Electric Distribution Network constraint management is employed by Distribution System Operators in order to keep inside desired safety bounds the aggregated power demand at each network substation. In our context, such aggregated power demand is due to residential users requiring electricity to the substation they are connected to. This enables saving in substation maintenance and energy peak production, as users typically tend to use little energy for most of the day, except for demand peaks, especially during evenings. The main workhorse to obtain such a goal is Demand Side Management, that is, trying to change the users demand in order to meet aggregated demand safety bounds. In this short paper, we introduce the problem and briefly review our recent approach to perform Demand Side Management for Electric Distribution Network constraint management, based on a network state estimator and a Model Predictive Control scheme. We also show experimental results on large scenarios using a real Electric Distribution Network in Denmark.
Electricity network constraint management using individualised demand aware price policies
Melatti I.;
2020-01-01
Abstract
Electric Distribution Network constraint management is employed by Distribution System Operators in order to keep inside desired safety bounds the aggregated power demand at each network substation. In our context, such aggregated power demand is due to residential users requiring electricity to the substation they are connected to. This enables saving in substation maintenance and energy peak production, as users typically tend to use little energy for most of the day, except for demand peaks, especially during evenings. The main workhorse to obtain such a goal is Demand Side Management, that is, trying to change the users demand in order to meet aggregated demand safety bounds. In this short paper, we introduce the problem and briefly review our recent approach to perform Demand Side Management for Electric Distribution Network constraint management, based on a network state estimator and a Model Predictive Control scheme. We also show experimental results on large scenarios using a real Electric Distribution Network in Denmark.Pubblicazioni consigliate
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