Probability-driven transmission expansion planning with high-penetration renewable power generation: A case study in northwestern China. (1st December 2019) - Liang, Z.; Chen, H.; Chen, S.; Lin, Z.; Kang, C.
Highlights: A hybrid probability uncertainty set is constructed with the density information. A probability-driven robust transmission expansion planning model is proposed. A slack linearization method is deployed to eliminate absolute value terms in H . The large-scale model is solved by a novel column-and-constraint generation method. Case studies on the real-world power system in northwestern China are conducted. Abstract: The incorporation of power generation derived from renewable energy sources, or renewable power generation (RPG), into conventional electric power grids has been rapidly increasing on a large scale in recent years. However, this process can be expected to inevitably increase the uncertainty associated with transmission line investment owing to the inherent uncertainty associated with RPG. While robust transmission expansion planning (RTEP) has been commonly employed for optimizing transmission line investments, this method suffers from serious disadvantages such as the neglect of available RPG probability information, overly conservative solutions, and exceedingly time consuming solution processes. The present work addresses these disadvantages by modeling the probability of RPG uncertainty according to RPG output probabilities obtained over a long-term planning horizon based on available historical data using a hybrid probability uncertainty set constructed using 1-norm and ∞-norm metrics. A probability-driven RTEP model is then proposed to obtain an optimal investment strategy that provides a security guarantee under the worst-case RPG probability distribution, while alleviating the need for overly conservative solutions with high total expansion costs. In addition, a modified column-and-constraint generation algorithm is developed to solve the proposed tri-level probability-driven RTEP model, where the large-scale inner bi-level component of the optimization problem is decomposed into several small-scale linear models that can be solved in parallel. The proposed algorithm requires no dual variables in the inner problem and eliminates all highly non-convex bilinear terms like those obtained in conventional RTEP solution algorithms. This can effectively increase the computational speed of the solution process. The effectiveness, good applicability, and robustness of the proposed model and solution algorithm are demonstrated by numerical applications based on a Garver's 6-bus test system and an existing electric grid system in northwestern China.