A MILP Model for Phase Identification in LV Distribution Feeders Using Smart Meters Data

Heidari-Akhijahani, Adel; Hojjatinejad, Saeed; Safdarian, Amir

doi:10.1109/sgc49328.2019.9056591

Cited by 7 publications

(11 citation statements)

References 13 publications

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“…It is noteworthy that the preliminary stage of using the proposed method is that the hosting phase of customers is known. If not, the method proposed in [36] can be used to identify the hosting phase of customers.…”

Section: Resultsmentioning

confidence: 99%

Unbalance mitigation by optimal placement of static transfer switches in low voltage distribution feeders

Heidari-Akhijahani

Safdarian

Lehtonen

2020

IET Generation, Transmission & Distribution

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Section: Resultsmentioning

confidence: 99%

Unbalance mitigation by optimal placement of static transfer switches in low voltage distribution feeders

Heidari-Akhijahani

Safdarian

Lehtonen

2020

IET Generation, Transmission & Distribution

Self Cite

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“…However, just like for the optimal measurement placement problem, it feels natural to associate binary variables to each user phase, to indicate whether a connection exists or not. Therefore, a number of works exploit MIP techniques, such as Akhijahani et al (2019) and Arya et al (2011). The tractability issues are similar to those of the optimal measurement placement problem.…”

Section: Phase Identificationmentioning

confidence: 99%

“…Arya et al (2011) solves a mixed-integer quadratic programming and MILP, making sure that the power demand from LV users matches the total transformer power per phase, ignoring line losses. Akhijahani et al (2019), instead, does include power flow equations, in a linearized form and solves a MILP problem, which aims to minimize the mean absolute difference between estimated and measured parameters. Active and reactive power are Fast convergence, converge guarantees, adds modeling errors Note: If the implementation separates the modeling and solving layer, the SE problem is addressed with mathematical optimization toolboxes and off-the-shelf solvers (which are specified below the reference).…”

Section: Phase Identificationmentioning

confidence: 99%

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Applications of optimization models for electricity distribution networks

Claeys

Vanin

Geth

2021

WIREs Energy & Environment

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Increased penetration of low‐carbon technologies, such as residential photovoltaic systems, electric vehicles, and batteries, can potentially cause voltage quality issues in distribution networks. Active distribution networks adopt control schemes where these assets are actively managed to prevent potential issues, increasing the network utilization. Mathematical optimization is a key technology in enabling such applications, either directly as the underlying solution, or for benchmarking effectiveness. As networks are operated closer to their engineering limits, models representing distribution network physics become increasingly important. This article reviews how distribution networks are modeled with varying degrees of detail in the context of optimization problems. It goes on to catalog the applications that use such models, and ends with an overview of toolchains to implement them, to enable the transition from the passive to active management of the distribution system. This article is categorized under: Energy Systems Analysis > Systems and Infrastructure

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“…Wang et al [28] propose a maximum-likelihood-estimation approach with binary variables, but these are successively relaxed. Heidari et al [29] propose a MILP technique with linearized power flow equations, which consists of minimizing the least absolute difference between measured values and system variables. This is solved in their follow-up work [30] with an accelerated Benders decomposition.…”

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confidence: 99%

Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

Vanin¹,

Acker²,

D’hulst³

et al. 2022

Preprint

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To address the challenges and exploit the opportunities that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these require accurate network models to work effectively, including user phase connectivity. However, such information is often not available. This paper proposes a novel method to identify the phase connectivity of single-and three-phase distribution consumers using smart meter measurements. The method is based on state estimation and mixed-integer linear programming, and requires shorter measurement collection times compared to statistical and machine learning-based techniques, for the same level of accuracy. Consequently, the duration of measurement efforts/campaigns aimed at obtaining this type of information reduces, accelerating the system knowledge acquisition process and potentially reducing the campaigns' costs. A technique is illustrated that allows to solve large networks in acceptable time despite the considerable number of integer variables. Extensive computational results are presented for publicly available low voltage feeders.

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A MILP Model for Phase Identification in LV Distribution Feeders Using Smart Meters Data

Cited by 7 publications

References 13 publications

Unbalance mitigation by optimal placement of static transfer switches in low voltage distribution feeders

Unbalance mitigation by optimal placement of static transfer switches in low voltage distribution feeders

Applications of optimization models for electricity distribution networks

Phase Identification of Distribution System Users Through a MILP Extension of State Estimation

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