TY - JOUR
T1 - Impacts of joint operation of wind power with electric vehicles and demand response in electricity market
AU - Wang, Xian
AU - Zhang, Huajun
AU - Zhang, Shaohua
AU - Wu, Lei
N1 - Publisher Copyright:
© 2021
PY - 2021/12
Y1 - 2021/12
N2 - This paper investigates the impacts of joint operation of wind power producers (WPPs) with electric vehicles (EVs) and demand response (DR) resources as a virtual power plant (VPP) in electricity markets. In particular, this paper aims at answering the question of whether these resources have incentives to voluntarily form a VPP to participate in the electricity market. For this purposes, two stochastic multi-period game models of the day-ahead (DA) electricity market are proposed based on the oligopolistic competition theory. The first model considers when the WPP, EV aggregator (EVA), and DR aggregator (DRA) form a VPP and bid in the DA market in a cooperative way, and the Shapley value method is applied to allocate the VPP's profit among the WPP, EVA and DRA; while the second model focuses on when they bid in the DA market in a non-cooperative way. In both models, the scenario generation and reduction technique is employed to simulate wind speed uncertainties, and a deviation penalty mechanism is applied when the WPP's actual output deviate from its cleared quantity in the market. In addition, the conditional value-at-risk (CVaR) is used to measure financial risks of the WPP and analyze its risk preference. Numerical examples show that the VPP mode can help the WPP to reduce its deviation between its actual output and cleared quantity and improve its competitiveness in the market. In addition, individual profits of the WPP, EVA and DRA will all increase in the VPP mode, which means that the WPP, EVA and DRA have high incentives to voluntarily form a VPP and participate in the electricity market.
AB - This paper investigates the impacts of joint operation of wind power producers (WPPs) with electric vehicles (EVs) and demand response (DR) resources as a virtual power plant (VPP) in electricity markets. In particular, this paper aims at answering the question of whether these resources have incentives to voluntarily form a VPP to participate in the electricity market. For this purposes, two stochastic multi-period game models of the day-ahead (DA) electricity market are proposed based on the oligopolistic competition theory. The first model considers when the WPP, EV aggregator (EVA), and DR aggregator (DRA) form a VPP and bid in the DA market in a cooperative way, and the Shapley value method is applied to allocate the VPP's profit among the WPP, EVA and DRA; while the second model focuses on when they bid in the DA market in a non-cooperative way. In both models, the scenario generation and reduction technique is employed to simulate wind speed uncertainties, and a deviation penalty mechanism is applied when the WPP's actual output deviate from its cleared quantity in the market. In addition, the conditional value-at-risk (CVaR) is used to measure financial risks of the WPP and analyze its risk preference. Numerical examples show that the VPP mode can help the WPP to reduce its deviation between its actual output and cleared quantity and improve its competitiveness in the market. In addition, individual profits of the WPP, EVA and DRA will all increase in the VPP mode, which means that the WPP, EVA and DRA have high incentives to voluntarily form a VPP and participate in the electricity market.
KW - Demand response
KW - Electric vehicle
KW - Electricity market
KW - Game model
KW - Virtual power plant
KW - Wind power producer
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U2 - 10.1016/j.epsr.2021.107513
DO - 10.1016/j.epsr.2021.107513
M3 - Article
AN - SCOPUS:85112769147
SN - 0378-7796
VL - 201
JO - Electric Power Systems Research
JF - Electric Power Systems Research
M1 - 107513
ER -