Invited Session Mon.2.MA 549

Monday, 13:15 - 14:45 h, Room: MA 549

Cluster 18: Optimization in energy systems [...]

Optimization in energy systems


Chair: Jon Lee



Monday, 13:15 - 13:40 h, Room: MA 549, Talk 1

Timo Lohmann
Stochastic hydro-thermal scheduling with CVaR risk constraints in deregulated markets

Coauthor: Steffen Rebennack


In the regulated electricity market, a power producer's goal is to satisfy his customers' electricity demand while minimizing his expected cost of operating his power system. In the deregulated market, a power producer has no longer to meet the electricity demand of his customers, but is able to trade the electricity in the market. This complicates the problem as uncertainty of spot prices and risk appetite have to be considered in addition. The objective of optimization is the maximization of expected net profit of the power system in the mid-term horizon. In our case the power system is hydro-dominated and the resulting multi-stage stochastic programming problem contains jointly uncertainties in the hydro inflows and electricity spot prices. These kinds of models are usually solved with the SDDP algorithm. For this special case we need to use a hybrid SDP/SDDP algorithm and we enhance it with CVaR risk constraints.



Monday, 13:45 - 14:10 h, Room: MA 549, Talk 2

Selvaprabu Nadarajah
Approximate linear programming relaxations for commodity storage real option management

Coauthors: Margot Francois, Nicola Secomandi


Real option management of commodity storage typically gives rise to an intractable Markov Decision Process (MDP). We derive novel approximate dynamic programs for this MDP from partitioned surrogate relaxations of an approximate linear program. We estimate lower bounds and dual upper bounds on the value of storage for a seasonal commodity (natural gas) and a non-seasonal commodity (oil). Our lower bounds essentially match the best known lower bounds for the natural gas storage instances, and are near-optimal for the oil instances, which are new. Our upper bounds either match or improve those available in the literature for natural gas, but are weaker than an exchange-option based upper bound from the literature for the oil instances. We use a tractable version of the problem to highlight the source of the bias in our estimated upper bounds.



Monday, 14:15 - 14:40 h, Room: MA 549, Talk 3

Yongpei Guan
A branch-and-cut algorithm for the Multi-stage Stochastic Unit Commitment Problem

Coauthors: Ruiwei Jiang, Jean-Paul Watson, Ming Zhao


Due to the uncertainty from both supply and demand sides, power grid operation is generally a stochastic nonlinear problem for regulated electricity market. In this talk, we propose a Multi-stage Stochastic Unit Commitment (MSUC) model to address this problem, where we approximate the nonlinear fuel cost functions by piecewise linear functions. Furthermore, we employ a branch-and-cut algorithm to solve MSUC by constructing strong inequalities for the substructures of the constraints.


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