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| 1 | Analysis of electric vehicle charging using the traditional generation expansion planning analysis tool WASP-IV显示文摘Electric vehicles(EV)are proposed as a measure to reduce greenhouse gas emissions in transport and support increased wind power penetration across modern power systems.Optimal benefits can only be achieved,if EVs are deployed effectively,so that the exhaust emissions are not substituted by additional emissions in the electricity sector,which can be implemented using Smart Grid controls.This research presents the results of an EV roll-out in the all island grid(AIG)in Ireland using the long term generation expansion planning model called the Wien Automatic System Planning IV(WASP-IV)tool to measure carbon dioxide emissions and changes in total energy.The model incorporates all generators and operational requirements while meeting environmental emissions,fuel availability and generator operational and maintenance constraints to optimize economic dispatch and unit commitment power dispatch.In the study three distinct scenarios are investigated base case,peak and off-peak charging to simulate the impacts of EV’s in the AIG up to 2025. | Aoife FOLEY Brian O GALLACHOIR | 2015 | Journal of Modern Power Systems and Clean Energy2015，3，2： | 8 |
| 2 | A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads显示文摘Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements.These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints,such as the valve point effect,power balance and ramprate limits.The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times.In this paper,multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model.Self-learning teaching-learning based optimization(TLBO)is employed to solve the non-convex non-linear dispatch problems.Numerical results onwell-known benchmark functions,as well as test systems with different scales of generation units show the significance of the new scheduling method. | Zhile YANG Kang LI Qun NIU Yusheng XUE Aoife FOLEY | 2014 | Journal of Modern Power Systems and Clean Energy2014，2，4： | 8 |
| 3 | Current methods and advances in forecasting of wind power generation显示文摘 | Aoife M. Foley Paul G. Leahy Antonino Marvuglia Eamon J. McKeogh | 2011 | Renewable Energy2011，，1： | 1 |
| 4 | Current methods and advances in forecasting wind power generation显示文摘 | Aoife M Foley Paul G Leahy Antonino Marvuglia | 2011 | Re- newable Energy2011，37，1： | 1 |
| 5 | Current methods and advances in forecasting of wind power generation显示文摘 | Aoife M Foley Paul G Leahy Antonino Marvuglia | 2011 | Renewable Energy2011，37，1： | 1 |
| 6 | Joint Estimation of Inconsistency and State of Health for Series Battery Packs显示文摘Battery packs are applied in various areas(e.g.,electric vehicles,energy storage,space,mining,etc.),which requires the state of health(SOH)to be accurately estimated.Inconsistency,also known as cell variation,is considered a significant evaluation index that greatly affects the degradation of battery pack.This paper proposes a novel joint inconsistency and SOH estimation method under cycling,which fills the gap of joint estimation based on the fast-charging process for electric vehicles.First,fifteen features are extracted from current change points during the partial charging process.Then,a joint estimation system is designed,where fusion weights are obtained by the analytic hierarchy process and multi-scale sample entropy to evaluate inconsistency.A wrapper is used to select the optimal feature subset,and Gaussian process regression is implemented to estimate the SOH.Finally,the estimation performance is assessed by the test data.The results show that the inconsistency evaluation can reflect the aging conditions,and the inconsistency does affect the aging process.The wrapper selection method improves the accuracy of SOH estimation by about 75.8%compared to the traditional filter method when only 10%of data is used for model training.The maximum absolute error and root mean square error are 2.58%and 0.93%,respectively. | Yunhong Che Aoife Foley Moustafa El‑Gindy Xianke Lin Xiaosong Hu Michael Pecht | 2021 | Automotive Innovation2021，4，1： | 1 |
| 7 | Current methods and advances in forecasting of wind power generation显示文摘 | Aoife M. Foley Paul G. Leahy Antonino Marvuglia Eamon J. McKeogh | 2011 | Renewable Energy2011，，1： | 1 |
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