Scenario reduction in stochastic programming: An approach using probability metrics

By Jitka Dupacová, Nicole Gröwe-Kuska, Werner Römisch. 2000

656 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2751bc1bdc71029faf3df6f151ba8ff861f4063f

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In Building and Construction

Citations in the corpus

Hourly Coordination of Electric Vehicle Operation and Volatile Wind Power Generation in SCUC (2012, background): “Table VIII shows the hourly commitment of thermal generators in which G3 is committed at additional hours as compared to Case 1.”
Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016, methodology): “To solve the scenario reduction problem, Römisch and coworkers (Dupacova et al., 2003; Heitsch & Römisch, 2003) propose two heuristic algorithms—forward selection and backward reduction—which are fast but do not guarantee optimality.”
Pricing Electricity in Pools With Wind Producers (2012, methodology): “An original set of 10 000 samples is first generated and subsequently reduced to 100 by applying the scenario reduction technique proposed in [22] and [23].”
Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics (2014, background): “The shortened and excess amount of energy required to balance the local load can be accommodated by trading with the main grid through the Point of Common Coupling (PCC) [31] or by running conventional units.”
Generation of multivariate scenario trees to model stochasticity in power management (2005, methodology): “…here in chronological order (i) bound-based approximationmethods [11], [8], [4], (ii) Monte Carlo-basedsampling schemes [21], [5], [2], [31], (iii) the moment-matching principle[19], [20], (iv) optimalapproximations based on probability metrics[25], [18], [7], [13], and (v) the use…”
Lectures on Stochastic Programming: Modeling and Theory (2009, methodology): “The Reader is referred to Kall and Mayer [122], Heitsch and Römisch [98], Hochreiter and Pflug [109], Casey and Sen [35], Pennanen [176], Dupačova, Growe-Kuska and Römisch [71], and the references therein.”
Chance-Constrained Day-Ahead Scheduling in Stochastic Power System Operation (2014, neutral): “1800 scenarios are originally generated which are reduced to 185 by the scenario reduction method [36].”
Market-clearing with stochastic security-part II: case studies (2005, methodology): “In addition, as mentioned in the companion paper, the use of scenario reduction techniques [15]–[17] is promising, as are decomposition techniques based on Benders’ decomposition [18]–[21] and “branch-and-price” decomposition [22]–[24].”
Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network (2013, methodology): “The alternative stochastic unit commitment policy, denoted as SUC2, is based on crossing the most severe system contingencies with wind production outcomes produced by the scenario reduction algorithm of Dupacova et al. (2003).”
Economic Assessment of Energy Storage in Systems With High Levels of Renewable Resources (2015, methodology): “In this paper, a backward reduction method introduced in [25] and [26] was employed to select 10 scenarios out of 1000 to be used within the stochastic unit commitment.”
Modeling Real-Time Balancing Power Market Prices Using Combined SARIMA and Markov Processes (2008, methodology): “A reduction method is described in [ 30 ] and [31].”
Risk-Constrained Bidding Strategy With Stochastic Unit Commitment (2007, background): “A practical way is to set the number of reduced scenarios when the objective function is not changing significantly or the relative distance between original scenarios and reduced scenarios is within an acceptable level [22], [23].”