Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability

Veviurko, G.; Böhmer, J.W.; Mackay, Laurens; de Weerdt, M.M.

doi:10.3390/en15041389

Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability

Title

Surrogate DC Microgrid Models for Optimization of Charging Electric Vehicles under Partial Observability

Author

Veviurko, G. (TU Delft Algorithmics)
Böhmer, J.W. (TU Delft Algorithmics)
Mackay, Laurens (DC Opportunities R&D)
de Weerdt, M.M. (TU Delft Algorithmics)

Date

2022

Abstract

Many electric vehicles (EVs) are using today’s distribution grids, and their flexibility can be highly beneficial for the grid operators. This flexibility can be best exploited by DC power networks, as they allow charging and discharging without extra power electronics and transformation losses. From the grid control perspective, algorithms for planning EV charging are necessary. This paper studies the problem of EV charging planning under limited grid capacity and extends it to the partially observable case. We demonstrate how limited information about the EV locations in a grid may disrupt the operation planning in DC grids with tight constraints. We introduce two methods to change the grid topology such that partial observability of the EV locations is resolved. The suggested models are evaluated on the IEEE 16 bus system and multiple randomly generated grids with varying capacities. The experiments show that these methods efficiently solve the partially observable EV charging planning problem and offer a trade-off between computational time and performance.

Subject

DC microgrid
partial observability
electric vehicle
optimization

To reference this document use:

http://resolver.tudelft.nl/uuid:4328fd28-3592-433b-a63f-4f6537da2cee

DOI

https://doi.org/10.3390/en15041389

ISSN

1996-1073

Source

Energies, 15 (4)

Part of collection

Institutional Repository