Optimization Of Hybrid Renewable Energy System Research Articles

Hybrid solar photovoltaic-wind turbine system for on-site hydrogen production: A techno-economic feasibility analysis of hydrogen refueling Station in South Korea's climatic conditions

The South Korean government has an ambitious policy to roll out hydrogen-powered vehicles across the country with 2000 hydrogen refueling stations (HRSs) infrastructure by 2050. However, the country currently lacks sufficient HRS infrastructure. In this context, this study proposes and investigates the technoeconomic feasibility and performance assessment of an optimal hybrid renewable energy system integrated with a vanadium redox flow battery for on-site hydrogen production. The system is designed to refuel a fleet of 20 fuel cell electric vehicles at seven South Korean locations with distinct climates. Based on the typical daily hydrogen and electric load profile of the HRS, the capital expenditure, net present cost, operation expenditure, levelized cost of hydrogen and levelized cost of energy were estimated to be, $5.95 - $13.2 M, $8.93 - $19.4 M, 82,670–202,184 $/yr, 8.77–19.1 $/kg and 2.1–4.58 $kWh, respectively.

Hybrid renewable energy systems for sustainable power supply in remote location: Techno-economic and environmental assessment

Electricity supply is inconsistent and unreliable in many remote areas of India, where depending solely on a single renewable energy source is impractical. In this context, this study investigates the potential of off-grid hybrid renewable energy systems (HRES) to meet the energy needs of a village community in India. Techno-economic analysis and life cycle assessment have been employed to compare eleven HRES combinations which combine photovoltaic (PV), wind turbine (WT), battery (BAT), diesel generator (DG), biogas generator (BG), converter (CONV), and electrolyser (ELEC). By optimising the size and capacity of each component in HRES, this study aims to identify the combination with the lowest levelised cost of electricity (LCOE). This research aligns with United Nations Sustainable Development Goal No. 7 to seek “Affordable and Clean Energy”. The findings highlight that HRES comprising PV/WT/BAT/CONV/DG exhibits the lowest LCOE (0.319 $/kWh) and net present cost (6.81 M$) among all combinations. In systems with partial reliance on diesel, integrating both PV and WT could reduce diesel consumption and increase the renewable fraction to 87.4 %. For HRES involving PV, a significant contribution to greenhouse gas emissions occurs during the construction stage. The WT/DG combination, with its high diesel dependency, has the largest global warming potential. The efforts from this study provide valuable insights into determining the optimal HRES for remote communities by considering their economic and environmental factors.

Optimal planning for hybrid renewable energy systems under limited information based on uncertainty quantification

Developing hybrid renewable energy systems (HRES) is crucial for ensuring affordable, sustainable, and reliable energy access in remote rural and island regions. Rich and detailed data play a pivotal role in the optimal design of HRES. However, remote areas often face data limitations due to inadequate metering infrastructure, which further increases uncertainties in HRES design. This paper proposes a planning optimization method that quantifies multiple uncertainties, requiring only limited information for the optimal HRES design. First, uncertainties in HRES planning are classified into different types based on intrinsic randomness, data availability, and impact level. Then, by integrating uncertainty quantification with credibility theory, knowledge-based scenario generation models are developed to leverage limited available information to generate extreme and typical operating conditions for a tailored HRES design. Finally, a fuzzy credibility-constrained stochastic-robust optimization model is proposed to optimize the design of HRES. A practical case facing significant data scarcity and gaps is used to validate the effectiveness of the proposed method. The proposed approach reduces the loss of load probability for HRES to 0.02% and lowers the levelized cost of energy to $0.16/kWh—33% lower than that of the deterministic method and 79% lower than that of the traditional stochastic method.

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.

Integrated Hybrid Renewable Energy System Optimization for Sustainable Agricultural Operations

Integrated Hybrid Renewable Energy System Optimization for Sustainable Agricultural Operations

High gain super lift Luo converter for hybrid energy management systems

Recently, there has been a resurgence of interest in the development of alternative energy supplies due to rising fossil fuel prices and issues about the effects of greenhouse gas emissions on the environment. Today's demand is the design of a hybrid electric power generation system that uses solar and wind energy for remote places. This paper presents the design of an optimized hybrid renewable energy system consisting of a photo voltaic wind turbine (savinous type) with a battery and a super lift LUO converter. A solar PV module with an intelligent inverter facilitates the tracking of the maximum power point and the implementation of a battery charge controller for greater battery lifespan as well as reliable performance. Higher power output and clean energy are available from the hybrid power generation system, which combines innovative techniques with solar and wind power.

Optimization of a hybrid renewable energy system for off-grid residential communities using numerical simulation, response surface methodology, and life cycle assessment

This paper presents an optimized hybrid renewable energy system tailored for off-grid residential communities, integrating wind, solar, and hydrogen technologies to meet electricity, cooling, heating, and water needs. The proposed optimization methodology combines numerical simulation, response surface methodology (RSM), and life cycle assessment (LCA), ensuring robust performance across diverse conditions. Key components include fuel cells, reverse osmosis systems, heat pumps, photovoltaic/thermal solar collectors, wind turbines, and hydrogen generation systems. The optimization process targets critical variables such as PVT panel area, the number of wind turbines, and fuel cell power. The optimal configuration was found to include 24 small-scale wind turbines, 159.46 m2 of PVT collectors, and 79.73 kW of fuel cell power. This configuration generated surplus electricity with net electricity usage (NEU) values of −123084.27 kWh/year in New York, −224245.29 kWh/year in Forks, and −215949.30 kWh/year in Santa Barbara. Additionally, the optimized systems achieved a significant reduction in life cycle cost (LCC), with Forks showing the lowest LCC at 304428.97 USD. The environmental impact was also minimized using the novel optimization approach, with the optimized systems achieving negative 100-year global warming potential (GWP100) values, indicating a net reduction in greenhouse gas emissions.

Optimal Hybrid Renewable Energy System to Accelerate a Sustainable Energy Transition in Johor, Malaysia

As the world’s second-largest palm oil producer, Malaysia heavily depends on its extensive oil palm cultivation, which accounts for nearly 90% of the country’s lignocellulosic biomass waste. Approximately 20–22 tonnes of empty fruit bunches (EFBs) can be derived from an initial yield of 100 tonnes of fresh fruit bunches (FFBs) from oil palm trees. The average annual amount of EFBs produced in Johor is 3233 tonnes per day. Recognising that urban areas contribute significantly to anthropogenic greenhouse gas emissions, and to support Malaysia’s transition from fossil fuel-based energy to a low-carbon energy system, this research employed HOMER Pro software 3.18.3 to develop an optimal hybrid renewable energy system integrating solar and biomass (EFB) energy sources in Johor, Malaysia. The most cost-effective system (solar–biomass) consists of 4075 kW solar photovoltaics, a 2100 kW biomass gasifier, 9363 battery units and 1939 kW converters. This configuration results in a total net present cost (NPC) of USD 44,596,990 and a levelised cost of energy (LCOE) of USD 0.2364/kWh. This system satisfies the residential load demand via 6,020,427 kWh (64.7%) of solar-based and 3,286,257 kWh (35.3%) of biomass-based electricity production, with an annual surplus of 2,613,329 kWh (28.1%). The minimal percentages of unmet electric load and capacity shortage, both <0.1%, indicate that all systems can meet the power demand. In conclusion, this research provides valuable insights into the economic viability and technical feasibility of powering the Kulai district with a solar–biomass system.

Design and Optimization ofHybrid Renewable Energy Systems forA Residential House in Babylon, Iraq: Insights From Homer Software Analysis

The study investigates the development and improvement of hybrid renewable energy systems for a residential residence in Babylon, Iraq, utilising the HOMER programme. The project aims to determine the most efficient and cost-effective energy system design for residential usage, in response to the growing global need for sustainable and renewable energy sources. The process entailed examining different permutations of solar photovoltaic (PV) panels, wind turbines, and battery storage devices. According to the results, an off-grid photovoltaic (PV) and battery system is the most cost-effective option, with the lowest upfront investment and ongoing expenses. Nevertheless, the integration of wind energy, although enhancing thedependability and variety of the system's energy sources, results in higher upfront and ongoing costs. These findings offer vital information for politicians and engineers to implement renewable energy solutions that are customised to Iraq's specific meteorological and economic circumstances.

A novel standalone hybrid renewable energy systems onboard conventional and autonomous tugboats

The recent surge in fuel costs and stringent gas emission regulations mandated by the International Maritime Organization (IMO) has prompted research into integrating renewable energy and energy management strategies (EMS) onboard ships. This study illustrates the computation, simulation, and optimization of a hybrid renewable energy system (HRES) to ensure continuous power supply for auxiliary loads and critical systems on both conventional and fully autonomous tugboats. The primary aim is to design an optimal HRES with minimal annualized cost of system (ACS) and a higher proportion of renewable energy while using an artificial bee colony (ABC) algorithm. Validation of optimization outcomes is conducted using particle swarm optimization (PSO), genetic algorithm (GA), and Hybrid Optimization of Multiple Energy Resources (HOMER) Pro. The HRES incorporates diesel generators (Gensets), photovoltaic (PV) arrays, vertical axis wind turbines (VAWT), and battery banks. The optimal HRES configuration for both conventional and fully autonomous tugboats is found to be Genset/PV/VAWT/Battery. We observe that the ABC algorithm exhibits superior convergence, reliability, cost-effectiveness, renewable energy fraction, and reduced carbon emissions compared to alternative algorithms. Results of robustness tests suggest that the shipload variation, fuel prices, temperature fluctuations, wind speed and solar irradiance along the navigation route have significant impact on the optimal HRES configuration. Ultimately, we conclude that the fully autonomous tugboat demonstrates superior performance in terms of costs, carbon dioxide emissions, and renewable energy fraction compared to its conventional counterpart.

A Case Study of Hybrid Renewable Energy System Optimization for an Island Community based on Particle Swarm Optimization

A Case Study of Hybrid Renewable Energy System Optimization for an Island Community based on Particle Swarm Optimization

On–off-Grid Optimal Hybrid Renewable Energy Systems for House Units in Iraq

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement.

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

Optimal planning and designing of microgrid systems with hybrid renewable energy technologies for sustainable environment in cities.

Although hybrid wind-biomass-battery-solar energy systems have enormous potential to power future cities sustainably, there are still difficulties involved in their optimal planning and designing that prevent their widespread adoption. This article aims to develop an optimal sizing of microgrids by incorporating renewable energy (RE) technologies for improving cost efficiency and sustainability in urban areas. Diverse RE technologies such as photovoltaic (PV) systems, biomass, batteries, wind turbines, and converters are considered for system configuration to obtain this goal. Net present cost (NPC) is this study's objective function for optimal sizing microgrid configuration. For demonstration, we assess the technical, economic factors, and atmospheric emissions of optimal hybrid renewable energy systems for Putrajaya City in Malaysia. The required solar radiation data, temperature, and wind speeds are collected from the NASA surface metrological database. From the quantitative analysis of simulations, the biomass-battery-based system has optimal economic outcomes compared to other systems with an NPC of around 1.07M$, while the cost of energy (COE) is 0.118 $/kWh. Moreover, environmentally safe nitrogen oxide emissions, carbon monoxide, and carbon dioxide concentrations exist. The grid-tied RE technology boasts cost-effectiveness, with an NPC of 348,318 $ and a COE of 0.0112 $/kWh. This study aids decision-makers in formulating policies for integrating hybrid RE systems in urban areas, promoting sustainable energy generation.

Optimal design and analysis of a grid‐connected hybrid renewable energy system using HOMER Pro: A case study of Tumbatu Island, Zanzibar

AbstractThis study addresses the pressing issue of quality electricity access in remote regions, with a specific focus on Tumbatu Island in Tanzania. Most studies on quality improvement concentrate on low‐voltage distribution lines and leave high‐voltage (HV) transmission lines behind. It is essential to address quality issues in HV lines due to their critical role in electricity transmission and distribution infrastructure. The objective of the study is to improve the voltage profile on the island's HV transmission line by identifying the optimal hybrid energy system comprising solar PV, wind turbine, and battery technologies. The study begins by presenting the total power demand and consumption on Tumbatu Island, which are important factors in designing an efficient energy system. The findings reveal a power demand of 7173 kW and a consumption of 28540 kWh/day, with an average scaled value of 1507.9 and 6000 kWh/day. To achieve the desired voltage profile improvement, the research incorporates HOMER Pro software for simulating and analyzing various hybrid system configurations. Essential input parameters, such as costs, resources, technology components, and load, are considered in the simulation process. The software generates a ranked list of options based on the system's net present cost (NPC). The simulation results demonstrate that the integration of solar PV, wind turbine, and HV lines provides a substantial improvement in the voltage profile, increasing it from 29.6 to 31.23 kV during peak demand periods. This solution proves to be the most economically viable, with the lowest NPC of $4,003,851 and a relatively short payback period of 3.79 years. In addition, a sensitivity analysis is performed to identify the most influential parameter in the system's performance. Wind speed is found to have the greatest impact, emphasizing its significance in the design and operation of the hybrid energy system. The implementation of this optimal hybrid renewable energy system on Tumbatu Island will not only improve the voltage profile and meet the island's energy needs but also shall contribute to global efforts in reducing pollution and the cost of electricity to the Tumbatu Island population. Moreover, it addresses the current and future demands for clean energy, underlining its importance in achieving sustainable and accessible electricity in sub‐Saharan Africa and beyond.

Optimal Sizing of Hybrid Renewable Energy System Using Two-Stage Stochastic Programming

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations.

Efficient integration of photo voltaic and hydro energy technologies for sustainable power generation in rural areas: A case study

This research aims to provide an efficient and cost-effective renewable energy supply. It assesses the potential for photovoltaic (PV) and hydro energy in Pirthala, Haryana, India, using HOMER Pro® v3.14.2. A Hybrid renewable energy system (HRES) can continuously power 855 homes. The optimal HRES configuration comprises well-optimized PV modules, hydro turbines, converters, and batteries. The top four configurations were selected based on criteria such as net present cost (NPC) and cost of energy production (COE). The most effective HRES configuration involves a 3461-kW solar array, a 98.1 kW hydro turbine, 304 lithium-ion batteries of 100 kWh, and a 2785-kW converter, achieving a 100 % integration of renewable energy. This ideal HRES was thoroughly assessed regarding economic, technical, and renewable energy considerations. The results and the optimized HRES configuration can serve as a valuable reference for similar initiatives in rural areas, contributing to adopting renewable energy sources and enhancing energy access and reliability.

Performance and Techno-Economic Analysis of Optimal Hybrid Renewable Energy Systems for the Mining Industry in South Africa

This paper presents an exploration of the potential of hybrid renewable energy systems (HRESs), combining floating solar photovoltaics (FPV), wind turbines, and vanadium redox flow (VRF) battery energy storage systems (BESSs) to expedite the transition from conventional to renewable energy for the mining sector in South Africa. The feasibility study assesses how to enhance the overall efficiency and minimize greenhouse gas emissions from an economic standpoint by using the Hybrid Optimization of Multiple Energy Resources (HOMER) grid software version 1.11.1 and PVsyst version 7.4. Furthermore, the BESS Covariance Matrix Adaptation Evolution Strategy (CMA-ES) dispatch algorithm is proposed to make the most of the battery storage capacity and capability, aligning it with the dynamic energy demand and supply patterns of an HRES. The proposed HRES includes a highly efficient SFPV with a performance ratio of 0.855 and an annual energy production of 15,835 MWh; a wind turbine (WT) operating for 2977 h annually, achieving a 25% wind penetration rate; and a dynamic VRF-BESS with a 15,439 kWh life throughput and a 3 s dispatch response time. This HRES has a CapEx of R172 million, a 23.5% Internal Rate of Return (IRR), and an investment payback period of 4.9 years. It offers a low Levelized Cost of Energy (LCoE) at 4.27 R/kWh, a competitive Blended Cost of Energy (BCoE) at 1.91 R/kWh, and a positive net present cost (NPC), making it economically advantageous without external subsidies. Moreover, it annually reduces CO2 emissions by 1,715,468 kg, SO2 emissions by 7437 kg, and NOx emissions by 3637 kg, contributing to a significant environmental benefit.

Techno-economic optimization of hydrogen generation through hybrid energy system: A step towards sustainable development

The present study aims to design and optimize a Hybrid Renewable Energy System (HRES) to facilitate a remote community's electricity and hydrogen load demand at Dungri, Uttarakhand, India. The methodology includes an assessment of energy demand, local resource availability, and the nature of the inhabitant. The findings shows techno-economically an optimal HRES, consisting of 90 kW PV, 25 kW Bio-generator, 30 kW electrolyser, 15 kW hydrogen tank, 10 kWh storage and a 30 kW converter with Net present value (NPV) of Rs. 78.4 lakhs, cost of energy (COE) as Rs. 7.61/kWh and cost of hydrogen (COH) Rs. 330/kg. Annual electricity generation is of 250641 kWh/year and yearly average hydrogen generation of 1838 kg through electricity consumption of 80462 kWh/year and can be utilized for 26280 km/year of operation of hydrogen fuelled buses. Furthermore, sensitivity analysis reveals that, rise in the hydrogen load, boost up the system's NPV and COE, whereas,the COH reduces. Amount of annual CO2 emission and avoided are 53.9 kg, 209.12 t, respectively. Moreover, it infers that the proposed system serves isolated rural communities with high-quality energy and satisfies local hydrogen fuel demand in the transportation sector in economically viable manner and achieves the SDG 7 & 8.

Multiple energy resources integration in the food industry: A technoeconomic analysis

This study focuses on exploring sustainable energy solutions for the industrial sector, with a particular emphasis on meeting the electrical and thermal needs of the local bakery industry. Several alternative energy sources were proposed in this study, including solar irradiation, and the utilization of agricultural residues (such as olive tree pruning and residues of tomato crop). An anaerobic digestion (AD) reactor, a photovoltaic (PV) system, wind turbines (WT), a gasification system, and a combined heat and power (CHP) unit were used as parts of the developed hybrid renewable energy system (HRES). The objective was to optimize the system's performance by considering renewable energy sources (RES) penetration, energy cost, and ecological footprint.Furthermore, this investigation identified research gaps in the supply of thermal energy demand using renewable energy sources in an industrial setting and evaluated the integration of waste-to-energy technologies in HOMER Pro. Through modelling, optimization, and evaluation, different HRES configurations were assessed to determine the most cost-effective and optimal system for the bakery industry. The optimal suggested HRES showed a 65% reduction (from 0.4362 €/kWh to 0.1533 €/kWh) in the levelized cost of electricity (LCOE). Gasification achieved better results than AD, with the addition of a gasifier leading to a 40% decrease in the LCOE. The renewable fraction for the optimal HRES was 87.9%. There was a reduction in carbon dioxide emissions by 63%, while the HRES had a payback time of 3.17 years. Overall, the findings of this study show that utilizing local biomass and waste can play a significant role in the energy transition, leading to cost reduction and improved economic performance.