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Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging
Technical Paper
2017-01-1701
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The goal of grid friendly charging is to avoid putting additional load on the electricity grid when it is heavily loaded already, and to reduce the cost of charging to the consumer. In a smart metering system, Day Ahead tariff (DA) prices are announced in advance for the next day. This information can be used for a simple optimization control, to select to charge at cheapest times. However, the balance of supply and demand is not fully known in advance and the Real-Time Prices (RTP) are therefore likely to be different at times. There is always a risk of a sudden price change, hence adding a stochastic element to the optimization in turn requiring dynamic control to achieve optimal time selection. A stochastic dynamic program (SDP) controller which takes this problem into account has been made and proven by simulation in a previous paper.
Since there are differences between the DA and the RTP tariff, this paper proposes a (1) predictor to create an unbiased estimate of the RTP tariff based on available data. It uses a regression on historical data to find the best prediction of the expected price. Finally, a (2) case study based on data from the Illinois Electricity Grid prices is presented to validate the SDP controller over several years of data. The stochastic optimization uses the RTP prices effectively, getting very close to the globally optimal charging price. However, the predictor achieves only a slight reduction in prediction uncertainty with this data sate, and it has a negligible effect on cost. This means that DA prices can be used as a fair prediction of RTP charging cost here. The SDPM successfully reacts in the case study and leads to savings on charging costs over the years presented.
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Citation
Mody, S. and Steffen, T., "Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging," SAE Technical Paper 2017-01-1701, 2017, https://doi.org/10.4271/2017-01-1701.Data Sets - Support Documents
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References
- Zubaryeva A. , Thiel C. , Barbone E. , and Mercier A. Assessing factors for the identification of potential lead markets for electrified vehicles in Europe: expert opinion elicitation Technol. Forecast. Soc. Change 1 16 Jul. 2012
- World energy outlook 2009 IEA (International Energy Agency) Paris 2009
- Nissan LEAF website https://www.nissan.co.uk/vehicles/new-vehicles/leaf/charging-range.html
- Kang J. E. and Recker W. W. An activity-based assessment of the potential impacts of plug-in hybrid electric vehicles on energy and emissions using 1-day travel data Transp. Res. Part D Transp. Environ. 14 8 541 556 Dec. 2009
- Tesla Motors Website https://www.tesla.com/en_GB/models
- Bauer P. , Zhou Y. Z. Y. , Doppler J. , and Stembridge , N. Charging of electric vehicles and impact on the grid MECHATRONIKA, 2010 13th Int. Symp. 2010
- Clement-Nyns K. , Haesen E. , and Driesen J. , Analysis of the impact of plug-in hybrid electric vehicles on residential distribution grids by using quadratic and dynamic programming World Electr. Veh. J. 3 2009
- Meyer J. , Hähle S. , Schegner P. , and Wald C. Impact of electrical car charging on unbalance in public low voltage grids,” in Proceeding of the International Conference on Electrical Power Quality and Utilisation, EPQU 2011 635 640
- Stroehle P. , Becher S. , Lamparter S. , Schuller A. , and Weinhardt C. The impact of charging strategies for electric vehicles on power distribution networks 2011 8th International Conference on the European Energy Market, EEM 11 2011 51 56
- Zhang L. , Brown T. , and Samuelsen G. S. Fuel reduction and electricity consumption impact of different charging scenarios for plug-in hybrid electric vehicles J. Power Sources 196 6559 6566 2011
- Duan Z. , Gutierrez B. , and Wang L. Forecasting plug-in electric vehicle sales and the diurnal recharging load curve IEEE Trans. Smart Grid 5 1 527 535 2014
- Mody S. and Steffen T. Optimal Charging of Electric Vehicles using a Stochastic Dynamic Programming Model and Price Prediction SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 8 2 379 393 2015
- Mody S. and Steffen T. Optimal Charging of EVs in a Real Time Pricing Electricity Market SAE Int. J. … 2013
- Shahidehpour M. , Yamin H. , and Li Z. Market Operations in Electric Power Systems 2002
- Han X. S. , Han L. , Gooi H. B. , and Pan Z. Y. Ultra-short-term multi-node load forecasting – a composite approach IET Gener. Transm. Distrib. 6 5 436 May 2012
- Hong , T. Wilson , and J. Xie J. Long term probabilistic load forecasting and normalization with hourly information IEEE Trans. Smart Grid 5 1 456 462 2014
- Singhal D. and Swarup K. S. Electricity price forecasting using artificial neural networks Int. J. Electr. Power Energy Syst. 33 3 550 555 2011
- Wu L. and Shahidehpour M. A hybrid model for integrated day-ahead electricity price and load forecasting in smart grid IET Gener. Transm. Distrib. 8 12 1937 1950 2014
- Allcott H. Rethinking real-time electricity pricing Resour. Energy Econ. 33 4 820 842 Nov. 2011
- The Brattle Group, Freeman Sullivan & Co, and Global Energy Partners A National Assessment of Demand Response Potential Ferc 254 2009
- Pricing R. R. Bringing Residential Real-Time Pricing to Scale in Illinois : Policy Recommendations 2 5 2009
- ComEd Electricity Website https://www.comed.com/Pages/default.aspx
- Quebec Hydro Electric Vehicle Charging Stations Technical Installations Guide 2012
- Mody , S. and Steffen , T. Optimal Charging of EVs in a Real Time Pricing Electricity Market SAE Int. J. Alt. Power. 2 2 337 349 2013 10.4271/2013-01-1445