Autonomous Hybrid Research Articles

Optimal multiobjective design of an autonomous hybrid renewable energy system in the Adrar Region, Algeria.

Extended power outages are not only a nuisance but a critical problem in the modern world, which demands a continuous supply of decent quality electricity. Hybrid renewable energy systems (HRES) within a microgrid (MG) play an important role in delivering energy to rural and off-grid areas and avoiding potential power outages. This research describes an in-depth study of the three phases, design, optimization, and performance analysis of a stand-alone hybrid microgrid for a residential area in a remote area in the province of Adrar in southern Algeria. The system is composed of photovoltaic (PV) modules and a wind turbine, a set of batteries as an energy storage unit, a diesel generator as a backup energy source, and an inverter. This paper investigates four recent methodologies based on Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Dragonfly Algorithm (MODA), and Multi-objective Evolutionary Algorithm (MOGA) to identify the optimal sizing of a microgrid (MG) integrated with hybrid renewable energy sources (RES). The proposed methods are carried out to select the optimal system size, which is a multi-objective problem involving the minimization of the annual cost of electricity (COE), and the loss of power supply probability (LPSP) simultaneously. To achieve this, the proposed methods are combined with energy management strategy (EMS) rules that coordinate energy flows between the various system components. The findings reveal that the MOPSO method has the most efficient hybrid renewable configuration with an annual generation cost of electricity (COE) of 0.2520 $/kWh and loss of power supply probability (LPSP) of 9.164%, which dominates the performance of MOALO (COE of 0.1625$/kWh and LPSP of 8.4872%), MOGA (COE of 0.1577$/kWh and LPSP of 10%), and MODA (COE of 0.02425$/kWh and LPSP of 7.8649%). Furthermore, a sensitivity analysis is performed for the effect that COE variants may have on the design variables.

Autonomous hybrid power plants based on renewable energy sources for off-grid rural electrification

Autonomous hybrid power plants based on renewable energy sources for off-grid rural electrification

Energy management in autonomous hybrid electric vehicles: A review

Energy management in autonomous hybrid electric vehicles: A review

A Perspective on Rehabilitation Through Open-Source Low-Cost 3D-Printed Distal to the Wrist Joint Transitional Prosthetics: Towards Autonomous Hybrid Devices

Over the years, patients with partial hand loss have relied on expensive prosthetics to recover some of the hand functionality. Fortunately, advancements in additive manufacturing desktop solutions allow transitional prosthetics prices to decrease. Therefore, the present work focused on providing a basic overview of the field and available low-cost 3D-printed upper-limb prosthetic devices. The aim was to develop a basic frame of reference on the field of transitional partial hand prosthetics. Concomitantly, this study also highlights additive manufacturing techniques on which further research can be done whilst helping to provide a new variant for an upper limb prosthetic device. The initial stages, current practices, and future possibilities were considered. Researchers and industry can utilize these findings to develop additional variants for the benefit of patients suffering from partial hand loss.

Model of optimizing operating modes of autonomous power supply systems based on hydrogen technologies for the needs of the Arctic

The possibilities of implementing a “hydrogen transition” in the local energy sector of the Arctic are considered based on the application of the concepts of environmentally friendly technologies and socio-technological transit according to the criterion of profit maximization. The relevance of the work is due to the reform of the electric power industry, which implies a phased transition to a competitive market model, when each manufacturer will be interested in maximizing its own profit and independently determine the volume of production of electrical and thermal energy based on modern technologies. An analysis of foreign experience in creating autonomous hybrid power systems was carried out. The article discusses the possibilities of using hydrogen produced from natural gas in fuel cells in decentralized electricity and heat supply systems. The conditions for the competitiveness of the new technology in comparison with centralized systems and decentralized ones based on mini-CHP with gas turbine or gas piston engines have been determined. It has been shown that fuel cell systems using natural gas as fuel are not energy-saving technologies, but can reduce emissions of carbon dioxide and other pollutants into the environment. Massive use of fuel cells in decentralized energy supply systems is possible only if their cost is reduced to less than $100/kW.

A novel all-in-one target-powered entropy-driven dynamic DNA networks to regulate the activity of CRISPR/AsCas12a for enhanced DNA detection

BackgroundThe non-enzyme autonomous DNA nanodevices have been developed to detect various analytes through the programmability of Watson-Crick base pairing. Nevertheless, by comparison with enzymatic biosensors, the usage of enzyme-free DNA networks to create biosensors for testing low amounts of targets is still subject to the finite number of cycles. Besides, these biosensors still require the incorporation of other amplification strategies to improve the sensitivity, which complicates the detection workflow and lacks of a uniform compatible system to respond to the target in one pot. ResultsHere, we put forward a novel way for rapid and sensitive DNA diagnostic via EDN (entropy-driven dynamic network) coupling with CRISPR/AsCas12a-powered amplification. In the absence of the target, the autonomous hybridization among the substrate and crRNA is kinetically hindered by enclosing complementary regions, which leads to the loss of the activation function of Cas12a. On the contrary, the target initiates the EDN, reconfiguring the activator strand from a duplex to branch construction, which provides a valid means to adjust the hybridization with crRNA, thereby controlling the indiscriminate collateral cleavage activities of the CRISPR/AsCas12a. Compared with the traditional EDN, synergistic activation between the EDN and the CRISPR catalyst could dramatically enhance the detection signal of the target in one pot. Furthermore, the proposed approach provides universal platforms through the rational functional and structural design of DNA assembly modules. Significance and noveltyOverall, this target-triggered EDN switches the activator strand to regulate the activity of AsCas12a (called TERA), which showed nearly one orders of magnitude sensitivity than the conventional Cas12a alone assay, resulting in a devisable universal CRISPR sensing platform that favours the fast, robust and one-pot detection of nucleic molecules.

Discrete control model Q-learning for an energy storage system with a hydrogen unit of an autonomous hybrid power plant of a railway substation

The paper considers the prospects for creating autonomous hybrid power plants using renewable energy sources and hydrogen as energy storage systems, as well as storage batteries, for the railway power supply system. A comparative analysis of various energy storage systems is carried out. A simulation model has been created that takes into account the characteristics of electric rolling stock trains, a model of the contact network of the inter-station zone of the railway stage, and a model of the energy storage system. Managing the flow of electricity in an autonomous hybrid power plant requires considering forecasts of electricity consumption and generation and the level of charge of the energy storage device. To solve the problem of synthesizing a control algorithm, the use of matrix Q-learning is proposed. The novelty of the study lies in the proposed approach of applying Q-learning to the problem under consideration based on discretization of input and output parameters of the model and verification of the approach on a simulation model built for a real railway section between the Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). It is shown that the use of the proposed system makes it possible to equalize the voltage in the network between traction substations and provide a significant increase in the capacity of the railway section, and the proposed method of matrix Q-learning makes it possible to synthesize an effective algorithm for controlling the energy storage system as part of an autonomous hybrid power plant.

Efficient power management strategies for AC/DC microgrids with multiple voltage buses for sustainable renewable energy integration

This study proposes a distinct coordination control and power management approach for hybrid residential microgrids (MGs). The method enhances the feasibility of hybrid MGs by reducing power loss on ILBCs. The MG has been modeled with solar and wind generators. The MG comprises multiple direct current (DC) and alternating current (AC) sub-microgrids (SMGs) with varying voltage levels. The coordination control and power management strategies for autonomous hybrid MGs with primary and secondary control levels. A novel technique is proposed to ensure seamless and precise power transfer among SMGs while minimizing the constant operation of ILBCs in islanded mode, with a focus on the secondary control level. The study uses MATLAB/Simulink to analyze on-grid, off-grid, and transient mode power transfer among MG. The MG has been operative during transient/faulty conditions. The results indicate that the proposed method demonstrates excellent adaptability in managing power flow.

Energy Management Control of an Autonomous Hybrid System Based on ANN and Golden Jackel Algorithm

Energy Management Control of an Autonomous Hybrid System Based on ANN and Golden Jackel Algorithm

Optimization of Power Control for Autonomous Hybrid Electric Vehicles with Flexible Power Demand

Abstract Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degree of freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an Approximate Dynamic Programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle (HEV), while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand.

Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system’s dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

Autonomous hybrid optimization of a SiO2 plasma etching mechanism

Computational modeling of plasma etching processes at the feature scale relevant to the fabrication of nanometer semiconductor devices is critically dependent on the reaction mechanism representing the physical processes occurring between plasma produced reactant fluxes and the surface, reaction probabilities, yields, rate coefficients, and threshold energies that characterize these processes. The increasing complexity of the structures being fabricated, new materials, and novel gas mixtures increase the complexity of the reaction mechanism used in feature scale models and increase the difficulty in developing the fundamental data required for the mechanism. This challenge is further exacerbated by the fact that acquiring these fundamental data through more complex computational models or experiments is often limited by cost, technical complexity, or inadequate models. In this paper, we discuss a method to automate the selection of fundamental data in a reduced reaction mechanism for feature scale plasma etching of SiO2 using a fluorocarbon gas mixture by matching predictions of etch profiles to experimental data using a gradient descent (GD)/Nelder–Mead (NM) method hybrid optimization scheme. These methods produce a reaction mechanism that replicates the experimental training data as well as experimental data using related but different etch processes.

Suppression of low‐frequency oscillations in hybrid/multi microgrid systems with an improved model predictive controller

AbstractGrid‐forming inverters are used for voltage regulation and frequency control in autonomous hybrid microgrids and multi‐microgrid systems by imitating synchronous generators. However, in microgrids with weak grids including low inertia levels and small X/R ratios, these inverters interact with each other, and as a result low‐frequency oscillations (LFO) arise. LFO impacts the frequency stability of multi‐microgrid systems. Nevertheless, LFO can be mitigated by the load‐frequency control system, which serves as a secondary control mechanism. However, the presence of wind turbines and photovoltaic systems in hybrid microgrids adds complexity to the operation of the load‐frequency control due to the uncertainty associated with these renewable energy resources, and various controllers have been employed. This paper proposes a novel approach to enhance the performance of the load‐frequency control system and suppress LFO. The presented technique reduces the complexity of the hybrid microgrid structure by reducing the number of controllers. The model predictive control (MPC) technique is utilized for load‐frequency control and the weight parameters of the MPC are determined using the rain optimization algorithm. The proposed method demonstrates improved dynamic response, reduced overshoot and undershoot responses, decreased controller complexity, and effective LFO suppression. The simulation results verify the effectiveness of the method.

Collective hydrogen stand-alone renewable energy systems for buildings in Spain. Towards the self-sufficiency

The article examines the feasibility of implementing standalone hydrogen-based renewable energy systems in Spanish residential buildings, specifically analyzing the optimization of a solar-battery and solar-hydrogen system for a building with 20 dwellings in Spain. The study initially assesses two standalone setups: solar-battery and solar-hydrogen. Subsequently, it explores scenarios where these systems are connected to the grid to only generate and sell surplus energy. A scenario involving grid connection for self-consumption without storage serves as a benchmark for comparison. All system optimizations are designed to meet energy demands without interruptions while minimizing costs, as determined by a techno-economic analysis. The systems are sized using custom software that incorporates an energy management system and employs the Jaya algorithm for optimization. The findings indicate that selling surplus energy can be economically competitive and enhance the efficiency of grid-connected self-consumption systems, representing the study's main innovation. The conclusion highlights the economic and technical potential of an autonomous hybrid energy system that includes hydrogen, with the significant remaining challenge being the development of a regulatory framework to support its technical feasibility in Spain.

Techno-economic study of a hybrid photovoltaic-diesel system for a remote area in southwest Algeria

Exploiting natural sun and wind resources in remote and isolated areas is undoubtedly an excellent decision to generate electrical energy due to their availability and cleanliness. Various systems were used to generate this energy, such as photovoltaics (PV), wind turbine sand other energy systems. Moreover, for optimum energy use, some of these systems are combined either with each other or with other conventional systems, such as diesel generators with PV systems (i.e., hybrid systems). This work aims to present a technical-economic study of PV/diesel autonomous hybrid systems to supply electrical power for an isolated house located in a hot desert climate, Adrar. For optimizing the hybrid systems, hourly input data of solar radiation and load were used according to two configurations, where the annual load is 11.2 kWh/day. The findings showed that the diesel system had a high cost, with a cost for energy of $0.407/kWh and a fuel price of $0.140/l. Among the hybrid power systems but with significant pollution, the proposed hybrid system 2 kw photovoltaic and diesel generator with 2.3 kW has important economic feasibility, where the energy cost amounted to $0.172/kWh. In addition, CO2 emissions are reduced by approximately 5 tons every year compared to an independent diesel generator system.

Using a soil moisture sensor-based smart controller for autonomous irrigation management of hybrid bermudagrass with recycled water in coastal Southern California

There is a lack of information on the reliability of smart soil moisture sensor-based irrigation controllers for autonomous urban landscape irrigation management using recycled water where water conservation is required, which is the main objective of this study. A three-year turfgrass irrigation research trial (2019–2021) using tertiary-treated recycled water (mean EC = 1.18 dS/m, pH = 7.5) was conducted in Irvine, California, USA. A total of 12 irrigation treatments were designed, including six soil moisture-based thresholds (for triggering and terminating irrigation events) × two irrigation frequency treatments (restricted versus on-demand). Weekly NDVI and canopy temperature data were collected during the summer irrigation season to assess the impact of irrigation treatments on the overall health and quality of 'Tifgreen 328' hybrid bermudagrass. Soil salinity and sodium adsorption ratio (SAR) were measured before and after the summer irrigation seasons. Findings revealed statistically significant effects of irrigation levels and irrigation frequency restrictions on NDVI over the three years. The temperature difference (dT) between turfgrass and air (Tc - Ta) was significantly affected by irrigation levels throughout the three years, while irrigation frequency restrictions showed a significant effect in the years 2021 and 2022. On-demand irrigation resulted in a 10% reduction in temperature difference values during the study period compared to restricted irrigation. Variations in soil salinity levels were observed during the experimental periods; however, both salinity and SAR increased as the study progressed. Results indicate that fully autonomous hybrid bermudagrass irrigation with recycled water, based on recommended soil moisture thresholds of 75% of field capacity over an extended period in a semiarid climate, may lead to unacceptable turfgrass quality.

Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

ОБОСНОВАНИЕ ПРИМЕНЕНИЯ ДИФФЕРЕНЦИРОВАННОГО СОЦИАЛЬНОГО ТАРИФА НА ЭЛЕКТРИЧЕСКУЮ ЭНЕРГИЮ ПРИ РЕАЛИЗАЦИИ ПРОЕКТА ЭЛЕКТРОСНАБЖЕНИЯ ОТ АВТОНОМНОЙ ГИБРИДНОЙ ЭНЕРГЕТИЧЕСКОЙ УСТАНОВКИ

The issue of uninterrupted power supply to remote settlements is relevant in many countries of the world, but especially in Russia. Modern autonomous hybrid power plants (AHPP), namely solar power plants (SPP), wind power plants (WPP) and other power plants using renewable energy sources (RES), combined with diesel electric power plants (DES), provide not only an increase in the amount of electricity generated, but, most importantly, they ensure uninterrupted power supply to consumers through a combination of renewable energy sources and covering the missing energy from diesel power plants. In the conditions of the northern areas of the Krasnoyarsk Region, this is especially important. ASPPs make it possible to reduce the cost of electrical energy by saving fuel consumption and budgetary subsidies for its supply to remote areas of the region. The purpose of the study was to substantiate the areas of application of autonomous hybrid power plants for the regions of the Far North with the possibility of establishing a differentiated social tariff for household consumers. To achieve the goal, load coverage graphs were constructed for Vanavara settlement; the parameters of the ASPP were studied for the conditions of Vanavara settlement; simulation of the operating modes of the ASPP was carried out; calculation of the main technical and economic indicators of the use of ASPP for power supply to Vanavara settlement was carried out; recommendations were given on rational areas of application of ASPPs with the possibility of applying special tariffs for the electricity they produce. The authors of the paper carried out a technical and economic assessment of ASUE in the Vanavara settlement with a capacity of 10 MW, including a solar power plant with a capacity of 2,5 MW. It was established that the cost of electrical energy in the first option when powered by diesel power plants is 36,75 rubles/kW.h. In the second option, the cost depends on the time of year and is 32,7 rubles/kW.h in winter, 11.6 in spring, 6.05 in summer, and 12,2 rubles/kW.h in autumn, which makes it possible to establish a differential social tariff for household consumers based on a lower cost in the summer in the amount of 7–9 rubles/kW.h.

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

The global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector.

Optimal design and analyzing the techno-economic-environmental viability for different configurations of an autonomous hybrid power system

In the present day, there is widespread acceptance of autonomous hybrid power systems (AHPSs) that rely on renewable energy sources (RESs), owing to their minimal adverse effects on the environment. This paper evaluates and compares three various AHPS configurations comprising photovoltaic (PV) modules, wind turbines (WTs), batteries, and diesel generators (DGs), using a recent optimization approach. A new optimizer 'Dandelion-Optimizer' (DO) is applied to tackle design problems. Real-time meteorological data from Siwa Oasis in northwest Egypt was utilized to determine an optimum design of system components for the purpose of providing sustainable power to this remote region. The system configurations are effectively modelled and optimized to achieve the minimum cost of energy (COE), while also minimizing the loss of power supply probability (LPSP) and carbon dioxide (CO2) emissions. As per the results, the last configuration (PV with both backup equipment) is the most optimal one in terms of the lowest cost, whereas the first configuration (PV and WT with both types of backup equipment) is the most optimal one with regards to the lowest carbon emissions.