Abstract
This paper considers an aggregator of Electric Vehicles (EVs) who aims to learn the aggregate power of his/her fleet while also participating in the electricity market. The proposed approach is based on a data-driven inverse optimization (IO) method, which is highly nonlinear. To overcome such a caveat, we use a two-step estimation procedure which requires solving two convex programs. Both programs depend on penalty parameters that can be adjusted by using grid search. In addition, we propose the use of kernel regression to account for the nonlinear relationship between the behavior of the pool of EVs and the explanatory variables, i.e., the past electricity prices and EV fleet’s driving patterns. Unlike any other forecasting method, the proposed IO framework also allows the aggregator to derive a bid/offer curve, i.e. the tuple of price-quantity to be submitted to the electricity market, according to the market rules. We show the benefits of the proposed method against the machine-learning techniques that are reported to exhibit the best forecasting performance for this application in the technical literature.
Highlights
According to the White Paper on transport of the European Commission (2011), one of the main goals to achieve a sustainable transport system is to halve the use of ‘conventionally fueled’ cars in urban transport by 2030; phase them out in cities by 2050; achieve essentially CO2-free city logistics in major urban centers by 2030
We propose here a multi-purpose application for the aggregator of electric vehicles (EVs) in order to forecast the EV-fleet power, and to derive a bid/offer curve according to the rules of the electricity market, e.g. see OMIE (2019)
Unlike existing works (Saez-Gallego et al, 2016; Saez-Gallego and Morales, 2017; Lu et al, 2018; Ruiz et al, 2013; Zhou et al, 2010), we address the EV-fleet power forecasting with an inverse optimization (IO) approach in which the prediction tool accounts for two distinctive features: (i) the pool of EVs may be equipped with V2G capabilities, and (ii) there may exist a strong nonlinear relationship between the EV-fleet power and the explanatory variables, namely past EVs’ charging/discharging patterns and past electricity prices
Summary
According to the White Paper on transport of the European Commission (2011), one of the main goals to achieve a sustainable transport system is to halve the use of ‘conventionally fueled’ cars in urban transport by 2030; phase them out in cities by 2050; achieve essentially CO2-free city logistics in major urban centers by 2030. Unlike existing works (Saez-Gallego et al, 2016; Saez-Gallego and Morales, 2017; Lu et al, 2018; Ruiz et al, 2013; Zhou et al, 2010), we address the EV-fleet power forecasting with an IO approach in which the prediction tool accounts for two distinctive features: (i) the pool of EVs may be equipped with V2G capabilities, and (ii) there may exist a strong nonlinear relationship between the EV-fleet power and the explanatory variables, namely past EVs’ charging/discharging patterns and past electricity prices To capture these nonlinear relations, we endogenously introduce kernels into the proposed IO approach. The rest of the document is organized as follows: Section 2 provides the IO methodology; Section 3 gives a general overview on the comparison methodologies; in Section 4, we analyze a case study for a residential aggregator of EVs; conclusions are duly drawn in Section 5; and, Appendix presents a mixed-integer linear programming problem to generate synthetic data on the behavior of an EV fleet
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