Optimal scheduling of reconfigurable grids considering dynamic line rating constraint

Mohamed, Mohamed A.; Awwad, Emad Mahrous; El-Sherbeeny, Ahmed M.; Nasr, Emad Abouel; Ali, Ziad M.

doi:10.1049/iet-gtd.2019.1570

Cited by 40 publications

(21 citation statements)

References 22 publications

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“…where ER is the total electric range, i.e., if mileage is more than this, the vehicle will shut down. Based on the literature, ER can take values of (20,30,49,60) depending on the car type. Having SoC and the battery capacity, the charging length can be well estimated by the following equation:…”

Section: Charging Modeling Of Hevsmentioning

confidence: 99%

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

et al. 2021

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Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

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Section: Charging Modeling Of Hevsmentioning

confidence: 99%

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

et al. 2021

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“…This article uses the PEM as a wellknown and successful approach, in which a problem with ρ uncertain parameter is solved 2ρ times to capture the uncertainty effects [24]. In 2ρ-PEM, each PDF is replaced by two generating points of concentration using the mean and the standard deviation parameters of the uncertainty [40]. Each of these points are then used as an input to the nonlinear problem, let us call it F, to transfer to the arbitrary output objective S:…”

Section: A Stochastic Framework Based On 2ρ-pemmentioning

confidence: 99%

A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids

Yin¹,

Hajjiah

Jermsittiparsert³

et al. 2021

IEEE Access

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Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems. In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators. This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids' components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM). Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure. We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching. Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids. The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid. The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction). Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr (4.40% reduction), which are notable values. The results advocate the quality and functionality of the proposed framework.

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“…Maximum power flow in i feeder S Line,t i Power flow in feeder i at time t S on Gi /S According to the united stated DOE department, microgrid is a set of coupled loads and distributed energy resources (DERs) with specific electric borders and can act as an independent well-regulated unit from the main grid (connecting or disconnecting) to provide either grid-connected or islanded operation [1]. Therefore, it can be supposed as a small-scale self-supplied grid that can help improving the electric power quality by generation at the consumption location [2], [3]. The microgrid technology can improve the system reliability by announcing self-healing capability in the low voltage level, upgrading the power quality, diminishing air pollution, reducing power losses, increasing revenues by lower T&D investments, providing growing space for renewable energy sources (RESs), and growing energy efficacy by getting compatible with the real-time market prices [4]- [7].…”

Section: Res Tmentioning

confidence: 99%

A Secured Energy Management Architecture for Smart Hybrid Microgrids Considering PEM-Fuel Cell and Electric Vehicles

Dong²,

et al. 2020

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This article assesses the energy management of reconfigurable residential smart hybrid AC/DC microgrids considering the combined heat and power (CHP) loads as well as the electric vehicles charging/discharging behaviors. A holistic model is developed for the proton exchange membrane fuel cell to retrieve the unwanted thermal energy generated at the operation time. The proposed model makes use of the unoccupied capacity of the fuel cell for producing/storing hydrogen for the later usage and increasing its efficiency. A stochastic framework is designed using point estimate method (PEM) to capture the uncertainties of the photovoltaic and wind turbine forecast error, power company price, the operating temperature of the proton exchange membrane fuel cell, the price for natural gas, price for selling hydrogen, and the pressure of the H2 and O2 in the fuel cell stack. The PEM approach has shown superior advantages in terms of accuracy and running time. Considering the complex and nonlinear structure of the proposed framework, a proficient optimization technique based on the teacher learning algorithm (TLA) is devised. A two-phase modification method is proposed to increase the algorithm variety and help its convergence characteristics. The performance of the proposed algorithm is compared with the TLA, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). For enhancing the security of the energy and data transaction within the system, a directed acyclic graph (DAG)-based security framework is introduced to guarantee the performance of the system against the subversive accesses. By using this scheme, the essential data of the units are recorded and secured in the form of public, private and transaction blockchains. The economic characteristics of the proposed method are assessed on a residential hybrid AC-DC microgrid test system.

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Optimal scheduling of reconfigurable grids considering dynamic line rating constraint

Cited by 40 publications

References 22 publications

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids

A Secured Energy Management Architecture for Smart Hybrid Microgrids Considering PEM-Fuel Cell and Electric Vehicles

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