Coordinating renewable generation, electricity demand, and battery storage requires scheduling methods that can represent both numerical operating limits and evolving policy constraints. This paper presents a neurosymbolic decision framework in which neural models generate day-ahead photovoltaic-generation and building-load forecasts, while a declarative symbolic layer determines storage power and grid exchange. The scheduling formulation combines real-valued storage variables with logical rules and cardinality constraints. It is evaluated using measured data from a university energy system over 124 daily scheduling instances. In the base problem, the symbolic formulation and a Gurobi-based mathematical-programming formulation attain the same aggregate grid-exchange objective on every evaluated day. A sequential Greedy strategy also matches the aggregate result under the simplified base assumptions. An additional experiment limits the number of hourly battery discharging intervals. Under this global restriction, the symbolic and mathematical-programming formulations preserve the reference objective on all 124 instances, whereas the Greedy strategy does so on 97 instances. The average runtime in the extended problem is 8.297 s for the symbolic formulation and 0.004 s for the mathematical-programming formulation. These results indicate that the symbolic approach offers a direct declarative representation of operational policies, while conventional mathematical programming remains computationally faster in the evaluated extension. The conclusions are restricted to deterministic, offline, single-site scheduling with point forecasts and idealized lossless storage.

This paper introduces an advanced learning-based fault-tolerant control strategy for DC-DC buck-boost converters, demonstrating significant improvements over classic approaches under challenging operating conditions. The proposed Learning-based Fault-Tolerant Control PI (LFTC-PI) integrates Fuzzy C-Means (FCM) clustering with adaptive parameter adjustment to enable fault detection and mitigation dynamically. In contrast to fixed-parameter robust controllers, which often depend on conservative designs, the LFTC-PI adaptively adjusts its control parameters based on operational data, allowing for prompt adjustments in response to component degradation and external disturbances. Comprehensive simulation studies demonstrate substantial performance improvements: reduced settling time, lower overshoot, and diminished steady-state error compared to classic fault-tolerant control strategies. Moreover, the proposed controller ensures stable operation even under simultaneous 40% inductance and capacitance degradation, with concurrent voltage and load disturbances. An analysis of computational complexity demonstrates the algorithm’s suitability for implementation on standard embedded systems. This approach represents a transition from reactive to proactive fault mitigation in power electronics, with potential applications in electric vehicle charging infrastructure and renewable energy systems, where reliability is critical.

The increasing penetration of renewable generation and distributed storage requires scheduling methods that combine accurate prediction with flexible constraint modeling. This paper proposes a neurosymbolic framework for renewable energy community scheduling in which neural forecasting modules estimate photovoltaic production and building-level demand, and a symbolic optimization layer computes storage and grid-exchange decisions using Answer Set Programming with Linear Constraints. The framework separates data-driven prediction from declarative decision modeling: forecasts provide time-indexed exogenous parameters, while technical limits, storage dynamics, and operational policies are encoded as symbolic constraints. We evaluate the approach on real data collected at the University of Calabria under configurations combining measured and predicted production and consumption. The results show that the Answer Set Programming with Linear Constraints scheduler computes optimal day-ahead schedules with negligible runtime for 24-hour horizons and obtains the same energy-exchange optimum as a Mixed-Integer Linear Programming baseline in the considered cases. Beyond numerical performance, the symbolic formulation supports incremental model extension: additional operational requirements, such as limiting battery discharge events to mitigate degradation, can be expressed through compact declarative rules without redesigning the solver. These results indicate that Answer Set Programming with Linear Constraints is a suitable optimization layer for energy-community scheduling when operational requirements are expected to evolve over time.

This study investigates the problems associated with the nonlinear power-voltage characteristics of photovoltaic (PV) systems, especially under partial shading conditions (PSC), which reduce energy efficiency and tracking accuracy. To overcome these limitations, two improved maximum power point tracking (MPPT) controllers based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques are proposed. The controllers are designed with a suggested architecture that uses the power-voltage derivative ([Formula: see text]) and the voltage time derivative ([Formula: see text]) as input features, enabling predictive, non-iterative control. This approach eliminates the steady-state oscillations inherent in conventional perturb-and-observe (P&O) algorithms and achieves superior dynamic response under rapidly changing environmental conditions. Simulation results demonstrate significant improvements compared with the traditional P&O method. The proposed ANN and ANFIS controllers achieved average tracking efficiencies ([Formula: see text]) of 99.4% and 99.75%, respectively, with a response time reduction of about 55% and steady-state oscillation suppression exceeding 70%. The ANFIS controller exhibited higher stability, reducing the duty-cycle fluctuation index ([Formula: see text]) by approximately 20% compared with the ANN controller, resulting in smoother and more reliable power extraction. A comparative evaluation with recently published metaheuristic and hybrid AI-based MPPT approaches confirmed that the proposed ANFIS model achieves equal or better performance while maintaining very low computational complexity. The average execution time per control step remained below 0.2 ms, confirming the suitability of both controllers for real-time deployment on low-cost digital signal processors (DSPs). These findings demonstrate that the proposed intelligent MPPT framework provides a fast, accurate, and computationally efficient solution for improving the reliability and energy yield of PV systems operating under dynamic and partially shaded conditions.

The increasing penetration of Non-Programmable Renewable Energy Sources (NPRES) is reducing system inertia and challenging the stability of modern power grids. Energy Storage Systems (ESSs) offer flexible solutions for supporting frequency regulation and providing further services such as peak shaving and load leveling. This study presents the StorInPlus device, a grid-following inverter integrated into a Virtual Energy Storage System (VESS) via the ViSto platform, designed to provide multiple ancillary services. A detailed simulation model was developed in Simulink using the Simscape Electrical library, including a double-loop control structure, a two-stage Phase-Locked Loop (PLL) for grid synchronization, and a mechanism for estimating frequency and Rate of Change of Frequency (RoCoF). The inverter’s active power reference combines a baseline contribution with service-specific components for Synthetic Inertia (SI), Fast Frequency Reserve (FFR), and secondary services. The model incorporates a DC-link, IGBT-based PWM modulation, an LCL filter, and a delta-star transformer. Simulation and experimental results provided by a laboratory prototype demonstrate that StorInPlus can effectively track grid conditions, provide rapid dynamic support, and enhance grid stability under high renewable penetration, validating its potential as a scalable solution for modern power systems.

Complex microgrids with the inclusion of renewable energy sources and multiple energy storage systems based on batteries and hydrogen storage require fast, reliable, and adaptable management approaches to achieve profitability. Uncertainty in power generation and demand levels renders the application of typical optimization methodologies impossible in real-time management scenarios, due to slow response times and the risk of power imbalances. In this paper, we introduce a new machine learning based approach to the real-time management of complex microgrids under uncertainty. A decision tree classifier is trained on solved-to-optimality mixed-integer linear programming problem instances. Then, a decision algorithm is proposed to dispatch the microgrid operation. The proposed machine learning approach shows promising performance on a real dataset simulation compared to the benchmark rule-based approach, resulting in a significant 22% yearly operational cost reduction tested on the real data.

The rapid integration of renewable energy has dramatically increased power system complexity, requiring decision-support tools that deliver accurate, physically consistent, and human-interpretable responses to operational queries. This work proposes DSM-EQA, a physics-informed multimodal large language model that seamlessly fuses operational time-series, meteorological data, and grid topology into a unified latent representation, adapts a pretrained language model via low-rank adaptation, injects physics knowledge through continuous prefix conditioning, and simultaneously generates fluent natural-language explanations and quantitative system indicators under strict power-balance, voltage security, and thermal-limit constraints enforced by differentiable regularization. Evaluated on a physics-verified benchmark of over 10,000 question-answer pairs across six core energy management tasks, DSM-EQA achieves 87.3% Decision Support Accuracy, 94.6% physics compliance, and 89.4% numerical accuracy surpassing GPT-3.5-Turbo by 29.2 percentage-points and GPT-4o by 16.7 percentage-points while reducing hallucinations to 3.2% (Cohen ' sZd = 0.82, Zp < .001), with benchmark case studies confirming 91% alignment with operator-validated reference decisions. DSM-EQA provides a strong, physically grounded foundation for trustworthy AI decision support in safety-critical energy infrastructure during the global energy transition.

The global transition towards sustainable transportation depends on the successful integration of renewable energy sources with electric vehicle (EV) charging systems. In South Asia a region shaped by rapid urbanisation, heavy fossil fuel import dependence, and worsening urban air quality this integration remains critically underdeveloped despite significant policy commitments. This systematic review, guided by PRISMA 2020 guidelines, examines the barriers that prevent green energy-EV integration across five key South Asian economies: India, Pakistan, Bangladesh, Sri Lanka, and Nepal. Unlike prior reviews that treat EV adoption or renewable energy deployment as separate concerns, this study is the first to apply a unified cross-country comparative framework specifically to the green energy-EV nexus in South Asia, delivering insights that country-level or general developing-country studies cannot provide. Country-specific findings show that India leads in deployment scale while Pakistan holds the greatest untapped renewable potential, yet both are held back by persistent structural problems such as grid instability, fragmented regulatory frameworks, high capital costs, and weak coordination between ministries. Nepal’s hydro-EV model and Sri Lanka’s experience with policy reversals offer contrasting paths that reveal both the opportunities and the pitfalls of green transport transitions in low- and middle-income settings. A frequency-weighted barrier severity scoring system, drawing on evidence counts from the reviewed literature, is used to rank barriers across countries. The review also acknowledges that cross-country comparisons are complicated by structural differences in GDP, population density, and institutional capacity, and a dedicated limitations section is included to address these confounding factors. The findings carry direct implications for policymakers, energy regulators, and development finance institutions working towards nationally determined contributions (NDCs) and the Sustainable Development Goals (SDGs).

A primary principle of modern distribution networks is to maintain the voltage profile within permissible limits while minimizing power losses. However, challenges arising from the uncertainty of electric vehicles (EVs) and wind power plants necessitate proper distribution network reconfiguration to maintain stability. Therefore, operators require fast and reliable tools for managing network configuration. In this research, a data-driven framework based on a Hybrid Transformer-CNN (HT-CNN) model is presented for network reconfiguration. In this framework, wind speed is first predicted using a TCN-BiGRU model. Then, the main HT-CNN model uses this prediction, along with load data and EV charging/discharging profiles, to simultaneously estimate bus voltages, switch statuses, and network losses. The proposed model, trained with wind, load, and EV charging data on the IEEE 33-bus system, reduced the loss error to 1.46 kW and the voltage error to 0.00417 per-unit, while predicting switch statuses with 94.96% accuracy. These errors are significantly lower than the standard 5% margin in network planning, confirming the model’s high reliability for identifying optimal configurations with minimal losses. In this study, EV charging and discharging data were simulated in MATLAB. This data, along with predicted wind data, was used to solve a linearized network reconfiguration problem in GAMS. Subsequently, the GAMS output was used to train and test the neural network model in Python. Finally, the accuracy and results of the methods were analyzed and validated using DIgSILENT software.

The rapid escalation of electric vehicle adoption places unprecedented pressure on power distribution infrastructure, generating load demand patterns that undermine the operational stability and resilience of the charging infrastructure.Maintaining stable and reliable operations requires moving beyond reactive interventions toward proactive, anticipatory management strategies. In this work, we propose a novel optimization framework that identifies the minimal corrective actions needed to preserve grid stability before instability occurs. Our approach embeds machine learning classifiers within a mixed-integer linear program (MILP) model to preemptively determine the optimal power curtailment factors for each charging station, using counterfactual explanations to generate real time stability-restoring decisions. The optimization model includes optimal power flow approximations as constraints, guaranteeing that the proposed solutions are consistent with the actual operating conditions of the distribution infrastructure. We benchmark our proactive approach against a reactive optimal power flow baseline, assuming full observability and perfect information, demonstrating that our framework achieves near-optimal real-time stability.

Microgrids are increasingly recognized as efficient and sustainable solutions for modern energy management, especially in scenarios requiring high resilience and self-sufficiency. The intermittent nature of renewable energy sources presents significant challenges in balancing energy supply and demand. The integration of both short- and long-term energy storage systems represents a viable solution to these issues. Among the various long-term storage systems, the strategic importance of hydrogen as a long-term storage solution within microgrids is well recognized, enabling greater integration of renewables, enhancing energy autonomy, and contributing to the decarbonization of energy systems. To ensure the proper operation of such systems, scheduling models that optimally coordinate these resources become necessary. However, it is challenging to find scheduling models in the literature that incorporate photovoltaic production and electrical load consumption forecast errors. This paper aims to address this lack in the literature, introducing a probabilistic approach to model photovoltaic production and electrical load consumption forecast errors to integrate into the scheduling model. For this purpose, a hydrogen-based microgrid modular configuration is proposed, integrating both a battery and a hydrogen-based storage system. On this microgrid configuration, a day-ahead (short-term) scheduling Mixed-Integer Linear Programming model is developed to schedule power flow among the different microgrid components minimising the operational costs and considering the impact of production and consumption forecast errors on system reliability, ensuring a robust energy management. This study is part of the "SmartHydroGrid" project, which aims to develop innovative digital solutions to optimize energy management of hybrid energy systems, multi-energy and multi-sector. Simulation results show an improvement from 10% to 20% in renewable energy utilization. The system achieves 80.1% self-sufficiency. Moreover, the probabilistic approach ensures robust scheduling decisions with less than 2% deviation from optimal costs across uncertainty scenarios. The primary objective of the project is to create a digital twin of a hybrid smart grid, enabling optimized and interconnected management of energy resources. The proposed model is validated using the data of a real-life application, the SmartHydroGrid deployed at the Techfem S.p.A. company site, in Italy.

This study develops a Mixed-Integer Linear Programming optimization model, implemented in Python with the Gurobi solver, to quantify the economic benefits of integrating Electric Vehicles into Renewable Energy Communities. The research investigates how coordinated Vehicle-to-Grid and Grid-to-Vehicle operations provide Ancillary Services while maximizing revenues from market incentives and local selfconsumption. By accounting for battery degradation and diverse driving cycles, the model evaluates the impact of these revenues on the Discounted Payback Period of the initial vehicle purchase cost. Results demonstrate that the vehicle fleet effectively balances grid requests and community energy sharing, significantly enhancing financial viability compared to standard mobility use. This integration reduces the payback period for all analyzed models, reaching a return on investment in less than seven years for affordable vehicles. The findings confirm that smart charging ensures grid stability and provides critical flexibility, accelerating the transition toward sustainable, community-based energy systems.

Concentrating Solar Power (CSP) plants represent a promising solution for flexible and dispatchable power generation, enabling a significant increase in the share of renewable energy in electricity production. However, an effective grid-integrated CSP solution requires careful assessment of plant design and power dispatching to maximize operators' profits while adhering to local regulations. This study introduces a novel methodology for designing and selecting the operational scenarios of a CSP plant to maximize the operator's profits within a national market framework. A case study of a multi-tower CSP system integrated within a Renewable Energy Community (REC) was considered, using real energy demand data and assuming Italian electricity prices and regulations. A numerical model was implemented to assess the CSP plant's performance, and a detailed cost analysis was conducted. The Levelized Cost of Electricity (LCOE) was calculated for fifty-six plant layouts, and the configuration with the lowest LCOE was selected to determine the plant arrangement needed to meet the REC members' power demand of 1 MWe. Fifteen dispatching scenarios were designed, considering tertiary regulation ancillary services, demand following, and simple energy supply. The profit for each scenario was then evaluated, and a comparative analysis was conducted. The results, following the outlined methodology, indicate that the lowest LCOE is 12.42 c kWh-1, and that three single-unit CSP plants are required to power the REC members. The multi-tower CSP system enables the plant operator to achieve the highest annual profit of 430 k by exclusively providing ancillary services to the electric grid during the evening hours.

A DC-nanogrid is a small-size DC smart grid (not over 10 kW) generally for residential applications, based on a DC bus, which can interconnect different generation plants with electric storage systems and at least an inverter to supply uninterruptible critical loads. In the next future, Renewable Energy Communities are intending to serve the power system in terms of flexibility, reliability and optimal management of distributed energy storage. The DC-nanogrids are the key technologies to the smart functioning of microgrids and key factors for building Renewable Energy Communities. In this study the environmental evaluation, via Life Cycle Assessment (LCA), of a DC-nanogrid for home application (called in ComESto project nGfHA) has been carried out. The focus of the study is twofold: the analysis of the nGfHA hardware and the use of a targeted approach for the detailed evaluation of the nGfHA operation. An experimental approach based on a four days' timeline has been implemented for the dynamic LCA evaluation of the nGfHA operations. The results highlight that the hardware of the nGfHA has secondary effects on the environmental impacts of the complete nGfHA configuration; the highest contribution to the environmental impact comes from the Li-ion energy storage, followed by PV, use phase and nGfHA hardware. Nevertheless, a sensitivity analysis shows that an extended lifetime of energy storage leads to equivalent environmental performances.

Applications such as fuel cell-based electric cars can benefit from the inherent characteristics of interleaved boost converters (IBCs), which include high voltage gain and minimal output voltage and input current ripples. To achieve effective regulation in the face of parameter changes, its critical to maintain the globally stable regulated IBC, especially as the use of highly variable loads increases. An innovative Integral sliding-mode controller along with a disturbance observer for the two-phase IBC (2PIBC) is suggested to ensure such large-signal stability. The time-domain approach is employed to verify the averaged model of the system, which forms basis of designing the controlled system. Several aspects of SMC design are discussed in detail, such as global stability, control signal creation, and sliding surface type selection. The proposed controller is shown to have excellent tracking capabilities with low computations. The use of the recommended control algorithm is demonstrated through MATLAB results and also using experiments based on prototype and using dSPACE 1104.

Renewable Energy Source plants represent an excellent compromise between environmental sustainability and power supply. At the same time, their adoption coincides with the gradual decommissioning of coal-fired power plants. This poses significant challenges for the electrical power system in terms of available power reserve and frequency regulation, making the electric power system more susceptible to abrupt changes in load and/or generation. This issue could be mitigated by integrating Distributed Energy Storage Systems with renewable energy sources through the adoption of a Virtual Energy Storage system approach. This integration would help to eliminate, or rather, contain, the frequency variations associated with an imbalance between demand and generation. A Multi-Agent System strategy is proposed to manage the energy flows of each energy storage system operating as a Virtual Energy Storage System within an energy community framework, under the oversight of a generic Distribution Network Operator. The goal is to compensate for power imbalances and therefore frequency observed by the Transmission Network Operator. After a brief mention of frequency regulation in a scenario of strong renewable energy source penetration, the proposed Multi-agent System approach, implemented in a Python simulation environment, is illustrated, and subsequently tested on a distribution network portion.

Distribution network reconfiguration stands out as a potent approach for reducing power losses in radial distribution feeders, where losses notably are more those observed in transmission systems. The distribution power losses incurred not only lead to costly operations but also contribute to a compromised voltage profile in electric energy grids. Habitual reconfiguration involves adjusting the form of the distribution grid to minimize electric energy losses, with a focus on the electricity demanded by end-consumers. The existing models predominantly address distribution network reconfiguration without accounting for the different types of load demand linked to points of consumption, each exerting distinct power loss effects in contrast to single-type demands. Despite the imperative load type diversity role, most models neglect this aspect. The limited studies incorporating load type into reconfiguration strategies tend to employ non-linear formulations, introducing complexities. While models with non-linear characteristics may be tackled via metaheuristic techniques, they fall short of guaranteeing optimal solutions. Alternatively, solving such models using solvers for non-linear equations in conventional tools of optimization demands computations that require significant time. To address these challenges, the present article proposes an impressive model for reconfiguring distribution networks that consider different load types. Notably, this model is designed for seamless implementation with linear procedures. Our study’s results demonstrate the exactitude of the introduced model in yielding accurate results for targeted reconfiguration challenges, coupled with the advantage of swift execution suitable for real-time reconfigurations.

Voltage Source Converters (VSCs) are essential for grid-connected systems, enabling efficient active and reactive power control. However, classic fixed-gain controllers struggle with nonlinear dynamics, parameter uncertainties, and harmonic distortions, leading to performance degradation under non-ideal conditions. This paper introduces an unsupervised learning-based inner control loop (UL-ICL) that dynamically adjusts controller gains and decoupling parameters using fuzzy c-means clustering. By addressing these challenges, the UL-ICL ensures accurate current tracking and robust decoupling, even under dynamic conditions. Simulation results validate its effectiveness, outperforming fixed-gain controllers in precision and resilience. The proposed method offers a promising solution for real-time control of grid-connected VSC systems.

This study proposes an advanced hybrid fault-tolerant control (FTC) architecture for permanent magnet synchronous motors (PMSMs) operating under speed sensor faults (SSFs), integrating model predictive control (MPC), third-order sliding mode control (TOR-SMC), and a model reference adaptive system (MRAS). The key innovation lies in the synergistic combination of MPC's predictive optimization with the robustness of TOR-SMC and the real-time adaptive estimation capability of MRAS, enabling reliable operation in the presence of sensor degradation or failure. A residual-based fault detection mechanism is embedded to monitor discrepancies between actual and estimated rotor speeds, enabling rapid fault identification and seamless transition to observer-based control. The proposed hybrid control system is designed within a hierarchical architecture, wherein MPC optimizes inverter switching actions, TOR-SMC ensures robust disturbance rejection and chattering suppression, and MRAS delivers high-fidelity speed estimation. Simulation studies under various operating scenarios-encompassing step changes in speed, variable torque loads, and fault scenarios-demonstrate that the system achieves a maximum speed estimation error of 1.8%, speed tracking accuracy of 97.6%, and a fault detection time of less than 2.5 ms, which is 41.3% faster than extended Kalman filter (EKF)-based schemes. Quantitatively, the proposed method reduces torque ripples by 32.5%, current overshoot by 35.7%, and transient response time by 27%, while improving overall fault tolerance by 63% compared to conventional FTC approaches. The TOR-SMC module contributes to a 78% reduction in chattering and ensures stable electromagnetic torque behavior even under dynamic torque disturbances. In parallel, MRAS offers faster convergence (2.5 ms) and smoother transitions compared to SMO and EKF observers, maintaining control integrity despite sensor anomalies. This comprehensive and modular FTC approach addresses a critical vulnerability in PMSM drive systems and is particularly well-suited for deployment in electric vehicles, aerospace systems, and renewable energy platforms, where high reliability, real-time responsiveness, and robustness to sensor degradation are paramount. The results confirm the proposed hybrid MPC-TOR-SMC-MRAS framework as a scalable and high-performance solution for next-generation motor control systems under fault-prone environments.

The rapid transition of the power sector towards decarbonization, decentralization, and digitalization has significantly increased the penetration of non-dispatchable renewable energy sources, introducing new challenges in frequency stability due to reduced system inertia. Smart grids, integrating distributed generation, energy storage systems, and advanced control strategies, are emerging as a key solution to address these issues. In this context, synthetic inertia has become a critical ancillary service, especially in low-inertia networks. Hydrogen-based storage systems, particularly in power-to-hydrogen-to-power configurations, offer promising capabilities for both renewable energy optimization and frequency support services. This study presents the dynamic modeling and simulation of a smart grid incorporating a hydrogen storage system alongside a photovoltaic generator and a lithium-ion battery, interconnected via a DC bus. The hydrogen subsystem includes a fuel cell, an electrolyzer, and a storage tank, enabling bidirectional energy exchange with the grid. Developed in the MATLAB/Simulink environment, the model evaluates the system's ability to absorb surplus renewable power, supply active power during demand peaks, and provide synthetic inertia for frequency regulation. Simulation results demonstrate the potential benefits of integrating hydrogen storage in enhancing the stability, flexibility, and resilience of smart grids.