基于对抗神经网络场景生成的源网荷储双层柔性规划

doi:10.16183/j.cnki.jsjtu.2024.214

上海交通大学学报 ›› 2026, Vol. 60 ›› Issue (4): 531-540.doi: 10.16183/j.cnki.jsjtu.2024.214

• 新型电力系统与综合能源 • 上一篇下一篇

基于对抗神经网络场景生成的源网荷储双层柔性规划

马溯¹, 柳璐¹(), 程浩忠¹, 张啸虎², 许凌², 娄为²

¹ 上海交通大学电力传输与功率变换控制教育部重点实验室, 上海 200240
² 国家电网有限公司华东分部, 上海 200120

收稿日期:2024-06-07 修回日期:2024-08-15 接受日期:2024-09-12 出版日期:2026-04-28 发布日期:2026-04-29
通讯作者: 柳璐 E-mail:liulu52@163.com
作者简介:马溯(1989—),博士生,从事电力系统规划研究.
基金资助:
国家电网公司总部科技项目资助(5100-202303410A-3-2-ZN)

Bi-Level Flexible Planning of Source-Grid-Load-Energy Storage Based on GAN

MA Su¹, LIU Lu¹(), CHENG Haozhong¹, ZHANG Xiaohu², XU Ling², LOU Wei²

¹ Key Laboratory Control of Power Transmission and Conversion of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
² East China Branch of State Grid Corporation of China, Shanghai 200120, China

Received:2024-06-07 Revised:2024-08-15 Accepted:2024-09-12 Online:2026-04-28 Published:2026-04-29
Contact: LIU Lu E-mail:liulu52@163.com

摘要/Abstract

摘要：

为应对高比例新能源消纳,有必要推进新能源与煤电规划与运行.为此,建立考虑不确定性场景生成的源网荷储柔性规划方法.首先,用对抗神经网络方法生成四季风光不确定性场景.然后,在进一步分析新能源不确定性和煤电灵活性改造特性基础上,建立考虑新能源与煤电源网荷储长短期时间尺度双层随机规划模型,探究考虑新能源决策与煤电灵活性改造的源网荷储柔性规划方案;采用场景准确性指标和灵活性不足指标,探究不确定性场景生成方法和规划结果之间相关性.最后,利用实际算例系统验证所提方法有效性.所提方案在夏、秋季节午时调峰高峰时段可发挥显著深度调峰作用,能适应未来大规模新能源消纳,提升源网荷储经济性.

关键词: 场景生成, 源网荷储, 柔性规划, 新能源, 煤电灵活性改造

Abstract:

To address the high penetration of renewable energy, it is necessary to advance the planning and operation of both renewable and coal-fired power, and to develop a source-grid-load-storage flexible planning method that considers uncertainty scenario generation. First, generative adversarial networks (GANs) are employed to generate seasonal (spring, summer, autumn, and winter) scenarios of wind and solar uncertainties. Based on a further analysis of renewable energy uncertainty and coal-fired power flexibility retrofit characteristics, a two-stage stochastic planning model with both long-term and short-term time scales is established, incorporating renewable energy and coal-fired power. This model explores flexible source-grid-load-storage planning schemes that account for renewable energy decisions and coal-fired power flexibility retrofit. Scenario accuracy indicators and insufficient flexibility indicators are used to investigate the correlation between uncertainty scenario generation methods and planning outcomes. Finally, the proposed method is validated via a practical case system. The proposed scheme exhibits a remarkable deep peak-regulation effect during severe noon peak-regulation periods in summer and autumn. It can accommodate large-scale renewable energy integration in the future and achieves superior economic performance for the source-grid-load-storage system.

Key words: scenarios generation, source-grid-load-energy storage, flexible planning, renewable energy, coal fired flexible retrofit

中图分类号:

TM715

马溯, 柳璐, 程浩忠, 张啸虎, 许凌, 娄为. 基于对抗神经网络场景生成的源网荷储双层柔性规划[J]. 上海交通大学学报, 2026, 60(4): 531-540.

MA Su, LIU Lu, CHENG Haozhong, ZHANG Xiaohu, XU Ling, LOU Wei. Bi-Level Flexible Planning of Source-Grid-Load-Energy Storage Based on GAN[J]. Journal of Shanghai Jiao Tong University, 2026, 60(4): 531-540.

图/表 8

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参考文献 13

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类别	置信度/%	方法	覆盖率/%	区间宽度/MW
春季场景	95	LGAN	94.56	625
		Markov	98.67	760
夏季场景	95	LGAN	93.06	288
		Markov	98.14	473
秋季场景	95	LGAN	92.62	248
		Markov	98.01	681
冬季场景	95	LGAN	95.76	920
		Markov	99.05	829

类别	场景S2	场景S4
春季场景	1.38	1.86
夏季场景	1.02	1.58
秋季场景	1.02	1.58
冬季场景	2.17	2.38

场景	线路投资成本/ 亿元	常规机组投资成本/ 亿元	新能源投资成本/ 亿元	储能投资成本/ 亿元	灵活性改造成本/ 亿元	投资成本/ 亿元	弃能成本/ 亿元	新能源维护成本/ 亿元	深调成本/ 亿元	需求响应成本/ 亿元	机组燃料成本/ 亿元	运行成本/ 亿元	总成本/ 亿元	新能源消纳率/%
F1	71.0	149.5	212.7	42.3	30.7	50.6	0	31.40	3.2	3.4	2339	2377	2883	100.0
F2	80.5	182.2	212.7	203.0	0	678.3	3.2	3.20	0	0	2343	2349	3027	99.0
F3	76.7	167.8	212.7	0	30.7	487.8	2.6	3.20	41.6	0	2342	2389	2877	99.2
F4	73.5	162.0	212.7	60.0	30.7	538.8	1.9	3.20	38.2	0	2341	2384	2923	99.4
F5	80.5	182.2	212.7	0	0	4754.0	31	3.16	0	0	2374	2409	2885	90.2

场景	年线路投资成本	年常规机组投资成本	年新能源投资成本	年储能投资成本	年灵活性改造成本	年投资成本	年弃能成本	年新能源维护成本	年深度调峰成本	年需求响应成本	年机组燃料成本	年运行成本	年总成本
D1	80.5	182.2	212.7	203.0	0	678.3	3.2	3.2	0	0	2343	2349	3027
D2,R1	71.0	149.5	212.7	42.3	30.7	506.1	0	3.2	31.4	3.4	2339	2377	2883
D3	68.2	149.5	212.7	26.2	35.7	492.3	0	3.2	33.2	3.4	2339	2379	2871
R2	76.0	100.0	263.7	84.6	56.5	580.8	2.2	3.9	33.8	0.5	2257	2298	2879
R3	79.6	90.7	289.2	84.6	56.5	600.6	4.1	4.3	35.3	2.3	2217	2263	2864

基于对抗神经网络场景生成的源网荷储双层柔性规划

Bi-Level Flexible Planning of Source-Grid-Load-Energy Storage Based on GAN

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