A Two-Level Scheduling Optimization Model for Building Microgrids Considering Demand Response and Uncertainties of Flexible Load Resources

doi:10.12204/j.issn.1000-7229.2022.06.014

Abstract

Abstract:

In order to solve the problem of building microgrid consuming renewable energy where distributed generation and flexible load resources are integrated, this paper takes electric vehicles as flexible resources, and constructs a scheduling optimization model for building microgrids considering demand response and charging/discharging uncertainty. Firstly, the price-based demand-response model and incentive-based demand-response model of flexible load resources are constructed. Secondly, the electric vehicle is regarded as an integrator of production and consumption, and Markov chain and information gap decision theory (IGDT) are used to deal with the uncertainties of charging and discharging, respectively. Finally, the deterministic scheduling optimization model is constructed by maximizing the net revenue of the system, photovoltaic accommodation, users’ satisfaction, and minimizing carbon dioxide emissions. The effectiveness of the constructed optimization model is verified by example analysis. The model not only enhances the clean energy accommodation rate of the system and reduces carbon dioxide emissions, but also can bring certain benefits to users, tap the potential of flexible load resources to participate in microgrid scheduling, and realize finally the win-win benefits of both supply and demand sides.

Key words: building microgrid, electric vehicle, demand response, uncertainty, Markov chain, information gap decision theory (IGDT), two-level optimization

CLC Number:

TM73

WANG Pingping, XU Jianzhong, YAN Qingyou, LIN Hongyu. A Two-Level Scheduling Optimization Model for Building Microgrids Considering Demand Response and Uncertainties of Flexible Load Resources[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(6): 128-140.

Figures/Tables 18

Fig.1 Energy supply structure of the system

Fig.2 Ladder-type compensation mechanism of IBDR

Fig.3 Solution steps

Fig.4 Structural framework of the optimization model

Fig.5 Fitting result of the milage

Fig.6 Curves of charging load and the number of EVs

Table 1 Case setup

Table 2 Emission factors of CO2

Fig.7 Curves of load and photovoltaic power

Table 3 Time division and charging prices

Table 4 Physical parameters of EVs

Fig.8 Output of generators in Case 1

Fig.9 Charging loads before and after PBDR and the output of generators

Fig.10 Charging loads before and after IBDR and the output of generators

Fig.11 Charging loads before and after EVs’ discharging and the output of generators

Fig.12 Charging loads with and without considering discharge uncertainty of EVs and the output of generators

Fig.13 Variation trajectory of revenue robustness and uncertainty coefficient

Table 5 Calculation result comparison of the cases

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