A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output
By Qianfan Wang, Yongpei Guan, Jianhui Wang. IEEE Transactions on Power Systems, 2012
471 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Finance
View Paper: https://www.semanticscholar.org/paper/90bd9694ba6d6c727040babb5a2caca45b8f5ac4
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Citations in the corpus
- Adaptive Robust Optimization With Dynamic Uncertainty Sets for Multi-Period Economic Dispatch Under Significant Wind (2014, background): “[31] proposes a chance-constrained two-stage stochastic UC for systems with wind power uncertainty.”
- Multi-Stage Robust Unit Commitment Considering Wind and Demand Response Uncertainties (2013, background): “More recently, to ensure high utilization of wind power, a chance constrained optimization model [12] and a robust optimization model [13] have been developed to solve the problem.”
- Energy Management and Optimization Methods for Grid Energy Storage Systems (2018, background): “stage stochastic program for unit commitment with uncertain wind power output is presented in [184].”
- Preventing undesirable behavior of intelligent machines (2019, background): “Although chance-constrained programs can be useful for many real problems [75, 76, 77], like approaches based on hard constraints on the feasible set, existing chance-constrained algorithms are mostly limited in applicability to problems in which the user has significant”
- On Ensuring that Intelligent Machines Are Well-Behaved (2017, background): “Although chance-constrained programs can be useful for many real problems [Houck, 1979; Gurvich et al., 2010; Wang et al., 2012], like other approaches based on hard constraints on the feasible set, their applicability is limited to problems where the user has significant knowledge about the…”
- Chance-Constrained Two-Stage Unit Commitment Under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition (2016, methodology): “2) CPX_BigM: It represents using CPLEX to directly compute the Big-M reformulation of CC_UC problems [21]– TABLE IV INTEGRALITY GAP OF BILINEAR AND BIG-M REFORMULATIONS FOR CC_UC PROBLEMS (RISK LEVEL OF CC_UC MODEL: 5%)”
- Data-driven Affinely Adjustable Distributionally Robust Unit Commitment (2018, methodology): “Monte Carlo simulation based method is usually employed to generate scenarios [3], [7].”
- Multi-Area Energy and Reserve Dispatch Under Wind Uncertainty and Equipment Failures (2013, background): “For instance, [11] deals with demand uncertainty, [12] considers wind power uncertainty, and [13] includes both demand and wind power uncertainties.”
- Stochastic Unit Commitment With Uncertain Demand Response (2013, background): “where denotes generator start-up and shut-down costs [2], represents the fuel cost for generator at time when”
- Distributionally Robust Chance Constrained Optimal Power Flow with Renewables: A Conic Reformulation (2018, background): “There are many works on solving OPF, unit commitment (UC) problem or reserve scheduling via stochastic programming [28, 32, 37], robust optimization [3, 11, 15, 16, 39, 40, 43, 49], and chance constrained program approaches [5, 4, 19, 27, 31, 35, 38, 44] (see [23] for some discussions).”
- A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty (2015, methodology): “Various unit commitment (UC) models have been proposed and can be generally divided into four categories based on the manner in which they address the uncertainty, namely, scenario-based stochastic programming [7]–[12], robust optimization [13]–[18], chance-constrained optimization [19]–[22] and ris”