Consensus ADMM and Proximal ADMM for economic dispatch and AC OPF with SOCP relaxation

Ma, Minyue; Fan, Lingling; Miao, Zhixin

doi:10.1109/naps.2016.7747961

Cited by 43 publications

(16 citation statements)

References 11 publications

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“…To evaluate performance, new realizations of load were generated using the same procedure summarized in (14) and ADMM was run twice per sample: once, uninterrupted, for K 4 iterations to provide a baseline, and a second time with aRNN predictions substituted for the current ADMM iterate at step k = 4, after which the λ's and y's continue to evolve. The total number of steps to convergence and associated accuracies were then compared.…”

Section: A Methods 1) Test Systemsmentioning

confidence: 99%

“…Interest in distributed optimization algorithms themselves is also on the rise due to the increasing prevalence of physically distributed, autonomous systems and the frequent intractability of centralized formulations that assume complete knowledge of the system's state and dynamics. The optimal power flow problem, in particular, is amenable to the use of distributed methods as evidenced by several recent reviews [8], [9] and applications utilizing ADMM [10], [11], [12], [13], [14], [15], [16]. This paper sits at the intersection of these two research areas and specifically aims to augment the latter with the former.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning-Accelerated ADMM for Distributed Optimal Power Flow

Biagioni,

Graf,

Zhang

et al. 2019

Preprint

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Section: A Methods 1) Test Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning-Accelerated ADMM for Distributed Optimal Power Flow

Biagioni,

Graf,

Zhang

et al. 2019

Preprint

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“…• Partially distributed: An improvement to the conventional ADMM was initially proposed in [28], which uses a consensus-based approach for a fully distributed update of primal and dual variables at each prosumer. However, this requires additional auxiliary variables and constraints to be included in the optimization framework [29], as well as the communication of the iteration wise solutions of each prosumer with all its neighbors. The partially distributed approach has one central node to check the power balance and the other nodes can act in a distributed manner to calculate reference commands for the LC (Fig.…”

Section: Figure 2 Community-based Market Structurementioning

confidence: 99%

Modelling and Simulation Approaches for Local Energy Community Integrated Distribution Networks

et al. 2022

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Due to the absence of studies of local energy communities (LECs) where the grid is represented, it is very difficult to infer implications of increased LEC integration for the distribution grid as well as for the wider society. Therefore, this paper aims to investigate holistic modelling and simulation approaches of LECs. To conduct a quantifiable assessment of different control architectures, LEC types and market frameworks, a flexible and comprehensive LEC modelling and simulation approach is needed. Modelling LECs and the environment they operate in involves a holistic approach consisting of different layers: market, controller, and grid. The controller layer is relevant both for the overall energy management system of the LEC and the controllers of single components in a LEC. In this paper, the different LEC modelling approaches in the reviewed literature are presented, several multilayered concepts for LECs are proposed, and a case study is presented to illustrate a holistic simulation where the different layers interact.

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“…This section discusses around four versions of the ADMM algorithms previously presented in the literature namely Regular (orignal algorithm [21]), Fast (acceleration via penalty parameter [21]), PJ (parallelized, regularized version [34]), and Fast-PJ (combination of acceleration, parallelization, and regularization), and the novel version Two-steps Fast-PJ Algorithm 1 by comparing the way penalty parameter (λ (k) t ) is updated for the optimal flexibility exchange problem in each algorithm. Therefore, it is possible to see the impact of acceleration, parallelization, regularization and the two-step algorithm modifications.…”

Section: E Distributed Algorithm Acceleration Comparisonmentioning

confidence: 99%

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

Olivella‐Rosell

Rul⋅lan

Lloret-Gallego

et al. 2020

IEEE Trans. Smart Grid

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The recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.

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Consensus ADMM and Proximal ADMM for economic dispatch and AC OPF with SOCP relaxation

Cited by 43 publications

References 11 publications

Learning-Accelerated ADMM for Distributed Optimal Power Flow

Learning-Accelerated ADMM for Distributed Optimal Power Flow

Modelling and Simulation Approaches for Local Energy Community Integrated Distribution Networks

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

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