Dual-data-model-driven Distributionally Robust Optimal Scheduling of Renewable Energy Microgrid
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摘要: 针对新建新能源微电网数据稀缺性和源荷不确定性的能量优化调度问题，文中提出了一种基于数据模型双驱动的微电网分布鲁棒优化调度框架。首先，通过神经网络与光伏发电物理模型相结合，利用历史气象数据增强场景生成的准确性和鲁棒性，以应对数据稀缺带来的问题。其次，通过引入基于Wasserstein距离的分布鲁棒优化策略和线性决策规则，将考虑源荷不确定性的微电网能量优化调度问题由复杂的半无限规划问题转化为易于求解的混合整数线性规划问题。所提出的分布鲁棒优化能源调度框架能够在低运营成本和高可靠性之间实现平衡，并适应光伏发电功率和其他因素的实时变化。最后，在3种典型气象条件下的实验对比结果验证了所提方法的有效性。Abstract: Aiming at energy optimal scheduling problems in newly established microgrids（MGs） with the data scarcity and the uncertainty of source and load, this paper proposes a dual-data-model-driven distributionally robust optimization（DRO） framework for MGs. Firstly, the accuracy and robustness of the scenario generation using historical meteorological data are enhanced by the integration of neural networks with photovoltaic physical generation models to address the problem of data scarcity. Secondly, by the introduction of the DRO strategy and linear decision rules based on the Wasserstein distance, the energy optimization scheduling problem of MGs considering the uncertainty of source and load is transformed from a complex semi-infinite programming（SIP） problem to a mixed-integer linear programming（MILP） problem that is easy to be solved. The proposed DRObased energy scheduling framework can realize the balance between low operation costs and high reliability, and can adapt to the real-time changes in photovoltaic generation power and other factors. Finally, the experimental comparison results under three typical weather conditions verify the effectiveness of the proposed method.
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