A dual time-scale optimal dispatch algorithm for PV systems: Integrating centralized optimal power dispatch with distributed power deviation absorption in DC smart grids

IF 5.3 Q2 ENGINEERING, ENVIRONMENTAL

Cleaner Engineering and Technology Pub Date : 2025-05-01 DOI:10.1016/j.clet.2025.100961

Jiahua Ni , Yuwei Chen , Arman Goudarzi , Tong Wang , Lingang Yang , Shengwei Mei

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Abstract

The integration of Photovoltaic (PV) systems into DC smart grids faces challenges due to solar power’s inherent unpredictability. Traditional dispatch methods struggle to effectively manage PV power deviations in real-time. This paper proposes a dual time-scale strategy integrating centralized optimization with distributed consensus. On the long-term scale, a convex relaxation-based optimal power flow model minimizes line losses and stabilizes voltages. For short-term adjustments, a distributed consensus algorithm dynamically allocates power deviations among PV sources using reserve capacity, eliminating the need for probabilistic uncertainty modeling. The approach is validated through IEEE 14-node simulations and hardware-in-loop (HIL) tests, with comparisons against centralized methods considering forecast errors. The results demonstrate enhanced voltage stability, highlighting the framework’s practicality for real-time grid management.

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光伏系统双时间尺度最优调度算法：直流智能电网集中最优调度与分布式功率偏差吸收相结合

由于太阳能固有的不可预测性，将光伏系统集成到直流智能电网中面临着挑战。传统的调度方法难以有效地实时管理光伏发电功率偏差。本文提出了一种集集中式优化和分布式共识于一体的双时间尺度策略。在长期尺度上，基于凸松弛的最优潮流模型可以最大限度地减少线路损耗并稳定电压。对于短期调整，分布式共识算法使用备用容量在光伏发电源之间动态分配功率偏差，从而消除了概率不确定性建模的需要。通过IEEE 14节点仿真和硬件在环（HIL）测试验证了该方法，并与考虑预测误差的集中式方法进行了比较。结果表明，该框架增强了电压稳定性，突出了实时电网管理的实用性。

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