Large-scale Unit Commitment under uncertainty

Tahanan, Milad; Ackooij, Wim van; Frangioni, Antonio; Lacalandra, F.

doi:10.1007/s10288-014-0279-y

Cited by 127 publications

(110 citation statements)

References 402 publications

(431 reference statements)

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“…(18) and PP processors to recover complete solutions to the SUC problem in Eqs. (10)- (17). Interactions between different processors are presented in Figure 1.…”

Section: Dual Maximization and Primal Recoverymentioning

confidence: 99%

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A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

Aravena¹,

Papavasiliou²,

Papalexopoulos³

2017

Recent Progress in Parallel and Distributed Computing

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We present a distributed computing architecture for smart grid management, composed of two applications at two different levels of the grid. At the high voltage level, we optimize operations using a stochastic unit commitment (SUC) model with hybrid time resolution. The SUC problem is solved with an asynchronous distributed subgradient method, for which we propose stepsize scaling and fast initialization techniques. The asynchronous algorithm is implemented in a high-performance computing cluster and benchmarked against a deterministic unit commitment model with exogenous reserve targets in an industrial scale test case of the Central Western European system (679 buses, 1037 lines, and 656 generators). At the distribution network level, we manage demand response from small clients through distributed stochastic control, which enables harnessing residential demand response while respecting the desire of consumers for control, privacy, and simplicity. The distributed stochastic control scheme is successfully tested on a test case with 10,000 controllable devices. Both applications demonstrate the potential for efficiently managing flexible resources in smart grids and for systematically coping with the uncertainty and variability introduced by renewable energy.

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“…(18) and PP processors to recover complete solutions to the SUC problem in Eqs. (10)- (17). Interactions between different processors are presented in Figure 1.…”

Section: Dual Maximization and Primal Recoverymentioning

confidence: 99%

“…We recover primal solutions by taking advantage of the fact that ðu (10)- (17). Therefore, in order to compute complete primal solutions and obtain upper bounds for problem in Eqs.…”

Section: Readmentioning

confidence: 99%

A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

Aravena¹,

Papavasiliou²,

Papalexopoulos³

2017

Recent Progress in Parallel and Distributed Computing

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show abstract

“…This section compares the computational performance of the proposed power-based formulation with two energy-based formulations, [1] and [4], which have been recognized as computationally efficient in the literature [14,18,23]. The following three formulations are then implemented:…”

Section: Numerical Resultsmentioning

confidence: 99%

“…e.g., minimum and maximum generation capacity constraints. The reader is referred to [11,14,16,18,19,[21][22][23]26] and references therein for further details.…”

Section: Introductionmentioning

confidence: 99%

Tight MIP formulations of the power-based unit commitment problem

2015

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This paper provides the convex hull description for the basic operation of slow-and quick-start units in power-based unit commitment (UC) problems. The basic operating constraints that are modeled for both types of units are (1) generation limits and (2) minimum up and down times. Apart from this, the startup and shutdown processes are also modeled, using (3) startup and shutdown power trajectories for slow-start units, and (4) startup and shutdown capabilities for quick-start units. In the conventional UC problem, power schedules are used to represent the staircase energy schedule; however, this simplification leads to infeasible energy delivery, as stated in the literature. To overcome this drawback, this paper provides a power-based UC formulation drawing a clear distinction between power and energy. The proposed constraints can be used as the core of any power-based UC formulation, thus tightening the final mixed-integer programming UC problem. We provide evidence that dramatic improvements in computational time are obtained by solving different case studies, for self-UC and network-constrained UC problems.

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“…The general optimal economic dispatch is realized by designing unit commitment [1] for obtaining the minimum overall generation cost [2]. The centralized optimization methods are generally used in the economic dispatch problems, such as lambda iterative algorithms [3] and interior point algorithms [4], which require the objective function to be derivative.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Randomized Gradient-Free Algorithm for the Non-Convex Economic Dispatch Problem

Xie

Cao

2018

Energies

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Abstract:In this paper, a distributed randomized gradient-free algorithm (DRGF) is employed to solve the complex non-convex economic dispatch problem whose non-convex constraints include valve-point loading effects, prohibited operating zones, and multiple fuel options. The DRGF uses the Gauss approximation, smoothing parameters, and a random sequence to construct distributed randomized gradient-free oracles. By employing a consensus procedure, generation units can gather local information through local communication links and then process the economic dispatch data in a distributed iteration format. Based on the principle of projection optimization, a projection operator is adopted in the DRGF to deal with the discontinuous solution space. The effectiveness of the proposed approach in addressing the non-convex economic dispatch problem is demonstrated by simulations implemented on three standard test systems.

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Large-scale Unit Commitment under uncertainty

Cited by 127 publications

References 402 publications

A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

Tight MIP formulations of the power-based unit commitment problem

A Distributed Randomized Gradient-Free Algorithm for the Non-Convex Economic Dispatch Problem

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