
1. 国网河南省电力有限公司经济技术研究院,河南,郑州,450000
2. 上海电力大学 电气工程学院,上海,200082
Published Online:23 October 2025,
Published:2025
移动端阅览
齐桓若, 陈晨, 郭放, 薛文杰, 闫向阳, 康祎龙, 刘俊成, 马思源. 考虑精细化充放电与碳效益的配电网储能多目标双层规划模型[J]. 中国电力, 2025, 58(10): 121-135.
QI Huanruo, CHEN Chen, GUO Fang, et al. Multi-objective Bi-level Planning Model for Distribution Network Energy Storage Considering Refined Charging/Discharging and Carbon Benefits[J]. 2025, 58(10): 121-135.
齐桓若, 陈晨, 郭放, 薛文杰, 闫向阳, 康祎龙, 刘俊成, 马思源. 考虑精细化充放电与碳效益的配电网储能多目标双层规划模型[J]. 中国电力, 2025, 58(10): 121-135. DOI: 10.11930/j.issn.1004-9649.202506048.
QI Huanruo, CHEN Chen, GUO Fang, et al. Multi-objective Bi-level Planning Model for Distribution Network Energy Storage Considering Refined Charging/Discharging and Carbon Benefits[J]. 2025, 58(10): 121-135. DOI: 10.11930/j.issn.1004-9649.202506048.
高比例新能源发展愿景下,为有效缩短储能回报周期、提升新能源消纳以及降低配电网碳排放,提出一种考虑精细化充放电与碳效益的配电网储能多目标双层规划模型。首先,基于Wasserstein距离和梯度惩罚的改进生成对抗网络(Wasserstein generative adversarial network with gradient penalty,WGAN-GP)以及K-中心聚类算法(K-medoids)生成光伏典型场景。其次,建立储能系统的充放电精细化模型,并基于储能降碳量和全生命周期碳排放量构建碳效益模型。然后,构建考虑精细化充放电与碳效益的双层配电网储能规划运行模型,以日总成本最小为上层目标,对储能进行优化配置;以运行成本最小、电压偏移量最小和储能碳效益最大为下层目标,实现配电网的优化运行。再次,利用跨层关联变量建模将双层模型转化为单层多目标模型,并采用归一化法向约束法(normalized normal constraint,NNC)求解多目标问题,采用熵权-逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)选取最优折中解。最后,基于IEEE 33节点系统进行算例仿真,验证模型有效性。
Under the vision of high-proportion renewable energy development
in order to effectively shorten the energy storage payback period
enhance renewable energy accommodation
and reduce distribution network carbon emissions
this paper proposes a multi-objective bilevel planning model for energy storage systems (ESSs) in distribution networks that considers refined charging/discharging strategies and carbon benefits. Firstly
typical photovoltaic scenarios are generated using an improved Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and the K-medoids clustering algorithm. Secondly
a refined charging/discharging model for ESS is established
and a carbon benefit model is constructed based on both the carbon emission reduction enabled by ESS and its lifecycle carbon emissions. And then
a bilevel ESS planning and operation model for distribution network is constructed considering the refined charging/discharging strategies and carbon benefits. The upper-level model aims to minimize the total daily cost for optimal energy storage configuration
while the lower-level model pursues the minimization of operational costs and voltage deviation
as well as the maximization of carbon benefits from energy storage
to achieve optimized distribution network operation. Subsequently
the bilevel model is transformed into a single-layer multi-objective model by modeling the inter-layer coupling variables. The multi-objective model is then solved using the normalized normal constraint (NNC) method
and the optimal compromise solution is selected via the entropy-weighted TOPSIS method. Finally
the effectiveness of the proposed model is verified through numerical case studies based on the IEEE 33-node system.
1李旭东, 谭青博, 赵浩辰, 等. 碳达峰背景下中国电力行业碳排放因素和脱钩效应[J]. 中国电力, 2024, 57 (5): 88- 98.
LI Xudong, TAN Qingbo, ZHAO Haochen, et al. Carbon emission factors and decoupling effects of China's power industry under the background of carbon peak[J]. Electric Power, 2024, 57 (5): 88- 98.
3陈奇芳, 李若凡, 夏明超, 等. 计及多维性能评估的新型配电网光伏选址定容方法[J]. 中国电力, 2024, 57 (10): 172- 178, 207.
CHEN Qifang, LI Ruofan, XIA Mingchao, et al. Photovoltaic site selection and capacity determination method for new distribution network considering multidimensional performance evaluation[J]. Electric Power, 2024, 57 (10): 172- 178, 207.
4解大, 代荣荣, 高少炜, 等. 基于保险精算理论的储能运营商利益分配策略[J]. 电力科学与技术学报, 2024, 39 (5): 247- 261.
XIE Da, DAI Rongrong, GAO Shaowei, et al. Benefit allocation strategy for energy storage operators based oninsurance actuarial theory[J]. Journal of Electric Power Science and Technology, 2024, 39 (5): 247- 261.
6李建林, 孙浩元, 张敏慧, 等. 计及风电平抑的电-氢混合储能容量优化配置[J]. 太阳能学报, 2025, 46 (6): 120- 129.
LI Jianlin, SUN Haoyuan, ZHANG Minhui, et al. Optimal capacity allocation of electricity-hydrogen hybrid energy storage considering wind power smoothing[J]. Acta Energiae Solaris Sinica, 2025, 46 (6): 120- 129.
7方晓涛, 严正, 王晗, 等. 考虑概率电压不平衡度越限风险的共享储能优化运行方法[J]. 上海交通大学学报, 2022, 56 (7): 827- 839.
FANG Xiaotao, YAN Zheng, WANG Han, et al. A shared energy storage optimal operation method considering the risk of probabilistic voltage unbalance factor limit violation[J]. Journal of Shanghai Jiaotong University, 2022, 56 (7): 827- 839.
8袁铁江, 郭建华, 杨紫娟, 等. 平抑风电波动的电-氢混合储能容量优化配置[J]. 中国电机工程学报, 2024, 44 (4): 1397- 1406.
YUAN Tiejiang, GUO Jianhua, YANG Zijuan, et al. Optimal allocation of power electric-hydrogen hybrid energy storage of stabilizing wind power fluctuation[J]. Proceedings of the CSEE, 2024, 44 (4): 1397- 1406.
10周勃, 李二超. 一种考虑风光不确定性的热电混合共享储能双层优化配置方法[J]. 太阳能学报, 2025, 46 (3): 189- 198.
ZHOU Bo, LI Erchao. A two-layer optimal allocation method for hybrid shared energy storage considering the uncertainty of wind power and photovoltaic[J]. Acta Energiae Solaris Sinica, 2025, 46 (3): 189- 198.
11高芊芊, 山雨琦, 朱晓荣. 基于合作博弈的共享混合储能电站规划[J]. 太阳能学报, 2024, 45 (12): 509- 519.
GAO Qianqian, SHAN Yuqi, ZHU Xiaorong. Planning of shared hybrid energy storage power station based on cooperative game[J]. Acta Energiae Solaris Sinica, 2024, 45 (12): 509- 519.
12马楠, 刘国伟, 吴杰康, 等. 租赁市场中储能容量配置双层鲁棒优化模型[J]. 电网技术, 2025, 49 (2): 653- 665.
MA Nan, LIU Guowei, WU Jiekang, et al. A double-layer robust optimization model for energy storage capacity allocation in the leasing market[J]. Power System Technology, 2025, 49 (2): 653- 665.
14南斌, 董树锋, 唐坤杰, 等. 考虑需求响应和源荷不确定性的光储微电网储能优化配置[J]. 电网技术, 2023, 47 (4): 1340- 1352.
NAN Bin, DONG Shufeng, TANG Kunjie, et al. Optimal configuration of energy storage in PV-storage microgrid considering demand response and uncertainties in source and load[J]. Power System Technology, 2023, 47 (4): 1340- 1352.
15袁世琦, 潘鹏程, 魏业文, 等. 园区综合能源系统低碳经济优化调度模型研究[J]. 太阳能学报, 2024, 45 (3): 347- 356.
YUAN Shiqi, PAN Pengcheng, WEI Yewei, et al. Study on low carbon economic optimal scheduling model of community integrated energy system[J]. Acta Energiae Solaris Sinica, 2024, 45 (3): 347- 356.
16王小虎, 楚春礼, 曹植, 等. 分布式光伏-储能系统经济-碳排放-能源效益实证分析——以山东省胶州光伏及其储能系统为例[J]. 中国环境科学, 2022, 42 (1): 402- 414.
WANG Xiaohu, CHU Chunli, CAO Zhi, et al. Empirical analysis of cost-CO2-energy benefits of distributed pho-tovoltaic-battery storage system—taking (PV-BSS) in a case study in rural Jiaozhou Shandong[J]. China Environmental Science, 2022, 42 (1): 402- 414.
17郝婷, 樊小朝, 王维庆, 等. 阶梯式碳交易下考虑源荷不确定性的储能优化配置[J]. 电力系统保护与控制, 2023, 51 (1): 101- 112.
HAO Ting, FAN Xiaochao, WANG Weiqing, et al. Optimal configuration of energy storage considering the source-load uncertainty under ladder-type carbon trading[J]. Power System Protection and Control, 2023, 51 (1): 101- 112.
18巩晋通. 考虑辅助服务和碳效益的光伏电站电化学储能优化配置[D]. 武汉: 武汉大学, 2022.
GONG Jintong. Optimal configuration of battery energy storage system for photovoltaic power station considering ancillary service and carbon benefits[D]. Wuhan: Wuhan University, 2022.
19刘道兵, 李珏岑, 齐越, 等. 考虑碳效益和运行策略的风电场储能优化配置[J]. 太阳能学报, 2025, 46 (2): 664- 675.
LIU Daobing, LI Juecen, QI Yue, et al. Wind farm energy storage optimization configuration considering carbon benefit and operation strategy[J]. Acta Energiae Solaris Sinica, 2025, 46 (2): 664- 675.
20赵璐, 巩晋通, 李园林, 等. 考虑碳效益和辅助调峰的光伏电站储能配置方法[J]. 武汉大学学报(工学版), 2023, 56 (1): 80- 88.
ZHAO Lu, GONG Jintong, LI Yuanlin, et al. Energy storage configuration method of photovoltaic power station considering carbon benefits and auxiliary peak shaving[J]. Engineering Journal of Wuhan University, 2023, 56 (1): 80- 88.
21李军徽, 张靖祥, 穆钢, 等. 辅助服务市场下独立储能调峰调频协同优化调度[J]. 中国电机工程学报, 2025, 45 (2): 650- 665.
LI Junhui, ZHANG Jingxiang, MU Gang, et al. Collaborative optimal dispatch of peak shaving and frequency modulation with independent energy storage based on auxiliary service market[J]. Proceedings of the CSEE, 2025, 45 (2): 650- 665.
24田圆, 陈红坤, 刘颖杰, 等. 辅助服务市场背景下灵活性资源调峰补偿价格决策方法[J]. 电力自动化设备, 2024, 44 (9): 154- 161, 188.
TIAN Yuan, CHEN Hongkun, LIU Yingjie, et al. Compensation price decision method for peak shaving of flexible resources in context of ancillary service market[J]. Electric Power Automation Equipment, 2024, 44 (9): 154- 161, 188.
25徐宁, 周波, 凌云鹏, 等. 现货市场下独立储能参与电能量与辅助服务协同优化策略[J]. 现代电力, 2025, 42 (1): 109- 116. DOI
XU Ning, ZHOU Bo, LING Yunpeng, et al. Collaborative optimization strategy of energy and auxiliary services with independent energy storage participating under the spot market[J]. Modern Electric Power, 2025, 42 (1): 109- 116. DOI
26王坤峰, 苟超, 段艳杰, 等. 生成式对抗网络GAN的研究进展与展望[J]. 自动化学报, 2017, 43 (3): 321- 332.
WANG Kunfeng, GOU Chao, DUAN Yanjie, et al. Generative adversarial networks: the state of the art and beyond[J]. Acta Automatica Sinica, 2017, 43 (3): 321- 332.
27ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial networks[C]//Proceedings of the 34th International Conference on Machine Learning. Sydney: JMLR. org, 2017: 214–223.
28GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017: 5769–5779.
29雷大勇. 交直流混合主动配电网规划模型及其求解方法研究[D]. 南昌: 南昌大学, 2022.
LEI Dayong. Research on the planning model and solution method of AC/DC hybrid active distribution network [D]. Nanchang: Nanchang University, 2022.
30米阳, 周杰, 卢长坤, 等. 基于改进生成对抗网络与碳足迹的配电网多目标双层规划[J]. 中国电机工程学报, 2024, 44 (21): 8421- 8435.
MI Yang, ZHOU Jie, LU Changkun, et al. A multi-objective bi-level planning of distribution network based on improved generative adversarial network and carbon footprint[J]. Proceedings of the CSEE, 2024, 44 (21): 8421- 8435.
31张忠会, 雷大勇, 蒋昌辉, 等. 基于二阶锥规划和NNC法的交直流混合配电网双层规划模型及其求解方法[J]. 中国电机工程学报, 2023, 43 (1): 70- 85.
ZHANG Zhonghui, LEI Dayong, JIANG Changhui, et al. A bi-level planning model and its solution method of AC/DC hybrid distribution network based on second-order cone programming and NNC method[J]. Proceedings of the CSEE, 2023, 43 (1): 70- 85.
32颜远, 林舜江, 刘明波. 考虑备用动作约束的含风电场电力系统多目标动态优化调度[J]. 电网技术, 2018, 42 (2): 479- 486.
YAN Yuan, LIN Shunjiang, LIU Mingbo. Multi-objective optimal dynamic dispatch of power system with wind farms considering reserve action constraints[J]. Power System Technology, 2018, 42 (2): 479- 486.
33赵书强, 汤善发. 基于改进层次分析法、CRITIC法与逼近理想解排序法的输电网规划方案综合评价[J]. 电力自动化设备, 2019, 39 (3): 143- 148, 162.
ZHAO Shuqiang, TANG Shanfa. Comprehensive evaluation of transmission network planning scheme based on improved analytic hierarchy process, CRITIC method and TOPSIS[J]. Electric Power Automation Equipment, 2019, 39 (3): 143- 148, 162.
34朱天曈, 丁坚勇, 郑旭. 基于改进TOPSIS法和德尔菲——熵权综合权重法的电网规划方案综合决策方法[J]. 电力系统保护与控制, 2018, 46 (12): 91- 99.
ZHU Tiantong, DING Jianyong, ZHENG Xu. A comprehensive decision-making method for power network planning schemes based on the combination of the improved TOPSIS method with Delphi-entropy weight method[J]. Power System Protection and Control
0
Views
0
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621