TY - JOUR
T1 - Probabilistic Model for Microgrids Optimal Energy Management Considering AC Network Constraints
AU - Javidsharifi, Mahshid
AU - Niknam, Taher
AU - Aghaei, Jamshid
AU - Shafie-Khah, Miadreza
AU - Catalao, Joao P.S.
N1 - Funding Information:
Manuscript received January 1, 2019; revised May 12, 2019; accepted July 3, 2019. Date of publication July 24, 2019; date of current version June 3, 2020. The work of J. P. S. Catalão was supported by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under POCI-01-0145-FEDER-029803 (02/SAICT/2017). (Corresponding authors: Miadreza Shafie-khah; João P. S. Catalão).
Publisher Copyright:
© 2007-2012 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - A new probabilistic approach for microgrids (MGs) optimal energy management considering ac network constraints is proposed in this paper. The economic model of an energy storage system (ESS) is considered in the problem. The reduced unscented transformation (RUT) is applied in order to deal with the uncertainties related to the forecasted values of load demand, market price, and available outputs of renewable energy sources (RESs). Moreover, the correlation between market price and load demand is taken into account. Besides, the impact of the correlated wind turbines (WT) on MGs' energy management is studied. An enhanced JAYA (EJAYA) algorithm is suggested to achieve the best solution of the considered problem. The effective performance of the presented approach is verified by applying the suggested strategy on a modified IEEE 33-bus system. It can be observed that for dealing with probabilistic problems, the suggested RUT-EJAYA shows accurate results such as those of Monte Carlo (MC) while the computational burden (time and complexity) is lower.
AB - A new probabilistic approach for microgrids (MGs) optimal energy management considering ac network constraints is proposed in this paper. The economic model of an energy storage system (ESS) is considered in the problem. The reduced unscented transformation (RUT) is applied in order to deal with the uncertainties related to the forecasted values of load demand, market price, and available outputs of renewable energy sources (RESs). Moreover, the correlation between market price and load demand is taken into account. Besides, the impact of the correlated wind turbines (WT) on MGs' energy management is studied. An enhanced JAYA (EJAYA) algorithm is suggested to achieve the best solution of the considered problem. The effective performance of the presented approach is verified by applying the suggested strategy on a modified IEEE 33-bus system. It can be observed that for dealing with probabilistic problems, the suggested RUT-EJAYA shows accurate results such as those of Monte Carlo (MC) while the computational burden (time and complexity) is lower.
KW - EJAYA algorithm
KW - microgrid (MG) energy management
KW - reduced unscented transformation (RUT)
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85086079677&partnerID=8YFLogxK
U2 - 10.1109/JSYST.2019.2927437
DO - 10.1109/JSYST.2019.2927437
M3 - Journal article
AN - SCOPUS:85086079677
SN - 1932-8184
VL - 14
SP - 2703
EP - 2712
JO - IEEE Systems Journal
JF - IEEE Systems Journal
IS - 2
M1 - 8771129
ER -