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Simulation Results for SMPC Reserve Provision
- Citation Author(s):
-
JacobThrän
Imperial College London - Submitted by:
- Jacob Thran
- Last updated:
- Tue, 08/06/2024 - 10:20
- DOI:
- 10.21227/ckmr-1g77
- Data Format:
- License:
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Abstract
This dataset contains simulation results for the research article "Reserve Provision from Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Control". Each CSV file corresponds to an experimental setup with a certain number of EVs included in the fleet and the risk-aversion determined by the risk-aversion factor Ω. Each CSV file contains ten columns that contain the following features: Penalty incurred, positive reserve commitment, negative reserve commitment, total cumulative energy trajectory, G2V charging, V2G charging, lower bound of the conceptual battery, upper bound of the conceptual battery, power boundary of the conceptual battery, electricity system price. Benchmark 1 shows the results for a naive algorithm that solely relies on deterministic predictions. Benchmark 2 is an algorithm that has perfect future knowledge and therefore does not need to rely on predictions. The raw data that was fed to the proposed algorithm is also provided.
The dataset can be used to visualise results from the simulations of the study. Illustrations of how the algorithm functions can be obtained by plotting the results in a single file. Note that some variables may need to be computed from others with equations for this given in the publication. To see that the algorithm makes optimal choices, it can be helpful to plot benchmark 2 as this has perfect foresight. To see results for different fleet sizes, revenue data should be aggregated within each CSV according to reserve price data given in the paper.
