Search Results (129)

Off-grid and isolated rural communities in developing countries with limited resources require energy supplies for daily residential use and social, economic, and commercial activities. The use of data from space assets and space-based solar power is a feasible solution for addressing ground-based energy insecurity when harnessed in a hybrid manner. Advances in space solar power systems are recognized to be feasible sources of renewable energy. Their usefulness arises due to advances in satellite and space technology, making valuable space data available for smart grid design in these remote areas. In this case study, an isolated village in Namibia, characterized by high levels of solar irradiation and limited wind availability, is identified. Using NASA data, an autonomous hybrid system incorporating a solar photovoltaic array, a wind turbine, storage batteries, and a backup generator is designed. The local load profile, solar irradiation, and wind speed data were employed to ensure an accurate system model. Using HOMER Pro software V 3.14.2 for system simulation, a more advanced AI optimization was performed utilizing Grey Wolf Optimization and Harris Hawks Optimization, which are two metaheuristic algorithms. The results obtained show that the best performance was obtained with the Grey Wolf Optimization algorithm. This method achieved a minimum energy cost of USD 0.268/kWh. This paper presents the results obtained and demonstrates that advanced optimization techniques can enhance both the hybrid system’s financial cost and energy production efficiency, contributing to a sustainable electricity supply regime in this isolated rural community. Full article

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations. Full article

The growing demand for electric vehicles (EV) has increased the need for reliable and sustainable charging infrastructure. To address this challenge, autonomous charging stations powered by renewable energy sources (RES) are a promising solution. This paper presents a simulation-based study that determines the optimal contribution of wind farms, photovoltaic systems, and energy storage to power an autonomous EV charging station. The simulation takes into account historical weather data, EV charging patterns, and renewable energy storage capacity. The results show that by combining RES and batteries, the charging station can operate autonomously minimizing the dependence on the power grid. Battery energy storage plays a key role in balancing intermittent RES generation and variable demand from the charging station. The simulation highlights the importance of adjusting parameters to optimize the energy utilization of the charging station and minimize the dependence on the grid. Further research is warranted to optimize the design, operation, and integration with advanced energy management systems to increase the efficiency and effectiveness of these charging stations. The development of a widespread autonomous charging infrastructure powered by renewable energy sources can accelerate the transition to clean transportation and support the energy system. Full article

Air–water heat pumps are gaining interest in modern architectures, and they are a suitable option as a replacement for fossil fuel-based heating systems. These systems consume less electricity by combining solar panels, a heat pump, thermal storage, and a smart control system. This study was applied to a completely ecological rural health sub-center built on the basis of recycled bottles, and that, for its regular operation, requires an energy system according to the needs of the patients in the rural community. Detailed analyses were performed for heating and hot water preparation in two scenarios with different conditions (standard and fully integrated). From a technical perspective, different strategies were analyzed to ensure its functionality. If the photovoltaic system is sized to achieve advanced control, the system can even operate autonomously. However, due to the need to guarantee the energy efficiency of the center, the analyses were performed with a grid connection, and it was determined that the photovoltaic system guarantees at least two-thirds of the energy required for its autonomous operation. The results show that the system can operate normally thanks to the optimal size of the photovoltaic system, which positively influences the rural population in the case under analysis. Full article

The integration of multi-source energy harvesting (EH) systems into silicon presents a promising avenue for powering autonomous, low-power devices, particularly in applications such as the Internet of Things (IoT), biomedical implants, and wireless sensor networks, where power efficiency and small-size solutions are crucial. This review provides a detailed technical assessment of energy harvesting schemes—including photovoltaic, mechanical, thermoelectric, and radio frequency energy harvesting—and the integration of their associated electronic circuits into silicon integrated solutions. The EH systems are critically analyzed based on their architectures, the number and type of input sources, and key performance metrics such as energy conversion efficiency, output power delivered to loads, silicon area footprint, and degree of integration (e.g., reliance on external components). By examining current advancements and practical implementations, crucial design parameters are assessed for state-of-the-art integrated silicon energy harvesting systems. Furthermore, based on current trends, future research directions are outlined to enhance EH efficiency, reliability, and scalability, paving the way for fully integrated silicon-based EH systems for the next-generation self-powered electronic devices. Full article

An analysis of the possibilities of using alternative energy to solve the problem of electricity shortages in developing countries shows that solar energy can potentially play an essential role in the fuel and energy complex. The geographical location, on the one hand, and the global development of solar energy technologies, on the other, create an opportunity for a fairly complete and rapid solution to problems of insufficient energy supply. An autonomous solar installation is expensive; 50% of the cost is solar modules, 45% of the cost consists of other elements (battery, inverter, charge controller), and 5% is for other materials. This work proposes the most efficient PV system, based on the technical characteristics of the SB and AB. It has a direct connection between the SB and AB and provides almost full use of the solar panel’s installed power with a variable orientation to the Sun. The development of a small solar photovoltaic (PV) installation, operating both in parallel with the grid and in autonomous mode, can improve the power supply of household consumers more efficiently and faster than the development of a large energy system. It is suggested that two minimized criteria be used to create a model for forecasting FOU. This model can be used with a genetic algorithm to make a prediction that fits a specific case, such as a time series representation based on discrete fuzzy sets of the second type. The goal is to make decisions that are more valid and useful by creating a forecast model and algorithms for analyzing small PV indicators whose current values are shown by short time series and automating the processes needed for forecasting and analysis. Full article

Photovoltaic (PV) systems are integral to global renewable energy generation, yet their efficiency and reliability are frequently compromised by undetected faults, leading to significant energy losses, increased maintenance costs, and reduced operational lifespans. To address these challenges, this study proposes a novel hybrid deep learning framework that combines Stacked Sparse Auto-Encoders (SSAE) for autonomous feature extraction with an Optimized-Multi-Layer Perceptron (OMLP) for precise fault classification. The SSAE extracts high-dimensional fault features from raw operational data, while the OMLP leverages these features to classify faults with exceptional accuracy. The model was rigorously validated using real-world PV datasets, encompassing diverse fault types such as partial shading, open circuits, and module degradation under dynamic environmental conditions. Results demonstrate state-of-the-art performance, with the model achieving 99.82% accuracy, 99.7% precision, 99.4% sensitivity, and 100% specificity, outperforming traditional machine learning and deep learning approaches. These findings highlight the framework’s robustness and reliability in real-world applications. By significantly enhancing fault detection accuracy and computational efficiency, the proposed approach optimizes PV system performance, reduces operational costs, and supports sustainable energy production. This study concludes that the hybrid SSAE-Optimized MLP model represents a scalable and efficient solution for improving the reliability and longevity of renewable energy infrastructure, setting a new benchmark for intelligent maintenance strategies in the field. Full article

This study presents the development and evaluation of a stand-alone solar dryer designed to improve the efficiency of bee bread dehydration. Unlike the electric prototype powered by conventional energy sources, the proposed system operates autonomously, utilizing solar energy as the primary drying agent. The drying chamber is equipped with solar collectors located in its lower section, which ensure convective heating of the product. Active convection is generated by a set of fans powered by photovoltaic panels, maintaining the drying agent’s temperature near 42 °C. The research methodology integrates both numerical simulation and experimental investigation. Simulations focus on the variations in temperature (288–315 K) and relative humidity (1–1.5%) within the honeycomb structure under convective airflow. Experimental trials examine the relationship between moisture content and variables such as bee bread mass, airflow rate, number of frames (5–11 units), and drying time (2–11 h). A statistically grounded analysis based on an experimental design method was conducted, revealing a reduction in moisture content from 16.2–18.26% to 14.1–15.1% under optimized conditions. Linear regression models were derived to describe these dependencies. A comparative assessment using enthalpy–humidity (I–d) diagrams demonstrated the enhanced drying performance of the solar dryer, which is attributed to its cyclic operation mode. The results confirm the potential of the developed system for sustainable and energy-efficient drying of bee bread in decentralized conditions. Full article

Hybrid renewable energy systems (HRESs) are being incorporated and evaluated within seaports to realize efficiencies, reduce dependence on grid electricity, and reduce operating costs. The paper adopts a genetic algorithm (GA)-based optimization framework to assess four energy management scenarios that embed wind turbines (WTs), photovoltaic energy (PV), an energy storage system (ESS), and an energy management system (EMS). The scenarios were developed based on different levels of renewable energy integration, energy storage utilization, and grid dependency to optimize cost and sustainability while reflecting the actual port energy scenario as the base case. Integrating HRES, ESS, and EMS reduced the port’s levelized cost of energy (LCOE) by up to 54%, with the most optimized system (Scenario 3) achieving a 53% reduction while enhancing energy stability, minimizing grid reliance, and maximizing renewable energy utilization. The findings show that the HRES configuration provides better cost, sustainability, and resiliency than the conventional grid-tied system. The unique proposed EMS takes it a step further, optimizing not just the energy flow but also the cost, making the overall system more efficient—and less costly—for the user. ESS complements energy storage and keeps it functional and reliable while EMS makes it completely functional by devising ways to reduce costs and enhance efficiency. The study presents the technical and economic viability of HRES as an economic and operational smart port infrastructure through its cost-effective integration of renewable energy sources. The results reinforce the move from conventional to sustainable autonomous port energy systems and lay the groundwork for forthcoming studies of DR-enhanced port energy management schemes. While prior studies have explored renewable energy integration within ports, many lack a unified, empirically validated framework that considers HRES, ESS, and EMS within real-world port operations. This research addresses this gap by developing an optimization-driven approach that assesses the techno-economic feasibility of port energy systems while incorporating real-time data and advanced control strategies. This study was conducted to enhance port infrastructure and evaluate the impact of HRES, ESS, and EMS on port sustainability and autonomy. By bridging the gap between theoretical modeling and practical implementation, it offers a scalable and adaptable solution for improving cost efficiency and energy resilience in port operations. Full article

The rapid acceptance of solar photovoltaic (PV) energy across various countries has created a pressing need for more coordinated approaches to the sustainable monitoring and maintenance of these widely distributed installations. To address this challenge, several digitization architectures have been proposed, with one of the most recently applied being the digital twin (DT) system architecture. DTs have proven effective in predictive maintenance, rapid prototyping, efficient manufacturing, and reliable system monitoring. However, while the DT concept is well established in fields like wind energy conversion and monitoring, its scope of implementation in PV remains quite limited. Additionally, the recent increased adoption of autonomous platforms, particularly robotics, has expanded the scope of PV management and revealed gaps in real-time monitoring needs. DT platforms can be redesigned to ease such applications and enable integration into the broader energy network. This work provides a system-level overview of current trends, challenges, and future opportunities for DTs within renewable energy systems, focusing on PV systems. It also highlights how advances in artificial intelligence (AI), the internet-of-Things (IoT), and autonomous systems can be leveraged to create a digitally connected energy infrastructure that supports sustainable energy supply and maintenance. Full article

Distribution networks are an indispensable component of modern economic societies. Against the background of building new power systems, the rapid growth of distributed renewable energy sources, such as photovoltaic and wind power, has introduced many challenges for distribution network planning (DNP), including different source-load compositions, complex network topologies, and varied application scenarios. Traditional heuristic algorithms are limited in scalability and struggle to address the increasingly complex optimization problems of DNP. The emergence of new artificial intelligence provides a new way to solve this problem. Based on the above discussion, this paper proposes a DNP method based on deep reinforcement learning (DRL). By defining state space and action space, a Markov decision process model tailored for DNP is formulated. Then, a multi-objective optimization function and a corresponding reward function including construction costs, voltage deviation, renewable energy penetration, and electricity purchase costs are designed to guide the generation of network topology schemes. Based on the proximal policy optimization algorithm, an actor-critic-based autonomous generation and adaptive adjustment model for DNP is constructed. Finally, the representative test case is selected to verify the effectiveness of the proposed method, which indicates that the proposed method can improve the efficiency of DNP and promote the digital transformation of DNP. Full article

The variability of renewable energy sources, storage limitations, and fluctuations in residential demand affect the reliability of sustainable energy systems, resulting in energy deficits and the risk of service interruptions. Given this situation, the objective of this study is to diagnose and optimize the reliability of a residential microgrid based on photovoltaic and wind power generation and battery energy storage systems (BESSs). To this end, genetic algorithms (GAs) and ant colony optimization (ACO) are used to evaluate the performance of the system using metrics such as loss of load probability (LOLP), loss of supply probability (LPSP), and availability. The test system consists of a 3.25 kW photovoltaic (PV) system, a 1 kW wind turbine, and a 3 kWh battery. The evaluation is performed using Python-based simulations with real consumption, solar irradiation, and wind speed data to assess reliability under different optimization strategies. The initial diagnosis shows limitations in the reliability of the system with an availability of 77% and high values of LOLP (22.7%) and LPSP (26.6%). Optimization using metaheuristic algorithms significantly improves these indicators, reducing LOLP to 11% and LPSP to 16.4%, and increasing availability to 89%. Furthermore, optimization achieves a better balance between generation and consumption, especially in periods of low demand, and the ACO manages to distribute wind and photovoltaic generation more efficiently. In conclusion, the use of metaheuristics is an effective strategy for improving the reliability and efficiency of autonomous microgrids, optimizing the energy balance and operating costs. Full article

Photovoltaic (PV) energy is gaining traction due to its direct conversion of sunlight to electricity without harming the environment. It is simple to install, adaptable in size, and has low operational costs. The power output of PV modules varies with solar radiation and cell temperature. To optimize system efficiency, it is crucial to track the PV array’s maximum power point. This paper presents a novel fixed-point FPGA design of a nonlinear maximum power point tracking (MPPT) controller based on synergetic control theory for driving autonomously standalone photovoltaic systems. The proposed solution addresses the chattering issue associated with the sliding mode controller by introducing a new strategy that generates a continuous control law rather than a switching term. Because it requires a lower sample rate when switching to the invariant manifold, its controlled switching frequency makes it better suited for digital applications. The suggested algorithm is first emulated to evaluate its performance, robustness, and efficacy under a standard benchmarked MPPT efficiency (${\eta }_{MPPT}$) calculation regime. FPGA has been used for its capability to handle high-speed control tasks more efficiently than traditional micro-controller-based systems. The high-speed response is critical for applications where rapid adaptation to changing conditions, such as fluctuating solar irradiance and temperature levels, is necessary. To validate the effectiveness of the implemented synergetic controller, the system responses under variant meteorological conditions have been analyzed. The results reveal that the synergetic control algorithm provides smooth and precise MPPT. Full article

This paper explores the development and optimization of a hybrid renewable energy system (HRES) integrated with a hybrid battery energy storage system (HBESS) to achieve energy autonomy for an e-Houseboat. The e-Houseboat is a floating residential unit equipped with advanced sustainable technologies, including photovoltaic panels, wind turbines, and a hybrid battery storage system consisting of lithium iron phosphate (LFP) and lead-acid batteries. The primary goal of this study was to design an energy-autonomous e-Houseboat capable of sustaining energy demands for at least one month without external power sources, regardless of the season. This study included a comprehensive analysis of energy generation potential from renewable sources across different European locations, detailed simulations of the energy storage system, and the development of energy management function for a houseboat automation system. The results demonstrate the feasibility of achieving the desired energy autonomy by leveraging the synergistic benefits of multiple energy storage technologies and optimizing energy management strategies. The experiment demonstrated that the implemented solutions enabled the facility to achieve energy autonomy for a period of 7 months. Full article