Real-Time Bi-directional Electric Vehicle Charging Control with Distribution Grid Implementation

As electric vehicle (EV) adoption is growing year after year, there is no doubt that EVs will occupy a significant portion of transporting vehicle in the near future. Although EVs have benefits for environment, large amount of un-coordinated EV charging will affect the power grid and degrade power quality. To alleviate negative effects of EV charging load and turn them to opportunities, a decentralized real-time control algorithm is developed in this paper to provide optimal scheduling of EV bi-directional charging. To evaluate the performance of the proposed algorithm, numerical simulation is performed based on real-world EV user data, and power flow analysis is carried out to show how the proposed algorithm improve power grid steady state operation. . The results show that the implementation of proposed algorithm can effectively coordinate bi-directional charging by 30% peak load shaving, more than 2% of voltage drop reduction, and 40% transmission line current decrease

QUERCETIN CAUSES TO DECREASE PROXIMAL TUBULES APOPTOTIC CELLS IN STREPTOZOTOCIN-INDUCED DIABETIC RAT

Enhanced oxidative stress and changes in antioxidant capacity are considered to play an important role in the pathogenesis of chronic diabetes mellitus. Wistar male rat (n=40) were allocated into three groups, control group (n=1O) and quercetin (QR) group that received 15mg/kg (IP) QR, (n= 10), and Diabetic group that received 55mgjkg (IP) streptozotocin (STZ) (n=20) which was subdivided to two groups of 10STZ group and treatment group. Treatment group received 55mgjkg (IP) STZ plus 15mg/jkg QR, daily for 4 weeks, respectivelyhowever, the control group just received an equal volume of distilled water daily(IP) . Diabetes were induced by a single (IP) injection of streptozotocin (55mg/kg). Animals were kept in standard condition. In 28 day after inducing diabetic 5 mL blood were collected for Total Af1tioxidant Capacity (TAC), Malondi Dehyde (MDA) and Oxidized Low density Lipoprotein (Ox-LDL) levels and kidney tissues of Rat in whole groups were removed then prepared for Apoptosis analysis by Tunel metho. Apoptotic cells significantly decreased in group that has received 15mg/kg (IP) quercetin (

Optimal Integration of Battery Energy Storage and Transportation Electrification in Distribution Grids

Two pioneer states, California and New York, have set their ambitious targets to get 100% and 70% of their electricity from renewable energy resources by 2045 and 2030, respectively. Aligned with these endeavors, currently 19.2% of the electricity in California is coming from the solar energy where the utilities are expected to add an additional 60% solar energy in the next five years. To achieve this goal, Electrification- the transition from non-electric end-use energy consumers to the electric consumers is a trend in the energy sector, as it facilitates to have access to sustainable and clean energy infrastructure. The fact that 28% of the energy in the USA is consumed by the transportation sector where 92% is provided by the fossil fuel energy motivates Electrication strongly. This revolution is specifically happening in the California state where 50% of the electric vehicle (EV) owners are living. The increasing penetration of renewable energies, such as solar energy, as well as Electrification in the electrical grids introduce new challenges for system operation and planning. Solar energy, inherently, is intermittent and shows stochastic behavior which makes it a non-dispatchable source of electricity. Therefore, the conventional models to capture its generation profile are no longer applicable. Also according to a new report by National Renewable Energy Laboratory (NREL) [MJL18], EVs are introduced as one of the most influential elements of Electrification. EVs can drastically change the load pattern, thus their integration in the power grids is carefully observed by the electric utilities.To mitigate the stochastic behavior of solar energy and make it a dispatchable resource, the energy storage can be utilized to capture its intermittency through the coordinated charging and discharging sequences. That is one reason why the California and New York states plan to integrate an additional 1.3 GW and 3.0 GW energy storage in their power grids by 2024 and 2030, respectively. Moreover, EVs are controllable loads that provide the flexibility and opportunity to shift their consumption profile according to the operating conditions of the power grids. Nevertheless, the deployment of energy storage is challenging as the generation profile of the solar energy should be modeled accurately, and an effective and optimal controller should be designed to coordinate the charging and discharging of the energy storage. Also designing an efficient charging load management system for EVs is a difficult task since all the involved entities in the load management decision making, such as the system operator, load aggregators, and the end customers, must be satisfied, and the safe and stable operation of the grid should be guaranteed. Accordingly, the optimal load management is a large scale problem, especially when it should be solved and repeated every several minutes for the whole power grid.The models proposed in the literature for the solar energy generation, load, and EV charging demand modeling either can not capture their stochasticity accurately or are not computationally efficient. Therefore, the coordination methods for energy storage as well as EV loads are not effective in accommodating renewable energies and Electrification in the power grids. In addition, the energy storage and load coordination methods are not scalable and suffer from a considerable computation burden when the number of energy resource units and controllable loads in the optimal decision-making increases.In this dissertation, (1) modeling of the solar generation, load demand, and EV charging load, (2) the integration of battery energy storage system (BESS) in the power grids, and (3) the large scale accommodation of EV loads are addressed. For the modeling, a probability model based on kernel density estimator (KDE) is proposed which, comparing to the previous models, provides a low-computation precise stochastic model. For the integration of BESS in the electrical grids, a mobile BESS (MBESS) is prototyped to capture the random behavior of the EV charging profile, reduce the charging demand cost and improve the reliability and resiliency of the charging service. The performance of MBESS is validated through the experiments in the Civic Center parking structure, in the City of Santa Monica, and it is shown that MBESS can effectively shave the peak load of the EV charging demand. To address the lack of scalability in the previous load management methods, the distributed optimization methods are used so that the optimal EV load coordination is solved through an iterative negotiation procedure. The scalability of the proposed methods is coming from the fact that each agent solves its desired problem locally while it exchanges the insensitive limited information with the others. The proposed methods satisfy all the agents and guarantee the power grid operation in the stability and safe region.The proposed probability model is shown to improve the accuracy of the solar energy profile up to 36.7%, the load demand profile up to 5.9%, and the EV charging parameters, including the arrival time, required charging energy, and the departure time up to 26.6%, 49.3%, and 41.21%, respectively. In addition, the experiments with MBESS verifies that it not only reduces the charging cost but also provides the emergency power to the charging system in the case of failure in the power grid, which is called islanded operation. Moreover, through the numerical simulations using real data, it is validated that the distributed multi-agent based methods for the load coordination can approximately decrease the convergence time and the communication overhead by 94% while the computation burden for the distribution system operator and the load aggregators reduces significantly. Also the load coordination results validate the efficacy of the proposed frameworks in accommodating the large populations of EV loads in the distribution grids by improving the voltage prole from 45% up to 93% and reducing the peak load from 50% up to 66%. The results show that an efficient load management system is a necessity for Electrification integration without any investment on the grid capacity expansion

Optimal Integration of Battery Energy Storage and Transportation Electrification in Distribution Grids

Two pioneer states, California and New York, have set their ambitious targets to get 100% and 70% of their electricity from renewable energy resources by 2045 and 2030, respectively. Aligned with these endeavors, currently 19.2% of the electricity in California is coming from the solar energy where the utilities are expected to add an additional 60% solar energy in the next five years. To achieve this goal, Electrification- the transition from non-electric end-use energy consumers to the electric consumers is a trend in the energy sector, as it facilitates to have access to sustainable and clean energy infrastructure. The fact that 28% of the energy in the USA is consumed by the transportation sector where 92% is provided by the fossil fuel energy motivates Electrication strongly. This revolution is specifically happening in the California state where 50% of the electric vehicle (EV) owners are living. The increasing penetration of renewable energies, such as solar energy, as well as Electrification in the electrical grids introduce new challenges for system operation and planning. Solar energy, inherently, is intermittent and shows stochastic behavior which makes it a non-dispatchable source of electricity. Therefore, the conventional models to capture its generation profile are no longer applicable. Also according to a new report by National Renewable Energy Laboratory (NREL) [MJL18], EVs are introduced as one of the most influential elements of Electrification. EVs can drastically change the load pattern, thus their integration in the power grids is carefully observed by the electric utilities.To mitigate the stochastic behavior of solar energy and make it a dispatchable resource, the energy storage can be utilized to capture its intermittency through the coordinated charging and discharging sequences. That is one reason why the California and New York states plan to integrate an additional 1.3 GW and 3.0 GW energy storage in their power grids by 2024 and 2030, respectively. Moreover, EVs are controllable loads that provide the flexibility and opportunity to shift their consumption profile according to the operating conditions of the power grids. Nevertheless, the deployment of energy storage is challenging as the generation profile of the solar energy should be modeled accurately, and an effective and optimal controller should be designed to coordinate the charging and discharging of the energy storage. Also designing an efficient charging load management system for EVs is a difficult task since all the involved entities in the load management decision making, such as the system operator, load aggregators, and the end customers, must be satisfied, and the safe and stable operation of the grid should be guaranteed. Accordingly, the optimal load management is a large scale problem, especially when it should be solved and repeated every several minutes for the whole power grid.The models proposed in the literature for the solar energy generation, load, and EV charging demand modeling either can not capture their stochasticity accurately or are not computationally efficient. Therefore, the coordination methods for energy storage as well as EV loads are not effective in accommodating renewable energies and Electrification in the power grids. In addition, the energy storage and load coordination methods are not scalable and suffer from a considerable computation burden when the number of energy resource units and controllable loads in the optimal decision-making increases.In this dissertation, (1) modeling of the solar generation, load demand, and EV charging load, (2) the integration of battery energy storage system (BESS) in the power grids, and (3) the large scale accommodation of EV loads are addressed. For the modeling, a probability model based on kernel density estimator (KDE) is proposed which, comparing to the previous models, provides a low-computation precise stochastic model. For the integration of BESS in the electrical grids, a mobile BESS (MBESS) is prototyped to capture the random behavior of the EV charging profile, reduce the charging demand cost and improve the reliability and resiliency of the charging service. The performance of MBESS is validated through the experiments in the Civic Center parking structure, in the City of Santa Monica, and it is shown that MBESS can effectively shave the peak load of the EV charging demand. To address the lack of scalability in the previous load management methods, the distributed optimization methods are used so that the optimal EV load coordination is solved through an iterative negotiation procedure. The scalability of the proposed methods is coming from the fact that each agent solves its desired problem locally while it exchanges the insensitive limited information with the others. The proposed methods satisfy all the agents and guarantee the power grid operation in the stability and safe region.The proposed probability model is shown to improve the accuracy of the solar energy profile up to 36.7%, the load demand profile up to 5.9%, and the EV charging parameters, including the arrival time, required charging energy, and the departure time up to 26.6%, 49.3%, and 41.21%, respectively. In addition, the experiments with MBESS verifies that it not only reduces the charging cost but also provides the emergency power to the charging system in the case of failure in the power grid, which is called islanded operation. Moreover, through the numerical simulations using real data, it is validated that the distributed multi-agent based methods for the load coordination can approximately decrease the convergence time and the communication overhead by 94% while the computation burden for the distribution system operator and the load aggregators reduces significantly. Also the load coordination results validate the efficacy of the proposed frameworks in accommodating the large populations of EV loads in the distribution grids by improving the voltage prole from 45% up to 93% and reducing the peak load from 50% up to 66%. The results show that an efficient load management system is a necessity for Electrification integration without any investment on the grid capacity expansion

EFFECT OF WATER MELON SEEDS EXTRACTS (Citrullus vulgaris) ON SPERMS IN DIABETIC RAT.

Citrullus vulgaris is an antioxidant and has been shown to reduce oxidative stress. Previous studies confirmed that antioxidants have essential effect on infertility through participating in reactive oxygen’s species. Chronic hyperglycemia is known to cause infertility in diabetes disease. Wistar male rats (n=40) were allocated into three groups: control group(n=10), Citrullus vulgaris seeds extract (CVE) group that received 55mg/kg by gavage method (n=10), and Diabetic group that received 55mg/kg (IP) streptozotocin (STZ) (n=20). The last group was subdivided into two groups of 10. STZ group and treatment group. Treatment group received 55mg/kg (IP) STZ plus 55mg/kg CVE, daily for 4weeks; however, the control group just received an equal volume of (0.9% NaCl) daily (gavage). Diabetes was induced by a single (IP) injection of streptozotocin (55mg/kg). Animals were kept in standard condition. In 28th day, 5cc bloodn sample was taken from every rat for biochemical analysis. Collecting epididymis tissues, they were prepared for sperm analysis by WHO method. In comparison to other groups, sperm parameters were significantly increased in groups that received 55mg/kg (CVE) (

A Robust Data-Driven Approach for Fault Detection in Photovoltaic Arrays

n this paper, a robust data-driven method for fault detectionin photovoltaic (PV) arrays is proposed. Our method is based onthe random vector functional-link networks (RVFLN) which has theadvantage of randomly assigning hidden layer parameters with no tuning. To eliminate the effect of measurement noise and overfitting in thetraining process which reduce the fault detection accuracy, the sparseregularization method is utilized which uses l2−norm with loss weighting factor to compute the output weights. To attain a strong robustnessagainst the outlier samples, the non-parametric kernel density estimationis employed to assign a loss weighting factor. Through rigorous simulation studies, we validate the performance of our proposed method in detectingthe short and open circuit faults based on only the output current andvoltage measurements of PV arrays. In addition to a stronger robustnesscomparing with the least square-support vector machine, we also showthat our proposed method provides 80% and 100% average detection accuracy for short circuit and open circuit, respectively