A decomposition and coordination approach for large-scale security constrained unit commitment problems with combined cycle units

Sun, Xiaojun; Luh, Peter B.; Bragin, Mikhail A.; Chen, Yonghong; Wang, Fengyu; Wan, Jie

doi:10.1109/pesgm.2017.8274098

Cited by 17 publications

(30 citation statements)

References 6 publications

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“…and p ↑,k−1 j(b) , are penalized in (27). As in [9], the absolute value penalties are used to avoid unnecessary linearization.…”

Section: ) Solve the Dso Problemmentioning

confidence: 99%

“…The penalization is implemented as discussed in [9]. Due to the pagination limit, we omit the procedure to derive the exact expressions for L c k andL c k and refer interested readers to [9] for details. 4) Update: Using the DSO and TSO solutions obtained at iteration k, the following parameters are updated:…”

Section: J(b)mentioning

confidence: 99%

“…Reference [7] derives a stepsizing formula that guarantees the convergence and quantifiable solution accuracy without requiring any knowledge of the optimal dual Lagrangian function. Previously, the SLR was applied to large-scale transmission UC models [8], [9], even with AC power flows [10].…”

Section: Introductionmentioning

confidence: 99%

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Toward Coordinated Transmission and Distribution Operations

Bragin

Dvorkin

Darvishi

2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Proliferation of smart grid technologies has enhanced observability and controllability of distribution systems. If coordinated with the transmission system, resources of both systems can be used more efficiently. This paper proposes a model to operate transmission and distribution systems in a coordinated manner. The proposed model is solved using a Surrogate Lagrangian Relaxation (SLR) approach. The computational performance of this approach is compared against existing methods (e.g. subgradient method). Finally, the usefulness of the proposed model and solution approach is demonstrated via numerical experiments on the illustrative example and IEEE benchmarks.Index Terms-Distribution system operations, transmission system operations, Surrogate Lagrangian Relaxation.

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“…and p ↑,k−1 j(b) , are penalized in (27). As in [9], the absolute value penalties are used to avoid unnecessary linearization.…”

Section: ) Solve the Dso Problemmentioning

confidence: 99%

Section: J(b)mentioning

confidence: 99%

See 1 more Smart Citation

Toward Coordinated Transmission and Distribution Operations

Bragin

Dvorkin

Darvishi

2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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“…Some the above difficulties have been overcome within subgradient incremental methods [14,15], Alternate Direction Method of Multipliers (ADMM) [16][17][18][19][20][21], surrogate subgradient method [22], and surrogate Lagrangian relaxation (SLR) [23][24]49]. The distributed and asynchronous incremental subgradient method [15] for optimizing convex dual functions consisting of a large number of components, which arise within Lagrangian relaxation framework with a large number of subproblems, overcomes the synchronization difficulty.…”

Section: Introductionmentioning

confidence: 99%

“…Our recent SLR method [23,24,49] has overcome major convergence difficulties of standard Lagrangian Relaxation such as high computational effort, zigzagging of multipliers, and the need to know the optimal dual value for convergence. In [49], it has been demonstrated that the method is capable of efficiently coordinating thousands of subsystems. These methods will be reviewed in more detail in Section II.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Coordination of Distributed Mixed-Integer Linear Subsystems via Surrogate Lagrangian Relaxation

Bragin¹,

Luh²,

Yan³

2020

Preprint

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With the emergence of Internet of Things that allows communications and local computations, and with the vision of Industry 4.0, a foreseeable transition is from centralized system planning and operation toward decentralization with interacting components and subsystems, e.g., self-optimizing factories. In this paper, a new “price-based” decomposition and coordination methodology is developed to efficiently coordinate subsystems such as machines and parts, which are described by Mixed-Integer Linear Programming (MILP) formulations, in a distributed and asynchronous way. To ensure low communication requirements, exchanges between the “coordinator” and subsystems are limited to “prices” (Lagrangian multipliers) broadcast by the coordinator, and to subsystem solutions sent to the coordinator. Asynchronous coordination, however, may lead to convergence difficulties since the order in which subsystem solutions arrive at the coordinator is not predefined as a result of uncertainties in communication and solving times. Under realistic assumptions of finite communication and solve times, convergence of our method is proved by innovatively extending Lyapunov Stability Theory. Numerical testing of generalized assignment problems through simulation demonstrates that the method converges fast and provides near-optimal results, paving the way for self-optimizing factories in the future.

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