190 research outputs found
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
Integrating Optimal EV Charging in the Energy Management of Electric Railway Stations
In this paper, an electric railway Energy Management System (EMS) with
integration of an Energy Storage System (ESS), Regenerative Braking Energy
(RBE), and renewable generation is proposed to minimize the daily operating
costs of the railway station while meeting railway and Electric Vehicle (EV)
charging demand. Compared to other railway EMS methods, the proposed approach
integrates an optimal EV charging policy at the railway station to avoid high
power demand due to charging requirements. Specifically, receding horizon
control is leveraged to minimize the daily peak power spent on EV charging. The
numerical study on an actual railway station in Chur, Switzerland shows that
the proposed method that integrates railway demand and optimal EV charging
along with ESS, RBE, and renewable generation can significantly reduce the
average daily operating cost of the railway station over a large number of
different scenarios while ensuring that peak load capacity limits are
respected.Comment: to appear in IEEE PowerTech, Belgrade, Serbia, 202
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