Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System
by
Amin Shokri Gazafroudi 1,*
, Francisco Prieto-Castrillo 1,2,3, Tiago Pinto 1, Javier Prieto 1,4, Juan Manuel Corchado 1,5 and Javier Bajo 1
Amin Shokri Gazafroudi 1,*, Francisco Prieto-Castrillo 1,2,3, Tiago Pinto 1, Javier Prieto 1,4, Juan Manuel Corchado 1,5 and Javier Bajo 1
1
BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
2
MediaLab, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA
3
Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
4
StageMotion, R&D Department, C/Orfebres 10, 34005 Palencia, Spain
5
Osaka Institute of Technology, Asahi-ku Ohmiya, Osaka 535-8585, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Pedro Faria
Energies 2017, 10(9), 1397; https://doi.org/10.3390/en10091397
Received: 3 August 2017 / Revised: 1 September 2017 / Accepted: 5 September 2017 / Published: 13 September 2017
(This article belongs to the Special Issue Distributed Energy Resources Management)
This paper proposes a predictive dispatch model to manage energy flexibility in the domestic energy system. Electric Vehicles (EV), batteries and shiftable loads are devices that provide energy flexibility in the proposed system. The proposed energy management problem consists of two stages: day-ahead and real time. A hybrid method is defined for the first time in this paper to model the uncertainty of the PV power generation based on its power prediction. In the day-ahead stage, the uncertainty is modeled by interval bands. On the other hand, the uncertainty of PV power generation is modeled through a stochastic scenario-based method in the real-time stage. The performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the Modified Stochastic Predicted Band (MSPB) method. Moreover, the impacts of energy flexibility and the demand response program on the expected profit and transacted electrical energy of the system are assessed in the case study presented in this paper.
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Keywords:
decision-making under uncertainty; domestic energy management system; energy flexibility; interval optimization; stochastic programming
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MDPI and ACS Style
Gazafroudi, A.S.; Prieto-Castrillo, F.; Pinto, T.; Prieto, J.; Corchado, J.M.; Bajo, J. Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System. Energies 2017, 10, 1397.
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