Citation

Goh, Hui Hwang and Shi, Shuaiwei and Liang, Xue and Zhang, Dongdong and Dai, Wei and Liu, Hui and Wong, Shen Yuong and Agustiono Kurniawan, Tonni and Goh, Kai Chen and Cham, Chin Leei (2022) Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty. Applied Energy, 327. p. 120094. ISSN 0306-2619

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Abstract

The benefits of more comprehensive energy use and promotion of renewable energy (RE) consumption enable large-scale microgrid deployment. However, the uncertainty of renewable energy sources and the diversity of load types pose a threat to the microgrid's stability. Recently, energy scheduling optimization for microgrids (MGs) has been primarily based on ideal models, but incorporating as many real-world characteristics as feasible can improve the reliability of the optimization outcome. This paper provides a multi-stage methodology for solving the energy management optimization (EMO) problem of MG under uncertainty considering carbon trading market and demand side response (DSR). To begin, scenario analysis method was used to address the uncertainty associated with RE in MG, and four typical scenarios of renewable energy were generated. Then, the flexible configuration and operational constraints of each power source in MG are dealt with under the premise of considering the carbon trading market. The third stage involved merging the characteristics of various load types and analyzing the response impacts of different percentage residential and industrial loads, respectively, using the price-based and load-transfer-based DSR approaches. Finally, quantum particle swarm optimization (QPSO) algorithm was used to obtain the optimal solution. The acquired results demonstrate the efficacy of the proposed multi-stage energy optimization framework, and support the following two conclusions: 1) The carbon trading market policy contributes to the reduction of carbon emissions and fossil fuel consumption. 2) A high load participation rate in DSR can increase MG operation economics by up to 27.48% compared to not considering DSR.

Item Type:	Article
Uncontrolled Keywords:	Energy management optimization, Microgrid, Stochastic analysis, Renewable energy, Quantum particle swarm optimization
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Nov 2022 03:01
Last Modified:	01 Nov 2022 03:01
URII:	http://shdl.mmu.edu.my/id/eprint/10596

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