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

doi:10.16183/j.cnki.jsjtu.2024.214

Abstract

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

CLC Number:

TM715

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.

Figures/Tables 8

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References 13

[14]	LIU S Y, CHEN C M, JIANG Y C, et al. Bi-level coordinated power system restoration model considering the support of multiple flexible resources[J]. IEEE Transactions on Power Systems, 2023, 38(2): 1583-1595. doi: 10.1109/TPWRS.2022.3171201 URL
[15]	WANG Y C, LOU S H, WU Y W, et al. Coordinated planning of transmission expansion and coal-fired power plants flexibility retrofits to accommodate the high penetration of wind power[J]. IET Generation, Transmission & Distribution, 2019, 13(20): 4702-4711. doi: 10.1049/gtd2.v13.20 URL
[16]	WANG Y C, LOU S H, WU Y W, et al. Flexible operation of retrofitted coal-fired power plants to reduce wind curtailment considering thermal energy storage[J]. IEEE Transactions on Power Systems, 2020, 35(2): 1178-1187. doi: 10.1109/TPWRS.59 URL
[17]	杨修宇, 穆钢, 柴国峰, 等. 考虑灵活性供需平衡的源-储-网一体化规划方法[J]. 电网技术, 2020, 44(9): 3238-3246.
	YANG Xiuyu, MU Gang, CHAI Guofeng, et al. Source-storage-grid integrated planning considering flexible supply-demand balance[J]. Power System Technology, 2020, 44(9): 3238-3246.
[18]	卓振宇, 张宁, 谢小荣, 等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(9): 171-191.
	ZHUO Zhenyu, ZHANG Ning, XIE Xiaorong, et al. Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(9): 171-191.
[19]	QIU J, ZHAO J H, DONG Z Y. Probabilistic transmission expansion planning for increasing wind power penetration[J]. IET Renewable Power Generation, 2017, 11(6): 837-845. doi: 10.1049/rpg2.v11.6 URL
[20]	郭咏涛, 向月, 刘俊勇. 面向高比例清洁能源消纳的含灵活性资源电力系统规划方案优选[J]. 上海交通大学学报, 2023, 57(9): 1146-1155. doi: 10.16183/j.cnki.jsjtu.2022.269
	GUO Yongtao, XIANG Yue, LIU Junyong. Optimal planning of power systems with flexible resources for high penetrated renewable energy accommodation[J]. Journal of Shanghai Jiao Tong University, 2023, 57(9): 1146-1155.
[21]	LI Z Z, YANG P, ZHAO Z L, et al. Retrofit planning and flexible operation of coal-fired units using stochastic dual dynamic integer programming[J]. IEEE Transactions on Power Systems, 2024, 39(1): 2154-2169. doi: 10.1109/TPWRS.2023.3243093 URL
[1]	黄强, 郭怿, 江建华, 等. “双碳” 目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509. doi: 10.16183/j.cnki.jsjtu.2021.272
	HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[2]	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2819.
	ZHANG Zhigang, KANG Chongqing. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819.
[3]	魏旭, 刘东, 高飞, 等. 双碳目标下考虑源网荷储协同优化运行的新型电力系统发电规划[J]. 电网技术, 2023, 47(9): 3648-3661.
	WEI Xu, LIU Dong, GAO Fei, et al. Generation expansion planning of new power system considering collaborative optimal operation of source-grid-load-storage under carbon peaking and carbon neutrality[J]. Power System Technology, 2023, 47(9): 3648-3661.
[4]	ZHUO Z Y, DU E S, ZHANG N, et al. Incorporating massive scenarios in transmission expansion planning with high renewable energy penetration[J]. IEEE Transactions on Power Systems, 2020, 35(2): 1061-1074. doi: 10.1109/TPWRS.59 URL
[5]	柳璐, 程浩忠, 吴耀武, 等. 面向高比例可再生能源的输电网规划方法研究进展与展望[J]. 电力系统自动化, 2021, 45(13): 176-183.
	LIU Lu, CHENG Haozhong, WU Yaowu, et al. Research progress and prospects of transmission expansion planning method for high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(13): 176-183.
[6]	ZIAEE O, ALIZADEH-MOUSAVI O, CHOOBINEH F F. Co-optimization of transmission expansion planning and TCSC placement considering the correlation between wind and demand scenarios[J]. IEEE Transactions on Power Systems, 2018, 33(1): 206-215. doi: 10.1109/TPWRS.2017.2690969 URL
[7]	HAN S, QIAO Y H, YAN J, et al. Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network[J]. Applied Energy, 2019, 239: 181-191. doi: 10.1016/j.apenergy.2019.01.193 URL
[22]	陈占鹏, 胡炎, 邰能灵, 等. 考虑灵活性与经济性的可再生能源电力系统源网联合规划[J]. 电力自动化设备, 2022, 42(9): 94-101.
	CHEN Zhanpeng, HU Yan, TAI Nengling, et al. Source-grid joint planning of renewable energy power system considering flexibility and economy[J]. Electric Power Automation Equipment, 2022, 42(9): 94-101.
[23]	张晓辉, 李阳, 钟嘉庆, 等. 基于安全因子及协同因子的源网多目标协调规划[J]. 电工技术学报, 2021, 36(9): 1842-1856.
	ZHANG Xiaohui, LI Yang, ZHONG Jiaqing, et al. Multi-objective coordinated planning of source network based on safety factor and coordination factor[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1842-1856.
[24]	李丹, 任洲洋, 颜伟, 等. 基于因子分析和神经网络分位数回归的月度风电功率曲线概率预测[J]. 中国电机工程学报, 2017, 37(18): 5238-5247.
	LI Dan, REN Zhouyang, YAN Wei, et al. Month-ahead wind power curve probabilistic prediction based on factor analysis and quantile regression neural network[J]. Proceedings of the CSEE, 2017, 37(18): 5238-5247.
[25]	董骁翀, 孙英云, 蒲天骄. 基于条件生成对抗网络的可再生能源日前场景生成方法[J]. 中国电机工程学报, 2020, 40(17): 5527-5536.
	DONG Xiaochong, SUN Yingyun, PU Tianjiao. Day-ahead scenario generation of renewable energy based on conditional GAN[J]. Proceedings of the CSEE, 2020, 40(17): 5527-5536.
[8]	ZHANG Y H, CHENG V, MALLAPRAGADA D S, et al. A model-adaptive clustering-based time aggregation method for low-carbon energy system optimization[J]. IEEE Transactions on Sustainable Energy, 2023, 14(1): 55-64. doi: 10.1109/TSTE.2022.3199571 URL
[9]	邵振国, 张承圣, 陈飞雄, 等. 生成对抗网络及其在电力系统中的应用综述[J]. 中国电机工程学报, 2023, 43(3): 987-1004.
	SHAO Zhenguo, ZHANG Chengsheng, CHEN Fei-xiong, et al. A review on generative adversarial networks for power system applications[J]. Proceedings of the CSEE, 2023, 43(3): 987-1004.
[10]	YUAN R, WANG B, SUN Y Q, et al. Conditional style-based generative adversarial networks for renewable scenario generation[J]. IEEE Transactions on Power Systems, 2023, 38(2): 1281-1296. doi: 10.1109/TPWRS.2022.3170992 URL
[11]	KANG M Y, ZHU R, CHEN D X, et al. A cross-modal generative adversarial network for scenarios generation of renewable energy[J]. IEEE Transactions on Power Systems, 2024, 39(2): 2630-2640. doi: 10.1109/TPWRS.2023.3277698 URL
[12]	孙大雁, 耿建, 杨胜春, 等. 新型电力系统环境下煤电与新能源等多元资源协调优化运行及其挑战[J]. 电网技术, 2024, 48(8): 3105-3113.
	SUN Dayan, GENG Jian, YANG Shengchun, et al. Challenges of coordinated and optimized operation of coal power and new energy and other diversified resources in new power system[J]. Power System Technology, 2024, 48(8): 3105-3113.
[13]	郇政林, 刘杰, 徐沈智, 等. 面向高比例新能源接入的源-荷-储灵活性资源协调规划[J]. 电网与清洁能源, 2022, 38(7): 107-117.
	HUAN Zhenglin, LIU Jie, XU Shenzhi, et al. Source-load-storage flexibility resource coordinated planning for high proportion of renewable energy[J]. Power System & Clean Energy, 2022, 38(7): 107-117.

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