Supplementary material of the work Inverse Optimization with Kernel Regression: Application to the Power Forecasting and Bidding of a Fleet of Electric vehicles

Overview

In this repository, we provide the input data for all cases of the inverse optimization methodology described in the work entitled Inverse Optimization with Kernel Regression: Application to the Power Forecasting and Bidding of a Fleet of Electric vehicles.

The paper can be accessed via arxiv in [1]. This article has been developed by R. Fernandez-Blanco, J. M. Morales, S. Pineda, and A. Porras, which are members of the OASYS group, and it has been possible thanks to the funding of the project Flexanalytics. We suggest you to visit the related links to know more our research.

Database

This respository contains 5 excel files, which are related to the following cases:

1. input_invfor_CS0_g2v: sync grid-to-vehicle case (Section 4.2 of the paper)

2. input_invfor_CS0_v2g: sync vehicle-to-grid case (Section 4.2 of the paper)

3. input_invfor_naive: naive case (Section 4.2 of the paper)

4. input_invfor_CSnot0_g2v: non-sync grid-to-vehicle case (Section 4.3 of the paper)

5. input_invfor_CSnot0_v2g: non-sync vehicle-to-grid case (Section 4.3 of the paper)

Note that electricity prices come from day-ahead electricity prices from Spain and the observed power is coming from the MILP model described in the appendix of the paper [1].

References

[1] Fernández-Blanco, R., Morales, J. M., Pineda, S., & Porras, A. (2020). Inverse Optimization with Kernel Regression: Application to the Power Forecasting and Bidding of a Fleet of Electric vehicles. arXiv preprint: https://arxiv.org/abs/1908.00399

License

Copyright 2019 Optimization and Analytics for Sustainable energY Systems (OASYS)

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