A Probabilistic Evaluation Method of Household EVs Dispatching Potential Considering Users’ Multiple Travel Needs

Gan, Lei; Chen, Xingying; Yu, Kun; Zheng, Jiyue; Du, Wei

doi:10.1109/tia.2020.2989690

Cited by 38 publications

(25 citation statements)

References 29 publications

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“…The relevant parameters of EVs are following Gaussian distribution with the mean and standard deviation in TABLE 1 . The plug-in and plug-out time are generated based on the multimodal normal distribution functions in [41] .…”

Section: Assumptions and Parametersmentioning

confidence: 99%

A distributed robust control strategy for electric vehicles to enhance resilience in urban energy systems

Dong

Zhang

et al. 2023

Advances in Applied Energy

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Section: Assumptions and Parametersmentioning

confidence: 99%

A distributed robust control strategy for electric vehicles to enhance resilience in urban energy systems

Dong

Zhang

et al. 2023

Advances in Applied Energy

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“…Using a copula-based sampling technique, a sequential sample of random trip parameters was carried out using the plug-in rate and coupling properties of the trip parameters [11]. Tis study considered customers' demands for multiple daily trips, from which the corresponding probability distributions of the various trip parameters were generated and ftted using a least square estimation-based parameter optimization approach.…”

Section: Ev Load Profle Simulationmentioning

confidence: 99%

Charging Management of Electric Vehicles on Loading Capability of Distribution System Equipment, Voltage Quality, and Energy Loss by Monte Carlo Simulation and Linear Programming

Sirisumrannukul

2023

International Transactions on Electrical Energy Systems

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This paper presents the impact of uncoordinated and coordinated charging management of electric vehicles (EVs) on the loading capability of major distribution system equipment, voltage quality, and energy loss in a distribution system. The main emphasis is given to the overloading of distribution transformers, primary feeders, and a substation transformer. The voltage quality of load points along the feeders and the system energy loss are also underlined. The load profile for uncoordinated EV charging is simulated by a Monte Carlo method with several deterministic and stochastic variables involved. To mitigate the overloading of the system components, a coordinated charging (also known as smart charging) model formulated as a linear programming problem is proposed with the objective of maximizing the total amount of energy consumption by EVs and the sum of all individual final states of charge (SoCs), and minimizing the sum of the absolute deviation of individual SoCs from the overall average SoC. The optimization problem is subject to equipment capability loading and planning criteria constraints with low, medium, and high EV penetration levels. The voltage quality problem and energy loss are also analyzed by an unbalanced three-phased power flow model. A case study of a real and practical 115/22 kV distribution system of the Provincial Electricity Authority (PEA) with a 50 MVA substation transformer, 5 feeders, and 732 distribution transformers shows that the possibility of overloaded system components, voltage drops along the feeders, and the system energy loss can be identified in the uncoordinated charging scenario and offer the readiness for equipment replacement and network reinforcement planning. The proposed smart charging model allows the distribution system to accommodate more EVs by appropriately managing the power and the start times of charging for the individual EVs over the timeslots of a day. The study results confirm no violation of the system components and voltage regulation imposed by the system planning guidelines. In addition, the system peak demand and the system energy loss are significantly lower in the smart charging scenario, thus deferring investment upgrades, offering better asset utilization, and retaining network security and service quality.

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“…Where Dtd, and Dtm is the hourly demand of the day, and the next day, respectively; and θtd is the hourly temperature of the day. A GC is a popular option for modeling the dependence structure between variables [31]. Parameters of the GC functions Rho are estimated by means of an approximation to Kendall's rank correlation (see table 1).…”

Section: Stlf For the Next-day Demandmentioning

confidence: 99%

Short-Term and Mid-Term Load Forecasting in Distribution System Using Copula

Ahmadi

Rivera

2020

Wseas Transactions on Systems and Control

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Short-term and (Mid\Long)-term load prediction are needed for the efficient management of distribution system. An accurate load forecasting could help distribution operators to achieve a great control and flexibility on energy. In addition, a reliable prediction would have significant impact on load power flow problems, operation management, planning, automation, and search market. This paper proposes a new method based on copula for Short-term, and (Mid/Long)-term load forecasting. Proposed method was used less historical data to predict the load consumption then the other methods, so it is useful in distribution system due to scarce installed meters. Copula is a recently developed statistical theory for multivariate probability analysis that has been proposed by Sklar in 1958. Copulas are used in popularity for financial and reliability application. Numerical tests results using database of Iran system’s operation (ISO) shows that this method provides a much better prediction performance in comparison with other methods employing the same data.

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A Probabilistic Evaluation Method of Household EVs Dispatching Potential Considering Users’ Multiple Travel Needs

Cited by 38 publications

References 29 publications

A distributed robust control strategy for electric vehicles to enhance resilience in urban energy systems

A distributed robust control strategy for electric vehicles to enhance resilience in urban energy systems

Charging Management of Electric Vehicles on Loading Capability of Distribution System Equipment, Voltage Quality, and Energy Loss by Monte Carlo Simulation and Linear Programming

Short-Term and Mid-Term Load Forecasting in Distribution System Using Copula

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