A-Priori Reduction of Scenario Approximation for Automated Generation Control in High-Voltage Power Grids With Renewable Energy
Lukashevich A. Bulkin A. Maximov Y.
2024Institute of Electrical and Electronics Engineers Inc.
IEEE Control Systems Letters
2024#81613 - 1618 pp.
Renewable energy sources (RES) are increasingly integrated into power systems to support the United Nations Sustainable Development Goals of decarbonization and energy security. However, their low inertia and high uncertainty pose challenges to grid stability and increase the risk of blackouts. Stochastic chance-constrained optimization, particularly data-driven methods, offers solutions but can be time-consuming, especially when handling multiple system snapshots. This letter addresses a dynamic joint chance-constrained Direct Current Optimal Power Flow (DC-OPF) problem with Automated Generation Control (AGC) to facilitate cost-effective power generation while ensuring that balance and security constraints are met. We propose an approach for a data-driven approximation that includes a priori sample reduction, maintaining solution reliability while reducing the size of the data-driven approximation. Both theoretical analysis and empirical results demonstrate the superiority of this approach in handling generation uncertainty, requiring up to twice less data while preserving solution reliability.
Data-driven control , optimization , power systems
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Skolkovo Institute of Science and Technology, Institute for Artificial Intelligence, Lomonosov Moscow State University, Moscow, 119191, Russian Federation
International Center of Corporate Data Analysis, Department of Engineering, Astana, 010010, Kazakhstan
Los Alamos National Laboratory, Theoretical Division T-5, Los Alamos, 87545, NM, United States
Skolkovo Institute of Science and Technology
International Center of Corporate Data Analysis
Los Alamos National Laboratory
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