Abstract
We present a model for operational stochastic short-term hydropower scheduling, taking into account the uncertainty in future prices and inflow, and illustrate how the benefits of using a stochastic rather than a deterministic model can be quantified. The solution method is based on stochastic successive linear programming. The proposed method is tested against the solution of the true non-linear problem in a principal setting. We demonstrate that the applied methodology is a first-order approximation to a formal correct head-of-water optimization and achieve good results in tests. How the concept of stochastic successive linear programming has been implemented in a prototype software for operational short-term hydropower scheduling is also presented, and the model's ability is demonstrated through case studies from Norwegian power industry. From these studies, improvements occurred in terms of the objective function value and decreased risk of spill from reservoirs.
Highlights
In Norway, power supply has traditionally been almost 100% hydropower
This paper presents a model for the short-term optimization of hydropower based on stochastic successive linear programming and illustrates through case studies that the proposed methodology may give improved decision support to producers acting under price and inflow uncertainty, compared to using a deterministic model
Summary of work and testing In this paper we have presented the concept of stochastic short-term hydropower scheduling implemented in the SHARM model, and illustrated the performance of the model by examples
Summary
In Norway, power supply has traditionally been almost 100% hydropower. Hydropower optimization is challenging, and the main reason is that decisions are coupled in time; the optimization problem includes state-variables such as reservoir levels and stochastic, climate dependent variables where the most important is inflow. In Scandinavia, uncertainty in variables such as inflow and market prices are handled by frequent reapplication of models with updated input parameters ( called rolling horizon), or by adding safety constraints that limit the characteristics of optimization models to produce too smart schedules for the hydropower system. The cost of such uncertainty imposed constraints is calculated from sensitivity analyses or based on specific and practical system experience. Continued operation with multiple re-runs or manual rules for maintaining system flexibility is difficult when the boundary conditions are constantly changing, in which case the safety limits should become an integrated part of the operational decisions
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