Stochastic Generation Capacity Expansion Planning with Approximate Dynamic Programming

摘要

Failing to consider the many long-term uncertainties that affect the performance of a power generation portfolio can result in suboptimal generation expansion plans. Further, traditional deterministic approaches can omit flexible plans that are able to adapt to future events. In this paper, we explore the use of approximate dynamic programming (ADP) to create forward-looking generation expansion plans. A case study is included with three sequential decision periods; three generation technologies; and four uncertainties: demand growth, natural gas prices, renewable portfolio standards, and the adoption of customer-driven solar generation. The flexible plans found through ADP show a 3 % reduction in total expected cost when compared to myopic planning heuristics while circumventing the computational burdens that accompany high-dimensional dynamic programs.