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The rapid increase in renewable energy integration and electric vehicle (EV) adoption creates significant challenges for the stability and efficiency of power distribution networks. This study addresses the need for optimized placement and sizing of Electric Vehicle Charging Stations (EVCSs), photovoltaic (PV) systems, and Distribution Static Compensators (DSTATCOMs) to enhance grid performance. The motivation for this work arises from the fluctuating nature of renewable energy generation and the unpredictable demands of EV charging, which strain existing infrastructure. To address these challenges, we propose a novel optimization framework that introduces the Renewable Distributed Generation Hosting Factor (RDG-HF) and Electric Vehicle Hosting Factor (EV-HF) as key metrics. These metrics, combined with the Hippopotamus Optimization Algorithm (HO), enable strategic planning within the IEEE 69-bus system. Simulation results demonstrate that the integrated placement of EVCSs, PVs, and DSTATCOMs reduces power losses by up to 31.5% and reactive power losses by up to 29.2%. An economic analysis further reveals payback periods ranging from 2.7 to 10.4 years and potential profits of up to $1,052,365 over 25 years. These findings highlight the importance of optimized integration in improving both technical performance and long-term economic benefits for distribution networks.

This research paper presents an advanced AI-driven hybrid power quality management system for electrical railways that addresses critical challenges in 25 kV AC traction networks through a novel integration of single-phase PV-UPQC with ANN-Lyapunov control architecture. The system effectively manages voltage unbalance exceeding 2%, high THD, voltage variations of ± 10%, and poor power factor through a dual-approach methodology combining ANN-based reference signal generation with Lyapunov optimization, enabling dynamic parameter tuning and real-time load adaptation. MATLAB/Simulink simulations validate the system’s superior performance, demonstrating significant improvements, including voltage unbalance reduction from 1.5 to 0.8%, THD reduction below 1%, unity power factor correction, 40% faster dynamic response, and DC link voltage regulation within ± 2%, while maintaining 95% overall system efficiency. Integrating ANN-based shunt and series APF control, Lyapunov optimization, and PV integration establishes a robust framework for enhanced energy efficiency and power quality management in modern railway systems.

This study presents the design and performance evaluation of a bidirectional electric vehicle charging system integrating solar photovoltaic energy with an Artificial Neural Network based control strategy. The proposed architecture employs a modified Single-Ended Primary Inductor Converter capable of supporting both Grid-to-Vehicle and Vehicle-to-Home operating modes while maintaining stable bidirectional power flow between the grid, photovoltaic source, and EV battery. The ANN controller dynamically regulates the duty cycle of the MOSFET switches using battery current feedback and reference current signals, enabling adaptive control under varying solar irradiance and grid conditions. Simulation results indicate that the proposed system achieves charging efficiencies above 90% while maintaining stable operation for both 72 V and 240 V EV battery configurations. Compared with conventional proportional–integral control approaches, the ANN controller demonstrates faster transient response and improved current regulation during dynamic operating conditions. The integration of solar photovoltaic energy further reduces reliance on grid power and enhances renewable energy utilization in EV charging infrastructure. These results indicate that the proposed ANN-controlled bidirectional charging system provides an efficient and flexible solution for renewable-integrated EV charging applications.

Residential buildings in hot-arid regions experience high cooling loads and rapidly increasing electricity demand, making cost-effective retrofits a strategic priority. This study evaluates techno-economic retrofit pathways that can move existing single-family homes in Saudi Arabia toward Near Zero-Energy Building (NZEB) performance. A detailed energy model of a representative detached villa in Riyadh was developed in DesignBuilder and calibrated against measured 2023 electricity bills (60,048 kWh/year), demonstrating close agreement between simulated and observed consumption in accordance with ASHRAE Guideline 14. The calibrated model was used to assess both individual and bundled retrofit measures, including external wall insulation, high-performance double glazing, extended horizontal shading devices, and a rooftop photovoltaic (PV) system sized to the available roof area. Results show that envelope retrofit packages combining insulation, glazing, and external shading can reduce annual electricity consumption by 34–37% (20,668–22,447 kWh) relative to the calibrated baseline, although shading alone exhibited weak standalone economic performance. Pareto-optimized scenarios highlight clear trade-offs between minimum energy use and economic performance: insulation-intensive packages achieve the lowest site energy consumption (37,601–38,473 kWh/year) but result in longer payback periods (72.4–108.9 months), whereas lower-cost configurations without insulation yield shorter payback periods (31.1 months) with slightly higher residual energy use. Integrating a 20.5 kWp rooftop photovoltaic system further reduces net grid electricity demand to 9,489–11,268 kWh/year, approaching near zero-energy performance; however, overall payback periods increased to 70–116 months due to the additional upfront investment required for PV systems.

The growing penetration of photovoltaic (PV) generation in multi-energy microgrids has amplified the challenges of maintaining real-time operational efficiency, reliability, and safety under conditions of renewable variability and forecast uncertainty. Conventional rule-based or optimization-based strategies often suffer from limited adaptability, while purely data-driven reinforcement learning approaches risk violating physical feasibility constraints, leading to unsafe or economically inefficient operation. To address this challenge, this paper develops a Physics-Informed Reinforcement Learning (PIRL) framework that embeds first-order physical models and a structured feasibility projection mechanism directly into the training process of a Soft Actor–Critic (SAC) algorithm. Unlike traditional deep reinforcement learning, which explores the state–action space without physical safeguards, PIRL restricts learning trajectories to a physically admissible manifold, thereby preventing battery over-discharge, thermal discomfort, and infeasible hydrogen operation. Furthermore, differentiable penalty functions are employed to capture equipment degradation, user comfort, and cross-domain coupling, ensuring that the learned policy remains interpretable, safe, and aligned with engineering practice. The proposed approach is validated on a modified IEEE 33-bus distribution system coupled with 14 thermal zones and hydrogen facilities, representing a realistic and complex multi-energy microgrid environment. Simulation results demonstrate that PIRL reduces constraint violations by 75–90% and lowers operating costs by 25–30% compared with rule-based and DRL baselines while also achieving faster convergence and higher sample efficiency. Importantly, the trained policy generalizes effectively to out-of-distribution weather conditions without requiring retraining, highlighting the value of incorporating physical inductive biases for resilient control. Overall, this work establishes a transparent and reproducible reinforcement learning paradigm that bridges the gap between physical feasibility and data-driven adaptability, providing a scalable solution for safe, efficient, and cost-effective operation of renewable-rich multi-energy microgrids.

The increasing penetration of photovoltaic (PV) systems and the need for reliable backup power solutions have led to the development of hybrid uninterruptible power supply (UPS) systems. These systems integrate PV energy storage with battery backup and grid power to optimize real-time energy management. This paper proposes an advanced energy management strategy and an artificial neural network (ANN)-based control method for PV-integrated hybrid UPS systems. The proposed strategy dynamically determines the optimal power-sharing ratio between battery storage and the grid based on real-time economic parameters, load demand, and battery state of charge (SoC). A centralized ANN-based controller ensures precise control of the LLC converter and rectifier, achieving stable and efficient power distribution. Additionally, a genetic algorithm is implemented to optimize the power sharing ratio, minimizing the LCOE under varying load and electricity pricing conditions. The proposed approach is validated through simulations, demonstrating significant improvements in cost-effectiveness, system stability, and dynamic adaptability compared to conventional control methods. These findings suggest that integrating ANN-based control with optimized energy management can enhance the efficiency and sustainability of hybrid UPS systems, particularly in fluctuating energy markets.

Two area multi-unit thermal hydro (TAMTH) system integrated with solar and dish-Stirling solar thermal system (DSTS) is investigated to regulate the frequency disturbance. An adaptive controller with a combination of Fuzzy Logic Control (FLC), Fractional Order proportional integral derivative (FOPID) and 2 Degree of Freedom PID (2 DOFPID) is designed to achieve frequency stability. The decisive parameters of the proposed Fuzzy based FOPID-2DOFPID (FFOPID-2DOFPID) controller are optimized by Crow Search Algorithm (CSA) and craziness factor of crow in CSA (CCSA). The proposed FFOPID-2DOFPID controller is enforced in each area for both thermal and hydro units to contribute a fine-tuned stable power system. The conformation of superiority of projected controller is presented by a comparative analysis with different kind of controllers along with some newly published research works. The comparative simulation performance analysis is performed by considering undershoot, overshoot and settling time of deviations to show the supremacy of the FFOPID-2DOFPID controller.

Climate change has prompted the adoption of sustainable measures to reduce greenhouse gas (GHG) emissions, particularly in urban transportation. The integration of renewable energy sources, such as solar energy, offers a promising strategy to enhance sustainability in urban transit systems. This study assessed solar irradiation along the tram route in Cuenca—an Andean city characterized by distinctive topographic and climatic conditions—with the aim of evaluating the technical feasibility of integrating solar energy into the tram infrastructure. A descriptive, applicative, and longitudinal approach was adopted. Solar irradiation was monitored using a system composed of a fixed station and a mobile station, the latter installed on a tram vehicle. Readings carried out over fourteen months facilitated the analysis of seasonal and spatial variability of the available solar resource. The fixed station recorded average irradiation values ranging from 3.80 to 4.61 kWh/m2·day, while the mobile station reported values between 2.60 and 3.41 kWh/m2·day, revealing losses due to urban shading, with reductions ranging from 14.7% to 18.8% compared to fixed-site values. It was estimated that a fixed photovoltaic system of up to 1.068 MWp could be installed at the tram maintenance depot using 580 Wp panels, with the capacity to supply approximately 81% of the annual electricity demand of the tram system. Complementary solar installations at tram stops, stations, and other related infrastructure are also proposed. The results demonstrate the technical feasibility of integrating solar energy—through fixed and mobile systems—into the tram infrastructure of Cuenca. This approach provides a scalable model for energy planning in urban transport systems in Andean contexts or other regions with similar characteristics.

This study aims to develop and apply a novel methodology to assess the scope, benefits and challenges of distributed photovoltaic generation (DG-PV). The research provides a replicable framework applicable to any country, as long as official energy consumption data are available and the nation is seeking to modify its energy matrix as part of a sustainable transition through the design of renewable-energy-based policies. To support the viability of the proposal, data from the Ecuadorian electrical system for the period between 2014 and 2024 were analyzed using technical, operational and socio-economic indicators defined in the methodology. These include renewable participation, energy diversification, DG-PV, technical efficiency, regulatory index, operational resilience and electrical coverage. The investigation concludes with the definition of a Distributed Photovoltaic Integration Index (DPII), which can be used to measure a country’s progress toward the proper implementation of renewable energy. The DPII supports informed decision-making by allowing utilities and policymakers to prioritize distributed photovoltaic integration and compare alternative energy transition scenarios. In the case of Ecuador, a DPII of 0.170 is obtained for 2024 compared to a value of 0 for 2014. This result is mainly due to an increase in renewable energy participation (P1), which rose from 0.49 to 0.76 during this period, largely supported by hydropower expansion. This value was obtained because over the last ten years, Ecuador has committed to implementing active policies that incorporate renewable energies, as well as other aspects such as technical efficiency and the expansion of electrical coverage. This approach offers a replicable quantitative tool for evaluating the integration of DG-PV, providing key information for energy planning and for the formulation of policies that promote the decarbonization, decentralization and digitalization of the national electrical system.

Variable transmission applications for light control or energy saving based on electrochromic materials have been successfully applied in the past in the building, sports, or automotive fields, although lower costs and ease of fabrication, installation, and maintenance are still needed for deeper market integration. In this study, all-printed large area (900 cm2 active area) flexible electrochromic devices were fabricated, and an autoregulating self-power supply was implemented through the use of organic solar cells. A new perspective was applied for automotive light transmission function, where portability and mechanical flexibility added new features for successful market implementation. Special emphasis was placed in applying solution-based scalable deposition techniques and commercially available materials (PEDOT-PSS as an electrochromic material; vanadium oxide, V2O5, as a transparent ion-storage counter electrode; and organic solar modules as the power supply). A straightforward electronic control method was designed and successfully implemented allowing for easy user control. We describe a step-by-step route following the design, materials optimization, electronic control simulation, in-solution fabrication, and scaling-up of fully functional self-powered portable electrochromic devices.