Distribution system state estimation using semidefinite programming

Klauber, Cecilia; Zhu, Hao

doi:10.1109/naps.2015.7335195

Cited by 19 publications

(20 citation statements)

References 23 publications

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“…Power flow (PF) and power system state estimation (PSSE) are central to planning, monitoring and control of electricity networks. Although these problems have been extensively studied in transmission networks [1], there is renewed interest in developing novel techniques for determining the system state in distribution grids [2]. Due to limited instrumentation, low investment interest in the past, and the sheer scale of residential electricity networks, low-voltage grids have limited observability [3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Observability in Distribution Grids Using Smart Meter Data

Bhela

Kekatos

Veeramachaneni³

2018

IEEE Trans. Smart Grid

132

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Due to limited metering infrastructure, distribution grids are currently challenged by observability issues. On the other hand, smart meter data, including local voltage magnitudes and power injections, are communicated to the utility operator from grid buses with renewable generation and demand-response programs. This work employs grid data from metered buses towards inferring the underlying grid state. To this end, a coupled formulation of the power flow problem (CPF) is put forth. Exploiting the high variability of injections at metered buses, the controllability of solar inverters, and the relative timeinvariance of conventional loads, the idea is to solve the non-linear power flow equations jointly over consecutive time instants. An intuitive and easily verifiable rule pertaining to the locations of metered and non-metered buses on the physical grid is shown to be a necessary and sufficient criterion for local observability in radial networks. To account for noisy smart meter readings, a coupled power system state estimation (CPSSE) problem is further developed. Both CPF and CPSSE tasks are tackled via augmented semi-definite program relaxations. The observability criterion along with the CPF and CPSSE solvers are numerically corroborated using synthetic and actual solar generation and load data on the IEEE 34-bus benchmark feeder.

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Section: Introductionmentioning

confidence: 99%

Enhancing Observability in Distribution Grids Using Smart Meter Data

Bhela

Kekatos

Veeramachaneni³

2018

IEEE Trans. Smart Grid

132

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“…where the given matrices M k depend on Y; see [15], [16]. The matrix variables V t 0 have been obtained upon relaxing the rank-one constraint V t =ṽ tṽ H t on the original system states for t ∈ T .…”

Section: Solving the P2l Tasksmentioning

confidence: 99%

Smart Inverter Grid Probing for Learning Loads: Part II—Probing Injection Design

Bhela

Kekatos

Veeramachaneni³

2019

IEEE Trans. Power Syst.

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This two-part work puts forth the idea of engaging power electronics to probe an electric grid to infer non-metered loads. Probing can be accomplished by commanding inverters to perturb their power injections and record the induced voltage response. Once a probing setup is deemed topologically observable by the tests of Part I, Part II provides a methodology for designing probing injections abiding by inverter and network constraints to improve load estimates. The task is challenging since system estimates depend on both probing injections and unknown loads in an implicit nonlinear fashion. The methodology first constructs a library of candidate probing vectors by sampling over the feasible set of inverter injections. Leveraging a linearized grid model and a robust approach, the candidate probing vectors violating voltage constraints for any anticipated load value are subsequently rejected. Among the qualified candidates, the design finally identifies the probing vectors yielding the most diverse system states. The probing task under noisy phasor and non-phasor data is tackled using a semidefinite-program (SDP) relaxation. Numerical tests using synthetic and real-world data on a benchmark feeder validate the conditions of Part I; the SDP-based solver; the importance of probing design; and the effects of probing duration and noise.

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“…Furthermore, the high resistance/resactance (R/X) ratios of distribution network branches could lead to worrying numerical stability issues for ADNSE. The SE numerical stability issue was well addressed by convex semidefinite programming proposed in [21], but with a considerable computation burden. Compared with integrated state estimation (ISE), multiarea distributed state estimation (DSE), in which the largescale network is divided into several subareas and the SE in each area is performed in parallel, shows a higher computational efficiency and a better numerical stability.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The problem (19) cannot be decomposed directly because the same augmented Lagrangian function couples the SE tasks across areas K and L. To enable a fully distributed scheme, the block coordinate descent (BCD) method is adopted. This will yield two separate sub-problems (20)- (21) and a multiplier update (22) as: where t represents the iteration number; and g ðtÞ is a step size parameter. Note that the augmented Lagrangian relaxation here severs as a local algorithm as J K ðx K Þ and J L ðx L Þ are nonconvex (nonlinear power flow equations).…”

Section: Augmented Lagrangian Relaxationmentioning

confidence: 99%

“…Note that the augmented Lagrangian relaxation here severs as a local algorithm as J K ðx K Þ and J L ðx L Þ are nonconvex (nonlinear power flow equations). Based on the distributed procedure (20)- (22), neighboring subareas K and L solve (20) and (21) independently. Meanwhile, only a very limited boundary information (x K ½L ðtÞ or x L ½K ðtÞ ) needs to be exchanged between subareas K and L at each successive iteration.…”

Section: Augmented Lagrangian Relaxationmentioning

confidence: 99%

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Multi-area distributed three-phase state estimation for unbalanced active distribution networks

Chen

Wei

Sun

et al. 2016

J. Mod. Power Syst. Clean Energy

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This paper proposes a new multi-area framework for unbalanced active distribution network (ADN) state estimation. Firstly, an innovative three-phase distributed generator (DG) model is presented to take the asymmetric characteristics of DG three-phase outputs into consideration. Then a feasible method to set pseudo-measurements for unmonitored DGs is introduced. The states of DGs, together with the states of alternating current (AC) buses in ADNs, were estimated by using the weighted least squares (WLS) method. After that, the ADN was divided into several independent subareas. Based on the augmented Lagrangian method, this work proposes a fully distributed three-phase state estimator for the multi-area ADN. Finally, from the simulation results on the modified IEEE 123-bus system, the effectiveness and applicability of the proposed methodology have been investigated and discussed.

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Distribution system state estimation using semidefinite programming

Cited by 19 publications

References 23 publications

Enhancing Observability in Distribution Grids Using Smart Meter Data

Enhancing Observability in Distribution Grids Using Smart Meter Data

Smart Inverter Grid Probing for Learning Loads: Part II—Probing Injection Design

Multi-area distributed three-phase state estimation for unbalanced active distribution networks

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