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Optimal placement and sizing of Distributed Generation (DG) are essential for future power planning of distribution networks. The performance of Gravitational Search Algorithm (GSA) and an Improved version of Gravitational Search Algorithm (IGSA) are compared in solving the optimization problem. The multi-objective function optimization includes power loss minimization (Ploss), Minimum bus voltage (Vbusmin), and an average voltage total harmonic distortion (THDv) are considered in this optimization problem and the IEEE 13-bus and IEEE 69-bus radial distribution network were applied on this study. The benefits due to the optimal placement and sizing of DG include power loss reduction, minimization of total harmonic distortion, and improvement of bus voltages, especially the weakest bus in the distribution network. The impact of DG installation at the proposed location with the proposed sizing on voltage stability margin also presented. The results show the Voltage Stability Margin (VSM) for both real power (P) and reactive power (Q) at the weakest bus after DG have improvement with present of DG.
Noor Ropidah Bujal; Marizan Sulaiman; Aida Fazliana Abd Kadir; Tamer Khatib; Naji Eltawil. A Comparison Between GSA and IGSA for Optimal Allocation and Sizing of DG and Impact to Voltage Stability Margin in Electrical Distribution System. Journal of Electrical Engineering & Technology 2021, 1 -18.
AMA StyleNoor Ropidah Bujal, Marizan Sulaiman, Aida Fazliana Abd Kadir, Tamer Khatib, Naji Eltawil. A Comparison Between GSA and IGSA for Optimal Allocation and Sizing of DG and Impact to Voltage Stability Margin in Electrical Distribution System. Journal of Electrical Engineering & Technology. 2021; ():1-18.
Chicago/Turabian StyleNoor Ropidah Bujal; Marizan Sulaiman; Aida Fazliana Abd Kadir; Tamer Khatib; Naji Eltawil. 2021. "A Comparison Between GSA and IGSA for Optimal Allocation and Sizing of DG and Impact to Voltage Stability Margin in Electrical Distribution System." Journal of Electrical Engineering & Technology , no. : 1-18.
This paper presents a grid impact assessment of a 5 MWp photovoltaic-based distribution unit on a 33 kV/23 MVA power distribution network with high penetration of renewable energy generation. The adapted network has an average load demand of 23 MVA, with a 3 MWp centralized PV system, and a number of decentralized PV systems of a capacity of 2 MWp. A grid impact assessment is done to an additional 5 MWp of PV generation as a centralized system as well as a number of decentralized systems. Power flow analysis is conducted to the grid considering different generation loading scenarios in order to study grid performance including active and reactive power flow, voltage profiles, distribution power transformers loading, transmission lines ampacity levels, and active and reactive power losses. On the other hand, the distribution of the decentralized systems is done optimally considering power distribution transformer loading and available area using the geographical information system. Finally, an economic analysis is done for both cases. Results showed that grid performance is better considering decentralized PV systems, whereas the active power losses are reduced by 13.43% and the reactive power losses are reduced by 14.48%. Moreover, the voltage of buses improved as compared to the centralized system. However, the decentralized PV systems were found to affect the power quality negatively more than the centralized system. As for the economic analysis, the decentralized PV system option is found slightly less profitable than the centralized system, whereas the simple payback period is 9 and 7 years, respectively. However, decentralized PV systems are recommended considering the technical implications of the centralized PV system.
Tamer Khatib; Lama Sabri. Grid Impact Assessment of Centralized and Decentralized Photovoltaic-Based Distribution Generation: A Case Study of Power Distribution Network with High Renewable Energy Penetration. Mathematical Problems in Engineering 2021, 2021, 1 -16.
AMA StyleTamer Khatib, Lama Sabri. Grid Impact Assessment of Centralized and Decentralized Photovoltaic-Based Distribution Generation: A Case Study of Power Distribution Network with High Renewable Energy Penetration. Mathematical Problems in Engineering. 2021; 2021 ():1-16.
Chicago/Turabian StyleTamer Khatib; Lama Sabri. 2021. "Grid Impact Assessment of Centralized and Decentralized Photovoltaic-Based Distribution Generation: A Case Study of Power Distribution Network with High Renewable Energy Penetration." Mathematical Problems in Engineering 2021, no. : 1-16.
This paper presents a novel research clustering scheme based on bibliometric analysis to identify the state of the art and the trends of electrical power system load shedding research area. The proposal of this paper involves two major steps. Firstly, the topic phrases are extracted using bibliometric analysis to identify the general trend of the research area. Secondly, the proposed novel research clustering scheme is performed. The proposed scheme focuses on identifying the popular topics, and research focuses of a research area. An extensive test is conducted by considering 1548 scholarly outputs from Scopus database, where 36 popular topics are extracted by using the proposed method. Results also show that microgrid cluster is the biggest cluster in this research area. It is found that the percentage of growth of the microgrid cluster is 39.6%. On the other hand, the percentage of publication growth for voltage stability cluster is found slightly above 9%. Similarly, the transient stability cluster has a publication growth of − 5.6%. Meanwhile, power system reliability cluster publication growth was 12.9%. Finally, the observation likewise reveals that China, Iran, and the UK were the most popular countries for these four popular topics.
Aziah Khamis; Tamer Khatib; Nor Aishah Muhammad; Razaman Ridzuan. A Novel Research Clustering Scheme Using Bibliometric Analysis: A Case Study of Global Trend in Electrical Power System Load Shedding. Iranian Journal of Science and Technology, Transactions of Electrical Engineering 2021, 1 -16.
AMA StyleAziah Khamis, Tamer Khatib, Nor Aishah Muhammad, Razaman Ridzuan. A Novel Research Clustering Scheme Using Bibliometric Analysis: A Case Study of Global Trend in Electrical Power System Load Shedding. Iranian Journal of Science and Technology, Transactions of Electrical Engineering. 2021; ():1-16.
Chicago/Turabian StyleAziah Khamis; Tamer Khatib; Nor Aishah Muhammad; Razaman Ridzuan. 2021. "A Novel Research Clustering Scheme Using Bibliometric Analysis: A Case Study of Global Trend in Electrical Power System Load Shedding." Iranian Journal of Science and Technology, Transactions of Electrical Engineering , no. : 1-16.
In this research, a novel design and operation of solar-based charging system for battery vehicle for a 50 km run is proposed. The proposal is aimed at replacing 110 existing diesel vehicles with 39 electric buses. Several operation scenarios for the charging stations are proposed and analyzed. Scenarios include two different battery charging methodologies and one hybrid option between electric buses and diesel vehicles. An energy model of the adapted electric buses is developed first. After that, load demand and needs including number of daily trips, number of passengers per hour, and hourly energy consumption are determined based on the developed model and gathered information. Results show that a 5.7 MWp photovoltaic system is required to power this transportation line with a loss of load probability of 5% and a trip cost per passenger of 2.05 USD. The simple payback period of the system is found to be 10 years, which is 40% of the system’s lifetime. The amount of CO2 mitigated by the proposed system is estimated as 1,629,387 (kg/year). The social impact of the proposed project is found acceptable; whereas, most of the current employees will keep their jobs with higher salaries by about 145% and less working hours by 50%. Moreover, it is expected that the proposed project will significantly increase the reliability, convenience, and sustainability of the transportation process.
Mohamed Salameh; Tamer Khatib; Khaled Alsahili. A Novel Design of Photovoltaic-Based Charging Station for Battery Vehicles with Dynamic Demand: A Case of Short Runs. International Journal of Photoenergy 2021, 2021, 1 -17.
AMA StyleMohamed Salameh, Tamer Khatib, Khaled Alsahili. A Novel Design of Photovoltaic-Based Charging Station for Battery Vehicles with Dynamic Demand: A Case of Short Runs. International Journal of Photoenergy. 2021; 2021 ():1-17.
Chicago/Turabian StyleMohamed Salameh; Tamer Khatib; Khaled Alsahili. 2021. "A Novel Design of Photovoltaic-Based Charging Station for Battery Vehicles with Dynamic Demand: A Case of Short Runs." International Journal of Photoenergy 2021, no. : 1-17.
This paper provides an improved method for predicting the I–V curve of the photovoltaic module using a hybrid machine learning system. The proposed method is based on a random forest algorithm and a cascade forward neural network. A random forest algorithm is used to predict a specific factor that is subsequently used as an input for the cascade neural network to remove the correlation between voltage and current. Then, the actual current is predicted using the cascade neural network. This procedure assures the ability of the proposed model to extract the I–V curve of any photovoltaic module regardless of its rating or type. A dataset that contains values for air temperature, solar radiation, voltage, and current of two polycrystalline photovoltaic modules is used in the training process of the proposed algorithm. The hybrid model has general inputs such as ambient temperature, solar radiation, and data from the photovoltaic module datasheet (Voc and Isc). The proposed model is trained, tested, and validated by 86% of the data. Meanwhile, 14% of the data are used for testing. Thus, the proposed model is tested using unknown data so as to avoid overfitting. Results show that the proposed model is very accurate in predicting I–V curves based on three types of errors which are mean absolute percentage error (0.68%), mean bias error (0.0191 A), and root-mean-squared error (0.04458 A). This hybrid model can be used to obtain the I–V curves for several types of photovoltaic modules.
Tamer Khatib; Rezeq Direya; Asmaa Said. An Improved Method for Extracting Photovoltaic Module I–V Characteristic Curve Using Hybrid Learning Machine System. Journal of Solar Energy Engineering 2021, 143, 1 .
AMA StyleTamer Khatib, Rezeq Direya, Asmaa Said. An Improved Method for Extracting Photovoltaic Module I–V Characteristic Curve Using Hybrid Learning Machine System. Journal of Solar Energy Engineering. 2021; 143 (5):1.
Chicago/Turabian StyleTamer Khatib; Rezeq Direya; Asmaa Said. 2021. "An Improved Method for Extracting Photovoltaic Module I–V Characteristic Curve Using Hybrid Learning Machine System." Journal of Solar Energy Engineering 143, no. 5: 1.
Most of the consumed energy in Palestine comes from Israel. Meanwhile, the Israeli government controls the amount of electricity for Palestinians due to political reasons. This has led to many electricity shortages, prompting the Palestinians to invest in grid connected photovoltaic systems to mitigate electricity shortages. However, the lack of experience and loose energy policies have negatively affected the electricity distribution network in Palestine. Thus, this paper aims to discuss the current energy policy model for photovoltaic generation in Palestine and the challenges facing it. Moreover, 15 photovoltaic systems are selected in this research for technical and economical evaluation, to first show the typical performance of photovoltaic systems in Palestine, and second, to prove that there are failure cases in many systems due to a number of behavioral and structural barriers. Finally, the paper proposes a suggestion of unbundling transmission lines in the region to address the current critical status of photovoltaic investment in Palestine. As a result, the typical average yield factor of photovoltaic systems in Palestine is in the range of 1368–1816 kWh/kWp per year with a payback period of 5.5–7.4 years. However, the percentage of failure for the installed systems is found to be 47%. Meanwhile, the low awareness and lack of non-technical information are the main behavioral barriers, while grid infrastructure, lack of technical standards and staff training as well as loose and discouraging policies are the most dominant structural barriers.
Tamer Khatib; Amin Bazyan; Hiba Assi; Sura Malhis. Palestine Energy Policy for Photovoltaic Generation: Current Status and What Should Be Next? Sustainability 2021, 13, 2996 .
AMA StyleTamer Khatib, Amin Bazyan, Hiba Assi, Sura Malhis. Palestine Energy Policy for Photovoltaic Generation: Current Status and What Should Be Next? Sustainability. 2021; 13 (5):2996.
Chicago/Turabian StyleTamer Khatib; Amin Bazyan; Hiba Assi; Sura Malhis. 2021. "Palestine Energy Policy for Photovoltaic Generation: Current Status and What Should Be Next?" Sustainability 13, no. 5: 2996.
This paper presents a smartphone application game that aims to increase the awareness of preschoolers on renewable energy. The age of the selected preschoolers is in the range of 4-6 years. The game is called DAYSAM, and it aims to increase awareness regarding photovoltaic arrays, wind turbines, mini-hydropower stations, energy efficiency, and risks that polar bears are facing. The game provides two superior features compared to other available games in Arabic language, targeting the same age group. Preschoolers from An-Najah Child Institute are selected to play this game to investigate the impact of this game. The preschoolers’ awareness is tested before and after playing the game using coloring sheets in an unsupervised coloring process. The results show that the proposed game has increased preschooler’s awareness of renewable energy. Before playing the game, none of the preschoolers recognized images like the photovoltaic array or the wind turbine. After playing the game the preschoolers recognized these devices in different situations and shapes. This indicates that such a game can be used as a fun and educational tool in nurseries that have Arabic communication medium to increase awareness of renewable energy.
Tamer Khatib; Haneen Alwaneh; Wajdi Mabroukeh; Yassmin Abu-Ghalion; Fatima Abu-Gadi; Aliaa Assali; Wilfried Elmenreich; Muna Zarour. Development of DAYSAM: An Educational Smart Phone Game for Preschoolers to Increase Awareness of Renewable Energy. Sustainability 2021, 13, 433 .
AMA StyleTamer Khatib, Haneen Alwaneh, Wajdi Mabroukeh, Yassmin Abu-Ghalion, Fatima Abu-Gadi, Aliaa Assali, Wilfried Elmenreich, Muna Zarour. Development of DAYSAM: An Educational Smart Phone Game for Preschoolers to Increase Awareness of Renewable Energy. Sustainability. 2021; 13 (1):433.
Chicago/Turabian StyleTamer Khatib; Haneen Alwaneh; Wajdi Mabroukeh; Yassmin Abu-Ghalion; Fatima Abu-Gadi; Aliaa Assali; Wilfried Elmenreich; Muna Zarour. 2021. "Development of DAYSAM: An Educational Smart Phone Game for Preschoolers to Increase Awareness of Renewable Energy." Sustainability 13, no. 1: 433.
Electricity access in many remote areas in Sarawak, Malaysia is very little due to some limitations including the complicated geographical factors and high costs. In fact, there are about 1623 locations in Sarawak without electricity, whereas 420 locations (small settlements) can be only electrified using isolated renewable energy microgrid. However, it is impossible to install individual systems for each location. Thus, the optimal clustering of these locations and the selection of sites that are located in the centers of these clusters is necessary. Therefore, in this research, image segmentation and regional technique are used to analyze the map of remote electrification in Sarawak. The image segmentation which includes color thresholding, circular hough transform, and K-means technique is used in this research to identify the optimal installation site. HOMER software is then used to optimize the proposed renewable energy systems. Results show that nine locations out of 420 locations are optimum locations for the installation of renewable power systems. These nine locations are the centers of the obtained nine clusters of communities and small villages. Finally, it is found that most of the recommended combinations are hybrid renewable energy systems, where photovoltaic and hydropower systems combination is found the best hybrid system for the rural areas in Sarawak.
Aziah Khamis; Tamer Khatib; Nur Amira Haziqah Mohd Yosliza; Aimie Nazmin Azmi. Optimal selection of renewable energy installation site in remote areas using segmentation and regional technique: A case study of Sarawak, Malaysia. Sustainable Energy Technologies and Assessments 2020, 42, 100858 .
AMA StyleAziah Khamis, Tamer Khatib, Nur Amira Haziqah Mohd Yosliza, Aimie Nazmin Azmi. Optimal selection of renewable energy installation site in remote areas using segmentation and regional technique: A case study of Sarawak, Malaysia. Sustainable Energy Technologies and Assessments. 2020; 42 ():100858.
Chicago/Turabian StyleAziah Khamis; Tamer Khatib; Nur Amira Haziqah Mohd Yosliza; Aimie Nazmin Azmi. 2020. "Optimal selection of renewable energy installation site in remote areas using segmentation and regional technique: A case study of Sarawak, Malaysia." Sustainable Energy Technologies and Assessments 42, no. : 100858.
In this research, an approach for predicting wind energy in the long term has been developed. The aim of this prediction is to generate wind energy profiles for four cities in Palestine based on wind energy profile of another fifth city. Thus, wind energy data for four cities, namely, Nablus city, are used to develop the model; meanwhile, wind energy data for Hebron, Jenin, Ramallah, and Jericho cities are predicted based on that. Three machine learning algorithms are used in this research, namely, Cascade-forward neural network, random forests, and support vector machines. The developed models have two input variables which are daily average cubic wind speed and the standard deviation, while the target is daily wind energy. The R-squared values for the developed Cascade-forward neural network, random forests, and support vector machines models are found to be 0.9996, 0.9901, and 0.9991, respectively. Meanwhile, RMSE values for the developed models are found to be 41.1659 kWh, 68.4101 kWh, and 205.10 kWh, respectively.
Tamer Khatib; Reziq Deria; Asma Isead. Assessment of Three Learning Machines for Long-Term Prediction of Wind Energy in Palestine. Mathematical Problems in Engineering 2020, 2020, 1 -11.
AMA StyleTamer Khatib, Reziq Deria, Asma Isead. Assessment of Three Learning Machines for Long-Term Prediction of Wind Energy in Palestine. Mathematical Problems in Engineering. 2020; 2020 ():1-11.
Chicago/Turabian StyleTamer Khatib; Reziq Deria; Asma Isead. 2020. "Assessment of Three Learning Machines for Long-Term Prediction of Wind Energy in Palestine." Mathematical Problems in Engineering 2020, no. : 1-11.
In this research, a differential protection technique for a power transformer is proposed by using random forest and boosting learning machines. The proposed learning machines aim to provide a protection expert system that distinguishes between different transformer status which are normal, inrush, overexcitation, CT saturation, or internal fault. Data for 20 different transformers with 5 operating cases are used in this research. The utilized random forest and boosting techniques are trained using these data. Meanwhile, the proposed models are validated by other measures such as out-of-bag error and confusion matrix. In addition, variable importance analysis that shows signal’s component importance inside a transformer at different instances is provided. According to the result, the proposed random forest model successfully identifies all of the current cases (100% accuracy for the conducted experiment). Meanwhile, it is found that it is less accurate as a conditional monitoring element with accuracy in the range of 97%–98%. On the other hand, the proposed boosting model identifies all of the currents for both cases (100% accuracy for the conducted experiment). In addition to that, a comparison is conducted between the proposed models and other AI-based models. Based on this comparison, the proposed boosting model is the simplest and the most accurate model as compared to other models.
Tamer Khatib; Gazi Arar. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques. Mathematical Problems in Engineering 2020, 2020, 1 -12.
AMA StyleTamer Khatib, Gazi Arar. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques. Mathematical Problems in Engineering. 2020; 2020 ():1-12.
Chicago/Turabian StyleTamer Khatib; Gazi Arar. 2020. "Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques." Mathematical Problems in Engineering 2020, no. : 1-12.
In this paper, an improved particle swarm optimization method (PSO) is proposed to optimally size and place a DG unit in an electrical power system so as to improve voltage profile and reduce active power losses in the system. An IEEE 34 distribution bus system is used as a case study for this research. A new equation of weight inertia is proposed so as to improve the performance of the PSO conventional algorithm. This development is done by controlling the inertia weight which affects the updating velocity of particles in the algorithm. Matlab codes are developed for the adapted electrical power system and the improved PSO algorithm. Results show that the proposed PSO algorithm successfully finds the optimal size and location of the desired DG unit with a capacity of 1.6722 MW at bus number 10. This makes the voltage magnitude of the selected bus equal to 1.0055 pu and improves the status of the electrical power system in general. The minimum value of fitness losses using the applied algorithm is found to be 0.0.0406 while the average elapsed time is 62.2325 s. In addition to that, the proposed PSO algorithm reduces the active power losses by 31.6%. This means that the average elapsed time is reduced by 21% by using the proposed PSO algorithm as compared to the conventional PSO algorithm that is based on the liner inertia weight equation.
Neda Hantash; Tamer Khatib; Maher Khammash. An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems. Applied Computational Intelligence and Soft Computing 2020, 2020, 1 -8.
AMA StyleNeda Hantash, Tamer Khatib, Maher Khammash. An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems. Applied Computational Intelligence and Soft Computing. 2020; 2020 ():1-8.
Chicago/Turabian StyleNeda Hantash; Tamer Khatib; Maher Khammash. 2020. "An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems." Applied Computational Intelligence and Soft Computing 2020, no. : 1-8.
In this paper, an optimization approach for designing a hybrid renewable energy system with zero load rejection is presented for a specific location in Malaysia. The proposed renewble energy system includes photovoltaic system, gas turbine generator and battery bank. The aim of the optimization process is to design the system with a loss of load probability that is less than 1%. An improved numerical algorithm is proposed in this paper. Moreover, a comparison between electrification options, including the existing gas-turbine-based generator (existing system), electricity grid and the proposed system, is presented in terms of the annualized total life-cycle cost. The results show that the proposed system can reduce the annual running cost by USD 2.1 million, while the electricity grid connection option can reduce the annual cost by USD 1.16 million as compared to the existing gas-turbine-based generator. In addition to this, the proposed optimization algorithm provides a reliable power system with zero load rejection based on simulation results.
Mohamed Atef; Tamer Khatib; Muhammad Faris Abdullah; Mohd Fakhizan Romlie. Optimization of a Hybrid Solar PV and Gas Turbine Generator System Using the Loss of Load Probability Index. Clean Technologies 2020, 2, 240 -251.
AMA StyleMohamed Atef, Tamer Khatib, Muhammad Faris Abdullah, Mohd Fakhizan Romlie. Optimization of a Hybrid Solar PV and Gas Turbine Generator System Using the Loss of Load Probability Index. Clean Technologies. 2020; 2 (3):240-251.
Chicago/Turabian StyleMohamed Atef; Tamer Khatib; Muhammad Faris Abdullah; Mohd Fakhizan Romlie. 2020. "Optimization of a Hybrid Solar PV and Gas Turbine Generator System Using the Loss of Load Probability Index." Clean Technologies 2, no. 3: 240-251.
A standalone photovoltaic system mainly consists of photovoltaic panels and battery bank. The use of such systems is restricted mainly due to their high initial costs. This problem is alleviated by optimal sizing as it results in reliable and cost-effective systems. However, optimal sizing is a complex task. Artificial intelligence (AI) has been shown to be effective in PV system sizing. This paper presents an AI-based standalone PV system sizing method. Differential evolution multi-objective optimization is used to find the optimal balance between system’s reliability and cost. Two objective functions are minimized, the loss of load probability and the life cycle cost. A numerical algorithm is used as a benchmark for the proposed method’s speed and accuracy. Results indicate that the AI algorithm can be successfully used in standalone PV systems sizing. The proposed method was roughly 27 times faster than the numerical method. Due to AI algorithm’s random nature, the proposed method resulted in the exact optimal solution in 6 out of 12 runs. Near-optimal solutions were found in the other six runs. Nevertheless, the nearly optimal solutions did not introduce major departure from optimal system performance, indicating that the results of the proposed method are practically optimal at worst.
Tamer Khatib; Dhiaa Halboot Muhsen. Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm. Sustainability 2020, 12, 2233 .
AMA StyleTamer Khatib, Dhiaa Halboot Muhsen. Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm. Sustainability. 2020; 12 (6):2233.
Chicago/Turabian StyleTamer Khatib; Dhiaa Halboot Muhsen. 2020. "Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm." Sustainability 12, no. 6: 2233.
In this paper, optimization of Electric Vehicle (EV) batteries and dedicated energy storage unit charging profiles were conducted for the sake of bidding into day-ahead ancillary service markets. The aim of the optimization is to provide the maximum operational profits for both the EV aggregator and dedicated energy storage unit administrator. Ancillary service algorithms were then introduced to simulate the response of the EV batteries and dedicated energy storage units. Results showed that the usage of dedicated energy storage units for bidding into the ancillary services markets is more profitable than the case of operating an EV aggregator.
Abdelrahman Aldik; Tamer Khatib. EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice. World Electric Vehicle Journal 2019, 11, 8 .
AMA StyleAbdelrahman Aldik, Tamer Khatib. EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice. World Electric Vehicle Journal. 2019; 11 (1):8.
Chicago/Turabian StyleAbdelrahman Aldik; Tamer Khatib. 2019. "EV Aggregators and Energy Storage Units Scheduling into Ancillary Services Markets: The Concept and Recommended Practice." World Electric Vehicle Journal 11, no. 1: 8.
Considering, the high penetration of plug-in electric vehicles (PHEVs), the charging and discharging of PHEVs may lead to technical problems on electricity distribution networks. Therefore, the management of PHEV charging and discharging needs to be addressed to coordinate the time of PHEVs so as to be charged or discharged. This paper presents a management control method called the charging and discharging control algorithm (CDCA) to determine when and which of the PHEVs can be activated to consume power from the grid or supply power back to grid through the vehicle-to-grid technology. The proposed control algorithm considers fast charging scenario and photovoltaic generation during peak load to mitigate the impact of the vehicles. One of the important parameters considered in the CDCA is the PHEV battery state of charge (SOC). To predict the PHEV battery SOC, a particle swarm optimization-based artificial neural network is developed. Results show that the proposed CDCA gives better performance as compared to the uncoordinated charging method of vehicles in terms of maintaining the bus voltage profile during fast charging.
Ahmed Aljanad; Azah Mohamed; Tamer Khatib; Afida Ayob; Hussain Shareef. A Novel Charging and Discharging Algorithm of Plug-in Hybrid Electric Vehicles Considering Vehicle-to-Grid and Photovoltaic Generation. World Electric Vehicle Journal 2019, 10, 61 .
AMA StyleAhmed Aljanad, Azah Mohamed, Tamer Khatib, Afida Ayob, Hussain Shareef. A Novel Charging and Discharging Algorithm of Plug-in Hybrid Electric Vehicles Considering Vehicle-to-Grid and Photovoltaic Generation. World Electric Vehicle Journal. 2019; 10 (4):61.
Chicago/Turabian StyleAhmed Aljanad; Azah Mohamed; Tamer Khatib; Afida Ayob; Hussain Shareef. 2019. "A Novel Charging and Discharging Algorithm of Plug-in Hybrid Electric Vehicles Considering Vehicle-to-Grid and Photovoltaic Generation." World Electric Vehicle Journal 10, no. 4: 61.
Most of Mauritania’s landscape is desert areas with very limited water sources. In the meanwhile, the current irrigation methods that are used in available oases are conventional and implied high water losses which lead to desertification of the oases and consequently destroy agriculture and livestock. Thus, this research aims to protect these oases by proposing an optimized photovoltaic based water irrigation system. Description of adopted oases areas characteristics and solar energy potential in Mauritania are provided. Moreover classifications of pumps, motors, irrigation systems and required PV power are given in this research. After that, a full and detailed design is proposed for water irrigation in Mauritanian’s oases. Results show that a photovoltaic power system with a capacity of 142.8 kWp is needed to power eight submersible pumps with a total power of 83.5 kW. The pumps are designed to deliver 80% of the available wells’ capacity for eight zones of date palm trees that are planted in Tawaz oasis which is located in Adrar region. It is also found that 250 storage tanks with capacity of 4 m3 for each are needed so as to maintain the availability of the system. A one year simulation of the proposed system is conducted, Based on the conducted simulation, the average cost of the pumped water by the system of the 8 wells is about 20.71 cent/m3 which is lower than the current pumping cost by 300%. It is also noted that the water loss of the former irrigation process is reduced significantly.
Tamer Khatib; Alia Saleh; Shaymaa Eid; Monera Salah. Rehabilitation of Mauritanian oasis using an optimal photovoltaic based irrigation system. Energy Conversion and Management 2019, 199, 111984 .
AMA StyleTamer Khatib, Alia Saleh, Shaymaa Eid, Monera Salah. Rehabilitation of Mauritanian oasis using an optimal photovoltaic based irrigation system. Energy Conversion and Management. 2019; 199 ():111984.
Chicago/Turabian StyleTamer Khatib; Alia Saleh; Shaymaa Eid; Monera Salah. 2019. "Rehabilitation of Mauritanian oasis using an optimal photovoltaic based irrigation system." Energy Conversion and Management 199, no. : 111984.
Jordan Valley area suffers from a lack of water because of the current political situation. Therefore, water distribution is being done on a periodic basis and farmers need to store water in artificial ponds on site so as to be able to irrigate their field during the anonymous days that water is not available from the main supply. However, artificial ponds may affect the environment negatively due to the plants that live in it such as algae, which attracts mosquitos and causes a bad smell. Thus, in this paper, a simple and low-cost photovoltaic based pumping system is proposed to inject a chemical material in the water of the artificial pond to get rid of algae. The proposed system consists of a pump that is powered by a photovoltaic module and pumps the proposed chemical material in the artificial pond using a rotary nozzle that is fixed on a pipe around the ponds. The system is affordable and reduces the production of the unwanted plants. As a result, the proposed system reduces chemical oxygen demand value, which is considered the main cause of algae blooming, from 7200 mg/L to 95 mg/L. The proposed product is powered by a 50 W foldable solar panel and it costs about 213 USD.
Tamer Khatib; Sora Qalalweh; Raghad Ameerah; Ismail Warad. An Efficient Method for Water Treatment of Artificial Ponds in Jordan Valley Based on Photovoltaic Pumping System. Agriculture 2019, 9, 151 .
AMA StyleTamer Khatib, Sora Qalalweh, Raghad Ameerah, Ismail Warad. An Efficient Method for Water Treatment of Artificial Ponds in Jordan Valley Based on Photovoltaic Pumping System. Agriculture. 2019; 9 (7):151.
Chicago/Turabian StyleTamer Khatib; Sora Qalalweh; Raghad Ameerah; Ismail Warad. 2019. "An Efficient Method for Water Treatment of Artificial Ponds in Jordan Valley Based on Photovoltaic Pumping System." Agriculture 9, no. 7: 151.
The increasing in energy demand leads to wide range of blackout crises around the worldwide. Load management is represented as one of the most important solutions to balance the energy demand with the available generation resource. Dynamic and adaptive method is required to sort all multi-objective sets of optimal solutions of customer load scheduling. A multi-objective optimization differential evolution (MODE) algorithm in this paper is used to obtain a set of optimal customer load management by minimizing the energy cost and customer’s inconvenience simultaneously. The obtained optimal set of solutions are sorted from the best to the worst using multi-criteria decision making (MCDM) methods. An integration of analytic hierarchy process (AHP) and technique for order preferences by similarity to ideal solution (TOPSIS) are used as MCDM methods. The effect of different time slots on the given optimal solutions are addressed for real customer’s data of a typical household. Results of simulation indicate that the proposed method manages to realize energy cost saving of 44%, 44% and 32% for 1, 5 and 10 min time slots, respectively. Moreover, the peak load savings are 42%, 31% and 41% for 1, 5 and 10 min time slots, respectively. Furthermore, the results are validated by other approaches presented earlier in literature to support the findings of the proposed method. The proposed method provides superior saving for energy cost and peak consumption as well as maintains an acceptable range of customer inconvenience.
Dhiaa Halboot Muhsen; Haider Tarish Haider; Yaarob Mahjoob Al-Nidawi; Tamer Khatib. Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods. Sustainable Cities and Society 2019, 50, 101651 .
AMA StyleDhiaa Halboot Muhsen, Haider Tarish Haider, Yaarob Mahjoob Al-Nidawi, Tamer Khatib. Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods. Sustainable Cities and Society. 2019; 50 ():101651.
Chicago/Turabian StyleDhiaa Halboot Muhsen; Haider Tarish Haider; Yaarob Mahjoob Al-Nidawi; Tamer Khatib. 2019. "Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods." Sustainable Cities and Society 50, no. : 101651.
From the growth of residential energy demands has emerged new approaches for load scheduling to realize better energy consumption by shifting the required demand in response to cost changes or incentive offers. In this paper, a hybrid method is proposed to optimize the load scheduling problem for cost and energy saving. The method comprises a multi-objective optimization differential evolution (MODE) algorithm to obtain a set of optimal solutions by minimizing the cost and peak of a load simultaneously, as a multi-objective function. Next, an integration of the analytic hierarchy process (AHP) and a technique for order preferences by similarity to ideal solution (TOPSIS) methods are used as multi-criteria decision making (MCDM) methods for sorting the optimal solutions’ set from the best to the worst, to enable the customer to choose the appropriate schedule time. The solutions are sorted based on the load peak and energy cost as multi-criteria. Data are for ten appliances of a household used for 24 h with a one-minute time slot. The results of the proposed method demonstrate both energy and cost savings of around 47% and 46%, respectively. Furthermore, the results are compared with other recent methods in the literature to show the superiority of the proposed method.
Dhiaa Halboot Muhsen; Haider Tarish Haider; Yaarob Al-Nidawi; Tamer Khatib. Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods. Electronics 2019, 8, 524 .
AMA StyleDhiaa Halboot Muhsen, Haider Tarish Haider, Yaarob Al-Nidawi, Tamer Khatib. Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods. Electronics. 2019; 8 (5):524.
Chicago/Turabian StyleDhiaa Halboot Muhsen; Haider Tarish Haider; Yaarob Al-Nidawi; Tamer Khatib. 2019. "Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods." Electronics 8, no. 5: 524.
Standalone photovoltaic system is promising sustainable energy source. Accurate modeling and sizing of these systems strongly affect the system’s feasibility. Thus, in this paper, optimal sizing of standalone photovoltaic system is conducted based on multi-objective differential evolution algorithm integrated with hybrid multi-criteria decision making methods. Multi-objective differential evolution algorithm is used to optimize set of configurations of a system by minimizing technical and cost objective functions simultaneously. After that, an analytical hierarchy process integrated with a technique for order preference by similarity to ideal solution are used to order preference of configurations based on the loss of load probability and life cycle cost of system. The results of the proposed sizing method are validated by a numerical method to explain the superiority of the proposed method. According to results, the proposed sizing method is faster than numerical method by around 27 times. This is because the multi-objective differential evolution algorithm requires roughly 0.23 of simulations that is required by numerical method. Furthermore, the performance of multi-objective differential evolution algorithm is evaluated by various metrics. As a result, for the adapted load demand, the optimal configuration is 63 PV modules and 66 battery unit with maximum capacity of 500Ah.
Dhiaa Halboot Muhsen; Moamen Nabil; Haider Tarish Haider; Tamer Khatib. A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods. Energy 2019, 174, 1158 -1175.
AMA StyleDhiaa Halboot Muhsen, Moamen Nabil, Haider Tarish Haider, Tamer Khatib. A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods. Energy. 2019; 174 ():1158-1175.
Chicago/Turabian StyleDhiaa Halboot Muhsen; Moamen Nabil; Haider Tarish Haider; Tamer Khatib. 2019. "A novel method for sizing of standalone photovoltaic system using multi-objective differential evolution algorithm and hybrid multi-criteria decision making methods." Energy 174, no. : 1158-1175.