This area develops methods and mathematical models to study evolutionary structural transformations and optimize the operating modes of power systems subjected to critical influences and stochastic disturbances.
Large-scale infrastructure failures, forced load restrictions, and rapid decentralization lead to rapid and unpredictable evolution, both at the power system level and in distribution networks. As a result of this evolution, classical deterministic models of optimal power flow and stability, as well as methods for controlling the operating modes of electrical networks based on a static “top-down” hierarchy, are losing their effectiveness.
We propose viewing the power system as a complex adaptive system in which structural evolution—in the form of self-organization within community-level microgrids—and physical operating modes are deeply interrelated and require dynamic joint optimization. We plan to develop new methods, algorithms, and models by utilizing empirical data obtained from previous studies, as well as big data analysis-based approaches to processing this data. This will enable us to derive optimal solutions for design, operational mode configuration, and management decisions, taking into account potential critical impacts.
Key approaches:
- Application of dynamic graph theory to model the forced fragmentation of centralized networks and their restructuring into self-organizing microgrids.
- Development of state-of-the-art mathematical models for solving problems of optimal flow distribution and economic dispatch under dynamically changing, highly uncertain operating conditions with limited resources.
- Development of algorithms to ensure voltage and frequency stability in operating modes that involve a temporary transition to island mode, and management of distributed resources in the absence of centralized dispatch control.
- Quantitative assessment of nonlinear changes in energy consumption and generation patterns; integration of the human factor of self-organization as a mathematical variable in system optimization processes.