203 research outputs found
A Gossip Algorithm based Clock Synchronization Scheme for Smart Grid Applications
The uprising interest in multi-agent based networked system, and the numerous
number of applications in the distributed control of the smart grid leads us to
address the problem of time synchronization in the smart grid. Utility
companies look for new packet based time synchronization solutions with Global
Positioning System (GPS) level accuracies beyond traditional packet methods
such as Network Time Proto- col (NTP). However GPS based solutions have poor
reception in indoor environments and dense urban canyons as well as GPS antenna
installation might be costly. Some smart grid nodes such as Phasor Measurement
Units (PMUs), fault detection, Wide Area Measurement Systems (WAMS) etc.,
requires synchronous accuracy as low as 1 ms. On the other hand, 1 sec accuracy
is acceptable in management information domain. Acknowledging this, in this
study, we introduce gossip algorithm based clock synchronization method among
network entities from the decision control and communication point of view. Our
method synchronizes clock within dense network with a bandwidth limited
environment. Our technique has been tested in different kinds of network
topologies- complete, star and random geometric network and demonstrated
satisfactory performance
Fast and revenue-oriented protection of radial LV cables with smart battery operation
Low-voltage radial electricity cables will have more and more difficulties to carry the increasing load of novel consumption devices (e.g. electric vehicles) and the expected generated input of decentrally-generated power (e.g. from photovoltaic cells). One solution to avoid replacement is to install a battery at the end of a cable which is expected to be overloaded frequently. The intelligent operation of this battery needs to combine the protection of the cable with optimizing its revenue, in order to be economically viable. This paper formulates the offline optimization problem and proposes two robust heuristic online strategies. We show in computer simulations that these heuristics, which make fast just-in-time responses, reliably deliver good results. Our second heuristic, H2, reaches up to 83% of the
approximated theoretical optimum
Recent approaches and applications of non-intrusive load monitoring
The Appliance Load Monitoring is vital in every energy consuming system be it commercial, residential or industrial in nature. Traditional load monitoring system, which used to be intrusive in nature require the installation of sensors to every load of interest which makes the system to be costly, time consuming and complex. Nonintrusive load monitoring (NILM) system uses the aggregated measurement at the utility service entry to identify and disaggregate the appliances connected in the building, which means only one set of sensors is required and it does not require entrance into the consumer premises. We presented a study in this paper providing a comprehensive review of the state of art of NILM, the different methods applied by researchers so far, before concluding with the future research direction, which include automatic home energy saving using NILM. The study also found that more efforts are needed from the researchers to apply NILM in appliance energy management, for example a Home Energy Management System (HEMS)
Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation
In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio
Privacy-Protecting Energy Management Unit through Model-Distribution Predictive Control
The roll-out of smart meters in electricity networks introduces risks for
consumer privacy due to increased measurement frequency and granularity.
Through various Non-Intrusive Load Monitoring techniques, consumer behavior may
be inferred from their metering data. In this paper, we propose an energy
management method that reduces energy cost and protects privacy through the
minimization of information leakage. The method is based on a Model Predictive
Controller that utilizes energy storage and local generation, and that predicts
the effects of its actions on the statistics of the actual energy consumption
of a consumer and that seen by the grid. Computationally, the method requires
solving a Mixed-Integer Quadratic Program of manageable size whenever new meter
readings are available. We simulate the controller on generated residential
load profiles with different privacy costs in a two-tier time-of-use energy
pricing environment. Results show that information leakage is effectively
reduced at the expense of increased energy cost. The results also show that
with the proposed controller the consumer load profile seen by the grid
resembles a mixture between that obtained with Non-Intrusive Load Leveling and
Lazy Stepping.Comment: Accepted for publication in IEEE Transactions on Smart Grid 2017,
special issue on Distributed Control and Efficient Optimization Methods for
Smart Gri
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