Solution sensitivity-based scenario reduction for stochastic unit commitment

Abstract

A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if many scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the US, FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial. Abstract A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment (SRUC) are viewed as first-stage decisions, and dispatch is relegated to the second stage. It is challenging to solve such a stochastic program if a lot of scenarios are incorporated. A heuristic scenario reduction method termed forward selection in recourse clusters (FSRC), which selects scenarios based on their cost and reliability impacts, is presented to alleviate the computational burden. In instances down-sampled from data for an Independent System Operator in the U.S., FSRC results in more reliable commitment schedules having similar costs, compared to those from a scenario reduction method based on probability metrics. Moreover, in a rolling horizon study, FSRC preserves solution quality even if the reduction is substantial.

Efficient samplings in stochastic optimization for reactive power planning problems

“…The cross entropy method (Fonteneau-Belmudes et al, 2011) is an iterative importance sampling that allows finding the appropriate importance function to find critical zones. A second type of methods include the scenario reduction methods (Bruninx et al, 2014 andGröwe-Kuska et al, 2003) and clustering techniques (Feng et al, 2016) which do not require any predefined division of the sample space but reduce the number of samples after their random generation. Scenario reduction methods use a probability-distance metric to distinguish and classify the samples.…”

Statistical reliability of wind power scenarios and stochastic unit commitment cost

“…Various alternative formulations for unit commitment under uncertainty have been proposed to reduce the computation times [32][33][34]. Moreover, scenario reduction techniques that are specified to SUC are proposed to decrease the computational demands to a degree [35,36]. Research that considers assessing the scenarios and comparing different scenario sets' performances according to SUC results is very limited.…”

Robust optimization vs. stochastic programming incorporating risk measures for unit commitment with uncertain variable renewable generation

“…The second stage decisions on the dispatch of each generator committed in the first stage are then made for each scenario. To cope with the computational difficulties caused by a large number of scenarios, scenario reduction techniques are used frequently [15,18]. Benders decomposition [41] and progressive hedging [11,14] are two methods to efficiently solve the SUC with a two-stage structure.…”