Stochastic Optimal Operation of the VSC-MTDC System with FACTS Devices to Integrate Wind Power

TL;DR

This paper introduces a stochastic conic programming approach for optimal operation of VSC-MTDC systems with FACTS devices, effectively integrating large-scale wind power while managing uncertainties and computational challenges.

Contribution

It develops a novel stochastic SOC-ACOPF model with a parallel Benders decomposition algorithm for efficient large-scale wind power integration.

Findings

The M-BDA method outperforms traditional approaches in convergence and efficiency.

The model successfully handles up to 50,000 wind scenarios.

The approach ensures feasible and optimal operation under wind power uncertainties.

Abstract

This paper proposes to use stochastic conic programming to address the challenge of large-scale wind power integration to the power system. Multiple wind farms are connected through the voltage source converter (VSC) based multi-terminal DC (VSC-MTDC) system to the power network supported by the Flexible AC Transmission System (FACTS). The optimal operation of the power network incorporating the VSC-MTDC system and FACTS devices is formulated in a stochastic conic programming framework accounting the uncertainties of the wind power generation. A methodology to generate representative scenarios of power generations from the wind farms is proposed using wind speed measurements and wind turbine models. The nonconvex transmission network constraints including the VSC-MTDC system and FACTS devices are convexified through the proposed second-order cone AC optimal power flow model (SOC-ACOPF) that can be solved to the global optimality using interior point method. In order to tackle the computational challenge due to the large number of wind power scenarios, a modified Benders decomposition algorithm (M-BDA) accelerated by parallel computation is proposed. The energy dispatch of conventional power generators is formulated as the master problem of M-BDA. Numerical results for up to 50000 wind power scenarios show that the proposed M-BDA approach to solve stochastic SOC-ACOPF outperforms the traditional single-stage (without decomposition) solution approach in both convergence capability and computational efficiency. The feasibility performance of the proposed stochastic SOC-ACOPF model is also demonstrated.