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The advent of modern artificial intelligence methods for performance improvement of optimal control strategy has paved a way for providing a reliable operation of power systems. Based on the modern advancements in such techniques, the present paper provides a detailed comparison for finding the optimal control strategy using such techniques. This is exhibited by developing a novel control strategy in the form of a 2nd order active disturbance rejection controller for concurrent frequency-voltage control of a hybrid power system. The hybrid power system comprises of renewable generations in the form of solar-thermal, wind plants. Moreover, the modern day electric vehicles (EVs) are also incorporated as energy storage and operate in vehicle-to-grid mode. The developed control strategy is compared with established industrial controllers to prove its dominance based on concurrent frequency-voltage control of the hybrid power system. Firstly, the controller gains are optimized using magnetotactic bacteria optimization (MBO) technique. Then, the developed control strategy is tuned using artificial neural network (ANN) methodology. Based on the simulation outcomes, the results for frequency deviations, voltage deviations and tie-line power deviations are compared with MBO and ANN optimized 2nd order active disturbance rejection controller. The simulations are carried out on one-area, two-area and standard IEEE-39 bus power systems for in depth validation. Results show that the ANN optimized 2nd order active disturbance rejection controller has superior performance with respect to MBO optimized one. The effects of modern day EVs and renewable generations on the power system is studied broadly.

The advent of modern artificial intelligence methods for performance improvement of optimal control strategy has paved a way for providing a reliable operation of power systems. Based on the modern advancements in such techniques, the present paper provides a detailed comparison for finding the optimal control strategy using such techniques. This is exhibited by developing a novel control strategy in the form of a 2nd order active disturbance rejection controller for concurrent frequency-voltage control of a hybrid power system. The hybrid power system comprises of renewable generations in the form of solar-thermal, wind plants. Moreover, the modern day electric vehicles (EVs) are also incorporated as energy storage and operate in vehicle-to-grid mode. The developed control strategy is compared with established industrial controllers to prove its dominance based on concurrent frequency-voltage control of the hybrid power system. Firstly, the controller gains are optimized using magnetotactic bacteria optimization (MBO) technique. Then, the developed control strategy is tuned using artificial neural network (ANN) methodology. Based on the simulation outcomes, the results for frequency deviations, voltage deviations and tie-line power deviations are compared with MBO and ANN optimized 2nd order active disturbance rejection controller. The simulations are carried out on one-area, two-area and standard IEEE-39 bus power systems for in depth validation. Results show that the ANN optimized 2nd order active disturbance rejection controller has superior performance with respect to MBO optimized one. The effects of modern day EVs and renewable generations on the power system is studied broadly.

SummaryExtensive research and development in the processes of production and generation of hydrogen are needed to make renewable hydrogen cost‐competitive with fossil hydrogen. As a result, developing hydrogen energy from low‐cost components and synthesizing it from renewable resources is an important issue that can be considered a global challenge. The current research provided a continuous H2 energy production from waste aluminum in a water medium using copper oxide nanoparticles (CuO NPs) as catalysts in the reaction. CuO NPs have been prepared by a green, easy, inexpensive, and systematic method using Catha edulis L. (Khat) extract as reducing and capping agents. Separately, CuO NPs prepared in an alkaline media using the conventional hydrothermal process have been described and reported for comparison. Multidisciplinary tools were used to characterize the powders for structural, morphological, and surface aspects. CuO NPs were homogeneous, well‐dispersed, and have fine diameters with an average of 6 nm, owing to the abundant biomolecules in the plant extract. The synthesized samples were utilized as catalysts for H2 production from industrial waste aluminum with a 1/0.02 ration of Al/CuO by weight. The primary results revealed a higher H2 production yield and rapid rate in alkaline than acidic mediums. Under all conditions, the CuO NPs prepared from the extract had better catalytic activity than those prepared by a conventional method. The results proved that the reaction temperature prompts the reaction kinetics more than other parameters. The optimum H2 yield could be optimized at a temperature of 70°C, 1/50 ratio of CuO/waste Al, and near‐neutral solutions. The above conditions are ideal for designing a continuous production reactor. The hydrogen generated using industrial waste aluminum in the present system is a cost‐effective and sustainable approach.

SummaryExtensive research and development in the processes of production and generation of hydrogen are needed to make renewable hydrogen cost‐competitive with fossil hydrogen. As a result, developing hydrogen energy from low‐cost components and synthesizing it from renewable resources is an important issue that can be considered a global challenge. The current research provided a continuous H2 energy production from waste aluminum in a water medium using copper oxide nanoparticles (CuO NPs) as catalysts in the reaction. CuO NPs have been prepared by a green, easy, inexpensive, and systematic method using Catha edulis L. (Khat) extract as reducing and capping agents. Separately, CuO NPs prepared in an alkaline media using the conventional hydrothermal process have been described and reported for comparison. Multidisciplinary tools were used to characterize the powders for structural, morphological, and surface aspects. CuO NPs were homogeneous, well‐dispersed, and have fine diameters with an average of 6 nm, owing to the abundant biomolecules in the plant extract. The synthesized samples were utilized as catalysts for H2 production from industrial waste aluminum with a 1/0.02 ration of Al/CuO by weight. The primary results revealed a higher H2 production yield and rapid rate in alkaline than acidic mediums. Under all conditions, the CuO NPs prepared from the extract had better catalytic activity than those prepared by a conventional method. The results proved that the reaction temperature prompts the reaction kinetics more than other parameters. The optimum H2 yield could be optimized at a temperature of 70°C, 1/50 ratio of CuO/waste Al, and near‐neutral solutions. The above conditions are ideal for designing a continuous production reactor. The hydrogen generated using industrial waste aluminum in the present system is a cost‐effective and sustainable approach.

Electric water heaters have the ability to store energy in their water buffer without impacting the comfort of the end user. This feature makes them a prime candidate for residential demand response. However, the stochastic and nonlinear dynamics of electric water heaters, makes it challenging to harness their flexibility. Driven by this challenge, this paper formulates the underlying sequential decision-making problem as a Markov decision process and uses techniques from reinforcement learning. Specifically, we apply an auto-encoder network to find a compact feature representation of the sensor measurements, which helps to mitigate the curse of dimensionality. A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation. In a simulation-based experiment using an electric water heater with 50 temperature sensors, the proposed method was able to achieve good policies much faster than when using the full state information. In a lab experiment, we apply fitted Q-iteration to an electric water heater with eight temperature sensors. Further reducing the state vector did not improve the results of fitted Q-iteration. The results of the lab experiment, spanning 40 days, indicate that compared to a thermostat controller, the presented approach was able to reduce the total cost of energy consumption of the electric water heater by 15%. Submitted to IEEE transaction on smart grid

Electric water heaters have the ability to store energy in their water buffer without impacting the comfort of the end user. This feature makes them a prime candidate for residential demand response. However, the stochastic and nonlinear dynamics of electric water heaters, makes it challenging to harness their flexibility. Driven by this challenge, this paper formulates the underlying sequential decision-making problem as a Markov decision process and uses techniques from reinforcement learning. Specifically, we apply an auto-encoder network to find a compact feature representation of the sensor measurements, which helps to mitigate the curse of dimensionality. A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation. In a simulation-based experiment using an electric water heater with 50 temperature sensors, the proposed method was able to achieve good policies much faster than when using the full state information. In a lab experiment, we apply fitted Q-iteration to an electric water heater with eight temperature sensors. Further reducing the state vector did not improve the results of fitted Q-iteration. The results of the lab experiment, spanning 40 days, indicate that compared to a thermostat controller, the presented approach was able to reduce the total cost of energy consumption of the electric water heater by 15%. Submitted to IEEE transaction on smart grid

Abstract Recently, biodiesel has been gaining market share against fossil-origin diesel due to its ecological benefits and because it can be directly substituted for traditional diesel oils. However, the high cost of the raw materials required to produce biodiesel make it more expensive than fossil diesel. Therefore, low-priced raw materials, such as waste cooking oil and animal fats, are of interest because they can be used to drive down the cost of biodiesel. We have produced biodiesel from camel fat using a transesterification reaction with methanol in the presence of NaOH. The experimental variables investigated in this study were the temperature (30–75 °C), reaction time (20–160 min), catalyst concentration (0.25–1.5%), and methanol/fat molar ratio (4:1–9:1). A maximum biodiesel yield of 98.6% was obtained. The fuel properties of biodiesel, such as iodine value, saponification value, density, kinematic viscosity, cetane number, flash point, sulfur content, carbon residue, water and sediment, high heating value, refractive index, cloud point, pour point, and distillation characteristics, were measured. The properties were compared with EN 14214 and ASTM 6751 biodiesel standards, and an acceptable level of agreement was obtained.