Tri-Level Integrated Optimization Design Method of a CCHP Microgrid with Composite Energy Storage

Yi Yan, Xuerui Wang, Ke Li, Xiaopeng Kang, Weizheng Kong and Hongcai Dai
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Yi Yan: Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Xuerui Wang: Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China
Ke Li: School of Control Science and Engineering, Shandong University, Jingshi Road 17923, Jinan 250061, China
Xiaopeng Kang: School of Control Science and Engineering, Shandong University, Jingshi Road 17923, Jinan 250061, China
Weizheng Kong: State Grid Energy Research Institute Co., Ltd., Binhe 18#, Beiqijia, Changping District, Beijing 102209, China
Hongcai Dai: State Grid Energy Research Institute Co., Ltd., Binhe 18#, Beiqijia, Changping District, Beijing 102209, China

Sustainability, 2022, vol. 14, issue 9, 1-29

Abstract: Combined cooling, heating, and power (CCHP) microgrids are important means of solving the energy crisis and environmental problems. Multidimensional composite energy storage systems (CESSs) are vital to promoting the absorption of distributed renewable energy using CCHP microgrids and improving the level of energy cascade utilization. In this context, this paper proposes a multi-energy coupling structure that includes a multidimensional CESS with a compressed air energy storage (CAES) connected to a CCHP microgrid. Dividing design and operation causes some problems, such as low operating efficiency and difficult energy matching of CESSs. To solve the existing problems, an integrated design method is proposed that considers the capacity configuration of the equipment and the optimal operation of the system on a multi-timescale. The optimization result of the capacity configuration level is used as the constraint of the operational control level, and the equipment output plan of the operational control level is used as the optimized operation strategy and parameters of the system. The C-NSGA-II algorithm is adopted at the capacity configuration level and day-ahead scheduling level. Rolling optimization is solved using the PSO algorithm. The final result that satisfied the output design was obtained after several iterations. The average daily cost and CO 2 emission reduction rate ( CO 2 ERR) of capacity configuration levels are $2241 and 45.02%. The best CO 2 ERRs of day-ahead scheduling optimization levels are 39.9% and 45.9% in summer and winter, where the operating cost saving rate (OCSR) are 30.5% and 38.3% separately. Examples show that the integrated design method presented in this paper has significant advantages in enhancing energy-grade matching and improving the economy and environmental protection of the system.

Keywords: integrated design method; CESS; CCHP; CAES (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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