Day-ahead Economic Dispatch of Integrated Energy System Considering Uncertainties of Wind Power and Electric Vehicles

doi:10.12204/j.issn.1000-7229.2020.12.008

Abstract

Abstract:

The uncertainty of wind power generation (WPG) output and charging and discharging of electric vehicles (EVs) has a great impact on the economy of integrated energy system(IES) operation. Considering the influence of the popularity of EVs on the IES, the operation models of various types of EVs are refined and differentiated according to the different travel and charging characteristics of EVs. Considering the uncertain probability of WPG output is difficult to obtain, a nonparametric fuzzy set based on imprecise Dirichlet model (IDM) is constructed on the basis of more historical data, so as to build a two-stage robust dispatch model for IES considering uncertainties of wind power and EVs. In order to obtain the optimal solution of the model, it is decomposed into a mixed integer linear programming (MILP) master problem and a sub-problem with max-min structure. By using duality theory and big M method, the sub-problem is transformed into a MILP problem, and the optimal solution of the main problem and the transformed sub-problem is iterated by column and constraint generation (C&CG) algorithm. Case studies show that the presented model can handle the uncertainties of wind power generation and EVs' behaviors.

Key words: wind power generation, electric vehicle, day-ahead economic dispatch, data-driven, adjustable robust optimization

CLC Number:

TM732

WANG Xi, XU Hao, WANG Haiyan, CHEN Bo, WANG Changhao, LIU Yang. Day-ahead Economic Dispatch of Integrated Energy System Considering Uncertainties of Wind Power and Electric Vehicles[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(12): 82-91.

Figures/Tables 16

Fig.1 Schematic diagram of the integrated energy system

Fig.2 Charging demand of EVs

Fig.3 Dispatch results of power

Fig.4 Dispatch results of heat

Table 1 Optimization results with or without bus participation

Fig.5 DED strategies with or without bus participation

Fig.6 Scatter of total operation cost

Table 2 Cost comparison of different optimization methods

Table 3 Comparison of computation time

Fig.7 Uncertain intervals of output of WPG

Table 4 Comparison of different quantity of historical data

Fig.A1 Prediction value of uncertain variables

Table A1 The parameters of CGs

Table A2 The parameters of storage systems

Table A3 TOU price of micro energy grid元·(kW·h)-1

Table A4 The parameters of EVs

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