Robust Coordinated Transmission and Generation Expansion Planning Considering Ramping Requirements and Construction Periods

TL;DR

This paper develops a robust planning model for transmission and generation expansion that accounts for ramping requirements, construction periods, and uncertainties in wind power and load, improving decision-making in renewable-rich power systems.

Contribution

It introduces a two-stage robust optimization framework with decoupled uncertainty sets for effective coordination of flexible resources in expansion planning.

Findings

The model effectively manages ramping and construction uncertainties.

Numerical results demonstrate improved robustness and resource coordination.

The approach is validated with real-world data simulations.

Abstract

Two critical issues have arisen in transmission expansion planning with the rapid growth of wind power generation. First, severe power ramping events in daily operation due to the high variability of wind power generation pose great challenges to multi-year planning decision making. Second, the long construction periods of transmission lines may not be able to keep pace with the fast growing uncertainty due to the increasing integration of renewable energy generation. To address such issues, we propose a comprehensive robust planning model considering different resources, namely, transmission lines, generators, and FACTS devices. Various factors are taken into account, including flexibility requirement, construction period, and cost. We construct the hourly net load ramping uncertainty (HLRU) set to characterize the variation of hourly net load including wind power generation, and the annual net load duration curve uncertainty (LDCU) set for the uncertainty of normal annual net load duration curve. This results in a two-stage robust optimization model with two different types of uncertainty sets, which are decoupled into two different sets of subproblems to make the entire solution process tractable. Numerical simulations with real-world data show that the proposed model and solution method are effective to coordinate different flexible resources, rendering robust expansion planning strategies.