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Benefits of Stochastic Optimisation with Grid Price Prediction for Electric Vehicle Charging
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
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.
CitationMody, 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.
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