Title: Large-scale hydropower models in StochasticPrograms.jl
Abstract: We present three large-scale hydropower planning models implemented in our
open-source software framework StochasticPrograms.jl developed using the Julia
programming langugage. The framework provides an expressive syntax for
formulating stochastic programming models and has distributed capabilities that
can handle large-scale instances. The three models describe different case
studies of the hydroelectric power plants in the Swedish river
Skellefte\alven. The models are two-stage stochastic programs with sampled
scenarios that describe uncertain electricity prices and local water inflows.
The first model is a day-ahead planning problem that concerns how to determine
optimal order strategies in a day-ahead energy market. We pose this problem
from the perspective of a hydropower producer, who participates in the Nordic
day-ahead market and operates in the Swedish river Skellefte\alven. We
implement the day-ahead model using our computational tools and then solve
large-scale instances of the problem in a distributed environment. A
statistically significant value of running stochastic planning is obtained
using a sample-based algorithm. Next, we consider a variation of the day-ahead
problem that includes preventive maintenance scheduling. We show how intricate
coordination between the submitteed market orders and the maintenance schedule
results in a larger value of the stochastic solution than the day-ahead
problem. The final model is a capacity expansion problem with a long planning
horizon. The same methodology is applied as when solving the first two
hydropower problems. However, the planning horizon is considerably longer, from
one year up to 20 years compared to a 24 hour horizon. We note that the
relative significance of the value of the stochastic solution is much greater
when comparing to the extra profits incurred from the capacity expansion
instead of the total profit.
Publication Year: 2021
Publication Date: 2021-11-03
Language: en
Type: preprint
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