Invited Session Tue.2.MA 549

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

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

Stochastic programming applications in energy systems


Chair: Suvrajeet Sen



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

Cosmin Petra
Scalable stochastic optimization of power grid energy systems

Coauthors: Mihai Anitescu, Miles Lubin


We present a scalable approach for solving stochastic programming problems, with application to the optimization of power grid energy systems with supply and demand uncertainty.
Our framework, PIPS, has parallel capabilities for both continuous and discrete stochastic optimizations problems. The continuous solver uses an interior-point method and a Schur complement technique to obtain a scenario-based decomposition. With an aim of providing a scalable solution for problems with integer variables, we also developed a
linear algebra decomposition strategy for simplex methods that is used in a parallel branch-and-bound framework.
We will also discuss application-specific algorithmic developments and computational results obtained on "Intrepid'' Blue Gene/P system at Argonne when solving unit commitment problems with billions of variables.



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

Diego Klabjan
Day ahead stochastic unit commitment with demand response and load shifting

Coauthors: Frank Schneider, Ulrich Thonemann


High costs for fossil fuels and increasing shares of intermittent energy sources are imposing big
challenges on power grid management. Uncertainty in generation as well as in demand for electric
energy call for
flexible generation capacity and stochastic optimization of generation schedules.
Emerging smart grid technology is one component believed to be a successful tool to increase
efficiency in power generation and to mitigate effects of increasing uncertainty. We
focus on the potential of demand side resources (DSRs) that can be dispatched to reduce load
at peak times. We present a stochastic dynamic programming model for the unit commitment
problem in a day ahead market and include dispatch decisions for DSRs. We model the effect of
load shifting to previous and subsequent periods that must be taken into account when making
dispatch decisions. We also present an approximate dynamic programming algorithm embedded
in a decomposition algorithm that enables us to capture effects of DSR dispatch on previous
periods and to solve both problems concurrently. Lower bounds on the optimal solution are



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

Boris Defourny
A quantile-based approach to unit commitment with wind

Coauthors: Ethan Fang, Warren B. Powell, Hugo P. Simao


Handling higher levels of uncertainty in the unit commitment problem (UC) is an important issue for the independent system operator (ISO) who is dealing with an increasing level of variable energy resources (VERs), and specifically energy from wind. Here, we focus on approximations that plan for uncertainty by adding to the original problem new penalties or constraints, and then view the weights of the new terms as tunable parameters. We investigate methods where the wind energy seen by the UC problem is a certain quantile of the forecasted wind distribution. The quantiles are then tuned based on a simulation of the recourse costs. The work is motivated by an analogy with newsvendor-type problems where the overage and underage costs of wind energy forecasts have to be estimated, given a day-ahead schedule.


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