Coordinated multi-level scheduling method considering uncertainty of renewable energy and load

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 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.

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Publication Details

Journal
Frontiers in Energy Research
Published
2025-08-18
DOI
https://doi.org/10.3389/fenrg.2025.1612065
Citations
2
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
1.46

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article

Coordinated multi-level scheduling method considering uncertainty of renewable energy and load

Moyan Zhu, Kangping Qin, Guangyu He, Min Wen et al.
2 citations
Frontiers in Energy Research
Smart Grid Energy Management
1.46
article

Coordinated multi-level scheduling method considering uncertainty of renewable energy and load

Moyan Zhu, Kangping Qin, Guangyu He, Min Wen, Kaiming Zou, Bingbing Song
article en
2 citations

Abstract

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 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.

Frontiers in Energy ResearchVol. 13
State Grid Corporation of China (China) (CN), Shanghai Jiao Tong University (CN)
Science and Technology Project of State Grid
Openalex Percentile: Top 16%
Smart Grid Energy Management
1.46
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Coordinated multi-level scheduling method considering uncertainty of renewable energy and load — Moyan Zhu, Kangping Qin, et al. · Frontiers in Energy Research (2025) | TGRS Research Map | TGRS