Optimization Of Hybrid System Research Articles

Hydropneumatic Storage Methodology Towards a New Era of Hybrid Energy System's Efficiency and Flexibility

This research explores the link between hydropneumatic energy storage capacity and the efficiency and flexibility of hybrid energy systems in water-energy solutions. A new methodology is introduced, featuring mathematical models, experimental data collection, and hydraulic simulations using 1D and 2D CFD models for hydropneumatics modeling. A promised energy storage efficiency of around 30-50% was obtained on a small lab scale. The optimization of hybrid systems through Solver and Python algorithms with various objective functions enables optimal design choices tailored to specific needs such as drinking water supply, irrigation, or industrial processes. Hydropneumatic vessels emerge as an effective storage solution, combining pumped storage and hydropower in one circuit. When integrated with renewable sources, such as solar (PV) and wind energy, they offer a flexible, long-lasting energy management system, applicable across different water-energy sectors to support Sustainable Development Goals. A case study with 4.8 Mm³/year water allocation, producing 1000 MWh of hydropower and 13500 MWh of solar energy, achieved 100% water reliability and a 25-year cash flow of 2.5 million euros.

Optimization of wind-solar hybrid system based on energy stability of multiple time scales and uncertainty of renewable resources

Optimization of wind-solar hybrid system based on energy stability of multiple time scales and uncertainty of renewable resources

Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework

This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets.

Stochastic optimization of a hybrid photovoltaic/fuel cell/parking lot system incorporating cloud model

The interaction and optimization of electric parking lots (EPLs) with hybrid renewable energy to exchange power and improve the system load reliability has been welcomed systems in energy industry, but it is accompanied by challenges such as uncertain parameters, and also implementing the optimal energy management. This paper proposes a novel stochastic optimization framework for a hybrid energy system including photovoltaic resources, hydrogen storage based-fuel cell integrated with an EPL (PV/FC/EPL) aiming for minimization of the total net present cost (TNPC) while ensuring the maximum probability of power shortage (PPSHmax) to meet a commercial load in Tianjin, China using the real meteorological data. A new improved kepler optimization algorithm (IKOA) utilizing the golden sine strategy is applied to optimize system component sizes and EPL charge/discharge. In a deterministic scenario, the optimal system configuration ensures cost-efficiency and reliability for load supply. Also, a new stochastic modeling framework is proposed using the Cloud Model (CM) to evaluate the effect of uncertainties in irradiance, temperature, and system demand on TNPC and PPSH. Deterministic findings reveal enhanced reliability and reduced TNPC through FC and EPL overlap during power shortages, compared to systems lacking EPL. Specifically, TNPC and PPSH are $8,632,643 and 1.45 %, respectively, for PPSHmax = 2 % over 20 years of operation. However, stochastic optimization demonstrates a 10.82 % increase in TNPC and an 8.67 % decrease in PPSH compared to the deterministic model under PPSHmax = 2 %, indicating heightened cost and reduced reliability when uncertainties are considered. This underscores the efficacy of the cloud model-based stochastic approach in addressing uncertainty in hybrid system optimization. Also, it reveals fluctuations in optimal cost and reliability values with changes in cloud droplet distribution around the expected value, emphasizing the significance of stochastic modeling for robust decision-making in hybrid energy systems. Moreover, evaluating the incorporating the penalty for the system's inability to supply the load, changing the storage investment cost, and interest rate variations on the system optimization cleared considerable effect of the system cost and reliability.

Techno-economic analysis of solar, wind and biomass hybrid renewable energy systems in Bhorha village, India

The present study investigates the potential use of Hybrid Renewable Energy Systems (Solar photovoltaic, wind, biomass, and diesel), both with and without the inclusion of battery/supercapacitor storage in the Bhorha village, Bihar, India. A comprehensive assessment of different possible system configurations is conducted using hybrid optimization model for electric renewable (HOMER) software to determine the most economically viable and efficient system for the designated place. The current analysis is focused on six distinct cases in the village community, in view of sufficing the daily energy requirement of 615.625 kWh and a peak demand of 86.47 kW, pertaining to major factors viz. system efficiency, financial viability, and ecological consequences. The primary aim of the research is to elucidate the comparative analysis of energy generation for different proposed designs of hybrid renewable energy systems. Detailed techno-commercial assessments are also carried out to examine the energy production, consumption, and financial impacts of each HRES configuration. The research outcome of this study obtained from HOMER software reveals that the optimized hybrid system comprises 86.7 kW solar photovoltaic, 30 kW wind turbine, 5 kW biogas generator, a 50 kW diesel generator, 280 kWh battery bank with nominal capacity, and 38.8 kW converter to sustain the for energy needs of the nominated place. This system has a minimum cost of energy of 0.309 $/kWh with a net present cost of $854894 along with operating cost 51847 $/year and net carbon dioxide emission of 56728 kg/yr. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems for a safe, sustainable and independent green future for the generations to come.

Optimizing hybrid renewable energy based automated railway level crossing in Bangladesh: Techno-economic, emission and sensitivity analysis

The railway system in Bangladesh, particularly the level crossing system, needs significant advancements, including a shift towards using renewable energy to power these crossings. As a solution, this study proposes optimal hybrid systems powered by renewable energy on an automated railway level crossing system, which is reliable, efficient, and sustainable. The main contribution of this study is to introduce an optimal hybrid renewable energy-based automated railway level crossing system in Bangladesh, focusing on technical and economic evaluation, emissions, and sensitivity assessment using HOMER Pro. The proposed system examines the optimal outcome for a 1 kW vertical-axis wind turbine and a 0.440 kW photovoltaic system at five selected locations. The results reveal satisfactory net present cost values amounting to USD 8495, USD 8505, USD 8564, USD 8262, and USD 8357 for Narayanganj, Cox’s Bazaar, Noakhali, Dinajpur, and Rajshahi respectively. Moreover, HOMER Pro indicates that the photovoltaic-wind turbine–grid-connected model offers a lower Cost of Energy which is around 0.03 USD/kWh compared to other configurations. The Internal Rate of Return for the selected locations are 20 %, 33.7 %, 31.5 %, 5.2 %, and 4.6 % for Narayanganj, Cox’s Bazaar, Noakhali, Dinajpur, and Rajshahi, respectively. The developed structure is projected to have a payback period ranging from 4.57 to 13.29 years across the five selected locations. Furthermore, the proposed systems reduce greenhouse gas emissions by up to 44.23 % compared to a grid-only system. Additionally, sensitivity analysis is performed by varying wind speed and solar irradiation to emphasize the robustness of the proposed systems.

A multiscale approach to optimize off-grid hybrid renewable energy systems for sustainable rural electrification: Economic evaluation and design

This article thoroughly investigates the design and optimization of an off-grid hybrid renewable energy system for a remote town in the province of Ankara, whose traditional power infrastructure is lacking. The goal is to provide an efficient and cost-effective energy system that can supply the village's power needs indefinitely. The optimization process entails selecting the best mix of renewable energy sources and sizing components to obtain the best economic and technical performance. To handle the complexity of the optimization problem, the Nelder-Mead simplex search method is used, taking into account the stochastic nature of renewable energy generation and the nonlinear features of RES-based power plants. The simulation results show that the suggested hybrid system outperforms traditional diesel power generators in terms of economic viability and environmental sustainability. The system assures consistent power supply by using reserve energy devices such as batteries, thereby minimizing the intermittent nature of renewable sources. With a competitive energy cost of €0.63/kWh, the optimized hybrid system ensures a dependable and continuous power supply, fulfilling the village's electrical requirement for an amazing 16 years.

Techno-Economic Analysis of Solar, Wind, and Biomass Hybrid Renewable Energy Systems to Meet Electricity Demand of a Small Village in Bihar State of India

This study examines the potential use of Hybrid Renewable Energy Systems (consisting of photovoltaic, wind, bio, and diesel sources) both with and without the inclusion of battery storage in the eastern region of India. An evaluation is conducted to determine the economic viability of several system configurations, and the most efficient system is selected using HOMER software. The investigation focused on six distinct scenarios to meet the energy needs of a village community. The goal was to satisfy a daily load need of 1093.7 kWh, with a peak demand of 153.63 kW. The study examined many factors, such as system efficiency, financial viability, and ecological consequences. The primary aim of the research was to compare the power costs associated with different designs of HRES. Detailed techno-commercial assessments were carried out to examine the energy production, consumption, and financial impacts of each scenario. This research provides valuable insights for individuals and organizations seeking reliable and long-lasting energy solutions by analyzing the potential benefits and drawbacks of implementing HRES in rural areas. An evaluation is conducted to determine the energy contribution of each element within an RES, as well as the influence of HRES on energy expenses and net present value. The findings of this study reveal that the optimized hybrid system comprises 133 kW photovoltaic arrays, a 130-kW wind turbine, a 0.2 kW biogas generator, a 100-kW diesel generator, a 540-kWh battery bank with nominal capacity, and a 58-kW converter. This system has a minimum COE of 0.347$/kWh and NPC of $1.71M. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems.

Cycle performance analysis of hybrid battery thermal management system coupling phase change material with liquid cooling for lithium-ion battery module operated at high C-rates

High performance battery thermal management system (BTMS) is urgently needed to satisfy the severe cooling demand of battery modules operated at high C-rates during continuous charging-discharging cycles. Hybrid BTMS coupling phase change material (PCM) with liquid cooling is a promising solution which attracts great attention in recent years. Here, cycle performance of the hybrid system is analyzed in detail via comparison with the pure PCM system to examine its reliability and demonstrate its superiority during continuous 3C-charging and 4C-discharging cycles. Effects of PCM melting temperature are studied and RT44HC is found a suitable PCM choice with appropriate melting temperature. Analysis of the first three cycles is proved important and basically sufficient as system thermal behavior stabilizes afterwards. Further, interacting mechanisms of expanded graphite (EG) content and battery distance on system cooling performance are elucidated and their possible combinations satisfying temperature requirements of battery module in both temperature rise and temperature uniformity are presented. Optimal combination of EG content and battery distance is 3 % and 3 mm. Effects of coolant velocity are also investigated and suitable velocity for the optimized hybrid system is set at 0.01 ms−1 to achieve balance between cooling performance and auxiliary energy cost.

Pra-studi Kelayakan Sistem Hibrida PV-Baterai-PLTD di Daerah Pedesaan Wilayah Maluku

This research addresses the high costs and logistical challenges of diesel-based electricity generation in the rural and remote areas of Maluku. The aim is to assess the feasibility of hybridizing diesel generators with solar photovoltaic (PV) and battery energy storage systems (BESS) to reduce energy costs and environmental impacts. A tailored methodology evaluated 42 locations to determine the optimal hybrid systems. The evaluation included preliminary solar potential, electricity consumption, hybrid options, generation costs, and capital costs. Findings indicate that systems with higher renewable energy fractions are more beneficial, reducing total diesel consumption from 30.5 million L/yr to 5.8 million L/yr and Levelized Cost of Energy (LCOE) from 0.62 USD/kWh to 0.42 USD/kWh. PV sizes ranged from 150-2400 kWp with renewable energy fractions from 80-93%. This method effectively prioritizes economically and technically suitable sites.

PV-Wind Hybrid System Optimization Using Improved Fuzzy Logic Control

PV-Wind Hybrid System Optimization Using Improved Fuzzy Logic Control

Synergizing perovskite solar cell and thermally regenerative thermocapacitive cycle toward full-spectrum solar energy conversion

To overcome the heat generation issues in a perovskite solar cell (PSC) and enhance overall energy conversion efficiency, a pioneering full-spectrum solar power hybrid system seamlessly synergizing a PSC, a thermally regenerative thermocapacitive cycle (TRTC) and a solar selective absorber (SSA) is proposed. By developing a mathematical model grounded in charge carrier dynamics and thermodynamics, operational restraints and key performance indicators are derived. Through exhaustive parametric studies, the coefficient of thermal conductance between SSA and TRTC, proportional coefficient of regenerator, absorption layer thickness of PSC, absorption layer (AL)/hole transport layer interface defect density, and electron transport layer/AL interface defect density are identified optimizable factors. Meanwhile, elevating the coefficient of thermal conductance between TRTC and environment, charging endpoint voltage can effectively enhance performance. Besides, the operating temperature of PSC within different interval leads to different impacts on performance. Predictive optimization reveals remarkable maximum efficiencies of energy and exergy for the optimized hybrid system (i.e., 28.64 % and 30.80 %), surpassing unoptimized PSCs by 44.56 % and 45.56 %, respectively. These findings not only demonstrate the potential of the hybrid system but also provide valuable insights for enhancing solar energy conversion in real-world applications.

An Hybrid Framework OTFS-OFDM Based on Mobile Speed Estimation

The Future wireless communication systems face the challenging task of simultaneously providing highquality service (QoS) and broadband data transmission, while also minimizing power consumption, latency, and system complexity. Although Orthogonal Frequency Division Multiplexing (OFDM) has been widely adopted in 4G and 5G systems, it struggles to cope with a significant delay and Doppler spread in high mobility scenarios. To address these challenges, a novel waveform named Orthogonal Time Frequency Space (OTFS). Designers aim to outperform OFDM by closely aligning signals with the channel behaviour. In this paper, we propose a switching strategy that empowers operators to select the most appropriate waveform based on an estimated speed of the mobile user. This strategy enables the base station to dynamically choose the waveform that best suits the mobile user’s speed. Additionally, we suggest retaining an Integrated Sensing and Communication (ISAC) radar approach for accurate Doppler estimation. This provides precise information to facilitate the waveform selection procedure. By leveraging the switching strategy and harnessing the Doppler estimation capabilities of an ISAC radar.Our proposed approach aims to enhance the performance of wireless communication systems in high mobility cases. Considering the complexity of waveform processing, we introduce an optimized hybrid system that combines OTFS and OFDM, resulting in reduced complexity while still retaining performance benefits.This hybrid system presents a promising solution for improving the performance of wireless communication systems in higher mobility.The simulation results validate the effectiveness of our approach, demonstrating its potential advantages for future wireless communication systems. The effectiveness of the proposed approach is validated by simulation results as it will be illustrated.

Improving wind power forecast accuracy for optimal hybrid system energy management

Abstract Due to its renewable and sustainable features, the wind energy is growing up around the word. However, the wind speed fluctuation induces the intermittent character of the generated wind power. Thus, wind power estimation, through wind speed forecasting, is very inherent to ensure an effective power scheduling. Four wind speed predictors based on deep learning networks and optimization algorithms, were developed. The designed topologies are the multilayer perceptron neural network, the long short-term memory network, the convolutional short-term memory network and the bidirectional short-term neural network coupled with the Bayesian optimization. The models performance was evaluated through evaluation indicators mainly, the root mean squared error, the mean absolute error and the mean absolute percentage. Based on the simulation results, all of them show a considerable prediction results. Moreover, the combination of the the long short-term memory network and the optimization algorithm is more robust on wind speed forecasting with a mean absolute error equal to 0.23 m/s. The estimated wind power was investigated for an optimal Wind/PV/Battery/Diesel energy management. The handling approach lies on the continuity of the load supply through the renewable sources as priority, the batteries on second order and finally the diesels. The proposed management strategy respects the designed criteria with satisfactory contribution percentage of the renewable sources equal to 71%.

Four-leg floating interleaved converters for electric vehicle applications with rule-based energy management algorithm

This work contributes to the optimization of hybrid system coupling a fuel cell (FC) to a supercapacitor (SC) for electric vehicles. The complementarity between these two energy sources allows the improvement of the global performances of the system. Our study focuses on the implementation of control and energy management techniques. Our objective is to have a better use of the storage system. In this context, our approach is to regulate the bus current and voltage and then to develop two real-time energy management strategies, one without considering and the other with considering the state of charge of the storage system. A comparison between these two strategies allowed us to understand the importance of the state of charge in the hybrid system. MATLAB/Simulink helped us in showing the performance obtained on a given mission profile of the vehicle dynamics.

Experimental optimization of a spectrum-splitting photovoltaic-thermoelectric hybrid system

This paper provides a comprehensive experimental optimization of a spectrum-splitting photovoltaic-thermoelectric hybrid system. A steady-state experimental setup is established. The properties of the coupling system with different splitter cutoff wavelengths and thermoelectric device structures are tested and compared with the single photovoltaic system under the same conditions. The effects of concentration ratio, cooling water temperature, and flow rate on the coupling properties are also experimentally investigated. The results show that among the three cutoff wavelengths tested, 880 nm, 990 nm, and 1100 nm, the coupling system using the 880 nm cutoff wavelength splitter performs best with the highest output power of 1.64 W. The coupling utilization can boost the power by 49.1 % compared to the 1.1 W power of the single photovoltaic system. Effective enhancement in the coupling property can be achieved by increasing the thermoelectric leg height, elevating the concentration ratio, lowering the cooling water temperature, and enhancing the flow rate. The coupling system output power is increased by 24.2 % by raising the thermoelectric leg height from 1.0 mm to 1.5 mm. When increasing the concentration ratio from 55 to 146, the total system power improves by 65 %, and the thermoelectric power ratio of the total power rises from 15.2 % to 37.5 %. The results can be used to guide the subsequent studies and practical applications of the spectrum-splitting photovoltaic-thermoelectric hybrid system.

Optimal Control and Optimization of Grid-Connected PV and Wind Turbine Hybrid Systems Using Electric Eel Foraging Optimization Algorithms.

This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system's architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load's power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems.

DESIGN AND DEVELOPMENT OF HYBRID POWER GENERATION BY SOLAR AND WIND ENERGY

This In our study, we delved into designing an optimal model for a hybrid solar-wind energy plant, meticulously considering various design parameters such as the number of photovoltaic modules, wind turbine height, number of turbines, and rotor diameter. Our aim was to minimize costs while ensuring consistent energy production. Our findings unequivocally revealed a distinct complementary relationship: during summer months, solar arrays predominantly met energy demands due to abundant solar radiation and minimal wind energy, whereas in winter, with higher wind speeds and reduced solar radiation, wind turbines became the primary energy suppliers. This study highlights the significant potential of leveraging synergies between solar and wind energy in an optimized hybrid system, ensuring reliable energy production year-round. Moreover, our project focused on exploring the feasibility of installing roof-mounted vertical wind turbines of various types, including those with shrouded blades, to enhance turbine efficiency. One notable advantage of vertical axis wind turbines is their ground-level installation, facilitating easy maintenance. Additionally, their omni-directional nature eliminates the need for precise alignment with the wind direction to generate power. Our main objective revolves around designing a self-starting vertical axis wind turbine using CATIA V5, aiming to contribute to the advancement of sustainable energy solutions. Key Words: Renewable energy source, vertical axis wind mill, power generation,catia v5

Towards a net-zero-energy building with smart control of Trombe walls, underground air ducts, and optimal microgrid composed of renewable energy systems

In recent years, permanent outages and lack of access to electricity have been significant problems in most cities of Iran. The increasing growth of fossil fuel consumption made it necessary to change the design of buildings. Designing a net-zero energy building (with natural ventilation and renewable energy systems) is a solution for reducing fossil fuels. In this paper, dynamic energy modeling of the building for the smart operation of the trapezoidal Trombe wall and underground air ducts is performed. In the first step, the Trombe Zones are considered for heating. Then the combination of the Trombe zones with underground air ducts for cooling is simulated. Then the system combination with a solar hot water system to supply hot water energy is evaluated. Finally, the economic analysis of the optimal hybrid system (with a photovoltaic system, wind turbine, battery, and inverter) for supplying the electricity of the building equipped with both passive energy strategies was evaluated by Homer software. The results show that by integrating the Trombe wall, the heating load is reduced by 44 %, and by integrating a coupled geothermal cooling system with the air duct and the Trombe wall, the cooling load is reduced by 39%. Also, the net present cost over the 25-year life of the optimized renewable system is reduced by about 12.5% due to both passive strategies. The average daily electricity energy demand of the base building is equal to 86.37 kWh and for the passive building, it is equal to 67.49 kWh.

Highly sensitive hybrid silicon carbonitride piezoresistive sensors enabled by coupling piezoelectric effect

Piezoresistive sensors with high working temperature and excellent sensitivity are highly desired for in-situ measuring pressure in various high temperature applications. Polymer-derived silicon carbonitrides (SiCNs) are a novel class of materials with excellent stability and piezoresistive performance at high temperature, yet challenging to build to meet the rapid growth of large-area electronics. This work demonstrates a thin and highly sensitive hybrid SiCN piezoresistive films enabled by coupling piezoelectric effect of Al-doped ZnO (AZO). The hybrid thin films with the optimal nano-domain phase of SiCN features ultrahigh gauge factors of 6518 to 31,195 upon various compressive stresses, which are significantly larger than those of single-layer SiCN and other existing piezoresistive materials. First-principles calculations reveal that the charges from AZO layer accumulate and move toward the carbon clusters in SiCN under compressive stress, resulting in greater tunneling currents between carbon clusters and more significant piezoresistive effect. Moreover, superior piezoresistive performance of the optimal hybrid system can be characterized by atomic-scale binding energy and band gap change, as formation of the hybrid structure facilitates interfacial charge separation and increases density of electron at conduction band. This work opens an avenue for improving sensitivity of piezoresistive sensors by effective coupling of piezoelectric materials.