Distributionally Robust Stochastic Optimal Power Flow Considering N-1 Security Constraints with renewable

Shiyuan Ni,Defei Yao,Yi Lin,Guilian Wu,Ruosi Zhang,Zehao Wang,M.M.H Mostafa,S Manickam

doi:10.1051/e3sconf/202126102017

Abstract

This paper proposes a data-driven stochastic optimal power flow model considering the uncertainties of renewable energy sources. The proposed model also focuses on the constraints of reactive voltage, aiming at improving the safety of voltage amplitude and reactive power output at each bus. Using data-driven linearization techniques, we simplified the calculation of system. In addition, Wasserstein ambiguity set was used to describe the uncertainties of renewable energy prediction error distribution, and a robust stochastic optimal power flow model considering N-1 security constraints is established. The simulation results on IEEE-39 system showed the accuracy and effectiveness of the distributionally robust optimization model and the reactive voltage constraint model provided a more stable operation schedule.

Highlights

The grid-connected operation of Renewable Energy Sources (RESs) makes the balance between the economic benefits of the transmission grid and the reliability or safety of the system more complicated
The probability distribution of prediction errors can only be calculated from limited data sets, so data-driven methods[2]have been widely used in stochastic optimal power flow (OPF) models with Conditional Value-at-Risk (CVaR) [3]
Robust Optimization (DRO) method [4] combined the above-mentioned stochastic OPF with the traditional robust optimization (RO), using historical data to estimate the parameters of the distribution[5]

Summary

Introduction

The grid-connected operation of Renewable Energy Sources (RESs) makes the balance between the economic benefits of the transmission grid and the reliability or safety of the system more complicated. The SO assumed that prediction errors of uncertainties followed a certain probability distribution, such as Gaussian distribution[1] These assumptions are unreasonable and cannot simulate the real power system operating characteristics due to the complex nonlinear phenomena. Robust Optimization (DRO) method [4] combined the above-mentioned stochastic OPF with the traditional RO, using historical data to estimate the parameters of the distribution[5] It minimized the operation cost of the system under the worst case of uncertainties distribution and considered both robustness and conservativeness. This paper establishes a multi-period data-based DRO model for the transmission grid OPF with renewable energy and storage, which fully takes into account the voltage magnitude constraints and reactive power constraints of each node in the system for optimization. The results on the IEEE-39 case showed that safer and more economical strategies for the system can be made base on our DRO model considering voltage and reactive power

Stochastic OPF model

Data-driven distributionally robust stochastic optimal power flow

Case studies

Conclusion