METHODS article
Front. Smart Grids
Sec. Smart Grid Technologies
Volume 4 - 2025 | doi: 10.3389/frsgr.2025.1632546
Generation of Unit Startup Schemes Driven by Multi-Scenario Constraints and a Comprehensive Evaluation Method for the Carbon Peak and Neutrality Targets
Provisionally accepted
- 1Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD., Guangdong, China
- 2Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China
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Abstract. The present paper proposes a multi-scenario constraint-driven method for generating unit startup schemes and conducting a comprehensive evaluation of carbon peak and neutrality targets. This method is intended to address the comprehensive optimization problem of unit startup schemes under the carbon peak and neutrality targets. The present study proposes a multifaceted indicator system, encompassing low-carbon targets, economic costs, and system stability. The system integrates the analytic hierarchy process to quantify indicator weights, and it devises a two-layer optimization model to achieve dynamic generation and iterative optimization of startup schemes. The innovative introduction of a scoring feedback mechanism enables the conversion of comprehensive evaluation results into constraint conditions, thereby facilitating the gradual optimization of the startup scheme until it approaches the comprehensive optimal solution. The experimental results demonstrate that this method significantly enhances the comprehensive performance of startup schemes, including low-carbon performance, economic efficiency, and stability. The closed-loop feedback mechanism employed achieves an effective balance between solution efficiency and scheme quality, thereby providing quantifiable decision support for low-carbon dispatch in power systems.
Keywords: Carbon Peak and Neutrality targets, low-carbon targets, economic costs, System stability, Low-carbon dispatch, Pumped storage, Battery Charging/Discharging, peak shaving
Received: 21 May 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Dong, Gao, Hu, Gao and Xing. 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: Yue Xing, Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu, China
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