计及源荷不确定性的智能小区能源系统两阶段鲁棒优化调度

doi:10.16088/j.issn.1001-6600.2023051804

广西师范大学学报（自然科学版） ›› 2024, Vol. 42 ›› Issue (3): 70-85.doi: 10.16088/j.issn.1001-6600.2023051804

计及源荷不确定性的智能小区能源系统两阶段鲁棒优化调度

杨安全¹, 代红¹, 赵清松², 钟浩¹, 马辉^1*

1.三峡大学电气与新能源学院, 湖北宜昌 443002;
2.沈阳工业大学电气工程学院, 辽宁沈阳 110870

收稿日期:2023-05-18 修回日期:2023-07-25 出版日期:2024-05-25 发布日期:2024-05-31
通讯作者: 马辉(1985—), 男, 河南开封人, 三峡大学副教授, 博士。E-mail: mahuizz119@126.com
基金资助:
国家自然科学基金(52107108); 梯级水电站运行与控制湖北省重点实验室基金(2022KJX05); 中国博士后基金(2020M683230)

Two-Stage Robust Optimal Scheduling of Energy Systems in Smart Community Considering Source-Load Uncertainty

YANG Anquan¹, DAI Hong¹, ZHAO Qingsong², ZHONG Hao¹, MA Hui^1*

1. China Three Gorges University, College of Electrical Engineering & New Energy, Yichang Hubei 443002, China;
2. Shenyang university of Technology, School of Electrical Engineering, Shenyang Liaoning 110870, China

Received:2023-05-18 Revised:2023-07-25 Online:2024-05-25 Published:2024-05-31

摘要/Abstract

摘要： 为解决电力系统中电源侧和负荷侧的不确定性对电网调度计划的影响,针对电源侧,考虑风电与光伏出力的不确定性,分别建立风电与光伏的概率密度函数模型,通过拉丁超立方采样方法生成场景并进行缩减,从而得出风电与光伏出力区间;针对负荷侧,考虑柔性负荷对电网消峰填谷的作用,提出基于智能小区的综合需求响应两阶段鲁棒优化模型。在日前阶段,以电网系统运行成本和碳交易成本最小为优化目标,考虑源荷的不确定性,基于价格需求响应模型,从而确定日前调度方案。在日内阶段,基于日前阶段优化结果,以智能小区运行成本和碳交易成本最小为优化目标,建立两阶段鲁棒优化模型,通过列约束生成(column-and-constraint generation,C&CG)算法将目标函数进行转换,采用Karush-Kuhn-Tucker条件和Big-M约束方法将max-min形式优化问题转化为混合整数线性规划(mixed integer linear programming,MILP)模型。最终,通过算例验证了所提模型的正确性以及算法的有效性。

关键词: 源荷不确定性, 综合需求响应, 两阶段鲁棒优化, 智能小区, 柔性负荷

Abstract: To address the impact of uncertainties in both the generation and demand sides of the power system on grid scheduling plans, a two-stage robust optimization model is proposed for integrated source-load uncertainty management. For the generation side, the uncertainties in wind and solar power outputs are considered. Probability density function models are established for wind and solar power, and the Latin hypercube sampling method is employed to generate scenarios and perform scenario reduction, resulting in power output intervals for wind and solar generation. For the demand side, the role of flexible loads in peak shaving and valley filling is considered, and an integrated demand response model based on smart communities is proposed. In the day-ahead stage, aiming to minimize the system operating cost and carbon trading cost while considering source-load uncertainties, a price-based demand response model is developed to determine the day-ahead scheduling plan. In the intra-day stage, based on the optimized results from the day-ahead stage, a two-stage robust optimization model is formulated with the objective of minimizing the operating cost and carbon trading cost of smart communities. The column-and-constraint generation (C&CG) algorithm is employed to convert the objective function, and the Karush-Kuhn-Tucker (KKT) conditions and Big-M constraint method are utilized to transform the max-min optimization problem into a mixed integer linear programming (MILP) model. The correctness of the proposed model and the effectiveness of the algorithm are verified through case studies.

Key words: source charge uncertainty, integrated demand response, two-stage robust optimization, intelligent community, flexible load

中图分类号: TK01;TM73

杨安全, 代红, 赵清松, 钟浩, 马辉. 计及源荷不确定性的智能小区能源系统两阶段鲁棒优化调度[J]. 广西师范大学学报（自然科学版）, 2024, 42(3): 70-85.

YANG Anquan, DAI Hong, ZHAO Qingsong, ZHONG Hao, MA Hui. Two-Stage Robust Optimal Scheduling of Energy Systems in Smart Community Considering Source-Load Uncertainty[J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(3): 70-85.

参考文献

[1] 吕佳炜,张沈习,程浩忠,等.考虑互联互动的区域综合能源系统规划研究综述[J].中国电机工程学报,2021,41(12):4001-4020. DOI: 10.13334/j.0258-8013.pcsee.201592.
[2] 黎博,陈民铀,钟海旺,等.高比例可再生能源新型电力系统长期规划综述[J].中国电机工程学报,2023,43(2):555-581. DOI: 10.13334/j.0258-8013.pcsee.212716.
[3] 舒印彪,张智刚,郭剑波,等.新能源消纳关键因素分析及解决措施研究[J].中国电机工程学报,2017,37(1):1-9. DOI: 10.13334/j.0258-8013.pcsee.162555.
[4] 陆俊,朱炎平,彭文昊,等.计及用电行为聚类的智能小区互动化需求响应方法[J].电力系统自动化,2017,41(17):113-120. DOI: 10.7500/AEPS20161206006.
[5] 伍惠铖,王淳,尹发根,等.考虑负荷转移和多重博弈的智能小区需求响应策略[J].电网技术,2019,43(12):4550-4557. DOI: 10.13335/j.1000-3673.pst.2018.2929.
[6] 廖望,刘东,巫宇锋,等.考虑源荷不确定性及用户响应行为的电力系统低碳经济调度[J].中国电机工程学报,2024,44(3):905-918.DOI: 10.13334/j.0258-8013.pcsee.222369.
[7] 郭尊,李庚银,周明,等.考虑网络约束和源荷不确定性的区域综合能源系统两阶段鲁棒优化调度[J].电网技术,2019,43(9):3090-3100. DOI: 10.13335/j.1000-3673.pst.2019.1113.
[8] 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. DOI: 10.13334/j.0258-8013.pcsee.170500.
[9] 李咸善,王苗.考虑源荷不确定性的多微电网鲁棒优化调度与VCG机制竞价策略[J].电网技术,2023,47(6):2330-2345. DOI: 10.13335/j.1000-3673.pst.2022.0843.
[10] 王灿,张雪菲,凌凯,等.基于区间概率不确定集的微电网两阶段自适应鲁棒优化调度[J/OL].中国电机工程学报:1-14[2024-03-10].http://kns.cnki.net/kcms/detail/11.2107.tm.20230308.1126.021.html.
[11] SABERI-BEGLAR K, ZARE K, SEYEDI H, et al. Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads[J]. Applied Energy, 2023, 329: 120265. DOI: 10.1016/j.apenergy.2022.120265.
[12] 赵冬梅,宋原,王云龙,等.考虑柔性负荷响应不确定性的多时间尺度协调调度模型[J].电力系统自动化,2019,43(22):21-30. DOI: 10.7500/AEPS20190428008.
[13] ZHENG S L, SUN Y, QI B, et al. Incentive-based integrated demand response considering S&C effect in demand side with incomplete information[J]. IEEE Transactions on Smart Grid, 2022, 13(6): 4465-4482. DOI: 10.1109/TSG.2022.3149959.
[14] 孙毅,胡亚杰,郑顺林,等.考虑用户响应特性的综合需求响应优化激励策略[J].中国电机工程学报,2022,42(4):1402-1412. DOI: 10.13334/j.0258-8013.pcsee.202244.
[15] 邓杰,姜飞,王文烨,等.考虑电热柔性负荷与氢能精细化建模的综合能源系统低碳运行[J].电网技术,2022,46(5):1692-1702. DOI: 10.13335/j.1000-3673.pst.2021.1373.
[16] 彭文昊,陆俊,冯勇军,等.计及用户参与不确定性的需求响应策略优化方法[J].电网技术,2018,42(5):1588-1594. DOI: 10.13335/j.1000-3673.pst.2017.1020.
[17] 蒋程,王硕,王宝庆,等.基于拉丁超立方采样的含风电电力系统的概率可靠性评估[J].电工技术学报,2016,31(10):193-206. DOI: 10.3969/j.issn.1000-6753.2016.10.023.
[18] MANSOURI S A, AHMARINEJAD A, NEMATBAKHSH E, et al. A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources[J]. Energy, 2022, 245: 123228. DOI: 10.1016/j.energy.2022.123228.
[19] 许周,孙永辉,谢东亮,等.计及电/热柔性负荷的区域综合能源系统储能优化配置[J].电力系统自动化,2020,44(2):53-59. DOI: 10.7500/AEPS20190620005.
[20] HOSSEINI S M, CARLI R, DOTOLI M. Robust optimal energy management of a residential microgrid under uncertainties on demand and renewable power generation[J]. IEEE Transactions on Automation Science and Engineering, 2021, 18(2): 618-637. DOI: 10.1109/TASE.2020.2986269.
[21] YUAN Z P, LI P, LI Z L, et al. Data-driven risk-adjusted robust energy management for microgrids integrating demand response aggregator and renewable energies[J]. IEEE Transactions on Smart Grid, 2023, 14(1): 365-377. DOI: 10.1109/TSG.2022.3193226.
[22] TSANG M Y, SHEHADEH K S, CURTIS F E. An inexact column-and-constraint generation method to solve two-stage robust optimization problems[J]. Operations Research Letters, 2023, 51(1): 92-98. DOI: 10.1016/j.orl.2022.12.002.
[23] 薛开阳,楚瀛,凌梓,等.考虑柔性负荷的综合能源系统低碳经济优化调度[J].可再生能源,2019,37(8):1206-1213. DOI: 10.3969/j.issn.1671-5292.2019.08.016.
[24] 刘蓉晖,李子林,杨秀,等.考虑用户侧柔性负荷的社区综合能源系统日前优化调度[J].太阳能学报,2019,40(10):2842-2850. DOI: 10.19912/j.0254-0096.2019.10.018.
[25] 陈灵敏,吴杰康,张文杰,等.基于鲁棒优化的微能源网综合需求响应协同调度策略[J].电力系统自动化,2021,45(21):159-169. DOI: 10.7500/AEPS20210201001.
[26] 周特,薛云飞,季节,等.考虑电力间接碳排放不确定性的电-冷-热综合能源系统两阶段鲁棒优化方法[J].电网技术,2024,48(1):50-60. DOI: 10.13335/j.1000-3673.pst.2023.0471.
[27] 尚文强,李广磊,丁月明,等.考虑源荷不确定性和新能源消纳的综合能源系统协同调度方法[J].电网技术,2024,48(2):517-532.DOI: 10.13335/j.1000-3673.pst.2023.0577.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

计及源荷不确定性的智能小区能源系统两阶段鲁棒优化调度

Two-Stage Robust Optimal Scheduling of Energy Systems in Smart Community Considering Source-Load Uncertainty

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

Metrics

本文评价

推荐阅读 10

[1]	闫文文, 文中, 王爽, 李国祥, 王博宇, 吴艺. 基于AA-CAES电站和综合需求响应的供暖期弃风消纳策略[J]. 广西师范大学学报（自然科学版）, 2024, 42(2): 55-68.
[2]	甘友春, 王灿, 贺旭辉, 张羽, 张雪菲, 王帆, 喻亚洲. 考虑光热电站和柔性负荷的电氢热综合能源系统联合优化运行[J]. 广西师范大学学报（自然科学版）, 2024, 42(2): 69-83.