generators. To this end, this paper presents a novel sensitivity analysis for system losses that highlights the nonlinear dependencies on power-injecting sources. It is shown that the parameters of the nonlinear model allow easy identification of a small subset of grid buses that are suitable candidates for large-power injection. At these buses, source sizing can be determined using standard optimal power flow techniques that minimize the energy cost function. The proposed allocation strategy is validated on the IEEE 69 and 85 benchmark bus grids, and its robustness is further confirmed through simulations that account for stochastic load fluctuations. Comparison with state of the art and robust source allocation approaches, such as the Iterative Search Method, shows that for similar accuracy the proposed method is two/three orders of magnitude faster.

The development of sophisticated and secure charging infrastructures that can effectively manage energy demand while ensuring resilience against cyber threats is imperative due to the widespread adoption of electric vehicles. In order to jointly validate cost-effective control algorithms and assess the resilience of electric vehicles’ charging systems under cyberattacks, this study suggests a real-time co-simulation framework that incorporates the electrical grid and the information and communication technology infrastructure. Thus, in addition to ICT models based on the OCPP 1.6 protocol and WebSocket communication, the framework employs a modified IEEE 13-Node Test Feeder simulated using Typhoon HIL. This setup allows a thorough assessment of the effects of control commands issued by the central system on power system dynamics and the potential for malicious interventions to spread across the infrastructure. Analysis of network traffic validates the integrity and timing of message exchange. Overall, this co-simulation environment offers a robust foundation for testing algorithmic strategies and modelling cyberattack scenarios, thereby supporting the development and validation of resilient and secure charging infrastructures.

Accurate forecasting of solar photovoltaic (PV) power is essential for grid stability and efficient energy management. This study investigates the impact of missing data, introduced through both random and block removal at varying percentages, on the performance of hybrid neural network architectures, Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), LSTM Autoencoder, and standalone LSTM models, for short-term PV power prediction. Using two real-world PV datasets representing semi-arid and desert climates, the focus is placed on non-parametric probabilistic forecasting, leveraging Kernel Density Estimation and quantile regression to generate confidence intervals that capture forecast uncertainty. Experimental results demonstrate that while all models experience performance degradation with increasing levels of missing data, hybrid architectures such as CNN–LSTM and LSTM Autoencoder exhibit greater robustness in capturing temporal patterns and maintaining forecast reliability. Notably, the CNN–LSTM model achieved a 19.35% reduction in quantile loss compared to the standalone LSTM under 30% random data removal. The findings underscore the importance of evaluating both predictive accuracy and uncertainty calibration under data scarcity scenarios to enable dependable solar energy forecasting and resilient energy management.

Abstract Accurate short-term photovoltaic (PV) power forecasts are critical for efficient grid balancing, yet training optimizers, often overlooked compared to neural network architectures, significantly influence prediction accuracy and convergence speed. Prior research primarily focuses on adjusting network architectures, typically employing a single optimizer (commonly Adam), thus leaving optimizer selection underexplored, especially under noisy and incomplete real-world PV data. This study systematically benchmarks four optimizers—Adam, Adaptive Gradient (Adagrad), Rectified Adam (RAdam), and Lookahead—across three deep-learning architectures (Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)-LSTM, and LSTM-Autoencoder) using data from two distinct PV sites. Unlike prior works, we assess optimizer effectiveness across a wide range of conditions, including varying training data lengths, sampling intervals, and missing data patterns (both random and block-wise). Using two real-world PV datasets representing semi-arid and desert climates, we analyze forecasting accuracy, convergence time, and robustness. Our empirical results demonstrate that RAdam consistently outperforms Adam by achieving up to 36% lower forecasting error under noisy and incomplete data conditions, while Lookahead offers up to 40% faster convergence in deep hybrid models. These gains translate into tighter reserve-margin planning and smoother inverter set-points, advancing state-of-the-art PV forecast pipelines. The paper concludes with optimizer-architecture recommendations for practitioners facing latency or compute constraints.

This paper outlines an innovative approach to enhance the predictability of solar photovoltaic power. By employing advanced machine learning techniques, specifically artificial neural networks, this study addresses the challenges posed by the intermittent nature of solar energy. The employed models incorporate probabilistic forecasting to provide not only precise power output predictions but also confidence intervals that signify the uncertainty in these predictions. This approach supports more effective integration of solar energy into power grids, facilitating better energy management and planning. The results indicate that our models can significantly improve the accuracy of solar power forecasting, crucial for optimizing grid operations and enhancing renewable energy adoption.

The increasing prevalence of Electric Vehicles (EVs) presents challenges in seamlessly integrating EV chargers. This article introduces an enhanced economic model predictive control strategy to address these challenges and alleviate grid congestion caused by EV energy demand. The approach is developed to operate with actual charging station infrastructures and tackles critical issues, including EV supply equipment management, defining charging profiles based on owner preferences, minimizing operational costs, and reducing peak power. It adeptly manages the standard for charger pilot signals and the non-ideal behavior of EV batteries across multiple vehicle types. Through simulations utilizing a comprehensive dataset, the strategy optimizes charging profiles, effectively reducing peak power and achieving cost savings across EV chargers. The study encompasses single charger responses to station-wide evaluations, showcasing the strategy’s applicability and superiority over uncoordinated approaches. This proposed approach, accounting for real-world factors like energy prices and grid demand, establishes itself as an optimal EV charging station operation tool.

Thermoforming is a process in which a sheet of thermoplastic material is heated, formed with a mould, and successively cooled to harden a product with a desired shape. During the heating phase, the sheet is heated up to the thermoplastic softening temperature by one or two benches of infrared radiative lamps called heaters. If the material sheet is heated and softened not properly, it can not be formed adequately, resulting in a scrap product. In this paper a sheet heating model is validated with a finite element analysis for quartz tube IR heaters. Quartz tubes are faster and more reactive than ceramic heaters, enabling the use of more flexible power regulation strategies. Hence, the validated model is uploaded over a HIL real-time testbench for rapid control prototyping, enabling the development of a model predictive controller for the optimal online regulation of the heating stage in the thermoforming process.

Digital twins (DTs) are a critical technology that bridges the gap between physical machinery and real-time manufacturing decisions. DTs provide a viable solution to digital and intelligent manufacturing challenges. They have proven effective in various scenarios, particularly core components in industrial machining applications, electrical machines, and drive systems. As Industry 4.0 advances, DTs are expected to enhance design optimization, fault diagnosis, and coordinated control, significantly strengthening these systems. This study proposes a DT framework for a Permanent Magnet Synchronous Machine (PMSM), a part of a machine tool spindle, to connect isolated machines to an interconnected system and monitor machine conditions in real time. The proposed approach facilitates the design of process monitoring and fault diagnosis methods for PMSM drives used in industrial applications by incorporating disturbances from multiple perspectives. It is asserted that DT modeling is highly comparable to the natural system. Subsequently, for various operating conditions, a simulated faulty condition is induced to demonstrate the real-time state of the PMSM while a bearing fault occurs.

Solar photovoltaic (PV) power forecasting is a crucial aspect of efficient energy management in the renewable energy sector. This study examines the use of artificial neural networks (ANNs) to forecast solar PV power output. It considers various factors influencing power output and investigates different ANNs for prediction. Real-world PV power data is collected and preprocessed for training and testing ANNs such as recurrent neural networks, autoencoders, and convolutional neural networks. The results show that ANNs, particularly Long Short-term memory (LSTM), accurately forecast PV power output in the short term. The study also analyzes the impact of panel ageing on PV power using machine learning models, revealing effective prediction of performance degradation. Clustering the dataset into sunny and cloudy subsets, and using separate models for each subset improves prediction accuracy. The study presents a comprehensive analysis of ANNs for PV power forecasting and the influence of panel ageing, highlighting the potential of machine learning for precise and reliable predictions.

The distribution system is highly penetrating with the Electric Vehicle charging stations for increasing Electric Vehicle demand for a sustainable future. The impact of this large-scale integration into the distribution network at a medium voltage level has to be analyzed before dealing with the optimization problem of Electric Vehicles. This article elucidates the modeling of Electric Vehicle charging behavior using a measurement dataset whose aggregation integrated into the medium voltage network is analyzed to study their impact on the power network using probabilistic load flow simulation. The medium voltage network selected is the 69 bus test network to which Electric Vehicle aggregation is imposed as PQ load, and the voltage distribution and the voltage unbalance factor for such integration are discussed in the article at three different time windows i.e., morning, midday and evening.

Smart gird is intelligent and interactive among its power devices using the communication network running a protocol for reliable transfer of messages from one device to another or with the system operator for critical operation and control of the substation. Employing protocol such as IEC61850 globally in the electrical substation need to be well tested for time critical operations, physical security of the substation with proper logic implementation for power flow in case of an event. If the substation is connected to the internet then the problem gets more complex with respect to cyber security challenges faced over this protocol.In this work, Hardware-in-the-loop simulation methodology of Smart grid including the physical IEC61850 communication net-work layer using Intelligent electronic devices of circuit breakers is presented. GOOSE messages and MMS server communication using IEC61850 in the smart grids are investigated inside the real-time simulation boundaries. The unique aspect of this work is the logic implementation among the protection devices are tested with real communication delays over the protocol that can occur between them, and these results are more realistic matching the real world scenario.

Modeling heating phenomena for industrial applications is crucial for both the optimization and performance control of these systems. In this work, we focus on creating a modular digital twin for modeling thermoforming systems based on a lumped parameter model. This paper discusses and demonstrates the validity of a complete model-based digital twin and data integration system to analyze a direct heating system with ceramic heating elements. A cascade model describing the system’s main features is presented: the heaters, the view factor explaining the effect of the heaters on the sheet, and finally, a heating model of a flat polymer sheet. Validation of the model is then done using finite element software. The proposed mathematical model has low complexity and is useful in the development of improved control strategies, optimization of geometric parameters, analysis of disturbance reduction techniques, heater characterization, and sensory system definition.

The electricity market is constantly growing and is facing unprecedented planning needs. Loads are no longer as systematic as in the past due to changing user habits and the likewise for the generation of energy due to the unpredictability of renewable sources. Demand response (DR) strategies are the basis of resource planning, but these methods cannot ignore the presence of accurate load models that can predict behavior. In this panorama, the prediction of the loads due to the recharging of electric vehicles, offers interesting ideas of complexity, which make it a topic of open research. In this paper we will show a method of reconstruction of charging profiles through Markov Chains, starting from distributions derived from experimental data.

The diffusion of electric vehicles is undoubtedly the problem of power distribution networks. It becomes therefore essential to have the tools that allow to measure the impact of EV penetration on distribution networks. This work deals with studying the impact of charging electric vehicles, imagining a distribution not only temporal, but also spatial. In fact, the proposed approach tries to take into account the movement of vehicles during a time interval. Depending on the distance traveled, the EV battery reaches a certain level of charge and therefore the EV may need to recharge at the next stop. The impact on the electrical demand of the substations is then estimated by combining the time-varying electrical load with the EV fleet charging behavior based on these geographically spread charging stations. Because of the stochastic nature of the problem, the analysis is based on Monte Carlo simulation to calculate reliability indexes for the power substations.

An electro-mechanical model of a SF6circuit breaker for medium voltage applications, i.e. for voltages up to 36 kV and short circuit currents up 50 kA is presented. The model is developed by starting from the well known Schwarz-Advonin arc model with the addition of the mechanical effects on the arc geometry and of the “puffer”. The puffer can blow the arc during its full duration and mainly during current zero crossing to extinguish it. This action is carefully exploited by breaker designers and often is the key aspect to obtain arc extinction in high current conditions.

The optimization of electrical machines can be managed using advanced computational intelligence algorithms. These algorithms can speed up the design phase and can improve the performances, being able to find out the optimal design also in problems involving a large number of physical and geometric parameters. In this paper, a new population based metaheuristic algorithms, named Social Network Optimization (SNO), has been used to find the optimal design of a tubular permanent magnet linear generator (TPMLG), in the context of a vehicular energy harvesting system.

In the near future Electrical Vehicless (EVs) will most likely replace conventional combustion-engine based ones. This and the increase in the use of renewable energy sources will have an important positive impact on our ecosystem. At the same time it will require a serious reanalysis and possibly redesign of the structure of our distribution network. In this paper we propose a model that, at a relatively fine level, describes the behavior of communities in terms of transport requirements, derives statistic driving behavior patterns and determines the corresponding induced Electric Vehicle Charging habits. The energy consumption of every simulated EV is computed taking into account the features of the trip and the vehicle itself. The model has been tailored to data extracted from a portion of the Milan (Italy) metropolitan area, but is trivially adaptable to any area for which a similar set of data is available. The obtained results are compared to data from similar works, showing good agreement. The outputs of the model, i.e. the energy consumption of the vehicles and their time-spatial distribution within a specific area (and consequently the loads on the distribution network nodes due to charging operations), have been (and will be) used to perform impact analyses on network performances and on personal mobility.

Point absorbers, i.e. wave energy converters (WECs) whose characteristic dimensions are small with respect to the typical wavelength, are designed to be installed in arrays, in order to get a cost-effective power generation. Starting from a coupled hydrodynamic - electromagnetic model of a heaving point absorber, this paper presents an in-depth analysis of the hydrodynamic interactions among devices in array. The final goal is to find out the configurations of a four WEC array, which maximize the energy absorption and the quality of the power output. At this aim, different topological layouts, distances among converters and incident wave directions are investigated. The results highlight that a proper design of the wave farms allows to significantly reduce power fluctuations without negative side effects on electricity production.

Probe insertion is traditionally used in the frequency domain to increase robustness of the harmonic balance method and avoid the DC degenerate solution. This technique evidences several good properties, for instance when simulating oscillators that are based on high quality factor resonators or AC coupled ones. In this paper the probe insertion technique is extended to the time domain, allowing the application of the shooting method to a wider class of circuits including for example crystal oscillators. Time domain methods, such as shooting, are superior in the simulation of strongly nonlinear, and, thanks to a recent extention, mixed analog/digital circuits. Moreover, time domain probe insertion can be used to find multiple steady state solutions and check their stability properties, without the need to compute the eigenvalues of the monodromy matrix.

This paper proposes a novel electrical power system architecture capable to efficiently transfer energy from a solar generator to high power electric thrusters of a spacecraft. The architecture is based on a maximum power point tracker that acts on a DC/DC converter connected between a subsection of the solar generator and the thrusters. The other sections of the solar generator, that deliver most of the total power, are directly connected to the thrusters. The novelty of the proposed architecture is that the maximum power point tracker is always operating and, while conditioning only a fraction of the total power, is able to transfer to the thrusters an amount of power greater than the handled one.

The synthesis of low depth command streams for power bridges is considered, with regards to drives for ac motors. Focus is on the exploitation of the degrees of freedom still available once the command rate is set. Using a nonlinear motor model, different options are evaluated up to the mechanical output. Options such as Delta Sigma Modulation (ΔΣM) and Pulse Width Modulation (PWM) are associated to tuples of merit factors defining points in a performance space. Standard ΔΣM is shown to favor perceived drive quality, while PWM helps keeping the switching rate low. Interpreting both PWM and ΔΣM as heuristics for Pulse Density Modulation (PDM), it is shown that ΔΣM may offer some commonly unexploited forms of tuning and may lead to a more flexible choice of placement on the performance space.

Periodic circuits driven by multitone signals are still a challenging simulation problem despite several numerical methods being presented in the literature. In this paper, a mixed time-frequency method for the solution of this problem and suitable for both autonomous and nonautonomous circuits is presented. The method is based on an extension of the envelope following method, which allows us to reduce the number of unknowns involved in the steady-state problem with respect to previous mixed time-frequency approaches, and a suitable reformulation of the periodicity constraint that allows us to obtain a significant acceleration in the determination of the solution by reducing the time interval along which the envelope analysis must be performed. The method is first presented for nonautonomous circuits and then extended to autonomous ones.

The probe-based harmonic balance (HB) method is a well-known and largely used tool to compute the steady state behavior of autonomous circuits (oscillators). In this paper, the method is extended to analyze coupled oscillators, where the working frequencies and conditions, i.e., pulling and locking modes, have great relevance. It is shown that probe insertion can be considered as a specific matrix-bordering technique applied to the Jacobian matrix of the HB method. It transforms the original coupled autonomous system in a non-autonomous one, forcing the circuit to lock to the probes themselves. In this context, a novel approach to find the steady state solution is introduced. This approach exploits the properties of the power exchanged among the probes and the coupled oscillators. Possibly, more than one steady state solution with the oscillators working in pulling or locking modes can be found.

In this paper the optimization of a Tubular Permanent Magnet-Linear Generator (TPM-LiG) for energy generation is presented. The application is related to the sea wave energy generation for small sensorized buoy. The optimization process is developed by means of an hybrid evolutionary algorithm widely presented in the paper. The advantage of this algorithm is in the wide exploration of the variables space and in the effective exploitation of the fitness function. The algorithm has been tested on a benchmark case and then applied to the optimization of the here considered device.

The shooting method is largely employed to determine the steady-state working condition of both autonomous and nonautonomous circuits. In general, the conventional shooting method employs the Newton algorithm to estimate a better approximation of the steady-state working condition. The Newton algorithm requires the computation of the Jacobian matrix and this seriously limits the use of the conventional shooting method to solve medium/large scale circuits. In this paper, an approach to efficiently determine the shooting matrix is presented. It is shown that the approach is also adequate to deal with mixed analog/digital circuits.

This paper describes an approach to determine the Floquet exponents and the related eigenfunctions of linear time-varying circuits, that represent a vehicle to implement the variational model of periodic nonlinear circuits. The Floquet exponents and eigenfunctions allow us to exploit the structure of the analytical solution of the linear time-varying circuit and, as shown, yield an efficient solution. The proposed approach allows the calculation of the Floquet exponents directly and is developed from the harmonic-balance formulation adopted to find the steady-state solution of the nonlinear periodic circuit.