Polynomial time method improves planning of power grid expansions
Polynomial-time algorithms for setting tight big-M coefficients in transmission expansion planning with disconnected buses
Discrete Mathematics
Summary
Electricity grids need new lines and connection points to handle more renewable energy and higher demand. Planning these expansions is complicated, especially when parts of the grid are disconnected. The authors developed a faster way to set important parameters called big-M coefficients by finding key paths in the grid more efficiently than before. This method helps improve the math models used to plan grid expansions, making them more accurate and easier to solve.
What this means in practice
- •For power system planners: Generate tighter mathematical constraints to model grid expansions efficiently when parts of the network are disconnected.
- •For energy grid operators: Improve operational decision support by integrating more accurate planning parameters for future grid growth involving renewable connections.
Authors
Behnam Jabbari-Marand, Adolfo R. Escobedo
Abstract
The increasing penetration of renewable energy and rising electricity demand are driving the need to integrate new buses and transmission lines into transmission grids. These trends are reshaping transmission expansion planning (TEP), motivating the development of effective methodologies to manage the resulting complexity. This paper introduces the longest shortest-path connection (LSPC) algorithm, a graph-based method to enhance the mixed-integer linear programming disjunctive formulation of TEP using valid inequalities (VIs). Traditional approaches for determining big-M coefficients in disconnected TEP networks typically rely on solving the computationally intensive longest path problem (LPP). In contrast, LSPC circumvents these limitations by efficiently identifying relevant power-flow paths between disconnected buses within the expansion network. We demonstrate that the VIs generated from these identified paths dominate those derived from LPP-based methods and other existing approaches.