Two-stage robust optimization for N-k contingency-constrained unit commitment

Wang, Qianfan; Watson, Jean‐Paul; Guan, Yongpei

doi:10.1109/tpwrs.2013.2244619

Cited by 184 publications

(104 citation statements)

References 15 publications

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“…However, due to its computational complexity, security-constrained unit commitment considering the full spectrum of NERC reliability standards has not attracted a comparable level of attention until very recently. Specifically, [26] and [29] consider the case of security-constrained unit commitment under the more general N-k reliability criterion. Similarly, [26,27] and [29] use robust optimization methods for identifying worst-case N-k contingencies.…”

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confidence: 99%

“…-We ensure the existence of a feasible post-contingency corrective recourse, taking into consideration generator ramping constraints and the no-contingency (nominal) state economic dispatch; -We consider the loss of both generation units and transmission lines; -We propose novel decomposition methods to solve the contingency-constrained CCUC efficiently, and show that models and methods proposed by [12], [19], [26], [27], and [29] are all special cases of our general approach.…”

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Contingency-constrained unit commitment with post-contingency corrective recourse

et al. 2014

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We consider the problem of minimizing costs in the generation unit commitment problem, a cornerstone in electric power system operations, while enforcing an N-k-ε reliability criterion. This reliability criterion is a generalization of the wellknown N-k criterion, and dictates that at least (1 − ε j ) fraction of the total system demand must be met following the failures of k or fewer system components. We refer to this problem as the Contingency-Constrained Unit Commitment problem, or CCUC. We present a mixed-integer programming formulation of the CCUC that accounts for both transmission and generation element failures. We propose novel cutting plane algorithms that avoid the need to explicitly consider an exponential number of contingencies. Computational studies are performed on several IEEE test systems and a simplified model of the Western US interconnection network, which demonstrate the effectiveness of our proposed methods relative to current state-of-the-art.

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Contingency-constrained unit commitment with post-contingency corrective recourse

et al. 2014

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“…One of the first applications of robust optimization to UC problems concerns contingencyconstrained UC problems with N − k generation security criterion (see [67]), where the traditional N − k condition is modeled by the so-called budgeted uncertainty set [68]. The work [67] is further extended to include transmission contingencies and post-contingency redispatch in [69]. The proposed model has the two-stage structure as described in (26), except here d is the uncertainty of generation and transmission outages.…”

Section: Robust Optimizationmentioning

confidence: 99%

Advanced optimization methods for power systems

Panciatici¹,

Campi

Garatti

et al. 2014

2014 Power Systems Computation Conference

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Abstract-Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both continuous and discrete variables. In the recent years, a number of complementary theoretical advances in addressing such problems have been obtained in the field of applied mathematics. The paper introduces a selection of these advances in the fields of non-convex optimization, in mixedinteger programming, and in optimization under uncertainty. The practical relevance of these developments for power systems planning and operation are discussed, and the opportunities for combining them, together with high-performance computing and big data infrastructures, as well as novel machine learning and randomized algorithms, are highlighted.

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“…In a weekly generation scheduling problem as considered in this paper, this approximation results in a large-scale formulation whose computational burden can be significantly higher than that of the robust optimization (RO) approach. In the literature, RO approaches have recently been applied in various problems, including UC (see, e.g., [21], [22], and [23]), system security (see, e.g., [24]), plug-in hybrid electric vehicles (see, e.g., [25]), and offering strategies (see, e.g., [26] and [27]). A key feature separating our approach from the classical RO ones is that we employ a vector autoregressive (VAR) model to analyze the seasonality of the water inflow, and accordingly generate the forecasted value and CI.…”

Section: Introductionmentioning

confidence: 99%

Weekly Two-Stage Robust Generation Scheduling for Hydrothermal Power Systems

Dashti

Conejo

Jiang

et al. 2016

IEEE Trans. Power Syst.

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Abstract-As compared to short-term forecasting (e.g., 1 day), it is often challenging to accurately forecast the volume of precipitation in a medium-term horizon (e.g., 1 week). As a result, fluctuations in water inflow can trigger generation shortage and electricity price spikes in a power system with major or predominant hydro resources. In this paper, we study a two-stage robust scheduling approach for a hydrothermal power system. We consider water inflow uncertainty and employ a vector autoregressive (VAR) model to represent its seasonality and accordingly construct an uncertainty set in the robust optimization approach. We design a Benders' decomposition algorithm to solve this problem. Results are presented for the proposed approach on a real-world case study.

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Two-stage robust optimization for N-k contingency-constrained unit commitment

Cited by 184 publications

References 15 publications

Contingency-constrained unit commitment with post-contingency corrective recourse

Contingency-constrained unit commitment with post-contingency corrective recourse

Advanced optimization methods for power systems

Weekly Two-Stage Robust Generation Scheduling for Hydrothermal Power Systems

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