Optimal scheduling of battery energy storage system operations under load uncertainty

被引:0
|
作者
Rafayal, Syed Mahbub [1 ]
Alnaggar, Aliaa [1 ]
机构
[1] Toronto Metropolitan Univ, Mech Ind & Mechatron Engn Dept, 350 Victoria St, Toronto, ON M5B 2K3, Canada
关键词
Distributionally robust optimization; Second-order cone programming; Probabilistic forecasting; Energy management; Battery energy storage system; MANAGEMENT; BUILDINGS; CONSUMPTION;
D O I
10.1016/j.apm.2024.115756
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper investigates the optimal scheduling of battery energy storage system operations considering energy load uncertainty. We develop a novel two-stage distributionally robust optimization model to determine an optimal battery usage schedule that minimizes the worst-case energy costs considering peak load costs. The model leverages deep-learning-based probabilistic forecasting in the construction of the ambiguity set. Specifically, we develop a Deep Autoregressive Recurrent Networks model to generate a probabilistic forecast of energy loads over a time horizon. The output of the forecasting model is then used to construct a marginal-moment ambiguity set for the distributionally robust optimization model. To solve the proposed model, we establish a closed-form characterization of the optimal second-stage objective function value. Leveraging this closed-form expression and using second-order conic duality, we derive an exact single-level mixed integer second-order conic reformulation of the problem. Extensive computational experiments, conducted on a real dataset, demonstrate the value of our proposed model and the effectiveness of the resulting battery schedule. The results show that the proposed model outperforms several benchmarks, including two-stage stochastic programming. Furthermore, the accuracy of the load forecast significantly impacts the effectiveness of the optimal battery schedule in eliminating peak loads by achieving up to 18% reduction in the maximum energy load.
引用
收藏
页数:25
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