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【Applied Energy最新原创论文】市场环境下考虑多时间尺度不确定性的水风光互补系统中期多阶段分布鲁棒调度

AEii国际应用能源  · 公众号  ·  · 2023-06-29 18:30

正文

原文信息

Medium-term multi-stage distributionally robustscheduling of hydro–wind–solar complementary systems in electricity marketsconsidering multiple time-scale uncertainties

原文链接:

https://www.sciencedirect.com/science/article/pii/S0306261923007353


Highlights

•Multi-stage distributionally robust scheduling model for hydro–wind–solar system.

•Multiple time-scale uncertainties are considered by ambiguity sets.

•Stochastic dual dynamic integer programming is modified to solve the model.

•Proposed scheduling model increases profits by 3.04% and lowers trading risks.

摘要

水风光互补系统在电力市场的联合交易有助于降低偏差成本并增加收益。然而,多种能源和市场价格的不确定性影响了交易策略。现有的中期调度方法假设随机变量的概率分布是已知的。新能源的短期波动性也被忽略,可能会导致收入损失和交易风险。为了解决上述问题,本文提出了一种电力市场环境下水风光互补系统的中期多阶段分布鲁棒调度方法。首先,在中期调度模型中加入小时级机组组合约束,以准确考虑新能源短期出力波动。基于修正的卡方距离设计了一种新的模糊集,以解决两个不同时间尺度上的概率分布不确定性。随后,提出了多阶段分布鲁棒优化调度模型来优化交易策略。最后,基于线性化和重构技术将所提出的多阶段分布鲁棒优化模型转化为一个大规模多阶段整数规划问题。对随机对偶动态整数规划算法进行了改进,以保证计算的可处理性。 以中国金沙江流域的溪洛渡-向家坝水电系统为例进行了研究。结果表明:1)相比于随机规划模型,多阶段分布鲁棒优化模型对分布不确定性具有更强的鲁棒性。当随机变量的概率分布发生变化时,多阶段分布鲁棒优化模型能得到更高的收益(+2.43%)和更低的收益标准差(-60.8%)。2)与多阶段随机规划模型、确定性模型、两阶段随机规划模型和两阶段分布鲁棒优化模型相比,多阶段分布鲁棒优化模型在能获得最高的收益和最低交易风险,并取得最佳的样本外性能。3) 将小时级机组组合约束纳入多阶段分布鲁棒优化模型增加了模型的计算复杂度,但也增加了总收入(+3.53%)和发电量(+3.31%)。


更多关于“ Distributionally robust optimization ”的文章请见:

https://www.sciencedirect.com/search?cid=271429&pub=Applied%20Energy&qs=Distributionally%20robust%20optimization

Abstr act

Joint trading of hydro–wind–solar complementary systems (HWSCSs) in the electricity market (EM) helps to reduce the imbalance cost and increase profits. However, multiple energy resources and market price uncertainties affect the trading strategies. Existing medium-term (MT) scheduling approaches assume that the probability distribution of the random variable is perfectly known. Short-term variations were also ignored, which led to revenue loss and trading risk. To address the above issues, this paper proposes an MT multi-stage distributionally robust optimization (MDRO) scheduling approach for a price-taking HWSCS in the EM. Firstly, hourly unit commitment (HUC) constraints are incorporated into the MT scheduling model to accurately capture short-term variations. A novel ambiguity set is designed based on the modified chi-square distance to address probability distribution uncertainties at two different time scales. Subsequently, an MDRO scheduling model is proposed to optimize the trading strategy. Finally, the proposed MDRO model is converted to a large-scale multi-stage integer programming problem based on linearization and reformation. The stochastic dual dynamic integer programming algorithm is modified to ensure computational tractability. Xiluodu-Xiangjiaba HWSCS, located in the Jinsha River in China, was selected as a case study. The results show that: 1) the MDRO model is more robust to distributional uncertainties than the multi-stage stochastic programming (MSSP) model. When the probability distribution of the random variable changes, the MDRO model yields a higher expected revenue (+2.43%) and a lower standard deviation (-60.8%) of revenue, which illustrates lower trading risk. 2) Compared with MSSP, deterministic, two-stage stochastic programming, and distributionally robust optimization models, the MDRO model exhibits the best out-of-sample performance in terms of the highest expected revenue and lowest trading risk. 3) Incorporating HUC constraints into the MDRO model helps to increase the total revenue (+3.53%) and energy generation (+3.31%) at the expense of increasing the computational burden.

Keywords

Hydro–wind–solar complementary system

Medium-term scheduling

Electricity market

Distributionally robust optimization

Stochastic dualdynamic integer programming

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