Deploying high penetration of variable renewables represents a critical pathway for deep decarbonizing the power sector. The conflict between their temporal variability and limited system flexibility has been largely ignored currently at planning stage. Here we present a novel capacity expansion model optimizing investment decisions and full-year, hourly power balances simultaneously, with considerations of storage technologies and policy constraints, such as carbon tax and renewable portfolio standards (RPS). Based on a computational efficient modeling formulation, all flexibility constrains (ramping, reserve, minimum output, minimal online/offline time) for the 8760-hour duration are incorporated. The proposed model is applied to the northwestern grid of China to examine the optimal composition and distribution of power investments with a wide range of renewable targets. Results indicate that the cost can increase moderately towards 45% of RPS, when properly designing the generation portfolio: prioritizing wind investments, distributing renewable investments more evenly and deploying more flexible mid-size coal and gas units. Reaching higher penetrations of renewables is expensive and the reductions of storage costs are critically important for an affordable low-carbon future. RPS or carbon taxes to reach a same target of emission reduction in China will result in similar overall costs but different generation mixes.
The Harvard-China Project adopted an Open Access policy in September 2017. Most journal articles published henceforth are available in the Harvard University open-access repository, DASH.