Renewable Energy Research and Applications
Volume:5 Issue: 2, Summer-Autumn 2024

This research proposes and evaluates an enhanced open-loop photovoltaic evacuated tube solar thermal collector hybrid energy system based on the developed multi-objective energy management strategy that manages and coordinates the hybrid system with a randomly unreliable grid power source to meet the health center's energy demand using TRNSYS software. A technical assessment of the system shows that the system is capable of meeting system load with a solar fraction of 67% even on days with an overcast sky level of radiation as low as 250 W/m2 and only 37.5% grid power availability. Overall, the system has a solar fraction of 80%. The implication of an 80% solar fraction is the large environmental benefit of reducing emissions and improved system economic viability, indicating that the formulated energy management achieves the goal of promoting renewable energy sources in the hybrid system. An economic analysis of the system revealed that it has a payback period of 6.9 years and Net Present Value of $36,985 at the end of the project's lifetime. This demonstrates that the upgrade of the traditional hybrid PVT with an evacuated tube collector operated based on the developed energy management strategy has met the goal of minimising emissions with significant environmental and economic savings.

This study aims to identify a favorable area for wind energy exploitation in the Littoral region of Cameroon. The study used data collected by the meteorological service at Douala International Airport. A probabilistic method based on the Weibull distribution with two parameters was used to assess the potential of the study area. Three methods were used to determine the parameters of this distribution: the maximum likelihood method, the WAsP method, and the energy pattern factor method. Statistical tests showed that the energy pattern factor method is more efficient, but the WAsP software provided acceptable results. The WAsP software was used to generate maps of the mean wind speed and wind power density at different heights. Two specific wind turbines were considered to calculate the annual energy production. The topography of the study area, the obstructions around the logger, and the roughness of the terrain were all taken into account when generating the maps for the different characteristics. Finally, maps at heights of 50 and 100 m were created using extrapolation techniques. Two zones with the highest power density were identified. In one of these locations, the wind power density could reach 54 W/m2 at a height of 100 m and the annual electrical output from a specific wind turbine could reach 1 GWh. The corresponding location is located at latitude 4.0661° North and longitude 9.8796° East.

The global electricity demand is rapidly growing due to population increase and industrialization. However, the reliance on fossil fuels and other non-renewable energy resources has resulted in climate change and other unsustainability-related issues. This study aims to determine the significant penetration levels of Solar PV on system operations and production costs based on the current year (business as usual scenario) and the accelerated Solar PV scenario (hypothetical future) in the Kenyan electricity generation system. A one-year dynamic analysis based on an hourly time step energy demand was performed using the Energy PLAN simulation tool. The current peak demand for electricity in Kenya was established to be 2,056.67 MW with an installed capacity of 3,074.34 MW with a 2.47% contribution by Solar PV while the curtailed energy was 285.51 GWh. The simulation results showed that large-scale installations of Solar PV can decrease CO₂-equivalent emissions from 0.134 Mt to 0.021 Mt. Both scenarios are presented in terms of their ability to avoid excess electricity production regarding system operations and production costs. Increasing the share of Solar PV in electricity generation is possible by as much as 39.56% (technical) and 30.54% (market economic) simulation. Additionally, the Solar PV electricity produced increased to 19.76 TWh/year from 11.90 TWh/year. Furthermore, the Market Economic Simulation showed that the total investment annual cost for Solar PV in the hypothetical future was low at 10 mEUR/Year. Therefore, large-scale installation of Solar PV in Kenya's energy system is feasible and economically viable based on technical analysis and economic analysis.

The objective of this study was to assess and evaluate the biogas yield of food wastes generated from the main campus of Ambo University's student cafeteria in a batch anaerobic digestion. Food waste from preprocessing and leftover from the student cafeteria were collected and measured. Standard techniques were used to analyze the physicochemical characteristics of the various food wastes, and the barrier solution was used to assess the amount of biogas and methane produced. The daily, weekly, monthly and yearly generated food wastes were: 1,283.02; 8,883.14; 38, 489.06; 204, 448.78kg respectively and the rate of generation of food waste was 0.37kg/capita/day. The moisture content ranged from 3.4±0.78% to 93.11±0.30%; total solids from 6.9±0.30% to 96.6±0.72%, VS of TS 82.1±0.59% to 98.1±0.75%; OC from 45.6±0.33% to 54.5±0.02%, C:N from 33.8% to 20.03±0.3%. The highest average biogas and percentage of methane were measured from FLM (12500±307.16ml and (81.65±2.58%) respectively while, the lowest average total biogas and percentage of methane were from the FPK (8590.33±260.77ml and (67.15±2.47%) respectively. The findings of this study revealed that the high quantity of food waste that was readily available at the study site and that could potentially be converted into high quantity and high-quality bio-methane which could serve two purposes producing of bio-fuels and reducing environmental degradation from the open disposal of food waste.

Recycling plastics into energy sources is the most promising method for cutting down on pollution and trash. In this regard, predictions of adiabatic engines using pistons with thermal barrier coatings (TBCs) were made to reduce in-cylinder heat rejection, safeguard the underlying metallic surfaces from thermal cracking, and indeed reduce engine emissions. This study compares the predicted thermal and physical parameters of Plastic Waste Oil (WP) with its diesel blends in fixed proportions of WP10D90 (10% plastic oil, 90% diesel), WP20D80, WP30D70, WP40D60, and WP50D50 to diesel values. The study further explores the concept of the utility function to evaluate the best-ranked fuel blend in each category of various performance characteristics namely BTE, BSFC, UHC, CO, and NOx. Additionally, the effect of the thermal barrier piston coating on CI engine performance metrics and emissions was studied and compared to those achieved with regular diesel oil. When compared to diesel, the results state that the WP40D60 blend has the highest brake thermal efficiency, i.e., 31.62% at 80% load, and the lowest NOx emissions at all load conditions. In addition, it was further observed that the WP20D80 has lower hydrocarbon (HC) emissions at 20% load and an increment in CO emissions for all blends and load combinations. Overall, WP30D70 has come up with the best fuel as per the Utility function.

Fuel cells have been identified as a promising technology to meet future electric power requirements. Out of various fuel cells, Proton Exchange Membrane Fuel Cells (PEMFC) has been staged up as they can operate at low temperatures and also have high power density. In this article, the flow field design of a Single Serpentine Flow Field (SSFF) has been modified to L-Serpentine Flow Field (LSFF) in order to reduce thermal and water management problems in PEMFC. A numerical study was conducted on 441 mm2 active area at 700C and 3 atm operating conditions, to evaluate various flow characteristics by comparing LSFF with SSFF, and it was observed that temperature and species flux distribution in LSFF enhanced significantly. The modification of the flow field yielded remarkable improvements in various aspects. These enhancements included a more uniform distribution of membrane water content, an impressive 8% increase in O2 consumption, a remarkable 22% improvement in product evacuation demonstrated by the H2O species profile, attributed to a 40% reduction in product travel distance. Additionally, a noteworthy 10% increase in power density was achieved. Despite a slight increase in pressure drop due to the additional bends and turns in the modified flow field, the impact on power density remained insignificant. These findings highlight the immense potential of the modified flow field to significantly enhance performance.

Most of the partial shading maximum power point tracking methods have been designed for the static shading pattern of the partial shading conditions, however, the irradiance pattern may change further when in partial shading mode. Therefore, to cover this research gap, a global maximum power point control under varying irradiance (GCVI) algorithm is proposed in this paper. The algorithm does not use any sensors to detect the change in the irradiance, instead, the change in the current values of the modules are continuously monitored to detect the change. The reference voltages across which the peaks on the power curve are scanned are obtained from the reference voltage generation process, the consideration of these reference points avoids the excessive power losses in the system. The verification of the working of the proposed algorithm is carried out by simulating the photovoltaic system model on SIMULINK in MATLAB software. Simulations are carried out in various scenarios to show the effectiveness of the control. The simulation results illustrate that with the change in the global maximum under partial shading, the system successfully retunes to the new maximum point; the maximum point retunes from 10 kW to 9.2 kW and from 13.8 kW to 11.5 kW for two different case scenarios. Further, the comparisons are also carried out with the previously reported methods.

Biodiesel, derived from biomass, offers significant environmental advantages by reducing CO2 and CO emissions and promoting energy self-sufficiency. Currently, biodiesel remains limited to DG sets used by a small number of farmers in India, with minimal adoption in the transportation sector. Numerous challenges impede biodiesel's acceptance. This research focuses on identifying challenges connected to India's biofuel policy, supply chain inefficiencies, and vehicle technology. In terms of cultivation, land management, and the delivery of high yielding varieties to farmers, biofuel policies have failed to encourage indigenous feedstock. Instead, the Biofuel Policy 2022 encourages the imported palm oil sterain. Inconsistencies in the supply chain caused by policies impair the cost effectiveness of biodiesel. Diesel engines in automobiles have compatibility concerns owing to corrosiveness and high fuel consumption due to the fuel's low calorific value. Furthermore, biodiesel causes substantial NOx emissions. This study offers policy-level solutions, such as encouraging the production of domestic feedstocks through efficient management of wastelands. In this approach, farmers may receive high yielding seeds at a reduced cost until the industry is self-sufficient. In addition, Policy Linked Incentive (PLI) scheme can be given to biodiesel producers. A policy like ethanol blending can also be implemented. The biodiesel supply chain, like that of Germany, the United States, Malaysia, and Indonesia, must be optimized. For the technological challenges in diesel engines, the government must use policy intervention, to incorporate engine components suitable for biodiesel, as well as upgrade diesel engines by calibrating electronic control units and with exhaust gas recirculation systems.

In this work, the photovoltaic power forecast for the next 24 hours by combining a time series forecasting model (LSTM) and a regression model (XGBoost) from direct irradiation only is performed. Several meteorological parameters such as irradiance, ambient temperature, wind speed, relative humidity, sun position, dew point were identified as influencing parameters of PV power variability. Thanks to the parameter extraction and selection techniques of the XGBoost model, only the direct irradiation could be kept as input parameters. The LSTM model was used to predict the direct irradiation for the next 24 hours and the XGBoost model to estimate the future power from the predicted irradiation. These models were developed under Python 3, the exploited data were downloaded in the PVGIS database for the city of Abomey-Calavi in Benin and the prediction was carried out on a panel of 1000W of peak power. An experimental validation was then performed by comparing the predicted irradiance values to the measured values on site. It was obtained for the LSTM model a root mean square error of 3.66 W/m2 and for the XGBoost model a root mean square error and a regression coefficient of 1.72 W and 0.992129 respectively. These results were compared to the LSTM-XGBoost performances with irradiation, temperature, sun position and wind speed as inputs. It was found that the use of irradiation alone as input did not as such impair the forecast performance. The proposed method was also found to be more efficient than LSTM and CNN models used alone.

In this paper, it is determined exactly how much of the loss of exergy in a specify component is concerning the own component and how much of the exergy loss is due to the effects of the rest of the components on that component. In this new method of exergy analysis, at first, the exergy loss in a component is classified as avoid. /unavoid categories. With this classification, it is possible to understand what quantity of the exergy loss of a component is eliminated by optimizing that component and how much of the exergy dissipation can never be eliminated and is related to the nature of the component. In the next classification, by categorizing the exergy loss into endo./exo., we can find out how much of the exergy destruction is due to the non-optimality of other components and has nothing to do with the component itself. Finally, the categories are divided into avoid-endo, unavoid-endo, avoid-exo and avoid-enxo. By performing this new method, the results demonstrate that the highest exergy destruction (1.976 MW) happens in the evaporator, 68% of which is unavoid-endo. exergy loss. The highest avoid. exergy loss relates to low pressure turbine (0.5791 MW). It is shown that optimizing of surrounding components of deaerator, economizers, and evaporators has a greater effect on decreasing the exergy dissipation of these own components, and the most Avoid. exergy destruction is in heat exchangers, pumps, condensers, turbines, expansion valves, reheaters, and superheaters.

The current research focuses on the utilization of three waste water streams from a power plant located in southwestern Iran for desalination purposes and to prevent the waste of heat from the boiler blowdown stream while reducing carbon dioxide emissions by preheating the cooling water. Three different scenarios are simulated using the Thermoflow-GT master 23 software, considering the conditions of power plant. The optimal values for the top brine temperature (TBT) of cooling water and the mass flow rate of the hot steam are selected by sensitivity analysis. The premier scenario consists of eight stages, with five stages dedicated to heat recovery (HGS) and three stages for heat rejection section (HRS). The optimal value for the TBT of cooling water is determined to be 90℃, the produced freshwater capacity in the desalination unit is found to be 1.69 kg/s, and the gain output ratio (GOR) of the system is about 3.60. The proposed unit requires 0.47 and 10.15 kg/s of hot steam and cooling water, respectively and the overall heat transfer coefficient is 2069.2 W/m2 ℃. In addition, the feasibility of utilizing a solar farm to generate the necessary thermal energy for the system is being evaluated.

The effect of biodiesel percentage in biodiesel-diesel blends on the engine under different engine operation conditions must be predicted to achieve high performance. The goal of the present paper was to model brake power, brake torque, thermal efficiency, and specific fuel consumption of a diesel engine fueled by algal biodiesel-diesel blends. The response surface methodology was successfully applied to model the performance indicators of biodiesel-diesel fueled OM 314 diesel engine at various engine loads and rotational speeds. Brake power, torque, and thermal efficiency increased by increasing engine load. Increasing engine rotational speed caused increase in brake power whereas highest brake torque and thermal efficiency was obtained in medium engine rotational speed. Increase of biodiesel percentage caused decrease in. Biodiesel had negative effects, but it had lower effects than engine load and rotational speed on the change of the engine performance indicators. Brake specific fuel consumption decreased by increasing load but it was lowest in medium rotational speeds. A quadratic model was suitably fitted to predict the effects of input-output variables with statistical significance of 1% probability level. The coefficient of determinations for prediction of the engine brake power, brake torque, thermal efficiency, and brake specific fuel consumption were 97.63, 99.74, 97.41, and 95.72%, respectively. The result of the present work is useful to find optimum biodiesel percentage and engine load and rotational speed to achieve high performance fuel blends.