Nonparametric Probabilistic Optimal Power Flow

With the increasing penetration of renewable energy, accurate and efficient probabilistic optimal power flow (POPF) calculation becomes more and more important to provide decision support for secure and economic operation of power systems. This paper develops a novel nonparametric probabilistic optimal power flow (N-POPF) model describing the probabilistic information by quantiles, which avoids any parametric probability distribution assumptions of random variables. A novel critical region integral method (CRIM) which combines the multiparametric programming theory and discrete integral is proposed to efficiently solve the N-POPF problem. In the CRIM, the critical region partitioning algorithm is firstly introduced into the POPF model to directly establish the mapping relationship from wind power to optimal solutions of the POPF problem. Besides, a discrete integral method is developed in the CRIM to achieve the probability convolution calculation based on quantiles. Comprehensive numerical experiments verify the superior performance of the proposed CRIM in estimation accuracy and computational efficiency, and demonstrate that N-POPF model significantly improves the accuracy of uncertainty analysis. In general, the proposed N-POPF model and CRIM form a new framework of POPF problem for power system analysis and operation.

Automated summary

This paper proposes a novel nonparametric probabilistic optimal power flow (N-POPF) model that uses quantiles to describe probabilistic information without making assumptions about the probability distribution of random variables. Additionally, a critical region integral method (CRIM) is introduced to efficiently solve the N-POPF problem by combining multiparametric programming theory and discrete integral. The experimental results demonstrate the superior performance of the proposed CRIM in terms of estimation accuracy and computational efficiency, and prove that the N-POPF model significantly improves the accuracy of uncertainty analysis.