Design Of Renewable Energy System Research Articles

Optimizing a hybrid wind-solar-biomass system with battery and hydrogen storage using generic algorithm-particle swarm optimization for performance assessment

This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m2, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.

Design and evaluation of a hybrid offshore wave energy converter and floating photovoltaic system for the region of Oran, Algeria

Introduction. This paper presents the novel design and analysis of a hybrid renewable energy system that combines a wave energy converter (WEC) with a floating photovoltaic (FPV) system for offshore installation, with a specific focus on Oran as a case study. The purpose of integrating these two technologies is to harness both wave and solar energy, thereby maximizing energy output and enhancing the reliability of renewable energy sources in offshore environments. The goal of this study is to develop a hybrid system that leverages the complementary nature of WEC and FPV technologies to maximize energy output and improve reliability. By integrating these technologies, the system aims to overcome the limitations of standalone energy systems. The methodology includes selecting suitable WEC and FPV technologies, optimizing their configurations, and analyzing their combined performance under various environmental conditions. To assess the energy production potential, structural stability, and economic feasibility of the hybrid system, computational simulations and data analysis are employed. This comprehensive approach ensures rigorous testing and optimization for real-world applications. The results demonstrate substantial improvements in energy yield and system resilience compared to standalone WEC or FPV systems. The hybrid system shows enhanced performance, particularly in consistent energy output and structural robustness. These findings indicate that combining WEC and FPV technologies can lead to more reliable and efficient offshore renewable energy solutions. The practical values are significant, providing insights into efficient and sustainable offshore renewable energy solutions. By focusing on Oran, it offers a localized perspective that can be adapted to similar coastal areas globally, contributing to the advancement of renewable energy technologies. The hybrid system’s enhanced reliability and efficiency support the broader goal of sustainable energy development in marine environments, highlighting its potential for widespread application and impact. References 23, tables 4, figures 17.

Advancing Economical and Environmentally Conscious Electrification: A Comprehensive Framework for Microgrid Design in Off‐Grid Regions

AbstractThe design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions.

Design and optimization of hybrid renewable energy systems for hydrogen production at Aksaray University campus

In this study, an off-grid HRES is proposed to ensure the electricity demands of the campus in a reliable, cost-effective, and non-polluting way for Aksaray University to have a sustainable and green campus. Within this framework, three HRESs were designed and compared using HOMER Pro software to find the optimum HRES, using a combination of different components related to zero carbon emissions and fully renewable energy sources, including transportation with environmentally friendly hydrogen fuel cell buses for students, academics, and staff. According to the optimization results obtained for the various configurations, the optimum HRES has a net cost of $20.3 million for the 25-year project life, with annual costs of $1.57 million. The levelized cost of electricity of the proposed system, represented by Scenario III, is calculated to be 0.327$/kWh. The PV panels produce 4,758,497 kWh/year at a levelized cost of 0.0404$/kWh, while the wind turbines produce electricity at a levelized cost of 0.0625$/kWh. The optimal system includes a 2000 kW electrolyzer that produces 73,061 kg of hydrogen annually, with a consumption rate of 46.4 kWh/kgH2. The hydrogen tank has an energy reserve of 83,333 kWh with a storage capacity of 2500 kg. The results indicate that Scenario III is a robust, cost-effective, and environmentally friendly energy solution for the campus, paving the way for a greener future. Furthermore, the proposed HRES model provides a practical framework that can influence campus energy policies and potentially serves as a model for other educational institutions that are interested in implementing sustainable energy solutions.

Typical daily scenario extraction method based on key features to promote building renewable energy system optimization efficiency

Given its intrinsic volatility and randomness, the effective design of a building renewable energy system (BRES) necessitates long-period, short-step boundaries. Nevertheless, this poses computational burdens. In this context, time-series aggregation (TSA) emerges as a valuable approach, which involves obtaining a set of typical scenarios to substitute original time series. Numerous studies have focused on mathematical algorithms to minimize the deviation between typical scenarios and original time series. However, the premise of enhancing model-solving efficiency by extracting typical scenarios is to ensure the optimality of planning. Henceforth, identifying and quantifying key features in the original time series that significantly influence the planning, and further extracting need to be addressed. This paper proposes a typical day extraction method based on key features, named the feature-based method. Key features are summarized through an extensive literature review and then quantitatively expressed through different indicator models. Finally, a feature-based mixed integer linear programming (MILP) model is established for extraction. To evaluate the effectiveness of the feature-based method, it is applied to design a BRES and compared with traditional TSA methods such as Averaging Method (AM) and k-means. Results indicate that the feature-based method can effectively retain key features, thereby achieving more efficient and accurate designs.

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.

Design and assessment of an advanced renewable energy system with hydrogen and monomethylhydrazine production for space shuttles

A new renewable energy-driven advanced plant is designed and developed to produce multiple useful outputs, namely electricity, heat, cooling, hydrogen, and monomethylhydrazine (as space shuttle fuel) using solar and wind energies locally available. A specific location needed for this kind of unique work is identified as the Kennedy Space Center (KSC), which is known as located on Merritt Island, in the state of Florida (USA). It is also one of the National Aeronautics and Space Administration's (NASA) ten field centers. Since December 1968, KSC has been NASA's primary launch center of American spaceflight, research, and technology. This paper presents a comprehensive analysis of the subsystems that constitute the newly proposed system potentially for KSC and highlights their complex relationships and synergies. Leveraging renewable energy sources, such as concentrated solar power (CSP) and wind energy, coupled with the subsystem uniquely, such as the regenerative Rankine cycle, multi-stage flash (MSF) desalination unit, hydrogen production process, and Li–Br absorption chiller, the KSC's system maximizes energy conversion efficiency while minimizing environmental impact. Noteworthy outcomes include daily water production of 177.63 kg/s and hydrogen production of 169.61 kg/day, alongside annual energy contributions of 212,692,688 kWh for CSP and 66,158,552 kWh for wind turbines. More than that, it produces 23.04 kg/s MMH as fuel. Additionally, the overall energy and exergy efficiencies of the system are 30.6% and 33% respectively. Despite challenges, such as low efficiencies in certain subsystems, like mono methyl hydrazine (MMH) production, ongoing research and development efforts aim to optimize processes and enhance sustainability.

Optimal Design of Hybrid Renewable Energy System on Grid Based on Energy Consumption: A Case Study

Optimal Design of Hybrid Renewable Energy System on Grid Based on Energy Consumption: A Case Study

Optimal selection and design of grid-connected hybrid renewable energy system in three selected communities of Rivers State

The pursuit of electrifying rural areas has been a multi-decade quest, emblematic of Nigeria's longstanding efforts to diversify its energy sources and reduce dependence on traditional fossil fuels. Accordingly, this study aims to evaluate the electrification status and perform a techno-economic analysis of a hybrid system to ensure electricity reliability, bill reduction and reduced grid demand for three representative rural communities in Rivers State using HOMER Pro Software. Subsequently, five hybrid system scenarios that encompassed different combinations of the grid, PV modules, wind and battery, were examined using the CRITIC-PROMETHEE II methodologies. Each community was assessed based on its own hourly electricity demand, using data obtained from the injection substation. The Aluu, Choba and Rumuekini communities experienced an average daily electricity supply of 4.6, 5.4 and 6.2 h respectively from the national grid with energy usage of 5436.4 MW for Aluu, 3375.4 MW for Choba and 1626.1 MW for Rumuekini, in 2022. The findings indicate that the grid/PV module was the optimal choice for the three communities. Moreover, the cost of energy (COE) for the proposed systems ranged from 0.0181 $/kWh to 0.0185 $/kWh, significantly lower than the grid's COE of 0.08625 $/kWh in the respective communities. This research further includes a sensitivity analysis of the optimal system, examining variations in PV cost, discount rate, inflation rate, annual scaled solar irradiance and electrical demand. The results provide a comprehensive framework to support decision-making, optimize system design and align renewable energy solutions with project goals and stakeholder priorities.

Impact of uncertainty in building operation patterns and electric energy demand on the design and techno-economic performance of solar photovoltaic systems in different climates

The optimal design of renewable energy systems for buildings requires accurately estimating building electric energy loads. While this energy demand highly depends on how occupants and facility managers use building systems, operation-related uncertainty is rarely accounted for in the renewable energy design workflow. This paper presents a comprehensive framework to quantify the impact of uncertainty in building operation patterns and electric demand on the techno-economic performance of grid-connected photovoltaic (PV) systems (with and without battery storage). A hybrid methodology is proposed, combining and comparing two approaches to studying building performance under uncertainty: (i) physics-based building performance simulations coupled with parametric variations of occupant behavioral profiles (e.g., austere vs. wasteful), and (ii) data-driven modeling using a large national building dataset. The approach is illustrated and validated on small, medium, and large-sized office buildings in different climate zones (hot dry, mixed marine, and cold humid). Results show that different building operational patterns could lead up to 60% increase in building electric demand, which translated to similar increases in PV energy capacity and the corresponding capital costs of the systems. The findings emphasize the urgent need to better account for uncertainty in building operational patterns in the design workflows of buildings with PV generation and energy storage capabilities.

Design and optimization of off-grid solar PV and biomass-based hybrid renewable energy system (HRES) for electrification of a rural community in Tharparkar, Pakistan

Design and optimization of off-grid solar PV and biomass-based hybrid renewable energy system (HRES) for electrification of a rural community in Tharparkar, Pakistan

Environmental and economic valuation of user behavior in the optimal design of renewable energy systems

Environmental and economic valuation of user behavior in the optimal design of renewable energy systems

Trends in Hybrid Renewable Energy System (HRES) Applications: A Review

Microgrids and hybrid renewable energy systems play a crucial role in today’s energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings.

Feasibility study of hybrid renewable energy systems for off-grid electrification in Kuwait’s rural national park reserve

ABSTRACT This study demonstrates the optimal design of a hybrid renewable energy system for the electrification of a potential rural national park reserve. The objective is to evaluate the feasibility of utilising renewable energy sources (RESs) to reduce GHG emissions. The core components studied are photovoltaic solar (PV) panels, wind turbines (WTs), diesel generators (DGs), and battery banks (BBs). The research involves estimating the reserve’s load profile, assessing the potentials of RESs, and designing various system configurations. Each configuration is evaluated to derive the most efficient option. For a building with an energy demand of 832,640 kWh/yr, it is found that PV-WT-BB is the best configuration which comprises of 500-kW PV, 200-kW WT, and 1424-kW BBs and contributes an annual generation of 1,821,732 kWh/year. With a net present cost of $2,206,308 for the lifetime of the project, it saves 757,162 kg/year of total GHG emissions if the reserve operated using DGs.

Design and Analysis of Solar Power Cart for Inhouse Application Through ANSYS

The design and analysis of a solar power cart for in-house applications using ANSYS software is presented in this paper. The solar power cart is a portable, self-contained unit equipped with solar panels, batteries, and power electronics, designed to provide a renewable energy source for various in-house applications. The objective of this study is to analyze the structural integrity and thermal performance of the solar power cart using ANSYS simulation tools. The structural analysis focuses on evaluating the cart's mechanical strength and durability under various loading conditions, including static and dynamic loads. Finite element analysis (FEA) is used to simulate the structural behavior of the cart's components, such as the frame, wheels, and mounting structures, to ensure they can withstand the operational loads. The thermal analysis investigates the heat distribution and dissipation within the cart to prevent overheating of the components and optimize the overall thermal performance. ANSYS Fluent is employed to simulate the airflow and heat transfer mechanisms, enabling the design of effective cooling systems and thermal management strategies. The results of the design and analysis are used to optimize the solar power cart's performance, efficiency, and reliability for in-house applications. The proposed design demonstrates the feasibility of using ANSYS software for the design and analysis of renewable energy systems, providing valuable insights for future research and development in this field.. Key Words: Hallow shaft, Catia software Ansys19.0

Control and mathematical design of PV-based renewable energy systems for LCL-SR filter-based power quality improvement

Power quality challenges arise when photovoltaic (PV) systems are integrated into power networks. This research work examines the control and mathematical design of an LCL-SR (series resistance) filter for power quality improvement in PV-based sustainable energy systems to meet this difficulty. Power quality may be improved via an LCL filter in grid-connected systems. A non-linear load-connected PV system incorporates a variety of power electronics controllers, including an MPPT controller, battery controller, and inverter current controller, which introduce harmonics into the grid. A passive filter, LCL-SR, is suggested for PV applications that interact with voltage source inverters and the utility grid or electric load to reduce harmonics. A detailed mathematical modeling is presented to design an LCL-SR filter, and its performance is evaluated under different load conditions in terms of THD (Total Harmonics Distortion) and efficiency. LCL-SR filter has 4.32% THD but in the case of LCL filter with parallel R is less THD, which is 3.61%. In terms of efficiency and THD LCL-SR is best among all compared filters which have THD is 4.32% along with an efficiency is 75.95%. The results are compared without a filter, and different configurations of LCL-SR are simulated to show the effectiveness of the proposed filter for PV & residential applications.

Investigation of the effects of synthetic wind speed parameters and wind speed distribution on system size and cost in hybrid renewable energy system design

The accuracy of wind speed data has an important impact on determining power output from a Hybrid Renewable Energy System including wind turbine. For a better estimation of energy price or payback period, it is crucial to determine both monthly average wind speed values and distribution parameters. Studies evaluating the sensitivity of wind speed distribution parameters concerning the optimal system size are lacking in the literature. This work fills the gap in the literature and for the first time, the effects of wind speed distribution parameters and wind speed distribution on the optimum system are statistically demonstrated. According to this case study, if these parameters are not set correctly, the Cost of Energy may be faulty between −14.024% and +37.804% for the Photovoltaic-Wind-Battery-Inverter system, may be faulty between −19.024% and +97.05% for the Wind-Battery-Inverter system and may be faulty between −56.437% and +65.250% for the Wind-Inverter system. When the default distribution parameters of the HOMER software are used, the errors are −0.609%, −18.30%, −34.215% respectively. This study reveals that in addition to monthly average wind speed values, distribution parameters should be chosen as accurately as possible. The innovative aspect of this work is the statistical presentation of how synthetic wind speed distribution can affect Hybrid Renewable Energy System sizing and at the same time Cost of Energy. Its importance is that it provides more precise results by guiding optimization studies using synthetic meteorological data, which is a popular field of study.

Optimal sizing of hybrid renewable energy systems using quasi-optimal control

This paper addresses the critical challenge of sizing hybrid renewable energy systems over their lifespan while accounting for uncertainties in energy sources and load demands. By leveraging stochastic programming, we introduce a novel modeling technique that ensures robust optimization without compromising numerical tractability. Unlike conventional methods, which determine system parameters for the entire project duration upfront, our approach enables annual adjustments to the renewable components’ sizing. This dynamic strategy alleviates early-stage under-utilization, aligning system capacity with evolving demand patterns. Central to our approach is the reformulation of the two-stage stochastic program as a quasi-optimal control. This reduces the number of optimization variables significantly, simplifying the solution process. Moreover, thousands of constraints are replaced with a system of differential equations, enhancing computational efficiency. By minimizing capital costs and dynamic operating expenses, we achieve an optimal system size. Through a real-world application in a rural area of South Africa, we demonstrate the effectiveness of our approach. The results showed a remarkable improvement in problem-solving efficiency, reduced under-utilization, and heightened adaptability to unpredictable weather conditions. This innovative framework represents a substantial leap forward in hybrid renewable energy systems design, offering a more agile and efficient solution for sustainable energy generation.

Optimal Design of Grid-Connected Hybrid Renewable Energy System Considering Electric Vehicle Station Using Improved Multi-Objective Optimization: Techno-Economic Perspectives

Electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) have been widely integrated into distribution systems. Electric vehicles (EVs) offer advantages for distribution systems, such as increasing reliability and efficiency, reducing pollutant emissions, and decreasing dependence on non-endogenous resources. In addition, vehicle-to-grid (V2G) technology has made EVs a potential form of portable energy storage, alleviating the random fluctuation of renewable energy power. This paper simulates the optimal design of a photovoltaic/wind/battery hybrid energy system as a power system combined with an electric vehicle charging station (EVCS) using V2G technology in a grid-connected system. The rule-based energy management strategy (RB-EMS) is used to control and observe the proposed system power flow. A multi-objective improved arithmetic optimization algorithm (MOIAOA) concept is proposed to analyze the optimal sizing of the proposed system components by calculating the optimal values of the three conflicting objectives: grid contribution factor (GCF), levelled cost of electricity (LCOE), and energy sold to the grid (ESOLD). This research uses a collection of meteorological data such as solar radiation, temperature, and wind speed captured every ten minutes for one year for a government building in Al-Najaf Governorate, Iraq. The results indicated that the optimal configuration of the proposed system using the MOIAOA method consists of eight photovoltaic modules, two wind turbines, and thirty-three storage batteries, while the fitness value is equal to 0.1522, the LCOE is equal to 2.66 × 10−2 USD/kWh, the GCF is equal to 7.34 × 10−5 kWh, and the ESOLD is equal to 0.8409 kWh. The integration of RESs with an EV-based grid-connected system is considered the best choice for real applications, owing to their remarkable performance and techno-economic development.

Modeling, design and optimization of integrated renewable energy systems for electrification in remote communities

Integrated renewable energy systems are becoming a promising option for electrification in remote communities. Integrating multiple renewable energy sources allows the communities to counteract the weaknesses of one renewable energy source with the strengths of another. This study aims to model, design and optimize integrated renewable energy systems consisting of solar photovoltaic (PV) panels, wind turbines, a biomass power generator, and storage batteries for applications in remote communities in Canada. Biomass is used as a fuel to produce electricity during periods when solar power and wind power are not capable of meeting the power demand. A methodology is developed to optimize the integrated renewable energy systems design, with the aim of minimizing the net present cost (NPC) and the levelized cost of electricity (LCOE) of the energy systems. Results show that the NPC is $3.61 M and the LCOE is $0.255/kWh for an optimized integrated renewable energy system in a sample remote community that has a peak power consumption of 238.7 kW and an average load demand of 2230 kWh/day. Through the present research, the integrated energy systems are evidenced to be an effective option for electrification in remote communities.