ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1612065
Coordinated Multi-Level Scheduling Method Considering Uncertainty of Renewable Energy and Load
Provisionally accepted
- 1East China Branch of State Grid Corporation of China, Shanghai, China
- 2Shanghai Jiao Tong University, Shanghai, China
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As renewable energy continues to be widely integrated, the energy structure is gradually transforming. The increasing grid connection of wind and photovoltaic power signifies a major shift in the energy mix. This change is particularly evident in heavy load areas at the regional grid and provincial dispatch levels, where uncertainties on both the supply and demand sides impact the daily operation of power systems. New dispatch strategies are urgently needed to address these uncertainties. This paper introduces a two-stage day-ahead and intra-day coordinated multi-level dispatch method that considers both the regional-level and provincial-level power systems, addressing supply-demand uncertainties from the perspective of regional grid-level and unmet load peak shaving. Unmet load refers to the load that cannot be met solely by the output of regional grid units. At the regional grid level, a unit dispatch model for unmet load peak shaving is developed. We introduce the concept of unmet load and, based on peak-valley weighting, propose a multi-province load peak shaving method, improving the approach to unmet load considerations. At the provincial level, a two-stage robust optimization dispatch model is constructed based on regional grid dispatch, and it is solved using the Karush-Kuhn-Tucker (KKT) conditions and the Column-and-Constraint Generation (C&CG) algorithm. Finally, case study results validate the proposed model's effectiveness, demonstrating its ability to provide an optimized coordinated grid-provincial dispatch strategy under supply-demand uncertainty.
Keywords: uncertainty, Coordinated scheduling, Unmet load, peak shaving, robust optimization, C&CG
Received: 15 Apr 2025; Accepted: 04 Jul 2025.
Copyright: © 2025 Song, Qin, Wen, Zhu, Zou and He. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Moyan Zhu, Shanghai Jiao Tong University, Shanghai, China
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