Paper Info
Title
Evaluation of prediction error effects in wind energy-based electric vehicle charging
Abstract
This paper first presents a battery operation scheduler for the sake of practical integration of wind energy generation and electric vehicle charging, and then measures its performance mainly focusing on the effect of wind speed prediction errors. The operation scheduler decides whether to charge or discharge a station battery on each time slot based on current wind speed reading and next speed prediction. Its control logic straightforwardly activates generation facilities according to the minimum wind speed for energy generation and the current battery capacity. Next-hour wind speed is predicted by an artificial neural network trained by a series of hour-by-hour speed records. The performance measurement results obtained from simulation show that the depletion ratio is affected by 6.8 % and the energy loss by 3.5 %. This result is valid for the whole given parameter range except only a few cases. Moreover, judging from the observation that the largest renewable energy loss is just 0.9 %, the battery management scheme overcomes the misprediction effect by adaptively compensating for the generation loss on each time slot.
Year
DOI
Venue
2013
10.1145/2513228.2513229
RACS
Keywords
Field
DocType
time slot,next speed prediction,battery operation scheduler,wind energy generation,current wind speed reading,prediction error effect,next-hour wind speed,minimum wind speed,electric vehicle,battery management scheme,wind speed prediction error,hour-by-hour speed record,smart grid,wind energy
Automotive engineering,Wind speed,Renewable energy,Smart grid,Electric vehicle,Generation loss,Simulation,Computer science,Real-time computing,Battery (electricity),Wind power,Electricity generation
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
4
Name
Order
Citations
PageRank
Junghoon Lee100.34
Gyung-Leen Park239968.77
Il-Woo Lee310418.64
Wan-Ki Park47613.62