Enhanced Decision Support for a Changing Electricity Landscape: The GenX Configurable Electricity Resource Capacity Expansion Model

Abstract

The electric power sector is currently undergoing several important transitions, which individually and collectively have the potential to transform the design, operation, and characteristics of electricity systems, including: decarbonization of electricity supplies; increased adoption of variable renewable energy and distributed energy resources; digitization of power systems; and electrification of greater shares of heating, transportation, and industry. In the face of these transformations, many conventional electricity resource capacity expansion models are no longer adequate for rigorous decision support and policy analysis. This working paper describes the formulation of “GenX," a highly-configurable electricity resource capacity expansion model that incorporates several state-of-the-art improvements in electricity system modeling to offer improved decision support for a changing electricity landscape. GenX is a constrained optimization model that determines the mix of electricity generation, storage, and demand-side resource investments and operational decisions to meet electricity demand in a future planning year at lowest cost subject to a variety of power system operational constraints and specified policy constraints, such as CO2 emissions limits. The appropriate level of model resolution with regards to chronological variability of electricity demand and renewable energy availability, power system operational detail and unit commitment constraints, and transmission and distribution network representation each vary for a given planning problem or policy question. As such, the GenX model is designed to be highly configurable, with several different degrees of resolution possible on each of these three key dimensions. The model is capable of representing a full range of conventional and novel electricity resources, including thermal generators, variable renewable resources (wind and solar), run-of-river, reservoir and pumped-storage hydroelectric generators, energy storage devices, demand-side flexibility, and several advanced technologies such as high temperature nuclear reactors and carbon capture and storage. Two optional modules also allow modeling of heat storage, industrial heat demand, and co-generation of heat and power; and distributed energy resources deployed at distribution voltages. The model has been implemented in Julia Language.