Enhancing the Spatio-temporal Observability of Grid-Edge Resources in
Distribution Grids
- URL: http://arxiv.org/abs/2102.07801v1
- Date: Mon, 15 Feb 2021 19:12:54 GMT
- Title: Enhancing the Spatio-temporal Observability of Grid-Edge Resources in
Distribution Grids
- Authors: Shanny Lin and Hao Zhu
- Abstract summary: Enhancing the convex-temporal observability of distributed energy resources (DERs) is crucial for achieving secure and efficient operations in distribution grids.
This paper puts forth a joint recovery framework for residential loads by leveraging the complimentary strengths of heterogeneous types of measurements.
- Score: 5.815007821143811
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Enhancing the spatio-temporal observability of distributed energy resources
(DERs) is crucial for achieving secure and efficient operations in distribution
grids. This paper puts forth a joint recovery framework for residential loads
by leveraging the complimentary strengths of heterogeneous types of
measurements. The proposed approaches integrate the low-resolution smart meter
data collected for every load node with the fast-sampled feeder-level
measurements provided by limited number of phasor measurement units. To address
the lack of data, we exploit two key characteristics for the loads and DERs,
namely the sparse changes due to infrequent activities of appliances and
electric vehicles (EVs) and the locational dependence of solar photovoltaic
(PV) generation. Accordingly, meaningful regularization terms are introduced to
cast a convex load recovery problem, which will be further simplified to reduce
computational complexity. The load recovery solutions can be utilized to
identify the EV charging events at each load node and to infer the total
behind-the-meter PV output. Numerical tests using real-world data have
demonstrated the effectiveness of the proposed approaches in enhancing the
visibility of these grid-edge DERs.
Related papers
- Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators [1.885025492232011]
It is an urgent problem that measurement devices are not installed in many low-voltage (LV) grids.
We present an approach to estimate pseudo-measurements for non-measured LV feeders based on the metadata of the respective feeder.
In the future, the approach can be adapted to other grid levels like substation transformers.
arXiv Detail & Related papers (2024-09-29T14:10:43Z) - A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems [67.52782366565658]
State-of-the-art recommender systems (RSs) depend on categorical features, which ecoded by embedding vectors, resulting in excessively large embedding tables.
Despite the prosperity of lightweight embedding-based RSs, a wide diversity is seen in evaluation protocols.
This study investigates various LERS' performance, efficiency, and cross-task transferability via a thorough benchmarking process.
arXiv Detail & Related papers (2024-06-25T07:45:00Z) - Filling the Missing: Exploring Generative AI for Enhanced Federated
Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data.
Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
arXiv Detail & Related papers (2023-10-21T12:07:04Z) - DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging
Scheduling in Large-scale Networked Facilities [5.78463306498655]
Electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and jeopardized stability.
Modern power grids require coordinated or smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion.
We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits.
arXiv Detail & Related papers (2023-05-18T14:03:47Z) - Federated Learning for Energy-limited Wireless Networks: A Partial Model
Aggregation Approach [79.59560136273917]
limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL)
We first devise a novel FL framework with partial model aggregation (PMA)
The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
arXiv Detail & Related papers (2022-04-20T19:09:52Z) - FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy
Providers [1.1254693939127909]
We propose a novel horizontal privacy-preserving federated learning framework for energy load forecasting, namely FedREP.
We consider a federated learning system consisting of a control centre and multiple retailers by enabling multiple REPs to build a common, robust machine learning model without sharing data.
For forecasting, we use a state-of-the-art Long Short-Term Memory (LSTM) neural network due to its ability to learn long term sequences of observations.
arXiv Detail & Related papers (2022-03-01T04:16:19Z) - An Energy Consumption Model for Electrical Vehicle Networks via Extended
Federated-learning [50.85048976506701]
This paper proposes a novel solution to range anxiety based on a federated-learning model.
It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
arXiv Detail & Related papers (2021-11-13T15:03:44Z) - Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load
Monitoring [2.2237337682863125]
Non-Intrusive Load Monitoring (NILM) is the task of inferring the energy consumption of each appliance given the aggregate signal recorded by a single smart meter.
We propose a novel two-stage optimization-based approach for energy disaggregation.
arXiv Detail & Related papers (2021-06-16T22:16:00Z) - A Hierarchical Deep Actor-Critic Learning Method for Joint Distribution
System State Estimation [1.7205106391379026]
Real-time monitoring of customers at the grid-edge has become a critical task.
We present a novel hierarchical reinforcement learning-aided framework to achieve near real-time solutions.
arXiv Detail & Related papers (2020-12-04T22:38:21Z) - Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable
Edge Computing Systems [87.4519172058185]
An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied.
A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem.
Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
arXiv Detail & Related papers (2020-02-20T04:58:07Z) - Targeted free energy estimation via learned mappings [66.20146549150475]
Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences.
FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions.
One strategy to mitigate this problem, called Targeted Free Energy Perturbation, uses a high-dimensional mapping in configuration space to increase overlap.
arXiv Detail & Related papers (2020-02-12T11:10:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.