Multi-timescale scheduling strategy for a 100 MW-level source-grid-load-storage integrated park

SHEN Qianfeng; BAI Yilin; WANG Junyue; SONG Zhengxiang; YANG Kun; MENG Jinhao

doi:10.19799/j.cnki.2095-4239.2025.0918

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Multi-timescale scheduling strategy for a 100 MW-level source-grid-load-storage integrated park

Energy Storage System and Engineering | 更新时间：2026-07-01

- Multi-timescale scheduling strategy for a 100 MW-level source-grid-load-storage integrated park
- Energy Storage Science and Technology Vol. 15, Issue 4, Pages: 1275-1291(2026)
- 作者机构：
  
  1.西安交通大学电气工程学院，陕西西安 710049
  2.中能智新科技产业发展有限公司，北京 100120
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.0918
  CLC： TM 73
- Received：15 October 2025，
  
  Revised：2025-12-01，
  
  Published：28 April 2026
- 稿件说明：
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沈钱锋, 白伊琳, 王俊月, 等. 百兆瓦级源网荷储一体化园区多时间尺度调度策略[J]. 储能科学与技术, 2026, 15(4): 1275-1291.

SHEN Qianfeng, BAI Yilin, WANG Junyue, et al. Multi-timescale scheduling strategy for a 100 MW-level source-grid-load-storage integrated park[J]. Energy Storage Science and Technology, 2026, 15(4): 1275-1291.
沈钱锋, 白伊琳, 王俊月, 等. 百兆瓦级源网荷储一体化园区多时间尺度调度策略[J]. 储能科学与技术, 2026, 15(4): 1275-1291. DOI： 10.19799/j.cnki.2095-4239.2025.0918.

SHEN Qianfeng, BAI Yilin, WANG Junyue, et al. Multi-timescale scheduling strategy for a 100 MW-level source-grid-load-storage integrated park[J]. Energy Storage Science and Technology, 2026, 15(4): 1275-1291. DOI： 10.19799/j.cnki.2095-4239.2025.0918.

摘要

随着全球能源结构向清洁低碳转型，高比例可再生能源并网带来的随机性与波动性对电力系统平衡能力提出了严峻挑战。传统“源随荷动”调度模式难以适应大规模新能源消纳需求，亟须探索源网荷储一体化协同调控技术。本工作以新疆某百兆瓦级工业园区为研究对象，提出日前优化、日内滚动修正、实时逻辑修正的“三段式”多时间尺度调度策略。为高效求解该高维调度模型，本工作设计了精英优选的粒子群优化(PSO)算法，通过逻辑判断生成初始解并引入精英粒子筛选机制，显著降低了寻优维度，有效避免了局部最优。仿真表明，所提精英优选PSO算法较基于逻辑初始化的PSO可使综合运行成本降低2.71%；且该策略通过多时间尺度协调控制，综合运行成本较单一日前调度降低26.49%。实际应用表明多时间尺度调度策略使得月弃风弃光率减少至4.9%，且月消纳新能源发电量可达62200.13 MWh。本研究开发的一体化智慧调控平台有效解决了百兆瓦级工业园区新能源消纳与电力平衡难题，为大型园区低碳转型提供了技术支撑，对推动新型电力系统建设与“双碳”目标实现具有重要意义。

Abstract

With the global transition toward clean and low-carbon energy systems

the randomness and volatility associated with high-penetration renewable energy integration pose significant challenges to power system balance. Traditional "source-follows-load" dispatching models struggle to accommodate large-scale renewable energy consumption. This study focuses on a 100 MW-level source-grid-load-storage integrated industrial park in Xinjiang and proposes a three-stage multi-timescale dispatching strategy

including day-ahead optimization

intraday rolling correction

and real-time rapid-response scheduling. To efficiently solve (PSO) the resulting high-dimensional dispatch model

an elite-preferred particle swarm optimization algorithm is developed. The algorithm generates initial solutions through logical judgment and introduces an elite particle screening mechanism

which significantly reduces the search dimension and effectively avoids local optima. Practical implementation demonstrates that monthly wind and solar curtailment was reduced to 4.9%

achieving renewable energy absorption of 62200.13 MWh per month. A self-developed smart control platform addresses the challenges of renewable energy integration and power balance in 100 MW-level industrial parks. This research fills the technical gap in large-scale integrated energy system regulation

provides a replicable solution for low-carbon transformation

and supports the construction of new power systems.

关键词

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references

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