Multistage Economic Scheduling Model of Micro-Energy Grids Considering Flexible Capacity Allocation

Hang Liu, Yongcheng Wang, Shilin Nie, Yi Wang and Yu Chen
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Hang Liu: School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
Yongcheng Wang: School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
Shilin Nie: Zhangshu Development and Reform Commission, Zhangshu 331200, China
Yi Wang: School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
Yu Chen: School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China

Sustainability, 2022, vol. 14, issue 15, 1-29

Abstract: Micro-energy grids integrating multiple energy sources can realize the efficient use of renewable energy and accelerate the process of energy transition. However, due to the uncertainty of renewable energy, the stability and security of system operations should be taken into account with respect to multi-energy coupling economic operations. Thus, it is essential to make flexible capacity allocations in advance of the actual scheduling of production in the micro-energy grid. With this motivation, this paper constructs a three-stage scheduling model corresponding to the running stage of the spot market. Specifically, the capacity of flexible, active devices is configured in the day-ahead stage; then, the intraday economic operation dispatching scheme is provided according to the capacity configuration. Based on the day-ahead and intraday optimization results, the system power balance is realized through the dispatching process using the reserve capacity of flexible active devices for deviations generated in the real-time stage of renewable energy. For the uncertainty of renewable energy output, the clustering method is applied to realize the clustering analysis of renewable energy output scenarios. In addition, the conditional value at risk (CVaR) theory is introduced to modify the three-stage stochastic optimization model, and the risk values caused by uncertainty are quantitatively evaluated. Finally, we simulate a practical case to verify the effectiveness of the proposed model. The results show that day-ahead flexible capacity allocation enhances the autonomy of the micro-energy grid system, ensures a certain degree of system operational security, and reduces balancing costs in the real-time stage. The higher the risk aversion factor, the more operational costs the system operator pays to avoid the risk. In addition, if the carbon penalty coefficient is higher, the overall carbon emission level of the micro-energy grid will decrease, but it will gradually converge to a minimal level. This paper guides the development of micro-energy grids and has important constructional significance for the construction of multi-energy collaborative mechanisms.

Keywords: micro-energy grids; multistage optimization; capacity allocation; CVaR; processing of uncertainty (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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