Optimal secondary distribution system design considering plug-in electric vehicles

Abdelsamad, Sherif F.; Morsi, Walid G.; Sidhu, T.S.

doi:10.1016/j.epsr.2015.09.009

Cited by 24 publications

(15 citation statements)

References 18 publications

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“…Home charging has individual charging points directly connected to the low‐voltage distribution network, with slow or average charging rate with the moderate power requirement of 1.6 to 9.6 kW. However, charging stations have several connecting points for fast charging connected to MV distribution networks . PEVs charge/discharge management plays a crucial role in EMS.…”

Section: Der Behaviormentioning

confidence: 99%

“…The uncontrolled charging of PEVs may occur coincidentally during peak hours because of the consuming habit, which will adversely affect the operational performance of power systems dramatically . Abdelsamad et al stated that uncoordinated charging leads to an overload of transformer and cable in the secondary distribution system (SDS). To mitigate these adverse effects of PEVs, smart charging is the solution.…”

Section: Der Behaviormentioning

confidence: 99%

“…However, charging stations have several connecting points for fast charging connected to MV distribution networks. 107,115 PEVs charge/discharge management plays a crucial role in EMS. State of charge of the battery, tentative travel distance, load conditions, and owner's willingness to participate in V2G energy transfer are few of the factors to decide the charging and discharging limits of PEVs in the parking lot.…”

Section: Pev Behavior and Ev-emsmentioning

confidence: 99%

“…The word Monte Carlo came from casinos in Monte Carlo, a concept based on random number generation in games at the casino. MC model has been used in previous studies 115,117,[140][141][142] ; to handle uncertainty in microgrid, PEV charging profile, charging power demand, storage capacity, reliability, and power quality are determined. MC used by a researcher where demand management and load modeling are required, especially where stochastic human interaction is involved.…”

Section: Uncertainty Management Techniquesmentioning

confidence: 99%

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Energy management system for smart grid: An overview and key issues

2020

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Summary Energy crisis and the global impetus to “go green” have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community.

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Section: Der Behaviormentioning

confidence: 99%

Section: Der Behaviormentioning

confidence: 99%

Section: Pev Behavior and Ev-emsmentioning

confidence: 99%

Section: Uncertainty Management Techniquesmentioning

confidence: 99%

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Energy management system for smart grid: An overview and key issues

2020

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“…This research provides better understanding of real distribution networks' operation, proposing specific operational points through minimizing electric vehicle charging effects. The probabilistic Monte Carlo method on high performance computers is used for the calculations.Energies 2018, 11, 2400 2 of 15 However, stochastic methods, and especially Monte Carlo [11], is computationally demanding even for today's standards; hence, specific operational points are developed in this study for a specific network [12].Electric vehicles charging would also require a grid expansion design factor [13]. Current research on electric vehicles, as far as the distribution network is concerned, gives emphasis to probabilistic methods in order to predict charging patterns, and predict the expected charging behavior.…”

mentioning

confidence: 99%

Calculating Operational Patterns for Electric Vehicle Charging on a Real Distribution Network Based on Renewables’ Production

Lazarou¹,

Vita²,

Ekonomou³

2018

Energies

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The connection of electric vehicles to distribution networks has been an emerging issue of paramount importance for power systems. On one hand, it provides new opportunities for climate change mitigation, if electric energy used for charging is produced from zero emission sources. On the other hand, it stresses networks that are now required to accommodate, in addition to the loads and production from distributed generation they are initially designed for, loads from electric vehicles charging. In order to achieve maximum use of the grid without substantially affecting its performance, these issues have to be addressed in a coordinated manner, which requires adequate knowledge of the system under consideration. It is advantageous that electric vehicle charging can be controlled to a certain degree. This research provides better understanding of real distribution networks' operation, proposing specific operational points through minimizing electric vehicle charging effects. The probabilistic Monte Carlo method on high performance computers is used for the calculations.Energies 2018, 11, 2400 2 of 15 However, stochastic methods, and especially Monte Carlo [11], is computationally demanding even for today's standards; hence, specific operational points are developed in this study for a specific network [12].Electric vehicles charging would also require a grid expansion design factor [13]. Current research on electric vehicles, as far as the distribution network is concerned, gives emphasis to probabilistic methods in order to predict charging patterns, and predict the expected charging behavior. This also affects the connection points for distributed generations, that could be optimally different if connected points are based on probabilistic methods, taking into consideration their intermittency [14]. As far as electric vehicles are concerned, the first step is to characterize their charging demand [15]. In some cases, the system is simulated as a whole, and the operational benefits are optimized based on electric charging owners' behavior [16]. Alternatively, they are connected in a way that relieves distribution system constraints [17]. Active distribution network management could be done by aggregating electric vehicle behavior in a probabilistic manner [12]. Probabilistic studies have also shown good correlation between electric vehicles and renewables [18]. In this research, electric vehicle and load aggregation is performed on the level of a secondary distribution system, at the point of medium voltage (MV)/low voltage (LV) connection, and it is fully controlled.Having mentioned the above, several studies have researched the emerging phenomenon of electric vehicles. All of them are consumer oriented, giving emphasis to electric vehicles per se, and showing minimal consideration for the electricity grid. On the other hand, the research presented in the current paper is electric grid oriented. It is focused in the procedure of creating optimal electric grid operation points for electric vehicle charging for a r...

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An optimization framework for capacity allocation and energy management of fast electric vehicle charging stations‐wind photovoltaic energy using artificial transgender longicorn algorithm

Arivalahan

Balaji

2022

Intl J of Energy Research

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Summary This paper proposes the optimal design for fast electric vehicle charging stations for the capacity configuration of components and the power scheduling strategy. The proposed approach is the artificial transgender longicorn algorithm, which is a simple and powerful controller. The optimal capacity of the system components and the optimal power scheduling approach are determined. First, an electric vehicle charging simulation model is built considering the demand response, which dynamically modified the charging expectation based on the electricity price at the time of use. Second, based on the system design, a multi‐objective optimization model is proposed with minimal objectives for the electricity cost and polluting emissions. Finally, a scenario analysis is carried out and it is concluded that renewable energy supplies, connection to the utility grid and demand response may help improve the performance of the optimization objectives. By then, the performance of the proposed technique is implemented in the MATLAB platform and compared with several existing approaches.

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Optimal secondary distribution system design considering plug-in electric vehicles

Cited by 24 publications

References 18 publications

Energy management system for smart grid: An overview and key issues

Energy management system for smart grid: An overview and key issues

Calculating Operational Patterns for Electric Vehicle Charging on a Real Distribution Network Based on Renewables’ Production

An optimization framework for capacity allocation and energy management of fast electric vehicle charging stations‐wind photovoltaic energy using artificial transgender longicorn algorithm

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