Urban distribution grid topology reconstruction via Lasso

918

370

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Section: A Connection Verificationmentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

918

370

“…Given noisy specifications, the CPSSE task was next tackled using actual data and the SDP-based solver of (15). Load and solar generation on the IEEE 34-bus grid was taken from the Pecan Street dataset between 10:00 a.m. and 4:00 p.m. on January 1, 2013 [19].…”

Section: Numerical Testsmentioning

confidence: 99%

“…In this test, the measurement noise was simulated as Gaussian with zero mean and standard deviation of 0.01 for voltages and 0.015 for power injections. The coupling equations in (15) were weighted by 0.035 and α was set to 2 in all tests.…”

Section: Numerical Testsmentioning

confidence: 99%

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Enhancing Observability in Distribution Grids Using Smart Meter Data

Bhela

Kekatos

Veeramachaneni³

2018

132

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.

“…The solutions to (12) and (20) were projected onto B by setting their L largest entries to one and nulling the rest. The convex problem in (20) was solved using both the PGD iterates of (14), and the FW iterates of (22). The nonconvex problem of (12) was solved solely by the PGD scheme, since the FW iterates are guaranteed to converge only under a non-trivial step size selection [32].…”

Section: Numerical Testsmentioning

confidence: 99%

Voltage Analytics for Power Distribution Network Topology Verification

Cavraro

Kekatos

Veeramachaneni³

2019

102

Abstract-Distribution grids constitute complex networks of lines oftentimes reconfigured to minimize losses, balance loads, alleviate faults, or for maintenance purposes. Topology monitoring becomes a critical task for optimal grid scheduling. While synchrophasor installations are limited in low-voltage grids, utilities have an abundance of smart meter data at their disposal. In this context, a statistical learning framework is put forth for verifying single-phase grid structures using non-synchronized voltage data. The related maximum likelihood task boils down to minimizing a non-convex function over a non-convex set. The function involves the sample voltage covariance matrix and the feasible set is relaxed to its convex hull. Asymptotically in the number of data, the actual topology yields the global minimizer of the original and the relaxed problems. Under a simplified data model, the function turns out to be convex, thus offering optimality guarantees. Prior information on line statuses is also incorporated via a maximum a-posteriori approach. The formulated tasks are tackled using solvers having complementary strengths. Numerical tests using real data on benchmark feeders demonstrate that reliable topology estimates can be acquired even with a few smart meter data, while the non-convex schemes exhibit superior line verification performance at the expense of additional computational time.Index Terms-Maximum likelihood; inverse covariance matrix estimation; linearized distribution flow model.