TY - JOUR
T1 - Stochastic Risk-Constrained Decision-making Approach for a Retailer in a Competitive Environment with Flexible Demand Side Resources
AU - Rashidizadeh-Kermani, Homa
AU - Vahedipour-Dahraie, Mostafa
AU - Anvari-Moghaddam, Amjad
AU - Guerrero, Josep M.
PY - 2019/2
Y1 - 2019/2
N2 - This paper presents a risk-averse stochastic bi-level programming approach to solve decision-making of a retailer in a competitive market under uncertainties. The retailer decides the level of involvement in day-ahead (DA) and regulation markets by making an optimal bidding strategy with the goal of expected profit maximization. Uncertainties associated with DA prices, up/down regulation market prices, customers’ demand and rival retailers’ behaviors are tackled through a stochastic programming model. In the proposed model responsive loads and electric vehicles (EVs) track the real-time prices and choose the proper retailer to minimize their payments in the competitive trading floor. The obtained nonlinear stochastic model is transformed into an equivalent linear single-level program by replacing the lower-level problem with its Karush–Kuhn–Tucker optimality conditions and using duality theory. Finally, the proposed methodology is evaluated by applying to a realistic case study and the results demonstrate the effectiveness of the proposed framework.
AB - This paper presents a risk-averse stochastic bi-level programming approach to solve decision-making of a retailer in a competitive market under uncertainties. The retailer decides the level of involvement in day-ahead (DA) and regulation markets by making an optimal bidding strategy with the goal of expected profit maximization. Uncertainties associated with DA prices, up/down regulation market prices, customers’ demand and rival retailers’ behaviors are tackled through a stochastic programming model. In the proposed model responsive loads and electric vehicles (EVs) track the real-time prices and choose the proper retailer to minimize their payments in the competitive trading floor. The obtained nonlinear stochastic model is transformed into an equivalent linear single-level program by replacing the lower-level problem with its Karush–Kuhn–Tucker optimality conditions and using duality theory. Finally, the proposed methodology is evaluated by applying to a realistic case study and the results demonstrate the effectiveness of the proposed framework.
KW - Demand response
KW - Conditional Value at Risk (CVaR)
KW - Competitive market
KW - Electric vehicle
KW - Stochastic bi-level program
UR - http://www.scopus.com/inward/record.url?scp=85053503191&partnerID=8YFLogxK
U2 - 10.1002/etep.2719
DO - 10.1002/etep.2719
M3 - Journal article
SN - 1430-144X
VL - 29
JO - International Transactions on Electrical Energy Systems
JF - International Transactions on Electrical Energy Systems
IS - 2
M1 - e2719
ER -