A distributed approach to the OPF problem

Erseghe, Tomaso

doi:10.1186/s13634-015-0226-x

Cited by 43 publications

(48 citation statements)

References 27 publications

(50 reference statements)

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“…Many works using ADMM for OPF do not consider line limits [14,15] as they add additional nonlinear inequality constraints to the problems. The recently published work [17] is one of the few that explicitly considers line limits.…”

Section: A 5-bus System With Line Limitsmentioning

confidence: 99%

“…C. 118-bus and 300-bus ALADIN vs. ADMM For the IEEE 118-bus and 300-bus test cases, we compare ALADIN with exact Hessians to ADMM results documented in the literature [14,15] supposing the authors thereof chose the parameters and their update rules optimally to facilitate fast convergence. We also adopt the grid partitioning from [14] for the 118-bus case.…”

Section: B 30-bus and 57-bus With Inexact Hessiansmentioning

confidence: 99%

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Toward Distributed OPF Using ALADIN

Engelmann

Jiang

Mühlpfordt

et al. 2019

IEEE Trans. Power Syst.

100

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The present paper discusses the application of the recently proposed Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method to non-convex AC Optimal Power Flow Problems (OPF) in a distributed fashion. In contrast to the often used Alternating Direction of Multipliers Method (ADMM), ALADIN guarantees locally quadratic convergence for AC-OPF. Numerical results for 5-300 bus test cases indicate that ALADIN is able to outperform ADMM and to reduce the number of iterations by about one order of magnitude. We compare ALADIN to numerical results for ADMM documented in the literature. The improved convergence speed comes at the cost of increasing the communication effort per iteration. Therefore, we propose a variant of ALADIN that uses inexact Hessians to reduce communication. Additionally, we provide a detailed comparison of these ALADIN variants to ADMM from an algorithmic and communication perspective. Moreover, we prove that ALADIN converges locally at quadratic rate even for the relevant case of suboptimally solved local NLPs.

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Section: A 5-bus System With Line Limitsmentioning

confidence: 99%

Section: B 30-bus and 57-bus With Inexact Hessiansmentioning

confidence: 99%

Toward Distributed OPF Using ALADIN

Engelmann

Jiang

Mühlpfordt

et al. 2019

IEEE Trans. Power Syst.

100

View full text Add to dashboard Cite

show abstract

“…Early works employ the Auxiliary Problem Principle [56,59], the Predictor Corrector Proximal Multiplier Method [58] and more recently the very popular Alternating Direction of Multipliers Method (admm) [34,58]. Especially admm has gained significant attention; thereby performing exhaustive simulation-based convergence analysis [34], investigating parameter update rules [35], and finally analyzing applicability to largescale systems [46]. However, for all the aforementioned algorithms convergence can, in general, not be guaranteed due to non-convexity.…”

Section: Brief Overview Of Existing Approachesmentioning

confidence: 99%

“…We remark that coupling via phases, voltages and power is just one possible problem formulation. For example in[35,46] the coupling is enforced via voltage consensus for auxiliary nodes and their next neighbors in the interior of each region.…”

mentioning

confidence: 99%

Optimal power flow: an introduction to predictive, distributed and stochastic control challenges

Faulwasser

Engelmann

Mühlpfordt

et al. 2018

At - Automatisierungstechnik

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The Energiewende is a paradigm change that can be witnessed at latest since the political decision to step out of nuclear energy. Moreover, despite common roots in Electrical Engineering, the control community and the power systems community face a lack of common vocabulary. In this context, this paper aims at providing a systems-and-control specific introduction to optimal power flow problems which are pivotal in the operation of energy systems. Based on a concise problem statement, we introduce a common description of optimal power flow variants including multi-stage-problems and predictive control, stochastic uncertainties, and issues of distributed optimization. Moreover, we sketch open questions that might be of interest for the systems and control community.

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“…In particular, a simple technique, which exploits the particular formulation of the branch current estimators, is proposed. Paper [12] presents a distributed approach to optimal power flow in an electrical network, suitable for application in a future smart grid scenario where access to resource and control is decentralized.…”

mentioning

confidence: 99%

Guest editorial introduction to the special issue on “advanced signal processing techniques and telecommunications network infrastructures for smart grid analysis, monitoring, and management”

Bracale

Barros

Cacciapuoti

et al. 2015

EURASIP J. Adv. Signal Process.

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Electrical power systems are undergoing a radical change in structure, components, and operational paradigms, and are progressively approaching the new concept of smart grids (SGs). Future power distribution systems will be characterized by the simultaneous presence of various distributed resources, such as renewable energy systems (i.e., photovoltaic power plant and wind farms), storage systems, and controllable/non-controllable loads. Control and optimization architectures will enable network-wide coordination of these grid components in order to improve system efficiency and reliability and to limit greenhouse gas emissions. In this context, the energy flows will be bidirectional from large power plants to end users and vice versa; producers and consumers will continuously interact at different voltage levels to determine in advance the requests of loads and to adapt the production and demand for electricity flexibly and efficiently also taking into account the presence of storage systems. This evolution of power distribution networks involves also an increasingly complex and performing Information and Communication Technology (ICT) infrastructure. An increasing and continuous flow of information will be exchanged among the various levels of such systems in order to ensure an optimized operation of the network and an efficient management of distributed resources. Then, SGs operate with two-way flows of both electricity and information. The necessary communication resources heavily depend on the choices related to the required signal processing for analysis, monitoring, and management of the network.Many research contributions have been published recently in the relevant literature, covering the innovative topics associated with this new concept of functional power systems. However, few papers have addressed the problems inherent to the study of the advanced signal processing techniques and telecommunications network infrastructures which are needed for the modern SGs. This is the reason for this special issue.There are many aspects where signal processing plays a crucial role for planning and operation of SGs. This special issue focuses on some of them, addressing in particular the development of new, advanced, signal processing methods for studying the characteristics of highly non-stationary waveforms involved in the analysis, monitoring and management of the electrical quantities at nodes/branches of the smart grids.Our call for papers attracted numerous submissions worldwide. In response to the call for papers for this special issue, after the review process, 12 were accepted for publication, with co-authors from Asia, Europe, South America, and the USA These papers are briefly described in the following.In [1], the authors use higher-order statistics-based feature extraction and regression algorithms implemented in artificial neural networks (ANNs) to classify power quality disturbances. The novel aspect of the method proposed is the use of a feature vector based on the combination of time and frequency d...

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A distributed approach to the OPF problem

Cited by 43 publications

References 27 publications

Toward Distributed OPF Using ALADIN

Toward Distributed OPF Using ALADIN

Optimal power flow: an introduction to predictive, distributed and stochastic control challenges

Guest editorial introduction to the special issue on “advanced signal processing techniques and telecommunications network infrastructures for smart grid analysis, monitoring, and management”

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