A progressive method to solve large-scale AC optimal power flow with discrete variables and control of the feasibility

Ruiz, Manuel; Maeght, Jean; Marie, Alexandre; Panciatici, Patrick; Renaud, A.

doi:10.1109/pscc.2014.7038395

Cited by 15 publications

(19 citation statements)

References 4 publications

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“…These computational challenges have been addressed by reformulating the MINLP into a Mathematical Program with Equilibrium Constraints (MPEC) as well as applying an algorithm based on progressive filtering and a gradual introduction of constraints. The solution strategy is explained in more detail in [14].…”

Section: ) Starting Point Initializationmentioning

confidence: 99%

Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

Konstantelos

Jamgotchian²,

Tindemans

et al. 2017

IEEE Trans. Smart Grid

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Abstract-This paper presents a computational platform for dynamic security assessment (DSA) of large electricity grids, developed as part of the iTesla project. It leverages High Performance Computing (HPC) to analyze large power systems, with many scenarios and possible contingencies, thus paving the way for pan-European operational stability analysis. The results of the DSA are summarized by decision trees of 11 stability indicators. The platform's workflow and parallel implementation architecture is described in detail, including the way commercial tools are integrated into a plug-in architecture. A case study of the French grid is presented, with over 8000 scenarios and 1980 contingencies. Performance data of the case study (using 10,000 parallel cores) is analyzed, including task timings and data flows. Finally, the generated decision trees are compared with test data to quantify the functional performance of the DSA platform.Index Terms-High performance computing, Monte Carlo methods, power system simulation, power system stability.

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Section: ) Starting Point Initializationmentioning

confidence: 99%

Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

Konstantelos

Jamgotchian²,

Tindemans

et al. 2017

IEEE Trans. Smart Grid

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“…are defined. For this purpose, a state-of-the-art tool [17] comprised of four hierarchical optimization levels is run to infer the operator's actions. The generated scenarios are consistent with what has been observed historically, but also capable of exploring marginal cases that have rarely occurred in the past.…”

Section: A Offline Computation Of Security Rulesmentioning

confidence: 99%

Online security assessment with load and renewable generation uncertainty: The iTesla project approach

Vasconcelos

Carvalho

Meirinhos

et al. 2016

2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Abstract-The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (HPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.

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“…This idea is intensively used in the on going European Project iTesla to build realistic base cases in Monte Carlo simulations; a paper [47] describing this work, is presented in PSCC 2014. Of course as any non global MINLP solver, only sub-optimal solutions are expected from MPEC solvers for non-convex MINLP problems.…”

Section: B Integer Variablesmentioning

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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A progressive method to solve large-scale AC optimal power flow with discrete variables and control of the feasibility

Cited by 15 publications

References 4 publications

Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

Implementation of a Massively Parallel Dynamic Security Assessment Platform for Large-Scale Grids

Online security assessment with load and renewable generation uncertainty: The iTesla project approach

Advanced optimization methods for power systems

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