Next Article in Journal
Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems
Next Article in Special Issue
Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review
Previous Article in Journal
Wake Management in Wind Farms: An Adaptive Control Approach
Open AccessArticle

Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response

GECAD-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, Portugal
*
Author to whom correspondence should be addressed.
Energies 2019, 12(7), 1248; https://doi.org/10.3390/en12071248
Received: 21 February 2019 / Revised: 25 March 2019 / Accepted: 27 March 2019 / Published: 1 April 2019
(This article belongs to the Special Issue Intelligent Energy Systems)
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. View Full-Text
Keywords: clustering; demand response; distributed generation; smart grids clustering; demand response; distributed generation; smart grids
Show Figures

Figure 1

MDPI and ACS Style

Silva, C.; Faria, P.; Vale, Z. Multi-Period Observation Clustering for Tariff Definition in a Weekly Basis Remuneration of Demand Response. Energies 2019, 12, 1248.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop