TY - JOUR
T1 - Risk-averse Probabilistic Framework for Scheduling of Virtual Power Plants Considering Demand Response and Uncertainties
AU - Vahedipour-Dahraie, Mostafa
AU - Rashidizadeh-Kermani, Homa
AU - Anvari-Moghaddam, Amjad
AU - Siano, Pierluigi
PY - 2020
Y1 - 2020
N2 - In this paper, a risk-based stochastic framework is presented for short-term energy and reserve scheduling of a virtual power plant (VPP) considering demand response (DR) participation. The VPP comprises several dispatchable generation units, battery energy storage systems (BESSs), wind power units, and flexible loads. The proposed scheduling framework is formulated as a risk-constrained stochastic program to maximize the VPP’s profit considering uncertainties of loads, wind energy and electricity prices as well as N-1 contingencies. The proposed model considers both supply and demand-sides capability for providing and deploying reserves in order to optimize the use of resources while satisfying N-1 security and other constraints. Moreover, the effect of risk-aversion on decision making of the VPP in the offering/bidding power and required reserve services is investigated by implementing conditional value-at-risk (CVaR) in the optimization model. The proposed scheme is implemented on a test VPP and the energy and reserve scheduling with and without DR participants is addressed in detail through a numerical study. Moreover, the effects of the operator’s risk-averse behavior on the VPP energy and reserve management and its security indices are investigated.
AB - In this paper, a risk-based stochastic framework is presented for short-term energy and reserve scheduling of a virtual power plant (VPP) considering demand response (DR) participation. The VPP comprises several dispatchable generation units, battery energy storage systems (BESSs), wind power units, and flexible loads. The proposed scheduling framework is formulated as a risk-constrained stochastic program to maximize the VPP’s profit considering uncertainties of loads, wind energy and electricity prices as well as N-1 contingencies. The proposed model considers both supply and demand-sides capability for providing and deploying reserves in order to optimize the use of resources while satisfying N-1 security and other constraints. Moreover, the effect of risk-aversion on decision making of the VPP in the offering/bidding power and required reserve services is investigated by implementing conditional value-at-risk (CVaR) in the optimization model. The proposed scheme is implemented on a test VPP and the energy and reserve scheduling with and without DR participants is addressed in detail through a numerical study. Moreover, the effects of the operator’s risk-averse behavior on the VPP energy and reserve management and its security indices are investigated.
KW - Virtual Power Plant
KW - Demand Response
KW - Energy Storage System
KW - Energy and reserve scheduling
KW - Virtual power plant (VPP)
KW - Energy storage system
KW - Demand response (DR)
UR - http://www.scopus.com/inward/record.url?scp=85084451616&partnerID=8YFLogxK
U2 - 10.1016/j.ijepes.2020.106126
DO - 10.1016/j.ijepes.2020.106126
M3 - Journal article
SN - 0142-0615
VL - 121
SP - 1
EP - 12
JO - International Journal of Electrical Power & Energy Systems
JF - International Journal of Electrical Power & Energy Systems
M1 - 106126
ER -