Wind power

Planning and scheduling in smart grids

The emergence of renewable energy sources in distribution grids creates several important challenges. For example, renewable power production will be more dependent on the weather, and distribution grids become congested if many consumers use electricity at the same time when charging their electric vehicles. Both aspects make it increasingly difficult to match power demand and supply while respecting the capacity constraints of the distribution grid.

In the GCP project we developed AI-based algorithms to schedule flexible consumption of consumers, taking into account grid capacity constraints and uncertainty in power consumption and renewable generation. Additionally, we significantly improved the computational performance of fundamental planning and scheduling algorithms from the artificial intelligence field. The key results were published in several international artificial intelligence conference proceedings and journals.

Publications

Point-Based Value Iteration for Finite-Horizon POMDPs

Erwin Walraven and Matthijs T. J. Spaan
Journal of Artificial Intelligence Research, vol. 65, pp. 307–341, 2019.

Planning under Uncertainty in Constrained and Partially Observable Environments

PhD dissertation, Delft University of Technology, 2019.

Column Generation Algorithms for Constrained POMDPs

Erwin Walraven and Matthijs T. J. Spaan
Journal of Artificial Intelligence Research, vol. 62, pp. 489–533, 2018.

Bootstrapping LPs in Value Iteration for Multi-Objective and Partially Observable MDPs

Diederik M. Roijers, Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 28th Int. Conference on Automated Planning and Scheduling, pp. 218–226, 2018.

Accelerated Vector Pruning for Optimal POMDP Solvers

Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 3672–3678, 2017.

Bounding the Probability of Resource Constraint Violations in Multi-Agent MDPs

Frits de Nijs, Erwin Walraven, Mathijs M. de Weerdt and Matthijs T. J. Spaan
Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 3562–3568, 2017.

Resource-constrained Multi-agent MDP Planning with Bounded Violation Probability

Frits de Nijs, Erwin Walraven, Mathijs M. de Weerdt and Matthijs T. J. Spaan
NIPS workshop on Learning, Inference and Control of Multi-Agent Systems, 2016.

Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply

Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 22nd European Conference on Artificial Intelligence, pp. 904–912, 2016.

Planning under Uncertainty for Aggregated Electric Vehicle Charging using Markov Decision Processes

Erwin Walraven and Matthijs T. J. Spaan
International Workshop on Artificial Intelligence for Smart Grids and Smart Buildings, 2016.

Planning under Uncertainty with Weighted State Scenarios (extended abstract)

Erwin Walraven and Matthijs T. J. Spaan
AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents, 2015.

Planning under Uncertainty with Weighted State Scenarios

Erwin Walraven and Matthijs T. J. Spaan
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, pp. 912–921, 2015.

A Scenario State Representation for Scheduling Deferrable Loads under Wind Uncertainty

Erwin Walraven and Matthijs T. J. Spaan
The 10th Annual Workshop on Multiagent Sequential Decision Making Under Uncertainty, 2015.