A. Tomasgard, C. Skar

Mathematical programming is frequently used to analyse the effect of implementing various climate and energy policies. A common feature of these problems is that the best long-term strategies, consider co-optimization of investments and short-term system operation. To construct solutions robust to the uncertainty, stochastic programming is often the method of choice. In these models there are uncertainties present at different time-scales. In a power system, for instance, investments in generation capacity are done under uncertainty, about short-term operating conditions (load, prices, intermittent production), but also about long-term developments (fuel prices, demand growth, policy regulation). Representing all the uncertainty in a single multi-stage tree will quickly produce an intractable problem, but there is a way to cast the problem such that it does not immediately explode in size. By making the assumption that observing realizations of short-term operational uncertainty does not provide updated information about future uncertainty (both short-term and long-term) the operational decisions in one stage of the tree, can be decoupled from decisions in the following stages. The resulting tree then have two types of nodes, strategic nodes (representing investment stages) which are affected by future uncertainty, and operational nodes which are only affected by short-term uncertainty. This greatly reduces the size of the problem and is the essence of multi-horizon tree formulation in stochastic programming. We present the EMPIRE model (European Model for Power system Investment with Renewable Energy ). Further give examples on use of the model, illustrated by several studies performed in cooperation with the “European Technology Platform for Zero Emission Fossil Fuel Power Plants” in order to inform EC policy makers.

Keywords: Climate policy, Energy policies, Stochastic Programming

P1 Plenary (Asgeir Tomargard)
May 31, 2016  10:15 AM
Salón de actos