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Reschedule of Distributed Energy Resources by an Aggregator for Market Participation

Pedro Faria (), João Spínola () and Zita Vale ()
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Pedro Faria: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Polytechnic Institute of Porto (IPP), Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal
João Spínola: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Polytechnic Institute of Porto (IPP), Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Zita Vale: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Polytechnic Institute of Porto (IPP), Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal

Energies, 2018, vol. 11, issue 4, 1-15

Abstract: Demand response aggregators have been developed and implemented all through the world with more seen in Europe and the United States. The participation of aggregators in energy markets improves the access of small-size resources to these, which enables successful business cases for demand-side flexibility. The present paper proposes aggregator’s assessment of the integration of distributed energy resources in energy markets, which provides an optimized reschedule. An aggregation and remuneration model is proposed by using the k-means and group tariff, respectively. The main objective is to identify the available options for the aggregator to define tariff groups for the implementation of demand response. After the first schedule, the distributed energy resources are aggregated into a given number of groups. For each of the new groups, a new tariff is computed and the resources are again scheduled according to the new group tariff. In this way, the impact of implementing the new tariffs is analyzed in order to support a more sustained decision to be taken by the aggregator. A 180-bus network in the case study accommodates 90 consumers, 116 distributed generators, and one supplier.

Keywords: aggregator; clustering; demand response; distributed generation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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