Multiobjective Synergistic Scheduling Optimization Model for Wind Power and Plug-In Hybrid Electric Vehicles under Different Grid-Connected Modes

Abstract

In order to promote grid’s wind power absorptive capacity and to overcome the adverse impacts of wind power on the stable operation of power system, this paper establishes benefit contrastive analysis models of wind power and plug-in hybrid electric vehicles (PHEVs) under the optimization goal of minimum coal consumption and pollutant emission considering multigrid connected modes. Then, a two-step adaptive solving algorithm is put forward to get the optimal system operation scheme with the highest membership degree based on the improvedεconstraints method and fuzzy decision theory. Thirdly, the IEEE36 nodes 10-unit system is used as the simulation system. Finally, the sensitive analysis for PHEV’s grid connected number is made. The result shows the proposed algorithm is feasible and effective to solve the model. PHEV’s grid connection could achieve load shifting effect and promote wind power grid connection. Especially, the optimization goals reach the optimum in fully optimal charging mode. As PHEV’s number increases, both abandoned wind and thermal power generation cost would decrease and the peak and valley difference of load curve would gradually be reduced.

The optimization model for multi-type customers assisting wind power consumptive considering uncertainty and demand response based on robust stochastic theory

“…EVs which could perform vehicle-to-grid have a better prospect than other types of EV with only the charging characteristic [28]. To make full use of EV and wind power's environment-friendly features, this paper assumes EV participates in PBDR and IBDR.…”

“…(29) and (30) are not nonlinear constraints, which are inconvenient to solve. The paper linearized them referenced to the literature [28].…”

“…Thermal units' operation parameters are taken from literature [28]. The maximum demand load is 2700 MW, customers demand load distribution in typically daily-load is shown in Fig.…”

“…The maximum limitation of storage energy is 10.8 kW h. The number of electric vehicles is assumed as 50,000 and the initial storage energy of storage energy is 0 kW h [28].…”

Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method

“…Accordingly, the site selection for EVCS is a multi-objective decision making problem. Nevertheless, the current studies on the EV predominantly focus on the following fields: battery management [1][2][3][4], charging scheduling [5][6][7][8], energy consumption [9][10][11][12] and the impacts on the power system [13,14] and so on. Up till now, only a few scholars have studied the topic of electric vehicle charging site selection, for instance, You and Hsieh [15] proposed a hybrid heuristic approach to address the selection of EVCS locations, Chung and Kwon [16] formulated a multi-period optimization model based on a flow-refueling location model for strategic charging station location planning, and Guo and Zhao [17] applied a fuzzy TOPSIS method to select the optimal EVCS site.…”