Day-ahead scheduling model for agricultural microgrid with pumped-storage hydro plants considering irrigation uncertainty

Wu, Yingjun, Chen, Runrun, Lin, Zhiwei, Chen, Yuyang, Chen, Zhaorui, Chen, Xuejie and Yuan, Jiangfan 2024. Day-ahead scheduling model for agricultural microgrid with pumped-storage hydro plants considering irrigation uncertainty. Journal of Energy Storage 95 , 112468. 10.1016/j.est.2024.112468

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Abstract

In agricultural microgrids, pumped-storage hydropower plants (PSHPs) have the dual functionality of generating electricity and providing irrigation water from downstream reservoirs. The amount of water supply for irrigation is subject to uncertainty due to effective precipitation and agricultural water demand. These uncertainties affect the water levels in the PSHP's downstream reservoir, potentially leading to violations of the state of charge (SoC) constraints in day-ahead scheduling. To address this issue, this paper proposes a day-ahead scheduling model for agricultural microgrids that incorporates the uncertainties in PSHP irrigation supply. The model redefines the SoC constraint boundaries for the PSHP's downstream reservoir. Optimal irrigation water supply ranges for different periods are determined using interval linear programming and a two-stage interactive algorithm, which accounts for uncertainties in effective precipitation and agricultural water demand. To manage the uncertainty in effective precipitation, a confidence interval model is constructed using Gaussian processes and probability theory. For agricultural water demand uncertainty, a confidence interval model is developed using chance-constrained programming and two-boundary methods. Simulation results validate the correctness of the proposed scheduling model. This approach ensures robust and reliable day-ahead scheduling in agricultural microgrids, accommodating the inherent uncertainties in irrigation water supply and thereby enhancing the operational stability of PSHPs.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	2352-152X
Date of Acceptance:	2 June 2024
Last Modified:	26 Jul 2024 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/170451

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