【目的】多能源微电网（multi-energy microgrid,MEMG）能够整合多种能量载体,实现更高的能源效率,助力实现“双碳”目标。【方法】文章提出了一种考虑不确定关联性与异常数据的MEMG数据驱动分布鲁棒优化调度方法。首先,基于Copula函数,提出了一种考虑不确定性相关性的模糊集,并基于此模糊集和机会约束,建立了考虑不确定性相关性的MEMG分布鲁棒机会约束优化调度模型;其次,由于分布鲁棒优化模型无法直接求解,文章基于对偶理论、McCormick松弛、条件风险价值近似,推导出所提模糊集中的最坏情况转化方法,从而将分布鲁棒模型转化为线性确定性模型,以便采用求解器高效求解;然后,提出了一种样本修剪算法,该算法通过迭代计算,生成剔除异常数据和极端数据的原始数据集子样本,以排除数据样本中异常值和极端值对分布鲁棒机会约束调度结果的负面影响;【结果】最后,案例仿真表明,文章所提的分布鲁棒模型方法能够有效剔除模糊集中的不切实际分布,将样本外成本降低8.16%,所提样本修剪算法能够有效剔除样本中异常值和极端值,将样本外成本进一步降低3.33%。【结论】文章所提方法在保证可靠性的同时提升调度经济性,具有明显的优越性。
Abstract
[Objective] Multi-energy microgrids (MEMGs) can integrate multiple energy carriers to improve energy efficiency, thereby contributing to the achievement of the "dual carbon" goals. [Methods] The paper proposes a data-driven distributionally robust optimization scheduling method for MEMGs that accounts for uncertainty correlations and outlier data. First, an ambiguity set incorporating uncertainty correlations is introduced based on the Copula function. A distributionally robust optimization scheduling model with opportunity constraints is then formulated, integrating the ambiguity set and opportunity constraints to address uncertainty correlations. Second, since the distributionally robust optimization model cannot be solved directly, a worst-case transformation method is derived for the proposed ambiguity set using dual theory, McCormick relaxation, and Conditional Value-at-Risk approximation. This transforms the distributionally robust model into a linear deterministic model, enabling efficient solution via optimization solvers. Subsequently, a sample pruning algorithm is proposed, which iteratively generates sub-samples from the original dataset by removing outliers and extreme data points. This approach mitigates the adverse effects of such data on the distributionally robust opportunity constraint scheduling results. [Results] Finally, case simulations demonstrate that the proposed distributionally robust model effectively eliminates unrealistic distributions in the ambiguity set, resulting in an 8.16% reduction in out-of-sample costs. The proposed sample pruning algorithm further reduces out-of-sample costs by 3.33%. [Conclusions] The proposed method enhances scheduling efficiency and ensuring reliability, which collectively demonstrate its clear superiority.
关键词
多能微电网 /
分布鲁棒优化 /
不确定相关性 /
机会约束 /
数据驱动
Key words
multi-energy microgrid /
distributionally robust optimization /
uncertainty correlation /
chance constraints /
data-driven
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参考文献
[1] Lu S, Gu W, Meng K, et al.Economic dispatch of integrated energy systems with robust thermal comfort management[J]. IEEE Transactions on Sustainable Energy, 2020, 12(1): 222-233.
[2] 张世旭,李姚旺,刘伟生,等.面向微电网群的云储能经济-低碳-可靠多目标优化配置方法[J].电力系统自动化,2024,48(01):21-30.
[3] 杨昆,刘通,柏林,等.基于谈判博弈的微电网群多主体共享储能容量优化配置策略[J].电测与仪表,2024,61(03):33-41.
[4] 郝元钊,吴豫,苗福丰,等.基于不动点映射的多微网与配电网主从博弈定价策略及均衡求解方法[J].电测与仪表,2023,60(11):37-44.
[5] 曾振松,郑洁云,魏鑫,等.微电网的氢-氨混合储能系统容量优化配置研究[J].广东电力,2024,37(07):42-49.
[6] 颜玉林,张籍,刘洋,等.基于一体式再生燃料电池的并网型微电网系统建模及分析[J].广东电力,2023,36(09):43-50.
[7] 李嘉丰,王莹,程晓绚,等.基于双层优化的微电网系统光储容量配置方法[J].分布式能源,2024,9(01):80-88.
[8] 高赐威,陈彦,石坤,等.基于潮流分布识别的园区微电网型虚拟电厂间分布式电力交易[J].供用电,2024,41(08):112-119.
[9] 李子凯,杨波,周忠堂,等.基于强化学习算法的微电网优化策略[J].山东电力技术,2024,51(06):27-35.
[10] 林泽源,王宗尧,张凡,等.考虑氢能应用的光伏直流微电网中储能容量配置寻优方法研究[J].高压电器,2024,60(07):78-87.
[11] 李翔宇,黎东阳,张正江,等.孤岛微电网系统的混合控制策略及动态滤波技术研究[J].全球能源互联网,2024,7(03):336-347.
[12] 李鸿,朱继忠,董瀚江.考虑协变量因素的多能微电网两阶段分布鲁棒优化调度[J/OL].中国电机工程学报,1-12[2024-06-18].
[13] Li Y, Han M, Shahidehpour M, et al.Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response[J]. Applied Energy, 2023, 335: 120749.
[14] Yang L, Li H, Zhang H, et al.Stochastic-distributionally robust frequency-constrained optimal planning for an isolated microgrid[J]. IEEE Transactions on Sustainable Energy, 2024.
[15] Liu H, Fan Z, Xie H, et al.Distributionally robust joint chance-constrained dispatch for electricity-gas-heat integrated energy system considering wind uncertainty[J]. Energies, 2022, 15(5): 1796.
[16] Mohajerin Esfahani P, Kuhn D.Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations[J]. Mathematical Programming, 2018, 171(1): 115-166.
[17] Arrigo A, Ordoudis C, Kazempour J, et al.Wasserstein distributionally robust chance-constrained optimization for energy and reserve dispatch: An exact and physically-bounded formulation[J]. European Journal of Operational Research, 2022, 296(1): 304-322.
[18] Li B, Jiang R, Mathieu J L.Integrating unimodality into distributionally robust optimal power flow[J]. Top, 2022, 30(3): 594-617.
[19] Esteban-Pérez A, Morales J M.Partition-based distributionally robust optimization via optimal transport with order cone constraints[J]. 4OR, 2022, 20(3): 465-497.
[20] Arrigo A, Kazempour J, De Grève Z, et al.Enhanced wasserstein distributionally robust OPF with dependence structure and support information[C]//2021 IEEE Madrid PowerTech. IEEE, 2021: 1-6.
[21] Xia X, Pan X, Li N, et al.GAN-based anomaly detection: A review[J]. Neurocomputing, 2022, 493: 497-535.
[22] Wang H, Bah M J, Hammad M.Progress in outlier detection techniques: A survey[J]. Ieee Access, 2019, 7: 107964-108000.
[23] Zhai R, Dan C, Kolter Z, et al.Doro: Distributional and outlier robust optimization[C]//International Conference on Machine Learning. PMLR, 2021: 12345-12355.
[24] Jiang N, Xie W.DFO: A framework for data-driven decision-making with endogenous outliers[J]. Preprint optimization-online. org, 2021.
[25] Rahimian H, Bayraksan G, Homem-de-Mello T. Identifying effective scenarios in distributionally robust stochastic programs with total variation distance[J]. Mathematical Programming, 2019, 173(1): 393-430.
[26] Li Y, Shen X.Anomaly detection and classification method for wind speed data of wind turbines using spatiotemporal dependency structure[J]. IEEE Transactions on Sustainable Energy, 2023, 14(4): 2417-2431.
[27] Zhai J, Wang S, Guo L, et al.Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid[J]. Applied Energy, 2022, 326: 119939.
[28] Zhai J, Jiang Y, Shi Y, et al.Distributionally robust joint chance-constrained dispatch for integrated transmission-distribution systems via distributed optimization[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2132-2147.
[29] Mitsos A, Chachuat B, Barton P I.McCormick-based relaxations of algorithms[J]. SIAM Journal on Optimization, 2009, 20(2): 573-601.
[30] Arrigo A, Kazempour J, De Grève Z, et al.Embedding dependencies between wind farms in distributionally robust optimal power flow[J]. IEEE Transactions on Power Systems, 2022.
基金
国家电网公司科学技术项目（多制约因素下大型城市电网与分布式资源互动控制技术研究及应用,5700-202311602A-3-2-ZN）; 江苏省基础研究计划自然科学基金项目 (BK20220842)
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