Source-load uncertainty-based multi-objective multi-energy complementary optimal scheduling

Yixiang Ma, Lean Yu, Guoxing Zhang, Zhiming Lu and Jiaqian Wu

Renewable Energy, 2023, vol. 219, issue P1

Abstract: The uncertainty of the source-load data, accompanied by the contradiction between different goal orientations, poses a challenge to the decision-making of the scheduling scheme. To solve the issue of multi-objective optimal scheduling under the condition of source-load uncertainty, this paper proposes a multi-objective multi-energy complementary optimal scheduling scheme based on source-load uncertainty. In the proposed method, four main steps: uncertainty analysis of the source-load data, design of multi-energy complementary scheduling scheme, optimal calculation of the scheduling scheme, and multi-scenario analysis, are involved. In addition, the effect of the source-load prediction on the optimal scheduling scheme is further analyzed for management implications. In the empirical analysis, the source-load data with 15-min intervals is introduced as the sample data, and different optimization algorithms and compromise solution determination methods are selected for comparative analysis. Compared with other optimization algorithms, the proposed method has an average decrease of 22.699%, 7.587% and 22.149% in the total cost of generation (TCG), the spinning reserve cost (CSR) and the carbon emission (CE), respectively, and the average increase in the rate of new energy generation (RNE) is 11.969%. The empirical analysis shows that the proposed method outperforms all benchmark methods, which can provide valuable insights for intraday rolling scheduling under the condition of source-load uncertainty and multi-objective optimization.

Keywords: Intraday rolling scheduling; Source-load uncertainty; Multi-objective optimization; Compromise solution; Multi-energy complementary; Prediction driven-decision making (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:219:y:2023:i:p1:s0960148123013988

DOI: 10.1016/j.renene.2023.119483

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