Motivations

The electricity power grid is one of the largest and most complex artificial systems currently in operation, with high criticality level given its wide social and economic impact. Such complexity is expected to increase in years to come to face possible climate changes while integrating intermittent renewable energy resources and distributed decisions taken at the consumer level. To limit building new power lines and expanding the power grid with costly equipment investments, new approaches relying on digital technologies are needed to leverage existing infrastructures by optimizing the management and operations of power grids: this is the “smart grid” era. In this context, new methods are needed to better understand and anticipate the behavior of the grid, with spatial and temporal multi-scale resolutions, both for predictive modeling and decision making. To complement historical physical models of the grid, coupled with standard optimization that are currently in use in study-tools of grid operators, learning-based methods promise to take into account expert knowledge to better assist them in analyzing demand forecasts and managing crisis situations.

Neural networks and machine learning have made great advances in the direction of continuously adapting to changes, acquiring new knowledge of systems, and improving context awareness. We anticipate that recent developments in neural networks will translate into applications in power systems, particularly to model ever larger datasets with spatio-temporal structure and capitalize on shared internet information. In addition, techniques that favor explainability and/or interpretability needed to assist operators in decision making are still in their infancy. Therefore, this special session aims at compiling the latest efforts and research advances in applying neural networks, machine learning, and other computational intelligence algorithms to augment existing study tools and simulators. Work that aims at accelerating research on a broader scale, enabling easier and more transparent iterations in the community, like open-sourcing dataset, releasing benchmark platforms or organizing data challenges will be very welcome.

Call for paper

Papers submitted to this special session should be submitted via the WCCI 2018 submission website here and respect the interactions provided therein.

In the submission form, when filling the "Main research topic*" field, make sure to select the special Session "Intelligent Power Systems" (S15. Special Session on Intelligent Power Systems)

The deadline for submission is: 1st of February 2018.

We encourage submission of papers using neural networks, machine learning, or other computational intelligence methods on problems related to power systems.

Topics of special interest this year include:

The topic of interest includes, but are not limited to:

• Building a computational intelligence power system community for decision making (assisting the operators, not only displaying new information with the integration of new tools)
  • Towards explainability/interpretability, some context awareness
  • Coupling decisions at different time scales
• Leveraging the use of existing physical simulators with learning
  • Meeting/crossing observational data, simulators and recent advances in AI
• Creating open initiatives between TSOs and Research in Universities :
  • Shared data on real system with confidentiality preservation
  • New Benchmarks
  • Challenge organization

Related topics also of interest include, but are not limited to:

The scope of the special session comprises all aspects of machine learning applied to smart grid management and operations, including but not limited to the following topics:

• Stochastic Load and/or Renewable Generation forecasting
  • At different spatial and time scales
• Power grid clustering and segmentation with visualization:
  • Operators naturally focus manually on zone of interests to study and solve a problem, how can we do it more automatically?
• Grid operation support for real-time:
  • Decision proposals to the operator under uncertainties.
• Learning with simulators:
  • Simulators are of great help to acquire data for free and learn from it, how can we leverage their usage augmenting them with machine learning.
• Calibrating simulators:
  • Learning the physical parameters of a given power grid with measurements
• Causal learning:
  • What are the factors that determine some observations on the grid? How can we influence those variables then?
• Multi-scale Reinforcement Learning:
  • From monthly to weekly to daily, how can we couple decisions taken at different time scale to better optimize grid management, especially between planned maintenance operations and near to real-time control room dispatching operations.
• Predictive Asset Management:
  • Grid Development and renewal requires smart planning to keep the system continuously running in normal conditions. Asset renewal will be quite high in the coming years for aging grids. There is a need today to estimate materials wearing given their historical operational and environmental conditions to prioritize renewals, as well as model the impact of applying different renewal policies.
• Collective Intelligence with open-source initiatives:
  • This applies to smart grid but to research as well: how can we ease collaboration and iterations with shared materials like data, benchmarks and challenges?