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Landfills have high potency as renewable energy sources by producing biogas from organic waste degradation. Landfills biogas (LFG) can be used for power plant purposes instead of allowing it to flare to the atmosphere which contributes to the global warming. The aim of this work was to introduce and examine an optimization model for maximizing the power generation of Al Ghabawi landfill in Amman city, Jordan. The optimization process focused on studying the effect of several operating parameters within the landfill power plant. To achieve this goal, a combustion model had been built and validated against a set of historical real data obtained from the landfill operator. In addition to that, an Artificial Neural Network (ANN) model had been built to perform a multi-objective optimization to obtain the optimal power generation conditions for Al Ghabawi landfill. The combustion model along with the ANN model aim to estimate the best engine operating conditions based on the actual daily data of the landfill. The engine operating parameters includes the intake pressure and temperature, the ignition time and the equivalence ratio. The results of the study indicate that the current operating parameters can be optimized to maximize the gensets power generation. Based on the daily data of the produced LFG, the optimal operating conditions for the landfill are 2.32 bar for the intake pressure, 303 K for the intake temperature, 0.9-1.0 for the equiveillance ratio and for the ignition time it is 13 degrees before the top dead center (BTDC). These optimized operating parameters can maximize the landfill power generation by at least 1 MW for each genset.

The construction industry is responsible for many negative environmental impacts that include emissions, waste generation, and the use of land, water, and energy. Most previous construction researc...

The use of intermittent power supplies, such as solar energy, has posed a complex conundrum when it comes to the prediction of the next days' supply. There have been several approaches developed to predict the power production using Machine Learning methods, such as Artificial Neural Networks (ANNs). In this work, we propose the use of weather variables, such as ambient temperature, solar irradiation, and wind speed, collected from a weather station of a Photovoltaic (PV) system located in Amman, Jordan. The objective is to substitute the aforementioned ambient temperature with the more realistic PV cell temperature with a desire of achieving better prediction results. To this aim, ten physics-based models have been investigated to determine the cell temperature, and those models have been validated using measured PV cell temperatures by computing the Root Mean Square Error (RMSE). Then, the model with the lowest RMSE has been adopted in training a data-driven prediction model. The proposed prediction model is to use an ANN compared to the well-known benchmark model from the literature, i.e., Multiple Linear Regression (MLR). The results obtained, using standard performance metrics, have displayed the importance of considering the cell temperature when predicting the PV power output. Keywords: Renewable Energy, Photovoltaic, Prediction, Cell temperature, Multiple Linear Regression, Artificial Neural Networks.JEL Classifications: C53, Q47.DOI: https://doi.org/10.32479/ijeep.9533

It has been shown that using fuel additives play an important role in enhancing the combustion characteristics in terms of efficiency and emissions. In addition, free piston engines have shown capable in reducing energy losses and presenting more efficient and reliable engines. In this context, the objective of the present work is to investigate the effect of using hydrogen as a fuel additive in natural gas homogeneous charge compression ignition free piston engine. To this aim, two models have been iteratively coupled: the combustion model that is used to calculate the heat release of the combustion and the scavenging model that is employed to determine the in-cylinder mixture state after scavenging in terms of its homogeneity and species mass fractions and to obtain the finial pressure and temperature of the in-cylinder mixture. In the former model, the 0-D approach through Cantera toolkit has been considered due to the fact that homogeneous charge compression ignition combustion is very rapid and the fuel-air mixture is well-homogenous, whereas in the latter model, 3-D-CFD approach through AN-SYS FLUENT software is considered to ensure precise calculations of the species exchange at the end of each engine cycle. The effect of hydrogen as a fuel additive has been quantified in terms of the combustion characteristics (e. g., ignition delay, heat release rate, engine overall efficiency and emissions, etc.). It has been shown that hydrogen addition reduces ignition delay time, decreases the in-cylinder peak pressure, while allowing the engine to operate with higher mechanical efficiency as it has high heat release rate, increases the NOx emission levels of the engine, but decreases the CO levels

As renewable energy application is gaining a wide acceptance by end users while considering the fact that renewable energy is intermittent, variable and cannot be expected; the need of storage systems is becoming a necessity at both micro and macro levels. Fuel cell technology is one of the most promising storage systems due to the fact that hydrogen has high energy density. This paper presents a design of stand-alone PV-PEMFC hybrid system for small house under Amman climate. The simulation results shows that the optimal size of PV array, fuel cell (PEMFC), inverter, electrolyzer (ELE) and H2 Tank capacity were 10 kW, 1 kW, 5 kW, 6 kW, and 5 kg respectively. Hydrogen proved itself as being a low carbon energy source, environmental friendly and with high energy content per unit mass. Due to fuel cells technology, hydrogen can be used for inter-season storage.

Solar thermal and Photovoltaic (PV) systems can save significant amount of non-renewable energy that are utilized to satisfy the energy services for buildings. The falling prices of PV in recent years increased the attractiveness of PV systems. The purpose of this research is to develop a systematic methodology for fair technical and economical comparison of reference and solar assisted systems. For this reason, the annual energy requirements for heating, cooling and DHW for a selected dormitory building under five representative climatic conditions were evaluated using the Hourly Analysis Program (HAP). At the same time, solar thermal and PV systems have been designed and simulated for the same building using Sketchup, Trnsys and PVsyst. Finally, a systematic comparison between the solar systems and five conventional systems have been carried out in terms of primary energy ratio, and Levelized Cost Of Energy (LCOE). For the case of Amman, solar thermal and electric systems achieved a non-renewable primary energy savings of 29% and 100%, respectively, whereas, they achieved LCOE savings of 15% and 62%, respectively compared to the best conventional system. The selection of the best solution for a given project requires a full understanding of the system performance and the interaction of its components rather than the technology efficiency only. This system performance depends on the building envelope, load patterns, availability of solar radiation, roof area availability, energy prices, and policies. Thus, a case-by-case analysis should be done for each project in each climatic region.

In this research, two identical solar stills were designed and constructed to investigate the effect of adding copper and aluminum oxide nanoparticles on the quantity of water produced by solar desalination. The two solar stills were installed side by side, and measurements were recorded simultaneously from both stills. The nanoparticles were added to one still, each at one time but with different concentrations, while the other contained only water. Data acquisition and a weather station were used to record the glass, water, and ambient temperatures in addition to the hourly solar radiation. It was found that the addition of nanoparticles increases the amount of condensate. The most efficient concentrations were found to be 0.4% of Al2O3 and 0.6% of CuO. At these concentrations, an increase in the efficiency of the still equals 7.8%, and 9.62% was recorded. Furthermore, it was found that CuO has a more pronounced effect on the condensate than Al2O3 at all concentrations except at 0.4% concentration.

This paper represents a case study for the potential of having hybrid energy system (PV/Wind/Diesel) with battery as a storage for powering a house located in a remote area in the city of Al-Tafilah in Jordan. It presents technoeconomic analysis of different hybrid system configurations, and potential of using renewable energy resources is evaluated. HOMER (Hybrid Optimization of Multiple Electric Renewables) optimization model is used to evaluate different possible systems combinations for supplying the load demand of 37.5 kWh/day with a 6.98 kW peak load. HOMER software tool was used to determine the optimum size and specifications of power generating systems. Ten results were found to be the best optimization solutions for each possible combination of using PV panels, wind turbines, diesel generators, converters and batteries. The best and most economical solution has been determined by HOMER, and was found to be where all the mentioned systems except wind turbines are used together. That optimal solution was found to be using a 12.1 kW PV system, a 7.7 kW Diesel generator, a 36 kWh nominal capacity battery bank and 4.82 kW converters. It has a minimum Net Present Cost (NPC) equal to 66,012 $, as well as a minimum Cost of Energy (COE) equal to 0.338$/kWh. Additionally, it has a lower gas emission than some of the other solutions.

In this experimental work, using nanofluids and water on the thermal performance of the two prismatic parabolic trough collectors (PTC) has been compared in the two prismatic PTC. Those prismatic PTCs installed side by side have been examined simultaneously for different days; this ensures that the comparison was conducted under identical conditions, one for the distilled water and the other for the proposed nanofluid. The proposed nanofluid that was utilized in our study has been produced by dispersing nanoparticles of Multi-walled carbon nanotubes (MWCNT) in a distilled water with a weight concentration of 0.1%. The key results showed that adding nanoparticles' positive impact increasing the base fluid’s thermophysical properties (Water), side by side with enhancing the heat transfer between the receiver tube and the working fluid. The average thermal efficiency of the PTC that used the proposed nanofluid showed a higher average value in comparison with the other PTC that used base fluid at the same weather conditions (ambient temperatures and solar irradiances). The tests were conducted for two different weather conditions; for the first set of experiments, the average thermal efficiencies observed were: 62.9% for nanofluid with 0.1% wt fraction and 33.4% for water. While for the second set, the average efficiencies obtained were: 60.45% for Nanofluid with 0.1% volume fraction and 32.2% for water. Finally, it was found that the average thermal efficiency of utilizing nanofluids had a strong dependence on the settling time after sonication, side by side with using the surfactant, where the highest average thermal efficiency was obtained immediately after sonication and utilizing Arabic Gum as a surfactant.

The interest in Bifacial PV modules has grown rapidly during the last few years. The main objective of this work is to experimentally investigate the electrical and thermal behavior of a bifacial module and to compare it with the performance of three monofacial modules (Half-cut, Mono-PERC, and Polycrystalline) under the Jordanian climate.Throughout the present work, four PV modules have been installed on the roof top of the Engineering School of the University of Jordan, with a tilt angle of 30° facing south. Several attempts have been done to design, calibrate, program, and install a complete data acquisition system that is capable of measuring all required system parameters: current, voltage, cell and ambient temperatures, and solar radiation on the front and rear sides of the panels. Measurements were conducted for five weeks.Temperature measurements for the PV modules showed that the measured temperatures are neither affected by the sensor position on the module surface nor by the module side at which it is installed. Moreover, the bifacial module temperatures were found to be similar to the temperature of other modules. From energetic point of view, the bifacial module produced 9.9%, 13.1% and 24.9% more energy compared to the Mono-PERC, Half-cut and Polycrystalline panels respectively for a period of five weeks during October and November. These results reveal that bifacial PV panels have a great potential to improve the energy production of a PV plant.

This study aims to develop a benchmark for the electric Energy Use Intensity (EUI) for residential buildings in Jordan. A benchmark was established through a tailor-designed questionnaire that was distributed on a random sample of senior students of the University of Jordan. About sixty responses were analyzed and the majority were obtained for buildings located in the middle region of Jordan (Amman and Zarqa) where the electricity is distributed by the Jordanian Electric Power Company (JEPCO). And the average electrical (EUI) of 31.6 and, and 22.5 kWh/m2.a for apartments and houses respectively were obtained.

The goal of this paper is to investigate performance enhancement of standard fixed photovoltaic (PV) solar systems when using single and dual sun tracking mechanisms, and compare it to concentrated photovoltaic (CPV) systems which inherently uses tracking systems. The study has two parts: experimental part and selected case study part. In the experimental analysis, characterization and performance comparison of four mounting types of standard PV systems are conducted. The PV panels are installed using either fixed mount, single axis (East-West tracking), single axis (NorthSouth tracking), or dual axis tracking. The analysis is done in Amman, Jordan. The data has been collected using data loggers every fifteen minutes, recorded then analyzed. The performance of these different mounting cases of PV system is compared to a CPV system installed in ASU. The results indicated that the Concentrated photovoltaic system utilized more electrical power than conventional photovoltaic. It produced 4.16% gain of power output compared with dual axes PV tracking system. The electrical power generation on the tracking surfaces were significantly greater than that on a fixed surface. There were increases of electrical power gain up to 37.72%, 24.05% and 16.56% for the two axes, east–west and north–south tracking, respectively, as compared with the fixed surface inclined 26 to the south in Amman, Jordan.

This chapter reviews the latest research and advancements in solar cooling technologies. In addition, it includes a discussion of the energetic, economic, and environmental indicators used to evaluate and compare different cooling options. Conventional vapor compression refrigeration systems driven by photovoltaic solar systems are gaining increased attention due to their maturity, modularity, and ease of installation. Solar thermal absorption and adsorption systems are slowly moving from demonstration to the market as well. Using solar desiccant cooling systems to dehumidify the air and improve its thermal comfort is economical, environmentally friendly, and easily achieved. Several combined ejector cooling systems can be utilized to improve their performance or create new functions. Hybrid solar cooling systems with various configurations are advancing and improving system efficiency and performance. Reviewed literature has revealed the great potential of solar energy to provide cooling with low energy consumption and reduced environmental impacts.

The use of data-driven ensemble approaches for the prediction of the solar Photovoltaic (PV) power production is promising due to their capability of handling the intermittent nature of the solar energy source. In this work, a comprehensive ensemble approach composed by optimized and diversified Artificial Neural Networks (ANNs) is proposed for improving the 24h-ahead solar PV power production predictions. The ANNs are optimized in terms of number of hidden neurons and diversified in terms of the diverse training datasets used to build the ANNs, by resorting to trial-and-error procedure and BAGGING techniques, respectively. In addition, the Bootstrap technique is embedded to the ensemble for quantifying the sources of uncertainty that affect the ensemble models' predictions in the form of Prediction Intervals (PIs). The effectiveness of the proposed ensemble approach is demonstrated by a real case study regarding a grid-connected solar PV system (231 kWac capacity) installed on the rooftop of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. The results show that the proposed approach outperforms three benchmark models, including smart persistence model and single optimized ANN model currently adopted by the PV system's owner for the prediction task, with a performance gain reaches up to 11%, 12%, and 9%, for RMSE, MAE, and WMAE standard performance metrics, respectively. Simultaneously, the proposed approach has shown superior in quantifying the uncertainty affecting the power predictions, by establishing slightly wider PIs that achieve the highest confidence level reaches up to 84% for a predefined confidence level of 80% compared to three other approaches of literature. These enhancements would, indeed, allow balancing power supplies and demands across centralized grid networks through economic dispatch decisions between the energy sources that contribute to the energy mix.

The high electricity consumption and cost at the University of Jordan has motivated the university to adopt a renewable energy and energy efficiency as one of its strategic objectives for the coming decade. The university has set forth an ambitious goal to achieve 100% electrical energy independence, relying mainly on renewable solar energy using photovoltaic (PV) panels. This study investigates different technical solutions of the grid connected solar PV system; fixed, single-axis and double–axis tracking PV modules. Moreover, two engineering models for the construction of such a project have been investigated; the Build Operate Transfer (BOT) model and the Engineering Procurement Construction (EPC) engineering model. The performance analysis was conducted in terms of final yield, land use and conversion efficiency, while the economic analysis investigates the simple payback period and internal rate of return. The simulation was carried out using a Trnsys model that has been experimentally validated by the authors. It was found that the most attractive choice is the EPC model using the fixed PV system for installation with 32% internal rate of return (IRR) and 3 years payback period. The required system size was 15030 kWp, with an estimated area of 150 thousand squared meters.

The variable renewable wind and solar resources have experienced a significant growth on its rate of deployment as a clean and competitive alternative for conventional power sources in Jordan. However, the variability of these sources have brought many technical challenges to grids. This paper presents a hybrid system that provides a firma capacity and improves dispatchability with an interesting financial perspective. This hybrid system includes a wind farm and a concentrated solar power plant with thermal energy storage. The performance analysis was conducted in terms of final yield and capacity factor, while the economic analysis investigated the levelized cost of electricity LCOE. The hybrid plant was simulated and optimized using TRNSYS 17 energy simulation software, minimizing the LCOE and considering a capacity factor higher than 65% as a constraint. Solar multiple and storage size were considered as decision variables. A strong complementarity between wind and direct normal solar radiation was observed in the selected location in Jordan, which emphasizes the attractiveness of the selected hybrid system. The optimal configuration of the CSP-wind hybrid system was obtained with a solar field of a 2.6 solar multiple and a 5 hours energy storage. The achieved capacity factor was 94%, and the LCOE is lower than those resulted for standalone CSP plants.

This study produces a technical and economic analysis of a farm- fed biogas plant that utilizes cow manure to produce electricity and heat via combined heat and power unit. The electricity generated is directly injected into the grid while heat is used within the biogas plant. The biogas system design was done depending on number of cows in the farm, which denotes the amount of manure available as feedstock. The economic performance of the proposed biogas plant was evaluated using various economic indicators. The levelized cost of electricity and the avoided greenhouse gas emissions were calculated too. The preliminary design and economic feasibility results proved the profitability of the manure-based biogas systems on wide range of farm sizes. The net present values of a 100-cow, 500-cow, 1,000-cow, 1,500-cow and 2,000-cow farms were all positive. The internal rate of return values were 11%, 12%, 13%, 16% and 17%, respectively, and the levelized cost of electricity values were 0.071, 0.069, 0.064, 0.055 and 0.055 JOD/kWh, respectively. The study also produced a design for a centralized anaerobic digestion plant depending on Jordan resources. The government role in promoting biogas-based electricity was discussed too.

The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate one day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kWac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15%, 1%, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) performance metrics, respectively, compared to the Persistence prediction model of literature.

A new system consisting of photovoltaic thermal-ground source heat pump (PV/T-GSHP) was proposed as a solution for electricity shortage and high electricity consumption in heating buildings in Jordan. The performances of the photovoltaic system and the ground source heat pump system were studied without coupling as in real life and with coupling in a hybrid system using TRNSYS software. The results show that this PV/T-GSHP hybrid system can reduce the photovoltaic panels' temperature by more than 20 °C, and improve the efficiency of electricity production by 9.5 %, simultaneously. And in heating season the average coefficient of performance of the heat pumps increased from 4.6 to 6.2 with a decreament in electricity consumption by 25.7 %. The life cycle cost of the hybrid system decreased by 3.9 % and the net present value inceased by 13.2 % compared to the two systems separated. The model of the PV/T-GSHP system was established using TRNSYS software, an optimmization was made to select the photovoltaic modules' temperature at which cooling starts, followed by technical and economical studies on both systems separated and coupled in a hybrid system. This hybrid system can provide guidance for future related project.

This paper assesses the economic sides of a grid-connected PV system based on a validated model and the feed in tariff regulation in Jordan. The economic model was built based on a calibrated design model at previous work for authors. An economic analysis is conducted based on a proposed PV plant to cover the Jordan University needs of electricity, considering the Engineering Procurement Construction (EPC) and the Build-operate-transfer (BOT) engineering models and three different sun tracking methods. The analysis showed that the most feasible choice is EPC model using the fixed tracking method. The IRR was 34.4% and a simple payback period of 2.86 years.

Several challenges affect solar PV (Photovoltaic) integration with Jordan electricity grid. Major challenges are PV power variability, uncertainty and constructing new transmission lines on unqualified grid. Conventional plants consume fuel to export the electricity to the grid and to the in-house load which requires, in average, 1.6∼7.8% from plant generated energy. This study aims to propose new method to indirectly integrate PV with the grid through conventional plants (Hybrid Model). The model consists of conventional power generation and PV plant. During solar radiation availability in-house load is fed from solar field, otherwise part of generated energy will be consumed by internal load. PV SYST 6.40 program was used to simulate a grid connected PV system (8 MWp) equal to the PV system capacity proposed for the hybrid model in the case study. The technical constraints and the economic performance in win-win scenarios were studied and compared. No technical constraints were found and the economic evaluation results of the PV system proposed for the hybrid model showed more attractive investment for Power Producers to install PV system in hybrid scheme (13.2%-IRR, 7 years pay-back period and 16,841,528 USD NPV) against gird-connected PV system (9.9%-IRR, 9 years pay-back period and 12,120,193 USD NPV). Avoided cost for transmission line and fuel saving when applying hybrid model counted as 2,095,000 USD and 782,248 USD/year respectively. Finally, applying hybrid model to operational plants in Jordan would result in 151 MWp PV integrated to the grid with Zero transmission cost.

The availability of solar radiation in phase with the seasonal as well as hourly cooling load profiles in most of the office buildings in the Mediterranean region, in addition to the large share of primary energy consumed for air- conditioning applications in office buildings create a high motivation for the utilization of solar cooling technology for such type of buildings. A solar heating and cooling system for an office building in Italy has been designed, installed and monitored within the framework of the EC co-funded project SOLERA aiming at developing highly integrated solar thermal heating and cooling system that is able to achieve a high solar fraction both for the heating and cooling seasons. The analysis of the system performance during 2011 is presented in this paper, with main focus on electricity consumption during summer. The analysis has been carried out according to the monitoring procedure developed within the frame of the IEA SHC Task 38.

The objective of this review article is to draw a picture about a promising solar cooling concept, based on the use of concentrating solar collectors, and to define the aspects that need to be considered in future developments. The following topics are covered: an overview of solar cooling systems utilizing concentrating solar collectors worldwide; the reasons behind the selection of these solar collection technologies for solar cooling applications; a quick assessment of the main performance figures for the different solar cooling schemes based on Monte Carlo simulations; the technical requirements of the technologies for future developments. Air-conditioning and refrigeration facilities driven by concentrating solar collectors are still infrequent and the outcomes of this review clearly present the small but steadily growing market of solar cooling systems coupled with concentrating solar collection technologies.