Search Results (67)

This research focuses on optimizing a grid-connected hybrid microgrid system (HMGS) for The Neotia University (TNU), West Bengal, India, utilizing renewable energy sources to improve sustainability and energy efficiency. The system integrates solar panels, wind turbines, and an existing diesel generator (DG) to meet campus energy demands, including electric vehicle (EV) charging facilities for residents and staff. The pelican optimization algorithm (POA) is employed to determine the optimal capacity of PV and wind turbine units for reducing energy costs, enhancing reliability, and minimizing carbon emissions. The results reveal a substantial decrease in the cost of energy (COE) from INR 11.74/kWh to INR 5.20/kWh. Full article

Starting from Ioffe’s description of a thermoelectric converter, we recover the optimal working points of conversion: the point of maximum efficiency and the one of maximal power. Inspired by biological converters’ optimization, we compute a third optimal point associated with cost of energy (COE). This alternative cost function corresponds to the amount of heat exchanged with the cold reservoir per unit of electric current used. This work emphasizes the symmetry between the efficiency and performance coefficient of the electric generator and heat pump modes. It also reveals the relation between their optimal working points. Full article

Greenhouse gas emissions have become a significant concern for many countries due to their effect on the global economy and environment. This work discusses a standalone hybrid renewable generation system (HRGS) for use in isolated areas with different load demand profiles. Three load profiles were studied in this work: educational, residential, and demand-side management (DSM)-based residential load profiles. To investigate the economic and environmental aspects, a proposed modified capuchin search algorithm (MCapSA) was implemented, and the obtained results were compared with those of different conventional optimal procedures, such as the genetic algorithm (GA), particle swarm optimization (PSO), and HOMER. The Levy flight distribution method, which is based on random movement, enhances the capuchin algorithm’s search capabilities. The cost of energy (CoE), electric source deficit (ESD), greenhouse gas (GHG) emissions, and renewable factor (RF) indicators were all optimized and estimated to emphasize the robustness of the proposed optimization technique. The results reveal that the shift in the residential load profile based on individual-household DSM-scale techniques leads to significant sharing of renewable sources and a reduction in the utilization of diesel generators, consequently diminishing GHG emissions. The proposed MCapSA achieved optimal values of economic and environmental aspects that are equal to or less than those achieved through PSO. From the overall results of the three scenarios, the modified algorithm gives the best solution in terms of GHG, COE, and ESD compared to other existing algorithms. The usage of MCapSA resulted in decreases in COE and GHG in three types of loads. The robustness and effectiveness of MCapSA are demonstrated by the fact that the DSM-based optimal configuration of the renewable energy sources produces the lowest CoE and GHG emissions of 0.106 USD/kWh and 137.2 kg, respectively. Full article

Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO₂-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations. Full article

The most rapid and efficient method to transport natural gas from its source to its destination is through a pipeline network. The optimal functioning of control stations for natural gas pipelines depends on the use of electrical devices, including data loggers, communication devices, control systems, surveillance equipment, and more. Ensuring a reliable and consistent power supply proves to be challenging due to the remote locations of these control stations. This research article presents a case study detailing the design and dynamic modeling of a hybrid power system (HPS) to address the specific energy needs of a particular natural gas pipeline control station. The HOMER Pro 3.17.1 software is used to design an optimal HPS for the specified location. The designed system combines a photovoltaic (PV) system with natural gas generators as a backup to ensure a reliable and consistent power supply for the control station. Furthermore, it provides significant cost savings, reducing the cost of energy (COE) by USD 0.148 and the annual operating costs by USD 87,321, all while integrating a renewable energy fraction of 79.2%. Dynamic modeling of the designed system is performed in MATLAB/Simulink R2022a to analyze the system’s response, including its power quality, harmonics, voltage transients, load impact, etc. The experimental results are validated using hardware in the loop (HIL) and OPAL-RT Technologies’ real-time OP5707XG simulator. Full article

Hybrid renewable energy sources (HRES) are increasingly being utilized to meet global energy demands, particularly in rural areas that rely on diesel generators and are disconnected from the utility grid, due to their environmental and human health benefits. This study investigates the performance of an off-grid, hybrid PV/diesel generator/battery system for a decentralized power plant in Kuakata, Bangladesh, meeting a load demand of 3000 kWh/day with a 501.61 kW peak load demand. HOMER Pro (hybrid optimization model for electric renewable) software (version 3.11) was used to simulate and optimize system operations utilizing real-time solar radiation and load profile data from that location. This study also includes a sensitivity analysis of the off-grid HRES system under different electrical load demands, project longevity, and derating variables. The results reveal that CO₂ emissions have potentially decreased by more than 30% and over 10 tons per year, respectively, when compared to traditional power plants. The optimized system’s net present cost (NPC) was determined to be around USD 5.19 million, with a cost of energy (COE) of USD 0.367 per kWh per unit with a 100% renewable component. Furthermore, the current study’s findings are compared to previous research that has resulted in an economical hybrid renewable energy system with an affordable COE. The hybrid energy system under consideration might also be applicable to other parts of the world with comparable climate conditions. Full article

The study objective was to field-validate the technical feasibility of a membrane- and adsorption-enhanced water gas shift reaction process employing a carbon molecular sieve membrane (CMSM)-based membrane reactor (MR) followed by an adsorptive reactor (AR) for pre-combustion CO₂ capture. The project was carried out in two different phases. In Phase I, the field-scale experimental MR-AR system was designed and constructed, the membranes, and adsorbents were prepared, and the unit was tested with simulated syngas to validate functionality. In Phase II, the unit was installed at the test site, field-tested using real syngas, and a technoeconomic analysis (TEA) of the technology was completed. All project milestones were met. Specifically, (i) high-performance CMSMs were prepared meeting the target H₂ permeance (>1 m³/(m².hbar) and H₂/CO selectivity of >80 at temperatures of up to 300 °C and pressures of up to 25 bar with a <10% performance decline over the testing period; (ii) pelletized adsorbents were prepared for use in relevant conditions (250 °C < T < 450 °C, pressures up to 25 bar) with a working capacity of >2.5 wt.% and an attrition rate of <0.2; (iii) TEA showed that the MR-AR technology met the CO₂ capture goals of 95% CO₂ purity at a cost of electricity (COE) 30% less than baseline approaches. Full article

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must thus be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform. Full article

The concept behind this research article is advancement towards utilizing renewable energy sources of wind–solar to generate electrical energy for E-bike (electric bike) charging stations. To optimize the design and operation control of the wind–solar E-bike charging station system, the development of modelling this hybrid power generation system, consisting of solar and wind energy combined with battery storage, is proposed and will be studied in this paper. A university campus setting is utilized for the case study by comparing offshore (Huangdao) and onshore (Laoshan) sites. The proposed research will focus on annual energy production (AEP) and system cost analysis. The proposed work’s main objectives are to analyze the wind/solar properties of the installation’s location using the last 20 years’ data, calculate the AEP for wind turbines and solar PV, and estimate how many E-bikes can be charged day/year with reliable operation. We have calculated that the hybrid power available is 27.08 kWh/day offshore and 22 kWh/day onshore. This research study concludes that on average, based on AEP, in the case of offshore, 5110 E-bikes can be charged per year and in the case of onshore, 4015 E-bikes can be charged per year. We have also calculated the COE (cost of energy) for 20 years for the proposed project, which is $0.62/kWh onshore and $0.46/kWh offshore. Full article

This paper proposes an optimal solution for the design of a hybrid power system that will supply a remote fishpond in eastern Serbia. In terms of structure, this off-grid system should be a hydro-photovoltaic-diesel-converter-battery setup. The optimization objectives are to minimize total net present cost (NPC) and greenhouse gas (GHG) emissions and to maximize total annual electricity generation based on the modification of hydro-turbine performance. This study considers the following three cases of a hydro-turbine with fixed propeller blades: having fixed guide vanes, for the annual average flow rate-Case 1; having adjustable guide vanes, for smaller flow rates-Case 2 and having adjustable guide vanes, for higher flow rates-Case 3. The optimization is performed using HOMER Pro v. 3.16.2 software. The results show that the total NPC, levelized cost of energy (COE) and GHG emissions in Case 3 are 16.6%, 16.8% and 13.1% lower than in Case 1, and 8.1%, 8% and 11.7% lower than in Case 2, respectively. It is also found that the total annual electricity generation and power output from the entire system in Case 3 are 33.3% and 1.2% higher than in Case 1, and 11.9% higher and not different than in Case 2, respectively. Full article

Access to cheap, clean energy has a significant impact on a country’s ability to develop sustainably. Fossil fuels have a major impact on global warming and are currently becoming less and less profitable when used to generate power. In order to replace the diesel generators that are connected to the university of Debre Markos’ electrical distribution network with hybrid renewable energy sources, this study presents optimization and techno-economic feasibility analyses of proposed hybrid renewable systems and their overall cost impact in stand-alone and grid-connected modes of operation. Metaheuristic optimization techniques such as enhanced whale optimization algorithm (EWOA), whale optimization algorithm (WOA), and African vultures’ optimization algorithm (AVOA) are used for the optimal sizing of the hybrid renewable energy sources according to financial and reliability evaluation parameters. After developing a MATLAB program to size hybrid systems, the total current cost (TCC) was calculated using the aforementioned metaheuristic optimization techniques (i.e., EWOA, WOA, and AVOA). In the grid-connected mode of operation, the TCC was 4.507 × 10⁶ EUR, 4.515 × 10⁶ EUR, and 4.538 × 10⁶ EUR, respectively, whereas in stand-alone mode, the TCC was 4.817 × 10⁶ EUR, 4.868 × 10⁶ EUR, and 4.885 × 10⁶ EUR, respectively. In the grid-connected mode of operation, EWOA outcomes lowered the TCC by 0.18% using WOA and 0.69% using AVOA, and by 1.05% using WOA and 1.39% using AVOA in stand-alone operational mode. In addition, when compared with different financial evaluation parameters such as net present cost (NPC) (EUR), cost of energy (COE) (EUR/kWh), and levelized cost of energy (LCOE) (EUR/kWh), and reliability parameters such as expected energy not supplied (EENS), loss of power supply probability (LPSP), reliability index (IR), loss of load probability (LOLP), and loss of load expectation (LOLE), EWOA efficiently reduced the overall current cost while fulfilling the constraints imposed by the objective function. According to the result comparison, EWOA outperformed the competition in terms of total current costs with reliability improvements. Full article

In this paper, a small-scale PV/Wind/Diesel Hybrid Microgrid System (HMS) for the city of Yanbu, Saudi Arabia is optimally designed, considering the uncertainties of renewable energy resources and battery degradation. The optimization problem is formulated as a multi-objective one with two objective functions: the Loss of Power Supply Probability (LPSP) and the Cost of Electricity (COE). An Improved Decomposition Multi-Objective Evolutionary Algorithm (IMOEAD) is proposed and applied to solve this problem. In this approach, different decomposition schemes are combined effectively to achieve better results than the classical MOEA/D approach. Twelve case studies are investigated based on different scenarios and different numbers of houses (5 and 10 houses). Each time, the suggested approach produced a set of solutions that formed a Pareto front (PF). Considering a variety of parameters, the optimal compromise option can be selected by the designer from the PF. Full article

The world is forced to think about alternate energy sources because fossil fuel stocks are unreliable, harmful, and depleting quickly. Deployments of microgrids powered by renewable energy are some of the most economical, effective, dependable, and sustainable answers to this problem. The design of a power system with the least amount of economic and environmental impact is the main challenge because the world is currently facing climate change disasters on a scale that has never been seen before. As a result, there is an urgent need to transition to renewable energy resources to meet energy demands. This study examines the creation of a hybrid microgrid to meet the electrical load requirements of the King Saud University campus in Riyadh by utilizing the site’s solar and wind potential. A software called HOMER Pro Version 3.14.5 is used to simulate the planned microgrid system. The software can run numerous simulations while taking into account various system configurations. The ultimate objective is to choose the best combination of different power sources to create a microgrid with low energy costs, dependability, minimal GHG emissions, and a high penetration of renewable energy. The solar, wind, and battery system connected to the grid was shown to be the most advantageous choice in terms of cost of energy (COE), net present cost (NPC), operational costs, and GHG emissions after the software ran numerous simulations. The most economically advantageous way to meet the load demands of a university campus while still achieving more than 82% renewable penetration is to use an optimal system architecture. In this study, the ideal system configuration is subjected to sensitivity analysis to confirm the system’s performance. This optimal system design is used as a benchmark for examining the potential usage of renewable energy in the education sector in Saudi Arabia in particular and in any educational facility worldwide in general. Full article

This paper presents four optimization outcomes for a diesel generator (DG), photovoltaic (PV), and battery hybrid generating radial system, to reduce the network losses and achieve optimum generated power with minimum costs. The effectiveness of the four utilized meta-heuristic algorithms in this paper (firefly algorithm, particle swarm optimization, genetic algorithm, and surrogate optimization) was compared, considering factors such as Cost of Energy (COE), the Loss of Power Supply Probability (LPSP), and the coefficient of determination (R²). The multi-objective function approach was adopted to find the optimal DG allocation sizing and location using the four utilized algorithms separately to achieve the optimal solution. The forward-backward sweep method (FBSM) was employed in this research to compute the network’s power flow. Based on the computed outcomes of the algorithms, the inclusion of an additional 300 kW DG in bus 2 was concluded to be an effective strategy for optimizing the system, resulting in maximizing the generated power with minimum network losses and costs. Results reveal that DG allocation using the firefly algorithm outperforms the other three algorithms, reducing the burden on the main DG and batteries by 30.48% and 19.24%, respectively. This research presents an optimization of an existing electricity network case study located on Tomia Island, Southeast Sulawesi, Indonesia. Full article

Solid oxide fuel cell (SOFC)–gas turbine (GT) hybrid systems can produce power at high electrical efficiencies while emitting virtually zero criteria pollutants (e.g., ozone, carbon monoxide, oxides of nitrogen and sulfur, and particulate matters). This study presents new insights into renewable hydrogen (RH₂)-powered SOFC–GT hybrid systems with respect to their system configuration and techno-economic analysis motivated by the need for clean on-demand power. First, three system configurations are thermodynamically assessed: (I) a reference case with no SOFC off-gas recirculation, (II) a case with cathode off-gas recirculation, and (III) a case with anode off-gas recirculation. While these configurations have been studied in isolation, here we provide a detailed performance comparison. Moreover, a techno-economic analysis is conducted to study the economic competitiveness of RH₂-fueled hybrid systems and the economies of scale by offering a comparison to natural gas (NG)-fueled systems. Results show that the case with anode off-gas recirculation, with 68.50%-lower heating value (LHV) at a 10 MW scale, has the highest efficiency among the studied scenarios. When moving from 10 MW to 50 MW, the efficiency increases to 70.22%-LHV. These high efficiency values make SOFC–GT hybrid systems highly attractive in the context of a circular economy as they outcompete most other power generation technologies. The cost-of-electricity (COE) is reduced by about 10% when moving from 10 MW to 50 MW, from USD 1976/kW to USD 1668/kW, respectively. Renewable H₂ is expected to be economically competitive with NG by 2030, when the U.S. Department of Energy’s target of USD 1/kg RH₂ is reached. Full article