孤岛微网极端场景储能容量优化配置方法研究

戴秦; 习卿钰; 王春鑫; 马利波

doi:10.19912/j.0254-0096.tynxb.2024-0776

PDF(1191 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (9) : 284-292. DOI: 10.19912/j.0254-0096.tynxb.2024-0776

孤岛微网极端场景储能容量优化配置方法研究

戴秦¹, 习卿钰¹, 王春鑫², 马利波²

作者信息 +

RESEARCH ON OPTIMAL ALLOCATION OF ENERGY STORAGE CAPACITY IN ISOLATED MICROGRID UNDER EXTREME SCENARIOS

Dai Qin¹, Xi Qingyu¹, Wang Chunxin², Ma Libo²

Author information +

文章历史 +

摘要

极端场景是威胁孤岛微网经济稳定运行的重要因素,计及系统运行经济成本及极端场景下的稳定运行约束,提出考虑极端场景的孤岛微网储能容量优化配置方法。首先,提出两段式孤岛微网储能容量优化配置数学模型,上、下层分别构建系统的容量优化配置模型及运行效益优化模型,并将极端场景下的稳定运行约束以罚函数方式引入到下侧的运行效益优化模型中;其次,为在极端场景样本数量不足的条件下尽可能保证系统的稳定运行,采用梯度惩罚的条件式Wasserstein距离生成对抗网络（CWGAN-GP）构建极端场景生成方法,通过大量生成极端场景以尽可能地保证容量优化配置结果的经济性及稳定性;最后,采用改进粒子群算法实现两段式孤岛微网储能容量优化配置数学模型求解。采用南方电网某省采集的孤岛电网场景数据验证所提方法,结果表明优化所得储能容量可有效满足系统稳定运行需求,各类运行场景下功率平衡越限率均为0,投资收益比可达5.53。

Abstract

Extreme scenarios are important factors that threaten the economic and stable operation of isolated island microgrids. Taking into account the economic cost of system operation and the constraints on stable operation under extreme scenarios, a method for optimizing the capacity allocation of isolated island microgrids is proposed. First, a two-stage isolated island microgrid energy storage capacity optimization and allocation mathematical model is proposed, with the upper and lower layers constructing the system capacity optimization and allocation model and the operation benefit optimization model, respectively, and the stable operation constraints under extreme scenarios are introduced into the lower operation benefit optimization model in the form of a penalty function. Secondly, in order to ensure the stable operation of the system under the condition of insufficient number of extreme scenario samples, CWGAN-GP is used to construct the extreme scenario generation method, and the extreme scenarios are generated in large quantities to ensure the economy and stability of the results of the optimal allocation of the capacity as much as possible. The proposed method is verified by using the scenario data of isolated island grid collected from a province of Southern Power Grid, and the results show that the optimized energy storage capacity can effectively ensure stable system operation. The power balance violation rate in various operation scenarios is 0, and the investment return ratio can reach 5.53.

导出引用

戴秦, 习卿钰, 王春鑫, 马利波. 孤岛微网极端场景储能容量优化配置方法研究[J]. 太阳能学报. 2025, 46(9): 284-292 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0776

Dai Qin, Xi Qingyu, Wang Chunxin, Ma Libo. RESEARCH ON OPTIMAL ALLOCATION OF ENERGY STORAGE CAPACITY IN ISOLATED MICROGRID UNDER EXTREME SCENARIOS[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 284-292 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0776

中图分类号： TM73

参考文献

[1] PANNALA S, PATARI N, SRIVASTAVA A K, et al.Effective control and management scheme for isolated and grid connected DC microgrid[J]. IEEE transactions on industry applications, 2020, 56(6): 6767-6780.
[2] FENG Z N, WEI F R, WU C T, et al.Novel source-storage coordination strategy adaptive to impulsive generation characteristic suitable for isolated island microgrid scheduling[J]. IEEE transactions on smart grid, 2023, 14(5): 3791-3803.
[3] 随权, 魏繁荣, 林湘宁, 等. 一种基于可控负荷效率控制的孤岛微网新型调度策略[J]. 中国电机工程学报, 2019, 39(24): 7168-7178, 7490.
SUI Q, WEI F R, LIN X N, et al.A novel dispatching strategy for isolated microgrid based on controllable load efficiency control[J]. Proceedings of the CSEE, 2019, 39(24): 7168-7178, 7490.
[4] MASHAYEKH S, STADLER M, CARDOSO G, et al.Security-constrained design of isolated multi-energy microgrids[J]. IEEE transactions on power systems, 2018, 33(3): 2452-2462.
[5] 吕智林, 孙顺吉, 汤泽琦, 等. 基于序列运算理论的微电网正负旋转备用容量优化[J]. 电力系统保护与控制, 2017, 45(20): 100-109.
LYU Z L, SUN S J, TANG Z Q, et al.Optimal scheduling of plus and minus spinning reserve in microgrid based on sequence operation theory[J]. Power system protection and control, 2017, 45(20): 100-109.
[6] LEE J, LEE S, LEE K.Multistage stochastic optimization for microgrid operation under islanding uncertainty[J]. IEEE transactions on smart grid, 2021, 12(1): 56-66.
[7] LI Y, YANG Z, LI G Q, et al.Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties[J]. IEEE transactions on industrial electronics, 2019, 66(2): 1565-1575.
[8] 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 B, DONG S F, TANG K J, 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.
[9] 谭颖, 吕智林, 李捷. 基于改进ELM的风/光/柴/储独立微网分布式电源多目标容量优化配置[J]. 电力系统保护与控制, 2016, 44(8): 63-70.
TAN Y, LYU Z L, LI J.Multi-objective optimal sizing method for distributed power of wind-solar-diesel-battery independent microgrid based on improved electromagnetism-like mechanism[J]. Power system protection and control, 2016, 44(8): 63-70.
[10] 赵超, 王斌, 孙志新, 等. 基于改进灰狼算法的独立微电网容量优化配置[J]. 太阳能学报, 2022, 43(1): 256-262.
ZHAO C, WANG B, SUN Z X, et al.Optimal configuration optimization of islanded microgrid using improved grey wolf optimizer algorithm[J]. Acta energiae solaris sinica, 2022, 43(1): 256-262.
[11] 左逢源, 张玉琼, 赵强, 等. 计及源荷不确定性的综合能源生产单元运行调度与容量配置两阶段随机优化[J]. 中国电机工程学报, 2022, 42(22): 8205-8215.
ZUO F Y, ZHANG Y Q, ZHAO Q, et al.Two-stage stochastic optimization for operation scheduling and capacity allocation of integrated energy production unit considering supply and demand uncertainty[J]. Proceedings of the CSEE, 2022, 42(22): 8205-8215.
[12] 易宇琴, 许加柱, 张伟明, 等. 考虑两阶段鲁棒优化配置的多微网合作博弈[J]. 电力系统自动化, 2023, 47(22): 149-156.
YI Y Q, XU J Z, ZHANG W M, et al.Multi-microgrid cooperative game considering two-stage robust optimal configuration[J]. Automation of electric power systems, 2023, 47(22): 149-156.
[13] 马燕峰, 傅钰, 赵书强, 等. 基于WGAN风光资源场景模拟和时序生产模拟的新能源电源容量配置[J]. 电力自动化设备, 2020, 40(11): 77-86.
MA Y F, FU Y, ZHAO S Q, et al.Capacity allocation of new energy source based on wind and solar resource scenario simulation using WGAN and sequential production simulation[J]. Electric power automation equipment, 2020, 40(11): 77-86.
[14] 杨和稳, 任增. 风光互补发电系统中抽水蓄能电站的优化配置[J]. 计算机仿真, 2015, 32(4): 111-115, 143.
YANG H W, REN Z.Research on optimal allocation of pumped storage station in wind and photovoltaic complementary generation system[J]. Computer simulation, 2015, 32(4): 111-115, 143.
[15] 李康平, 张展耀, 王飞, 等. 基于GAN场景模拟与条件风险价值的独立型微网容量随机优化配置模型[J]. 电网技术, 2019, 43(5): 1717-1725.
LI K P, ZHANG Z Y, WANG F, et al.Stochastic optimization model of capacity configuration for stand-alone microgrid based on scenario simulation using GAN and conditional value at risk[J]. Power system technology, 2019, 43(5): 1717-1725.
[16] 王东风, 刘婧, 黄宇, 等. 结合太阳辐射量计算与CNN-LSTM组合的光伏功率预测方法研究[J]. 太阳能学报, 2024, 45(2): 443-450.
WANG D F, LIU J, HUANG Y, et al.Photovoltaic power prediction method combinating solar radiation calculation and CNN-LSTM[J]. Acta energiae solaris sinica, 2024, 45(2): 443-450.
[17] 赵寒亭, 张耀, 霍巍, 等. 基于纵向联邦学习的短期风电功率协同预测方法[J]. 电力系统自动化, 2023, 47(16): 44-53.
ZHAO H T, ZHANG Y, HUO W, et al.Collaborative forecasting method for short-term wind power based on vertical federated learning[J]. Automation of electric power systems, 2023, 47(16): 44-53.
[18] 余光正, 陆柳, 汤波, 等. 基于云图特征提取的改进混合神经网络超短期光伏功率预测方法[J]. 中国电机工程学报, 2021, 41(20): 6989-7003.
YU G Z, LU L, YANG B, et al.An improved hybrid neural network ultra-short-term photovoltaic power prediction method based on cloud map feature extraction[J]. Proceedings of the CSEE, 2021, 41(20): 6989-7003.
[19] YU G Z, LU L, TANG B, et al.Ultra-short-term wind power subsection forecasting method based on extreme weather[J]. IEEE transactions on power systems, 2023, 38(6): 5045-5056.
[20] QIN Z J, CHEN X W, HOU Y H, et al.Coordination of preventive, emergency and restorative dispatch in extreme weather events[J]. IEEE transactions on power systems, 2022, 37(4): 2624-2638.
[21] ZULOAGA S, VITTAL V.Integrated electric power/water distribution system modeling and control under extreme mega drought scenarios[J]. IEEE transactions on power systems, 2021, 36(1): 474-484.
[22] GULRAJANI I, AHMED F, ARJOVSKY M, et al.Improved training of wasserstein gans[J]. Advances in neural information processing systems, 2017, 30.
[23] 黄金鑫, 李华强, 陆杨. 基于蒙特卡洛模拟和频谱分析法的孤岛微电网储能容量配置[J]. 电网技术, 2020, 44(5): 1622-1629.
HUANG J X, LI H Q, LU Y.Energy storage capacity configuration of isolated microgrid based on Monte Carlo simulation and spectrum analysis[J]. Power system technology, 2020, 44(5): 1622-1629.
[24] 黄文鑫, 吴军, 郭子辉, 等. 一种极端台风灾害下电网韧性评估及差异化规划方法[J]. 电力系统自动化,2023, 47(5): 84-91.
HUANG W X, WU J, GUO Z H, et al.A resilience assessment and differentiated planning method for power grids under extreme typhoon disasters[J]. Automation of electric power systems, 2023, 47(5):84-91.
[25] SALIMANS T, GOODFELLOW I, ZAREMBA W, et al. Improved techniques for training GANs[EB/OL].2016: 1606.03498. https://arxiv.org/abs/1606.03498v1.
[26] 符杨, 廖剑波, 李振坤, 等. 考虑越限风险的主动配电网日前优化调度及运行[J]. 中国电机工程学报, 2017, 37(21): 6328-6338.
FU Y, LIAO J B, LI Z K, et al.Day-ahead optimal scheduling and operating of active distribution network considering violation risk[J]. Proceedings of the CSEE, 2017, 37(21): 6328-6338.