Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response
Distributed energy resources can improve the operation of power systems, improving economic and technical efficiency. Aggregation of small size resources, which exist in large number but with low individual capacity, is needed to make these resources’ use more efficient. In the present pap...
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doaj-1002b6b0c3f643ae8ed2c69770b5f99d2020-11-25T00:15:25ZengMDPI AGEnergies1996-10732019-04-01127124810.3390/en12071248en12071248Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand ResponseCátia Silva0Pedro Faria1Zita Vale2GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, IPP-Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, PortugalGECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, IPP-Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, PortugalGECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, IPP-Polytechnic Institute of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, PortugalDistributed energy resources can improve the operation of power systems, improving economic and technical efficiency. Aggregation of small size resources, which exist in large number but with low individual capacity, is needed to make these resources’ use more efficient. In the present paper, a methodology for distributed resources management by an aggregator is proposed, which includes the resources scheduling, aggregation and remuneration. The aggregation, made using a k-means algorithm, is applied to different approaches concerning the definition of tariffs for the period of a week. Different consumer types are remunerated according to time-of-use tariffs existing in Portugal. Resources aggregation and remuneration profiles are obtained for over 20.000 consumers and 500 distributed generation units. The main goal of this paper is to understand how the aggregation phase, or the way that is performed, influences the final remuneration of the resources associated with Virtual Power Player (VPP). In order to fulfill the proposed objective, the authors carried out studies for different time frames (week days, week-end, whole week) and also analyzed the effect of the formation of the remuneration tariff by considering a mix of fixed and indexed tariff. The optimum number of clusters is calculated in order to determine the best number of DR programs to be implemented by the VPP.https://www.mdpi.com/1996-1073/12/7/1248clusteringdemand responsedistributed generationsmart grids |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cátia Silva Pedro Faria Zita Vale |
spellingShingle |
Cátia Silva Pedro Faria Zita Vale Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response Energies clustering demand response distributed generation smart grids |
author_facet |
Cátia Silva Pedro Faria Zita Vale |
author_sort |
Cátia Silva |
title |
Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response |
title_short |
Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response |
title_full |
Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response |
title_fullStr |
Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response |
title_full_unstemmed |
Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response |
title_sort |
multi-period observation clustering for tariff definition in a weekly basis remuneration of demand response |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2019-04-01 |
description |
Distributed energy resources can improve the operation of power systems, improving economic and technical efficiency. Aggregation of small size resources, which exist in large number but with low individual capacity, is needed to make these resources’ use more efficient. In the present paper, a methodology for distributed resources management by an aggregator is proposed, which includes the resources scheduling, aggregation and remuneration. The aggregation, made using a k-means algorithm, is applied to different approaches concerning the definition of tariffs for the period of a week. Different consumer types are remunerated according to time-of-use tariffs existing in Portugal. Resources aggregation and remuneration profiles are obtained for over 20.000 consumers and 500 distributed generation units. The main goal of this paper is to understand how the aggregation phase, or the way that is performed, influences the final remuneration of the resources associated with Virtual Power Player (VPP). In order to fulfill the proposed objective, the authors carried out studies for different time frames (week days, week-end, whole week) and also analyzed the effect of the formation of the remuneration tariff by considering a mix of fixed and indexed tariff. The optimum number of clusters is calculated in order to determine the best number of DR programs to be implemented by the VPP. |
topic |
clustering demand response distributed generation smart grids |
url |
https://www.mdpi.com/1996-1073/12/7/1248 |
work_keys_str_mv |
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