A Power-Balanced Clustering Algorithm to Improve Electrical Infrastructure Resiliency

Huo, Chen; Cotilla‐Sanchez, Eduardo

doi:10.23919/pscc.2018.8442565

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…As a consequence of Corollary 2.1, the reduced current balance and power-flow equations for the network defined by d1N, d1E can be computed via equation (1.4) and (1.5), with respect to a single iteration of the Kron reduction with the nodes d1N chosen as a reference. However, by storing the sequential mappings in d1H, various levels of resolution can be introduced to a reduced order model by: (i) selecting the nodes in d1N as reference nodes, and (ii) additionally selecting trees or sub-trees as reference nodes for a Kron reduction of N. Reducing radial networks was performed in a similar fashion by [25] as a preprocessing step to their k−nearest neighbors clustering approach. We add to this discussion now with the rigorous proof of the compatibility of this topological reduction with the iterative Kron reduction.…”

Section: Algorithm 21 Degree One Reductionmentioning

confidence: 99%

“…While community detection methods have applications in power systems, these approaches are not in of themselves appropriate for constructing a dynamically consistent reduced order model. The work of Huo & Cotilla-Sanchez seeks to [25] preserve dynamical features of clustered communities by scoring the clusters based on power flow characteristics and applying an evolutionary algorithm. Other works in circuit design have focused on network reductions which preserve static power-flow computations, e.g.…”

Section: Contributionmentioning

confidence: 99%

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Structure- and Physics-Preserving Reductions of Power Grid Models

Grudzien¹,

Deka²,

Chertkov³

et al. 2018

Multiscale Model. Simul.

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The large size of multiscale, distribution and transmission, power grids hinder fast system-wide estimation and real-time control and optimization of operations. This paper studies graph reduction methods of power grids that are favorable for fast simulations and follow-up applications. While the classical Kron reduction has been successful in reduced order modeling of power grids with traditional, hierarchical design, the selection of reference nodes for the reduced model in a multiscale, distribution and transmission, network becomes ambiguous. In this work we extend the use of the iterative Kron reduction by utilizing the electric grid's graph topology for the selection of reference nodes, consistent with the design features of multiscale networks. Additionally, we propose further reductions by aggregation of coherent subnetworks of triangular meshes, based on the graph topology and network characteristics, in order to preserve currents and build another power-flow equivalent network.Our reductions are achieved through the use of iterative aggregation of sub-graphs that include general tree structures, lines, and triangles. Important features of our reduction algorithms include that: (i) the reductions are, either, equivalent to the Kron reduction, or otherwise produce a powerflow equivalent network; (ii) due to the former mentioned power-flow equivalence, the reduced network can model the dynamic of the swing equations for a lossless, inductive, steady state network; (iii) the algorithms efficiently utilize hash-tables to store the sequential reduction steps. The third feature allows for easy re-introduction of detailed models into the reduced, conceptual network, and makes the final reduced order model backward compatible with a sequence intermediate, partially reduced networks with varying resolution -the ordered sequence of iterative reductions corresponds to a sequence of reduced order models. The performance of our graph reduction algorithms, and features of the reduced grids, are discussed on a real-word transmission and distribution grid. We produce visualizations of the reduced models through open source libraries and release our reduction algorithms with example code and toy data. Alamos, NM 87545 (backhaus@lanl.gov). arXiv:1707.03672v3 [cs.SY] 4 Oct 2018 the grid and also made issues regarding grid stability and control of paramount importance [3,43,17]. Dynamic forcing from the distribution grid has historically been much smaller than the transmission components. For example, the amount of inertia and damping in the distribution grid are limited [22]. However, rooftop solar, the internet of things [28], and other resources have cultivated the demand for decentralized resource generation and control in the distribution sub-grid [18,34,16,46,12]. With this demand comes the need for multiscale, dynamical models of the grid.Owing to the large size and dense interconnections, the control, optimization and dynamical simulation of detailed grids faces implementation issues [5,4]. Operational demands requi...

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Section: Algorithm 21 Degree One Reductionmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Structure- and Physics-Preserving Reductions of Power Grid Models

Grudzien¹,

Deka²,

Chertkov³

et al. 2018

Multiscale Model. Simul.

View full text Add to dashboard Cite

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“…The hybrid K-means/evolutionary algorithm in [13], and particle swarm optimization algorithm in [14] based on electrical distance considers the cluster size index in the fitness function for partitioning the power distribution network. The authors in [15] introduces the power mismatch index (PMI) in K-nearest neighbor clustering algorithm based on electrical distance for power grid partitioning to improve the system resiliency. The mixed integer linear program is formulated to maximize the critical loads to be picked up while satisfying the operational constraints for power grid partitioning in [16] to form resilient sub-networks.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Day-Ahead Prediction of Resilient Power Distribution Network Partitions

Shah¹,

Wies²

2021

Preprint

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The conventional power distribution network is being transformed drastically due to high penetration of renewable energy sources (RES) and energy storage. The optimal scheduling and dispatch is important to better harness the energy from intermittent RES. Traditional centralized optimization techniques limit the size of the problem and hence distributed techniques are adopted. The distributed optimization technique partitions the power distribution network into sub-networks which solves the local sub problem and exchanges information with the neighboring sub-networks for the global update. This paper presents an adaptive spectral graph partitioning algorithm based on vertex migration while maintaining computational load balanced for synchronization, active power balance and sub-network resiliency. The parameters that define the resiliency metrics of power distribution networks are discussed and leveraged for better operation of sub-networks in grid connected mode as well as islanded mode. The adaptive partition of the IEEE 123-bus network into resilient sub-networks is demonstrated in this paper.

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