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Stochastic evaluation of aggregator business models - Optimizing wind power integration in distribution networks
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems. (PSMIX)ORCID iD: 0000-0002-9860-4472
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems. (PSMIX)ORCID iD: 0000-0003-3014-5609
2014 (English)In: Proceedings - 2014 Power Systems Computation Conference, PSCC 2014, 2014Conference paper, Published paper (Refereed)
Abstract [en]

In order to limit the environmental impact of electricity production, renewable energy sources are expected to expand significantly. The current grid and production structure are not designed to absorb such quantities of intermittent power output and smart grids can provide promising solutions, like demand response. This paper presents the technical advantages of managing flexible demand through Aggregators in a centralized fashion. An optimization model is developed to evaluate the economic benefits induced by adapting instantaneous electricity consumption to renewable generation. The model presented can easily be adapted in a more general context, and tested for different scenarios. Further, a use case related to the Smart Grids project on the Swedish island of Gotland is simulated. The simulation results show that the Aggregator solution is technically feasible, but that the current market design is a barrier for a successful implementation.

Place, publisher, year, edition, pages
2014.
Keyword [en]
Aggregators, Demand Side Management, Distribution Networks, Local Supply/Demand Matching, Wind Power Integration, Electric power distribution, Electric power generation, Electric power transmission networks, Electric utilities, Environmental impact, Optimization, Renewable energy resources, Stochastic models, Stochastic systems, Wind power, Demand side managements, Electricity production, Electricity-consumption, Renewable energy source, Stochastic evaluations, Wind power integrations, Smart power grids
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-167897DOI: 10.1109/PSCC.2014.7038104Scopus ID: 2-s2.0-84924366449ISBN: 9788393580132 (print)OAI: oai:DiVA.org:kth-167897DiVA: diva2:820140
Conference
2014 Power Systems Computation Conference, PSCC 2014, 18 August 2014 through 22 August 2014
Note

QC 20150611

Available from: 2015-06-11 Created: 2015-05-22 Last updated: 2016-11-11Bibliographically approved

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Sandels, ClaesNordström, Lars

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CiteExportLink to record
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Citation style
  • apa
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  • modern-language-association-8th-edition
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  • de-DE
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  • sv-SE
  • Other locale
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Output format
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