Comparing scenario reduction methods for stochastic transmission 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 naive 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.