Search Results (42)

The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure. Full article

This paper discusses a thorough analysis, as well as the design, of an environmentally friendly, single-stage Organic Rankine Cycle (ORC) system, particularly optimized for untapped geothermal applications in Pakistan that are secluded and off-grid, to tackle the severe energy crises choking this country and its resources. Keeping in mind its Global Warming Potential (GWP), as well as its performance in the ORC, r600a was chosen as the operating fluid. This study focuses on varying the temperature, pressure, and mass flow rate of not only the geothermal reservoir but that of the operating fluid in the ORC as well. The impacts of adjusting these parameters on the net power output, cycle efficiency, and component-wise exergy destruction, as well as the total exergy destruction, are examined extensively. Analyses of the component-wise exergy destruction found that the maximum exergy destruction occurred in the evaporator, whereas it was discovered that decreasing the condenser pressure below 350 kPa led to negative exergy destruction values, although the total exergy destruction remained positive. Full article

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

Transitioning to sustainable energy systems is crucial for reducing greenhouse gas (GHG) emissions, especially in remote industrial operations where diesel generators remain the dominant power source. This study examines the feasibility of integrating a redox flow battery (RFB) storage system to optimize wind energy utilization at the Raglan mining site in northern Canada, with the goal of reducing diesel dependency, enhancing grid stability, and improving energy security. To evaluate the effectiveness of this hybrid system, a MATLAB R2024b-based simulation model was developed, incorporating wind energy forecasting, load demand analysis, and economic feasibility assessments across multiple storage and wind penetration scenarios. Results indicate that deploying 12 additional E-115 wind turbines combined with a 20 MW/160 MWh redox flow battery system could lead to diesel savings of up to 63.98%, reducing CO₂ emissions by 68,000 tonnes annually. However, the study also highlights a key economic challenge: the high Levelized Cost of Storage (LCOS) of CAD (Canadian dollars) 7831/MWh, which remains a barrier to large-scale implementation. For the scenario with high diesel economy, the LCOS was found to be CAD 6110/MWh, and the corresponding LCOE was CAD 590/MWh. While RFB integration improves system reliability, its economic viability depends on key factors, including reductions in electrolyte costs, advancements in operational efficiency, and supportive policy frameworks. This study presents a comprehensive methodology for evaluating energy storage in off-grid industrial sites and identifies key challenges in scaling up renewable energy adoption for remote mining operations. Full article

The Raglan mining site in northern Quebec relies on diesel for electricity and heat generation, resulting in annual emissions of 105,500 tons of CO₂ equivalent. This study investigates the feasibility of decarbonizing the site’s power generation system by integrating a renewable energy network of wind turbines and a pumped hydro storage plant (PHSP). It uniquely integrates PHSP modeling with a dynamic analysis of variable wind speeds and extreme climatic conditions, providing a novel perspective on the feasibility of renewable energy systems in remote northern regions. MATLAB R2024b-based simulations assessed the hybrid system’s technical and economic performance. The proposed system, incorporating a wind farm and PHSP, reduces greenhouse gas (GHG) emissions by 50%, avoiding 68,500 tons of CO₂ equivalent annually, and lowers diesel consumption significantly. The total investment costs are estimated at 2080 CAD/kW for the wind farm and 3720 CAD/kW for the PHSP, with 17.3 CAD/MWh and 72.5 CAD/kW-year operational costs, respectively. The study demonstrates a renewable energy share of 52.2% in the energy mix, with a payback period of approximately 11 years and substantial long-term cost savings. These findings highlight the potential of hybrid renewable energy systems to decarbonize remote, off-grid industrial operations and provide a scalable framework for similar projects globally. Full article

Standalone wind–solar–diesel–storage microgrids serve as a crucial solution for achieving energy self-sufficiency in remote and off-grid areas, such as rural regions and islands, where conventional power grids are unavailable. Addressing scheduling optimization challenges arising from the intermittent nature of renewable energy generation and the uncertainty of load demand, this paper proposes an adaptive optimization scheduling method (DQN-PSO) that integrates Deep Q-Network (DQN) with Particle Swarm Optimization (PSO). The proposed approach leverages DQN to assess the operational state of the microgrid and dynamically adjust the key parameters of PSO. Additionally, a multi-strategy switching mechanism, incorporating global search, local adjustment, and reliability enhancement, is introduced to jointly optimize both clean energy utilization and power supply reliability. Simulation results demonstrate that, under typical daily, high-volatility, and low-load scenarios, the proposed method improves clean energy utilization by 3.2%, 4.5%, and 10.9%, respectively, compared to conventional PSO algorithms while reducing power supply reliability risks to 0.70%, 1.04%, and 0.30%, respectively. These findings validate the strong adaptability of the proposed algorithm to dynamic environments. Further, a parameter sensitivity analysis underscores the significance of the dynamic adjustment mechanism. Full article

The worldwide growing adoption of Light Electric Vehicles (LEVs) indicates that such technology might in the near future be decisive for improving the sustainability of transportation. The segment of LEVs has some peculiar features compared to electric mobility in general, which then deserve a devoted investigation. Stakeholders are called to implement the most appropriate technology depending on the context, by taking into account multi-faceted factors, which are the investigation object of this work. At first, a methodology is formulated for estimating the power and energy impact of LEVs recharging. Based on this, and assessed that the load constituted by LEVs is in general modest but might create some problems in lowly structured networks, it becomes conceivable to develop Charging Station (CS) technologies which are alternative to the grid connection at a point of delivery. Yet, it is fundamental to develop accurate methodologies for the techno-economic and environmental analysis. This work considers a use case developed at the University of Brescia (Italy): a CS operating off-grid, powered by PhotoVoltaics (PV). Its peculiarity is that it is transportable, which makes it more appealing for rural/remote areas or when the charging demand is highly not homogeneous in time. On these grounds, this work specializes to a context where the proposed solution might be more appealing: small isolated islands, in particular Favignana in Sicily (Italy). It is estimated that the adoption of the proposed off-grid CS is by far advantageous as regards the greenhouse gases emissions but it is more economically profitable than the grid connection only if the number of users per day is less than order of 200. Hence this work provides meaningful indications on the usefulness of off-grid CS powered by PV in peculiar contexts and furnishes a general method for their techno-economic and environmental assessment. Full article

As the need to monitor agriculture parameters intensifies, the development of new sensor nodes for data collection is crucial. These sensor types naturally require power for operation, but conventional battery-based power solutions have certain limitations. This study investigates the potential of harnessing the natural temperature gradient between soil and air to power wireless sensor nodes deployed in environments such as agricultural areas or remote off-grid locations where the use of batteries as a power source is impractical. We evaluated existing devices that exploit similar energy sources and applied the results to develop a state-of-the-art device for extensive testing over a 12-month period. Our main objective was to precisely measure the temperature on a thermoelectric generator (TEG) (a Peltier cell, in particular) and assess the device’s energy yield. The device harvested 7852.2 J of electrical energy during the testing period. The experiment highlights the viability of using environmental temperature differences to power wireless sensor nodes in off-grid and battery-constrained applications. The results indicate significant potential for the device as a sustainable energy solution in agricultural monitoring scenarios. Full article

Hybrid energy systems (HESs) are gaining prominence as a practical solution for powering remote and rural areas, overcoming limitations of conventional energy generation methods, and offering a blend of technical and economic benefits. This study focuses on optimizing the sizes of an autonomous microgrid/HES in the Kingdom of Saudi Arabia, incorporating solar photovoltaic energy, wind turbine generators, batteries, and a diesel generator. The innovative reinforcement learning neural network algorithm (RLNNA) is applied to minimize the annualized system cost (ASC) and enhance system reliability, utilizing hourly wind speed, solar irradiance, and load behavior data throughout the year. This study validates RLNNA against five other metaheuristic/soft-computing approaches, demonstrating RLNNA’s superior performance in achieving the lowest ASC at USD 1,219,744. This outperforms SDO and PSO, which yield an ASC of USD 1,222,098.2, and MRFO, resulting in an ASC of USD 1,222,098.4, while maintaining a loss of power supply probability (LPSP) of 0%. RLNNA exhibits faster convergence to the global solution than other algorithms, including PSO, MRFO, and SDO, while MRFO, PSO, and SDO show the ability to converge to the optimal global solution. This study concludes by emphasizing RLNNA’s effectiveness in optimizing HES sizing, contributing valuable insights for off-grid energy systems in remote regions. Full article

Access to reliable and sustainable energy in remote areas remains a pressing global challenge, significantly affecting economic development and the quality of life. This study focuses on the implementation of fully off-grid wind–biogas hybrid power systems to address this issue, with a focus on remote healthcare camp facilities. This paper investigates the performance of a hybrid renewable energy system within the context of one of Jordan’s northern remote areas, the Zaatari Syrian Refugee Camp, assessing its efficiency and environmental impact by taking the Zaatari hospital as the case study. Simulations were conducted to evaluate system components, including wind turbines, biogas generators, and diesel generators. A comprehensive evaluation was conducted, encompassing both the operational efficiency of the system and its impact on the environment. This study also considered various scenarios (SC#), including grid availability and autonomy levels, to optimize system configurations. The techno-economic assessment employed the levelized cost of energy (LCOE) as a key performance indicator, and sensitivity analyses explored the impact of diesel costs and wind power fluctuations on the system. Additionally, environmental assessment was conducted to evaluate the environmental effects of hybrid systems, with a specific focus on reducing greenhouse gas emissions. This investigation involved an examination of emissions in three different scenarios. The results indicate that the lowest LCOE that could be achieved was 0.0734 USD/kWh in SC#1 with 72.42% autonomy, whereas achieving 100% autonomy increased the LCOE to 0.1756 USD/kWh. Additionally, the results reveal that in scenarios SC#2 and SC#3, which have a higher proportion of diesel generator usage, there were elevated levels of NOx and CO₂ emissions. Conversely, in SC#1, which lacks diesel generators, emissions were notably lower. The proposed hybrid system demonstrates its potential to provide a reliable energy supply to healthcare facilities in remote regions, emphasizing both economic feasibility and environmental benefits. These findings contribute to informed decision making for sustainable energy solutions in similar contexts, promoting healthcare accessibility and environmental sustainability. Full article

This paper proposes an optimal solution for the design of a hybrid power system that will supply a remote fishpond in eastern Serbia. In terms of structure, this off-grid system should be a hydro-photovoltaic-diesel-converter-battery setup. The optimization objectives are to minimize total net present cost (NPC) and greenhouse gas (GHG) emissions and to maximize total annual electricity generation based on the modification of hydro-turbine performance. This study considers the following three cases of a hydro-turbine with fixed propeller blades: having fixed guide vanes, for the annual average flow rate-Case 1; having adjustable guide vanes, for smaller flow rates-Case 2 and having adjustable guide vanes, for higher flow rates-Case 3. The optimization is performed using HOMER Pro v. 3.16.2 software. The results show that the total NPC, levelized cost of energy (COE) and GHG emissions in Case 3 are 16.6%, 16.8% and 13.1% lower than in Case 1, and 8.1%, 8% and 11.7% lower than in Case 2, respectively. It is also found that the total annual electricity generation and power output from the entire system in Case 3 are 33.3% and 1.2% higher than in Case 1, and 11.9% higher and not different than in Case 2, respectively. Full article

Hybrid power systems based on renewable energy sources and diesel generators are efficient solutions for supplying electricity to remote and off-grid locations. One of the most crucial problems in hybrid power systems is frequency regulation, which is established by balancing the supplied power with the load demand using the load frequency control approach. Since most feedback signals are analog and the control setups are digital, the resulting control system is a sampled-data system, which requires careful designs for both the control law and the sampling frequency to guarantee closed-loop stability. This paper is concerned with the state-feedback load frequency regulation for hybrid wind–diesel power systems under event-triggered implementation. It is assumed that the full state measurement is available for feedback and that sensors and controllers communicate over a shared digital network. To mitigate the communication load on the network, an event-triggering mechanism is constructed by emulation, based on the time-regularization principle in the sense that each consecutive triggering instant is speared by a specified minimum dwell time. The closed-loop system is described as a hybrid dynamical system to account for mixed dynamical behaviors naturally arising in networked control systems. By means of appropriate Lyapunov functions, the closed-loop stability is ensured under the proposed triggering rule. Moreover, the enforced dwell time between transmissions ensures that the accumulation of sampling times is prevented, which is crucial for the event-triggering condition to be implementable in practice. The required conditions to apply this technique are derived in terms of a linear matrix inequality. Numerical simulations on an isolated hybrid power system were implemented to demonstrate the efficiency of the proposed method. Comparative simulations with relevant techniques in the literature were carried out, which showed that the proposed approach can produce fewer transmission numbers over the network. Full article

The majority of remote locations not connected to the main electricity grid rely on diesel generators to provide electrical power. High fuel transportation costs and significant carbon emissions have motivated the development and installation of hybrid power systems using renewable energy such these locations. Because wind and solar energy is intermittent, such sources are usually combined with energy storage for a more stable power supply. This paper presents a modelling and sizing framework for off-grid hybrid power systems using airborne wind energy, solar PV, batteries and diesel generators. The framework is based on hourly time-series data of wind resources from the ERA5 reanalysis dataset and solar resources from the National Solar Radiation Database maintained by NREL. The load data also include hourly time series generated using a combination of modelled and real-life data from the ENTSO-E platform maintained by the European Network of Transmission System Operators for Electricity. The backbone of the framework is a strategy for the sizing of hybrid power system components, which aims to minimise the levelised cost of electricity. A soft-wing ground-generation-based AWE system was modelled based on the specifications provided by Kitepower B.V. The power curve was computed by optimising the operation of the system using a quasi-steady model. The solar PV modules, battery systems and diesel generator models were based on the specifications from publicly available off-the-shelf solutions. The source code of the framework in the MATLAB environment was made available through a GitHub repository. For the representation of results, a hypothetical case study of an off-grid military training camp located in Marseille, France, was described. The results show that significant reductions in the cost of electricity were possible by shifting from purely diesel-based electricity generation to an hybrid power system comprising airborne wind energy, solar PV, batteries and diesel. Full article

Electric vehicles (EVs) have become popular in reducing the negative impact of ICE automobiles on the environment. EVs have been predicted to be an important mode of mass transit around the globe in recent years. Several charging stations in island and remote areas are dependent on off-grid power sources and renewable energy. Solar energy is used in the daytime as it is based on several environmental components. The creation of efficient power trackers is necessary for solar arrays to produce power at their peak efficiency. To deliver energy during emergencies and store it in case there is an excess, energy storage systems are required. It has long been known that reliable battery management technology is essential for maintaining precise battery charge levels and avoiding overcharging. This study suggests an ideal deep-learning-assisted solar-operated off-board smart charging station (ODL-SOOSCS) design method as a result. The development of on-board smart charging for mass transit EVs is the main goal of the ODL-SOOSCS technique that is being described. In the ODL-SOOSCS approach described here, a perovskite solar film serves as the generating module, and the energy it generates is stored in a module with a hybrid ultracapacitor and a lithium-ion battery. Broad bridge converters and solar panels are incorporated into the deep belief network (DBN) controller, which doubles as an EV charging station. An oppositional bird swarm optimization (OBSO) algorithm is used as a hyperparameter optimizer to improve the performance of the DBN model. Moreover, an MPPT device is exploited for monitoring and providing maximal output of the solar panel if the power sources are PV arrays. The proposed system combines the power of metaheuristic optimization algorithms with deep learning techniques to create an efficient and smart charging station for mass transport passenger vehicles. This integration of two powerful technologies is a novel approach toward solving the complex problem of charging electric vehicles in mass transportation systems. The experimental validation of the ODL-SOOSCS technique is tested on distinct converter topologies. A widespread experimental analysis assures the promising performance of the ODL-SOOSCS method over other current methodologies. Full article

Renewable energy is considered to be sustainable solution to the energy crisis and climate change. The transition to renewable energy needs to be considered on a sectoral basis and one such sector that can potentially decarbonized with renewable energy is the telecommunication sector. Several base transceiver stations (BTS) in remote regions have unstable electric supply systems. Diesel generators (DG) are a common solution to energy problems on such telecommunication sites. However, they have high fuel costs on the global market and contribute to high carbon emissions. Hybrid renewable energy systems may provide a stable power output by integrating multiple energy sources, essential for supplying a dependable and uninterrupted power supply in the context of the telecom sector, notably base transceiver stations (BTS). Deploying such a system might also help BTS, which relies mainly on diesel generators with battery storage backup, reduce operational costs and environmental problems. This study presents the framework for large-scale photovoltaic system penetration based on techno-economic analysis (based on actual on ground data with least assumptions) in base transceiver stations (BTS) encapsulating telecom sector spread across various geographical regions. The proposed framework includes a mathematical model complemented with system design in HOMER software tool. The techno-economic aspects of the study were spread across 2, 12 and 263 sites, along with comparison analysis of photovoltaic system installation with and without energy storage devices, respectively. The sites included both on-grid and off-grid sites, which were exposed to high levels of power outages and subjected to reliance on costly and environmentally hazardous diesel generators. Optimization results showed that the photovoltaic system with a diesel generator and battery storage system provide a promising solution to the energy problem, with an average decrease in LCOE of 29%, DG hour’s reduction by 82% with 92% reduction in carbon emission and a reduction in NPC of 34% due to the high availability of solar. The techno-economic analysis indicated that optimized photovoltaic system and storage results in both on–off grid BTS sites with better options, amid low cost of energy and free accessibility of solar. Moreover, the results spread across geographical regions aiming at a reliable and environmentally friendly option that reduces load on utility grid across on-grid BTS sites and substantial overall reduction in diesel usage. Full article