Nested Benders’s decomposition of capacity-planning problems for electricity systems with hydroelectric and renewable generation

Abstract

Nested Benders’s decomposition is an efficient means to solve large-scale optimization problems with a natural time sequence of decisions. This paper examines the use of the technique to decompose and solve efficiently capacity-expansion problems for electricity systems with hydroelectric and renewable generators. To this end we develop an archetypal planning model that captures key features of hydroelectric and renewable generators and apply it to a case study that is based on the Columbia River system in the northwestern United States of America. We apply standard network and within-year temporal simplifications to reduce the problem’s size. Nevertheless, the remaining problem is large-scale and we demonstrate the use of nested Benders’s decomposition to solve it. We explore refinements of the decomposition method which yield further performance improvements. Overall, we show that nested Benders’s decomposition yields good computational performance with minimal loss of model fidelity.