Stochastic security for operations planning with significant wind power generation

By François Bouffard, Francisco D. Galiana. IEEE Power & Energy Society General Meeting, 2008

637 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/71a20bae001d070101a1c4021551bc7ad8e92ead

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Citations in the corpus

Multi-Stage Robust Unit Commitment Considering Wind and Demand Response Uncertainties (2013, background): “Recent studies have focused on developing stochastic optimization models with the objective of minimizing the total expected cost, including a short-term stochastic rolling unit commitment model [4]–[6], a stochastic unit commitment model to calculate reserve requirements by simulating the wind powe”
Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland (2011, background): “Compared to [17] and [18], the unit commitment algorithm presented in this paper allows different unit commitment schedules for each set of wind power, load and replacement reserves forecasts as long as unit restrictions concerning such as start-up times are respected.”
Two-Stage Minimax Regret Robust Unit Commitment (2013, methodology): “, see [47] and [13], among others), we apply the sample average approximation (SAA) algorithm”
The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming (2014, methodology): “[9] a stochastic programming has been used for market clearing and considered load and wind prediction error as normal distributed random variables.”
A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty (2015, methodology): “The objective function of SUC is usually the combination of the cost of here-and-now decisions and the expected cost associated with the redispatch of generators, the load shedding and wind curtailment in the wait-and-see decisions [8], [9].”
Multi-Area Energy and Reserve Dispatch Under Wind Uncertainty and Equipment Failures (2013, neutral): “The conclusions drawn from this study are provided in Section IV.”
Economic-environmental energy and reserve scheduling of smart distribution systems: A multiobjective mathematical programming approach (2014, background): “MinimizefF; Fg ð16Þ The total expected cost (F) of the distribution network represents one of the objective functions that should be minimized [25,32].”
Unit Commitment for Systems With Significant Wind Penetration (2009, background): “Stochastic security with wind generation is examined in [12], which formulates a market-clearing problem capable of accounting for wind power.”
A Review of Power System Flexibility With High Penetration of Renewables (2019, methodology): “The same concepts of LOLP, EENS, and VOLL are used in the stochastic SCOPF formulation of [15], [63].”
Unit Commitment With Continuous-Time Generation and Ramping Trajectory Models (2016, methodology): “A day-ahead UC model with stochastic security was formulated in [12] which is capable of accounting for non-dispatchable and variable wind power generation.”
Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation (2010, background): “Some authors, such as [10] and [11], have suggested that power systems with high wind penetrations could benefit from using a stochastic day-ahead unit commitment, which explicitly accounts for wind uncertainty.”
Wind power forecasting : state-of-the-art 2009. (2009, background): “A market clearing formulation with demand response and wind curtailment, which is somewhat similar to the one below, is presented in Bouffard and Galiana [347].”
Solving Multiperiod OPF Problems Using an AC-QP Algorithm Initialized With an SOCP Relaxation (2017, methodology): “Some recent techniques for addressing uncertainty include adding a heuristically chosen set of scenarios to the OPF problem [50]– [52], assuming a closed-form uncertainty distribution to analytically reformulate the OPF problem [53], [54], and scenario approaches that offer performance guarantees [5”
Chance-Constrained Economic Dispatch With Non-Gaussian Correlated Wind Power Uncertainty (2017, background): “Other papers, such as [25], [26], point out that it is preferable to adopt different distributions to model the forecast errors in different spatial-temporal scales: Cauchy distribution for ultra-short-term prediction, Beta for short-term one, and Gaussian for large scale of wind farms.”
Adaptive Robust Optimization With Dynamic Uncertainty Sets for Multi-Period Economic Dispatch Under Significant Wind (2014, background): “[6] presents a short-term forward electricity marketclearing model under net load uncertainty, for the purpose of”
Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power (2009, methodology): “The mathematical model developed in [7] to deal with the uncertainty associated to wind power is a probabilistic security-constrained market-clearing procedure [6]–[9] based on the concept of the error-free scenario.”
Bringing Wind Energy to Market (2012, neutral): “Representative references include [8], [9], [14], [16], [17], [20]–[22], [25], and [30].”