Day-ahead tariffs for the alleviation of distribution grid congestion from electric vehicles

O׳Connell, Niamh; Wu, Qiuwei; Østergaard, Jacob; Nielsen, Arne Hejde; Tae, Seung; Ding, Yi

doi:10.1016/j.epsr.2012.05.018

Cited by 110 publications

(93 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A Danish 0.4 kV distribution system from the Bornholm Island with topology described in [32] is employed to demonstrate the performance of the proposed model. There are 33 cables and 33 buses in this system, and 12 DGs are assumed to be connected to the 0.4 kV buses of the system.…”

Section: Case Studies and Discussionmentioning

confidence: 99%

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

Wen

Liu

et al. 2016

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

In recent years, much attention has been devoted to the development and applications of smart grid technologies, with special emphasis on flexible resources such as distributed generations (DGs), energy storages, active loads, and electric vehicles (EVs). Demand response (DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations. Despite their potential contributions to power system secure and economic operation, uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned. In addition, the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments. In this context, this paper aims to propose a DR based congestion management strategy for smart distribution systems. The general framework and procedures for distribution congestion management is first presented. A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established. Subsequently, the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices. The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.

show abstract

Section: Case Studies and Discussionmentioning

confidence: 99%

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

Wen

Liu

et al. 2016

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…In the following discussion, a brief explanation is given of the performance of these models. Also, input data including the rate of power consumption and the rate of demand generation and output parameters are also the answer to the management of charging and discharging electric vehicles in the standard network [6,7].…”

Section: -Materials and Methodsmentioning

confidence: 99%

Comparison and Evaluation of the Performance of Various Types of Neural Networks for Planning Issues Related to Optimal Management of Charging and Discharging Electric Cars in Intelligent Power Grids

Moradzadeh¹,

Khaffafi²

2018

ESJ

View full text Add to dashboard Cite

The use of electric vehicles in addition to reducing environmental concerns can play a significant role in reducing the peak and filling the characteristic valleys of the daily network load. In other words, in the context of smart grids, it is possible to improve the battery of electric vehicles by scheduling charging and discharging processes. In this research, the issue of controlling the charge and discharge of electric vehicles was evaluated using a variety of neural models, until the by examining the effect of the growth rate of the penetration level of electric vehicles of the hybrid type that can be connected to the distribution network, the results of the charge management and discharge model of the proposed response are examined. The results indicate that due to increased penetration of these cars is increased the amount of responses to charge and discharge management. In this research, a variety of neural network methods, a) neural network method using Multilayer Perceptron Training (MLP), b) neural network method using Jordan Education (RNN), c) neural network method using training (RBF ) Was evaluated based on parameters such as reduction of training error, reduction of network testing error, duration of run and number of replications for each one. The final results indicate that electric vehicles can be used as scattered power plants, and can be useful for regulating the frequency and regulation of network voltages and the supply of peak traffic. This also reduces peak charges and incidental costs, which ultimately helps to further network stability. Finally, the charge and discharge management response reflects the fact that intelligent network-based models have the ability to manage the charge and discharge of electric vehicles, and among the models the amount of error reduction training and testing is very favourable for both RNN, MLP.

show abstract

“…DSO [6,7] has the main responsibility for resolving the congestion in distribution networks, can choose to reinforce the network through long term planning or employ market methods so as to incentivize the DERs to respect the system capacity limits. Compared to direct control methods for congestion management [8,9], market-based methods maximizes the social welfare, cause least discomfort to customers and encourage more participation in the energy planning.…”

Section: Introductionmentioning

confidence: 99%

Efficient Energy Management System for Smart Grid Using Bacterial Foraging Optimization Technique

Raviprabhakaran¹

2017

ijecs

View full text Add to dashboard Cite

Distributed devices in smart grid systems are decentralized and connected to the power grid. The connection made through different types of equipment transmits. This produces the numerous energy losses, when power flows from one bus to another bus. The most efficient approaches to reduce energy losses is to integrate the renewable energy resources in Distributed Generation's (DG's). The uncertainty of DG may cause instability issues. The major issue includes congestion in the power grid due to the sudden power consumption by the customers, which affects the efficient energy delivery. Energy management with DG regulation is one of the most efficient solutions to solve these instability issues. In the considered power system with DG's and consumers, the Locational Marginal Pricing (LMP) based unified Energy Management System (uEMS) model is considered. This model increases the profit benefits for DG's and increases the stability of Distributed Energy System (DES). In this paper, the Bacterial Foraging Optimization (BFO) is employed to reduce losses i.e. based on Loss Reduction Allocation (LRA) method. Using LRA method the energy loss reduction is calculated and this model accurately rewards DG contribution and offers a good competitive market. Moreover, the entire DG's profit is increase by the BFO technique. The IEEE 37 bus feeder system is to be considered to validate the proposed uEMS model to increase the DG system stability. Furthermore, this implementation gives the idea of formulating efficient energy management system of future Indian scenario.

show abstract

Day-ahead tariffs for the alleviation of distribution grid congestion from electric vehicles

Cited by 110 publications

References 18 publications

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

Comparison and Evaluation of the Performance of Various Types of Neural Networks for Planning Issues Related to Optimal Management of Charging and Discharging Electric Cars in Intelligent Power Grids

Efficient Energy Management System for Smart Grid Using Bacterial Foraging Optimization Technique

Contact Info

Product

Resources

About