Impact of Smart Metering Data Aggregation on Distribution System State Estimation

Chen, Qipeng; Kaleshi, Dritan; Fan, Zhong; Armour, Simon

doi:10.1109/tii.2016.2573272

Cited by 54 publications

(29 citation statements)

References 18 publications

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“…A number of papers have deviated from the conventional approach towards selecting W W W . For instance, in [15], using active/reactive power data history, non-diagonal terms have been added to the weight matrix to obtain better WLS accuracy, by modeling the existing correlation between the different measurement samples. This problem has been analyzed in details in [16] for modeling the correlations in measurement error distributions of different variables that are measured by the same device (smart meters and PMUs.)…”

Section: Fundamentals Of Sementioning

confidence: 99%

“…A) Probabilistic and Statistical Approaches: Methods based on probabilistic and statistical techniques, which employ spatial/temporal correlation and historic probability distribution data, are widely used for generating reasonable pseudomeasurements and assessing their uncertainty. This includes empirical studies [45], Gaussian Mixture Models (GMMs) and Expectation Maximization (EM) [46] [47], time-varying variance and mean modeling [44], correlation analysis (between total and individual consumption) [48], nodal active-reactive correlation analysis [15], internodal and intranodal correlation modeling [16], intertemporal correlation analysis [6], multivariate complex Gaussian modeling [49], and constrained optimization [50].…”

Section: Dsse Problem Formulationmentioning

confidence: 99%

“…Different types of cyber- [99]. Eavesdropping defines a situation in which an unauthorized party seeks to gather system data by tapping into the communication infrastructure, compromising data privacy and confidentiality of users [100] [15]. A classification of different papers with respect to the issue of cyber-security can be seen in Table V.…”

Section: Cyber-securitymentioning

confidence: 99%

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A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Dehghanpour

Wang

et al. 2019

IEEE Trans. Smart Grid

334

173

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This paper presents a review of the literature on State Estimation (SE) in power systems. While covering some works related to SE in transmission systems, the main focus of this paper is Distribution System State Estimation (DSSE). The paper discusses a few critical topics of DSSE, including mathematical problem formulation, application of pseudomeasurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security. Both conventional and modern data-driven and probabilistic techniques have been reviewed. This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE.

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Section: Fundamentals Of Sementioning

confidence: 99%

Section: Dsse Problem Formulationmentioning

confidence: 99%

Section: Cyber-securitymentioning

confidence: 99%

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A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Dehghanpour

Wang

et al. 2019

IEEE Trans. Smart Grid

334

173

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“…As already mentioned (Sections I and III), in the case of DG SE the system observability is achieved using a large number of power injection measurements. These values are either pseudomeasurements with a noise standard deviation σ at the level of 10 −1 [21], or virtual measurements, such as zero power injections, with σ at the level of 10 −5 . Unfortunately, these particular measurements generate an ill-conditioned gain matrix G [16] and affect remarkably the SE accuracy [17].…”

Section: Solving the D-wtvse Problem Via Admmmentioning

confidence: 99%

“…Further, non-technical restrictions also affect the accuracy of the pseudo loads. A comprehensive study on the DG SE and the relevant challenges is presented in [21]. The privacy restrictions with respect to the data aggregation from smart meters are emphasized.…”

Section: Introductionmentioning

confidence: 99%

A regularized state estimation scheme for a robust monitoring of the distribution grid

Tsitsimelis

Antón-Haro

2020

International Journal of Electrical Power & Energy Systems

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In this paper, we propose a regularized state estimation (SE) scheme for the distribution grid. The ultimate goal is to track accurately the system state at a faster time scale according to the requirements of the new operational environment. The SE algorithm operates at two different time scales in which the set of available measurements are different. At the main time instants (every 15 minutes) the set of observations comprises SCADA measurements, pseudomeasurements and micro Phasor Measurement Units (µPMUs). In this case, we resort to a Regularized version of the Normal Equations based SE (R-NESE). In the intermediate time instants, only a reduced number of µPMU measurements is available. To circumvent observability issues, we exploit the fact that the voltage drop in adjacent buses is limited and, on that basis, a regularized weighted total variation estimation (WTVSE) problem is formulated. Then, the impact of in-line voltage regulators (IVLRs) is explicitly taken into consideration and that, forces us to decompose and solve the original SE problem for a number of smaller regions (D-WTVSE). The latter can be iteratively solved by resorting to the Alternating Direction Method of Multipliers (ADMM). Complementarily, we also present a µPMU placement method (µPP) in order to improve the conditioning of the R-NESE problem. This problem can be posed as a mixed integer semidefinite programming model (MISDP) and, thus, can be efficiently solved. The performance of the proposed scheme is numerically assessed on (mostly) a 95-bus distribution system for a number of realistic conditions of noise, load and photovoltaic generation profiles. A number of benchmarks are provided, as well.

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Low and medium voltage distribution network planning with distributed energy resources: a survey

Pham,

Shah,

Dao

et al. 2024

Electr Eng

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The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined. Graphical abstract

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Impact of Smart Metering Data Aggregation on Distribution System State Estimation

Cited by 54 publications

References 18 publications

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

A regularized state estimation scheme for a robust monitoring of the distribution grid

Low and medium voltage distribution network planning with distributed energy resources: a survey

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