Adaptive Robust Optimal Scheduling of Combined Heat and Power Microgrids Based on Photovoltaic Mechanism/Data Fusion-Driven Power Prediction

Yueyang Xu, Yibo Wang (), Chuang Liu, Jian Xiong, Mo Zhou and Yang Du
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Yueyang Xu: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Yibo Wang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Chuang Liu: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Jian Xiong: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Mo Zhou: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Yang Du: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Energies, 2025, vol. 18, issue 3, 1-23

Abstract: In order to effectively deal with the adverse effects of the randomness of photovoltaic output on the operation of combined heat and power (CHP) microgrids, this paper proposes an adaptive robust optimal scheduling strategy for CHP microgrids based on photovoltaic mechanism/data fusion-driven power prediction. Firstly, the mechanism of the clear sky radiation model is used to calculate the photovoltaic clear sky limit output and random output, and the latter is reorganized in different periods by using the idea of similar days. Then, the data-driven random prediction results are superimposed with the clear sky limit output, the photovoltaic mechanism/data fusion-driven power prediction model is established, and the fusion-driven power prediction framework is provided. Secondly, the boundary information of uncertain factors is deeply explored, and an adaptive robust uncertainty set considering the confidence interval of predictive error statistical information is constructed. On this basis, a robust optimization model of CHP microgrids with the lowest operating cost is proposed, and the optimization model is solved by column and constraint generation algorithm. Finally, the rationality and effectiveness of the proposed model are verified through simulation examples and analytical calculations.

Keywords: combined heat and power microgrid; adaptive robust optimization; photovoltaic power prediction; similar day; mechanism/data fusion-driven (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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