Complementary Intelligent Optimization Operation Strategy of Wind-Solar-Hydro Multi-energy Power System

doi:10.12096/j.2096-4528.pgt.19173

Abstract

Abstract:

For high-ratio renewable energy access to the grid, the utilization of wind-solar-hydro complementary power generation can provide a smooth and stable power supply. This paper proposed a short-term optimal operation method based on stochastic programming for the windsolar-hydro multi-energy power system, using big data and artificial intelligence technology. Firstly, based on the variational autoencoder (VAE), a renewable energy scenarios generation method was proposed, which can generate diverse scenarios that meet the characteristics of renewable energy output and accurately describe the correlation of renewable energy output. Secondly, based on the scenarios method, a short-term optimization operation model of wind-solar-hydro was established and a piecewise linearization method was used to transform various nonlinear constraints into linear constraints, which can achieve fast solution while ensuring the accuracy of the model. Finally, through the simulation of the wind-solarhydro power system in downstream of the Yalong River, it demonstrates the effectiveness of the intelligent optimization operation strategy proposed in this paper.

Key words: power system, complementation of windsolar-hydro, scenarios generation, variational autoencoder, optimal operation

CLC Number:

TM71

Wei HU,Yuchen QI,Hongxuan ZHANG,Ling DONG,Yanhe LI. Complementary Intelligent Optimization Operation Strategy of Wind-Solar-Hydro Multi-energy Power System[J]. Power Generation Technology, 2020, 41(1): 9-18.

References

1	马苗苗, 邵黎阳, 潘军军, 等. 风光互补发电系统的监督预测协调控制[J]. 中南大学学报(自然科学版), 2018, 49 (10): 2602- 2609.
2	庄雅妮, 杨秀媛, 金鑫城. 风光储联合发电运行技术研究[J]. 发电技术, 2018, 39 (4): 296- 303.
3	程海花, 寇宇, 周琳, 等. 面向清洁能源消纳的流域型风光水多能互补基地协同优化调度模式与机制[J]. 电力自动化设备, 2019, 39 (10): 61- 70.
4	吴萍, 陈昊, 赵兵, 等. 风光火打捆交直流混联外送系统交互影响及稳定性研究[J]. 电网技术, 2016, 40 (7): 1934- 1942.
5	陈宇, 彭潇, 丁婧, 等. 电动汽车参与风电场输出功率波动平抑方法研究[J]. 发电技术, 2019, 40 (1): 91- 98.
6	李伟楠, 王现勋, 梅亚东, 等. 基于趋势场景缩减的水风光协同运行随机模型[J]. 华中科技大学学报(自然科学版), 2019, 47 (8): 120- 127.
7	李姚旺, 苗世洪, 罗星, 等. 含压缩空气储能电力系统日前-日内协调调度策略[J]. 中国电机工程学报, 2018, 38 (10): 2849- 2860, 3136.
8	朱祥祥, 黄崇鑫, 岳东, 等. 基于区间不确定性的微网有功-无功联合优化调度[J]. 电力系统自动化, 2016, 40 (15): 39- 46.
9	赵书强, 金天然, 李志伟, 等. 考虑时空相关性的多风电场出力场景生成方法[J]. 电网技术, 2019, 43 (11): 3997- 4004.
10	丁明, 宋晓皖, 孙磊, 等. 考虑时空相关性的多风电场出力场景生成与评价方法[J]. 电力自动化设备, 2019, 39 (10): 39- 47.
11	仉梦林, 胡志坚, 王小飞, 等. 基于动态场景集和需求响应的二阶段随机规划调度模型[J]. 电力系统自动化, 2017, 41 (11): 68- 76.
12	叶荣, 陈皓勇, 王钢, 等. 多风电场并网时安全约束机组组合的混合整数规划解法[J]. 电力系统自动化, 2010, 34 (5): 29- 33, 65.
13	S?nderby, Casper Kaae, Raiko, Tapani, et.al. Ladder Variational Autoencoders[C]//Proceedings of the Advances in Neural Information Processing Systems 29(NIPS 2016), Barcelona, Spain, 2016.
14	Matt J. Kusner, Brooks Paige, Gelly S, JM Herná ndez-Lobato. Grammar variational autoencoder[C]//Proceedings of the 34th International Conference on Machine Learning, ICML'17, Sydney, Australia, 2017.
15	Kingma D P, Welling M. Auto-encoding variational bayes[C]//Proceedings of the 2nd International Conference on Learning Representations. Banff, Canada: ICLR, 2014.
16	吴世勇, 周永, 王瑞, 等. 雅砻江流域建设风光水互补千万千瓦级清洁能源示范基地的探讨[J]. 四川水力发电, 2016, 35 (3): 105- 108.
17	罗崇伸, 张蕴华, 胡学翠, 等. 雅砻江下游水能资源开发全面完成雅砻江公司着手打造风光水互补清洁能源示范基地[J]. 四川水力发电, 2016, 35 (2): 119- 120.
18	黄炜斌, 马光文, 王和康, 等. 雅砻江下游梯级电站群中长期优化调度模型及其算法研究[J]. 水力发电学报, 2009, 28 (1): 1- 4.

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