Influenced by the nonlinear dynamics of mathematical models found in power systems, an analysis of the dynamical behaviour of the coupled swing equation is performed. The coupled formulation explains the interactions between interconnected synchronous machines, which play a critical role in modern power systems. This paper examines analytically and numerically the emergence and evolution of oscillatory periodic solutions under primary resonance. As the control parameter is altered, the system undergoes a cascade of period-doubling bifurcations, which leads to loss of stability and the onset of complex dynamical behaviour can be observed. Phase portraits, time series and Poincaré maps are used to characterise the transition between different dynamical patterns to identify the precursors to instability. A thorough understanding of the system’s behaviour shows valuable insights into bifurcation with the appearance of the initial period doubling phenomena, depicting early indicators of adverse effects that can occur within an interconnected power system.
The study involves the integration of stochastic modeling and optimization of the on-grid photovoltaic (PV) system with Homer Pro and Python tools. Probabilistic functions were applied, along with 3 metrics (coefficient of determination R², Akaike AIC information criterion and Kolmogorov-Smirnov KS test) on consumption data from the Cotopaxi Technical University (UTC). Under the UTC, there was a variation in daily radiation between 3.98 - 4.55 kWh/m²/day, while the monthly demand varied from a minimum equal to 521 kWh to a maximum of 4,529.00 kWh. The results of the optimal photovoltaic system involved several technical aspects, with which it must have a capacity of 6.20 kW, annual production of 6,764.00 and a capacity factor of 9.62%. In relation to the economic aspects, the economic viability of the system was demonstrated by having a levelized cost of energy (LCOE) of 0.085 USD/kWh and a net present cost (NPC) of $47,711.87. This study is essential for modelling the charging behaviour, which improves the robustness of the system which provides solid support for sustainable energy planning.
Active and reactive power management and scheduling in power systems is critical for ensuring stable energy availability for residential, commercial, and industrial end-users. To address the scheduling of active/reactive power proportions, this paper proposes a novel Advanced Ensemble Optimization Framework named GIRCEDUMDA_AVOA_GWGwDVO. This hybrid methodology integrates the Artificial Vultures Optimization Algorithm (AVOA) for exploration, the Group-Weighted Genetic Algorithm with Dynamic Variable Optimization (GWGwDVO) for exploitation, and the Grouped Importance RCEDUMDA for statistical resource allocation. The strategy optimizes the parameter values through a phase-specific evaluation budget of a determined number of iterations (in a benchmarking that maximum accept 5000 iterations), significantly reducing performance indicators (risk based and expected cost). Simulation results on IEEE 30, 118, and Indian 62 bus systems demonstrate a convergence improvement of 37% and a computational efficiency increase of 42% compared to traditional methods. Extensive error analysis and numerical stability assessments confirm the robustness of the proposed approach.
This paper discusses the limitations associated with traditional energy meters, which include delayed data collection, the inability to provide real-time data, and inefficient billing systems. This paper also discusses the impact of these limitations on user experience. A cost-effective Internet of Things (IoT)-based smart energy meter is proposed, and the proposed system includes the accurate collection and processing of data by utilizing NodeMCU ESP8266 and ZMPT101B and ACS712 current and voltage sensors. The proposed system also includes the ability to display data locally and to monitor data remotely by utilizing the Blynk mobile application. The proposed system also includes the ability to compute data and to control the load remotely. The proposed system includes a custom-designed printed circuit board to make the system compact and efficient. The proposed system also includes empirical results, and the proposed system can be considered efficient for real-world applications.
This study presents the development of an intelligent sensorless control system for a pumping unit driven by an asynchronous motor under variable hydraulic load conditions. The proposed approach integrates an electromechanical motor model based on the Park–Gorev transformation with a hydraulic load model and applies an extended Kalman filter (EKF) for real-time estimation and prediction of stator current and rotor speed. Unlike conventional systems, the method eliminates the need for pressure and flow sensors by relying solely on electrical measurements. Simulation and experimental results demonstrate high accuracy, with deviations below 5%. The developed model improves energy efficiency, enhances system reliability, and enables stable operation under fluctuating conditions, making it suitable for industrial pumping applications.
This work presents an intelligent energy management approach for a connected hybrid system composed of photovoltaic (PV) and tidal energy, optimized to supply electricity for the TIMAB Gabes industry. The suggested configuration enables various sources to supply power to the load either independently or concurrently, contingent upon meteorological conditions. The strategy is based on an optimistic management algorithm considering production, prioritized load consumption, and variable electricity tariffs. To do this, we first present the modeling of the different sources equipped with the controllers, allowing their production to be maximized. In the second part, the energy management algorithm is developed to guarantee a consistent allocation of energy transfers between sources, load, and the grid. The manager is finally implemented in the MATLAB/Simulink simulator. The assessment of the complete hybrid system's functionality has been conducted. The obtained results demonstrate the effectiveness of the system in ensuring reliable operation and improving the utilization of renewable energy resources, making it a suitable solution for industrial applications.
This paper presents an Algorithm for solving the Optimal Reactive Power Dispatch problem under exponential load modeling. Traditional formulations assume constant power demands, which neglect the voltagedependent nature of real-world loads. In this work, we incorporate an exponential load model that accurately represents residential, commercial, industrial, and rural consumption patterns. The proposed EBOA combines the standard BOA with crossover mechanisms to improve exploration and exploitation capabilities. The algorithm is tested on the IEEE 30-bus system using both the traditional constant-power model and the proposed exponential load representation. Compared to conventional constant-power modeling, the exponential model reduces active power losses by up to 24.7%, improves voltage deviation by 91.1%, and enhances voltage stability by approximately 38% in the IEEE 30-bus system. These results highlight that incorporating voltage-dependent characteristics leads to more realistic and economically efficient reactive power dispatch solutions.
Predicting how much power solar panels will produce is a major hurdle for stable energy grids, largely because solar output is notoriously unpredictable, nonlinear, and constantly shifting. To tackle this, we’ve developed a hybrid deep learning framework designed for more accurate short-term forecasting. Our approach uses "CEEMDAN" to break down complex solar data into manageable components, paired with Iterative Filtering (IF) to pull out the most meaningful hidden patterns. These refined insights are then processed through a Bidirectional Long Short-Term Memory ("BiLSTM" ) network. By combining these tools, our model captures the deep, time-sensitive relationships in solar generation that traditional methods often miss. Each component is then modeled using "BiLSTM" networks to capture bidirectional temporal dependencies, and the final forecast is obtained through signal reconstruction. The proposed model is evaluated using real-world PV datasets collected from Tindouf (Algeria), and its performance is compared with benchmark deep learning and decomposition-based approaches. Testing proves that this framework markedly boosts both the accuracy and reliability of solar power predictions. With an R^2 of 99.06 %, an "RMSE" of 158.20 "kW" , and an "nRMSE" of 6.15%, the model consistently leads the way over standard LSTM models and other hybrid setups. These outcomes validate the idea that combining "CEEMDAN" decomposition, precise time-frequency feature extraction, and bidirectional deep learning creates a powerful, dependable solution for short-term forecasting. By blending these specific techniques, the system manages to capture the nuance of solar data more effectively than previous methods.
Accurate and computationally efficient forecasting is essential for the undisturbed operation of power systems. Traditional methods cannot detect the nonlinear behaviour of modern electrical grids, affected by various factors such as seasonal variations, peak-hour demand fluctuations the increasing penetration of renewable energy sources, etc. To overcome these limitations, an adaptive hybrid method is proposed, combining the Multi Model Partitioning Filter (MMPF) with Nonlinear Autoregressive Exogenous (NARX) models and a Genetic Algorithm for Resource Allocation (GARA). The MMPF structure remains unchanged, implementing NARX submodels to detect the non-linear data characteristics. Additionally, the GARA optimizes the MMPF overall weights through an evolutionary, procedure. The new method is evaluated using real data from the Hellenic grid and compared against two established methods, MMPF–SVM and MMPF–GA. The results indicate that the proposed method achieves better forecasting accuracy and reduced computational burden, making it suitable for applications including autonomous or mission-critical energy networks.
The paper aims to develop a model for forecasting energy production from a photovoltaic (PV) system given a short range of data. Real data for a recently installed PV system is available. An analytical model based on time series analysis has been developed. A feature of modeling is the insufficient amount of available data needed to make predictions. This limitation necessitates the use of a relatively simple forecasting model, such as the linear autoregressive model AR(1). To achieve better forecasting accuracy, a set of technologies, such as optimization, the least squares method, model predictive control, and a sliding procedure for shifting the beginning of historical data, was used. A comparison between actual and forecasted values has been made where possible. The proposed innovative approach is a good tool for planning energy production from photovoltaic systems. This can be beneficial for the declared power generation of an energy supplier, which has a cost-effective result. In addition, the forecast of photovoltaic system generation can help in the correct design of the inverter and battery parameters of the PV system.
This article presents a new way of combining simulations performed in MATLAB 21/Simulink with DIgSILENT PowerFactory 2024software, in order to optimize the use of electrical compensators known as STATCOM in modern electrical systems. The main objective is to address instability problems generated by the increasing use of renewable energies, such as solar or wind. The proposal includes a detailed model and highly advanced adaptive control, which allows fast correction of electrical voltage problems in just 18.7 milliseconds, keeping overshoots below 1.8%. This clearly exceeds the requirements of the IEEE 421.5-2022 international standard. The control system uses advanced vector modulation techniques in dq coordinates, resulting in a very high efficiency of 98.2%, operating normally under conditions of ±50 MVAr. Tests show significant improvements in power quality, with a 43.8% reduction in harmonic distortion (THDv) and 62% fewer losses caused by imbalances. The proposed solution fully complies with important international standards such as IEEE 1547-2018 and NERC PRC-025-2, demonstrating its effective use in real and smart grids. This method also has great potential for application in very high voltage electrical systems (500 kV or higher). The study also paves the way for future research on how to further improve real-time communications, achieving very small delays (less than 100 microseconds).
An improved method is introduced for the simultaneous reduction of both total active power loss and total voltage deviation in power grids. Multi-objective particle swarm optimization is utilized to select the optimal sizing and placement of distributed generators, subject to the overall power balance in the network and physical constraints of the power grid components. The optimization process produces a Pareto optimal set from which the best compromise solution is selected using fuzzy set theory. The proposed algorithm is tested on the IEEE 69-bus system, and results are compared to other modern multi-objective algorithms. Finally, a real-world case is studied where the proposed algorithm is employed to find the optimal DGs sizing and placement to improve the performance of Al-Dhahiriya City Distribution System.
The increasing penetration of renewable energy is reducing the role of traditional synchronous machines and introducing new stability challenges in modern power systems. Inverter-based resources enable renewable integration but lack natural inertia and strong reactive power capability, leading to frequency instability, weak voltage regulation, and higher sensitivity to disturbances. This study investigates how combining battery energy storage systems with VSC-HVDC transmission can enhance grid stability. Two MATLAB/Simulink models are developed: a grid-connected PV system with BESS and a multi-terminal VSC-HVDC system integrating offshore wind power. Both systems are tested under grid faults and load disturbances using grid-forming and grid-following control strategies. Results show that grid-forming converters outperform grid-following ones in inertia emulation, frequency response, and fault ride-through capability. Coordinated BESS–HVDC operation improves transient stability, dynamic voltage support, and reliable renewable power transfer. The findings align with IEEE 2800-2022, demonstrating advanced coordinated control of storage.
To comprehend the global energy supply chain`s development and explore how Moroccan wind energy supply chain can be improved to be more localized and resilient, this paper examines wind energy supply chain dynamics through three-ways analytical framework: (1) a narrative analysis of global best practices and challenges, (2) a critical analysis of both international and Moroccan wind energy supply chain structures, and (3) conbination of a SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis and Porter`s Five Forces of Morocco's wind energy ecosystem. The research presents, that while global supply chains face material shortage and logistical complexities, Morocco's emerging market uncovers twofold characteristics as a recipient of global technology transfers and as a potential regional hub with a distinct competitive advantage. The results show that Morocco can exploit its strategic 52% renewable energy target by 2030 by addressing vital weaknesses in national manufacturing capacity while leveraging international partnerships. This study contributes (i) a novel mixed analytical framework for wind energy supply chains, (ii) empirical validation of Morocco's positioning between global dependencies and local opportunities, and (iii) policy recommendations for supply chain localization that balance international standards with emerging market realities.
This work proposes the methodological basis in the development of a robust power system planning model for capital and intermediate cities using machine learning and computational intelligence techniques. The model, following the methodological basis, seeks to optimize the distribution of energy resources through the analysis of key variables, such as energy demand, generation capacity, use of renewable sources, operating costs and physical distribution infrastructure. The integration of artificial intelligence techniques (and availability of data) allows capturing complex patterns in the operation of the electric system, improving demand prediction and generation dispatch optimization. To this end, a data set is implemented that includes historical information on energy consumption, meteorological variables and characteristics of the electrical infrastructure.
The widespread reliance on conventional energy sources such as coal, oil, and natural gas has driven the focus toward developing renewable energy alternatives. Renewable sources like solar and wind energy are mature, cost-effective, and widely utilized. Additionally, fuel cell technology has reached an advanced stage of development. These energy sources are not only abundant and cost-free but also environmentally friendly. Combining these resources leads to the creation of a hybrid energy system. The proposed hybrid system, integrating solar energy, wind energy, and fuel cells, is highly effective for distributed energy production.
The transition toward climate neutrality goals by 2050 is a key priority of the European Union (EU), which has adopted an ambitious legislative and strategic framework to decarbonize all economic sectors. Among these, the transport sector – particularly road freight transport – remains one of the most challenging to decarbonize due to its heavy reliance on fossil fuels, complex logistics chains, and significant operational and geographical constraints. As a result, this sector is widely recognized as “hard-to-abate” and requires integrated policies that combine technological, infrastructural, and behavioral changes. To address these challenges, the EU has progressively implemented a comprehensive normative system over the past two decades, including high-level strategies and targets (e.g., the European Green Deal, the Fit for 55 package), emission standards, carbon pricing mechanisms (ETS and ETS II), and infrastructure regulations (e.g., AFIR), along with a growing portfolio of Renewable Energy Directives (RED I, II, III). These measures aim at reducing the carbon footprint by promoting energy efficiency, renewable fuels, electrification of freight vehicles, and development of alternative fuel infrastructure. This paper provides a structured and holistic review of EU freight decarbonization policies, with a particular emphasis on the alignment between legislative frameworks and real-world implementation strategies. Its original contribution is bridging regulatory theory and operational feasibility, offering a practical reference for policymakers and transport planners. Unlike previous literature reviews, this study integrates the ASI FOR FREIGHT research project – funded under Italy’s National Recovery and Resilience Plan within the National Center for Sustainable Mobility (MOST), Spoke 10 – as a concrete case study that tests how EU decarbonization policies are applied in practice. Through a combined policy review and applied analysis, the study explores the regulatory landscape and its implications for heavy and light freight transport. This dual approach enhances methodological clarity and provides original insights into how European legislation supports the transition toward sustainable freight systems, thereby informing policymakers, stakeholders, and researchers.
Battery factories play a crucial role in meeting the growing demand for energy storage solutions. However, it is essential that such factories maintain high-quality production standards in their battery manufacturing processes to ensure reliable and safe performance. This thesis focuses on the design and implementation of a machine learning-based monitoring system built to detect quality degradation factors in battery factories. The monitoring system proposed in this research leverages the power of machine learning techniques to identify and analyze factors that can potentially contribute to quality degradation in battery production. By continuously monitoring various parameters and variables throughout the manufacturing process, this system can effectively detect deviations and anomalies that may indicate the presence of quality issues.
Tunnel ventilation control is intended to guarantee a safe and comfortable environment for users. The controllers are responsible for managing concerns caused by gases released in the tunnels, such as carbon monoxide, nitrogen oxide, and dust. This study focuses on reconfigurable fuzzy controllers, considering traffic density in order. To lower carbon monoxide levels and smoke opacity, hence improving air quality. The global ventilation system consists of five cascaded reconfigurable fuzzy controllers, a global reconfiguration management block, and a zoning module. The global control system, managed by the reconfiguration approach, ensures real-time corrections and coordination among the different controllers. A reconfigurable fuzzy logic controller (RFLC) was designed to optimise the indoor environment and validated using simulation data. The RFLC was implemented on an FPGA using the Xilinx 14.7 development platform, combining both behavioral and structural VHDL descriptions. The developed system provides a maximum frequency of 91.106 MHz.
Gas-insulated substations have emerged over the air-insulated substations due to their stable operation with rapid development in transmission systems. However, abnormalities during switching operations in Gas-Insulated Switchgear pose significant challenges to system insulation and reliability, especially while operating with extra high voltages. This study explores the mechanisms contributing to its generation, emphasising the modelling of the disconnector switch under the influence of trapped charges and its diverse operating conditions in a 550 kV GIS, using a recurrent flash approach. This paper presents the examination of these overvoltage characteristics under various scenarios, with cautious observation of key factors used in its modelling to assess the disconnector full-scale type testing and to assist decisions on the various operating conditions. The reliability of the model is validated through simulation results and supported by field data from multiple studies. These outcomes inspire methods for optimising insulation design and operating protocols, hence boosting the system.
