基于数据驱动自适应鲁棒优化的电-热耦合系统协同调度

Coordinated dispatching of integrated electric and heat systems based on the data-driven adaptive robust optimization

随着电-热系统的紧密耦合,电-热联合调度已成为当前研究的热点。通过挖掘供热系统在源、网、荷侧的灵活性,为电力系统的风电消纳提供了额外空间。然而,可用的风电出力难以准确预测,且其相应的概率分布无法事先获取,而鲁棒调度又过于保守。为了解决这一问题,提出一种基于数据驱动的电-热耦合系统自适应鲁棒调度方法。该方法结合随机规划和鲁棒优化的优势,利用历史数据模拟最坏的概率分布场景,实现了经济性和保守性的平衡。通过在包含6节点电网和6节点热网的耦合系统进行仿真测试,验证所提方法的有效性。

Due to the tight coupling of electric and heating systems,combined heat and power dispatching has become a hot topic.By using the flexibility of heating systems in the aspects of sources,networks and loads,additional space for wind power penetration in the power systems has been provided.However,the available wind power output is difficult to forecast accurately,and its probability distribution cannot be obtained beforehand,while robust dispatch tends to be overly conservative.To address this issue,a data-driven adaptive robust dispatching method is proposed for integrated electric and heat systems.Combining the advantages of stochastic programming and robust optimization,the method simulates worst-case probability distribution scenarios using historical data to achieve a balance between economic efficiency and conservatism of dispatch strategy.The effectiveness of the proposed method is validated through simulation tests on a system consisting of a 6-bus electric network and a 6-node heating network.