OPTIMAL SCHEDULING OF THERMAL-PV-STORAGE SYSTEM FOR REGIONAL POWER GRID PHOTOVOLTAIC PEAK SHAVING

Ying Haotian; He Xu; Fan Yuxin; Xie Yurong; Zhu Peiwang; Xiao Gang

doi:10.19912/j.0254-0096.tynxb.2024-1401

PDF(2204 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 49-57. DOI: 10.19912/j.0254-0096.tynxb.2024-1401

Special Topics of Academic Papers at the 27th Annual Meeting of the China Association for Science and Technology

OPTIMAL SCHEDULING OF THERMAL-PV-STORAGE SYSTEM FOR REGIONAL POWER GRID PHOTOVOLTAIC PEAK SHAVING

Ying Haotian¹, He Xu², Fan Yuxin², Xie Yurong³, Zhu Peiwang¹, Xiao Gang¹

Author information +

History +

Abstract

Aiming at the problem of difficult peak-shaving of traditional units under high proportion of photovoltaic installation in a regional power grid of an oil field, this paper proposes a scheduling strategy of thermal-PV-storage system for photovoltaic peak-shaving in regional power grids by combining the characteristics of photovoltaic power generation and the operating characteristics of units of different types in power plants. Firstly, the distribution of photovoltaic power generation is mastered based on the probabilistic forecasting of photovoltaic power day-ahead, and a mathematical model of the start-stop peak-shaving units is established based on actual data. Then, an upper-level optimization model is established to solve the optimal scheduling of the start-stop peak-shaving stage of the unit with the goal of minimizing the fluctuation of the equivalent net load of the system; the lower-level model is based on the dynamic characteristics of each unit of the thermal-PV-storage multi-energy complementary system, and the solution model is established with the goal of optimizing the operating economy within the system scheduling cycle. Finally, based on the stochastic optimization method, the system scheduling is optimized by calling the CPLEX solver through Matlab. The calculation results show that after using this scheduling strategy, the system's photovoltaic energy curtailment ratio and operating cost are decreased by 32.91% and 19.95% respectively, indicating that the proposed scheduling strategy can effectively improve the regional power grid's photovoltaic absorption capacity and reduce the system's operating cost.

Key words

photovoltaic power / combined cycle power stations / energy storage / multiple energy complementation / wind and photovoltaic power consumption / optimal scheduling

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Ying Haotian, He Xu, Fan Yuxin, Xie Yurong, Zhu Peiwang, Xiao Gang. OPTIMAL SCHEDULING OF THERMAL-PV-STORAGE SYSTEM FOR REGIONAL POWER GRID PHOTOVOLTAIC PEAK SHAVING[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 49-57 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1401

References

[1] 国家发展改革委, 国家能源局. “十四五”现代能源体系规划[R]. 发改能源〔2022〕210号, 2022.
Nation Development and Reform Commission, National Energy Adminstration. 14th five-year plan for renewable energy development[R].Development and Reform Energy [2022] No. 210, 2022.
[2] 江涵, 岳程燕, 严兴煜, 等. 高比例可再生能源系统惯量约束对灵活性分析的影响研究[J]. 电力系统保护与控制, 2021, 49(18): 44-51.
JIANG H, YUE C Y, YAN X Y, et al.Influence of system inertia on flexibility resource analysis for an interconnection system with a high proportion of intermittent renewable energy[J]. Power system protection and control, 2021, 49(18): 44-51.
[3] 李明节, 陈国平, 董存, 等. 新能源电力系统电力电量平衡问题研究[J]. 电网技术, 2019, 43(11): 3979-3986.
LI M J, CHEN G P, DONG C, et al.Research on power balance of high proportion renewable energy system[J]. Power system technology, 2019, 43(11): 3979-3986.
[4] 赵书强, 吴杨, 李志伟, 等. 考虑风光出力不确定性的电力系统调峰能力及经济性分析[J]. 电网技术, 2022, 46(5): 1752-1761.
ZHAO S Q, WU Y, LI Z W, et al.Analysis of power system peaking capacity and economy considering uncertainty of wind and solar output[J]. Power system technology, 2022, 46(5): 1752-1761.
[5] 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 Z G, KANG C Q.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.
[6] 王晓东, 苗宜之, 卢奭瑄, 等. 基于SCM-ANFIS负荷预测的储能电站调峰控制策略[J]. 太阳能学报, 2018, 39(6): 1651-1657.
WANG X D, MIAO Y Z, LU S X, et al.Peak regulation control strategy of energy storage power station based on SCM-ANFIS load forecast[J]. Acta energiae solaris sinica, 2018, 39(6): 1651-1657.
[7] 安源, 郑申印, 苏瑞, 等. 风光水储多能互补发电系统双层优化研究[J]. 太阳能学报, 2023, 44(12): 510-517.
AN Y, ZHENG S Y, SU R, et al.Research on two-layer optimization of wind-solar-water-storage multi energy complementary power generation system[J]. Acta energiae solaris sinica, 2023, 44(12): 510-517.
[8] WANG Z N, FANG G H, WEN X, et al.Coordinated operation of conventional hydropower plants as hybrid pumped storage hydropower with wind and photovoltaic plants[J]. Energy conversion and management, 2023, 277: 116654.
[9] 李军徽, 张嘉辉, 穆钢, 等. 储能辅助火电机组深度调峰的分层优化调度[J]. 电网技术, 2019, 43(11): 3961-3970.
LI J H, ZHANG J H, MU G, et al.Hierarchical optimization scheduling of deep peak shaving for energy-storage auxiliary thermal power generating units[J]. Power system technology, 2019, 43(11): 3961-3970.
[10] 李炳阳, 李新利, 杨国田, 等. 风光火储联合供应火电厂厂用电系统优化调度[J]. 热力发电, 2024, 53(2): 142-152.
LI B Y, LI X L, YANG G T, et al.Optimized dispatching of wind-PV-thermal-storage for auxiliary power system of thermal power plant[J]. Thermal power generation, 2024, 53(2): 142-152.
[11] 王淑云, 娄素华, 吴耀武, 等. 计及火电机组深度调峰成本的大规模风电并网鲁棒优化调度[J]. 电力系统自动化, 2020, 44(1): 118-125.
WANG S Y, LOU S H, WU Y W, et al.Robust optimal dispatch of large-scale wind power integration considering deep peak regulation cost of thermal power units[J]. Automation of electric power systems, 2020, 44(1): 118-125.
[12] 邓婷婷, 娄素华, 田旭, 等. 计及需求响应与火电深度调峰的含风电系统优化调度[J]. 电力系统自动化, 2019, 43(15): 34-41.
DENG T T, LOU S H, TIAN X, et al.Optimal dispatch of power system integrated with wind power considering demand response and deep peak regulation of thermal power units[J]. Automation of electric power systems, 2019, 43(15): 34-41.
[13] 李铁, 李正文, 杨俊友, 等. 计及调峰主动性的风光水火储多能系统互补协调优化调度[J]. 电网技术, 2020, 44(10): 3622-3630.
LI T, LI Z W, YANG J Y, et al.Coordination and optimal scheduling of multi-energy complementary system considering peak regulation initiative[J]. Power system technology, 2020, 44(10): 3622-3630.
[14] 周凌安, 申建建, 李继红, 等. 日启停燃气机组调峰运行方法[J]. 中国电机工程学报, 2017, 37(20): 5913-5921.
ZHOU L A, SHEN J J, LI J H, et al.Peak regulation method for daily start-stop gas-fired units[J]. Proceedings of the CSEE, 2017, 37(20): 5913-5921.
[15] 杨丽, 吴雨茜, 王俊丽, 等. 循环神经网络研究综述[J]. 计算机应用, 2018, 38(增刊2): 1-6, 26.
YANG L, WU Y Q, WANG J L, et al.Research on recurrent neural network[J]. Journal of computer applications, 2018, 38(S2): 1-6, 26.
[16] 毕锐, 丁明, 徐志成, 等. 基于模糊C均值聚类的光伏阵列故障诊断方法[J]. 太阳能学报, 2016, 37(3): 730-736.
BI R, DING M, XU Z C, et al.PV array fault diagnosis based on FCM[J]. Acta energiae solaris sinica, 2016, 37(3): 730-736.
[17] 龙小慧, 秦际赟, 张青雷, 等. 基于相似日聚类及模态分解的短期光伏发电功率组合预测研究[J]. 电网技术, 2024, 48(7): 2948-2957.
LONG X H, QIN J Y, ZHANG Q L, et al.Short-term photovoltaic power prediction study based on similar day clustering and modal decomposition[J]. Power system technology, 2024, 48(7): 2948-2957.
[18] 金伟勇, 卢丽娜, 赖欢欢, 等. 基于功率特征的K-ISSA-LSTM光伏功率预测[J]. 太阳能学报, 2024, 45(2): 429-434.
JIN W Y, LU L N, LAI H H, et al.K-ISSA-LSTM photovoltaic power prediction based on power characteristic[J]. Acta energiae solaris sinica, 2024, 45(2): 429-434.
[19] 刘一欣, 郭力, 王成山. 微电网两阶段鲁棒优化经济调度方法[J]. 中国电机工程学报, 2018, 38(14): 4013-4022, 4307.
LIU Y X, GUO L, WANG C S.Economic dispatch of microgrid based on two stage robust optimization[J]. Proceedings of the CSEE, 2018, 38(14): 4013-4022, 4307.
[20] HE Y Y, XU Q F, WAN J H, et al.Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function[J]. Energy, 2016, 114: 498-512.
[21] 康岩松, 臧顺来. 基于多种策略的改进粒子群优化算法[J]. 东北大学学报(自然科学版), 2023, 44(8): 1089-1097.
KANG Y S, ZANG S L.Improved particle swarm optimization algorithm based on multiple strategies[J]. Journal of Northeastern University(natural science), 2023, 44(8): 1089-1097.
[22] ZHANG Y, WANG J X, WANG X F.Review on probabilistic forecasting of wind power generation[J]. Renewable and sustainable energy reviews, 2014, 32: 255-270.
[23] 万灿, 宋永华. 新能源电力系统概率预测理论与方法及其应用[J]. 电力系统自动化, 2021, 45(1): 2-16.
WAN C, SONG Y H.Theories, methodologies and applications of probabilistic forecasting for power systems with renewable energy sources[J]. Automation of electric power systems, 2021, 45(1): 2-16.