Iterative Decomposition of Joint Chance Constraints in OPF
In chance-constrained OPF models, joint chance constraints (JCCs) offer a stronger guarantee on security compared to single chance constraints (SCCs). Using Boole's inequality or its improved versions to decompose JCCs into SCCs is popular, yet the conservativeness introduced is still significant. In this letter, a non-parametric iterative framework is proposed to achieve the decomposition of JCCs with negligible conservativeness. An adaptive risk allocation strategy is also proposed and embedded in the framework. Results on an IEEE test case show that the conservativeness using the framework is nearly eliminated, thereby reducing the generation cost considerably.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Infinite-dimensional spherical-radial decomposition for probabilistic functions, with application to constrained optimal control and Gaussian process regression
The spherical-radial decomposition (SRD) is an efficient method for estimating probabilistic functions and their gradients defined over finite-dimensional elliptical distributions. In this work, we generalize the SRD to …
Scheduling HVAC loads to promote renewable generation integration with a learning-based joint chance-constrained approach
The integration of distributed renewable generation (DRG) in distribution networks can be effectively promoted by scheduling flexible resources such as heating, ventilation, and air conditioning (HVAC) loads. However, fi…
Computational EfficiencySchedulingEvolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem
Chance-constrained problems involve stochastic components in the constraints which can be violated with a small probability. We investigate the impact of different types of chance constraints on the performance of iterat…
Chance Constrained Stochastic Optimal Control for Linear Systems with Time Varying Random Plant Parameters
We propose an open loop control scheme for linear systems with time-varying random elements in the plant's state matrix. This paper focuses on joint chance constraints for potentially time-varying target sets. Under assu…
Flipping-based Policy for Chance-Constrained Markov Decision Processes
Safe reinforcement learning (RL) is a promising approach for many real-world decision-making problems where ensuring safety is a critical necessity. In safe RL research, while expected cumulative safety constraints (ECSC…
Reinforcement Learning (RL)Safe Reinforcement Learning