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A framework to quantify the value of model enhancements (VOMEs) in transmission planning models is proposed and applied to a case study of the large‐scale, long‐term planning of the Western Electricity Coordinating Council (WECC) system. The VOME, which is closely related to the concept of the value of information from decision analysis, quantifies the probability‐weighted improvement in the system performance resulting from changes in decisions that result from model enhancements. The WECC case study shows that it is practical to quantify VOME and illustrates the type of insights that can be obtained. The values of four types of model enhancements are compared. The results show major benefits from considering long‐run uncertainty using multiple scenarios of technology, policy, and economics; these benefits are as much as 14% of total benefits of new transmission built in the first ten years. However, less benefit is obtained from more temporal granularity, more complex network representations, and inclusion of unit commitment constraints and costs. This framework can be applied to quantify the VOMEs in any planning context, such as integrated resource planning. 

Policy, technology, and economic uncertainties affect the net benefits of grid reinforcements, and should be considered in planning. Stochastic optimisation can improve the robustness and expected performance of transmission plans, but is computationally intensive because model size grows as more scenarios are considered. Therefore, the ability to find a small number of scenarios while still capturing the benefits of stochastic programming is crucial. In this study, the authors evaluate the performance of several promising scenario sampling methods. Criteria for comparison include an index of the economic consequences of simplifying scenarios (the expected cost of naïve solution), changes in first‐stage investment decisions, and maximum regret. The results of an application to multidecadal planning of the Western Electricity Coordinating Council system show that solutions perform well when based on scenarios chosen by either a distance‐based method or the stratified scenario section method with moment‐matched probabilities. In particular, for this application, these methods’ results closely resemble solutions obtained from a much larger model using the full scenario set, and surprisingly have a lower worst case regret. Thus, careful scenario reduction can result in useful models that are more easily solved or, alternatively, can be expanded to accommodate other important features of power systems and markets.