Authors:
Ramón Christen
1
;
Vincent Layec
2
;
Gwendolin Wilke
1
and
Holger Wache
2
Affiliations:
1
Department of Computer Science, University of Applied Science Lucerne, Rotkreuz and Switzerland
;
2
Institute of Information Systems, University of Applied Science and Arts of Northwestern Switzerland, Olten and Switzerland
Keyword(s):
Smart Grid, Power Grid, Grid Capacity, Grid Reinforcement, Load Prediction, Load Management.
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Load Balancing in Smart Grids
;
Smart Grids
Abstract:
The electrification of the global energy system and the shift towards distributed power production from sustainable sources triggers an increased network capacity demand at times of high production or consumption. Existing energy management solutions can help mitigate resulting high costs of large-scale physical grid reinforcement, but often interfere in customer processes or restrict free access to the energy market. In a preceding paper, we proposed the RLS regional load shaping approach as a novel business model and load management solution in middle voltage grid to resolve this dilemma: market-based incentives for all stakeholders are provided to allow for flexible loads that are non-critical in customer processes to be allocated to the unused grid capacity traditionally reserved for N-1 security of supply. We provide a validation of the technical aspects of the approach, with an evaluation of the day-ahead load forecasting method for industry customers and a load optimization he
uristics. The latter is tested by a simulation run on a scenario of network branch with provoked capacity bottlenecks. The method handles all provoked critical network capacity situations as expected.
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