Bayesian linear state estimation using smart meters and PMUs measurements in distribution grids

Schenato, Luca; Barchi, Grazia; Macii, David; Arghandeh, Reza; Poolla, Kameshwar; Meier, Alexandra von

doi:10.1109/smartgridcomm.2014.7007708

Cited by 76 publications

(83 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As explained in [13], several different sources of information can be available to solve the SE problem: 1) Pseudo-measurements, i.e., load estimations S psd based on predictions and/or known installed load capacity at every bus. Since these pseudo-measurements are estimations rather than actual measurements, we model their uncertainty using a Gaussian noise with a relative large standard deviation (a typical value can be σ psd ≈ 50% [12]). 2) Virtual measurements, i.e., buses with zero-injections, no loads connected.…”

Section: B Measurementsmentioning

confidence: 99%

Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

Picallo

Anta

Schutter

2019

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

The lack of measurements in distribution grids poses a severe challenge for their monitoring: since there may not be enough sensors to achieve numerical observability, load forecasts (pseudo-measurements) are typically used, and thus an accurate state estimation is not guaranteed. However, an estimation is required to control distribution grids given the increasing amount of distributed generation. Therefore, we consider the problem of optimal sensor placement to improve the state estimation accuracy in large-scale, 3-phase coupled, unbalanced distribution grids. This is a combinatorial optimization problem whose optimal solution is unpractical to obtain for large networks. We explore the properties of different metrics in the context of optimal experimental design, like convexity and modularity, to propose and compare several tight lower and upper bounds on the performance of the optimal solution. Moreover, we show how to use these bounds to choose near-optimal solutions. We test the method on two IEEE benchmark test feeders, the 123-bus and the 8500-node feeders, to show the effectiveness of the approach.

show abstract

Section: B Measurementsmentioning

confidence: 99%

Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

Picallo

Anta

Schutter

2019

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

show abstract

“…This triad is also valuable for organising research efforts shedding light into these challenges. Technological adaptation-oriented research has contributed with knowledge on the integration of electricity storage in distribution systems [37][38][39][40][41], integration of distributed generation sources from wind [42][43][44], solar [45][46][47][48][49], CHP [50][51][52], and micro-CHP [53][54][55][56], integration of EVs [57][58][59][60], integration of smart meters [61][62][63][64][65][66], implementation of DR [67][68][69][70][71], deployment of active distribution management systems [72][73][74][75][76], and advanced grid monitoring and control [77][78][79], as well as the use of artificial intelligence methods [80][81][82], and machine learning applicati...…”

Section: Electricity Distribution Adaptation To a Smarter And More Sumentioning

confidence: 99%

The challenging paradigm of interrelated energy systems towards a more sustainable future

Soares

Martins

Carvalho³

et al. 2018

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

This paper brings together several contemporary topics in energy systems aiming to provide a literature review based reflection on how several interrelated energy systems can contribute together to a more sustainable world. Some directions are discussed, such as the improvement of the energy efficiency and environmental performance of the systems, the development of new technologies, the increase of the use of renewable energy sources, the promotion of holistic and multidisciplinary studies, and the implementation of new management rules and "eco-friendly and sustainable" oriented policies at different scales. The interrelations of the diverse energy systems are also discussed in order to address their main social-economic-environmental impacts. The subjects covered include the assessment of the electricity market and its main players (demand, supply, distribution), the evaluation of some urban systems (buildings, transportation, commuting), the analysis of the implementation of renewable energy cooperatives, the discussion of the diffusion of the electric vehicle and the importance of new bioenergy systems. This paper also presents relevant research carried out in the framework of both the Energy for Sustainability Initiative of the University of Coimbra and the Sustainable Energy Systems focus area of the MIT-Portugal Program. To conclude, several research topics that should be addressed in the near future are proposed.

show abstract

“…so that (12) and (14) are satisfied and so is (9). Remark 5: The convex approximation in (14) may cause a loss of optimality in the OPF problem.…”

Section: Stochastic Voltage Limitsmentioning

confidence: 99%

Stochastic Optimal Power Flow in Distribution Grids under Uncertainty from State Estimation

Picallo

Anta

Schutter

2018

2018 IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

The increasing amount of controllable generation and consumption in distribution grids poses a severe challenge in keeping voltage values within admissible ranges. Existing approaches have considered different optimal power flow formulations to regulate distributed generation and other controllable elements. Nevertheless, distribution grids are characterized by an insufficient number of sensors, and state estimation algorithms are required to monitor the grid status. We consider in this paper the combined problem of optimal power flow under state estimation, where the estimation uncertainty results into stochastic constraints for the voltage magnitude levels instead of deterministic ones. To solve the given problem efficiently and to bypass the lack of load measurements, we use a linear approximation of the power flow equations. Moreover, we derive a transformation of the stochastic constraints to make them tractable without being too conservative. A case study shows the success of our approach at keeping voltage within limits, and also shows how ignoring the uncertainty in the estimation can lead to voltage level violations.

show abstract

Bayesian linear state estimation using smart meters and PMUs measurements in distribution grids

Cited by 76 publications

References 17 publications

Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

The challenging paradigm of interrelated energy systems towards a more sustainable future

Stochastic Optimal Power Flow in Distribution Grids under Uncertainty from State Estimation

Contact Info

Product

Resources

About