The Future of Distribution Operations and Planning: The Electric Utility Environment Is Changing

Vadari, Subramanian

doi:10.1109/mpe.2019.2945344

Cited by 20 publications

(10 citation statements)

References 1 publication

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“…Naturally, the regression coefficients W are also described in matrix form, and thus the approximated function is h W (X) = W, X , which allows embedding kernels for flexible feature mapping. For example, a 2 nd -degree polynomial kernel is equivalent to the quadratic voltage features in (2). When unmeasured neighbors make perfect reconstruction impossible, a 3 rd -or higher degree kernel can automatically be used for good approximation within our highly flexible framework.…”

Section: B Support Matrix Regressionmentioning

confidence: 99%

“…For degradability, one intuitive way is to follow the highly resembled candidate model, e.g., LR, which prepossesses the quadratic features φ(x) to stay consistent with (2). Such a way is easy to understand but suffers from limited approximation capability with unobservability in distribution grids.…”

Section: A Guarantee Physical Mapping Recovery With Efficiencymentioning

confidence: 99%

“…. Besides, many customers can own ancillary devices with active controllers to support reactive power for voltage regulation, but the dynamic model and control rules may be unavailable to the utility [2]. For example, the Volt/VAr droop control of a smart inverter follows a combined curve of several piece-wise linear sections [40], which is different from the function types in basic power flow model.…”

Section: A Guarantee Physical Mapping Recovery With Efficiencymentioning

confidence: 99%

“…Such DER deployment not only contributes to cleaner energy but also brings in significant challenges for sustainable operation [1]. Therefore, new technologies are developed for advanced and automated distribution management, e.g., hosting capacity analysis of DERs, fault identification [2], coordinated control among smart inverters [3], etc. However, many of these new technologies require to know an accurate power flow model, difficult to obtain in many distribution grids with increasing uncertainties due to fast DER integration [4].…”

Section: Introductionmentioning

confidence: 99%

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Support Matrix Regression for Learning Power Flow in Distribution Grid With Unobservability

Yuan

Weng

2022

IEEE Trans. Power Syst.

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Increasing renewable penetration in distribution grids calls for improved monitoring and control, where power flow (PF) model is the basis for many advanced functionalities. However, unobservability makes the traditional way infeasible to construct PF analysis via admittance matrix for many distribution grids. While data-driven approaches can approximate PF mapping, direct machine learning (ML) applications may suffer from several drawbacks. First, complex ML models like deep neural networks lack the degradability and explainability to the true system model, leading to overfitting. There are also asynchronization issues among different meters without GPS chips. Last but not least, bad data is quite common in the distribution grids. To resolve these problems all at once, we propose a variational support matrix regression (SMR). It provides structural learning to (1) embed kernels to regularize physical form in observable area while achieving good approximation at unobservable area, (2) integrate temporal information into matrix regression for asynchronized data imputation, and (3) define support matrix for margins to be robust against bad data. We test the performance for mapping rule learning via IEEE test systems and a utility distribution grid. Simulation results show high accuracy, degradability from data-driven model to physical model, and robustness to data quality issues.

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Section: B Support Matrix Regressionmentioning

confidence: 99%

Section: A Guarantee Physical Mapping Recovery With Efficiencymentioning

confidence: 99%

Section: A Guarantee Physical Mapping Recovery With Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Support Matrix Regression for Learning Power Flow in Distribution Grid With Unobservability

Yuan

Weng

2022

IEEE Trans. Power Syst.

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“…In [10], a review is presented that aims to highlight the participation and importance of deep learning technologies in power grids, highlighting recent advances and future perspectives for dealing with big data and decision-making strategies. These issues, which are interlinked with aspects of power grid management, have proved to be, to a certain extent, a priority, because over the last two decades power grids have been the target of radical changes as a corollary of the intensive integration of new technologies (e.g., renewables, controllable electronic loads, electric mobility, energy storage systems, electrical railway systems, and new lighting systems) and the consequent need for power management resources covering the needs of all technologies [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Review of a Disruptive Vision of Future Power Grids: A New Path Based on Hybrid AC/DC Grids and Solid-State Transformers

et al. 2021

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Power grids are evolving with the aim to guarantee sustainability and higher levels of power quality for universal access to electricity. More specifically, over the last two decades, power grids have been targeted for significant changes, including migration from centralized to decentralized paradigms as a corollary of intensive integration of novel electrical technologies and the availability of derived equipment. This paper addresses a review of a disruptive vision of future power grids, mainly focusing on the use of hybrid AC/DC grids and solid‑state transformers technologies. Regarding hybrid AC/DC grids in particular, they are analyzed in detail in the context of unipolar and bipolar DC grids (i.e., two‑wire or three‑wire DC grids), as well as the different structures concerning coupled and decoupled AC configurations with low‑frequency or high‑frequency isolation. The contextualization of the possible configurations of solid‑state transformers and the different configurations of hybrid transformers (in the perspective of offering benefits for increasing power quality in terms of currents or voltages) is also analyzed within the perspective of the smart transformers. Additionally, the paper also presents unified multi‑port systems used to interface various technologies with hybrid AC/DC grids, which are also foreseen to play an important role in future power grids (e.g., the unified interface of renewable energy sources and energy storage systems), including an analysis concerning unified multi‑port systems for AC or DC grids. Throughout the paper, these topics are presented and discussed in the context of future power grids. An exhaustive description of these technologies is made, covering the most relevant and recent structures and features that can be developed, as well as the challenges for the future power grids. Several scenarios are presented, encompassing the mentioned technologies, and unveiling a progressive evolution that culminates in the cooperative scope of such technologies for a disruptive vision of future power grids.

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Deep Reinforcement Learning-based Resource Allocation for 5G Machine-type Communication in Active Distribution Networks with Time-varying Interference

Cheng

Yang

et al. 2022

Mobile Netw Appl

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The Future of Distribution Operations and Planning: The Electric Utility Environment Is Changing

Cited by 20 publications

References 1 publication

Support Matrix Regression for Learning Power Flow in Distribution Grid With Unobservability

Support Matrix Regression for Learning Power Flow in Distribution Grid With Unobservability

Review of a Disruptive Vision of Future Power Grids: A New Path Based on Hybrid AC/DC Grids and Solid-State Transformers

Deep Reinforcement Learning-based Resource Allocation for 5G Machine-type Communication in Active Distribution Networks with Time-varying Interference

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