Bi‐level scheduling of large‐scale electric vehicles based on the generation side and the distribution side

Abstract

With the intensification of energy shortages and climate warming, how to achieve the coordinated dispatch of electric vehicles (EVs) and high-proportion renewable energy systems (RESs) in the power system has been attracting more and more attentions. From the generation side and the distribution side, this work investigates the problem of large-scale EVs charging and discharging scheduling via the collaborative optimization among thermal power plants, wind and solar power, and EVs. To this end, an energy scheduling system (ESS) including power transmission and distribution is established, based on which a bi-level scheduling strategy is proposed. The upper level coordinately optimizes EVs, thermal power units, wind and solar power generation, and grid base loads to achieve the minimization of power supply costs. By introducing the node loss sensitivity (NLS) and node electricity price (NEP), the lower level allocates EVs to charging nodes to attain the minimization of the grid loss cost and charging cost. Finally, via the IEEE33-node standard power grid and the ESS model, it is verified that the proposed bi-level scheduling strategy can reduce the overall cost (node loss and charging cost) by 28.5%, and the wind curtailment rate and photovoltaic curtailment rate are 0.0% and 0.4%, respectively, making full use of renewable energy and adapting to its uncertainty.

Optimal Scheduling of Electric Vehicles Charging and Discharging Strategy Based on Real‐Time Price Considering Multi‐party Interest

“…[11], realizing rolling optimization and real-time update of charging price and charging control strategy. Reference [12] designed a two-level optimization model, which simultaneously considers the operation cost of the distribution network and the interests of EV owners to achieve the spatial-temporal scheduling of EVs. However, this paper did not consider the modeling process of EV, and the data from the upper and lower levels were also inconsistent.…”

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

“…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.…”

“…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. A novel interactive network model between the distribution network and EVs was proposed in, 14 which reduces the charging cost and has a smoothing effect on the load curve.…”

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

“…There is a contradiction between the volatility of new energy and the stability of power grid requirements. Many scholars have researched enhancing the consumption of new energy and improving the utilization of RES 3 . studied in the microgrid system, by introducing node loss sensitivity and node electricity price, the impact of EV charging and discharging on minimizing power supply cost, reducing node loss cost and charging cost, and appropriately improving RES consumption.…”

“…Many scholars have researched enhancing the consumption of new energy and improving the utilization of RES. 3 studied in the microgrid system, by introducing node loss sensitivity and node electricity price, the impact of EV charging and discharging on minimizing power supply cost, reducing node loss cost and charging cost, and appropriately improving RES consumption. 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.…”