RESUMEN
HRES (Hybrid Renewable Energy Systems) has been designed because of the increasing demand for environmentally friendly and sustainable energy. In this study, an Improved Subtraction-Average-Based Optimizer (ISABO) is presented for optimizing the HRES system by wind power, fuel cells, and solar energy. The suggested approach, by introducing adaptive mechanisms and enhancing processes, improves the performance of the traditional subtraction-average-based optimization. Optimization aims to provide reliable and efficient energy while lowering system expenses. The efficacy of ISABO is evaluated for this goal and compared with other optimization techniques. According to the findings, The ISABO algorithm, when equipped with adaptive mechanisms, surpasses conventional optimization techniques by achieving a 12 % decrease in Net Present Cost (NPC) and Levelized Cost of Electricity (LCOE) along with a 45 % cost reduction in electrolyzers. Through simulations, it has been shown that the ISABO algorithm ensures the lowest average NPC at $1,357,018.15 while also upholding system reliability with just a 0.8 % decline in Load Point Supply Probability (LPSP) in the event of a PV unit failure. This research validates that hybrid PV/wind/fuel cell systems present superior cost-effectiveness and reliability, thereby opening doors for more economical renewable energy solutions. The study reveals hybrid PV/wind/fuel cell systems are more cost-effective than purely wind, PV, or fuel cell systems. This advancement in HRES design and optimization techniques will enable more cost-effective renewable energy options.
RESUMEN
The growing demand for renewable energy systems is driven by climate change concerns, government support, technological advancements, economic viability, and energy security. These factors combine to create a strong momentum towards a clean and sustainable energy future. Governments, governments, and individuals are increasingly aware of the environmental impacts of traditional energy sources and adopting renewable energy solutions. Hybrid Renewable Energy Systems (HRES) are developed as an effective way of meeting the energy demands in remote locations. The complexity of the system components and the fluctuation of renewable energy sources make it difficult to design an economical and effective HRES. In this study, the Improved Aquila Optimization (IAO) approach has been suggested as a powerful tool to optimize the HRES design. The study addresses the implementation of the IAO approach in the design of HRES and emphasizes its advantages over other optimization techniques. Through extensive simulations and analyses, our findings demonstrate the superior performance of the IAO algorithm in improving the efficiency and cost-effectiveness of HRES. The optimization process using IAO resulted in a significant reduction in overall system costs, achieving an estimated Net Present Cost (NPC) of $201,973. It translates to a cost reduction of 25% compared to conventional optimization techniques. Furthermore, our analysis reveals that the IAO approach enhances the utilization of renewable energy sources, leading to a 15% increase in overall energy generation efficiency. These results highlight the effectiveness of the IAO approach in addressing the challenges associated with designing HRES. By significantly reducing costs and improving efficiency, it facilitates the adoption of sustainable energy systems in remote areas. The outcomes of this study emphasize the importance of utilizing advanced optimization techniques, such as IAO, to ensure the economic viability and environmental sustainability of HRES.
RESUMEN
The worldwide use of clean and environmentally friendly renewable energy sources, has been increasing to prevent global warming and climate change. In this study, a hybrid renewable energy system (HRES) including biomass and solar as the source, has been investigated for Mehmet Akif Ersoy University Istiklal Campus in Burdur, Türkiye. The campus has an animal farm consisting of 300 cattle and 200 sheep. Therefore, manure of the animals will be used as the resource for biomass generation. HOMER software is used to simulate the system and to find the size and the quantity of the equipment according to the meteorological and biomass capacity of the campus. The optimum system is determined by means of net present cost (NPC) and the cost of energy (COE). In the simulation, wind energy is also investigated but since the wind speed is not sufficient to produce energy in the region, it is not considered in the optimum system. The optimum system is determined to be grid connected biomass-solar system with 5000 kW PV panels and a 1500 kW biomass generator assisted by the grid of 3000 kW. Also, the NPC of the system is estimated to be USD 18.800.000 and the COE for the system is calculated as 0,107 USD/kWh. The system also reduces the emissions causing the global warming.
RESUMEN
In the current energy and environmental framework, the environmental impact of the road transport sector and the urban waste management and disposal are extremely important for highly crowded cities. This work assesses the energy, economic and environmental performance of an innovative paradigm for the full decarbonisation of the road transport sector. This problem is integrated with the management of the organic fraction of municipal solid waste. In particular, the proposed technology is based on an anaerobic digestion plant coupled with a biogas upgrading unit, for the production of biomethane. In addition, photovoltaic panels and solar thermal collectors are also considered for matching electrical and thermal demands, in order to achieve a fully-renewable system. To this scope, the system also includes suitable thermal and electric storages. The economic analysis also considers specific public funding policies, currently available for this technology. This system aims to be a novel paradigm in the energy scenario of waste disposal and road transport sector refurbishment. TRNSYS software was adopted to perform an accurate dynamic simulation for a one-year operation of the system. The anaerobic digestion model is developed by the authors in MatLab and integrated in TRNSYS, for dynamic simulation purpose. Results show that the plant is almost self-sufficient due to the integration of storage systems for both the thermal and electric energy. The photovoltaic system is able to reduce by 45% the energy dependence from the grid. Energy and environmental analyses show a Primary Energy Saving of 126% and a reduction of CO2 equivalent emissions by 112%. The economic feasibility analysis shows a promising Simple Payback period of 6 years.
RESUMEN
Hybrid renewable energy systems (HRES) are a nexus of various renewable energy sources that have been proposed as a solution to circumvent various issues of renewable energy systems when installed in isolation. During the operation of an HRES, efficient demand-side management of energy and real-time power trading requires accurate estimation of real-time variables for each sub-component. Generally, these variables are corrupted by measurement errors. To address this issue, in this study, we present various frameworks for reconciliation strategies that can be used to rectify the inconsistencies in the sensor measurements of HRES. Specifically, in this study, we evaluate the efficacy of various static and dynamic reconciliation strategies such as Regularized Particle filter (RPF), Ensemble Kalman filter (EnKF), and Extended Kalman filter (EKF) of a candidate HRES system with a solar panel, a fuel cell, and an electrolyzer. Proposed frameworks are evaluated using various simulation-based validation studies. To this end, we have considered different operational scenarios, namely, (i) single-rate sampling, (ii) multi-rate sampling, and (iii) sensor outage, to make the study comprehensive. Simulation results indicate that RPF yields the best estimation accuracy for all three operational scenarios with a performance improvement of 75% from EKF and by 50% from EnKF, with only a fractional increment in computational time.