Capacity planning of renewable energy systems using stochastic dual dynamic programming

We present a capacity expansion model for deciding the new electricity generation and transmission capacity to complement an existing hydroelectric reservoir system. The objective is to meet a forecast demand at least expected cost, namely the capital cost of the investment plus the expected discounted operating cost of the system. The optimal operating policy for any level of capacity investment can be computed using stochastic dual dynamic programming. We show how to combine a multistage stochastic operational model of the hydro system with a capacity expansion model to create a single model that can be solved by existing open-source solvers for multistage stochastic programs without the need for customized decomposition algorithms. We illustrate our method by applying it to a model of the New Zealand electricity system and comparing the solutions obtained with those found in a previous study. • The policy graph concept enables infinite horizon investment models in SDDP.jl. • Integrating investments in SDDP models give cost minimizing renewable capacities. • Linear functions can represent variable generation from endogenous capacities.