Optimal electric vehicles charging scheduling for energy and reserve markets considering wind uncertainty and generator contingency

Abstract

Decarburization of electrical systems encourage high wind power into electric power systems and the electrification of transport sectors through electric vehicles (EVs). The increasing penetration of uncertain wind power generation and transportation networks via EV charging stations has introduced challenges for system operators to manage power systems and market operations. In this context, this paper presents a stochastic AC security-constrained unit commitment (SCUC) model to clear day-ahead energy and reserve markets considering transportationelectricity networks through EV charging stations in the presence of uncertain wind power and generator contingency. Pre-contingency ranking is a common strategy for reducing the time of the SCUC problem, but it provides high-impact outages. To address this issue, generator outages ranked first are identified using the postcontingency generator response ranking approach. The main contribution of this

Spatial optimization of integrated energy system including hydrogen electrolysis center based on mixed integer second‐order cone programming

“…Bao et al 11 considers the problems of power fluctuations and high peak loads caused by high‐power charging at charging stations. By controlling the battery energy storage system, a two‐layer optimal control strategy is formed, and a calculation example is given to verify that the charging load curve can still be improved under large disturbances 12 . studied the method of stochastic optimization to control the uncertain wind power generation system and used the IEEE 118‐bus system for numerical calculation, which proved that the operating cost can be reduced 13 .…”

“…By controlling the battery energy storage system, a two-layer optimal control strategy is formed, and a calculation example is given to verify that the charging load curve can still be improved under large disturbances. 12 studied the method of stochastic optimization to control the uncertain wind power generation system and used the IEEE 118-bus system for numerical calculation, which proved that the operating cost can be reduced. 13 introduced node power loss sensitivity and electricity price in the optimization goal to reduce network loss and charging costs.…”

Comparison of wind speed forecasting models for power reserve scheduling in the congested transmission network

“…Optimally minimized power reserve with a satisfying security level reduces the operation costs [18,19]. To assess the power reserve that is needed in a given period, the probabilistic approaches have been widely applied to evaluate the reserve requirements because of the intermittent and volatile nature of the integrated renewable energy sources [20][21][22][23]. The previous study [24] has developed a distributionally robust coordinated power reserve scheduling model considering the probability distribution of the wind power forecast, aiming to minimize the total operation cost of conventional reserve, while satisfying the security requirement.…”

Optimal scheduling of integrated energy systems including hydrogen electrolyzers, HFCVs, and electric vehicles

“…Li et al 4 considers the cost of carbon trading for the fast‐charging EVs in the virtual power plant model and adopts the spatiotemporal two‐dimensional scheduling method to optimize the charging of EVs, achieving lower energy and carbon trading costs. Gupta et al 5 through the energy scheduling of EVS charging stations, a Stochastic AC Safe Unit Constraint model is used to optimize the charging process, reduce the fluctuation of wind power with strong uncertainty, and also have a certain optimization effect on the transportation network. Using the improved multiobjective particle swarm optimization, a multiobjective optimization was carried out for a typical IES including wind, storage, heat, and dispatchable loads 6 …”