Distributionally robust optimal dispatching of hydro-thermal-load resources for high penetration of wind system with dynamic power regulation margin

YANG Hongming, LIU Junpeng, LIANG Rui, LIAO Shengtao

Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (9) : 2327-2337.

PDF(1182 KB)
中国科学院数学与系统科学研究院期刊网
PDF(1182 KB)
Systems Engineering - Theory & Practice ›› 2021, Vol. 41 ›› Issue (9) : 2327-2337. DOI: 10.12011/SETP2020-1690

Distributionally robust optimal dispatching of hydro-thermal-load resources for high penetration of wind system with dynamic power regulation margin

  • YANG Hongming1, LIU Junpeng1, LIANG Rui1, LIAO Shengtao2
Author information +
History +

Abstract

The high penetration of wind power generation poses a great challenge to the power system safety dispatching since they're highly volatile and intermittent. In view of the anti-peak regulation characteristics and uncertainty of wind power, a distributional robust optimal dispatching method of hydro-thermal-load resources for high penetration of wind system with dynamic power regulation margin is proposed in this paper. Firstly, the moment uncertainty set of net load (the difference between wind power and load power) fluctuation rate is proposed to describe the randomness of system power change. Then, the dynamic power regulation margin model of the system is established considering the regulation ability of hydropower, thermal power and load power. Secondly, by means of the distributionally robust conditional value at risk (DR-CVaR) which can describe tail probability well, the risk of wind abandonment caused by the insufficient power regulation margin of the system under severe wind conditions is presented. A distributional robust optimal dispatching model is established to minimize the system operating costs, risk costs of wind abandonment and maximize the total dynamic regulation margin. The model is transformed into a semidefinite programming problem by dual optimization theory. The proposed method can effectively improve the permeation level of wind power, ensure economic operation and improve the ability to response the uncertain fluctuations of net load.

Key words

high penetration of wind system / dynamic power regulation margin / distributionally robust optimization / conditional value at risk (CVaR)

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
YANG Hongming , LIU Junpeng , LIANG Rui , LIAO Shengtao. Distributionally robust optimal dispatching of hydro-thermal-load resources for high penetration of wind system with dynamic power regulation margin. Systems Engineering - Theory & Practice, 2021, 41(9): 2327-2337 https://doi.org/10.12011/SETP2020-1690

References

[1] 李隽. 中国能源转型与"十四五"电力规划研究[EB/OL]. http://news.bjx.com.cn/html/20201201/1119138.shtml.Li J. Research on china's energy transformation and "14th five-year plan" power planning[EB/OL].http://news.bjx.com.cn/html/20201201/1119138.shtml.
[2] 李生虎, 董王朝. 基于NSTPNT的风电系统可靠性对风电预测误差灵敏度[J]. 系统工程理论与实践, 2019, 39(5):1340-1350.Li S H, Dong W C. Reliability sensitivity of wind power system to forecast error using NSTPNT method[J]. Systems Engineering-Theory & Practice, 2019, 39(5):1340-1350.
[3] Sanjari M J, Gooi H B, Nair N C. Power generation forecast of hybrid PV-wind system[J]. IEEE Transactions on Sustainable Energy, 2020, 11(2):703-712.
[4] 马燕峰, 陈磊, 李鑫, 等. 基于机会约束混合整数规划的风火协调滚动调度[J]. 电力系统自动化, 2018, 42(5):127-132+175.Ma Y F, Chen L, Li X, et al. Inductive power and signal synchronous transmission based on parallel paths of fundamental wave and harmonic wave[J]. Automation of Electric Power Systems, 2018, 42(5):127-132+175.
[5] 白杨, 汪洋, 夏清, 等. 水-火-风协调优化的全景安全约束经济调度[J]. 中国电机工程学报, 2013, 33(13):2-9.Bai Y, Wang Y, Xia Q, et al. A full-scenario SCED with coordinative optimization of hydro-thermal-wind power[J]. Proceedings of the CSEE, 2013, 33(13):2-9.
[6] 陈爱康, 胡静哲, 陆轶祺, 等. 梯级水光蓄系统规划关联模型的建模[J]. 中国电机工程学报, 2020, 40(4):1106-1116+1403.Chen A K, Hu J Z, Lu Y Q, et al. Research on corre-lation model of cascade hydro-photovoltaic-pumped storage hybrid generation planning[J]. Proceedings of the CSEE, 2020, 40(4):1106-1116+1403.
[7] Yang H M, Yu Q, Liu J P, et al. Optimal wind-solar capacity allocation with coordination of dynamic regulation of hydropower and energy intensive controllable load[J]. IEEE Access, 2020, 8:110129-110139.
[8] Zhang N, Hu Z G, Shen B, et al. An integrated source-grid-load planning model at the macro level:Case study for China's power sector[J]. Energy, 2017, 126:231-246.
[9] 鲁宗相, 李海波, 乔颖. 含高比例可再生能源电力系统灵活性规划及挑战[J]. 电力系统自动化, 2016, 40(13):147-158.Lu Z X, Li H B, Qiao Y. Power system flexibility planning and challenges considering high proportion of renewable energy[J]. Automation of Electric Power Systems, 2016, 40(13):147-158.
[10] 苏承国, 申建建, 王沛霖, 等. 基于电源灵活性裕度的含风电电力系统多源协调调度方法[J]. 电力系统自动化, 2018, 42(17):111-122.Su C G, Shen J J, Wang P L, et al. Coordinated dis-patching method for wind-turbine-integrated power system with multi-type power sources based on power flexibility margin[J]. Automation of Electric Power Systems, 2018, 42(17):111-122.
[11] 王洪坤, 王守相, 潘志新, 等. 含高渗透分布式电源配电网灵活性提升优化调度方法[J]. 电力系统自动化, 2018, 42(15):86-93.Wang H K, Wang S X, Pan Z X, et al. Optimized dis-patching method for flexibility improvement of distribution network with high-penetration distributed generation[J]. Automation of Electric Power Systems, 2018, 42(15):86-93.
[12] Lannoye E, Flynn D, O'Malley M. Transmission, variable generation, and power system flexibility[J]. IEEE Transactions on Power Systems, 2015, 30(1):57-66.
[13] 谭忠富, 邢通, 德格吉日夫, 等. 基于CVaR的能源互补联合系统优化配置模型研究[J]. 系统工程理论与实践, 2020, 40(1):170-181.Tan Z F, Xing T, De G, et al. Optimized configu-ration model for energy complementary system based on CVaR[J]. Systems Engineering-Theory & Practice, 2020, 40(1):170-181.
[14] Delage E, Ye Y Y. Distributionally robust optimization under moment uncertainty with application to data-driven problems[J]. Operations Research, 2010, 59(3):595-612.
[15] Yang H M, Zhang Y F, Qiu D, et al. Distributionally robust optimization of controllable load bidding simul-taneously in day-ahead and real-time electricity market[J]. IEEE Transactions on Power Systems, 2018, 33(1):1089-1091.
[16] Rockafellar R, Uryasev S. Optimization of conditional value-at-risk[J]. Journal of Risk, 2000, 2:21-41.
[17] Yang H M, Zhang J, Qiu J, et al. A practical pricing approach to smart grid demand response based on load classification[J]. IEEE Transactions on Smart Grid, 2017, 9(1):179-190.
[18] 林俐, 邹兰青, 周鹏, 等. 规模风电并网条件下火电机组深度调峰的多角度经济性分析[J]. 电力系统自动化, 2017, 41(7):21-27.Lin L, Zhou L Q, Zhou P, et al. Multi-angle economic analysis on deep peak regulation of thermal power units with large scale wind power integration[J]. Automation of Electric Power Systems, 2017, 41(7):21-27.
[19] 王若谷, 王建学, 张恒, 等. 水电机组调峰服务的成本分析及实用补偿方法[J]. 电力系统自动化, 2011, 35(23):41-46.Wang R G, Wang J X, Zhang H, et al. A cost analysis and practical compensation method for hydropower units peaking service[J]. Automation of Electric Power Systems, 2011, 35(23):41-46.

Funding

National Natural Science Foundation of China (71931003, 72061147004); Research Project of Hunan Provincial Science and Technology Department (2019WK2011, 2019GK5015)
PDF(1182 KB)

564

Accesses

0

Citation

Detail
Sections
Recommended

/

〈 〉