A Chance-Constrained Unit Commitment With an $n-K$ Security Criterion and Significant Wind Generation

Pozo, David; Contreras, Javier

doi:10.1109/tpwrs.2012.2227841

Cited by 158 publications

(98 citation statements)

References 17 publications

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“…Pozo and Contreras (2013) and Wang et al (2012) propose a chance-constrained programming approach to deal with the joint energy and reserve scheduling UC where one or several constraints must be satisfied with a given probability. One of the key assumptions in two-stage stochastic programming and chance-constrained programming is that the decision maker has complete information on the distribution of the uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

“…in Galiana 2004, 2005a;Restrepo and Galiana 2011;Karangelos and Bouffard 2012) or may not be known (e.g. in Arroyo and Galiana 2005;Street et al 2011;Pozo and Contreras 2013). Bouffard and Galiana (2008), Restrepo and Galiana (2011) and Pozo and Contreras (2013) include stochastic demand and wind with full knowledge of probability distributions of the uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Robust unit commitment with $$n-1$$ n - 1 security criteria

Gourtani

Pozo

et al. 2016

Math Meth Oper Res

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The short-term unit commitment and reserve scheduling decisions are made in the face of increasing supply-side uncertainty in power systems. This has mainly been caused by a higher penetration of renewable energy generation that is encouraged and enforced by the market and policy makers. In this paper, we propose a two-stage stochastic and distributionally robust modeling framework for the unit commitment problem with supply uncertainty. Based on the availability of the information on the distribution of the random supply, we consider two specific models: (a) a moment model where the mean values of the random supply variables are known, and (b) a mixture distribution model where the true probability distribution lies within the convex hull of a finite set of known distributions. In each case, we reformulate these models through Lagrange dualization as a semi-infinite program in the former case and a one-stage stochastic program in the latter case. We solve the reformulated models The research of this author is supported by EPSRC Grant EP/M003191/1. using sampling method and sample average approximation, respectively. We also establish exponential rate of convergence of the optimal value when the randomization scheme is applied to discretize the semi-infinite constraints. The proposed robust unit commitment models are applied to an illustrative case study, and numerical test results are reported in comparison with the two-stage non-robust stochastic programming model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust unit commitment with $$n-1$$ n - 1 security criteria

Gourtani

Pozo

et al. 2016

Math Meth Oper Res

Self Cite

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“…Commonly adopted approaches to address these challenges include advanced forecasting techniques [1][2][3] and improved system operational uncertainty modeling [4][5][6][7][8][9][10][11][12][13][14][15][16]. Traditionally, the system operators make the commitment and dispatch decisions based on a deterministic renewable point forecast.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, many researchers have applied different operational schemes to incorporate stochastic wind power generation [4][5][6][7][8][9][10][11][12][13][14][15][16]. These approaches mainly includes deterministic unit commitment (DUC) with dynamic reserves [4][5][6], scenario-based stochastic UC (SUC) [4][5][6][7], intervalbased UC (IUC) [8][9][10], robust UC (RUC) [11][12], chanceconstrained UC [13], as well as hybrid and unified approaches [14] [15].…”

Section: Introductionmentioning

confidence: 99%

“…These approaches mainly includes deterministic unit commitment (DUC) with dynamic reserves [4][5][6], scenario-based stochastic UC (SUC) [4][5][6][7], intervalbased UC (IUC) [8][9][10], robust UC (RUC) [11][12], chanceconstrained UC [13], as well as hybrid and unified approaches [14] [15]. The traditional DUC typically keep contingency reserves to address the possibility for a generation or line outage.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating stochastic methods in power system operations with wind power

Wang

Zhou

Liu

et al. 2016

2016 IEEE International Energy Conference (ENERGYCON)

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Abstract-Wind power is playing an increasingly important role in electricity markets. However, it's inherent variability and uncertainty cause operational challenges and costs as more operating reserves are needed to maintain system reliability. Several operational strategies have been proposed to address these challenges, including advanced probabilistic wind forecasting techniques, dynamic operating reserves, and various unit commitment (UC) and economic dispatch (ED) strategies under uncertainty. This paper presents a consistent framework to evaluate different operational strategies in power system operations with renewable energy. We use conditional Kernel Density Estimation (KDE) for probabilistic wind power forecasting. Forecast scenarios are generated considering spatiotemporal correlations, and further reduced to lower the computational burden. Scenario-based stochastic programming with different decomposition techniques and interval optimization are tested to examine economic, reliability, and computational performance compared to deterministic UC/ED benchmarks. We present numerical results for a modified IEEE-118 bus system with realistic system load and wind data.

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Joint generation and reserve scheduling of wind‐solar‐pumped storage power systems under multiple uncertainties

Huang

Zhou

Zhang³

et al. 2019

Int Trans Electr Energ Syst

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Summary Due to uncertain nature, large‐scale renewable integration, such as wind and solar energy, has challenged secure operation of power systems. With excellent peak clipping and valley filling capability, pumped storage power is often installed to stabilize fluctuation of renewable power output and improve economy of system operation. This paper aims at exploiting an approach to jointly scheduling generation and reserve for wind‐solar‐pumped storage power systems, taking multiple uncertainties (including wind and solar power output, load change, and generator failure) into account. Uncertainties are treated accordingly by two categories: continuous and discrete. To quantify reserve, continuous uncertainties (forecast errors of wind farm output considering forced outage of wind turbines, solar power output and load) are modeled probabilistically, then discretized into multistate units, further coupled with discrete generator outage to establish capacity outage probability table (COPT) and formulate relationship between reserve and reliability index loss‐of‐load probability (LOLP). Next, a security‐constrained unit commitment (SCUC) model is formulated to handle multiple uncertainties properly, to schedule generation and reserve jointly. Therein, operational characteristics of pumped storage power station are deliberated; stochastic and affinely adjustable robust optimization (AARO) method is adopted to address discrete and continuous uncertainties. Finally, case study is implemented on New England 39‐bus system; it is verified that pumped storage station can effectively improve system economy (accounting for 3.8% of total installed capacity, it can reduce the system cost by 6.9%), proposed that method to coping with multiple uncertainties and transmission constraints is effective, and proposed that approach can assure system reliability in an economic way.

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A Chance-Constrained Unit Commitment With an $n-K$ Security Criterion and Significant Wind Generation

Cited by 158 publications

References 17 publications

Robust unit commitment with $$n-1$$ n - 1 security criteria

Robust unit commitment with $$n-1$$ n - 1 security criteria

Evaluating stochastic methods in power system operations with wind power

Joint generation and reserve scheduling of wind‐solar‐pumped storage power systems under multiple uncertainties

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