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Aiming to address power consumption issues of various equipment in metro stations and the inefficiency of peak shaving and valley filling in the power supply system, this study presents an economic optimization scheduling method for the multi-modal “source-network-load-storage” system in metro stations. The proposed method, called the Improved Gray Wolf Optimization Algorithm (IGWO), utilizes objective evaluation criteria to achieve economic optimization. First, construct a mathematical model of the “sourcenetwork- load-storage” joint system with the metro station at its core. This model should consider the electricity consumption within the station. Secondly, a two-layer optimal scheduling model is established, with the upper model aiming to optimize peak elimination and valley filling, and the lower model aiming to minimize electricity consumption costs within a scheduling cycle. Finally, this paper introduces the IGWO optimization approach, which utilizes meta-models and the Improved Gray Wolf Optimization Algorithm to address the nonlinearity and computational complexity of the two-layer model. The analysis shows that the proposed model and algorithm can improve the solution speed and minimize the cost of electricity used by about 5.5% to 8.7% on the one hand, and on the other hand, it improves the solution accuracy, and at the same time effectively realizes the peak shaving and valley filling, which provides a proof of the effectiveness and feasibility of the new method.

In this paper, an improved sag control strategy based on automatic SOC equalization is proposed to solve the problems of slow SOC equalization and excessive bus voltage fluctuation amplitude and offset caused by load and PV power variations in a stand-alone DC microgrid. The strategy includes primary and secondary control. Among them, the primary control suppresses the DC microgrid voltage fluctuation through the I and II section control, and the secondary control aims to correct the P-U curve of the energy storage system and the PV system, thus reducing the steady-state bus voltage excursion. The simulation results demonstrate that the proposed control strategy effectively achieves SOC balancing and enhances the immunity of bus voltage. The proposed strategy improves the voltage fluctuation suppression ability by approximately 39.4% and 43.1% under the PV power and load power fluctuation conditions, respectively. Furthermore, the steady-state deviation of the bus voltage, ΔUdc is only 0.01--0.1 V, ensuring stable operation of the DC microgrid in fluctuating power environments. [ABSTRACT FROM AUTHOR]