Shenglu Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shenglu Wang has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Kairui Feng
State Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai China
1 shared publication - Mengqi Xue
Tongji University
1 shared publication - Yue Song
Tongji University
1 shared publication
Latest Publications
- 28 October 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Control Systems Letters
- Vol. 9, 2489-2494
Power Flow Security Maximization via Inverse Chance Constrained Optimization
The chance constrained optimal power flow (CC-OPF) essentially finds the low-cost generation dispatch scheme ensuring operational constraints are met with a specified probability, termed the security level. While the security level is a crucial input parameter, how it shapes the CC-OPF feasibility boundary has not been revealed. Changing the security level from a parameter to a decision variable, this paper proposes a security maximization approach based on the chance constrained DC-OPF model, termed inverse CC-OPF (ICC-OPF), that seeks the maximum security level achievable by the system. To efficiently solve the ICC-OPF, we design a Newton-Raphson-like iteration algorithm leveraging the duality-based sensitivity analysis of an associated surrogate problem. Numerical experiments validate the proposed approach, revealing complex feasibility boundaries for security levels that underscore the importance of coordinating security levels across multiple chance constraints.
- 28 October 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Control Systems Letters
- Vol. 9, 2489-2494
Power Flow Security Maximization via Inverse Chance Constrained Optimization
The chance constrained optimal power flow (CC-OPF) essentially finds the low-cost generation dispatch scheme ensuring operational constraints are met with a specified probability, termed the security level. While the security level is a crucial input parameter, how it shapes the CC-OPF feasibility boundary has not been revealed. Changing the security level from a parameter to a decision variable, this paper proposes a security maximization approach based on the chance constrained DC-OPF model, termed inverse CC-OPF (ICC-OPF), that seeks the maximum security level achievable by the system. To efficiently solve the ICC-OPF, we design a Newton-Raphson-like iteration algorithm leveraging the duality-based sensitivity analysis of an associated surrogate problem. Numerical experiments validate the proposed approach, revealing complex feasibility boundaries for security levels that underscore the importance of coordinating security levels across multiple chance constraints.